uk health research analysis 2020

Health Research Classification System

UK Health Research Analysis

Public Health Research

Health Research Analysis

An accurate picture of current research investment is important for planning research funding. The UKCRC has carried out analyses of the health research portfolios of government and charity organisations funding health related research in the UK, using a bespoke Health Research Classification System.

The first UK Health Research Analysis report, published in 2006, was the first ever national analysis of health research. It

Largest analysis of UK health research funding published

A report published on 28 January 2020 delivers the most comprehensive analysis of UK health research funding ever compiled. Bringing together data from 146 charity, professional and public sector organisations, the UK Health Research Analysis 2018 found that in 2018 these funders contributed £4.8 billion to support research to improve human health.

The report was compiled by a collaboration of 12 public and charitable funders led by the Medical Research Council, on behalf of the UK Clinical Research Collaboration (UKCRC). The UKCRC partnership supports coordination and collaboration between the major stakeholders that influence clinical research in the UK, and these efforts are underpinned by an analysis of UK health research funding every four to five years.

This latest report is the fourth in a series that charts changes in health research over the last 14 years. From 2004 to 2009, the reports found that public and charitable support for health research grew, with a compound annual growth rate (CAGR) of 8.5%, then over the last ten years funding for health research remained, in real terms, almost level with a CAGR of 1.5% (2009 to 2018).

The report notes that main areas to receive the highest increases in proportion of investment were research activities important for translation - research that aids translating scientific discoveries into new treatments and healthcare benefits. Early detection of disease and the development and evaluation of new treatments were the main areas that received increased funding, totalling £523 million over 14 years.

Half of public and charity support for health research was directed at basic science, which underpins further study or seeks to identify the causes and progression of disease. It is this discovery science that will provide a pipeline of new potentially transformative ideas, technologies and methods for the future.

Funders have continued to increase funding for other areas, such as prevention-related research (e.g. vaccine development and smoking/dietary interventions), that were identified as having lower investment in previous UK health research analyses.

The report highlights that public and charity funders support research into a diverse range of disease areas, with the largest increase in support for research into infectious diseases. While disease burden in the UK in this area is low, this research includes efforts to combat the global health risk of pandemics and antimicrobial resistance.

Cancer research continued to attract a substantial proportion of total funding, with high levels of charity support. Cancer is the disease that leads to the highest UK disease burden, according to the World Health Organisation.

The report includes data from 82 new funders – bringing the total of 146 funders to more than double the number included in the 2014 report – and included coding of over 22,500 projects.

It is the first report in the series to undertake a detailed analysis of international funding support for UK research teams (totalling £240 million income to the UK in 2018) and the first to record UK funder contributions to research overseas (totalling £220 million funding to 66 countries in 2018).

In addition to the £4.8 billion of public and charity 2018 funding detailed in this report, the authors have provided estimates for the total expenditure on health research in the UK. Estimates for expenditure in the pharmaceutical sector (£4.3 billion in 2017, continuing the recent recovery following the economic crisis of 2008), brought the estimated total for UK health research to £8.67 billion.

To download the report see the HRCS Online website .

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Article Contents

Introduction, acknowledgements, conflict of interests, data availability.

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Analysis of the UK Government’s 10-Year Drugs Strategy—a resource for practitioners and policymakers

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Adam Holland, Alex Stevens, Magdalena Harris, Dan Lewer, Harry Sumnall, Daniel Stewart, Eilish Gilvarry, Alice Wiseman, Joshua Howkins, Jim McManus, Gillian W Shorter, James Nicholls, Jenny Scott, Kyla Thomas, Leila Reid, Edward Day, Jason Horsley, Fiona Measham, Maggie Rae, Kevin Fenton, Matthew Hickman, Analysis of the UK Government’s 10-Year Drugs Strategy—a resource for practitioners and policymakers, Journal of Public Health , Volume 45, Issue 2, June 2023, Pages e215–e224, https://doi.org/10.1093/pubmed/fdac114

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In 2021, during a drug-related death crisis in the UK, the Government published its ten-year drugs strategy. This article, written in collaboration with the Faculty of Public Health and the Association of Directors of Public Health, assesses whether this Strategy is evidence-based and consistent with international calls to promote public health approaches to drugs, which put ‘people, health and human rights at the centre’. Elements of the Strategy are welcome, including the promise of significant funding for drug treatment services, the effects of which will depend on how it is utilized by services and local commissioners and whether it is sustained. However, unevidenced and harmful measures to deter drug use by means of punishment continue to be promoted, which will have deleterious impacts on people who use drugs. An effective public health approach to drugs should tackle population-level risk factors, which may predispose to harmful patterns of drug use, including adverse childhood experiences and socioeconomic deprivation, and institute evidence-based measures to mitigate drug-related harm. This would likely be more effective, and just, than the continuation of policies rooted in enforcement. A more dramatic re-orientation of UK drug policy than that offered by the Strategy is overdue.

In 2021, the UK Government published its 10-year drugs strategy, From Harm to Hope 1 (hereafter referred to as ‘the Strategy’) following Dame Black’s Independent Review of Drugs. 2 This is during a period of escalating drug-related deaths in the UK 3–5 surpassing the rates of many countries. 6 The following analysis, undertaken with the Faculty of Public Health and Association of Directors of Public Health, assesses whether the Strategy is evidence-based and consistent with the call from the highest coordination forum of the United Nations (UN) to ensure drug strategies promote public health and human rights. 7 The Strategy is structured under three strategic priorities: to ‘Break drug supply chains’, ‘Deliver a world-class treatment and recovery system’, and ‘Achieve a generational shift in demand for drugs’. This article discusses drug-related harm in the UK, the Strategy’s three pillars, and highlights missing elements of policy.

