critical analysis nhs

Knowledge & Library Services

Critical appraisal.

Critical appraisal is the process of carefully and systematically assessing the outcome of scientific research (evidence) to judge its trustworthiness, value and relevance in a particular context.

Critical appraisal looks at the way a study is conducted and examines factors such as internal validity, generalisability and relevance.

Critical appraisal is an essential step in the process of evidence-based practice. Acquiring critical appraisal skills, and adopting them into your everyday practice will enable you to make informed, evidence-based decisions.

Critical Appraisal Tools

Whether you are training to be a doctor, nurse or other health professional, or you’re a school student thinking about studying medicine, S4BE Contributors pull together all the resources on EBH into one interactive space, where we hope you will find the right information for you and help you meet other like-minded students.

Critically Appraise a Randomised Controlled Trial: Bite-Sized Videos

Barts Health Knowledge and Library Services have created a series of bite-sized videos to take you through the process of critically appraising a randomised controlled trial. You can watch part 1 below or view the full playlist on YouTube .

Critically Appraising the Evidence Base - eLearning

The critical appraisal programme has been designed to enable the healthcare workforce (clinical and non-clinical) to build confidence in the critical appraisal process when applying and evaluating research.

As the next step to research review, critical appraisal is the process of assessing the reliability, importance, and applicability of evidence. It can be applied to a range of written work such as formal research projects, studies and professional development to check validity and determine value.

This programme aims to support NHS colleagues with understanding the different methods and tools to carry out critical appraisal of research, through 8 bite-sized modules .

By the end of the programme, learners will be able to:

• describe the following terms; critical appraisal, bias, internal validity and external validity
• distinguish between different types of study designs and their strengths and limitations
• assess the appropriateness of methods used to conduct a randomised controlled trial, systematic review, diagnostic study and qualitative study
• interpret commonly reported results found in clinical papers
• identify different types of critical appraisal tools and their strengths and limitations

Applicable to health and care staff at all levels, this elearning may be ‘dipped into’ for reference or completed as a whole to obtain a certificate.

Get in contact

Email :  [email protected]

Phone : 01271 322363

Level 1 North Devon District Hospital, Raleigh Park, Barnstaple, EX31 4JB

E-mail:   [email protected]

Phone: 01392 406800

Exeter Health Library Level 2 PMS/RILD Building Royal Devon & Exeter Hospital Barrack Road Exeter, Devon EX2 5DW

Library Search Hub

Find a Book

Find a Journal

Last updated: July 26, 2023

Our site uses cookies to help give you a better experience. By continuing to use it you consent to the use of cookies as set out in our privacy policy.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings
• Advanced Search
• Journal List
• Patient Prefer Adherence

A critical analysis of national policies, systems, and structures of patient empowerment in England and Greece

Markella boudioni.

1 NIHR Imperial Biomedical Research Centre & Patient Experience Research Centre, Imperial College London

Susan McLaren

2 Institute for Leadership and Service Improvement, Faculty of Health and Social Care, London South Bank University, London, UK

Graham Lister

Comparison of patient empowerment (PE) policies in European countries can provide evidence for improvement and reform across different health systems. It may also influence patient and public involvement, patient experience, preference, and adherence.

The objective of this study was to compare PE within national policies, systems, and structures in England and Greece for achieving integrated people-centered health services.

We performed a critical search and review of policy and legislation papers in English and Greek languages. This included 1) general health policy and systems papers, 2) PE, patient and/or public involvement or patients’ rights policy and legislation (1990–2015), and 3) comparative or discussion papers for England and/or Greece.

A total of 102 papers on PE policies, systems, and structures were identified initially; 80 papers were included, in which 46 were policy, legislative, and discussion papers about England, 21 were policy, legislation, and discussion papers about Greece, and 13 were comparative or discussion papers including both the countries. In England, National Health Service policies emphasized patient-centered services, involvement, and empowerment, with recent focus on patients’ rights; while in Greece, they emphasized patients’ rights and quality of services, with recent mentions on empowerment. The health ombudsman is a very important organization across countries; however, it may be more powerful in Greece, because of the nonexistence of local mediating bodies. Micro-structures at trusts/hospitals are comparable, but legislation gives more power to the local structures in Greece.

PE policies and systems have been developed and expressed differently in these countries. However, PE similarities, comparable dimensions and mechanisms, were identified. For both the countries, comparative research and these findings could be beneficial in building connections and relationships, contributing to wider European and international developments on PE, involvement, and patients’ rights and further impact on patient preferences and adherence.

Introduction

Patient empowerment (PE) and its benefits 1 – 6 have been recognized internationally and in Europe; empowerment and engagement are goals of a global strategy on the achievement of integrated, people-centered health services between 2016 and 2026. 1 The Tallinn Charter recognized the importance of making health systems more responsive to patients’ needs, preferences, and expectations, committing WHO Member States in Europe to strengthening health systems. 4 The European Community White Paper (2008/2013) recognized citizens’ rights to be empowered in relation to health and health care, encompassing participation and influence on decision-making and competences needed for well-being. 5 The value of patient engagement and empowerment has been discussed in international forums, bringing forward proposals for strengthening national approaches to patient engagement and the advocacy capacity of patients. 7 Recently, WHO called for action on PE, 8 recognizing that patient, family, and community engagement are assets for building capacity and quality of care. 9

Diverse models of PE have been adopted internationally encompassing patients’ rights legislation (the Netherlands and Greece), introducing ombudsperson services (Austria, Finland, Hungary, Norway, Greece, and England) and increasing patient participation in care decision-making in England. 6 , 10 – 14 A developing consensus recognizes that PE is increasingly important to health governance, resulting in better system responsiveness to health consumer’s views, preferences, and self-management of health. Both England and Greece, which have not been compared in relation to empowerment before, are engaged in professional, political, and public discussions about PE, sharing common European health policies, standards, and targets. 1 , 4 Both the countries have recognized the benefits of making health systems and are more patient-centered and responsive by adopting PE strategies. 15 It is acknowledged that they may have different health and welfare systems, PE national policies, systems, and development. This diversity can only be helpful in providing evidence for improving policies, organizational systems, management, professional practices, and patient experiences. Comparison of PE policies across two different health and welfare systems can illuminate similarities and differences, providing evidence for improvement and reform. 16 – 18 It can also influence patient and public involvement in such policies and systems and impact on patient preferences and adherence.

The term “patient empowerment” is used as an inclusive term here, encompassing different levels, strategies, methods, and dimensions of involvement/participation, including patient and public involvement (PPI) and patients’ rights across countries. It refers to all mechanisms enabling patients to gain control and make choices in their health and health interventions, 19 the act or process of conferring authority, ability, or control. 20 More choice, more information, and more personalized care may be elements leading to better health literacy, informed decision-making, and real empowerment of patients to improve their health, health services, and systems. There are many concepts and definitions relating to PE ( Table 1 ); these have been discussed elsewhere and we do not revisit them in this paper. 14 , 19 – 22 This paper aimed to compare PE within national policies, systems, and structures in England and Greece.

Patient empowerment concepts and definitions

A wide-ranging critical policy and legislation review of papers in English and Greek language, using a structured approach was undertaken. Three categories of papers were reviewed: 1) general health policy and systems papers; 2) PE, PPI, or patients’ rights policy and legislation (1990–2015); and 3) comparative or discussion papers, for England and/or Greece. Inclusion criteria were the terms “patient empowerment,” “patient (and public) involvement,” “patients’ rights,” “patient engagement,” and “patient participation;” papers in English and Greek languages were included.

The review was conducted between April 2006 and September 2015. Combination of the following terms were searched: “patient empowerment,” “patient (and public) involvement,” “patients’ rights,” “patient engagement,” “patient participation,” “citizenship,” “health policy” (implementation of health policy), and “organizational systems” (strategies, systems, structures, or mechanisms). The following databases, search engines, and websites were used:

• Department of Health in England, Ministry for Health and Social Solidarity (MHSS) in Greece, National Health Services (NHSs) in both countries and other governmental.
• King’s Fund, Picker Institute Europe, World Health Organisation, European Community, Greek National Centre for Social Research (Eθνικό Κέντρο Κοινωνικών Eρευνών – EΚΚE), Greek National Documentation Centre (Eθνικό Κέντρο Tεκμηρίωσης).
• Electronic databases: Medline, CINAHL, Greek medical databases, that is, Iatrotek.org , MedNet.gr.

All papers were screened by MB; summaries and any data considered dubious were discussed with the other co-authors. Only papers agreed by all the three authors were included. Exclusion criteria were that 1) they did not contribute to new knowledge in relation to the terms and aims and 2) they were not about Greece or England.

Initially, 102 papers on PE policies, systems, and structures were identified. Of them, 80 papers were used and analyzed in the following sections: 46 were policy, legislative, and discussion papers about England; 21 were policy, legislation, and discussion papers about Greece; and 13 were comparative or discussion papers including both the countries. A short introduction about the general organization of the English and Greek NHSs is first presented, followed by the PE policies, systems, and structures in the two NHSs.

Established in 1948, the NHS centralized system, funded through national taxation, delivers services through public providers, devolving purchasing responsibilities to local bodies, that is, primary care trusts, clinical commissioning groups (CCGs). 23 , 24 Health care spending is 9.1 of GDP (2013), medium compared to other countries and arguably equitable. 11 , 25 – 27 Public health funding is high; private out-of-pocket funding is moderate. 26 All citizens and residents are insured, but have limited provider choice or access to specialists. Only 10% have private health insurance, which gives higher quality care access and reduced waiting times. 11 , 28 Initiatives focused on improving efficiency, responsiveness, and equity of the system, that is, foundation hospitals (2004) have greater management, financial responsibilities, and freedoms. Such measures aimed to reduce waiting lists, improve quality of provision, increase funding and staff numbers, encourage innovation, and extend patient choice. 23 Despite these, it has been argued that patients are prevented from taking control of their health care and frontline professionals from revolutionizing services for patients’ benefit. 29 Although UK health policy has focused on controlling spending, the system faces serious financial strains, waiting lists, and explicit rationing for some types of care 11 ( Table 2 ).

Main characteristics of the English and Greek National Health Services

Abbreviations: PCT, primary care trust; CCG, clinical commissioning group.

The NHS inception in 1983 guaranteed free health care for all residents; introduced state responsibility for health care services provision, equal access, decentralization of planning, primary care development, exclusive employment of health care staff, and unification of main insurance funds. 30 Health care spending is 9.8 of GDP (2013), medium compared to other countries. 27 The system is characterized by a public–private mix for funding and delivery, high out-of-pocket payments, and little regulation of access to health care providers. 11 , 26 , 31 Types of coverage available are the NHS, health insurance funds (occupation-based), and private health insurance. 23 Most Greeks (95%–97%) have private insurance for hospital care. The health insurance system is employer-based; employers enroll their employees in a “social” payer system, the ministry controls employee contribution rates, insurance benefits and the doctors’ social insurance funds. 11 NHS services are provided through a public hospital network, delivering to inpatients and outpatients. Advances in accessing health care services include development of rural surgeries, primary health centers, and public and regional teaching hospitals. There are many challenges, that is, integrating primary care services, high level of pharmaceutical expenditure and modernizing NHS management. 23 Public primary health care services are insufficiently developed, with the exception of some rural areas. Long waiting times for NHS care are partly due to provider shortages caused by low reimbursement rates. 11 , 32 , 33 The system is characterized by over-centralization, fragmentation of coverage (with many insurance funds), regressive financing extensive user charges and informal payments, inefficient resource allocation, perverse incentives for providers, and heavy reliance on unnecessarily expensive inputs 26 , 32 , 35 ( Table 2 ).

PE policies

NHS policies emphasized the need for patient/user input to service planning, development, delivery at all levels, monitoring, evaluation, audit, and their outcomes in the 1990s. 35 – 39 The Patients Charter presented the first “aspirational” vision for hospital patients; the standards were not legal rights, but “major and specific standards” encompassing respect for privacy, dignity, religious and cultural beliefs, continuity of care, and quality of nursing. 35 Other “rights” addressed waiting times, information (about cancellations), and timely responsiveness to complaints. 35

The NHS Plan and “Shifting the Balance of Power” were the first patient-centered strategies in the 2000s encapsulating a vision where patients had more say about health care provision, marking the need for organizational and cultural change. 40 , 41 Subsequently, significant policies, legislations, and frameworks aimed to empower patients and the public. 24 , 43 – 53 Importantly, patient and public involvement in planning, development, and making decisions affecting services operation became a duty enacted in the Health and Social Care Acts in 2001 and 2003. 43 , 54 A subsequent Act (2006) placed a legal duty on health organizations to involve users/representatives through consultation, providing them with information about planning services, proposals for change, and decisions affecting service operation 55 ( Table 3 ).

A summary of patient empowerment policies and legislation in England and Greece (1990–2015)

The Next Stage Review (2008) placed quality at the heart of care, concentrating on patient-reported outcome measures, detailing elements of PE encompassing more information, choice, partnership working, and quality of care. 56 Other guides and programs supported community, patient, and public engagement in health care. 57 , 58 The NHS Constitution brought together and explained patient’s rights and public, patient, and staff responsibilities, thereby empowering all, for the first time in 2009. 59 The Health Act 2009 placed a duty on all NHS providers to have regard to the Constitution, proposing measures to improve care quality, service performance, and public health. 60 In 2013, new rights for both patients and staff were added, and patient involvement was updated in the Constitution 61 ( Table 4 ).

A summary of patients’ rights and entitlements in England and Greece

Abbreviations: NHS, National Health Service; GP, general practitioner.

In the 2010s, the NHS White Paper Equity and Excellence envisioned “an NHS genuinely centered on patients and carers” encompassing an information revolution, greater choice and control through shared decision-making, increased patient control over records, and equity for everyone. 62 The NHS 2010–2015 Plan together with more recent operating frameworks are even more empowering, emphasizing driving change through patient influence and integrating information around patients. 63 , 64 A key tenet is “people must be given rights and entitlements, with greater control over their own health and care;” explicitly referring to rights, full choice of primary and secondary care services, more personal health responsibility, all increasing patient satisfaction. A significant proportion of provider income is linked to patient experience and satisfaction by linking payment to patient satisfaction, giving providers incentives to understand and improve. 63 With the Health and Social Care Act (2012), NHS duties to offer more patient choice were enacted in law, including individual and public participation. 65 The Health and Care System (2013) gave local communities more say in care through health and well-being boards, envisioning that “everyone has greater control of their health and their wellbeing, supported to live longer, healthier lives by high quality health and care services that are compassionate, inclusive and constantly-improving.” 66 The NHS 5-year Forward View emphasized a new relationship with patients and communities, that is, empowerment with access to electronic medical records. 67 Subsequently, the NHS England Business Plan (2015–2016) commits to innovative engagement with diverse communities and citizens, placing citizens at the center of design for new services and care models 68 ( Table 3 ).

Health care reforms in the 1990s and 2000s utilized legislation addressing patients’ rights and service quality. 68 The Conservatives’ Reform in 1992 introduced patients’ rights based on the European Charter; the most significant being rights of information, complaint, appropriate services, respect, and choice/refusal of treatment 69 ( Table 4 ). The Health care Reform in 1997 also emphasized patients’ rights and effective hospital management of utilizing user’s views in decision-making, through establishment of statutory bodies for rights’ protection at national and hospital level 70 ( Table 3 ).

The following Health care Reform (2001) focused on Greek citizens and their interests, reinforcing the statutory bodies established in 1997 (below). 71 This reform aimed to a patient-centered NHS, incorporating basic measures of universal coverage and equity in service delivery, efficiency, and quality. 34 Reforms were to be achieved through the establishment of National Health Institute’s Regional Health Authorities and improved public hospitals with appointment of professional managers. The later Health care Reform (2005) addressed living conditions in the wider public health context; health priorities emphasizing equal access and need satisfaction. Official endorsement of collaboration of hospitals and nongovernmental health organizations was given. 72 Patients’ rights continued to be reinforced in 2008 with the MHSS’ mission emphasizing equality, quality of services, and protection of individual and social rights. 73

Despite subsequent lack of reforms, recent National Action Plans were patient empowering. The National Action Plan for Public Health (2008–2012) explicitly mentions “information” and “empowerment of citizens.” 74 The National Action Plan for Human Rights (2013) explains rights to health and refers to “empowerment” as mirroring positive health, emphasizing social and individual capacities. 75

PE systems and structures

National organizations, systems, and initiatives.

National organizations’ monitoring, regulating, and inspecting health care services, that is, the Care Quality Commission (2009), replacing the Health Care (2004) and the Mental Health Act Commissions (1983), have been active since the 1980s. 76 , 77 National organizations empowering, protecting, and strengthening patients and public have also been active; of these, the Health Service Ombudsman, established in 1973, considers complaints that the NHS has not acted properly, fairly, or provided a poor service. 78 This statutorily independent investigator has powers to summon witnesses and access records, breaches of which can be treated as akin to contempt of court. 14 , 78 Other bodies now abolished, that is, the Commission for Patient and Public Involvement in Health (CPPIH) (2004–2007) and the National Resource Centre for Patient and Public Involvement (2006–2009), aimed to promote PPI value, and the latter to create a single point for information and advice ( Table 5 ). 79 , 80

A summary of NHS patient empowerment systems and mechanisms in England and Greece (1973–2015)

Changes implemented at the macro level included several choice initiatives, that is, the “Choose and Book” service, enabling patients requiring elective care to see information about hospitals and book first appointments through general practice surgeries or a booking service. 81 Patient feedback was facilitated via annual hospital patient surveys, undertaken by NHS acute hospital trusts between 1997 and 2013. Over time, surveys expanded to include topics of public and/or political interest, that is, waiting times, single sex wards, and cleanliness. Wider annual national surveys were undertaken for inpatients (2002–2007) and outpatients (2003, 2004/2005) together with surveys on specific services or conditions. 82 In 2013, the Friends and Family Test was introduced, asking service users if they would introduce a service to friends and families. 83 A new NHS Citizen Program was co-designed (2013), enabling NHS England to directly engage citizens in a publicly accountable and transparent forum, providing a framework for citizens to engage commissioners and providers of services, offering views, insights, and solutions, holding them to account. Since the program began, NHS England has facilitated workshops and regional events, with face to face and digital participation. 83

Local and trust/hospital systems and structures

Systems empowering patients and the public at the micro local/hospital level are also prevalent ( Table 5 ). Community Health Councils (1974), the first formal structures to represent public interests locally, were abolished in 2003. 84 Their role was taken over by other services:

• Overview and Scrutiny Committees (2000), a statutory service, established to look at the local NHS work with local authority councillors having the powers to review and scrutinize the planning, provision, and operation of health services and to make improvement in recommendations. 85
• Independent Complaints Advocacy Service (ICAS) (2003), another statutory service, launched aiming to provide independent support to patients/carers wishing to complain about their NHS treatment and care locally. 45
• Patient Advice and Liaison Services (PALS) (2002) established aiming to provide accessible support, advice, and information to patient/carers in NHS settings. 40
• Patient and Public Involvement Forums (PPIFs) (2003), independent bodies made up of volunteers, set up to monitor the NHS quality from the patient perspective. PPIFs were supported by the CPPIH. 40

Recently, NHS Citizen Development Sites (2014) provide opportunities for people in local areas to make links with the NHS Citizen Program, bringing public voice to the board of NHS England. 86

The Health and Welfare Inspectorate, established to provide good quality services to citizens, has been active since 2001. 87 It has a repressive and preventative role by conducting checks and audits and suggesting imposition of punishments or staff disciplinary control, simultaneously checking relevant procedures and making propositions. Although under the jurisdiction of the MHSS, it has full inspection independence collaborating with the Patients’ Rights Protection and Control Committee (PRPCC) (below). 88 Other national bodies promote, protect, and strengthen patients’ rights. The Greek ombudsman for Health and Welfare, established in 2004 89 as part of the Greek Ombudsman duties, 70 is the most prevalent. It suggests measures to the ministry for restoration and protection of patients’ rights; elimination of bad management, improvements in health and social welfare services, and relationships with citizens 89 ( Table 5 ).

National statutory bodies to protect patients’ rights were introduced in 1997. 70 The Patients’ Rights Protection Independent Service (PRPIS) was established to monitor patients’ rights protection, investigate formal written complaints, and report progress to the Ministry’s Secretary General. It requires annual hospital reports on their Office for Communication with Citizen (OCC) activities encompassing annual complaints, their resolution, time spent on complaint investigations, and decisions taken. PRPIS has the right to suggest solutions, request further investigations, and make recommendations. Although part of the MHSS, it has independent status. 70 PRPIS proposed establishment of reception desks (welcome points) in all public hospitals, staffed by OCCs, to make available information leaflets on patients’ rights and PRPIS functions. 71

The PRPCC was also established (1997) to ensure hospital compliance with patients’ rights regulation and conduct in-depth, national investigations of complaints. 70 It comprises a State Legal Council representative and representatives of 13 professional Greek institutions from a spectrum of professions. Once a decision regarding a patient complaint is made, the Ministry’s Secretary General is notified and ensures that appropriate actions are taken by hospital management. However, it has been under-functioning since 2005.

In 1999, two new bodies answering directly to the ministry were launched within the PRPIS: an “Office for the Protection of Individuals with Psychological Disorders” to protect the rights of these individuals and a “Special Committee for the Rights of Individuals with Psychological Disorders’ to ensure compliance with the Office.” 89 A national inpatient survey (2002) was also introduced following the 2001 reforms, aiming to evaluate public hospitals by measuring users’ satisfaction; 90 plans for ongoing annual surveys, however, were not materialized.

Hospital systems and structures

Statutory bodies were also established within all hospitals in 1997. 70 OCCs acted as information points for patients/service users, registering complaints, operating under direct supervision of hospital chairmen/chief executives. 71 They became “Offices of Patients’ Reception,” and their role changed slightly to welcoming and directing patients, accompanying them to the appropriate hospital services in 2001; thus, reception desks (welcome points) were created in all public hospitals. 71 In 2010, MHSS’ circulars and targets included commencement of quality monitoring processes and reinforcement of the OCCs. 92 A circular reminded all hospitals that “an Office of Patients’ Reception – Office of Communication with Citizen, staffed suitably, should exist in visible location near the entrance in every infirmary.” 93 Their aim was the reception, information, update, direction, and support of all patients and those accompanying them, about complaint procedures and information on hospital rules. Thus, a double more inclusive role with extensive responsibilities was allocated to the offices. The same year they were renamed to “Offices for Citizen’s Support,” responsible for the patient welcoming, information, movement control, administrative support, management and dispatch of concerns, complaints, and overall protection of patients’ rights. 94

Citizens Rights’ Three-member Protection Committees (CRPCs), established in 1997, investigated complaints and protected and promoted patients’ rights in collaboration with the OCCs for which it had a monitoring function. Its membership should include a user representative; the Board of Directors should ensure that all hospital patients were aware of their rights. CRPCs membership comprised a hospital manager/chair, the directors of medical and nursing services (user representative details not stated). In addition, other structures were introduced in 2010 reinforcing patient organizations and volunteering, and also evaluation, monitoring, and reporting, that is, Consultation Councils for Transparency and Monitoring in Health and Social Welfare, the “Registry for Volunteering in Health and Social Care.” 94

Cross-national analysis and discussion

This is the first paper to compare national policies, systems, and structures for PE in England and Greece. Notwithstanding its limitations, that is, based on a critical policy and legislation review in English and Greek language, and not taking into account patients, public, and other stakeholders’ perspectives and preferences, it has highlighted the important differences and similarities.

The NHSs and PE policies

English and Greek NHSs, having been developed differently, have diverse characteristics and face substantive challenges ( Table 2 ) with significant implications for PE. Both NHSs, committed to equity in service access, face serious financial strain, patients experience disempowering effects of waiting lists (both), rationing (England), extensive user charges, and informal payments (Greece). Despite these challenges, both the countries have recognized the benefits of making health systems more patient-centered and responsive by adopting PE strategies and have invested in infrastructures to support policy implementation despite financial constraints. 15

In England, policies and legislation have focused on patient-centered quality services, explicit PPI, engagement, empowerment, and more recently on patients’ rights. In Greece, the policy and legislative focus has been on patients’ rights, statutory bodies to protect rights and quality of services, with only a very recent focus on “empowerment” ( Table 3 ). Patients’ rights encompass human, social, and individual rights, addressing quality and accessibility of health care as well as basic consumer rights, balancing the partnership between providers and individual receivers of care. 95 Common dimensions of PPI and patients’ rights are information and complaining. Patients’ “voice” and “choice” are other common dimensions, requisite for PPI and underpinning values for all rights. 14 , 21 , 96 Although these may be comparable elements, their focus, emphasis, and implementation may differ, that is, information, complaining, “choice,” and “voice” may have different meanings and take different forms in the two countries. The timing and the development of patients’ rights also differ greatly; since the introduction of patients’ rights in 1992, there have not been great developments in Greek legislation, perhaps with the exception of the most recent introduction of the term “empowerment,” while patients’ rights have been developed and expanded greatly since the NHS Constitution in 2009 in England ( Table 4 ). However, there may be recent convergence of policies; patients’ rights were introduced as important PE mechanisms in England in 2009 and have been strengthened since then; and PE has been emphasized in Greek policies more recently.

Challenges existed in implementing PE policies and reforms in both the countries slowing down the pace of change. In England, reports noted barriers to patients taking control of their health and health professionals revolutionizing services. 11 , 29 In Greece, a need exists to modernize organizational management, tackle sources of over centralization/fragmentation of services and resolve factors which led to a hiatus in reforms post 2008. 31 , 25 , 29 The recent economic crisis has severely affected the implementation of policies in health and health care in Greece, that is, health care budgets have been slashed; in England, recent health budget cuts are also evident. 97 , 98 However, in both the countries, recent developments suggest positive attempts to move policy implementation forward, that is, the National Action Plan for Human Rights 75 in Greece explains and emphasizes on rights to health, and the recent NHS England Business Plan influences empowerment and engagement of citizens in decisions about future service design. 66

These developments may not be suprising, as policy makers are expected to implement human-centered approaches, safeguarding dignity and rights to face financial cuts and crisis. 99 , 100

Diverse systems and structures have been implemented in both the countries to support PE, patient and public involvement, and patients’ rights ( Tables 4 and ​ and5). 5 ). At a macro-level, the independent health ombudsman role is of vital importance in both the countries. However, in Greece because of the nonexistence of local independent organizations or mediating bodies, its role assumes greater importance. National organizations/committees have been set up to protect rights in both the countries, that is, PRPIS and PRPCC in Greece and the NHS citizens in England.

At trust/hospital micro-level, committee structures with patient representation have also been implemented in both the countries to support PE. Although PALSs and OCCs’ functions are comparable, PALS have been stable over the years, but legislation in Greece continues to devolve power to OCCs, although one could argue that their name change and slightly diminished role may have created confusion among staff and patients, limiting their functions ( Table 5 ). Furthermore, policy recommendations and implementation guidance for local organizations have been weak in England and required improvement; in contrast, in Greece some guidance and monitoring systems for OCCs is in place. Other PE mechanisms at the local level, that is, the ICAS in England and the Citizen’s Rights Protection Committees (CRPCs) in Greece are acknowledged as areas of difference between the two systems. Important systemic and cultural connotations for PE, essential in dealing with the deficiencies of each system and alternative coping mechanisms are highlighted elsewhere, that is, the role of the voluntary sector in both the countries and the family’s role in Greece. 101 , 102

At both macro- and micro-level, monitoring, audit and evaluation investigating PE issues from both patient and professional perspectives is helpful in providing evidence for improving policies, organizational systems, management, professional practices, and the patient experience. In both the countries, patient surveys ( Table 4 ) have been utilized for these purposes, most frequently and consistently in England; in Greece, greater use of survey research or use of alternative feedback forums (currently lacking) would give greater weight to policy development and reform.