Drug-related harm in the UK

‘Drug-related harm’ encompasses the negative health and social impacts associated with illicit drug use, and drug market involvement. As acknowledged by the Strategy, and the Black Review, 2 which preceded it, current approaches have not effectively reduced many of these harms. Various health and social issues including socioeconomic deprivation, mental and physical health problems, stigma, trauma and homelessness may both predispose to and be exacerbated by drug dependence. 8–10

Amongst the health harms related to drug use, drug-related deaths provide the most obvious metric. Between 2010 and 2019, age standardized drug-related mortality rates increased in Scotland by 171% (from 90 to 244 per million) 3 ; Northern Ireland by 149% (from 35 to 87 per million) 4 ; and England and Wales by 61% (from 31 to 49 per million). 5 There are likely multiple reasons for these increases. 2 , 11 A common argument is that deaths increased because people with drug dependencies are older, with comorbidities increasing overdose risk. Two recent studies, however, demonstrated ageing alone does not explain the increase. 12 , 13 Other potential contributory factors include: (i) increasing polydrug use, with the risk of opioid overdose increasing with concomitant benzodiazepine, gabapentinoid, and alcohol use 14–17 ; (ii) increasing homelessness and incarceration, which are associated with mortality risk, and human immunodeficiency virus (HIV) and hepatitis C (HCV) transmission 9 , 10 , 18 ; (iii) changing patterns of socioeconomic deprivation, which is strongly associated with drug-related harm 19–21 and (iv) cuts to services that protect against all-cause and drug-related mortality. 2 , 21 , 22

The Strategy makes some unsupported assumptions about the relationship between drugs and social problems. It suggests that drugs ‘blight’ neighbourhoods, stopping them from reaching their potential, implying drugs cause socioeconomic deprivation as opposed to the latter creating conditions in which drug markets flourish. Socioeconomic deprivation and adverse childhood experiences are inter-related 23 with both associated with harmful patterns of drug use, 19 , 20 , 24 Furthermore, disinvestment in health and social services in socioeconomically deprived areas since 2010 may have contributed to increasing harm. 22

Drug-related harms to third parties include acquisitive crime and drug-related violence. However, in some instances the Strategy exaggerates the causative relationship between drugs and crime. For example, it states drugs ‘contribute’ to almost half of all homicides, seemingly implying causation. In 2020, 48% of homicides were in some way related to drugs—in most cases, the victim or perpetrator was known to use or deal drugs, sometimes recently. 25 In a small proportion of cases, motives were related to obtaining drugs or drug proceeds, 25 but for the most part, it is not clear that drugs caused the homicides, and in no cases is it clear stricter drug controls would have prevented them.

Breaking drug supply chains

The first pillar of the strategy aims to: reduce drug availability by targeting supply chains, including international, wholesale and retail providers, with a particular focus on ‘county lines dealing’ (when drugs are transported from cities to other areas, and sold using a mobile phone ‘line’).

There is some evidence that limiting the supply of a drug increases its purity adjusted price, 26 which can reduce demand for that drug, 27 thereby reducing hospital attendances and overdoses related to its use. 28 There are, however, three issues with enforcement-led efforts to reduce drug supply.

First, there is limited evidence of their effectiveness. The Government has highlighted there is a lack of relevant evaluative research, 29 and available evidence does not suggest that arresting dealers or seizing drugs has a long-term impact on supply. 30 Internationally, there have been some isolated reductions in drug supply, for example, after global market disruption interrupted heroin supply in Australia in 2000 31 and Western Europe in 2010 32 ; and controls on precursor chemicals in the USA in 1989 and 2006 impacted cocaine availability. 33 These reductions were, however, temporary, and it is not clear what caused them when other efforts have not had the same impact. Despite recent seizures, global production and purity of drugs continues to increase 34 and the UK has amongst the cheapest heroin and cocaine in Europe. 35

Second, there is limited understanding of how restricting the supply of certain drugs affects the supply of, demand for, and harm related to other drugs. For example, during ‘droughts’ of specific drugs, people may use adulterated drugs, alternative drugs, or resort to polydrug use. 36–39

Third, enforcement may have unintended consequences on the drug market and people who use drugs, leading to increased harm. Focussing on the most violent and exploitative forms of supply, such as those associated with county lines dealing 40 may shape the market to adopt less harmful practices. 41 , 42 However, as the Black Review highlighted, 2 arresting suppliers can create conditions that favour competition, promoting innovation and violence. 41–51

Delivering a world-class treatment and recovery system

The second pillar of the Strategy aims to: rebuild treatment services following significant disinvestment; promote integration of drug treatment, health and criminal justice services; and improve employment and accommodation opportunities.

Additional drug treatment funding promised by the Strategy is welcome; however, this follows years of sustained disinvestment, 2 associated with reductions in numbers of people in treatment, 52 and an increase in the proportion of people using opioids and crack cocaine not engaged with services. 53 Furthermore, drugs workers have experienced increasing caseloads and greater administrative responsibilities, sometimes limiting their capacity to provide psychosocial interventions. 54

The Strategy suggests ‘recovery from drug addiction’ is a key aspect of its approach. As the UK Government Recovery Champion highlights, recovery and harm reduction should not be considered as opposing approaches, and the full range of evidence-based interventions should be provided. 55 Opioid agonist therapy (OAT—treatment of opioid dependence with methadone or buprenorphine) reduces the risks of all-cause mortality, overdose, suicide, self-harm, HIV and HCV, improves quality of life 9 , 56–61 and duration of OAT improves survival. 62–65 Whilst modelling demonstrates comprehensive OAT and harm reduction programmes reduce drug-related mortality, 66–68 this is dependent on retention in treatment, which should be a key indicator. Focusing on treatment completion may incentivise premature OAT cessation, limiting treatment benefits and the impact of additional funding.

Increased funding and targeted commissioning could allow the introduction of innovative interventions, including drug checking and diamorphine-assisted treatment—neither of which the Strategy mentions. No intervention alone will avert the drug-related death crisis, but in combination with wider treatment systems, these evidence-based interventions could have beneficial impacts on patterns of harm. 69–72 Local areas may need additional funding and technical support to commission diamorphine-assisted treatment, which is more expensive than oral OAT. 73 These costs, however, are compensated by greater savings to wider services, including related to reductions in acquisitive crime. 73 Drug checking, on the other hand, is expanding, as the UK’s first regular Home Office licensed, local authority funded drug checking service launches in Bristol. 74

Despite their relevance, the Strategy does not mention HCV and HIV prevention. An estimated 89% of people infected with HCV in the UK have injected drugs 75 and a recent outbreak of HIV occurred amongst people who inject drugs in Glasgow. 76 The UK is a leader in providing HCV treatment for people who inject drugs, with clear reductions in chronic infections and liver-related deaths. 12 , 77–80 However, achieving the World Health Organization (WHO) target of ‘eliminating HCV as a public health problem’ 75 will depend on preventing reinfection, with HCV infection a critical indicator for assessing the success of drug treatment and harm reduction systems. 81

People with drug dependencies often have co-occurring health problems. People in drug treatment are getting older, and more deaths are caused by long-term conditions than overdoses. 12 , 82 Office for Health Improvement and Disparities data suggest 63% of people starting drug treatment have a mental health need 53 and people with substance dependence are at greater risk of suicide. 83 A recent study demonstrated that one in fourteen opioid-related deaths in England occur amongst people recently discharged from hospital, 84 highlighting the need to improve integration between healthcare and drug treatment services. Drug services will need to recruit more clinically trained staff to identify and manage co-occurring health issues, which will be challenging as the workforce has been depleted by disinvestment. Furthermore, hospital care for people with drug dependence requires improvement. Stigmatizing attitudes towards people who use drugs and fear of opioid withdrawal are key barriers to healthcare access, 85–87 underpinned by hospital policies that create significant procedural barriers to providing OAT. 88

Drug-related harm remains a key issue in prison, with overdose risk substantially elevated in the month following release 89–91 and incarceration a risk factor for HIV and HCV. 18 Prison OAT reduces mortality and drug use in prison and critically also mortality following release. 92 , 93 The Strategy’s proposed zero-tolerance approach to drugs is inconsistent with the Inspectorate of Prisons acknowledgement of the importance of harm reduction strategies in prisons. 94 Proposed alternatives to prison OAT, including detoxification, are experimental, and it is necessary to demonstrate they do not increase drug-related deaths (during and after incarceration) compared to OAT. Evaluations of previous Drug Recovery Wings, which utilized abstinence and harm reduction-based approaches, highlighted potential benefits but identified challenges, particularly related to limited support on release. 95 The Strategy recognizes the need for improved inter-agency coordination during and following incarceration, however recommendations from the Advisory Council on the Misuse of Drugs (ACMD) to improve custody-community transitions have not been realized. 96

Achieving a generational shift in the demand for drugs

The third pillar of the Strategy aims to: reduce demand for drugs by applying ‘tougher and more meaningful consequences’ to deter use, delivering education programmes in schools and supporting at risk families.

The assumption that the threat of punishment will reduce demand is not supported by evidence, with no clear relationship between the stringency of drug laws and drug use prevalence. 97–101 The Home Office previously concluded ‘levels of drug use are influenced by factors more complex and nuanced than legislation and enforcement alone’. 102 These may include socioeconomic deprivation 19 and adverse childhood experiences 24 ; factors that may be exacerbated by the health and social harms associated with contact with the criminal justice system. 103 Additionally, the stigma associated with punitive policies may deter people with drug dependence from seeking support. 104

The Strategy’s proposed ‘tough consequences out of court disposal schemes’ provide an opportunity to divert people from the criminal justice system. Available evidence tentatively suggests diversion schemes reduce re-offending more effectively and cost-effectively than criminal sanctions. 105–108 However, there is limited research evaluating their impacts on drug-related harms 109 and existing diversion schemes vary in approach and ethos. Whilst diversion schemes may mitigate some of the harms associated with criminal sanctions, most are still designed to negatively impact people who use drugs, which may exacerbate the issues predisposing to harmful use.

The Government’s subsequent White Paper, SWIFT, CERTAIN, TOUGH (in consultation), proposes escalating consequences for drug possession including: mandatory drugs awareness courses, random drug testing (and expansion of drugs tested for on arrest), passport and driving licence confiscation, wearable drug monitors and exclusion orders prohibiting attendance of particular venues. 110 These proposals raise significant concerns. Mandatory drugs awareness courses will require payment, with non-attendance and non-payment punished with fines or criminal charges, placing an inequitable burden on the socioeconomically deprived, who are the most likely to be caught. Passport and driving license confiscations may affect employment prospects and will disproportionately impact the rights of people who use drugs. The intention to ensure ‘more people face consequences of their use’ with expanded drug testing is likely to ‘widen the net’, with more people receiving punishments that may escalate to criminal sanctions with questionable justification. Furthermore, the Strategy implies people could be coerced into drug treatment, contravening human rights and medical ethics norms, 111 with limited evidence that coerced treatment reduces future drug use. 111 , 112

It remains to be seen how proposed schemes will contribute to the stigma faced by people who use drugs, and whether they will reproduce the ethnic and socioeconomic disparities apparent in current enforcement. People who are black are nearly nine times more likely to be stopped and searched for drugs than people who are white and are more likely to be arrested, prosecuted, and sentenced to immediate custody. 113 Whilst the Strategy recognizes the problem of disproportionate policing, plans to expand punishments that inequitably impact the socioeconomically deprived do not align with efforts to reduce inequalities and ‘level up’ communities. 114

What’s missing?