The NHSs policies and legislations linked to PE were very different in England and Greece; in turn, PE systems, structures, and mechanisms reflect these influences in England and Greece. Different language and terminology have been used, that is, “patient (and public) involvement” and “engagement” in England, “patients’ rights” and “responsibilities” in Greece. However, PE similarities, comparable dimensions, and mechanisms were also identified.

Future studies could draw on these findings and explore how the implementation of PE policies and legislation may influence PE within organizations, that is, hospitals, in both individual and organizational levels. Further research in implementation of national systems, structures, and mechanisms, investigating PE from both patient and professional perspectives could provide evidence for improving policies, organizational systems, management, professional practices, and patient experience, preference, and adherence. For both the countries, comparative research could be beneficial in building connections and relationships, help to bridge the gap between research and policy implementation and contribute to wider European and international developments on PE, involvement and patients’ rights. Furthermore, the importance of patient and public involvement in such developments and how these factors impact on patient preference and adherence should be explored and highlighted.

Acknowledgments

We would like to thank all those who helped us with the literature review. This work was part of a PhD study, funded privately and supported by London South Bank University.

The authors report no conflicts of interest in this work.

• Library Workshops and 1-1s
• Request a talk or workshop
• Clinical Resources
• Critical Appraisal
• Keep up-to-date
• KnowledgeShare
• Coronavirus resources
• Print Copy Scan
• KCH Trust researcher support
• Library Guides Home Page

Reading a medical journal article or paper can seem like a complex task, but breaking the process down into steps should enable you to build up the skills to do this well:

- Skimming the article in the first instance to look for the author's main points and conclusions

- Being familiar with the way that many journal articles are structured (abstract, method, results, discussion etc)

- Reflecting on and being critical of what you are reading. A checklist or toolkit such as the ones below, can guide you through this process in a structured way.

Regular sign-up sessions for staff at GSTT and KCH are available. These introductory sessions aim to increase your confidence in your critical appraisal skills. Narrated slide decks are available on both qualitative and quantitative methods and groups of staff can request  a session building on the material in these.

On this page you will find links to resources providing guidance on critical appraisal, quantitative research and statistics and qualitative research. If you have any suggestions for other resources please contact [email protected] .

Critically Appraising the Evidence Base:e-learning

• Critically Appraising the Evidence Base If you want the skills to know what good evidence looks like, then you need to understand the basics of critical appraisal. Take a look at these 8 e-learning sessions (30 mins each) to understand and apply the basics of critical appraisal. more... less... It's not just essential for good evidence-based practice. It can enrich your own research and learning, and you can apply it to other areas of your life, in and out of work. To develop these skills, the NHS Knowledge for Healthcare Learning Academy has partnered with Health Education England elearning for healthcare (HEE elfh) to develop a new e-learning programme: Critically Appraising the Evidence Base. There are 8 sessions in total: Introduction to Critical Appraisal; Introduction to Health Inequalities; Introduction to Critical Appraisal of Randomised Controlled Trials; Introduction to Interpreting Results for Critical Appraisal; Introduction to Critical Appraisal of Systematic Reviews; Introduction to Critical Appraisal of Qualitative Studies; Introduction to Critical Appraisal of Diagnostic Studies; and Introduction to Critical Appraisal Tools. The sessions are short, taking about 30 minutes to complete. By the end of the programme, you will be able to understand and apply the basics of critical appraisal.

Critical appraisal resources

New books are published regularly on critical appraisal - check Library Search  to see what is available. 

• BMJ - How to read a paper article series by Trisha Greenhalgh. Articles include: 'Getting your bearings (deciding what the paper is about)'; 'Statistics for the non-statistician'; 'Papers that report drug trials' and 'Papers that go beyond numbers (qualitative research).
• Centre for evidence-based medicine (CEBM) From the University of Oxford. Checklists, workshops and other resources.
• Critical Appraisal of Evidence elearning King's Health Partners staff can access this presentation on the Learning Hub which provides a brief introduction to critical appraisal of evidence and evidence-based practice.
• 'How to read a paper' elearning King's Health Partners staff and students can access a Learning Hub module on "How to read a paper"

Qualitative Research

• An introduction to reading and appraising qualitative research, BMJ 2008
• Qualitative Research Glossary

Tools/evaluation checklists

• Critical Appraisal Skills Programme (CASP) - eight critical appraisal tools to be used when reading research. - tools for Systematic Reviews, Randomised Controlled Trials, Cohort Studies, Case Control Studies, Economic Evaluations, Diagnostic Studies, Qualitative studies and Clinical Prediction Rule.
• JBI’s critical appraisal tools JBI’s critical appraisal tools assist in assessing the trustworthiness, relevance and results of published papers. They provide checklists for Analytical Cross Sectional Studies; Case Control Studies; Case Reports; Case Series; Cohort Studies; Diagnostic Test Accuracy Studies; Economic Evaluations; Prevalence Studies; Qualitative Research; Quasi-Experimental Studies; Randomized Controlled Trials; Systematic Reviews and Text and Opinion.
• Mixed Methods Appraisal Tool (MMAT) Appraisal tool used for qualitative, quantitative, and/or mixed methods studies. Helpful for those working on a Mixed Methods Review.
• EQUATOR Network The EQUATOR (Enhancing the QUAlity and Transparency Of health Research) Network is an international initiative that seeks to improve the reliability and value of published health research literature. It contains reporting guidelines for the main study types, including COREQ for qualitative research.
• CONSORT Statement - set of recommendations for reporting Randomised Controlled Trials, providing a minimum set of items to be included. - Use the checklist to help you check for any gaps in an RCT paper you are reading. - please note this is not designed explicitly to be a critical appraisal tool as it focuses on the minimum to be expected more... less... - aims to help authors improve the reporting of systematic reviews and meta-analyses by - use the checklist to help you .in a review you are reading
• PRISMA Statement - aims to help authors improve the reporting of systematic reviews and meta-analyses by providing a minimum set of items to be included. - use the checklist to help you check for any gaps.in a review you are reading - please note this is not designed explicitly to be a critical appraisal tool as it focuses on the minimum to be expected

Quantitative Research

• Blinding Bandolier information on blinding
• Critical Appraisal of Quantitative Research
• Quantitative Research Glossary

• p-values and confidence intervals Bandolier guide
• Statistical advice for King's Health Partners staff 1hr booked appointments with a professional statistician are available for KHP staff
• IoPPN Biostatistics & Health Informatics Department The Department runs courses with discounts for staff at King's Health Partners.
• << Previous: Clinical Resources
• Next: Keep up-to-date >>
• Last Updated: Sep 28, 2023 6:22 PM
• URL: https://libguides.kcl.ac.uk/NHS

Library Services

UCL LIBRARY SERVICES

• Library skills

LibrarySkills@UCL for NHS staff

• Evaluating information & critical appraisal
• Library skills for NHS staff
• Accessing resources
• Evidence-based resources
• Bibliographic databases
• Developing your search
• Reviewing and refining your search
• Health management information
• Critical Appraisal of a quantitative study (RCT)
• Critical appraisal of a qualitative study
• Critical appraisal of a systematic review
• Referencing basics
• Referencing software
• Referencing styles
• Publishing and sharing research outputs
• Publishing a protocol for your systematic or scoping review
• Communicating and disseminating research
• Help and training

Evaluating information sources

To critically analyse /appraise or evaluate, means to:

• break something down into its component parts
• provide your opinion on each part, by asking the right kind of analytical questions
• support your opinions with evidence

What is critical appraisal?

Once you have identified evidence for your search topic, you should critically appraise it. This is particularly important in medicine, nursing, pharmacy and other health sciences, where poorly conducted research can have a negative impact on patient outcomes.

Critical appraisal is the process of carefully and systematically examining research to judge its trustworthiness , and its value and relevance in a particular context. ( Burls 2009 , p.1)

The following resources will help inform most critical appraisal projects.

• Critical Appraisal Skills Programme (CASP) Tools (checklists) for systematic reviews, randomised controlled trials, cohort studies, case control studies, economic evaluations, diagnostic studies, qualitative studies, clinical prediction rule

CASP checklists are widely used. See our additional guidance and examples for using CASP checklists for selected study designs:

• Critical appraisal of a quantitative study (RCT)

Additional resources:

• Joanna Briggs Institute Critical Appraisal Tools Similar to CASP, but with the addition of tools for: analytical cross-sectional studies, case reports, case series, diagnostic test accuracy studies, quasi-experimental studies, text and opinion.
• EQUATOR Network An international initiative, seeking to improve the reliability and value of published health research literature. It includes reporting guidelines for the main study types.
• PRISMA Statement Not designed explicitly to be a critical appraisal tool, but aims to help authors improve the reporting of systematic reviews or meta-analyses, by providing a minimum set of items to be included.

Bibliometrics

Bibliometrics is concerned with the analysis of research based on citation counts and patterns. The individual measures used are also commonly referred to as bibliometrics, or citation metrics.

• Bibliometrics A guide to bibliometrics and support available at UCL, including the UCL Bibliometrics policy on the responsible use of metrics.

Evaluating for reliability

There are checklists available, which can help you to determine if a source of information is reliable. We recommend the following:

WHAT is the resource (book, article or website)?

WHO is the author (are they a qualified person or organisation)?

WHY has the resource been written (to try and sell you something, or persuade you to do something)?

WHEN  was it published or updated?

WHERE is the information from (is it from a peer reviewed journal or an anonymous blog post)?

• CRAAP Test Information from Meriam Library, California State University, who developed the tool.
• PROMPT: tutorial Tutorial from the Open University, who developed the PROMPT checklist.
• SIFT 'Sifting through the Pandemic' blog posts by Mike Caulfield.

A method called lateral reading involves reading about a source (e.g. website) on other trustworthy websites, such as fact-checking sites (e.g. Politfact). You should look outside the website, and don't just rely on the 'About us' section.

Related guides

• LibrarySkills@UCL: Evaluating information
• << Previous: Health management information
• Next: Critical Appraisal of a quantitative study (RCT) >>
• Last Updated: Aug 30, 2023 9:47 AM
• URL: https://library-guides.ucl.ac.uk/nhs

• International edition
• Australia edition
• Europe edition

NHS is in crisis but the roots go much further back than Omicron

Analysis: Critical incidents show much of the health service is already overwhelmed after years of understaffing

• Coronavirus – latest updates
• See all our coronavirus coverage

Back in October Sajid Javid insisted : “We don’t believe that the pressures currently faced by the NHS are unsustainable.” It was a statement that did not age well. It led to snorts of derision from senior doctors and hospital bosses, some of whom wondered what planet the health secretary was living on.

Days later Roland Sinker, the chief executive of Cambridge university hospitals NHS trust, gave a vivid insight into how easily NHS hospitals can get overwhelmed. He told a staff Zoom meeting that the loss of 150 out of 900 beds at the trust’s main hospital – Addenbrooke’s, one of the NHS’s finest – meant that “we could barely function before Covid”. With “150 beds out of 900” unavailable, “this is ceasing to function as a hospital”.

Days earlier a patient had died in the back of an ambulance outside the hospital . Its inability to cope with demand was so worrying, Sinker said, that it might have to send patients to London or Birmingham. “I’m much more anxious and scared now” than when he took over in 2015, he admitted, adding: “You’d have to be asleep to not realise the profound nature of the crisis we’re in.”

Fast forward two months and a growing number of NHS trusts in England, struggling in the face of an Omicron-driven surge in staff sickness, are now having to declare a “critical incident” – an admission that they cannot cope with the extreme pressure they are facing. “Unfortunately this does mean postponing some non-urgent operations and outpatient appointments to accommodate those patients with the most urgent clinical need,” Aaron Cummins, the chief executive of the university hospitals of Morecambe Bay trust in Cumbria, explained in a statement on Monday . It is easy to see such situations as proof that those places are overwhelmed.

On Tuesday it emerged that the NHS ambulance service covering the north-east of England has begun asking friends and relatives of people who have dialled 999 to bring them to hospital themselves, even if they are having a suspected stroke or heart attack, because it cannot guarantee that paramedics will reach them in anywhere near the expected response times.

Ministers prefer to trumpet the record funding the NHS is now receiving, and that the largest number of people ever now work in it in England, than hear such stories of the inability of the nation’s most important service to do its job.

At Tuesday’s Covid press conference Boris Johnson declined to define what an “overwhelmed” NHS looked like. He denied suggestions that it was already in that state, though did concede that “there will be a difficult period for our wonderful NHS in the next few weeks because of Omicron”. The reality is that today’s pileup of problems shows that much of the NHS is already overwhelmed.

As the NHS Providers boss, Chris Hopson, points out , while the service’s current difficulties are the result of “an exceptional Covid surge”, years of government decision-making have left it in this weakened state. “Recent winters and NHS performance pre-Covid show that, after [a] decade of longest/deepest financial squeeze in NHS history, NHS has serious capacity/demand mismatch and broken workforce model,” he said.

The NHS in England has had close to 100,000 vacancies for years now, including for about 40,000 nurses and 10,000 doctors. The price of persistent understaffing is paid daily by health personnel routinely working extra hours to ensure patients get good care; that goodwill helps keep the NHS from falling over. Patients experience it in the form of delays in getting help or having a diagnostic test. And it is also felt by a service which, in some parts, too often teeters perilously close to falling over, usually under the extra strain winter brings, but sometimes in summer too, and certainly as a result of Omicron.

Analysis by Stephen Rocks of the Health Foundation thinktank shows that the UK has only 3.93 hospital doctors per 100,000 people – the lowest in Europe. It also has just 2.42 beds per 1,000 people, the second lowest in Europe after Sweden, which has only 2.07. And it also ranks low down the international league table for its supply of nurses, CT scanners and MRI scanners.

The size of the NHS budget is important. But providing high-quality healthcare involves more than that. It also involves giving the service the staff it needs and taking firm action to reduce illness.

Today’s immediate, escalating NHS crisis has long roots, which are also political roots. Years of decisions by David Cameron, Theresa May and Boris Johnson, especially their inaction on staffing, left it enfeebled and woefully underprepared for this level of extreme pressure, which the critical incidents and unavailability of ambulances dramatically illustrate. The service’s many supporters can only hope that it does not break altogether in the coming days.

• Coronavirus
• Health policy
• Public services policy

Most viewed

• - Google Chrome

Intended for healthcare professionals

• Access provided by Google Indexer
• My email alerts
• BMA member login
• Username * Password * Forgot your log in details? Need to activate BMA Member Log In Log in via OpenAthens Log in via your institution

Search form

• Advanced search
• Search responses
• Search blogs
• Using data for...

Using data for improvement

Read the full collection.

• Related content
• Peer review
• Amar Shah , chief quality officer and consultant forensic psychiatrist, national improvement lead for the Mental Health Safety Improvement Programme
• East London NHS Foundation Trust, London, E1 8DE, UK
• amarshah{at}nhs.net @DrAmarShah

What you need to know

Both qualitative and quantitative data are critical for evaluating and guiding improvement

A family of measures, incorporating outcome, process, and balancing measures, should be used to track improvement work

Time series analysis, using small amounts of data collected and displayed frequently, is the gold standard for using data for improvement

We all need a way to understand the quality of care we are providing, or receiving, and how our service is performing. We use a range of data in order to fulfil this need, both quantitative and qualitative. Data are defined as “information, especially facts and numbers, collected to be examined and considered and used to help decision-making.” 1 Data are used to make judgements, to answer questions, and to monitor and support improvement in healthcare ( box 1 ). The same data can be used in different ways, depending on what we want to know or learn.

Defining quality improvement 2

Quality improvement aims to make a difference to patients by improving safety, effectiveness, and experience of care by:

Using understanding of our complex healthcare environment

Applying a systematic approach

Designing, testing, and implementing changes using real-time measurement for improvement

Within healthcare, we use a range of data at different levels of the system:

Patient level—such as blood sugar, temperature, blood test results, or expressed wishes for care)

Service level—such as waiting times, outcomes, complaint themes, or collated feedback of patient experience

Organisation level—such as staff experience or financial performance

Population level—such as mortality, quality of life, employment, and air quality.

This article outlines the data we need to understand the quality of care we are providing, what we need to capture to see if care is improving, how to interpret the data, and some tips for doing this more effectively.

Sources and selection criteria

This article is based on my experience of using data for improvement at East London NHS Foundation Trust, which is seen as one of the world leaders in healthcare quality improvement. Our use of data, from trust board to clinical team, has transformed over the past six years in line with the learning shared in this article. This article is also based on my experience of teaching with the Institute for Healthcare Improvement, which guides and supports quality improvement efforts across the globe.

What data do we need?

Healthcare is a complex system, with multiple interdependencies and an array of factors influencing outcomes. Complex systems are open, unpredictable, and continually adapting to their environment. 3 No single source of data can help us understand how a complex system behaves, so we need several data sources to see how a complex system in healthcare is performing.

Avedis Donabedian, a doctor born in Lebanon in 1919, studied quality in healthcare and contributed to our understanding of using outcomes. 4 He described the importance of focusing on structures and processes in order to improve outcomes. 5 When trying to understand quality within a complex system, we need to look at a mix of outcomes (what matters to patients), processes (the way we do our work), and structures (resources, equipment, governance, etc).

Therefore, when we are trying to improve something, we need a small number of measures (ideally 5-8) to help us monitor whether we are moving towards our goal. Any improvement effort should include one or two outcome measures linked explicitly to the aim of the work, a small number of process measures that show how we are doing with the things we are actually working on to help us achieve our aim, and one or two balancing measures ( box 2 ). Balancing measures help us spot unintended consequences of the changes we are making. As complex systems are unpredictable, our new changes may result in an unexpected adverse effect. Balancing measures help us stay alert to these, and ought to be things that are already collected, so that we do not waste extra resource on collecting these.

Different types of measures of quality of care

Outcome measures (linked explicitly to the aim of the project).

Aim— To reduce waiting times from referral to appointment in a clinic

Outcome measure— Length of time from referral being made to being seen in clinic

Data collection— Date when each referral was made, and date when each referral was seen in clinic, in order to calculate the time in days from referral to being seen

Process measures (linked to the things you are going to work on to achieve the aim)

Change idea— Use of a new referral form (to reduce numbers of inappropriate referrals and re-work in obtaining necessary information)

Process measure— Percentage of referrals received that are inappropriate or require further information

Data collection— Number of referrals received that are inappropriate or require further information each week divided by total number of referrals received each week

Change idea— Text messaging patients two days before the appointment (to reduce non-attendance and wasted appointment slots)

Process measure— Percentage of patients receiving a text message two days before appointment

Data collection— Number of patients each week receiving a text message two days before their appointment divided by the total number of patients seen each week

Process measure— Percentage of patients attending their appointment

Data collection— Number of patients attending their appointment each week divided by the total number of patients booked in each week

Balancing measures (to spot unintended consequences)

Measure— Percentage of referrers who are satisfied or very satisfied with the referral process (to spot whether all these changes are having a detrimental effect on the experience of those referring to us)

Data collection— A monthly survey to referrers to assess their satisfaction with the referral process

Measure— Percentage of staff who are satisfied or very satisfied at work (to spot whether the changes are increasing burden on staff and reducing their satisfaction at work)

Data collection— A monthly survey for staff to assess their satisfaction at work

How should we look at the data?

This depends on the question we are trying to answer. If we ask whether an intervention was efficacious, as we might in a research study, we would need to be able to compare data before and after the intervention and remove all potential confounders and bias. For example, to understand whether a new treatment is better than the status quo, we might design a research study to compare the effect of the two interventions and ensure that all other characteristics are kept constant across both groups. This study might take several months, or possibly years, to complete, and would compare the average of both groups to identify whether there is a statistically significant difference.

This approach is unlikely to be possible in most contexts where we are trying to improve quality. Most of the time when we are improving a service, we are making multiple changes and assessing impact in real-time, without being able to remove all confounding factors and potential bias. When we ask whether an outcome has improved, as we do when trying to improve something, we need to be able to look at data over time to see how the system changes as we intervene, with multiple tests of change over a period. For example, if we were trying to improve the time from a patient presenting in the emergency department to being admitted to a ward, we would likely be testing several different changes at different places in the pathway. We would want to be able to look at the outcome measure of total time from presentation to admission on the ward, over time, on a daily basis, to be able to see whether the changes made lead to a reduction in the overall outcome. So, when looking at a quality issue from an improvement perspective, we view smaller amounts of data but more frequently to see if we are improving over time. 2

What is best practice in using data to support improvement?

Best practice would be for each team to have a small number of measures that are collectively agreed with patients and service users as being the most important ways of understanding the quality of the service being provided. These measures would be displayed transparently so that all staff, service users, and patients and families or carers can access them and understand how the service is performing. The data would be shown as time series analysis, to provide a visual display of whether the service is improving over time. The data should be available as close to real-time as possible, ideally on a daily or weekly basis. The data should prompt discussion and action, with the team reviewing the data regularly, identifying any signals that suggest something unusual in the data, and taking action as necessary.

The main tools used for this purpose are the run chart and the Shewhart (or control) chart. The run chart ( fig 1 ) is a graphical display of data in time order, with a median value, and uses probability-based rules to help identify whether the variation seen is random or non-random. 2 The Shewhart (control) chart ( fig 2 ) also displays data in time order, but with a mean as the centre line instead of a median, and upper and lower control limits (UCL and LCL) defining the boundaries within which you would predict the data to be. 6 Shewhart charts use the terms “common cause variation” and “special cause variation,” with a different set of rules to identify special causes.

A typical run chart

• Download figure
• Open in new tab
• Download powerpoint

A typical Shewhart (or control) chart

Is it just about numbers?

We need to incorporate both qualitative and quantitative data to help us learn about how the system is performing and to see if we improve over time. Quantitative data express quantity, amount, or range and can be measured numerically—such as waiting times, mortality, haemoglobin level, cash flow. Quantitative data are often visualised over time as time series analyses (run charts or control charts) to see whether we are improving.

However, we should also be capturing, analysing, and learning from qualitative data throughout our improvement work. Qualitative data are virtually any type of information that can be observed and recorded that is not numerical in nature. Qualitative data are particularly useful in helping us to gain deeper insight into an issue, and to understand meaning, opinion, and feelings. This is vital in supporting us to develop theories about what to focus on and what might make a difference. 7 Examples of qualitative data include waiting room observation, feedback about experience of care, free-text responses to a survey.

Using qualitative data for improvement

One key point in an improvement journey when qualitative data are critical is at the start, when trying to identify “What matters most?” and what the team’s biggest opportunity for improvement is. The other key time to use qualitative data is during “Plan, Do, Study, Act” (PDSA) cycles. Most PDSA cycles, when done well, rely on qualitative data as well as quantitative data to help learn about how the test fared compared with our original theory and prediction.

Table 1 shows four different ways to collect qualitative data, with advantages and disadvantages of each, and how we might use them within our improvement work.

Different ways to collect qualitative data for improvement

• View inline

Tips to overcome common challenges in using data for improvement?

One of the key challenges faced by healthcare teams across the globe is being able to access data that is routinely collected, in order to use it for improvement. Large volumes of data are collected in healthcare, but often little is available to staff or service users in a timescale or in a form that allows it to be useful for improvement. One way to work around this is to have a simple form of measurement on the unit, clinic, or ward that the team own and update. This could be in the form of a safety cross 8 or tally chart. A safety cross ( fig 3 ) is a simple visual monthly calendar on the wall which allows teams to identify when a safety event (such as a fall) occurred on the ward. The team simply colours in each day green when no fall occurred, or colours in red the days when a fall occurred. It allows the team to own the data related to a safety event that they care about and easily see how many events are occurring over a month. Being able to see such data transparently on a ward allows teams to update data in real time and be able to respond to it effectively.

Example of a safety cross in use

A common challenge in using qualitative data is being able to analyse large quantities of written word. There are formal approaches to qualitative data analyses, but most healthcare staff are not trained in these methods. Key tips in avoiding this difficulty are ( a ) to be intentional with your search and sampling strategy so that you collect only the minimum amount of data that is likely to be useful for learning and ( b ) to use simple ways to read and theme the data in order to extract useful information to guide your improvement work. 9 If you want to try this, see if you can find someone in your organisation with qualitative data analysis skills, such as clinical psychologists or the patient experience or informatics teams.

Education into practice

What are the key measures for the service that you work in?

Are these measures available, transparently displayed, and viewed over time?

What qualitative data do you use in helping guide your improvement efforts?

How patients were involved in the creation of this article

Service users are deeply involved in all quality improvement work at East London NHS Foundation Trust, including within the training programmes we deliver. Shared learning over many years has contributed to our understanding of how best to use all types of data to support improvement. No patients have had input specifically into this article.

This article is part of a series commissioned by The BMJ based on ideas generated by a joint editorial group with members from the Health Foundation and The BMJ , including a patient/carer. The BMJ retained full editorial control over external peer review, editing, and publication. Open access fees and The BMJ ’s quality improvement editor post are funded by the Health Foundation.

Competing interests: I have read and understood the BMJ Group policy on declaration of interests and have no relevant interests to declare.

Provenance and peer review: Commissioned; externally peer reviewed.

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ .

• ↵ Cambridge University Press. Cambridge online dictionary , 2008. https://dictionary.cambridge.org/ .
• Provost LP ,
• Braithwaite J
• Neuhauser D
• Donabedian A
• Mohammed MA
• Davidoff F ,
• Dixon-Woods M ,
• Leviton L ,
• ↵ Flynn M. Quality & Safety—The safety cross system: simple and effective. https://www.inmo.ie/MagazineArticle/PrintArticle/11155 .

• Critical Thinking and Decision Making

In this Critical Thinking module delegates discover that, with increased responsibility in an ever-changing world, they will need a toolkit to become more naturally empowered to form a sound judgement and make good decisions.

The key to this module is taking the most complex of subjects and making them simple. In just a few moves delegates will be able to cut through the fog and clearly see the problem, ask the right questions and make informed decisions from a simple trilogy of answers, helping you to form better judgements and decisions, both individually and collectively.

Who is it for?

Managers and any individual who is part of the decision-making process.

Course Objectives:

By the end of this programme, participants will be able to:

• Understand how individual and organisational character drive our reasoning and
• Improve ‘Judgement & Decision Making’ to drive the organization forward
• Learn how to influence others effectively and make good team decisions

Course Content:

• The Chemistry of Change
• The change curve – organizational and personal effects
• Problem and critical analysis
• Judgement and decision-making criteria – priorities and preferences
• Power of influence – taking people with you
• Actions, Summary & Close

Indicative Duration:

Contact our Academy team today for a consultation to discuss your specific education and training needs.