The Strategy states it is taking a new approach; however, most elements are a continuation of former approaches proposed in the context of existing legislation, rather than allowing for legislative reform to decriminalize the possession of drugs and facilitate innovative interventions.

The Strategy suggests that decriminalization risks increasing drug use; however, this is not supported by evidence. 97–101 Whilst criminalization has no clear benefits, it causes significant harm to people who use drugs. 115 Since the Misuse of Drugs Act 1971 was introduced, more than three million criminal records have been generated for drugs offences. 116 In 2017, 60% of prosecutions for drug offences in England and Wales were for possession rather than supply, including 36% for the possession of cannabis. 113 In the UK, decriminalization has been recommended by bodies including the 2019 Health and Social Care Committee on Drug Policy 117 ; the Royal College of Physicians 118 ; the Royal Society of Public Health and the Faculty of Public Health. 119 Internationally, over 30 countries have some degree of decriminalization, 101 and it has been recommended by the highest coordination forum of the UN, comprising the Executive Heads of organizations including the WHO and the UN Office for Drugs and Crime. 7

The Government has resisted the introduction of overdose prevention centres, 120 despite promising evidence they could reduce drug-related deaths and engage the most marginalized with services. 70 , 121 The introduction of pilot sites has been recommended by numerous health, academic and third sector organizations, 122 , 123 the ACMD, 21 the 2019 Health and Social Care Committee on Drugs Policy 117 and the Scottish Drug Deaths Taskforce. 124 Although overdose prevention centres may be provided in the UK with agreement from local agencies, 125 legislative change would facilitate pilots, allowing evaluations of their effectiveness and cost-effectiveness. 120 Currently, legislation also creates barriers to providing smoking paraphernalia to engage people who use crack cocaine with services, 126 as is the case in other countries. 127

There was no opportunity for public consultation in the Strategy’s development. For other health and social policies, research and commissioning, the views of the public are included as a matter of priority. 128 , 129 Generally, the views of people who use drugs, who entreat that there should be ‘nothing about us without us’, 130 have not been adequately considered when developing drugs strategies. 131 Communities of people who use drugs, and UN agencies, have highlighted human rights implications, including the right to non-discrimination, should be a primary consideration in developing drug strategies. 7 , 132 , 133 The Strategy does not mention human rights, and punitive policies and restrictions on access to harm reduction programmes are often at odds with human rights norms. 115 , 134

Stigma related to drug use, including that propagated by the language used to describe people who use drugs, 135 creates barriers to seeking support. 104 The Strategy identifies the need to reduce stigma. However, the Government has also suggested that stigma is a valued means to deter drug use initiation. 136 Elements of the Strategy could be seen as promoting stigma, for example referring to acquisitive crime in terms of’[t]he innocent families whose homes are broken into by addicts seeking to feed their habits’. 1 Independent anti-stigma campaigns have been launched, 137 , 138 but the evidence for their effectiveness is limited, 139 as sources of stigma are complex, 140 and efforts would need to translate into policy and practice to have meaningful impact.

There are significant inconsistencies between the Strategy and the call from the highest coordination forum of the UN to promote public health approaches to drugs, putting ‘people, health and human rights at the centre’. 7 A public health approach should tackle upstream factors predisposing to harmful drug use alongside many other health and social disadvantages. Whilst promised investment in drug treatment is welcome and likely to be beneficial, this alone will not solve the drug-related death crisis. Realizing the potential benefits of additional funding and achieving the ambition to develop a ‘world class treatment and recovery system’ will depend on addressing fundamental flaws in the Strategy’s approach. Furthermore, an effective public health strategy should reflect best evidence. Whilst the Strategy states evidence is ‘at the heart’ of its approach, this is not always the case as it continues to promote un-evidenced and harmful measures to deter drug use with punishment.

We believe a public health approach to drugs would be more effective than policies rooted in criminalization and enforcement. Framing drug use as something deserving of punishment promotes stigmatizing attitudes, which pose a barrier to accessing support and approaches that do not adequately consider the views and human rights of people who use drugs. For more than fifty years, this has failed to effect improvements and a more dramatic re-orientation of the UK response to drugs is overdue.

This article was written in collaboration with the Faculty of Public Health and the Association of Directors of Public Health.

AH is a volunteer harm reduction adviser for the Loop—a not-for-profit drug checking service provider. AS is a trustee for Harm Reduction International. DS is a volunteer for the Loop. JN is a Trustee at Cranstoun, a third sector drug and alcohol treatment provider. JS works as a pharmacist prescriber with a third sector drug and alcohol treatment provider. LR is Director of Corporate Services for the Hepatitis C Trust. ED is the UK Government Recovery Champion. FM is the Director of the Loop.

No funding supported this research.

No new data were generated or analyzed in support of this research.

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£4.8bn was invested in UK health research projects in 2018, new report shows.

Published: 30 January 2020

The NIHR is pleased to announce the publication of the UK Health Research Analysis 2018 , which provides a comprehensive view of the current levels of health-related research funding in the UK.

It reveals that, overall, £4.8billion was contributed to health research in 2018.

Authored by the Medical Research Council with contribution from the NIHR and 11 other public and charitable funders, the report collates data from 146 charity, professional and public sector organisations and over 22,500 individual research projects.

Basic science

The report outlines how half of public and charity support for health research was directed at ‘basic science’, which underpins further study or seeks to identify the causes and progression of disease. It is this discovery science that will provide a pipeline of new potentially transformative ideas, technologies and methods for the future.

Funding trends – areas of increase

Examining trends in health research, the report shows that while the largest overall funding increase in 2018 was for research into infectious diseases, the areas receiving the highest proportional increases in investment were those involved in translational research – the kind that ‘translates’ scientific discoveries into usable treatments and other healthcare benefits.

Early detection of disease and the development and evaluation of new treatments were other areas that received increased funding, with around £548 million invested over the last 14 years.

Long-term view

This latest report is the fourth in a series published by the UK Clinical Research Collaboration (UKCRC), which supports co-ordination and collaboration between the major stakeholders that influence clinical research in the UK.

The report also charts changes in health research over the last 14 years. From 2004 to 2009, the report found that public and charitable support for health research grew, with a compound annual growth rate (CAGR) of 8.5%, then over the last ten years funding for health research remained, in real terms, almost level with aCAGR of 1.7% (2009 to 2018.)

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The road to the ultimate UK health research dataset

By Eva Garritsen, Data and Impact Officer, AMRC

Published: 12 February 2020

AMRC’s annual data collection, Spring 2019 - I remember like it was yesterday. It wasn’t a usual data collection, oh no, this one was different. Because in addition to the usual drill of submitting research expenditure and grants data, this time our members could opt in to share their grants data and participate in the UK Health Research Analysis 2018 . The what, you say? For those of you who don’t know, the UK Health Research Analysis 2018 is the fourth in a nationwide health research landscaping report series. This series provides an overview of non-commercial health research funding in the UK.

A record number of AMRC participants

As you can imagine, this was a HUGE opportunity for our charities to demonstrate and highlight the unique role they play in the health research landscape. AMRC was on a mission to involve as many charities in this analysis as possible, and we’re happy to say it was a great success. 91 AMRC members provided their grants data for the analysis, almost doubling the number that participated back in 2014 and allowing for the most accurate representation of the charity sector to date!

The inclusion of 91 charities’ data was easier said than done. Before we got to this impressive number, we first needed permission from each charity to share their data and make it public. We then went through an extensive data cleaning process and, with the assistance of the grant awards database Dimensions , auto-coded more than 8,000 member charity-funded research projects. This also involved a lot of back and forth communications with the participating charities to ensure their datasets were complete and up to date. Hundreds of emails, spreadsheets and forms later, we had it: the final AMRC charity dataset was ready to share with the Medical Research Council team for analysis. Once MRC had summarised all the main findings from AMRC members and other public funders the Health Research Analysis Forum, of which AMRC is a member, helped to finesse it.

Good things come to those who wait

The Medical Research Council team set their sights on publishing the report in Summer 2019. Unfortunately, it wasn't meant to be, with the largest dataset yet and an impromptu General Election the publication date was pushed back to 28 January 2020. But the resulting report and accompanying dataset was well worth the wait! It is the most comprehensive view of public and charitable health research funding in the UK, consisting of more than 22,500 health and biomedical research active awards from 146 organisations.

Our team couldn’t wait to make use of the dataset! So, in the very same week, we developed an infographic to show the unique role our members play in the UK’s health research landscape. Our members provide the largest proportion of public funding for multiple stages of the research process and they account for the highest percentage of research spend in cancer, cardiovascular, inflammatory and immune health areas.

The beauty of this report and dataset is that it can be used to inform a range of activities: from identifying potential collaborators, to shaping new research priorities to highlighting research gaps. We've already seen some great examples of this from our members MQ: Transforming mental health and Marie Curie . It’s a fantastic resource that we encourage all our member charities to use. And when you do, don’t forget to let us know!

Largest analysis of UK health research funding published

28th January 2020

Guts UK is a member of the Association of Medical Research Charities (AMRC). Did you know that members of this association fund over 40% of UK medical research? Since 2008 – these crucial charities have invested over £13 billion in research in the UK.

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Open Access

Peer-reviewed

Research Article

Capturing the experiences of UK healthcare workers during the COVID-19 pandemic: A structural topic modelling analysis of 7,412 free-text survey responses

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Supervision, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation UCL, London, United Kingdom

Roles Formal analysis, Writing – review & editing

Roles Formal analysis, Writing – original draft, Writing – review & editing

Affiliation King’s College London, London, United Kingdom

Roles Funding acquisition, Writing – review & editing

Affiliation South London and Maudsley NHS Trust, London, United Kingdom

Danielle Lamb,
Liam Wright,
Hannah Scott,
Bethany Croak,
Sam Gnanapragasam,
Mary Docherty,
Neil Greenberg,
Matthew Hotopf,
Sharon A. M. Stevelink,

Published: October 7, 2022
https://doi.org/10.1371/journal.pone.0275720
Reader Comments

Healthcare workers (HCWs) have provided vital services during the COVID-19 pandemic, but existing research consists of quantitative surveys (lacking in depth or context) or qualitative interviews (with limited generalisability). Structural Topic Modelling (STM) of large-scale free-text survey data offers a way of capturing the perspectives of a wide range of HCWs in their own words about their experiences of the pandemic.

In an online survey distributed to all staff at 18 geographically dispersed NHS Trusts, we asked respondents, “Is there anything else you think we should know about your experiences of the COVID-19 pandemic?”. We used STM on 7,412 responses to identify topics, and thematic analysis on the resultant topics and text excerpts.