Contact Academy team

• Academy Courses
• Introduction to Project Management
• Clinical Supervision Supervisor Training
• Introduction to Continence Care
• Introduction to Dementia
• Introduction to ECG
• Introduction to Venepuncture
• Introduction to Wound Management
• Managing Fluid Balance
• Pressure Ulcer Care
• Understanding Nutrition and Hydration
• Action Learning Set Facilitator Training
• Problem Solving Group Facilitation
• Simulation Facilitator Training
• Train the Trainer
• Healthcare Support Worker Development
• Care Planning
• Co-ordinated Care - A Multi-disciplinary approach
• Appraisals and Review Meetings
• Coaching Skills
• Developing Cultural Competence for Leaders
• Developing Your Team
• Management Essentials
• Managing Difficult Conversations
• Mastering Organisational Relationships
• System Leadership
• OSCE Preparation Training – Adult Acute
• Developing an organisational 'Just Culture'
• Duty of Candour and Being Open Principles
• Incident Response
• Introduction to Complex Systems and Systems Thinking
• Introduction to Human Factors
• Investigator Preparation Training
• Just Culture
• Purpose of Patient Safety Incident Response
• Developing Your Confidence
• Effective Communication Persuasion and Influence
• Essential Writing Skills
• Giving and Receiving Feedback
• Presentations Skills
• Personal Development Courses
• Clinical Courses
• Education and Training Courses
• Integrated Care
• Management and Leadership Courses
• Patient Safety Courses

• Research article
• Open Access
• Published: 21 January 2021

Evaluation and improvement of the National Early Warning Score (NEWS2) for COVID-19: a multi-hospital study

• Ewan Carr   ORCID: orcid.org/0000-0002-1146-4922 1   na1 ,
• Rebecca Bendayan 1 , 2   na1 ,
• Daniel Bean 1 , 3 ,
• Matt Stammers 4 , 5 , 6 ,
• Wenjuan Wang 7 ,
• Huayu Zhang 8 ,
• Thomas Searle 1 , 2 ,
• Zeljko Kraljevic 1 ,
• Anthony Shek 9 ,
• Hang T. T. Phan 4 , 5 ,
• Walter Muruet 7 ,
• Rishi K. Gupta 10 ,
• Anthony J. Shinton 6 ,
• Mike Wyatt 11 ,
• Ting Shi 8 ,
• Xin Zhang 12 ,
• Andrew Pickles 1 , 2 ,
• Daniel Stahl 1 ,
• Rosita Zakeri 13 , 14 ,
• Mahdad Noursadeghi 15 ,
• Kevin O’Gallagher 13 , 14 ,
• Matt Rogers 11 ,
• Amos Folarin 1 , 3 , 16 , 17 ,
• Andreas Karwath 18 , 19 , 20 ,
• Kristin E. Wickstrøm 21 ,
• Alvaro Köhn-Luque 22 ,
• Luke Slater 18 , 19 , 20 ,
• Victor Roth Cardoso 18 , 19 , 20 ,
• Christopher Bourdeaux 11 ,
• Aleksander Rygh Holten 23 ,
• Simon Ball 20 , 24 ,
• Chris McWilliams 25 ,
• Lukasz Roguski 3 , 16 , 19 ,
• Florina Borca 4 , 5 , 6 ,
• James Batchelor 4 ,
• Erik Koldberg Amundsen 21 ,
• Xiaodong Wu 26 , 27 ,
• Georgios V. Gkoutos 18 , 19 , 20 , 24 ,
• Jiaxing Sun 26 ,
• Ashwin Pinto 6 ,
• Bruce Guthrie 8 ,
• Cormac Breen 7 ,
• Abdel Douiri 7 ,
• Honghan Wu 3 , 16 ,
• Vasa Curcin 7 ,
• James T. Teo 9 , 13 ,
• Ajay M. Shah 13 , 14 &
• Richard J. B. Dobson 1 , 2 , 3 , 16 , 17  

BMC Medicine volume  19 , Article number:  23 ( 2021 ) Cite this article

19k Accesses

57 Citations

73 Altmetric

Metrics details

The National Early Warning Score (NEWS2) is currently recommended in the UK for the risk stratification of COVID-19 patients, but little is known about its ability to detect severe cases. We aimed to evaluate NEWS2 for the prediction of severe COVID-19 outcome and identify and validate a set of blood and physiological parameters routinely collected at hospital admission to improve upon the use of NEWS2 alone for medium-term risk stratification.

Training cohorts comprised 1276 patients admitted to King’s College Hospital National Health Service (NHS) Foundation Trust with COVID-19 disease from 1 March to 30 April 2020. External validation cohorts included 6237 patients from five UK NHS Trusts (Guy’s and St Thomas’ Hospitals, University Hospitals Southampton, University Hospitals Bristol and Weston NHS Foundation Trust, University College London Hospitals, University Hospitals Birmingham), one hospital in Norway (Oslo University Hospital), and two hospitals in Wuhan, China (Wuhan Sixth Hospital and Taikang Tongji Hospital). The outcome was severe COVID-19 disease (transfer to intensive care unit (ICU) or death) at 14 days after hospital admission. Age, physiological measures, blood biomarkers, sex, ethnicity, and comorbidities (hypertension, diabetes, cardiovascular, respiratory and kidney diseases) measured at hospital admission were considered in the models.

A baseline model of ‘NEWS2 + age’ had poor-to-moderate discrimination for severe COVID-19 infection at 14 days (area under receiver operating characteristic curve (AUC) in training cohort = 0.700, 95% confidence interval (CI) 0.680, 0.722; Brier score = 0.192, 95% CI 0.186, 0.197). A supplemented model adding eight routinely collected blood and physiological parameters (supplemental oxygen flow rate, urea, age, oxygen saturation, C-reactive protein, estimated glomerular filtration rate, neutrophil count, neutrophil/lymphocyte ratio) improved discrimination (AUC = 0.735; 95% CI 0.715, 0.757), and these improvements were replicated across seven UK and non-UK sites. However, there was evidence of miscalibration with the model tending to underestimate risks in most sites.

Conclusions

NEWS2 score had poor-to-moderate discrimination for medium-term COVID-19 outcome which raises questions about its use as a screening tool at hospital admission. Risk stratification was improved by including readily available blood and physiological parameters measured at hospital admission, but there was evidence of miscalibration in external sites. This highlights the need for a better understanding of the use of early warning scores for COVID.

Peer Review reports

Key messages

The National Early Warning Score (NEWS2), currently recommended for stratification of severe COVID-19 disease in the UK, showed poor-to-moderate discrimination for medium-term outcomes (14-day transfer to intensive care unit (ICU) or death) amongst COVID-19 patients.

Risk stratification was improved by the addition of routinely measured blood and physiological parameters routinely at hospital admission (supplemental oxygen, urea, oxygen saturation, C-reactive protein, estimated glomerular filtration rate, neutrophil count, neutrophil/lymphocyte ratio) which provided moderate improvements in a risk stratification model for 14-day ICU/death.

This improvement over NEWS2 alone was maintained across multiple hospital trusts, but the model tended to be miscalibrated with risks of severe outcomes underestimated in most sites.

We benefited from existing pipelines for informatics at King’s College Hospital such as CogStack that allowed rapid extraction and processing of electronic health records. This methodological approach provided rapid insights and allowed us to overcome the complications associated with slow data centralisation approaches.

As of 9 December 2020, there have been > 67 million confirmed cases of COVID-19 disease worldwide [ 1 ]. While approximately 80% of infected individuals have mild or no symptoms [ 2 ], some develop severe COVID-19 disease requiring hospital admission. Within the subset of those requiring hospitalisation, early identification of those who deteriorate and require transfer to an intensive care unit (ICU) for organ support or may die is vital.

Currently, available risk scores for deterioration of acutely ill patients include (i) widely used generic ward-based risk indices such as the National Early Warning Score (NEWS2, [ 3 ]), (ii) the Modified Sequential Organ Failure Assessment (mSOFA) [ 4 ] and Quick Sequential Organ Failure Assessment [ 5 ] scoring systems, and (iii) the pneumonia-specific risk index, CURB-65 [ 6 ] which combines physiological observations with limited blood markers and comorbidities. NEWS2 is a summary score of six physiological parameters or ‘vital signs’ (respiratory rate, oxygen saturation, systolic blood pressure, heart rate, level of consciousness, temperature and supplemental oxygen dependency) used to identify patients at risk of early clinical deterioration in the United Kingdom (UK) National Health Service (NHS) hospitals [ 7 , 8 ] and primary care. Some components (in particular, patient temperature, oxygen saturation, and supplemental oxygen dependency) have been associated with COVID-19 outcomes [ 2 ], but little is known about their predictive value for COVID-19 disease severity in hospitalised patients [ 9 ]. Additionally, a number of COVID-19-specific risk indices are being developed [ 10 , 11 ] as well as unvalidated online calculators [ 12 ], but generalisability is unknown [ 13 ]. A Chinese study has suggested a modified version of NEWS2 with the addition of age only [ 14 ] but without any data on performance. With near-universal usage of NEWS2 in UK NHS Trusts since March 2019 [ 15 ], a minor adaptation to NEWS2 would be relatively easy to implement.

As the SARS-Cov2 pandemic has progressed, a number of risk prediction models to support clinical decisions, triage, and care in hospitalised patients have been proposed [ 13 ] incorporating potentially useful blood biomarkers [ 2 , 16 , 17 , 18 , 19 ]. These include neutrophilia and lymphopenia, particularly in older adults [ 11 , 18 , 20 , 21 ]; neutrophil-to-lymphocyte ratio [ 22 ]; C-reactive protein (CRP) [ 13 ]; lymphocyte-to-CRP ratio [ 22 ]; markers of liver and cardiac injury such as alanine aminotransferase (ALT), aspartate aminotransferase (AST), and cardiac troponin [ 23 ]; and elevated d -dimers, ferritin and fibrinogen [ 2 , 6 , 8 ].

Our aim is to evaluate the NEWS2 score and identify which clinical and blood biomarkers routinely measured at hospital admission can improve medium-term risk stratification of severe COVID-19 outcome at 14 days from hospital admission. Our specific objectives were as follows:

To explore independent associations of routinely measured physiological and blood parameters (including NEWS2 parameters) at hospital admission with disease severity (ICU admission or death at 14 days from hospital admission), adjusting for demographics and comorbidities

To develop a prediction model for severe COVID-19 outcomes at 14 days combining multiple blood and physiological parameters

To compare the discrimination, calibration, and clinical utility of the resulting model with NEWS2 score and age alone using (i) internal validation and (ii) external validation at seven UK and international sites

A recent systematic review found that most existing prediction models for COVID-19 had a high risk of bias due to non-representative samples, model overfitting, or poor reporting [ 13 ]. The analyses presented here build upon our earlier work [ 24 ] which suggested that adding age and common blood biomarkers to the NEWS2 score could improve risk stratification in patients hospitalised with COVID-19. While incorporating external validation, this preliminary work was limited in that the training sample comprised 439 patients (the cohort available at the time of model development). In the present study, we (i) expand the cohort used for model development to all 1276 patients at King’s College Hospital (KCH), (ii) use hospital admission (rather than symptom onset) as the index date, (iii) consider shorter-term outcomes (3-day ICU/death), (iv) improve the reporting of model calibration and clinical utility, and (v) increase the number of external sites from three to seven.

Study cohorts

The KCH training cohort ( n  = 1276) was defined as all adult inpatients testing positive for severe acute respiratory syndrome coronavirus 2 (SARS-Cov2) by reverse transcription polymerase chain reaction (RT-PCR) between 1 March and 31 April 2020 at two acute hospitals (King’s College Hospital and Princess Royal University Hospital) in South East London (UK) of Kings College Hospital NHS Foundation Trust (KCH). All patients included in the study had symptoms consistent with COVID-19 (e.g. cough, fever, dyspnoea, myalgia, delirium, diarrhoea). For external validation purposes, we used seven cohorts:

Guy’s and St Thomas’ Hospital NHS Foundation Trust (GSTT) of 988 cases (3 March 2020 to 26 August 2020)

University Hospitals Southampton NHS Foundation Trust (UHS) of 633 cases (7 March to 6 June 2020)

University Hospitals Bristol and Weston NHS Foundation Trust (UHBW) of 190 cases (12 March to 11 June 2020)

University College Hospital London (UCH) of 411 cases (1 February to 30 April 2020)

University Hospitals Birmingham (UHB) of 1037 cases (1 March to 31 June 2020)

Oslo University Hospital (OUH) of 163 cases (6 March to 13 June 2020)

Wuhan Sixth Hospital and Taikang Tongji Hospital of 2815 cases (4 February 2020 to 30 March 2020)

Data were extracted from structured and/or unstructured components of electronic health records (EHR) in each site as detailed below.

For all sites, the outcome was severe COVID-19 disease at 14 days following hospital admission, categorised as transfer to the ICU/death (WHO-COVID-19 Outcomes Scales 6–8) vs. not transferred to the ICU/death (scales 3–5) [ 25 ]. For nosocomial patients (patients with symptom onset after hospital admission), the endpoint was defined as 14 days after symptom onset. Dates of hospital admission, symptom onset, ICU transfer, and death were extracted from electronic health records or ascertained manually by a clinician.

Blood and physiological parameters

We included blood and physiological parameters that were routinely obtained at hospital admission and which are routinely available in a wide range of national and international hospital and community settings. Measures available for fewer than 30% of patients were not considered (including Troponin-T, Ferritin, d -dimers and glycated haemoglobin (HbA1c), Glasgow Coma Scale score). We excluded creatinine since this parameter correlates highly ( r  > 0.8) with, and is used in the derivation of, estimated glomerular filtration rate. We excluded white blood cell count (WBCs) which is highly correlated with neutrophil and lymphocyte counts.

The candidate blood parameters therefore comprised albumin (g/L), C-reactive protein (CRP; mg/L), estimated glomerular filtration rate (GFR; mL/min), haemoglobin (g/L), lymphocyte count (× 10 9 /L), neutrophil count (× 10 9 /L), platelet count (PLT; × 10 9 /L), neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-CRP ratio [ 22 ], and urea (mmol/L). The candidate physiological parameters included the NEWS2 total score, as well as the following parameters: respiratory rate (breaths per minute), oxygen saturation (%), supplemental oxygen flow rate (L/min), diastolic blood pressure (mmHg), systolic blood pressure (mmHg), heart rate (beats/min), and temperature (°C). For all parameters, we used the first available measure up to 48 h following hospital admission.

Demographics and comorbidities

Age, sex, ethnicity and comorbidities were considered. Self-defined ethnicity was categorised as White vs. non-White (Black, Asian, or other minority ethnic) and patients with ethnicity recorded as ‘unknown/mixed/other’ were excluded ( n  = 316; 25%). Binary variables were derived for comorbidities: hypertension, diabetes, heart disease (heart failure and ischemic heart disease), respiratory disease (asthma and chronic obstructive pulmonary disease (COPD)), and chronic kidney disease.

Data processing

King’s college hospital.

Data were extracted from the structured and unstructured components of the electronic health record (EHR) using natural language processing (NLP) tools belonging to the CogStack ecosystem [ 26 ], namely MedCAT [ 27 ] and MedCATTrainer [ 28 ]. The CogStack NLP pipeline captures negation, synonyms, and acronyms for medical Systematised Nomenclature of Medicine Clinical Terms (SNOMED-CT) concepts as well as surrounding linguistic context using deep learning and long short-term memory networks. MedCAT produces unsupervised annotations for all SNOMED-CT concepts (Additional file  1 : Table S1) under parent terms Clinical Finding, Disorder, Organism, and Event with disambiguation, pre-trained on MIMIC-III [ 29 ]. Starting from our previous model [ 30 ], further supervised training improved detection of annotations and meta-annotations such as experiencer (is the annotated concept experienced by the patient or other), negation (is the concept annotated negated or not), and temporality (is the concept annotated in the past or present) with MedCATTrainer. Meta-annotations for hypothetical, historical, and experiencer were merged into “Irrelevant” allowing us to exclude any mentions of a concept that did not directly relate to the patient currently. Performance of the NLP pipeline for comorbidities mentioned in the text was evaluated on 4343 annotations in 146 clinical documents by a clinician (JT). F1 scores, precision, and recall are presented in Additional file  2 : Table S2.

Guy’s and St Thomas’ NHS Foundation Trust

Electronic health records from all patients admitted to Guy’s and St Thomas’ NHS Foundation Trust who had a positive COVID-19 test result between 3 March and 21 May 2020, inclusive, were identified. Data were extracted using structured queries from six complementary platforms and linked using unique patient identifiers. Data processing was performed using Python 3.7 [ 31 ]. The process and outputs were reviewed by a study clinician.

University Hospitals Southampton

Data were extracted from the structured components of the UHS CHARTS EHR system and data warehouse. Data were transformed into the required format for validation purposes using Python 3.7 [ 31 ]. Diagnosis and comorbidity data of interest were gathered from the International Statistical Classification of Diseases (ICD-10) coded data. No unstructured data extraction was required for validation purposes. The process and outputs were reviewed by an experienced clinician prior to analysis.

University Hospitals Bristol and Weston NHS Foundation Trust

Data were extracted from UHBW electronic health records system (Medway). ICD-10 codes were used for diagnosis and comorbidity data. Data were transformed in line with project specifications and exported for analysis in Python 3.7 [ 31 ].

University College Hospital London

Dates of hospital admission, symptom onset, ICU transfer, and death were extracted from electronic health records. The outcome (14-day ICU/death) was defined in UCLH as ‘initiation of ventilatory support (continuous positive airway pressure, non-invasive ventilation, high-flow nasal cannula oxygen, invasive mechanical ventilation, or extracorporeal membrane oxygenation) or death’ which is consistent WHO-COVID-19 Outcomes Scales 6–8.

Wuhan cohort

Demographic, premorbid conditions, clinical symptoms or signs at presentation, laboratory data, and treatment and outcome data were extracted from electronic medical records using a standardised data collection form by a team of experienced respiratory clinicians, with double data checking and involvement of a third reviewer where there was disagreement. Anonymised data was entered into a password-protected computerised database.

University Hospitals Birmingham

Dates of hospital admission, symptom onset, ICU transfer, and death were extracted from electronic health records using the Prescribing Information and Communications System (PICS) system. The extracted data was transformed into the required format for validation purposes using Python 3.8 [ 31 ]. Diagnosis and comorbidity data of interest were gathered from ICD-10 coded data. The outcomes (3- and 14-day ICU/death) were defined consistent with WHO-COVID-19 Outcomes Scales 6–8.

Oslo University Hospital

All admitted patients with confirmed COVID-19 by positive SARS-CoV2 PCR were included in a quality registry. Data input into the register was manual. Register data was supplemented with test results from the laboratory information system (LIS) by matching exported Excel files from the register with exported Excel files from LIS. The fidelity of the match was checked against the original data source manually for a small number of patients. Only patients with symptoms consistent with COVID-19 were included in the study.

Statistical analyses

All continuous parameters were winsorized (at 1% and 99%) and scaled (mean = 0; standard deviation = 1) to facilitate interpretability and comparability [ 32 ]. Logarithmic or square root transformations were applied to skewed parameters. To explore independent associations of blood and physiological parameters with 14-day ICU/death (objective 1), we used logistic regression with Firth’s bias reduction method [ 33 ]. Each parameter was tested independently, adjusted for age and sex (model 1), and then additionally adjusted for comorbidities (model 2). P values were adjusted using the Benjamini-Hochberg procedure to keep the false discovery rate (FDR) at 5% [ 34 ].

To evaluate NEWS2 and identify parameters that could improve prediction of severe COVID-19 outcomes (objectives 2 and 3), we used regularised logistic regression with a least absolute shrinkage and selection operator (LASSO) estimator that shrinks parameters according to their variance, reduces overfitting, and enables automatic variable selection [ 35 ]. The optimal degree of regularisation was determined by identifying a tuning parameter λ using cross-validation. To avoid overfitting and to reduce the number of false-positive predictors, λ was selected to give a model with an area under the receiver operating characteristic curve (AUC) one standard error below the ‘best’ model. To evaluate the predictive performance of our model on new cases of the same underlying population (internal validation), we performed nested cross-validation (10-folds the for inner loop; 10-folds/1000 repeats for the outer loop). Discrimination was assessed using AUC and Brier score. Missing feature information was imputed using k -nearest neighbour (kNN) imputation ( k  = 5). All steps (feature selection, winsorizing, scaling, and kNN imputation) were incorporated within the model development and selection process to avoid data leakage that would otherwise result in optimistic performance measures [ 36 ]. All analyses were conducted with Python 3.8 [ 31 ] using the statsmodels [ 37 ] and Scikit-Learn [ 38 ] packages.

We evaluated the transportability of the derived regularised logistic regression model in external validation samples from GSTT ( n  = 988), UHS ( n  = 633), UHBW ( n  = 190), UCH ( n  = 411), UHB ( n  = 1037), OUH ( n  = 163), and Wuhan ( n  = 2815). Validation used LASSO logistic regression models trained on the KCH training sample, with code and pre-trained models shared via GitHub. Footnote 1 Models were assessed in terms of discrimination (AUC, sensitivity, specificity, Brier score), calibration, and clinical utility (decision curve analysis, number needed to evaluate) [ 32 , 39 ]. Moderate calibration was assessed by plotting model-predicted probabilities ( x -axis) against observed proportions ( y -axis) with locally estimated scatterplot smoothing (LOESS) and logistic curves [ 40 ]. Clinical utility was assessed using decision curve analysis where ‘net benefit’ was plotted against a range of threshold probabilities. Unlike diagnostic performance measures, decision curves incorporate preferences of the clinician and patient. The threshold probability ( p t ) is where the expected benefit of treatment is equal to the expected benefit of avoiding treatment [ 41 ]. Net benefit was calculated by counting the number of true positives (predicted risk >  p t and experienced severe COVID-19 outcome) and false positives (predicted risk >  p t but did not experience severe COVID-19 outcome) and using the below formula:

Our model was developed as a screening tool, to identify at hospital admission patients at risk of more severe outcomes. The intended treatment for patients with a positive result from this model would be further examination by a clinician, who would make recommendations regarding appropriate treatment (e.g. earlier transfer to the ICU, intensive monitoring, treatment). We compared the decision curve from our model to two extreme cases of ‘treat none’ and ‘treat all’. The ‘treat none’ (i.e. routine management) strategy implies that no patients would be selected for further examination by a clinician; the ‘treat all’ strategy (i.e. intensive management) implies that all patients would undergo further assessment. A model is clinically beneficial if the model-implied net benefit is greater than either the ‘treat none’ or ‘treat all’ strategies.

Since the intended strategy involves a further examination by a clinician, and is therefore low risk, our emphasis throughout is on avoiding false negatives (i.e. failing to detect a severe case) at the expense of false positives. We therefore used thresholds of 30% and 20% (for 14-day and 3-day outcomes, respectively) to calculate sensitivity and specificity. This gave a better balance of sensitivity vs. specificity and reflected the clinical preference to avoid false negatives for the proposed screening tool.

Sensitivity analyses

We conducted five sensitivity analyses. First, to explore the ability of NEWS2 to predict shorter-term severe COVID-19 outcome, we developed models for ICU transfer/death at 3 days following hospital admission. All steps described above were repeated, including training (feature selection) and external validation. Second, following recent studies suggesting sex differences in COVID-19 outcome [ 18 ], we tested interactions between each physiological and blood parameters and sex using likelihood-ratio tests. Third, we repeated all models with adjustment for ethnicity in the subset of individuals with available data for ethnicity ( n  = 960 in the KCH training sample). Fourth, to explore the differences between community-acquired vs. nosocomial infection, we repeated all models after excluding 153 nosocomial patients ( n  = 1123). Finally, we considered an alternative baseline model of ‘NEWS2 only’. Our primary analyses used a baseline model of ‘NEWS2 + age’ because NEWS2 is rarely used in isolation for prognostication and treatment decisions will incorporate other patient characteristics such as age.

Descriptive analyses

The KCH training cohort comprised 1276 patients admitted with a confirmed diagnosis of COVID-19 (from 1 March to 31 April 2020) of whom 389 (31%) were transferred to the ICU or died within 14 days of hospital admission, respectively. The validation cohorts comprised 6237 patients across seven sites. At UK NHS trusts, 30 to 42% of patients were transferred to the ICU or died within 14 days of admission. Disease severity was lower in the Wuhan sample, where 4% were transferred to the ICU or died. Table  1 presents the demographic and clinical characteristics of the training and validation cohorts. The UK sites were similar in terms of age and sex, with patients tending to be older (median age 59–74) and male (58 to 63%) but varied in the proportion of patients of non-White ethnicity (from 10% at UHS to 40% at KCH and UCH). Blood and physiological parameters were broadly consistent across UK sites.

Logistic regression models were used to assess independent associations between each variable and severe COVID-19 outcome (ICU transfer/death) in the KCH cohort. Additional file  3 : Table S3 presents odds ratios adjusted for age and sex (model 1) and comorbidities (model 2), sorted by effect size. Increased odds of transfer to the ICU or death by 14 days were associated with NEWS2 score, oxygen flow rate, respiratory rate, CRP, neutrophil count, urea, neutrophil/lymphocyte ratio, heart rate, and temperature. Reduced odds of severe outcomes were associated with lymphocyte/CRP ratio, oxygen saturation, estimated GFR, and albumin.

Evaluating NEWS2 score for prediction of severe COVID-19 outcome

Logistic regression models were used to evaluate a baseline model containing hospital admission NEWS2 score and age for the prediction of severe COVID-19 outcomes at 14 days. Internally validated discrimination for the KCH training sample was moderate (AUC = 0.700; 95% confidence interval (CI) 0.680, 0.722; Brier score = 0.192; 0.186, 0.197; Table  2 ). Discrimination remained poor-to-moderate in UK validation sites (AUC = 0.623 to 0.729) but was moderate-to-good in Norway (AUC = 0.786) and Wuhan hospitals (AUC = 0.815) (Figs.  1 and 2 ). Calibration was inconsistent with risks underestimated in some sites (UHS, GSTT) and overestimated in others (UHBW, UHB; Fig.  2 ).

Improvement in the area under the curve (AUC) for supplemented NEWS2 model for 14-day ICU/death at training and validation sites

Calibration (logistic and LOESS curves) of supplemented NEWS2 model for 14-day ICU/death model at validation sites

Supplementing NEWS2 with routinely collected blood and physiological parameters

We considered whether routine blood and physiological parameters could improve risk stratification for medium-term COVID-19 outcome (ICU transfer/death at 14 days). When adding demographic, blood, and physiological parameters to NEWS2, nine features were retained following LASSO regularisation, in order of effect size: NEWS2 score, supplemental oxygen flow rate, urea, age, oxygen saturation, CRP, estimated GFR, neutrophil count, and neutrophil/lymphocyte ratio. Notably, comorbid conditions were not retained when added in subsequent models, suggesting most of the variance explained was already captured by the included parameters. Internally validated discrimination in the KCH training sample was moderate (AUC = 0.735; 95% CI 0.715, 0.757) but improved compared to ‘NEWS2 + age’ (Table  2 ). This improvement over NEWS2 alone was replicated in validation samples (Fig.  1 ). The supplemented model continued to show evidence of substantial miscalibration.

For the 3-day endpoint, 13% of patients at KCH ( n  = 163) and between 16 and 29% of patients in the UK and Norway were transferred to the ICU or died (Table  1 ). The 3-day model retained just two parameters following regularisation: NEWS2 score and supplemental oxygen flow rate. For the baseline model (‘NEWS2 + age’), discrimination was moderate at internal validation (AUC = 0.764; 95% CI 0.737, 0.794; Additional file  4 : Table S4) and external validation (AUC = 0.673 to 0.755), but calibration remained poor (Additional file  5 : Figure S1). Moreover, the supplemented model (‘NEWS2 + oxygen flow rate’) showed smaller improvements in discrimination compared to those seen at 14 days. For the KCH training cohort, internally validated AUC increased by 0.025: from 0.764 (95% CI 0.737, 0.794) for ‘NEWS2 + age’ to 0.789 (0.763, 0.819) for the supplemented model (‘NEWS2 + oxygen flow rate’). At external validation, improvements were modest (UHBW, OUH) or negative (GSTT) in some sites, but more substantial in others (UHS, UCH). Moreover, model calibration was considerably worse for the supplemented 3-day model (Additional file  5 : Figure S1).

We found no evidence of difference by sex (results not shown) and the findings were consistent when additionally adjusting for ethnicity in the subset of individuals with ethnicity data and when excluding nosocomial patients (Additional file  6 : Table S5). Discrimination for the alternative baseline model of ‘NEWS2 only’ (Additional file  7 : Table S6) showed a similar pattern of results as those for ‘NEWS2 + age’, except that improvements in discrimination for the supplemented model (‘All features’) were larger in most sites.

Decision curve analysis

Decision curve analysis for the 14-day endpoint is presented in Fig.  3 . At KCH, the baseline model (‘NEWS2 + age’) offered small increments in net benefit compared to the ‘treat all’ and ‘treat none’ strategies for risk thresholds in the range 25 to 60%. This was replicated in all validation cohorts except for UHBW and OUH where the net benefit for ‘NEWS2 + age’ was lower than the ‘treat none’ strategy beyond the 40% risk threshold. The supplemented model (‘All features’) improved upon ‘NEWS2 + age’ and the two default strategies in most sites across the range 20 to 80%, except for (i) UHBW, where ‘treat none’ was superior beyond thresholds of 55%, and (ii) GSTT, where ‘treat all’ was superior up to a threshold of 30% and no improvement was seen for the supplemented model.

Net benefit of supplemented NEWS2 model for 14-day ICU/death compared to default strategies (‘treat all’ and ‘treat none’) at training and validation sites

For the 3-day endpoint, the improvement in net benefit for the supplemented model over the two default strategies was smaller, compared to the improvements seen at 14 days (Additional file  8 : Figure S2). At three sites (UHBW, GSST, and Wuhan), neither the baseline (‘NEWS2 + age’) nor the supplemented (‘All features’) models offered any improvement over the ‘treat all’ or ‘treat none’ strategies. At KCH and UHS, net benefit for ‘NEWS2 + age’ was higher than the default strategies for a range of risk thresholds but was not increased further by the supplemented (‘NEWS2 + oxygen flow rate’) model.

Principal findings

This study is amongst the first to systematically evaluate NEWS2 for severe COVID-19 outcome and carry out external validation at multiple international sites (five UK NHS Trusts, one hospital in Norway, and two hospitals in Wuhan, China). We found that while ‘NEWS2 + age’ had moderate discrimination for short-term COVID-19 outcome (3-day ICU transfer/death), it showed poor-to-moderate discrimination for the medium-term outcome (14-day ICU transfer/death). Thus, while NEWS2 may be effective for short-term (e.g. 24 h) prognostication, our results question its suitability as a screening tool for medium-term COVID-19 outcome. Risk stratification was improved by adding routinely collected blood and physiological parameters, and discrimination in supplemented models was moderate-to-good. However, the model showed evidence of miscalibration, with a tendency to underestimate risks in external sites. The derived model for 14-day ICU transfer/death included nine parameters: NEWS2 score, supplemental oxygen flow rate, urea, age, oxygen saturation, CRP, estimated GFR, neutrophil count, and neutrophil/lymphocyte ratio. Notably, pre-existing comorbidities did not improve risk prediction and were not retained in the final model. This was unexpected but may indicate that the effect of pre-existing health conditions could be manifest through some of the included blood or physiological markers.

Overall, this study overcomes many of the factors associated with a high risk of bias in the development of prognostic models for COVID-19 [ 13 ] and provides some evidence to support the supplementation of NEWS2 for clinical decisions with these patients.

Comparison with other studies

A systematic review of 10 prediction models for mortality in COVID-19 infection [ 10 ] found broad similarities with the features retained in our models, particularly regarding CRP and neutrophil levels. However, existing prediction models suffer several methodological weaknesses including overfitting, selection bias, and reliance on cross-sectional data without accounting for censoring. Additionally, many existing studies have relied on single-centre or ethnically homogenous Chinese cohorts, whereas the present study shows validation across multiple and diverse populations. A key strength of our study is the robust and repeated external validation across national and international sites; however, evidence of miscalibration suggests we should be cautious when attempting to generalise these findings. Future research should include larger collaborations and aim to develop ‘from onset’ population predictions.

NEWS2 is a summary score derived from six physiological parameters, including oxygen supplementation. Lack of evidence for NEWS2 use in COVID-19 especially in primary care has been highlighted [ 9 ]. The oxygen saturation component of physiological measurements added value beyond NEWS2 total score and was retained following regularisation for 14-day endpoints. This suggests some residual association over and above what is captured by the NEWS2 score and reinforces Royal College of Physicians guidance that the NEWS2 score ceilings with respect to respiratory function [ 42 ].

Cardiac disease and myocardial injury have been described in severe COVID-19 cases in China [ 2 , 23 ]. In our model, blood Troponin-T, a marker of myocardial injury, had additional salient signal but was only measured in a subset of our cohort at admission, so it was excluded from our final model. This could be explored further in larger datasets.

Strengths and limitations

Our study provides a risk stratification model for which we obtained generalisable and robust results across seven national and international sites with differing geographical catchment and population characteristics. It is amongst the first to evaluate NEWS2 at hospital admission for severe COVID-19 outcome and amongst a handful to externally validate a supplemented model across multiple sites.

However, some limitations must be acknowledged. First, there are likely to be other parameters not measured in this study that could substantially improve the risk stratification model (e.g. radiological features, obesity, or comorbidity load). These parameters could be explored in future work but were not considered in the present study to avoid limiting the real-world implementation of the risk stratification model. Second, our models showed better performance in UK secondary care settings amongst populations with higher rates of severe COVID-19 disease. Therefore, further research is needed to investigate the suitability of our model for primary care settings which have a high prevalence of mild disease severities and in community settings. This would allow us to capture variability at earlier stages of the disease and trends in patients not requiring hospital admission. Third, while external validation across multiple national and international sites represents a key strength, we did not have access to individual participant data and model development was limited to a single site (KCH). Although we benefited from existing infrastructure to support rapid data analysis, we urgently need infrastructure to support data sharing between sites to address some of the limitations of the present study (e.g. miscalibration) and improve the transferability of these models. Not only would this facilitate external validation, but more importantly, it would allow multi-site prediction models to be developed using pooled, individual participant data [ 43 ]. Fourth, our analyses would have excluded patients who experienced severe COVID-19 outcome at home or at another hospital, after being discharged from a participating hospital. Fifth, our model was restricted to blood and physiological parameters measured at hospital admission. This was by design and reflected the aim of developing a screening tool for risk stratification at hospital admission. However, future studies should explore the extent to which risk stratification could be improved by incorporating repeated measures of NEWS2 and relevant biomarkers.

The NEWS2 early warning score is in near-universal use in UK NHS Trusts since March 2019 [ 15 ], but little is known about its use for COVID-19 patients. Here, we showed that NEWS2 and age at hospital admission had poor-to-moderate discrimination for medium-term (14-day) severe COVID-19 outcome, questioning its use as a tool to guide hospital admission. Moreover, we showed that NEWS2 discrimination could be improved by adding eight blood and physiological parameters (supplemental oxygen flow rate, urea, age, oxygen saturation, CRP, estimated GFR, neutrophil count, neutrophil/lymphocyte ratio) that are routinely collected and readily available in healthcare services. Thus, this type of model could be easily implemented in clinical practice, and predicted risk score probabilities of individual patients are easy to communicate. At the same time, although we provided some evidence of improved discrimination vs. NEWS2 and age alone, given miscalibration in external sites, our proposed model should be used as a complement and not as a replacement for clinical judgement.

Availability of data and materials

Code and pre-trained models are available at https://github.com/ewancarr/NEWS2-COVID-19 and openly shared for testing in other COVID-19 datasets.

Source text from patient records used at all sites in the study will not be available due to inability to safely fully anonymise up to the Information Commissioner Office (ICO) standards and would be likely to contain strong identifiers (e.g. names, postcodes) and highly sensitive data (e.g. diagnoses).

A subset of the KCH dataset limited to anonymisable information (e.g. only SNOMED codes and aggregated demographics) is available on request to researchers with suitable training in information governance and human confidentiality protocols subject to approval by the King’s College Hospital Information Governance committee; applications for research access should be sent to [email protected] . This dataset cannot be released publicly due to the risk of re-identification of such granular individual-level data, as determined by the King’s College Hospital Caldicott Guardian.

The GSTT dataset cannot be released publicly due to the risk of re-identification of such granular individual-level data, as determined by the Guy’s and St Thomas’s Trust Caldicott Guardian.

The UHS dataset cannot be released publicly due to the risk of re-identification of such granular individual-level data, as determined by the University Hospital Southampton Caldicott Guardian.

The UCH data cannot be released publicly due to conditions of regulatory approvals that preclude open access data sharing to minimise the risk of patient identification through granular individual health record data. The authors will consider specific requests for data sharing as part of academic collaborations subject to ethical approval and data transfer agreements in accordance with the GDPR regulations.

The Wuhan dataset used in the study will not be available due to the inability to fully anonymise in line with ethical requirements. Applications for research access should be sent to TS and details will be made available via https://covid.datahelps.life/prediction/ .

The OUH dataset cannot be released publicly due to the risk of re-identification of such granular individual-level data.

https://github.com/ewancarr/NEWS2-COVID-19

WHO. WHO COVID-19 dashboard. 2020. https://who.sprinklr.com/ .

Google Scholar  

Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 2020;395:1054–62.

Article   CAS   Google Scholar  

Scott LJ, Redmond NM, Tavaré A, Little H, Srivastava S, Pullyblank A. Association between National Early Warning Scores in primary care and clinical outcomes: an observational study in UK primary and secondary care. Br J Gen Pract. 2020. https://doi.org/10.3399/bjgp20X709337 .

Lambden S, Laterre PF, Levy MM, Francois B. The SOFA score—development, utility and challenges of accurate assessment in clinical trials. Crit Care. 2019;23:374.

Article   Google Scholar  

Liu S, Yao N, Qiu Y, He C. Predictive performance of SOFA and qSOFA for in-hospital mortality in severe novel coronavirus disease. Am J Emerg Med. 2020. https://doi.org/10.1016/j.ajem.2020.07.019 .

Lim WS, van der Eerden MM, Laing R, Boersma WG, Karalus N, Town GI, et al. Defining community acquired pneumonia severity on presentation to hospital: an international derivation and validation study. Thorax. 2003;58:377–82.

Royal College of Physicians. National Early Warning Score (NEWS) 2: standardising the assessment of acute-illness severity in the NHS. Updated report of a working party. London: RCP; 2017.

Smith GB, Prytherch DR, Meredith P, Schmidt PE, Featherstone PI. The ability of the National Early Warning Score (NEWS) to discriminate patients at risk of early cardiac arrest, unanticipated intensive care unit admission, and death. Resuscitation. 2013;84:465–70.

Greenhalgh T, Treadwell J, Burrow R. NEWS (or NEWS2) score when assessing possible COVID-19 patients in primary care. Cent Evid-Based Med Nuffield Dep Prim Care Health Sci Univ Oxf. 2020;20. https://www.cebm.net/covid-19/should-we-use-the-news-or-news2-score-when-assessing-patients-with-possible-covid-19-inprimary-care/ .

Ji D, Zhang D, Xu J, Chen Z, Yang T, Zhao P, et al. Prediction for progression risk in patients with COVID-19 pneumonia: the CALL score. Clin Infect Dis. https://doi.org/10.1093/cid/ciaa414 .

Shi Y, Yu X, Zhao H, Wang H, Zhao R, Sheng J. Host susceptibility to severe COVID-19 and establishment of a host risk score: findings of 487 cases outside Wuhan. Crit Care. 2020;24:108.

COVIDAnalytics. https://www.covidanalytics.io/calculator . Access date 21 April 2020.

Wynants L, Calster BV, Bonten MMJ, Collins GS, Debray TPA, Vos MD, et al. Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal. BMJ. 2020;369. https://doi.org/10.1136/bmj.m1328 .

Liao X, Wang B, Kang Y. Novel coronavirus infection during the 2019–2020 epidemic: preparing intensive care units—the experience in Sichuan Province, China. Intensive Care Med. 2020;46:357–60.

NHS England » National Early Warning Score (NEWS). https://www.england.nhs.uk/ourwork/clinical-policy/sepsis/nationalearlywarningscore/ . Access date 23 April 2020.

Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020;395:497–506.

Li K, Wu J, Wu F, Guo D, Chen L, Fang Z, et al. The clinical and chest CT features associated with severe and critical COVID-19 pneumonia. Invest Radiol. 2020;Publish Ahead of Print. doi: https://doi.org/10.1097/RLI.0000000000000672 .

Xie J, Tong Z, Guan X, Du B, Qiu H. Clinical characteristics of patients who died of coronavirus disease 2019 in China. JAMA Netw Open. 2020;3:e205619.

Zhang J-J, Dong X, Cao Y-Y, Yuan Y-D, Yang Y-B, Yan Y-Q, et al. Clinical characteristics of 140 patients infected with SARS-CoV-2 in Wuhan, China. Allergy. 2020;75:1730-41. 

Ruan Q, Yang K, Wang W, Jiang L, Song J. Clinical predictors of mortality due to COVID-19 based on an analysis of data of 150 patients from Wuhan, China. Intensive Care Med. 2020;46:846–8. 

Guan W, Ni Z, Hu Y, Liang W, Ou C, He J, et al. Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med. 2020. https://doi.org/10.1056/NEJMoa2002032 .

Lagunas-Rangel FA. Neutrophil-to-lymphocyte ratio and lymphocyte-to-C-reactive protein ratio in patients with severe coronavirus disease 2019 (COVID-19): a meta-analysis. J Med Virol. 2020;92:1733-4. 

Guo T, Fan Y, Chen M, Wu X, Zhang L, He T, et al. Cardiovascular implications of fatal outcomes of patients with coronavirus disease 2019 (COVID-19). JAMA Cardiol. 2020. https://doi.org/10.1001/jamacardio.2020.1017 .

Carr E, Bendayan R, Bean D, Stammers M, Wang W, Zhang H, et al. Evaluation and improvement of the National Early Warning Score (NEWS2) for COVID-19: a multi-hospital study. medRxiv. 2020. https://www.medrxiv.org/content/10.1101/2020.04.24.20078006v4.article-info .

Marshall JC, Murthy S, Diaz J, Adhikari NK, Angus DC, Arabi YM, et al. A minimal common outcome measure set for COVID-19 clinical research. Lancet Infect Dis. 2020;20:e192–7.

Jackson R, Kartoglu I, Stringer C, Gorrell G, Roberts A, Song X, et al. CogStack - experiences of deploying integrated information retrieval and extraction services in a large National Health Service Foundation Trust hospital. BMC Med Inform Decis Mak. 2018;18. https://doi.org/10.1186/s12911-018-0623-9 .

Kraljevic Z, Bean D, Mascio A, Roguski L, Folarin A, Roberts A, et al. MedCAT -- Medical Concept Annotation Tool. ArXiv191210166 Cs Stat. 2019.  https://arxiv.org/abs/1912.10166 .

Searle T, Kraljevic Z, Bendayan R, Bean D, Dobson R. MedCATTrainer: a biomedical free text annotation interface with active learning and research use case specific customisation. ArXiv190707322 Cs. 2019.  https://arxiv.org/abs/1907.07322 .

Johnson AEW, Pollard TJ, Shen L, Lehman LH, Feng M, Ghassemi M, et al. MIMIC-III, a freely accessible critical care database. Sci Data. 2016;3:1–9.

Bean D, Kraljevic Z, Searle T, Bendayan R, Pickles A, Folarin A, et al. Treatment with ACE-inhibitors is associated with less severe disease with SARS-Covid-19 infection in a multi-site UK acute Hospital Trust. medRxiv. 2020;:2020.04.07.20056788.  https://www.medrxiv.org/content/10.1101/2020.04.07.20056788v1.full-text .

Van Rossum G, Drake FL. Python 3 reference manual. Scotts Valley: CreateSpace; 2009.

Steyerberg E. Clinical prediction models. 2nd ed. Cham: Springer; 2019.

Book   Google Scholar  

Firth D. Bias reduction of maximum likelihood estimates. Biometrika. 1993;80:27–38.

Benjamini Y, Hochberg Y. Controlling the false discovery rate - a practical and powerful approach. J R Stat Soc Ser B-Methodol. 1995;57:289–300.

Tibshirani R. Regression shrinkage and selection via the Lasso. J R Stat Soc Ser B Methodol. 1996;58:267–88.

Kuhn M, Johnson K. Applied predictive modeling. New York: Springer; 2013.

Seabold S, Perktold J. statsmodels: econometric and statistical modeling with Python. In: 9th Python in Science conference. Austin: SciPy; 2010.  http://conference.scipy.org/proceedings/scipy2010/pdfs/seabold.pdf .

Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: machine learning in Python. J Mach Learn Res. 2011;12:2825–30.

Romero-Brufau S, Huddleston JM, Escobar GJ, Liebow M. Why the C-statistic is not informative to evaluate early warning scores and what metrics to use. Crit Care Lond Engl. 2015;19:285.

Van Calster B, McLernon DJ, van Smeden M, Wynants L, Steyerberg EW, Bossuyt P, et al. Calibration: the Achilles heel of predictive analytics. BMC Med. 2019;17:230.

Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Mak. 2006;26:565–74.

NEWS2 and deterioration in COVID-19. RCP London. 2020. https://www.rcplondon.ac.uk/news/news2-and-deterioration-covid-19 . Access date 24 April 2020.

Ahmed I, Debray TP, Moons KG, Riley RD. Developing and validating risk prediction models in an individual participant data meta-analysis. BMC Med Res Methodol. 2014;14:3.

Download references

Acknowledgements

This paper represents independent research part-funded by the National Institute for Health Research (NIHR) Biomedical Research Centres at South London and Maudsley NHS Foundation Trust, London AI Medical Imaging Centre for Value-Based Healthcare, and Guy’s and St Thomas’ NHS Foundation Trust, both with King’s College London. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health and Social Care. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. We would also like to thank all the clinicians managing the patients, the patient experts of the KERRI committee, Professor Irene Higginson, Professor Alastair Baker, Professor Jules Wendon, Dan Persson, and Damian Lewsley for their support.

The authors acknowledge the use of the research computing facility at King’s College London, Rosalind ( https://rosalind.kcl.ac.uk ), which is delivered in partnership with the National Institute for Health Research (NIHR) Biomedical Research Centres at South London & Maudsley and Guy’s and St Thomas’ NHS Foundation Trusts, and part-funded by capital equipment grants from the Maudsley Charity (award 980) and Guy’s and St Thomas’ Charity (TR130505). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, King’s College London, or the Department of Health and Social Care.

GVG also acknowledges the support from the NIHR Birmingham ECMC, NIHR Birmingham SRMRC, Nanocommons H2020-EU (731032), and the NIHR Birmingham Biomedical Research Centre. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the National Institute for Health Research, the Medical Research Council, or the Department of Health. The funding organisations had no role in the design of this study, data collection, analysis or interpretation, or preparation of the manuscript and did not approve or disapprove of or delay publication of the work. Furthermore, the UHB data collection was supported by the PIONEER Acute Care Hub and HDR-UK Better Care Programme. This work uses data provided by patients and collected by the NHS as part of their care and support. We would like to acknowledge the contribution of all staff, key workers, patients, and the community who have supported our hospitals and the wider NHS at this time.

DMB is funded by a UKRI Innovation Fellowship as part of the Health Data Research UK MR/S00310X/1 ( https://www.hdruk.ac.uk ).

RB is funded in part by grant MR/R016372/1 for the King’s College London MRC Skills Development Fellowship programme funded by the UK Medical Research Council (MRC, https://mrc.ukri.org ) and by grant IS-BRC-1215-20018 for the National Institute for Health Research (NIHR, https://www.nihr.ac.uk ) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London.

RJBD is supported by the following: (1) NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London, London, UK; (2) Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and Wellcome Trust; (3) The BigData@Heart Consortium, funded by the Innovative Medicines Initiative-2 Joint Undertaking under grant agreement No. 116074. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA; it is chaired by DE Grobbee and SD Anker, partnering with 20 academic and industry partners and ESC; (4) the National Institute for Health Research University College London Hospitals Biomedical Research Centre; (5) the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London; (6) the UK Research and Innovation London Medical Imaging & Artificial Intelligence Centre for Value Based Healthcare; (7) the National Institute for Health Research (NIHR) Applied Research Collaboration South London (NIHR ARC South London) at King’s College Hospital NHS Foundation Trust.

KOG is supported by an MRC Clinical Training Fellowship (MR/R017751/1).

WW is supported by the Health Foundation grant.

AD and VC acknowledge the support from the National Institute for Health Research (NIHR) Applied Research Collaboration (ARC) South London at King’s College Hospital NHS Foundation Trust and the Royal College of Physicians, as well as the support from the NIHR Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust and King’s College London. VC is additionally supported by Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation, and Wellcome Trust.

RZ is supported by a King’s Prize Fellowship.

AS is supported by a King’s Medical Research Trust studentship.

JTHT is supported by London AI Medical Imaging Centre for Value-Based Healthcare (AI4VBH) and the National Institute for Health Research (NIHR) Applied Research Collaboration South London (NIHR ARC South London) at King’s College Hospital NHS Foundation Trust.

FB and PTTH are funded by the National Institute for Health Research (NIHR) Biomedical Research Centre, Data Sciences at University Hospital Southampton NHS Foundation Trust, and the Clinical Informatics Research Unit, University of Southampton.

JB is funded by the Clinical Informatics Research Unit, University of Southampton, and part-funded by the Global Alliance for Chronic Disease (GDAC).

A Pinto is part-funded by UHS Digital, University Hospital Southampton, Tremona Road, Southampton.

AJS is supported by a Digital Health Fellowship through Health Education England (Wessex).

HW and HZ are supported by the Medical Research Council and Health Data Research UK Grant (MR/S004149/1), Industrial Strategy Challenge Grant (MC_PC_18029), and Wellcome Institutional Translation Partnership Award (PIII054). XW is supported by the National Natural Science Foundation of China (grant number 81700006).

AMS is supported by the British Heart Foundation (CH/1999001/11735), the National Institute for Health Research (NIHR) Biomedical Research Centre at Guy’s and St Thomas’ NHS Foundation Trust and King’s College London (IS-BRC-1215-20006), and the Fondation Leducq. AP is partially supported by NIHR NF-SI-0617-10120. This work was supported by the National Institute for Health Research (NIHR) University College London Hospitals (UCH) Biomedical Research Centre (BRC) Clinical and Research Informatics Unit (CRIU), NIHR Health Informatics Collaborative (HIC), and by awards establishing the Institute of Health Informatics at University College London (UCL). This work was also supported by Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation, and the Wellcome Trust.

RKG is funded by the NIHR (DRF-2018-11-ST2-004). MN is funded by the Wellcome Trust (207511/Z/17/Z).

The work was supported by MRC Health Data Research UK (HDRUK/CFC/01), an initiative funded by the UK Research and Innovation, Department of Health and Social Care (England) and the devolved administrations, and leading medical research charities. AK is funded by a MRC Rutherford Fellowship MR/S003991/1 (as part of Health Data Research UK https://www.hdruk.ac.uk ).

Author information

Ewan Carr and Rebecca Bendayan are joint first authors.

Authors and Affiliations

Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, 16 De Crespigny Park, London, SE5 8AF, UK

Ewan Carr, Rebecca Bendayan, Daniel Bean, Thomas Searle, Zeljko Kraljevic, Andrew Pickles, Daniel Stahl, Amos Folarin & Richard J. B. Dobson

NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London, London, UK

Rebecca Bendayan, Thomas Searle, Andrew Pickles & Richard J. B. Dobson

Health Data Research UK London, University College London, London, UK

Daniel Bean, Amos Folarin, Lukasz Roguski, Honghan Wu & Richard J. B. Dobson

Clinical Informatics Research Unit, University of Southampton, Coxford Rd., Southampton, SO16 5AF, UK

Matt Stammers, Hang T. T. Phan, Florina Borca & James Batchelor

NIHR Biomedical Research Centre at University Hospital Southampton NHS Trust, Coxford Road, Southampton, UK

Matt Stammers, Hang T. T. Phan & Florina Borca

UHS Digital, University Hospital Southampton, Tremona Road, Southampton, SO16 6YD, UK

Matt Stammers, Anthony J. Shinton, Florina Borca & Ashwin Pinto

School of Population Health and Environmental Sciences, King’s College London, London, UK

Wenjuan Wang, Walter Muruet, Cormac Breen, Abdel Douiri & Vasa Curcin

Usher Institute, University of Edinburgh, Edinburgh, UK

Huayu Zhang, Ting Shi & Bruce Guthrie

Department of Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK

Anthony Shek & James T. Teo

UCL Institute for Global Health, University College London Hospitals NHS Trust, London, UK

Rishi K. Gupta

University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, UK

Mike Wyatt, Matt Rogers & Christopher Bourdeaux

Department of Pulmonary and Critical Care Medicine, People’s Liberation Army Joint Logistic Support Force 920th Hospital, Kunming, Yunnan, China

King’s College Hospital NHS Foundation Trust, London, UK

Rosita Zakeri, Kevin O’Gallagher, James T. Teo & Ajay M. Shah

School of Cardiovascular Medicine & Sciences, King’s College London British Heart Foundation Centre of Excellence, London, SE5 9NU, UK

Rosita Zakeri, Kevin O’Gallagher & Ajay M. Shah

UCL Division of Infection and Immunity, University College London Hospitals NHS Trust, London, UK

Mahdad Noursadeghi

Institute of Health Informatics, University College London, London, UK

Amos Folarin, Lukasz Roguski, Honghan Wu & Richard J. B. Dobson

NIHR Biomedical Research Centre at University College London Hospitals NHS Foundation Trust, London, UK

Amos Folarin & Richard J. B. Dobson

College of Medical and Dental Sciences, Institute of Cancer and Genomics, University of Birmingham, Birmingham, UK

Andreas Karwath, Luke Slater, Victor Roth Cardoso & Georgios V. Gkoutos

Institute of Translational Medicine, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK

Andreas Karwath, Luke Slater, Victor Roth Cardoso, Lukasz Roguski & Georgios V. Gkoutos

Health Data Research UK Midlands, Birmingham, UK

Andreas Karwath, Luke Slater, Victor Roth Cardoso, Simon Ball & Georgios V. Gkoutos

Department of Medical Biochemistry, Blood Cell Research Group, Oslo University Hospital, Oslo, Norway

Kristin E. Wickstrøm & Erik Koldberg Amundsen

Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, Oslo, Norway

Alvaro Köhn-Luque

Department of Acute Medicine, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway

Aleksander Rygh Holten

University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK

Simon Ball & Georgios V. Gkoutos

Department of Engineering Mathematics, University of Bristol, Bristol, UK

Chris McWilliams

Department of Pulmonary and Critical Care Medicine, Shanghai East Hospital, Tongji University, Shanghai, China

Xiaodong Wu & Jiaxing Sun

Department of Pulmonary and Critical Care Medicine, Taikang Tongji Hospital, Wuhan, China

Xiaodong Wu

You can also search for this author in PubMed   Google Scholar

Contributions

The corresponding author, Dr. Ewan Carr, is the guarantor of the manuscript. JT, AMS, RD, EC, and RB conceived the study design and developed the study objectives. JT, RD, AF, LR, DB, ZK, TS, and AS were the leads to develop the CogStack platform. DB, ZK, TS, and AS were responsible for the data extraction and preparation. EC, RB, AP, and DS contributed to the statistical analyses. All authors contributed to the interpretation of the data. AMS, JT, KO, and RZ provided clinical input. All authors contributed to interpret the data and draft the article and provided final approval of the manuscript. DMB, ZK, AS, TS, JTHT, LR, and KN performed the data processing and software development. KOG, RZ, and JTHT performed the data validation. At GSTT, WW and WM were responsible for the data extraction and preparation. WW performed the model validation. AD and VC contributed to the interpretation of the data. At UHS, MS and FB were responsible for the data extraction and preparation. MS, HP, and AS contributed to the statistical analysis. All authors contributed to the interpretation of the data. MS and AP provided clinical input. MS and HP performed the data/model validation. At UCH, RKG and MN were responsible for the data extraction, preparation, and model validation. At UHBW, MR and MW were responsible for the data extraction and preparation. CM and CB conducted the data and model validation. For the Wuhan cohort, XZ, XW, and JS extracted the data from the EHR system. HW and HZ preprocessed the raw data and conducted the prediction model validations. BG, HW, HZ, TS, and JS interpreted the data and results. At UHB/UoB, UHB IT and AK were responsible for the data extraction and preparation. AK, LS, VRC, and GVG performed the model validation. AK, GVG, and SB contributed to the interpretation of the data. At OUH, KW, EKA, and ARH were responsible for the data extraction and preparation. AKL contributed to the statistical analysis and performed the model validation. All authors contributed to the interpretation of the data. The views expressed are those of the authors and not necessarily those of the MRC, NHS, the NIHR, or the Department of Health and Social Care. The funders of the study had no role in the study design, data collection, data analysis, data interpretation, writing of the report, or the decision to submit the article for publication.