We identified 33 topics, grouped into two domains, each containing four themes. Our findings emphasise: the deleterious effect of increased workloads, lack of PPE, inconsistent advice/guidance, and lack of autonomy; differing experiences of home working as negative/positive; and the benefits of supportive leadership and peers in ameliorating challenges. Themes varied by demographics and time: discussion of home working decreasing over time, while discussion of workplace challenges increased. Discussion of mental health was lowest between September-November 2020, between the first and second waves of COVID-19 in the UK.

Our findings represent the most salient experiences of HCWs through the pandemic. STM enabled statistical examination of how the qualitative themes raised differed according to participant characteristics. This relatively underutilised methodology in healthcare research can provide more nuanced, yet generalisable, evidence than that available via surveys or small interview studies, and should be used in future research.

Citation: Lamb D, Wright L, Scott H, Croak B, Gnanapragasam S, Docherty M, et al. (2022) Capturing the experiences of UK healthcare workers during the COVID-19 pandemic: A structural topic modelling analysis of 7,412 free-text survey responses. PLoS ONE 17(10): e0275720. https://doi.org/10.1371/journal.pone.0275720

Editor: Gouranga Lal Dasvarma, Flinders University, AUSTRALIA

Received: February 17, 2022; Accepted: September 21, 2022; Published: October 7, 2022

Copyright: © 2022 Lamb et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The free-text data used in this study cannot be made publicly available due to stipulations set out by the ethics committee. The code used in the analysis is available at https://osf.io/4d8tf/ .

Funding: Funding for NHS CHECK has been received from the following sources: Medical Research Council (MR/V034405/1); UCL/Wellcome (ISSF3/ H17RCO/C3); Rosetrees (M952); NHS England and Improvement; Economic and Social Research Council (ES/V009931/1); as well as seed funding from National Institute for Health Research Maudsley Biomedical Research Centre, King's College London, National Institute for Health Research Health Protection Research Unit in Emergency Preparedness and Response at King's College London. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: MH, RR, and SW are senior NIHR Investigators. SW has received speaker fees from Swiss Re for two webinars on the epidemiological impact of COVID 19 pandemic on mental health. RR reports grants from DHSC/UKRI/ESRC COVID-19 Rapid Response Call, grants from Rosetrees Trust, grants from King’s Together rapid response call, grants from UCL (Wellcome Trust) rapid response call, during the conduct of the study; & grants from NIHR outside the submitted work. MH reports grants from DHSC/UKRI/ESRC COVID-19 Rapid Response Call, grants from Rosetrees Trust, grants from King’s Together rapid response call, grants from UCL Partners rapid response call, during the conduct of the study; grants from Innovative Medicines Initiative and EFPIA, RADAR-CNS consortium, grants from MRC, grants from NIHR, outside the submitted work. SS reports grants from UKRI/ESRC/DHSC, grants from UCL, grants from UKRI/MRC/DHSC, grants from Rosetrees Trust, grants from King’s Together Fund, and an NIHR Advanced Fellowship [ref: NIHR 300592] during the conduct of the study. NG reports a potential COI with NHSEI, during the conduct of the study; and I am the managing director of March on Stress Ltd which has provided training for a number of NHS organisations although I am not clear if the company has delivered training to any of the participating trusts or not as I do not get directly involved in commissioning specific pieces of work. DL is funded by the NIHR ARC North Thames. The views expressed in this publication are those of the authors and not necessarily those of the National Institute for Health Research or the Department of Health and Social Care. 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. Other authors report no competing interests. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Introduction

Research examining mental health and wellbeing outcomes for healthcare workers (HCWs) during the COVID-19 pandemic has largely consisted of online surveys. Such methods provide results quickly, involve low burden on participants, and can have relatively large samples. However, many of these studies fail to have sufficiently well-defined samples or response rates to support claims of representativeness [ 1 ]. Nonetheless, studies using these methods have found that HCWs report high prevalence of symptoms of psychological distress, anxiety, and depression, with those who are female, nurses, with inadequate access to PPE, and lack of support from superiors or peers tending to report poorer outcomes [ 2 – 5 ]. There is also some evidence that key workers (including HCWs) report higher levels of depression and anxiety symptoms than non-key workers [ 6 ].

It is widely accepted that self-report surveys, while providing interesting and sometimes useful snapshots of the prevalence of mental health symptoms, often miss deeper and more detailed insights about why people may be struggling, and how they could be better supported. In addition, surveys tend to over-estimate the prevalence of mental health disorders [ 7 ]. Qualitative research can offer depth and nuance about the factors associated with poor mental health and what sorts of support processes may be beneficial, but typically is conducted using one-to-one interviews or small focus groups. This drastically limits the number of participants and variety of perspectives that are feasible to include. While generalisability is not typically the goal of qualitative research [ 8 ], from a policy-making and organisational perspective it is desirable to hear from a large and diverse sample in order to ensure systems and support are set up in ways that benefit as many of the workforce as possible.

One way to gain valuable, open-ended insights from a much greater volume of participants than traditional one-to-one interviews, is to provide respondents to quantitative surveys with free-text boxes to report openly on specific questions posed. While there are limitations, e.g. the inability for an interviewer to ask follow up questions or probe more deeply, the requirement that participants are literate (and computer literate/have computer access, where the survey is online), there are also advantages in terms of confidentiality (leading, potentially, to more honest reporting), and the ease and convenience of data collection for participants and researchers [ 9 ]. Where the quantity of data collected is prohibitive to manual coding, e.g. where thousands of responses have been collected, computerised text mining methods provide a way to help analyse responses.