Corresponding author

Correspondence to Ewan Carr .

Ethics declarations

Ethics approval and consent to participate.

The KCH component of the project operated under London South East Research Ethics Committee (reference 18/LO/2048) approval granted to the King’s Electronic Records Research Interface (KERRI); specific work on COVID-19 research was reviewed with expert patient input on a virtual committee with Caldicott Guardian oversight. The UHS validation was performed as part of an urgent service evaluation agreed with approval from trust research leads and the Caldicott Guardian. For UCH, ethical approval was given by East Midlands - Nottingham 2 Research Ethics Committee (REF: 20/EM/0114; IRAS: 282900). The UHB component was operated under the PIONEER Health Data Research Hub in Acute Care ethical approval provided by the East Midlands Derby REC (reference: 20/EM/0158). For UHBW, the project was considered as service evaluation by the organisational review board. Informed consent was deemed unnecessary due to the retrospective observational nature of the data. Ethical approval for GSTT was granted by the London Bromley Research Ethics Committee (reference 20/HRA/1871) to the King’s Health Partners Data Analytics and Modelling COVID-19 Group to collect clinically relevant data points from patient’s electronic health records. The Wuhan validation was approved by the Research Ethics Committee of Shanghai Dongfang Hospital and Taikang Tongji Hospital. For the OUH validation, a project protocol was approved by the Regional Ethical Committee of South-East Norway (Reference number 137045) and the OUH data protection officer (Reference number 20/08822). Informed consent in the OUH cohort was waived because of the strictly observational nature of the project.

Consent for publication

Not applicable.

Competing interests

JTHT received research support and funding from InnovateUK, Bristol-Myers-Squibb, iRhythm Technologies, and holds shares < £5000 in Glaxo Smithkline and Biogen.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

James T Teo, Ajay M Shah, and Richard J B Dobson are joint last authors.

Supplementary Information

Additional file 1: table s1..

SNOMED terms.

Additional file 2: Table S2.

F1, precision and recall for NLP comorbidity detection.

Additional file 3: Table S3.

Logistic regression models for each blood and physiological measure tested separately in the KCH training cohort, for 14- and 3-day ICU/death.

Additional file 4: Table S4.

Internally validated discrimination for KCH training sample based on nested repeated cross-validation.

Additional file 5: Figure S1.

Calibration (logistic and LOESS curves) of supplemented NEWS2 model for 3-day ICU/death model at validation sites.

Additional file 6: Table S5.

Univariate logistic regression models for sensitivity analyses showing odds ratios of ICU/death at 3- and 14-days for subsets of the training cohort.

Additional file 7: Table S6.

Discrimination for all models in training and validation cohorts, including alternative baseline model of ‘NEWS2 only’.

Additional file 8: Figure S2.

Net benefit of supplemented NEWS2 model for 3-day ICU/death compared to default strategies (‘treat all’ and ‘treat none’) at training and validation sites.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Cite this article.

Carr, E., Bendayan, R., Bean, D. et al. Evaluation and improvement of the National Early Warning Score (NEWS2) for COVID-19: a multi-hospital study. BMC Med 19 , 23 (2021). https://doi.org/10.1186/s12916-020-01893-3

Download citation

Received : 29 September 2020

Accepted : 16 December 2020

Published : 21 January 2021

DOI : https://doi.org/10.1186/s12916-020-01893-3

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• NEWS2 score
• Blood parameters
• Prediction model

BMC Medicine

ISSN: 1741-7015

• Submission enquiries: [email protected]
• General enquiries: [email protected]

• Open Access
• Published: 14 December 2021

A critique of the English national policy from a social determinants of health perspective using a realist and problem representation approach: the ‘Childhood Obesity: a plan for action’ (2016, 2018, 2019)

• Naomi Griffin 1 , 2 ,
• Sophie M. Phillips 1 , 2 ,
• Frances Hillier-Brown 1 , 3 ,
• Jonathan Wistow 1 , 4 ,
• Hannah Fairbrother 5 ,
• Eleanor Holding 6 ,
• Katie Powell 6 &
• Carolyn Summerbell 1 , 2  

BMC Public Health volume  21 , Article number:  2284 ( 2021 ) Cite this article

13k Accesses

10 Citations

33 Altmetric

Metrics details

The UK government released Chapter 1 of the ‘Childhood Obesity: a plan for action’ (2016), followed by Chapter 2 (2018) and preliminary Chapter 3 was published for consultation in 2019 (hereon collectively ‘ The Policy’) . The stated policy aims were to reduce the prevalence of childhood obesity in England, addressing disparities in health by reducing the gap (approximately two-fold) in childhood obesity between those from the most and least deprived areas.

Combining a realist approach with an analysis of policy discourses, we analysed the policies using a social determinants of health (SDH) perspective (focusing on socio-economic inequalities). This novel approach reveals how the framing of policy ‘problems’ leads to particular approaches and interventions.

While recognising a social gradient in relation to obesity measures, we critique obesity problem narratives. The Policy included some upstream, structural approaches (e.g. restrictions in food advertising and the soft-drinks industry levy). However, the focus on downstream individual-level behavioural approaches to reduce calorie intake and increase physical activity does not account for the SDH and the complexity and contestedness of ‘obesity’ and pays insufficient attention to how proposals will help to reduce inequalities. Our findings illustrate that individualising of responsibility to respond to what wider evidence shows is structural inequalities, can perpetuate damaging narratives and lead to ineffective interventions, providing caution to academics, practitioners and policy makers (local and national), of the power of problem representation. Our findings also show that the problem framing in The Policy risks reducing important public health aims to encourage healthy diets and increase opportunities for physical activity (and the physical and mental health benefits of both) for children to weight management with a focus on particular children.

Conclusions

We propose an alternative conceptualisation of the policy ‘problem’, that obesity rates are illustrative of inequality, arguing there needs to be policy focus on the structural and factors that maintain health inequalities, including poverty and food insecurity. We hope that our findings can be used to challenge and strengthen future policy development, leading to more effective action against health inequalities and intervention-generated inequalities in health.

Peer Review reports

Childhood obesity has been identified as a public health priority in high income countries across the world [ 1 ]. In response, countries have developed national and local policies, and have implemented multiple public health interventions, to try and tackle the problem [ 1 ]. Taking England as an example, childhood obesity has been identified as a policy priority since 1991 [ 2 ]. Jebb and colleagues [ 3 ] described the evolution of policy and actions to tackle obesity in England up to 2013, concluding that rigorous evaluations of effectiveness were rare, and that the limited evidence of tangible success, despite substantial investment of resources, reinforces the magnitude of the challenge to the whole of society. More recently, Theis and White [ 4 ] analysed English government obesity policies using theoretical frameworks and an intensive applied thematic analysis approach. The analysis revealed that National obesity policy proposals rely heavily on individual level behaviour, are repeated with no reference to previous policies, and are proposed with limited guidance on implementation. Croker and colleagues [ 5 ] conducted a mapping study of national policies for pre-school children obesity in England from a behavioural science perspective. They found that much of the policy activity is focussed on education and suggested that upstream policies which act on food systems should be strengthened. Although the importance of the socioeconomic patterning of childhood obesity is acknowledged in these existing analyses of policy, it was not the focus of their analyses.

The recent 2019 Chief Medical Officers report Time to Solve Childhood Obesity [ 6 ] underlines the importance of the social determinants of health in understanding childhood obesity rates. The most recent national policy for tackling childhood obesity in England has been published as chapters, first in 2016 ‘Childhood Obesity: a plan for action’ [ 7 ], followed by ‘Childhood Obesity: a plan for action: Chapter 2’ in 2018 [ 8 ]. A preliminary Chapter 3 was opened for consultation in 2019 in the green paper ‘Advancing our health: prevention in the 2020s’ [ 9 ]. For convenience the chapters will collectively be referred to in this paper as The Policy . The stated aim of The Policy was to significantly reduce the prevalence of childhood obesity in England, and to address disparities in health by reducing the gap in childhood obesity between those from the most and least deprived areas. Although the creation of a policy addressing childhood obesity was generally welcomed by public health bodies at the time of publication, there is concern that they focus too heavily on individual behaviour change rather than upstream (stealth) interventions [ 4 , 5 , 10 , 11 ]. With plans underway for Chapter 3 of The Policy [ 9 ] potentially delayed due to a Government focus on Covid-19, it is our hope that government will revisit and review the aims of The Policy with a focus on the structural influences of health inequalities and poverty on health outcomes.

Childhood obesity and inequalities in England

The relationship between social disadvantage and health is well documented (see for example: [ 12 , 13 , 14 , 15 , 16 ]). There is a social gradient that can be mapped onto childhood obesity data (as measured by Body Mass Index) [ 14 ] with higher prevalence seen in children from areas of higher socio-economic deprivation [ 17 , 18 ]. This pattern has been evidenced globally [ 1 ]. Children living in the most deprived areas in England are twice as likely to measure as ‘obese’ than children in the most affluent groups [ 19 ], and the gap between the most and least deprived is growing with a plateauing of prevalence for the most advantaged [ 14 ].

Social determinants of health

A social determinants of health (SDH) perspective explores how individual experience of health is affected by micro and macro social and political contexts which lead to health inequalities [ 15 ]. The Marmot Review [ 13 ] was critical in the development of SDH perspectives designed to shape policy in England which emphasised the importance of the ‘causes of the causes’ of health and health inequalities. The review argues that ‘ health is closely linked to the conditions in which people are born, grow, live, work and age and inequities in power, money and resources – the social determinants of health’ ([ 13 ], p5). Early years; education; work; income; and communities were identified as key examples of where the social gradient in health is persistent [ 13 ]. Health policy has been critiqued for neglecting structural forces as causal factors in producing social and economic inequalities and health inequalities (e.g. [ 13 , 14 , 20 ]). Even when the social determinants of health are acknowledged in policy and policy networks, structural factors that create and sustain inequalities are often not meaningfully addressed [ 15 ].

Childhood obesity policy context and the social determinants of health

Traditionally, policy discourses around obesity have focused on personal responsibility and individualism, with an absence of engagement with the social determinants of health [ 20 , 21 ]. Individualistic approaches are evident in the wealth of research examining risk factors for childhood obesity, which focus on implementing changes to lifestyle behaviours [ 11 ] with minimal consideration of the wider social determinants of health. A pattern in approaches to obesity interventions lacking complexity has also been found at a local authority level in England [ 22 ]. Existing systematic reviews of the effectiveness of interventions to prevent childhood obesity which focus on health inequalities [ 23 , 24 ] found that most interventions did not report their results by socio-economic status nor used a social determinants of health approach to intervention development or implementation. There is often little mention of economic, cultural and social issues in relation to obesity and where wider determinants such as socioeconomic status, food insecurity, or education level are mentioned, behavioural and lifestyle modifications are still prioritised [ 20 , 21 ]. This is despite the UK having one of the highest levels of children living in severely food insecure households in Europe [ 25 ], and evidence that austerity and budget cuts have negatively impacted Local Authority capacity to reduce health inequalities [ 26 ].

The aim of our research was to analyse The Policy using a social determinants of health (SDH) perspective. Our findings can then be used to challenge and strengthen future policy development, leading to more effective action against health inequalities and intervention-generated inequalities in health. The Covid-19 pandemic has further exposed social gradients in health, with those experiencing poverty and disadvantage being hit hardest [ 27 ], and worsening food insecurity [ 28 ]. Food bank use has significantly increased; Trussell Trust [ 29 ] reported that food bank use had increased by 74% over the past 5 years, with 1.9 million emergency food supplies delivered to individuals across the UK between April 2019-March 2020, and 700,000 of these parcels went to children. It is critical that consideration is given to these issues in future policy proposals [ 30 , 31 ]. With plans underway for Chapter 3 of The Policy [ 9 ] potentially delayed due to a Government focus on Covid-19, it is our hope that government will revisit and review the aims of The Policy with a focus on health inequalities and poverty, using a stronger critical structural lens.

We used a novel methodological approach, employing and integrating Pawson and Tilley’s [ 32 ] realistic evaluation with Bacchi’s ‘What’s the Problem Represented to be?’ (WPR) approach [ 33 , 34 ] to analyse The Policy [ 7 , 8 , 9 ]. Pawson and Tilley’s [ 32 ] realist approach was used to understand the proposed pathways for reducing inequalities, assessing the inherent ‘programme theories’ within The Policy : what the policy proposes to do and the intended results, and what the (sometimes implied) pathways to said results are, and how success will be measured. These proposed pathways were then assessed based on how embedded they were in the realities of policy implementation and how they take account of external factors on policy processes. Bacchi’s WPR approach [ 33 ] was used to analyse government and external discourses around The Policy , aiming to uncover how policy ‘problems’ are discursively created within policy documents through the way ‘problems’ are represented. This approach asks the researcher to start with policy proposals and reflect on what the proposals imply that ‘the problem’ is (e.g., a proposal to increase training implies a lack of training to be the problem). Importantly, the way policy ‘problems’ are discursively produced can also set the parameters for the discourse that follows [ 33 , 34 ]. In this way, the WPR tool affords a productive means of identifying and interrogating the power of narratives that may otherwise be taken for granted. Our methodology acknowledges that all policy documents contribute to and are informed by wider narratives which frame proposals and interventions and therefore interrogating said narratives can help to understand their effects. Table 1 provides an overview of the criteria used to extract data from the policy documents.

Data extraction and analysis

The Policy documents were independently double data extracted between October 2019 and May 2020. Researchers with different academic backgrounds extracted and analysed the policies (NG & SMP), to allow broader identification and interpretation, and to enable a more diverse discussion of the findings [ 35 ]. We developed a coding framework based on the questions in Table 1 , then extracted data from the policy documents using coding software. The two researchers carried out data extraction of policy documents separately, then brought together the extracted data to identify any differences or disagreements through several data extraction and analysis meetings, moderated by a 3 rd researcher (FHB). All authors were then given the opportunity to comment on findings and analysis at several stages of the analysis and writing process.

Our findings begin by outlining the key proposals (see supplementary Table S1 ), the proposed pathways to change from the proposals, the proposed measurements and what they tell us about the policy aims and scope. Then the framing of the ‘problem’ (in Bacchi’s sense [ 33 , 34 ]) is discussed in depth, drawing from wider evidence to illustrate the framing of the policy ‘problem’ of obesity in the context of wider research that illustrates complexity and contested nature of the topic (see Supplementary Table S2 for examples of ‘problems’ as represented in The Policy ). We then discuss the policy’s approach to inequalities, highlighting fundamental gaps in between proposed aims to reduce inequalities in child health and the proposed pathways to do this. Situating the problem representation in The Policy within a context of policy absences and alternative conceptualisations illustrates the effects of problem framing, allowing for the re-imagining of policy approaches to, and discourses around, the public health priority of ‘childhood obesity’ and its relationship with inequalities.

Reviewing the key policy proposals

The Policy outlines that it is a response to the growing prevalence of childhood obesity (as measured by BMI) in England. The Policy states that the rising level of childhood obesity will result in rising obesity levels in adulthood that will cause other associated health problems, increasing chronic disease related to obesity (targeting an anticipated threat). The Policy predicts that this link will result in greater long-term cost to the NHS for obesity related health problems. Morbidities that have been linked to obesity (particularly type 2 diabetes) in adulthood and the link between obesity in childhood and adulthood are given to justify the policy’s pertinence, proposing to reduce the cost to the NHS by reducing the risk of health problems associated with obesity in adulthood through obesity reduction in children. The Policy proposals (Table S1 ) imply that behaviour change and reduction in obesity and child health inequalities will follow from the proposals.

The key proposals in The Policy suggest that it will tackle obesity through lowering sugar consumption, the reformulation of products and increase physical activity, and (after consultation and publication of Chapter 2) reducing promotion and advertising of unhealthy food and drink. An overview of the key proposals in The Policy (see Table S1 ) indicate that despite the different system levels that The Policy proposals cover the focus of proposals in on individual behaviour change without adequate engagement with wider determinants. Although the implementation of ‘upstream’ approaches such as the sugar tax and financial support in the case of the Healthy Start Scheme (HSS) are welcomed, The Policy focuses heavily on individual choice and behaviour (particularly of parents). Our findings support those of Chapman et al. ([ 36 ], p.20) that The Policy ‘ replicated a wider trend in which only aspirations for individual-level behaviours were articulated with precision .’

Due perhaps to the brevity of the policy documents, how the impact of the policies listed in the proposal will be measured beyond the National Child Measurement Programme (see further discussion below) is unclear. For example, the measurement of mandatory calorie labelling, TV advertising restrictions, and local area changes is not outlined, which makes assessing the pathways to impact difficult. Ofsted are granted responsibility for tracking progress in schools. The ‘Sugar Tax’ is being monitored by industry responses, but it is not clear how directly the impact will be measured in terms of obesity prevalence. There is limited engagement with external influences on impact and implementation of the policy proposals, and its successes, supporting the findings of Theis and White ([ 4 ], p126) that the proposals do ‘not readily lead to implementation’.

What’s the problem represented to be? Defining the ‘childhood obesity’ policy problem

The Policy ’s definition of ‘obesity’ focuses on child weight status where the determinants of change are physical activity levels and calorie intake (i.e., calories consumed vs energy expended): ‘ at its root obesity is caused by an energy imbalance: taking in more energy through food than we use through activity ’ ([ 7 ], p.3). However, the causes of ‘obesity’ (as defined by BMI) are embedded in an extremely complex biological system that interact with cultural, structural and economic contextual factors, none of which exist in isolation [ 37 ]. Systemic factors such as money, power and resources are necessary for understanding the social gradient seen in obesity data [ 38 ]. The focus of energy balance at an individual level does not acknowledge the complex and contested nature of causes, its contested relationship to health, and how ‘obesity’ is defined and measured, within wider public health research (see for example, [ 39 , 40 , 41 , 42 ]). Therefore, explanations of BMI data which rely on individual energy imbalance must be challenged.

A narrow definition of obesity is also reflected in the key measure highlighted in the policy documents being the National Child Measurement Programme (NCMP) which is based on BMI (body mass index). Measures which rely on BMI (designed for use in adults), and the NCMP in particular [ 41 , 42 , 43 ], have been criticised for simplicity and for generalising a relationship between weight and health (see for example [ 44 , 45 ]). Such a measure implies a definition of obesity which is not about the presence of illness or health problems, instead categorising individuals as overweight or obese based simply on height and weight [ 41 , 44 ]. BMI is not a measure of overall health and thus the limitations of BMI (and any such screening method) and its complex association with health needs to be acknowledged. The tracking of childhood obesity as measured by BMI into adulthood (stated as a reason for the need to tackle childhood obesity) is also not clear cut. Increased likelihood of obesity in adulthood is apparent in those with obesity in childhood and adolescence; however, a high proportion (70%) of adults that fall into the obesity category did not in childhood or adolescence [ 46 ]. Evidence has suggested an association between childhood obesity (as measured by BMI) and later adult morbidity (e.g cardiovascular disease and metabolic health risks); however, this is far from conclusive, and the nature of the relationship is unclear [ 47 , 48 ]. The combining of, and interchangeable use of, ‘obesity’ and ‘overweight’ in the policy also paints a misleading picture as morbidity correlation and risk differs between the categories. Where complexity and contextual factors are absent in policy proposals and the measurement of policy outcomes, it is implied that they are not relevant to understanding the policy ‘problem’.

The effects of problem framing

Obesity is framed as an avoidable financial cost to health services in The Policy which perpetuates a ‘burden’ narrative [ 38 ]. It is worth recognising that individuals (the general public) have little control over how resources are distributed and budgets allocated within health systems. Difficult decisions on where to invest in public health often need to be made, especially where resources are scarce, and preference can swing to the treatment of ‘identifiable victims’ rather than investment in long-term prevention activities [ 49 ]. There is also a notable absence of the impact of austerity on health budgets and spending and child health inequalities in the policy documents, even when referring to inequalities and poverty, despite links made between poverty and childhood obesity. The absence of the impact of austerity on NHS and local public health budgets in The Policy purports a narrative that focuses on individual responsibility rather than a service provision issue (i.e. those that require healthcare are a ‘burden’ on limited resources rather than that there is a resourcing issue that is negatively impacting individuals requiring healthcare). Focussing on the individual (or parents) as responsible for making changes to childhood obesity levels contributes to a narrative of blame [ 50 ] that does not account for structural inequalities and social determinants of health beyond individual control [ 21 ]. Individual blame narratives, then, work to further justify a focus on individual level behaviour change in policy rather than a focus on the SDH which can explain the gradient in BMI population data relative to socio-economic deprivation.

Stigma was given as a reason for the need for a childhood obesity policy, as children deemed overweight or obese are likely to experience ‘ bullying, stigmatization and low self-esteem ' ([ 8 ] p6). However, as there was no targeted response to stigma itself. In reviewing the literature, the attention paid to stigma is necessary. The physical and psychological harms caused by stigma, and the negative impact that stigma has on quality of healthcare have been evidenced [ 51 ]. Not only is stigma likely to impact an individual’s health and wellbeing, stigma and misinformation about ‘obesity’ also cause barriers to appropriate and timely treatment of many health concerns, not just those that have been linked to weight status [ 51 , 52 ]. Pont et al. [ 52 ] explain that stigma is purported by some as a way to motivate individual weight loss, to tackle the ‘problem’ of obesity; an approach which overlooks the complexity of understanding individual BMI (overstating the control individuals have over it), the contested nature of the links between ‘obesity’ and negative health outcomes, and the negative health outcomes that result from stigma. Interventions which promote stigmatizing messages are likely to have the lowest compliance, whereas interventions which make no reference to obesity at all have been found to be most effective in encouraging health promoting behaviours [ 53 ]. By framing stigma as the result of obesity, rather than a problem to challenge, The Policy narratively supports individual behaviour change and responsibility, rather than addressing the wider determinants that are necessary to understand the social gradient seen in BMI data and the negative impacts of weight stigma.

Individualising and oversimplifying discourses and evidence around obesity are common within policies and policy networks and perpetuate narratives of individual blame and responsibility for one’s own health status [ 21 , 54 ]. Stigmatizing policy narratives can detract from structural factors within the SDH which account for many adverse health outcomes and health inequalities that have been linked to obesity [ 21 ], which is particularly concerning in the context of policy focussed on children. How obesity is discussed at policy level is critical for public understanding of the topic [ 53 ], therefore attention must be paid to the effects of policy narratives and how they can perpetuate stigma.

The policy and health inequalities

We found several gaps between the proposals in The Policy and anticipated outcomes proposed. The fundamental gap identified is that inequality is referred to in the introduction as a crucial element and the conclusion of the policy states that inequality will be reduced as a result of the implementation of the policy and that support is needed for ‘those who need it most’ ([ 7 ], p7). However, how this will be achieved in practice is left unclear. Black and minority ethnic families are identified as more likely to be affected by obesity but no explanation for why or how such groups will be affected by the plans is given. Local authorities are encouraged to focus on health inequality, but specific guidance (and support) is unclear. For example, there is recognition of need for greenspace and inequality in access to greenspace, but The Policy does not say how it will address this.

Another gap is related to mandatory action or legislation aimed at the early years, a key life stage for understanding the impacts of the SDH and therefore interventions to reduce health inequalities [ 13 , 14 ]. The Policy presents statistics on the prevalence of obesity of children aged 5 years and suggests ‘… helping to improve the health of our children and give future generations the best possible start in life. ’ ([ 8 ], p.4). The reference to early years consists of voluntary food and physical activity guidelines [ 7 ] and suggests research is undertaken exploring curriculum development that supports good physical development in the early years, but with no details on the research or proposed timescales [ 8 ]. Although there is engagement with early years in the proposed Chapter 3 [ 9 ], there is no reference to inequalities.

Inequalities and healthy food ‘choices’

The Policy has a focus on making healthier food choices without consideration of food insecurity, food bank use and poverty. The Policy proposes ideas around ‘choice’ and ‘informed decisions’, for example ‘ I want to see parents empowered to make informed decisions about the food they are buying for their families when eating out .’ ([ 8 ] p.5). However, it lacks consideration of the accessibility of a balanced diet due to: affordability of food, practical considerations on physical cooking equipment and energy costs of preparing and cooking food, skipping meals, needing to use food banks [ 31 , 55 ] or availability of healthy food options where they live [ 56 ].

Food insecurity is associated with poorer diets among children [ 28 ], due to limited access to sufficient, varied and healthy foods [ 57 ]. Despite this association, there is only one instance where The Policy demonstrate an awareness that healthy food is not accessible to all, through a commitment to continue investment in the Healthy Start Scheme. HSS provides pregnant women and families with children under the age of four on low incomes vouchers for milk, fruit, vegetables and vitamins [ 58 ]. However, the value of HSS vouchers have remained the same since introduction in 2009 (£3.10 per voucher), despite increasing food prices [ 59 , 60 , 61 ]. There is minimal emphasis on the HSS in The Policy , evidence of poor implementation of the HSS, and a lack of presence of HSS in the preliminary Chapter 3 of The Policy [ 9 ]. A 30% decrease in families eligible for the HSS between 2011 and 2018 [ 30 ] and recent uptake data demonstrating that less than half of eligible families registered and received HSS vouchers in England [ 62 ] bringing the scheme’s effectiveness into question. Reasons for the decline may be due to lack of awareness about the scheme and difficulties with the application process [ 30 , 61 ].

The Policy proposes making healthier choices easier by providing nutritional information through front-of-pack food labelling, implying the ‘problem’ is a lack of information when making food purchasing choices. However, such approaches have the potential to widen health inequalities due to the high level of agency involved [ 63 ]. Greater use of UK front-of-pack food labelling by those from more affluent backgrounds, compared with those from disadvantaged backgrounds, is acknowledged [ 64 ]. Also, evidence of the effectiveness of front-of-pack labelling is mainly generated using simulated conditions and does not consider financial aspects of purchasing behaviour: a strong driver for those experiencing food and time poverty [ 65 ].

Physical activity and inequalities

In 2019, 24% of children from less affluent backgrounds were classified as physically inactive, in comparison with 12% of children from more affluent backgrounds [ 66 ], trends that have been consistently reported since 2015 [ 67 ]. The physical activity proposals in The Policy centre around advice to schools and funding for cycling and walking initiatives. However, The Policy lacks engagement with wider determinants of active travel including environmental constraints, distance from school, and time poverty [ 68 , 69 , 70 ] and unmeasured factors found to be associated with cycling including home and social arrangements that facilitate cycling and owning a bike [ 71 ]. The proposals do not demonstrate how they are going to target children from less affluent backgrounds to increase physical activity and reduce these inequalities.

In Chapter 2 the money generated by the sugar levy was reported as lower than expected as soft drink manufacturers reformulated products to avoid it. Though a sign of success of the policy, a consequence of this reformulation means less money generated for investment in public health programmes (the PE and sport premium) than was originally estimated, which is not addressed in Chapter 2 or the proposed Chapter 3 [ 7 , 8 , 9 ]. The extent to which the premium will support all children, and reduce inequality, through increasing physical activity in school is then brought into question. Questions have been raised about the consistency and accountability of the PE and sport premium in schools, with some aspects of funding lacking clarity about how it will be distributed [ 72 ]. As the premium is another initiative that is not targeted based on need, the initiative is unlikely to address inequalities in access to physical activity.

The Policy , described by government as ‘world-leading’ and the first of its kind for children, repeats many of the mistakes of obesity policies that have been shown to be either ineffective or even have adverse effects. The overall problem framing of ‘obesity’ risks reducing the important public health aims to encourage healthy diets and increase opportunities for physical activity (and the physical and mental health benefits of both) for children to weight management, with a focus on particular children, to damaging effect. We have highlighted that individualising of responsibility to respond to systemic factors and structural inequalities may perpetuate damaging narratives and lead to ineffective interventions and ineffective individual treatment. The damaging effects of stigma should not be overlooked, recognising the barriers caused by stigma to opportunities to health promoting behaviours, to positive health outcomes, and to timely and appropriate treatment of health problems. Therefore, careful consideration of the framing of ‘obesity’ is needed from researchers, policy makers (national and local), and public health practitioners as the public health priority of childhood obesity continues to develop and implementation of The Policy continues to unfold.

Our approach asked, ‘can the problem be conceptualized differently?’ [ 33 ]. From our findings we propose an alternative conceptualisation that obesity rates are illustrative of inequality, as shown by the social gradient, with BMI trends at a population level highlighted in the policy illustrating this. Therefore, rather than ‘obesity’ being the ‘problem’, which we have demonstrated as complex and contested in relation to definition and relationship to health outcomes, we propose that the ‘problem’ to be addressed is inequality. For Chaufan et al. [ 73 ], policies that seriously consider the relationship between childhood obesity and socioeconomic inequality, including making poverty and the wider social determinants of health central to their proposals, offer the greatest potential to promote better child health and reduce obesity inequalities. Given the complex and contested relationship between ‘obesity’ and health, it stands that articulating the target policy ‘problem’ as inequality itself (for example, the health gap and access to healthy food and physical activity opportunities) will be more effective in improving health outcomes for all children.

We therefore support a focus on structural inequalities and the social determinants of health (including food security, poverty and environment), rebalancing responsibility away from the individual. The government must work to remove barriers to healthy eating and physical activity particularly those most impacted by health inequalities, regardless of weight, for healthier outcomes for all. Policies must demonstrate how they will tackle inequality and ensure that what is proposed will not widen inequalities through effective engagement with the evidence of the SDH.

Availability of data and materials

The qualitative data extracted and analysed during the current study is not publicly available but can be discussed or made available from the corresponding author on reasonable request. All documents analysed are publically available and referenced in this article.

World Health Organization: Report of the commission on ending childhood obesity: implementation plan: executive summary. 2017. [ https://apps.who.int/iris/bitstream/handle/10665/259349/WHO-NMH-PND-ECHO-17.1-eng.pdf?sequence=1&isAllowed=y ]

Google Scholar  

Department of Health. The health of the Nation - a strategy for health in England. White Paper. HMSO; 1992.

Jebb SA, Aveyard PN, Hawkes C. The evolution of policy and actions to tackle obesity in England. Obes Rev. 2013;14:42–59.

Article   PubMed   Google Scholar  

Theis D, White M. Is obesity policy in England fit for purpose? Analysis of government strategies and policies, 1992-2020. Milbank Q. 2013:126–70.

Croker H, Russell SJ, Gireesh A, Bonham A, Hawkes C, Bedford H, et al. Obesity prevention in the early years: a mapping study of national policies in England from a behavioural science perspective. PLoS One. 2020;15(9):e0239402.

Article   PubMed   PubMed Central   CAS   Google Scholar  

Davies SC. Time to solve childhood obesity, CMO Special Report. London: Department of Health and Social Care; 2019.

HM Government. Childhood Obesity: a plan for action. Policy Document. London, 2016. [ https://www.gov.uk/government/publications/childhood-obesity-a-plan-for-action ] Accessed 10 Dec 2020.

HM Government: Childhood Obesity: a plan for action: Chapter 2. Policy Document. London; 2018. [ https://www.gov.uk/government/publications/childhood-obesity-a-plan-for-action-chapter-2 ] Accessed 10 December 2020.

Department of Health and Social Care: Advancing our health: prevention in the 2020’s. Consultation Document. 2019. [ https://www.gov.uk/government/consultations/advancing-our-health-prevention-in-the-2020s/advancing-our-health-prevention-in-the-2020s-consultation-document ] Accessed 8 Oct 2020.

Knai C, Petticrew M, Mays N. The childhood obesity strategy. BMJ. 2016:354–i4613.

Nobles JD, Summerbell C, Brown T, Jago R, Moore T. A secondary analysis of the childhood obesity prevention cochrane review through a wider determinants of health lens: implications for research funders, researchers, policymakers and practitioners. Int J Behav Nutr Phys Act. 2021;18(22):998–1003.

Black D, Morris JN, Smith C, Townsend P, Davidson N, Whitehead M. The black report: inequalities in health. London: Department for Health and Social Care; 1980.

Marmot Review. Fair society, healthy lives: strategic review of health inequalities in England post-2010. London: The Marmot Review; 2010.

Marmot M, Allen J, Boyce T, Goldblatt P, Morrison J. Health equity in England: The Marmot Review 10 years on. London: Institute of Health Equity; 2020.

MacKenzie M, Collins C, Connolly J, Doyle M, McCartney G. Working-class discourses of politics, policy and health: ‘I don’t smoke; don’t drink. The only thing wrong with me is my health’. Policy Polit. 2017;45(2):231–49.

Article   Google Scholar  

Wistow J, Blackman T, Byrne D, Wistow G. Studying health inequalities: an applied approach. Bristol: Policy Press; 2015.

Book   Google Scholar  

Chung A, Backholer K, Wong E, Palermo C, Keating C, Peeters A. Trends in child and adolescent obesity prevalence in economically advanced countries according to socioeconomic position: a systematic review. Obes Rev. 2016;17(3):276–95.

Article   PubMed   CAS   Google Scholar  

El-Sayed AM, Scarborough P, Galea S. Unevenly distributed: a systematic review of the health literature about socioeconomic inequalities in adult obesity in the United Kingdom. BMC Public Health. 2012;12(1):1–2.

NHS Digital. National Child Measurement Programme, England 2019/2021 School Year, Official National Statistics. 2020. [ https://digital.nhs.uk/data-and-information/publications/statistical/national-child-measurement-programme/2019-20-school-year ] Accessed 5 Jan 2021.

Kriznik NM, Kinmonth AL, Ling T, Kelly MP. Moving beyond individual choice in policies to reduce health inequalities: the integration of dynamic with individual explanations. J Public Health. 2018;40(4):764–75.

Article   CAS   Google Scholar  

Medvedyuk S, Ali A, Raphael D. Ideology, obesity and the social determinants of health: a critical analysis of the obesity and health relationship. Crit Public Health. 2018;28(5):573–85.

Nobles J, Christensen A, Butler M, Radley D, Pickering K, Saunders J, et al. Understanding how local authorities in England address obesity: A wider determinants of health perspective. Health Policy. 2019;123(10):998–1003.

Brown T, Moore T, Hooper L, Gao Y, Zayegh A, Ijaz S, et al. Interventions for preventing obesity in children. Cochrane Database Syst Rev. 2019;12:CD001871.

Hillier-Brown FC, Bambra CL, Cairns-Nagi JM, Kasim A, Moore HJ, Summerbell CD. A systematic review of the effectiveness of individual, community and societal level interventions at reducing socioeconomic inequalities in obesity amongst children. BMC Public Health. 2014;14:830.

Food Foundation. New evidence of child food insecurity in the UK. 2017. [ https://foodfoundation.org.uk/new-evidence-of-child-food-insecurity-in-the-uk/ ] Accessed 6 Oct 2020.

Holding E, Fairbrother H, Griffin N, Wistow J, Powell K, Summerbell C. Exploring the local policy context for reducing health inequalities in children and young people: an in depth qualitative case study of one local authority in the North of England, UK. BMC Public Health. 2021;21(1):1–4.

Bambra C, Riordan R, Ford J, Matthews F. The COVID-19 pandemic and health inequalities. J Epidemiol Community Health. 2020;74(11):964–8.

PubMed   Google Scholar  

Power M, Pybus KJ, Pickett KE, Doherty B. “The reality is that on Universal Credit I cannot provide the recommended amount of fresh fruit and vegetables per day for my children”: Moving from a behavioural to a systemic understanding of food practices. Emerald Open Res. 2021;18(3):3.

The Trussell Trust: End of year stats. 2020. [ https://www.trusselltrust.org/news-and-blog/latest-stats/end-year-stats/ ] Accessed 2 Oct 2020.

Crawley H, Dodds R: The UK healthy start scheme. What happened? What next?. First Steps Nutrition Trust; 2018 [ https://static1.squarespace.com/static/59f75004f09ca48694070f3b/t/5b8e2d0e575d1f6f1e5d2dcd/1536044307456/Healthy_Start_Report_for_web.pdf ] Accessed 5 Oct 2020.

Select Committee on Food, Poverty, Health and the Environment. Hungry for change: fixing the failures in food. 2020. [ https://committees.parliament.uk/publications/1762/documents/17092/default/ ] Accessed 8 Oct 2020.

Pawson R, Tilley N. Realist evaluation. London: Sage; 2004.

Bacchi C. Analysing policy: What’s the problem represented to be? London: Pearson Education; 2009.

Bacchi C. Problematizations in health policy: questioning how “problems” are constituted in policies. SAGE Open. 2016;6(2):1–16.

Smith B, McGannon KR. Developing rigor in qualitative research: problems and opportunities within sport and exercise psychology. Int Rev Sport Exerc Psychol. 2018;11(1):101–21.

Chapman P, Lindsey I, Dodd-Reynolds C, Oliver E, Summerbell C. Targeting childhood obesity through primary schools: reviewing alignment amongst English policies for physical activity and healthy eating. Child Adolesc Obes. 2020;3(1):20–41.

Lakerveld J, Mackenbach J. The upstream determinants of adult obesity. Obes Facts. 2017;10(3):216–22.

Article   PubMed   PubMed Central   Google Scholar  

L’Hôte E, Fond M, Volmert A. Communicating about obesity: a frameworks strategic report. Washington, DC: Frameworks Institute; 2018. https://www.frameworksinstitute.org/wp-content/uploads/2018/03/communicatingaboutobesity_strategicreport_3-23-18.pdf

Baum F, Fisher M. Why behavioural health promotion endures despite its failure to reduce health inequities. Sociol Health Illn. 2014;36:213–25.

Correia JC, Somers F, Golay A, Pataky Z. Obesity : eat less and move more ? Not so easy. Rev Med Suisse. 2020;687:573–7.

Gillborn S, Rickett B, Muskett T, Woolhouse M. Apocalyptic public health: exploring discourses of fatness in childhood ‘obesity’policy. J Educ Policy. 2020;35(1):3–22.

Evans B, Colls R. Measuring fatness, governing bodies: the spatialities of the body mass index (BMI) in anti-obesity politics. Antipode. 2009;41(5):1051–83.

Evans B, Colls R, Hoerschelmann K. ‘Change4Life for your kids’: embodied collectives and public health pedagogy. Sport Educ Soc. 2011;16(3):323–41.

Salas XR, Forhan M, Caulfield T, Sharma AM, Raine K. A critical analysis of obesity prevention policies and strategies. Can J Public Health. 2017;108(5):e598–608.

Chiappetta S, Sharma AM, Bottino V, Stier C. COVID-19 and the role of chronic inflammation in patients with obesity. Int J Obes. 2020;44(8):1790–2.

Simmonds M, Llewellyn A, Owen CG, Woolacott N. Predicting adult obesity from childhood obesity: a systematic review and meta-analysis. Obes Rev. 2016;17(2):95–107.

Umer A, Kelley GA, Cottrell LE, Giacobbi P Jr, Innes KE, Lilly CL. Childhood obesity and adult cardiovascular disease risk factors: a systematic review with meta-analysis. BMC Public Health. 2017;17(1):683.

Lloyd LJ, Langley-Evans SC, McMullen S. Childhood obesity and risk of the adult metabolic syndrome: a systematic review. Int J Obes. 2012;36(1):1–1.

Richardson AK. Investing in public health: barriers and possible solutions. J Public Health. 2012;34(3):322–7.

Salas XR. The ineffectiveness and unintended consequences of the public health war on obesity. Can J Public Health. 2015;106(2):e79–81.

Rubino F, Puhl RM, Cummings DE, Eckel RH, Ryan DH, Mechanick JI, et al. Joint international consensus statement for ending stigma of obesity. Nat Med. 2020;26(4):485–97.

Pont SJ, Puhl R, Cook SR, Slusser W. Stigma experienced by children and adolescents with obesity. Pediatrics. 2017;140:6.

Puhl R, Peterson JL, Luedicke J. Fighting obesity or obese persons? Public perceptions of obesity-related health messages. Int J Obes. 2013;37(6):774–82.

Thibodeau PH, Perko VL, Flusberg SJ. The relationship between narrative classification of obesity and support for public policy interventions. Soc Sci Med. 2015;141:27–35.

Harvey K. “When I go to bed hungry and sleep, I’m not hungry”: Children and parents' experiences of food insecurity. Appetite. 2016;99:235–44.

Colls R, Evans B. Making space for fat bodies?: A critical account of ‘the obesogenic environment’. Prog Hum Geogr. 2014;38:733–53.

Skafida V, Treanor MC. Do changes in objective and subjective family income predict change in children’s diets over time? Unique insights using a longitudinal cohort study and fixed effects analysis. J Epidemiol Community Health. 2014;68(6):534–41.

Healthy Start [ https://www.healthystart.nhs.uk/ ] Accessed 8 Oct 2020.

Department for Environment Food and Rural Affairs. Food statistics in your pocket: prices and expenditure. [ https://www.gov.uk/government/publications/food-statistics-pocketbook/food-statistics-in-your-pocket-prices-and-expenditure#uk-retail-price-changes-by-food-group-2009-to-2019 ] Accessed 6 Oct 2020.

Lucas PJ, Jessiman T, Cameron A. Healthy start: the use of welfare food vouchers by low-income parents in England. Soc Policy Soc. 2015;14(3):457–69.

McFadden A, Green JM, Williams V, McLeish J, McCormick F, Fox-Rushby J, et al. Can food vouchers improve nutrition and reduce health inequalities in low-income mothers and young children: a multi-method evaluation of the experiences of beneficiaries and practitioners of the Healthy Start programme in England. BMC Public Health. 2014;14(1):1–3.

Healthy Start Uptake Data [ https://www.healthystart.nhs.uk/healthy-start-uptake-data/ ] Accessed 5 Oct 2020.

Adams J, Mytton O, White M, Monsivais P. Why are some population interventions for diet and obesity more equitable and effective than others? The role of individual agency. PLoS Med. 2016;13(4):e1001990.

Department of Health and Social Care. Tackling obesity: empowering adults and children to live healthier lives; 2020 [ https://www.gov.uk/government/publications/tackling-obesity-government-strategy/tackling-obesity-empowering-adults-and-children-to-live-healthier-lives ] Accessed 29 Oct 2020.

Puddephatt JA, Keenan GS, Fielden A, Reaves DL, Halford JC, Hardman CA. ‘Eating to survive’: A qualitative analysis of factors influencing food choice and eating behaviour in a food-insecure population. Appetite. 2020;147:104547.

Sport England. Active lives data tables; 2020 [ https://www.sportengland.org/know-your-audience/data/active-lives/active-lives-data-tables?section=children_and_young_people_surveys ] Accessed 10 Mar 2021.

Fuller E, Mindell J, Prior G. Health survey for England 2015. NHS Digital; 2016.

Ahern SM, Arnott B, Chatterton T, de Nazelle A, Kellar I, McEachan RR. Understanding parents’ school travel choices: a qualitative study using the theoretical domains framework. J Transp Health. 2017;4:278–93.

Kalenkoski CM, Hamrick KS. How does time poverty affect behavior? A look at eating and physical activity. Appl Econ Perspect Policy. 2013;35(1):89–105.

Stewart O, Moudon AV, Claybrooke C. Common ground: eight factors that influence walking and biking to school. Transp Policy. 2012;24:240–8.

McKay A, Goodman A, Van Sluijs E, Millett C, Laverty AA. Cycle training and factors associated with cycling among adolescents in England. J Transp Health. 2020;16:100815.

Lindsey I. Analysing policy change and continuity: physical education and school sport policy in England since 2010. Sport Educ Soc. 2020;25(1):27–42.

Chaufan C, Yeh J, Ross L, Fox P. You can’t walk or bike yourself out of the health effects of poverty: active school transport, child obesity, and blind spots in the public health literature. Crit Public Health. 2015;25(1):32–47.

Download references

Acknowledgements

We thank Dr Iain Lindsey for helpful and supportive discussions in the development of this research project.

This study is funded by the National Institute for Health Research (NIHR) School for Public Health Research (Grant Reference Number PD-SPH-2015). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. The NIHR School for Public Health Research is a partnership between the Universities of Sheffield; Bristol; Cambridge; Imperial; and University College London; The London School for Hygiene and Tropical Medicine (LSHTM); LiLaC – a collaboration between the Universities of Liverpool and Lancaster; and Fuse - The Centre for Translational Research in Public Health a collaboration between Newcastle, Durham, Northumbria, Sunderland and Teesside Universities.

Author information

Authors and affiliations.

Fuse, the NIHR (UK) Centre for Translational Research in Public Health (website), Newcastle, UK

Naomi Griffin, Sophie M. Phillips, Frances Hillier-Brown, Jonathan Wistow & Carolyn Summerbell

Department of Sport and Exercise Sciences, Durham University, Durham, UK

Naomi Griffin, Sophie M. Phillips & Carolyn Summerbell

Population Health Sciences Institute, Newcastle University, Newcastle, UK

Frances Hillier-Brown

Department of Sociology, Durham University, Durham, UK

Jonathan Wistow

Health Sciences School, University of Sheffield, Sheffield, UK

Hannah Fairbrother

ScHARR, School of Health and Related Research, University of Sheffield, Sheffield, UK

Eleanor Holding & Katie Powell

You can also search for this author in PubMed   Google Scholar

Contributions

N.G., S.P & F.H-B wrote the main manuscript. All authors reviewed the manuscript. N.G & C.S. carried out final review edits for submission. N.G & S.P carried out double blind data extraction and thematic analysis of policy documents. N.G., J.W., H.F., E.H., K.P. & C.S., developed the theoretical framework for data extraction and analysis. The author(s) read and approved the final manuscript.

Corresponding author

Correspondence to Naomi Griffin .

Ethics declarations

Ethics approval and consent to participate.

Not applicable.

Consent for publication

Competing interests.

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: table s1..

Key Proposals outlined in The Policy , presented independently by chapter.

Additional file 2: Supplementary Table S2.

Examples of problem representations using Bacchi’s (2009) ‘What’s the problem represented to be?’ approach to analyse The Policy

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Cite this article.

Griffin, N., Phillips, S.M., Hillier-Brown, F. et al. A critique of the English national policy from a social determinants of health perspective using a realist and problem representation approach: the ‘Childhood Obesity: a plan for action’ (2016, 2018, 2019). BMC Public Health 21 , 2284 (2021). https://doi.org/10.1186/s12889-021-12364-6

Download citation

Received : 09 June 2021

Accepted : 29 November 2021

Published : 14 December 2021

DOI : https://doi.org/10.1186/s12889-021-12364-6

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Childhood Obesity
• Health inequalities
• Social Determinants of Health
• Health Policy

BMC Public Health

ISSN: 1471-2458

• Submission enquiries: [email protected]
• General enquiries: [email protected]

Strengthening NHS management and leadership

26 February 2022

• Bryan Jones
• Quality improvement
• Share on Twitter
• Share on LinkedIn
• Share on Facebook
• Share on WhatsApp
• Share by email
• Link Copy link
• Download publication Download

Introduction

Why management matters, addressing the challenges facing nhs managers, the role of national and local leaders, recommendations for the messenger review.

• Good management is key to the NHS’s ability to provide high-quality services and to maximise the impact of its resources in the face of growing demand for care. However, in recent years, the importance of good management has been somewhat forgotten in the policy debate at the expense of a focus on leadership.
• As part of the Health Foundation’s research on management in the health service, we interviewed NHS managers and leaders in England to understand the challenges they face, what works well and what could be done differently. In this long read, we set out some of the insights from these interviews, focusing mainly on the role and practice of managers, and how they are trained and supported.
• This coincides with government commissioning a review of leadership in health and social care in England, led by Sir Gordon Messenger, which is expected to report to the Secretary of State for Health and Social Care at the end of March. Here, we conclude with a series of recommendations for the Messenger review to consider, focused around the need to: better support providers and systems to tackle variation in management practice; improve access to training and development opportunities; ensure training equips managers and leaders with the skills they need today; tackle the reporting burden facing managers, and ensure the role of managers and leaders is better understood and valued. 
• Arguing that we should better value NHS managers and leaders, and increase the support available to them, might not be the most politically popular case. But it is the right thing to do, and indeed will be essential if the NHS is to improve the quality and efficiency of its services as it recovers from the COVID-19 pandemic. 

NHS leaders and managers can get a bad press. Blame for delays, waste and inefficiency in the health service tends to be laid firmly at their door, often without any real attempt to understand the pressures and constraints facing them. So it is refreshing to see Sir Gordon Messenger and Dame Linda Pollard , the chairs of the latest England-wide review of health and social care leadership and management, acknowledge the excellence of many leaders and managers in the NHS, as well as the strain under which they operate.

The fact that management and leadership are each receiving the same level of attention from the review is also encouraging. In recent years, the importance of good management has been somewhat forgotten in the policy debate at the expense of a focus on leadership . Given that the NHS employs over 30,000 managers (see Box 1), it is important this imbalance is addressed.  

Box 1: Management – key definitions and figures

What are management and leadership.

Management involves the control, monitoring or organisation of people, processes and systems in order to achieve specific goals. It has been described as consisting of six key tasks : planning, allocating resources, coordinating the work of others, motivating staff, monitoring output and taking responsibility for the process.

Meanwhile leadership refers to influencing and inspiring others in pursuit of common goals, setting the tone and direction for a group or organisation, and identifying and framing problems for others to solve. In practice, leadership and management are closely interconnected and health care employees at all levels often have to deploy both leadership and management skills in order to carry out their job effectively.

How many managers are there working in the NHS in England?

• In September 2021 there were just over 34,300 full-time equivalent managers . 12,000 of whom are classified as senior managers. However, this figure may not include all staff who have some management responsibilities.
• It is estimated that around a third of NHS managers are doctors and nurses with part-time management roles.
• Managers account for around 2% of the NHS workforce, considerably less than the 9.5% of the workforce in the wider UK economy made up of managers, directors and senior officials.
• The NHS management population peaked at just over 38,300 full-time equivalent managers in April 2010 before falling to a low of 26,000 in May 2013.

We interviewed NHS managers and leaders in England in autumn 2021 with a view to understanding the challenges they face, what works well and what could be done differently. In this long read, we set out some of the insights from these interviews, focusing mainly on the role and practice of managers and how they are trained and supported – this is, we believe, the area that warrants most attention. We also present some recommendations for the Messenger review to consider on how the challenges facing NHS managers could be addressed.

What impact does management have on NHS performance?

Good management underpins the success of many high performing, ‘well led’ NHS organisations . It is key to the NHS’s ability to provide high-quality services and to maximise the impact of resources in the face of growing demand for care. Managers also play a pivotal role in improving the way services are delivered. Some evidence on the impact of management on health care providers’ performance is summarised in Box 2.

Box 2: Evidence on the impact of management on NHS performance

A number of academic studies have highlighted the positive contribution that effective management plays in health care:

• Improvements in  health care providers’ ‘management practice’ scores have been associated with improved clinical outcomes. For example, in the UK a one point improvement in the management practice score is associated with a 6% fall in the rate of deaths from heart attacks ( Dorgan, Bloom, Van Reenan 2010 ).
• A statistically significant correlation between the proportion of managers in a provider organisation and its performance has been observed. An increase in managers, from 2% to 3% of the workforce, has been associated with a 15% reduction in infection rates and a 5% improvement in hospital efficiency ( Kirkpatrick 2018 ).
• However, other research has produced findings that are more equivocal. One recent study found no evidence of an association between quantity of management and various measures of hospital performance ( Asaria et al 2022 ), though it did find some evidence that higher quality management is associated with better performance.
• Middle managers have been shown to play a particularly critical role in creating the conditions for innovation and improvement to flourish in  health care organisations. As well as encouraging and supporting teams to identify and test new ideas, they can unlock barriers to innovation, for example by finding the necessary resources to support scoping and testing activities ( Birken 2018 ; Engle 2017 ; Gutberg and Berta 2017 ).

What type of organisational culture facilitates good management?

The organisational culture in which managers work has a critical bearing on their ability to do their job effectively. As well as maintaining stability at executive level, both in terms of personnel and strategic approach, many of the best performing provider organisations in England have focused on developing an inclusive and respectful culture and promoting good communication across the workforce. For example , the leaders and managers of these organisations are often skilled in brokering agreement between a diverse range of professionals and in ensuring that change is shaped and owned by front-line teams, rather than imposed from above and driven by a handful of senior figures. They also support initiatives aimed at breaking down inter-professional boundaries and fostering a sense of shared purpose across the organisation. The presence of an inclusive culture, as described in the NHS People Plan , geared towards learning and knowledge sharing, is critical in enabling managers to communicate well, build effective teams and establish good relationships with their senior colleagues, peers and direct reports.