This study used structural topic modelling [ 10 ], a text mining technique, on free-text responses to an online survey investigating healthcare workers’ experience of the COVID-19 pandemic between April 2020 and January 2021. Structural topic modelling (STM) enables the extraction of themes from free-text responses and the quantitative summary and analysis of these themes alongside other participant data such as demographics, using regression models. This method has been used previously in health science [ 11 ], but remains relatively underutilised in this area of research. The aim of this study was to use STM to analyse responses from a broad question about HCW experiences of the pandemic, to allow HCWs to identify what was most salient and important to them.

Ethical approval for the study was granted by the Health Research Authority (reference: 20/HRA/210, IRAS: 282686) and local Trust Research and Development approval. Cohort data are collected via Qualtrics online survey software, pseudonymised and held on secure university servers. Participants are aware that they can withdraw from the study at any time, and there is signposting to support services if participants feel they need it. Only those consenting to be contacted about further research will be invited to participate in further components.

Data collection

The data used in this analysis comes from the NHS CHECK study (for more information about the wider study, see [ 12 ]), a longitudinal cohort study of the mental health and wellbeing of HCWs during the COVID-19 pandemic in England. All staff members in 18 participating NHS Trusts were invited to complete an online survey once between April 2020 and January 2021. At the end of the survey was a free-text box where participants were asked, “Is there anything else you think we should know about your experiences of the COVID-19 pandemic?”. The responses to this question form the data used in this analysis.

Data analysis

We performed STM, implemented with the stm R package [ 13 ] to extract topics from responses. We performed STM using unigrams (i.e. single words). STM treats documents as a probabilistic mixture of topics, and topics as a probabilistic mixture of words. STM is a “bag of words” approach that uses correlations between word frequencies within documents to define topics. An advantage of STM is that it allows for inclusion of covariates in the estimation model, so the estimated proportion of a text devoted to a topic can differ according to document metadata (e.g., participant characteristics). We included participant’s age (modelled with natural splines with 3 degrees of freedom to account for potential non-linearities in the association: [ 14 ], date of response (natural splines, degrees of freedom 3), sex, marital status (married, co-habiting, single, divorced, widowed), ethnicity (White, Mixed Race, Black, Asian, Other) and NHS trust (18 categories) in our models, each collected at baseline. There was only a small amount of item missingness (6.1% for age; 2.2% for sex; 2.1% for marital status; 1.9% for ethnicity; 3.8% for role), so we used complete case data.

Prior to performing the analysis, we cleaned free-text response using an iterative process. We excluded responses containing fewer than five words and removed words which appeared in fewer than five responses. We also removed common “stop” words (“the”, “and”, “I”, etc.) from the analysis. Spelling mistakes were identified with the Hunspell spellchecker [ 15 ], amended manually if they had five or more occurrences and replaced using the Hunspell suggested word function if the number of occurrences was fewer than five. Where the algorithm provided multiple suggestions, the word with the highest frequency across responses was used. To reduce data sparsity, in the structural topic models, we used word stemming using the Porter [ 16 ] algorithm (e.g., help, helping and helped become help ). Data cleaning was carried out in R version 3.6.3 [ 17 ].

We ran STM models, which included between two to 50 topics and selected the final models based on visual inspection of the residuals and lower bound statistics of the model solutions. After selecting a final model (with 35 topics), we carried out two analyses, a thematic analysis of the topics and qualitative text excerpts, and a regression analysis of participant characteristics and generated themes. In line with previous work using this methodology [ 18 ], for the thematic analysis, we generated short descriptive titles for the 35 topics, firstly individually, and then via collaborative discussion. We dropped one topic due to incoherence between the exemplar texts, and merged two topics due to similarity. The remaining 33 topics were grouped, again, initially individually, and then in an agreed thematic structure via a collaborative process involving interpretation of the content of the topics. The exemplar responses for each topic were selected based on the ‘Theta’ assigned to them. Theta is the estimated proportion of text the response contains on a given topic. The closer Theta is to 1, the more likely the responses are to form a single semantically coherent topic. Only responses with a Theta greater than 0.3 were included in the analysis. Descriptive summaries were written for each theme and subtheme, and the thematic analysis proceeded via an iterative process of reading and re-reading exemplar responses. We identified responses that captured the essence of each of the themes generated, and reflected on meanings and interpretations of these in terms of the overarching narrative of the themes and data, and in terms of our own perspectives. While the generation of themes was supported by STM, rather than initial familiarisation with every text excerpt (which, while usual in reflexive thematic analysis, was not possible with over 7,000 responses), the subsequent stages of meaning-making and discussion of our own positionality was in keeping with recent guidance regarding reflexive thematic analysis [ 19 ].

Reflexivity

The researcher team comprised women and men, researchers and clinicians, and diversity in terms of age and ethnic background. The non-clinical researchers on the research team have extensive experience of conducting qualitative mental health research with occupational groups, particularly healthcare workers, and all have colleagues, friends, or family members who worked in healthcare roles through the pandemic. Several of the clinical researchers worked on the frontline during the pandemic in mental health roles. As such, all authors had some existing perspectives on the pressures faced by healthcare workers during this time (and before), whether first or second hand. We therefore undertook extensive discussion within the team throughout the analysis process of our subjective interpretations of the data, and how these may have been influenced by our own experiences and perspectives.

Patient and public involvement

Frontline NHS staff proposed this research, and we tested the proposal’s acceptability and approach with a small informal reference group of front-line staff (psychologists, managers, intensivists and trainee psychiatrists) and refined it accordingly. We have developed an advisory group of NHS staff (clinical, managerial, auxiliary, students), who meet online and contribute virtually to provide input on methods, recruitment strategy, communications, and interpretation of findings.