Our interviews highlighted a number of challenges to tackle for improving management practice in the NHS. In this section we describe some of these and suggest ways they could be addressed.

Rationalising managerial workload

A decade ago, the role of an NHS middle manager was described in an NIHR-funded study as having strong similarities to the highly paid ‘extreme jobs’ found in the world of international finance and management consulting. The financial rewards may have been very different, but the pace and intensity of the work, the long hours and the punishing delivery targets were largely the same. Little seems to have changed since then.

For many managers the pressure is every bit as intense as the NHS strives to tackle the care backlog in the wake of COVID-19, as well as the longer term pressures from increasing demand for care. According to our interviewees, finding a way to juggle an impossibly long and complex list of tasks, all of which are billed as urgent priorities, is still a constant challenge for a large proportion of NHS managers, especially for those working in front-line clinical settings.

It is important that managerial workload is feasible and management time is spent where it can add most value. All employers, as well as regional and national bodies, should reflect on how they can reduce the upward reporting burdens on managers and the volume of priority tasks they are assigned. Those organisations that have stopped ‘boiling the ocean’ , or expecting managers to deliver everything at once, and focused instead on establishing a few clear overarching quality goals , have helped to establish a climate in which managers are able to function effectively. In these organisations, clarity of purpose and an emphasis on ensuring their core goals are aligned and interlocking creates a context in which managers at all levels can be much more strategic about how they use their time and what they focus on.

Ensuring managers have the licence, space and ‘air cover’ from their leadership teams to lead the development of creative solutions to quality challenges also matters. Management teams need to earmark time away from their usual activities to reflect on the way that their service is managed and identify ways to improve it. This is key for enabling managers to switch from a mode of ‘firefighting’ to one in which they are able to identify and tackle emerging problems, or avoid them altogether.

Addressing variation in management practice and training

The question of whether managers should be subject to a set of agreed professional standards and national regulation governing their conduct, responsibilities and development has been at the forefront of many national reviews of NHS leadership and management over the past decade (including the Kerr , Rose , Smith , Dalton , Berwick and Francis reviews). The debate has been highly contentious . Proponents have argued that regulation has the potential to reduce variation in management performance, as well as bringing full-time managers in line with the medical and nursing professions and, in doing so, raising the status and profile of managers. Meanwhile, critics have pointed to the costs and feasibility of regulation and questioned whether it is really an effective lever for tackling variation. Some have worried that it may just provide another stick with which to beat an already beleaguered profession.

While the prospects of achieving a consensus on regulation appear slim, there is general agreement on both sides of the debate that there is a pressing need to achieve greater consistency in a number of key areas relating to management. Here we look at two particular issues, which emerged during our interviews: addressing the style of personality led management found in many organisations and improving the training and development of managers.

Reducing the prevalence of personality led management

A strong theme that emerged in our interviews was the extent to which the style and practice of NHS management varies, not just between organisations but within them. In many NHS trusts, for example, a group of clinical service lines is led by a ‘triumvirate’ consisting of a clinical lead, an operational lead and a nursing lead. But if you look beyond this standard structure, at the way in which power and responsibility is divided between the triumvirate in different groups, even within the same trust, a more varied picture emerges.

In some places, longstanding governance conventions and practices mean that one of the leads has a higher profile and reach than their peers. Elsewhere, it is the personalities and preferences of the respective leads that shape their management style and approach. Sometimes there are engaged and assertive clinical and nursing leads with a wealth of management experience who take the time to form an effective and even-handed partnership with their operational lead. In other cases, the operational lead dominates management decision making and the main function of the clinical and nursing leads is to review and sign off decisions.

Each of these versions of the triumvirate may work perfectly well for the individuals in post at the time and allow them to manage the division effectively. But this style of ‘personality led management’ (that is, contingent on the personality, style, interests and preferences of the individual) presents some risks. Chief among them is the inherent instability it can cause. If one person moves on to a new post and is replaced by someone with a different style, or a different set of priorities and preferences, then an entirely new working relationship needs to be struck, and staff are forced to adapt to a new management approach and set of power dynamics.

While ‘personality led management’ can be seen in the senior and middle management tiers of many NHS organisations, our interviewees suggested that it is not entrenched everywhere in the NHS. Some organisations have developed a consistent management culture and standardised way of working that is sufficiently well embedded that new appointees fit seamlessly into the team. As well as seeking to embed the right values and behaviours, leaders and managers of high-performing services have created management structures and processes geared towards ensuring consistency, clarity and stability. They have created standard job descriptions, and established managerial protocols, procedures and competency frameworks at each level to ensure that there is clarity about what each role involves, the skills and experience required and where the boundaries between specific roles lie. Underpinning them is often a transparent training and development pathway to ensure that prospective managers know what they need to do to progress their career, and what support they will get to do so.

The priority now is to ensure that good practice of this kind is the norm, rather than the exception. There is no single way of doing this. In large provider organisations it may be a question of gradually building on the success of high-performing, well-managed divisions and services as part of an organisation-wide strategy. In some areas, it may be appropriate for provider collaboratives and integrated care systems to pool resources with a view to driving change on a collaborative basis. Whatever path is chosen, it is important that organisations and systems are prepared to allocate sufficient time, resources and priority to planning and implementing their chosen approach.

While improving management practice is sometimes discussed as a route to tackling variation in performance between providers, it is important to remember that there can also be huge variation in performance within individual providers. Ensuring greater consistency in the approach to management within an organisation, and replicating good management practice across the whole organisation, can therefore be important levers for addressing this type of performance variation.

Improving access to training and development

A common complaint from the managers we interviewed is that they have had to work out for themselves how to be a manager. Sometimes there is training available, but they have not had the support to take time away from their day job to take advantage of it. Sometimes there is nothing suitable on offer at all. But what virtually all managers find is that the onus is firmly on them, unless they have a supportive supervisor, to find relevant training and then justify why it is necessary. Few it seems have access to a structured development programme in which they are expected to participate. As a result, there is significant variation in the training and skills of NHS managers.

There has been some progress over the past decade in increasing the availability of training programmes. According to our interviewees, for those who want it, it is often now easier than it was to find training in specific management skills, such as how to manage a budget or a team. Meanwhile, an increasing number of NHS trusts have understood the value of creating a standardised development offer for their managers and leaders (for example, Sheffield Teaching Hospitals NHS Foundation Trust). There are also a plethora of national schemes open to managers (see Box 3). And the importance of making line management training more readily available was recently acknowledged in the NHS People Plan .

Nonetheless, evidence that structured training for managers is widely recognised as important is elusive. The persistence of certain stereotypes about what it takes to become a manager may be partly to blame for this. In the case of clinicians, many of our interviewees drew attention to what they felt was an unhelpful assumption that clinicians’ medical training and clinical decision-making expertise provides them with the requisite skills to take on leadership and management responsibilities. As a result, they argued, many clinicians find themselves in management roles without any grounding in the theory and practice of management, and are then forced to play catch up for the rest of their careers. According to our interviewees, the presence of very senior NHS managers on management training modules, designed for far more junior colleagues, is much more common than it should be – testament to the lack of a structured development pathway earlier in their careers.

A clinical background offers many advantages for prospective managers, and it is clear that more needs to be done to encourage clinicians interested in leadership and management to take on responsibilities in this area. After all, clinical leadership at NHS trust board level has been associated with a range of performance benefits . But, in the view of our interviewees, the NHS lags behind other  health care systems in Europe and North America in terms of preparing clinicians for a management and leadership career.

Box 3: Examples of management development and training programmes

There is an increasing array of options for those looking to develop their leadership and management skills in health. Many higher education institutions offer courses in health care leadership and management, while a range of immersive development opportunities allow early career professionals to gain valuable experience.

NHS Graduate Management Training Scheme (GMTS)

The GMTS is a structured 2-year programme aimed at university graduates, providing placements across community, primary, secondary and tertiary care – with opportunities to specialise in different areas of management including finance, policy and human resources. The scheme provides opportunities for mentorship in addition to formal qualifications through the NHS Leadership Academy.

NHS Leadership Academy

The NHS Leadership Academy offers a range of development programmes leading to a qualification, with target audiences ranging from early career professionals to senior leaders looking to move up to board roles. The Elizabeth Garrett Anderson programme leads to a postgraduate degree in Healthcare Leadership accredited by both the University of Birmingham and University of Manchester.

Faculty of Medical Leadership and Management (FMLM)

The FMLM was established in 2011 by the UK medical royal colleges as a professional home for medical leadership. Over the past decade, the FMLM has run a 12-month immersive fellowship scheme for early career health care professionals, allowing them to step away from traditional NHS careers and gain exposure to a diverse range of organisations.

Full-time managers are also affected by some unhelpful assumptions, according to our interviewees. Whereas new doctors in training are generally afforded a certain amount of latitude and protection by colleagues because they are still learning their craft and adjusting to professional life, it is rare to see new junior managers offered the same understanding. They may be managers in training, but they are often expected to perform complex tasks that require a detailed understanding of NHS structures, regulations and processes with only the minimum of support or training. Rota management, highlighted in Box 4, is just one example. In many health care organisations, access to structured training is just as haphazard for full-time managers as it is for clinical managers.

Box 4: Training and development of rota managers

Medical rota managers typically work within human resources teams in hospitals, with responsibility for the work schedules of hundreds of clinical staff. These roles are made up of individuals working at a band 3 or band 4 level. Due to the banding of these roles, when recruited they are not typically expected to have qualifications or experience in managing complex rostering systems and patterns, and have minimal opportunities to gain formal or informal education in this area.

This can be a problem given the complexity of the work, which involves not only meeting the workforce requirements of clinical services but also the contractual technicalities of the staff. If rotas are not managed effectively, this can result in inadequate staffing, creating risks to patient safety, and distress among clinicians who feel their contractual rights are not being met. The result can be tensions between clinical and HR staff groups. While rota managers are often blamed for these tensions, the issue lies with a system that often puts people into management roles they are not fully equipped or supported to do.

How could this management training deficit be addressed? One priority is to ensure management training has the status, profile and resources it deserves. There is a good argument for making such training a core and non-negotiable element of the development of clinical managers and full-time managers alike, meaning that everyone with management responsibility would be expected to undertake some form of accredited training.

Another priority is to widen access to management development opportunities. According to our interviewees, it is often extremely difficult for junior staff who aspire to management (such as those working in administrative roles), to get the training and development they need to do so. Broadening training access, and promoting management among staff groups from which few have historically progressed into management, will help to increase the size and diversity of the NHS management talent pool. Further action is also needed to address the barriers to management careers faced by minority communities and those from backgrounds currently under-represented in management.

A further priority is to establish how and by whom training should be commissioned and delivered. It makes little sense for every employer to develop their own bespoke, standalone offer, although there are clear advantages in ensuring that training content reflects the context in which managers work. For this reason, it may well make sense to develop new accredited training programmes on a regional scale, as part of an integrated training and talent management strategy, which will provide economies of scale as well as a consistent approach. Nonetheless, care will be needed to ensure that any regional offers complement and build on existing established local programmes, such as those put in place by large teaching trusts.

Strengthening collaboration and improvement skills

There is a long list of competencies and skills that management development programmes should include, and this has been amply described elsewhere . However, there are key management challenges facing health care in the years ahead that require particular skills which have not traditionally been the focus of such programmes. According to our interviewees, among those management skills set to play a pivotal role in today’s increasingly networked, place-based, data-driven and improvement focused health care landscape, are:

• Collaborative leadership skills : A vital skill for leaders and managers at every organisational level is the capacity to work effectively with their peers across the local health care system. Leaders who are used to exercising their positional authority to drive change in their own organisation need a different skillset when operating at system level. In this place-based context, progress is contingent on leaders’ relational authority, which is built on trust and mutual respect, and requires well developed influencing and relationship skills. With integrated care systems set to become statutory bodies in 2022 there needs to be a greater emphasis on collaborative leadership skills in the training of all leaders and managers.
• Performance measurement and analysis skills : The ability to interrogate a performance dashboard and lead a team discussion aimed at understanding why a performance issue has emerged, and what approaches, methods and tools might be used to address it, is a key skill for service managers. Equally important is the capacity to identify suitable metrics to evaluate whether any changes made to a process or system achieve the intended outcome. According to our interviewees, many service managers lack confidence and expertise in these areas.
• Quality improvement skills : A core function of operations management is to improve the systems and processes that underpin the delivery of care and ensure that there is a consistent approach to managing quality . For example, it is important to understand how to identify and tackle problems that cause delay, waste and duplication within services, or impede patient flow along care pathways spanning multiple organisations. An awareness of specific quality improvement approaches is essential for managers to help redesign services and pathways and tackle unnecessary variation. Managers also need to be well versed in the relational aspects of change, such as how to involve and engage colleagues and patients. Yet in the NHS, quality improvement rarely features in the formal training and development of operations managers, or in their job descriptions.
• Technology appraisal and implementation skills : An understanding of how to deploy new technologies is becoming an increasingly important skill for NHS managers, whether to improve service quality or deliver service changes (for example, those seen in response to COVID-19 ). However, there is very little training or support available to managers in how to critically appraise and effectively implement technological and digital solutions. 

Strengthening these skills as part of an integrated and aligned training and development offer for managers will have significant benefits for health care providers in the coming decades.

Raising the profile of NHS managers

Many previous leadership and management reviews have underlined the importance of changing the way in which NHS managers are perceived. The persistence of anti-management narratives arguing that the NHS has far too many managers, diverting resources away from front-line care, suggests that more still needs to be done in this respect. Some criticisms of NHS managers are ideologically-driven, representing a broader antipathy to the public sector. In many other cases, perceptions of managers are undermined by the frequent failure to acknowledge their contribution to the NHS . Even within the health service, it is rare to see managers mentioned in reports of successful innovation or best practice, even if they were instrumental in getting them off the ground.

Why is it so hard to shift attitudes towards managers? One issue may be that there is little understanding of what managers in the NHS do. Patients rarely encounter an operations manager, service manager or general manager, or anyone in the dozens of other managerial roles in the NHS – unless the manager happens to be a clinician who also works as a manager. Even some clinicians struggle to say exactly what it is their own service and operations managers do day to day. This creates the space for misplaced assumptions and stereotypes to emerge.

Another issue is that while many clinicians have a royal college or professional society in their corner, with good connections to national bodies and influence with the media, the bodies representing professional managers do not yet have the same reach. There are signs that this may be changing thanks to the efforts of networks like Proud2bOps (see Box 5) and bodies like Managers in Partnership , but there is still a lot of ground to make up.

Box 5: Management networks

Proud2bOps is a grassroots network founded in 2017, aiming at bringing together operational managers under one banner to share learning, provide peer support and consolidate the voices of NHS operational managers to advocate for themselves and their services. The network now operates nationally with over 750 members across the country working at the levels of Divisional General Manager, Deputy Chief of Operations or Chief of Operations. Proud2bOp’s impact includes co-creating the Aspiring Chief Operating Officer Programme with NHS England and NHS Improvement.

Facilitated by the NHS Leadership Academy, #ProjectM is aimed at supporting and connecting managers in health and care. #ProjectM is not membership based, instead using Twitter as a platform to connect all those with management responsibilities in health services enabling peer-to-peer support, sharing of learning and mentoring opportunities to develop.

As such, there is an onus on policymakers and other influential figures in health care to make the case for why management matters and why managers deserve our respect. This is vital if the NHS is to retain good managers and recruit talented people into the profession.

Much of the responsibility for tackling the challenges outlined above lies with local leaders. The task of ensuring that pockets of good management practice are replicated across organisations and wider systems, for example, lies predominantly with organisation and system leaders. Local leaders can also help to ease the burden on managers of front-line services by stripping back the number of delivery targets and reporting requirements they face. However, national policymakers have several critical roles to play.

First, it is important they champion the cause of managers and celebrate the contribution they make to the delivery of quality health care. NHS England and NHS Improvement’s new 10-year strategy for NHS human resources and organisational development does underline the talent and ability present within the NHS’s managerial ranks, but these sentiments need to be echoed across the whole of government.

Second, and related to this, particular attention needs to be given at national level to attracting and retaining good managers and leaders, especially in organisations with performance challenges. Given that the median tenure of NHS trust chief executives is reported to be only 3 years, and that many of our interviewees cited a high level of managerial turnover in their trusts as a particular challenge for improving performance, it is clear that more work is required in this area. Financial incentives, such as relocation allowances, performance-related pay and loyalty bonuses probably have a role to play here, but they will need to be accompanied by efforts to boost the professional kudos associated with turning around struggling organisations. As long as leadership and management roles in large metropolitan teaching trusts continue to be seen as far more prestigious than those in other trusts, including in smaller urban centres or rural areas, then the latter will find it harder to recruit and retain talented managers and leaders. Recruitment and retention are not the only reasons why trusts in smaller urban centres or rural areas sometimes face performance challenges, but they certainly play an influential role.

Third, policymakers have an important contribution to make in creating a climate in which leaders and managers can flourish. Providing long-term strategic clarity, while avoiding overly prescriptive interventions and disruptive reorganisations, helps give organisations the security they need to set long-term objectives aligned with national goals. And by cutting the upward reporting burden, tackling ‘ priority thickets ’, and avoiding a punitive culture , national policymakers can help provide leaders and managers with the space they need to focus on improving services. Indeed, punitive policy and regulatory measures may have the effect of deterring innovation by leaders and managers, and encouraging a focus on compliance with short-term targets, rather than on long-term transformation.

Finally, policymakers need to consider the question of how to fund any increase in training provision. Ensuring that there are sufficient resources available to train and support senior leaders is relatively feasible given the modest size of the health and social care leadership population (and there are some excellent programmes already in place). Providing high-quality training for the NHS’s army of junior and middle managers is a more significant undertaking. Yet it is important that funding is made available if local organisations and systems are to have the resources they need to address their training and development priorities. After all, a lack of resources is one of the reasons why the recommendations of previous reviews to strengthen the management training offer have not been realised. So policymakers need to ensure that extra investment is made available over the next decade.

In summary, we have highlighted several important areas for the Messenger review to consider:

• Support providers and systems to tackle variation in management practice . Management practice and culture varies considerably across the health and care landscape, including within individual providers. Such variation is unlikely to be resolved through a one-size-fits-all intervention. Instead, local leaders need to be encouraged and supported to develop strategies tailored to local needs and context to ensure good practice is replicated across organisations and systems.
• Improve access to training and development opportunities . Training and development opportunities are currently patchy and hard to access for many current and prospective managers and leaders. Significant resources need to be earmarked to strengthen the infrastructure for training, development and talent management. It could make sense to develop new accredited training offers and programmes on a regional basis, while ensuring these complement existing local programmes and are tailored to local needs.
• Ensure training equips managers and leaders with the skills they need today . Training must include the knowledge and skills managers and leaders need to flourish in today’s landscape, which is networked, place-based, data-driven and improvement focused. This includes more emphasis on collaborative leadership skills, so that managers and leaders can work effectively with their peers across the local health care system, as well as on performance measurement, quality improvement and technology appraisal and implementation skills.
• Tackle the reporting burden and ‘priority thickets’ facing managers . Employers, and regional and national bodies have a responsibility to help ensure management workloads are feasible and that management time is spent where it can add most value. They should reflect on how they can reduce the upward reporting burden, tackle priority thickets and avoid unnecessary reorganisations – all of which can consume management capacity and make it harder to manage effectively.
• Ensure the role of managers and leaders is better understood and valued . Employers, regional and national bodies should work collaboratively to shift perceptions of NHS managers and leaders and to challenge negative stereotypes, as well as ensuring that remuneration supports effective recruitment and retention. This will be particularly important to help employers recruit and retain good managers, particularly in those parts of the country facing recruitment challenges.

Arguing that we should better value NHS managers and leaders, and increase the support available to them, might not be the most politically popular case to make given some of the myths and negative stereotypes that have been allowed to take root. But it is the right thing to do – and indeed will be essential – if the NHS is to improve the quality and efficiency of its services, and meet the growing demand for care as it recovers from the pandemic.

Share this page:

Health Foundation @HealthFdn

• Work with us

We look for talented and passionate individuals as everyone at the Health Foundation has an important role to play.

The Q community

Q is an initiative connecting people with improvement expertise across the UK.

Quick links

• News and media
• Events and webinars

Hear from us

Receive the latest news and updates from the Health Foundation

• 020 7257 8000
• [email protected]
• The Health Foundation Twitter account
• The Health Foundation LinkedIn account
• The Health Foundation Facebook account

Copyright The Health Foundation 2023. Registered charity number 286967.

• Accessibility
• Anti-slavery statement
• Terms and conditions
• Privacy policy

Log in using your username and password

• Search More Search for this keyword Advanced search
• Latest content
• For authors
• Browse by collection
• BMJ Journals More You are viewing from: Google Indexer

You are here

• Volume 11, Issue 5
• Critical care work during COVID-19: a qualitative study of staff experiences in the UK
• Article Text
• Article info
• Citation Tools
• Rapid Responses
• Article metrics

• http://orcid.org/0000-0002-5829-6137 Catherine M Montgomery 1 ,
• http://orcid.org/0000-0002-4397-6404 Sally Humphreys 2 ,
• Corrienne McCulloch 3 ,
• Annemarie B Docherty 3 , 4 ,
• http://orcid.org/0000-0002-3273-1727 Steve Sturdy 1 , 5 ,
• http://orcid.org/0000-0002-6771-8733 Natalie Pattison 6 , 7
• 1 Centre for Biomedicine, Self and Society , University of Edinburgh , Edinburgh , UK
• 2 Critical Care and Research & Development , West Suffolk NHS Foundation Trust , Suffolk , UK
• 3 Anaesthetics, Theatres and Critical Care , NHS Lothian , Edinburgh , UK
• 4 Centre for Medical Informatics, The Usher Institute , University of Edinburgh , Edinburgh , UK
• 5 Science, Technology and Innovation Studies , University of Edinburgh , Edinburgh , UK
• 6 School of Health and Social Work , University of Hertfordshire , Hertfordshire , UK
• 7 Nursing , East and North Hertfordshire NHS Trust , Stevenage , UK
• Correspondence to Dr Catherine M Montgomery; Catherine.montgomery{at}ed.ac.uk

Objective To understand National Health Service (NHS) staff experiences of working in critical care during the first wave of the COVID-19 pandemic in the UK.

Design Qualitative study using semistructured telephone interviews and rapid analysis, interpreted using Baehr’s sociological lens of ‘communities of fate’.

Participants Forty NHS staff working in critical care, including 21 nurses, 10 doctors and advanced critical care practitioners, 4 allied health professionals, 3 operating department practitioners and 2 ward clerks. Participants were interviewed between August and October 2020; we purposefully sought the experiences of trained and experienced critical care staff and those who were redeployed.

Setting Four hospitals in the UK.

Results COVID-19 presented staff with a situation of extreme stress, duress and social emergency, leading to a shared set of experiences which we have characterised as a community of fate. This involved not only fear and dread of working in critical care, but also a collective sense of duty and vocation. Caring for patients and families involved changes to usual ways of working, revolving around: reorganisation of space and personnel, personal protective equipment, lack of evidence for treating COVID-19, inability for families to be physically present, and the trauma of witnessing extreme patient acuity and death on a large scale. The stress and isolation of working in critical care during COVID-19 was mitigated by strong teamwork, camaraderie, pride and fulfilment.

Conclusion COVID-19 has changed working practices in critical care and profoundly affected staff physically, mentally and emotionally. Attention needs to be paid to the social and organisational conditions in which individuals work, addressing both practical resourcing and the interpersonal dynamics of critical care provision.

• intensive & critical care
• qualitative research
• organisation of health services

Data availability statement

The datasets used and analysed during the current study are available from the corresponding author on reasonable request.

This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See:  https://creativecommons.org/licenses/by/4.0/ .

http://dx.doi.org/10.1136/bmjopen-2020-048124

Statistics from Altmetric.com

Request permissions.

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

Strengths and limitations of this study

This is the first study to provide a sociological analysis of critical care work during the first wave of the COVID-19 pandemic in the UK.

International studies of staff experience of COVID-19 have focused on individualised mental health outcomes; we use the theoretical concept of ‘communities of fate’ to add value to existing approaches.

Our sample included a range of professional groups and explicitly sought to capture the experiences of both experienced and redeployed staff.

Our sample was limited due to the fact that participants were self-selecting and came from a small number of sites.

Medical and nursing staff made up the majority of participants in our sample; our findings may over-represent the experiences of these professional groups thus limiting wider generalisability.

Introduction

COVID-19 has placed unprecedented demands on the UK National Health Service (NHS). Around 8% of all hospital admissions, over 14 000 patients, have been admitted to critical care services with COVID-19 since February 2020. 1 2 Critical care services were rapidly expanded to meet these demands. 3 Research from China and France into the experiences of healthcare staff demonstrates the enormous pressure COVID-19 has placed on doctors and nurses, ranging from issues of healthcare service organisation to personal mental health and well-being. 4 5 In the UK context, Vindrola-Padros et al report on anxiety and distress caused by limited personal protective equipment (PPE) for healthcare workers, lack of routine testing and unmet training needs for redeployed staff. 6

Research on healthcare staff during the pandemic has predominantly focused on the psychological impacts of working in critical care during this time. A European-wide study identified high levels of self-reported burnout (51%) during the pandemic among intensive care unit (ICU) staff respondents. 5 We know that burnout, moral injury and moral distress are significant issues in critical care staff, and prior to COVID-19 were already the subject of a call to arms in the international critical care community. 7 Prevalence in critical care staff ranges from 6% to 47%, with worse burnout compared with areas such as palliative care, and significant emotional labour associated with working in critical care. 8–10

While much of the research to date has taken a narrowly psychological approach to staff experience, focusing on poor mental health outcomes such as burnout, we aimed to adopt a broader, sociological lens. As Matthewmann and Huppatz note, “As the discipline charged with making sense of contemporary social cohesion and transformation, sociology is well placed to comment on coronavirus and its profound consequences.” 11 The sociology of pandemics draws attention to the way in which social institutions—including healthcare systems—change when biological environments change and threaten established ways of living and acting in the world. 12 Social fragilities and structural deficiencies are laid bare and para-epidemics—of fear, of explanation and moralisation, and of action—proliferate. 13 14 In relation to the impacts of COVID-19 on mental health, members of the Society and Mental Health COVID-19 Expert Group recently argued against the pathologisation of responses to the pandemic and instead called for attention to the social substrates of poor mental health. 15 This view is echoed by a recent paper by Vera San Juan et al , emphasising the importance of socio-ecological approaches to healthcare worker well-being during the COVID-19 pandemic. 16 We accordingly aimed to provide an alternative to the discourse of individual psychological responses in healthcare workers by exploring issues including changing work organisation and its impacts, identity and care work, and interpersonal/professional relationships.

We draw on the sociological concept ‘community of fate’ to illuminate the experiences of frontline staff working in critical care during the first wave of the COVID-19 pandemic in the UK. Analysing the 2003 SARS outbreak in Hong Kong, Baehr describes a ‘community of fate’ as a particular form of group cohesion arising from a situation of extreme duress ( figure 1 ). 17 18 Such communities are socially productive; in the face of an existential threat, they mobilise around shared purpose and resources (including organisation and leadership) to instantiate collective action. A key hallmark of such communities is ‘a common focus of sustained attention, and an intense feeling of horizontal interconnectedness’. 18

• Download figure
• Open in new tab
• Download powerpoint

Key features of communities of fate (adapted from Baehr, 2005). 17

We conducted qualitative research using semistructured telephone interviews and rapid analysis. 19 20 Qualitative research can contribute to the evidence base of managing COVID-19 by accessing how frontline staff manage their day-to-day work, why particular approaches work or not from the point of view of those implementing them, and what could be done to improve the experience of caring for patients and families in critical care. 21 Critical care is used as a term throughout to encompass intensive care/ICUs, intensive therapy units and critical care/critical care units.

Sampling and recruitment

Using principles of maximum variation sampling, 22 we recruited 40 frontline staff members working in critical care from four hospitals in the UK, including nurses, medical staff, allied health professionals (AHPs) and ward clerks. The hospitals were all located in urban areas and served populations of between 500 000 and 2 000 000 inhabitants. At the time of the study, hospital A had more than 25 critical care beds, hospitals B and D had between 15 and 20 critical care beds, and hospital C had fewer than 10 critical care beds. All increased their capacity to deal with the COVID-19 surge in the first wave of the pandemic. We recruited 18 participants from hospital A, 6 from each of hospitals B and C, and 10 from hospital D. The sample was diverse with respect to age, gender and experience working in critical care ( table 1 ). The study was advertised using posters, email and word of mouth. Snowball sampling, which takes advantage of the social networks of identified respondents, provided the research team with an escalating set of participants.

• View inline

Sample description

Interested participants were provided with information about the study, contacted the principal investigator and were subsequently provided with a participant information sheet. Once participants had agreed, verbal informed consent was digitally recorded prior to interview.

Data collection and analysis

Telephone interviews were conducted between August and October 2020 by CM (sociologist), SH (research nurse/scholar) and NP (clinical professor of nursing). The research team included the additional expertise of a critical care consultant, professor of sociology and nurse researcher in critical care, with significant combined qualitative research experience. Interviews were digitally recorded and professionally transcribed. Interviews lasted from between approximately 30 and 80 min. Semistructured interviews covered staff’s experiences of working in critical care during the first wave of the pandemic (roughly between March and July 2020). Questions related to changes in working practice, interaction with patients, technology for family communication, end of life, learning and training, and personal well-being and support.

Data were analysed by team members following the rapid analysis methods proposed by Hamilton 19 and elaborated by Taylor et al . 20 In the first stage, key issues were noted on a structured summary template describing: participant and data collection details, deductive and inductive headings, quotations and the analyst’s reflections. Deductive aspects of the summary template were developed from the research questions. Following an initial testing period, additional, inductively generated subheadings were added. Summarised data were then transferred to a matrix to ‘streamline the process of noting simultaneously and systematically similarities, differences and trends in responses across groups of informants’. 23 Transparent team review and discussion across all transcripts took place to enhance confirmability, trustworthiness, dependability and credibility. 24 Early findings were discussed within the team and subsequently interpreted using the sociological lens of ‘communities of fate’. 17

Patient and public involvement

Patients and/or the public were not involved in the design, conduct, reporting or dissemination of this study.

COVID-19 presented staff in critical care with a rapidly changing situation and guidance; staff found themselves working in a state of constant flux. Participants’ accounts reflected changes every shift, with one person saying every day felt like the first day in a new job. Below, we describe the features of working in this extreme pandemic context in relation to the seven features of a community of fate, highlighting not just changes in working practice but the social corollaries of these changes. Illustrations from the data are given in figures 2–5 .

Data extracts illustrating critical care as a community of fate during COVID-19. ITU, intensive therapy unit; PPE, personal protective equipment.

Data extracts illustrating critical care as a community of fate during COVID-19. ITU, intensive therapy unit; ODP, operating department practitioner; PPE, personal protective equipment.

Data extracts illustrating critical care as a community of fate during COVID-19. ICU, intensive care unit; ITU, intensive therapy unit.

Data extracts illustrating critical care as a community of fate during COVID-19. ACCPs, advanced critical care practitioners; ICU, intensive care unit; NHS, National Health Service; ODP, operating department practitioner; PPE, personal protective equipment.

Danger recognition: fear and dread of COVID-19

Many staff members commented on the anxiety they felt in anticipation of working in critical care during COVID-19. During the early stage of the pandemic, these anxieties were heightened by media reports of overwhelmed hospitals in Italy and exhausted healthcare workers in China. Staff were also acutely aware of their personal risk of catching COVID-19 and taking this home to their families. Particularly affected were those from ethnic minorities and those with at-risk and shielding family members. One black African nurse living in a multigenerational household described the impact the death of a fellow nurse from COVID-19 had had on her, and how her sister, also a nurse, had also fallen ill with COVID-19. Such experiences heightened the anxiety of working in critical care; they also entailed considerable emotional labour—something described by several respondents when talking about how they had sought to reassure their partners and children that they were safe at work. A number of participants had experienced the death of colleagues, which was deeply affecting.

Moral density: purpose and duty

In spite of—indeed because of—this existential threat, many staff members spoke of a strong sense of duty in relation to working during COVID-19. Recognition of the danger the pandemic posed to the population as a whole was a powerful motivator, prompting several redeployed staff members in the sample to proactively volunteer. Shared professional commitment was also a powerful factor, with some expressing that this was simply their job, and others that it was what they had been trained to do. This collective vocation to provide care and be present created a common sense of purpose, which cut across professions and hierarchies.

Trial: ordeals in critical care

Working in COVID-19 critical care was extremely challenging. The main ordeals that staff described were as follows (see also figures 3 and 4 ):

Dislocation

Setting up COVID-19 critical care facilities often involved converting clinical areas, including wards and theatre recovery areas, to new purposes and assimilating redeployed staff into newly assembled critical care teams. Adapting to these new circumstances proved challenging, with staff reporting difficulties locating equipment and supplies, or identifying who was in charge. Lack of familiarity with other team members was exacerbated by PPE, which rendered identification and recognition difficult. Redeployed staff without previous critical care experience faced particularly acute challenges adapting to unfamiliar language and processes, with some saying they felt ill equipped to deal with even basic tasks such as how to record observations or wash patients. This was particularly so for the operating department practitioners (ODPs) in our sample.

Responsibility

Staff described a rapid acceleration in levels of responsibility. This included managing the dual tasks of caring for critically ill patients while also training non-critical care staff, which created additional cognitive and emotional demands: some senior nursing staff reported not taking a break for 6 or 7 hours due to anxiety over leaving inexperienced staff. These demands were magnified for staff from smaller units with a smaller pool of experienced staff to draw on for redeployment into critical care. Extreme stress was reported by senior nursing staff trying to maintain adequate staffing levels in these contexts. The sheer number of patients also exacerbated the burdens of responsibility. The decision to abandon existing guidance on minimum staff:patient ratios 25 was perceived to be unsafe by some nursing staff, and led to a loss of confidence, even for some experienced nurses. Some reported suffering from extreme anxiety and a sense of loss of control when attempting to look after patients safely and with dignity, while nurses in several units expressed sadness at their inability to provide as much care as usual in terms of washing, turning and personal care.

Caring for patients

Staff commented repeatedly on the physical and emotional intensity of caring for critically ill patients with COVID-19, and their acute awareness of how frightening it was for patients. While the majority of staff felt safe and protected by their PPE, it nonetheless made caring for patients difficult due to loss of manual dexterity, numbing of the senses, loss of visual and audio cues, heat, weight, dehydration, facial pain and the fact that everyone looked the same. This also disrupted professional interaction: staff were sometimes unable to recognise who had the expertise to respond to a given request for help, and it hindered tasks that depended on coordinated teamwork such as proning or turning patients.

The lack of an evidence base for treating COVID-19 also led to fundamental uncertainty about what care to provide. Medical and nursing staff alike commented on the simultaneous information vacuum and information deluge, compounded by the lack of a central, controlled source for information about clinical practice, with much communicated by word of mouth. AHPs described new challenges associated with ‘caring at a distance’, when direct access to patients was either not possible or limited. For example, one dietitian spoke of how relying on verbal reports from nursing staff via telephone rather than seeing the patient and their charts themselves made it difficult to assess nutritional status and ensure appropriate supplies.

End of life

While many participants were used to caring for dying patients, COVID-19 brought new difficulties. With families unable to visit, staff’s emotional relationship with patients was intensified, with many staff members saying they felt they had to take on the family’s role of ‘being there’ for the patient. All the staff members we interviewed expressed deep sadness at witnessing the deaths of patients with COVID-19 who were not allowed to have a family member present in person. Staff who sat with patients at the end of life often found it heartbreaking to be party to this very intimate moment between a patient and their family, for example, while holding a telephone to the patient’s ear. Staff reported that the decision to allow family members in again towards the end of life had made a huge difference and ‘humanised the process again’.

Staff also encountered new challenges after a patient had died. Some nurses reported that protocols regarding what to do after a patient had died from COVID-19 were not clear at the start, for example, around last offices, infection control and what to do with patient belongings. One experienced nurse described how upsetting it was to be tasked with moving people who had died into body bags and onto trolleys for the morgue.

The severity of illness and high death rate in COVID-19 critical care, while difficult for all staff, was particularly hard for redeployed theatre and recovery staff, whose work usually involves patients who improve. The ODPs we spoke to reported having no training or experience in communicating with families at end of life. Ward clerks were also affected by the sheer numbers of deaths and caring for these patients’ families. As the first port of call for families phoning up, this work could be very emotionally intense; as one ward clerk observed, ‘sometimes you could just listen to them, even though you couldn’t help them’.

Interaction with families

Caring for families is a large part of critical care work, 26 27 and particularly important at the end of a patient’s life. 28 29 With visitors excluded from patients’ bedsides due to COVID-19, staff experienced additional demands to keep families informed while navigating the constraints of communicating ‘virtually’ using digital technologies/telephone. Staff spoke of the peculiar difficulties of avoiding unwarranted optimism or pessimism when families were unable to witness for themselves how a patient might be progressing or deteriorating. The need to communicate at a distance also made it particularly challenging for staff to break bad news. Most staff felt unprepared to have these conversations, and consultants, in particular, reported having some of the most upsetting conversations of their careers.

The emotional strain of facing these trials, combined with the sense of isolation, was often severe. Some nursing staff reported experiencing acute fear, stress, anxiety, exhaustion and burnout, particularly in smaller units. Staff spoke of crying on the way to work, breaking down in tears on shift and crying after leaving work. Fear for the consequences of what they perceived to be inadequate staffing levels, inexperienced staff and a high volume of critically ill patients left some nursing staff feeling ‘broken’. Across the sample, staff reported a range of negative impacts, such as sleep disturbance, panic attacks, weight loss/gain, and feelings of guilt, grief, anger, sadness and dread. Some accessed professional mental health services, either within the Trust, through their general practitioner or privately.

Closure: isolation and the ‘COVID-19 bubble’

With COVID-19 critical care facilities physically and socially isolated from other parts of the hospital for infection control purposes, and many staff members removed or redeployed from their usual workplaces and colleagues, work in what some staff referred to as the ‘COVID-19 bubble’ could feel like a collective exile. Several participants voiced disappointment that COVID-19 seemed to be treated as ‘a problem for critical care’ rather than the hospital at large, such was their sense of isolation. This was magnified for staff working night shifts, who commented on feeling forgotten, having less food and drink available, fewer redeployed staff to support them and less visibility of senior managers.

Material and organisational resources: learning and creativity

By contrast, staff also took pride in the many ways they had managed to adapt to the challenges posed by new working arrangements. Staff experienced a steep learning curve: consultants described the challenges of treating patients in the absence of an evidence base, while nurses spoke of learning to manage a large number of patients who were considerably sicker than usual and whose condition could deteriorate quickly. Both groups spoke of the quick, self-directed learning needed to stay abreast of rapidly changing treatment protocols. While some felt that ‘nothing prepares you to work in a pandemic’, those with experience of working in previous infectious disease outbreaks such as SARS or H1N1, as well as those involved in protocol development and training for emergency situations, felt better prepared.

Rapid learning across units was helped by the relative homogeneity of the cohort of patients with COVID-19. The following training was specifically mentioned as helpful: locally run, structured competency and skills training; the FutureLearn COVID-19 Critical Care course; and the frequent Intensive Care Society webinars. For nursing staff, much of the training occurred on the job; those who had been redeployed particularly valued the opportunity to shadow staff and/or to have a more experienced buddy. Staff across nursing and medical teams identified platforms like WhatsApp as a crucial means of sharing information and updates, moving away from traditional modes of communication.

There was widespread praise for the speed with which new systems had been put in place and change effected in the NHS. A particular success was the use of tablets and mobile phones to connect families and patients via synchronous/asynchronous video-conferencing. Staff observed that the more frequently they were able to connect with families, the easier this virtual relationship became, the more patients were individuated, and the more satisfactory the caring relationship.

Axis of convergence: teamwork

Almost everyone we interviewed articulated positive aspects of their experience during the pandemic. Foremost among these was the sense of teamwork and camaraderie that had developed as staff pulled together. Many felt proud to have been part of the pandemic response, and spoke of the satisfaction of being part of something and working for a common purpose, while the influx of redeployed staff was often felt to be a source of both moral and practical support. Newly qualified and experienced nurses alike said that working during COVID-19 reaffirmed the values that had taken them into the profession in the first place.

Teamwork, and the mutual support it provided, also characterised some of the measures that proved most effective in meeting the challenges of COVID-19. Daily team huddles were observed to be useful for identifying and trouble-shooting local issues, while shared conversations within the team about difficult shifts and patients who had died was helpful in coping with emotionally difficult experiences. As a result, various staff said they had gained improved clinical, operational and management skills, increased resilience, confidence and self-esteem.

Social rituals: donning and doffing

In a setting that depends so heavily on teamwork, it would be futile to try and draw any hard-and-fast distinction between routine and ritual: both serve at once to provide reassurance and to affirm a common identity in the face of disorder and danger. In the context of COVID-19, however, some routines acquired special significance. The donning and doffing of PPE was one such. Staff spoke of the benefits of going into critical care in pairs to check both on PPE fit and each other’s well-being. They also found creative ways to decorate their PPE, turning it from a faceless signifier of risk into an expression of individuality. In so doing, they transfigured the fear of entering critical care into a moment of human solidarity and interconnectedness.

Using the sociological concept of ‘community of fate’, our analysis shows how working practice in critical care changed during the first wave of the COVID-19 pandemic in the UK, and how staff mobilised their collective resources to provide care to patients. In Baehr’s Weberian use of the term, ‘community of fate’ does not imply fatalism. On the contrary, it denotes the condition of purposeful collective action that may be attained by a group of people facing a common crisis. Employing the concept to analyse our interviewees’ testimony not only helps explain staff experience in the face of extremity, but highlights the crucial role of solidarity and teamwork in achieving a functioning COVID-19 critical care system.

That collective achievement should not blind us to the anguish that many participants endured and its lasting damage: several staff expressed a deep reluctance to return to critical care to tackle a second wave, something highlighted in other qualitative studies of frontline staff experience. 30 Nor should we assume that the communities of fate that coalesced in response to the first wave will survive as the pandemic becomes increasingly protracted and challenges recur. As Baehr notes “Where all hope is gone, resources spent, and action deemed hopeless, communities of fate are impossible.” 18 It therefore behoves us to ensure that resources are available and the conditions for good care and staff well-being are optimised. While some have suggested this should focus on individual well-being initiatives, including mindfulness and other coping strategies, 31 our analysis underscores the importance of structural resilience in critical care and attending to the conditions under which teams can prosper.

Our study is not alone in emphasising the importance of taking healthcare workers’ experiences into account during the COVID-19 pandemic. Indeed, our findings echo and amplify many of those from the Rapid Research, Evaluation and Appraisal Lab Study of the perceptions and experiences of healthcare workers during COVID-19. 6 16 32 While the latter drew on a sample consisting primarily of doctors, our study adds the voices of nurses and other professionals to the evidence base, as well as extending its geographical reach beyond London to other parts of the UK. By drawing on sociological theory to interpret our data, we provide an alternative lens through which to understand social cohesion and transformation in critical care during pandemic times.

Some limitations to our study apply, namely the self-selecting sample, the small number of sites from which we recruited and the small number of AHPs. As such, our findings may over-represent the experiences of nursing and medical staff, and further research should consider a broader range of experiences from across the professions. While we recruited from a range of critical care settings across the UK, these were all located in urban areas and staff working in rural areas may have different experiences. Nonetheless, we believe the concept of ‘communities of fate’ is likely to have broad theoretical generalisability. Finally, the rapid analytical methods we used were designed to structure the analysis thematically, and were not explicitly oriented to exploring differences by demographic variables, such as gender and ethnicity. Studies of staff experience during COVID-19 have shown how these variables shape experiences; 32 33 we acknowledge the importance of recognising the ‘stratified forms of risks and vulnerabilities facing diverse groups of healthcare workers both within and across health systems’. 34

A key strength of this study is its in-depth focus on critical care during the first wave of the COVID-19 pandemic in the UK. Our data have alerted us to a range of specific measures that might be implemented at the local level to help critical care staff contend with the challenges posed by COVID-19, and we have made our recommendations available and accessible. 35 They join a growing body of guidance and resources aimed at helping staff maintain mental health, well-being and resilience through the pandemic. 36 37 As has been noted elsewhere, 16 with some notable exceptions, 38 these resources are overwhelmingly oriented towards supporting individuals. Yet as Rose et al 15 argue, individualised psychology-based interventions will be ineffective unless the social preconditions for well-being are in place. Our own findings strongly support that view. While personalised support is to be welcomed, attention also needs to be paid to the social and organisational conditions in which individuals work.

Our research shows the importance, on the one hand, of building and facilitating teamwork within and across critical care; and on the other hand, of addressing the sense of isolation that critical care staff felt from other parts of the healthcare system. Resourcing is one aspect of this: our data attest to the need to address anxieties around practical issues such as staffing levels and PPE availability. But equally important is how institutions and national bodies develop transparent plans to deal with COVID-19 and meaningfully engage with frontline staff. Responsibility is key, and time, energy and resource must underpin the professional duty of care to healthcare colleagues in order to comprehensively manage surge situations like the COVID-19 pandemic.

To the best of our knowledge, this is the first sociological analysis of healthcare staff’s experiences of working in critical care during the first wave of the COVID-19 pandemic in the UK. Our findings provide timely insight into the challenges of critical care work during the first wave of COVID-19 and suggest the importance of moving beyond an individualised understanding of staff well-being to consider the social and organisational factors at stake.

Ethics statements

Patient consent for publication.

Not required.

Ethics approval

Ethical approval was granted by the University of Edinburgh School of Social and Political Science Research Ethics Committee; HRA approval (20/HRA/3270) was also obtained.

Acknowledgments

We would like to thank all the NHS staff who took part in this study for giving up their time and sharing their experiences with us for this research.

• Data.gov.uk
• Haase JE , et al
• Azoulay E ,
• De Waele J ,
• Ferrer R , et al
• Vindrola-Padros C ,
• Andrews L ,
• Dowrick A , et al
• Gozal D , et al
• Chuang CH ,
• Martins Pereira S ,
• Teixeira CM ,
• Carvalho AS , et al
• Sorensen R ,
• Matthewman S ,
• Dingwall R ,
• Hoffman LM ,
• Staniland K
• Pickersgill M
• Manning N ,
• Bentall R , et al
• Vera San Juan N ,
• Aceituno D ,
• Djellouli N , et al
• Henshall C ,
• Kenyon S , et al
• Thorogood N
• Denzin NK ,
• NHS England
• Davidson JE
• Cabilan CJ ,
• Faculty of Intensive Care Medicine
• Pattison N ,
• Turnock C , et al
• Bennett P ,
• Johnston S , et al
• Aughterson H ,
• McKinlay AR ,
• Fancourt D , et al
• Hoernke K ,
• Djellouli N ,
• Andrews L , et al
• Regenold N ,
• Vindrola-Padros C
• Montgomery C ,
• Docherty A ,
• Humphreys S , et al
• NHS Education for Scotland
• Intensive Care Society

Twitter @Cath_Montgomery, @Research2Note, @abdocherty79, @drnatpat

Contributors CMM and ABD conceived the study, and all authors contributed to the study design. CMM, SH and NP collected the data. CMM, SH, CMc, SS and NP analysed the data and all authors contributed to data interpretation. CMM, SS and NP wrote the first draft of the article and all authors contributed to subsequent revisions. All authors had full access to the data.

Funding This research was funded by Medical Research Scotland through a COVID-19 Research Grant (CVG-1739-2020) and supported in part by the Wellcome Trust (209519/Z/17/Z).

Disclaimer The funders of the study had no role in study design, data collection, data analysis, data interpretation or writing of the manuscript.

Competing interests None declared.

Provenance and peer review Not commissioned; externally peer reviewed.

Read the full text or download the PDF:

Exploring the benefits and limitations of transactional leadership in healthcare

Affiliation.

• 1 Institute of Health, University of Cumbria, Lancashire, England.
• PMID: 33089675
• DOI: 10.7748/ns.2020.e11593

Leadership theory is a crucial aspect of nursing and the focus of a large body of literature. However, it remains a challenging concept to define and has been subject to various interpretations. Much of the literature on leadership is contradictory, with some studies claiming particular leadership styles are more effective than others. This article discusses the benefits and limitations of one approach to leadership - transactional leadership - and considers its use in nursing practice. The author suggests that transactional leadership remains useful as an approach to meeting short-term goals and completing tasks, but that it should be combined with other leadership styles to maximise its effectiveness in healthcare settings.

Keywords: shared leadership; leadership; leadership development; leadership frameworks; leadership models; leadership skills; transformational leadership.

© 2020 RCN Publishing Company Ltd. All rights reserved. Not to be copied, transmitted or recorded in any way, in whole or part, without prior permission of the publishers.

• Delivery of Health Care
• Leadership*
• Nursing Care*

Cookies on GOV.UK

We use some essential cookies to make this website work.

We’d like to set additional cookies to understand how you use GOV.UK, remember your settings and improve government services.

We also use cookies set by other sites to help us deliver content from their services.

You have accepted additional cookies. You can change your cookie settings at any time.

You have rejected additional cookies. You can change your cookie settings at any time.

• Health and social care

Using NHS data to improve healthcare

Professor Sir Chris Whitty writes for The Times on how using data effectively and safely can improve patient care and bolster research

This article was originally published in The Times.

The NHS uses data every day for healthcare. All of us who use the NHS contribute to the data; all of us who use it benefit from the data being used effectively and safely. It serves three purposes all of which, done properly, improve healthcare now or in the future. The first is data for direct patient care. If data cannot be shared between different parts of the NHS, doctors and other healthcare professionals treating patients in one part of the NHS cannot access important information about us.

The second is to improve the effectiveness of the NHS. The third is to collate data for medical research to improve healthcare in the future. The more complete the data for all of these uses, the more effective current and future healthcare will be. By allowing the use of our data we benefit ourselves and others, but we also have an absolute expectation that these data will be kept securely, privately and only used for legitimate purposes.

Having our data shared across the NHS to assist directly in our own clinical care is a benefit to the great majority of patients in the NHS. We may receive care both from our GP and in hospital, and some people are treated in several different parts of the NHS. If a doctor or nurse in one part of the NHS does not know important facts from another, this can reduce the effectiveness of treatment and in some cases can be dangerous.

Currently the NHS has multiple data systems that do not talk to one another; we need to change that. Healthcare workers sometimes worry that sharing or accessing data might infringe some rule. When this is done in good faith for patient care, using standard guidelines and methods, it is extremely unlikely to cause any concern; indeed there is a strong clinical responsibility to share data when this will benefit an individual patient. The Information Commissioner, National Data Guardian and I have recently jointly written about this .

Using data integrated from patients across the NHS to improve its efficiency and effectiveness benefits us all. The more inclusive of everyone the data are, the easier it is for the NHS to make sure its resources are used efficiently to optimise healthcare for all of us. Covid-19 demonstrated this; by bringing together data from across the country and combining data from different parts of the system the NHS became steadily more effective at managing healthcare both for those who had Covid-19 and those needing care for other health problems. Knowing where there is ICU capacity, which A&E departments are under greater pressure, and optimising operation lists are all examples of things which need live, inclusive data.

The extraordinary improvements in health which have occurred as a result of medical research are the third area where using our patient data can be transformational. Again, taking Covid-19 as an example, we used data from patients from across the country, rapidly combined, to determine the safety and effectiveness of vaccines, drugs and other medical interventions.

Over a million UK citizens volunteered to take part in trials and other formal studies, and we should be very grateful to them. Many more contributed data which help to deliver new treatments, and assess their effectiveness for others in the future. Without data used in this way, the speed of medical improvements will be slowed down, and research aiming to improve future healthcare for diseases such as cancers or heart disease will be impossible.

People should know how their data is used and be involved in these decisions, so it is welcome that NHS England has today announced a programme of national conversations with the public on data policies and programmes. I encourage people to take part. The NHS has a responsibility to use data to improve individual patient care, its own effectiveness and support research. When we withhold our data from the NHS this can only reduce the effectiveness of care for us and others now, and medical advances for others in the future.

Is this page useful?

• Yes this page is useful
• No this page is not useful

Help us improve GOV.UK

Don’t include personal or financial information like your National Insurance number or credit card details.

To help us improve GOV.UK, we’d like to know more about your visit today. We’ll send you a link to a feedback form. It will take only 2 minutes to fill in. Don’t worry we won’t send you spam or share your email address with anyone.

We have moved to a new website

Helping our heroes, every moment counts.

Critical NHS was founded as a community based non-profit organisation established in March 2020 to support NHS front line staff and those most vulnerable groups in response to the outbreak of coronavirus, and ‘in-turn’ to help sustain local businesses and restaurants as suppliers.

Within the first four weeks we were able to raise enough funds to provide 25,000 meals across a network of more than 45 local businesses, developing a roster of deliveries co-ordinated across 50 volunteers.  This was supplemented by the provision of personal protective equipment (PPE), smartphones and tablets (so loved ones could see one another from hospital wards), care packages, and groceries including fresh vegetables, eggs, milk. bread, flour, pasta, tinned foods, toiletries and toilet paper.

Expanding quickly, and working with multiple partners, we were able to provide upwards of +1,500 meals a day to NHS frontline staff at 19 hospitals across London, Surrey, Sussex & Cornwall; and we continue to fund meals to a number of care and community centres that cater for more vulnerable individuals affected by the pandemic. The funds raised by individual donations and our sponsors has helped subsidise over 125,000 meals and has provided a much-needed lifeline to many local businesses who may otherwise have ceased trading.

Points of Light Award

SUPPORTING THE FRONTLINE

#DoshforNosh

Instagram: @criticalnhs.

Your donations will help us support the essential workers through this pandemic

Twitter: @criticalnhs

Set up to support the critical care staff at 16 hospitals across London over the next few weeks and months, and in turn, support our local shops and restaurants.

Testimonials

The staff of Critical Care who are working tirelessly have been kept afloat by this support … I am proud to work for our NHS and please do not underestimate how much your kindness and generosity means to us all.

“Thank you for the opportunity and, most of all, the lifeline that you have provided for me and my family.”

“Seeing how hard it is for the frontline NHS staff in helping the country ease the pain of the pandemic, I felt we as a firm, and myself personally, had to do something significant. The work that Critical NHS is doing is fantastic and I hope others will join us in offering whatever support they can.” 

LATEST NEWS

Critical NHS is a NOT FOR PROFIT initiative registered as Critical Support C.I.C, a Community Interest Company Registered in the UK N°  12556208

Get in touch.