Descriptive statistics

Sample descriptive statistics are displayed in Table 1 . There were 7,412 participants (35% of total sample) who provided a valid free-text response. The distribution of responses according to age, sex, marital status, ethnicity, role, and NHS trust type was similar among those who provided a free-text response and those who did not.

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https://doi.org/10.1371/journal.pone.0275720.t001

Thematic analysis

From the 33 topics, two overarching domains were identified, personal (45.8% of text), and professional (52.9% of text), each containing four themes (see Table 2 ), although many themes contained elements of both the personal and the professional, and were often interlinked.

https://doi.org/10.1371/journal.pone.0275720.t002

The personal domain contained themes related to physical health (15.4% of text), mental health (12.4% of text), social impact (10.0% of text) and restrictions and rules (8.0% of text). The professional domain contained themes related to home working (17.1%) of text), workplace challenges (13.7% of text), workplace culture (11.3% of text), and new roles (10.9% of text). See Fig 1 .

Source: Drawn by the authors from data analysed for this paper.

https://doi.org/10.1371/journal.pone.0275720.g001

After each quote used to illustrate the themes below is the role of the participant, grouped by job role (doctors, nurses, Allied Health Professionals (AHPs), and non-clinical staff). Sub-themes are indicated by bold text.

Social impact.

Many participants discussed how the pandemic and subsequent lockdown restrictions prevented them from fulfilling their personal caring responsibilities , which was upsetting for them and their relatives:

“ Having an elderly bed bound mum , who has carers visiting her , and it was extremely difficult for me and her when I couldn’t visit her during lockdown .” ( Non - clinical staff )

Some participants faced challenges due to schools and nurseries being shut, for example, “ Hard to balance work and childcare especially as single parent .” ( Doctor ).

Social isolation was identified as challenging: “ Not being able to see family members or friends quite upsetting ” ( Non - clinical staff ). Respondents reported the struggles of not seeing family and friends for long periods of time due to not living locally, and several respondents even described the breakdown of their relationships due to the time spent apart, for example, “ This has contributed to the end of my marriage .” ( AHP ). These issues negatively their affected mental health, as identified in the theme below.

Some respondents reported that being isolated from colleagues by working from home also impacted on them personally, feeling as though they were making less of a contribution than those in hospitals: “ Working at home can be isolating sometimes and normal work can seem less relevant making you feel less useful than clinical colleagues .” ( Non - clinical staff ).

Participants described various experiences of bereavement (mostly deaths of relatives) during the pandemic, some directly caused by COVID-19. Some detailed how the emotional toll of these bereavements has been exacerbated by the social isolation the restrictions have caused and not being able to be comforted in person by loved ones, for example, “ Due to bereavement and family illness I have been significantly impacted due to not being able to see family .” ( AHP ).

Pandemic restrictions and rules.

For some, the response at the national political level was felt to be poor, with a lack of trust in Government . The frustration was particularly related to the pace of the response, considered as slow by some, as well as advice being conflicting, with mixed messages: “ Poor and conflicting information from Government ” ( Non - clinical staff ). Others felt there was a disconnect between the practice undertaken at their organisation and Government guidelines, for example, “ Conflicting information from management vs Government information is stressful .” ( Non - clinical staff ).

A number of participants expressed considerable frustration with seeing individuals not adhering to rules such as social distancing. Some felt such actions were “ selfish and inconsiderate ” ( AHP ), and reported that this made them feel angry:

“ I have been angry to how people are allowed to flout or twist the rules imposed by the Government during this pandemic . I am also angry that the Government do not do more to reduce the cases .” ( AHP ).

Others expressed difficulty with adherence to rules, particularly in a healthcare setting, given the stringent nature of the rules, as well as their constant changes and related confusion–” It’s difficult sticking to the regulations within healthcare settings and it’s all overwhelming ” ( Nurse ).

Physical health.

Many respondents described their experience of COVID-19 symptoms , such as loss of taste, headaches, fatigue, and cough. Some commented on the longevity of their symptoms:

“ Mild initial infection has led to long - standing problems with breathlessness , dry cough , hoarse voice , concentration problems , balance issues , neurological problems , unexplained rashes , skin lesions and crushing fatigue at times .” ( Non - clinical staff ).

Participants reported not being able to get antigen testing despite symptoms, which fuelled further frustration and distrust in government, but some discussed the positives of participation in vaccination trials:

“ I’m also participating in the Oxford Vaccine Trial and am appreciating regular swab tests from this . It is very nice to have feedback that at the time of each test I know my likely COVID status .” ( Doctor ).

Some felt that those who were shielding or had recognised risk/comorbidity factors were being looked down upon by colleagues due to their inability to carry out their normal duties, and these feelings were damaging to morale and team cohesion.

“ COVID related bullying is happening and no one ( even managers ) know how to deal with it . I loved my job before COVID; now we are divided . People with underlying health concerns are treated as being slackers . Laughed at for being concerned and there is an age split between young and older .” ( AHP ).

In addition, many respondents expressed fear of infection with COVID-19. Participants commented that this fear was coupled with the worry that they would then pass on the infection to their household: “ Fear of getting the virus and giving it to my husband or other member of my family . Fear of dying of it myself and leaving my husband .” ( Nurse ). Certain factors seemed to exacerbate this concern such as their family member having an underlying health condition or being in a high risk group: “ Very worried about having BAME [Black and minority ethnicity] partner and bringing the virus back to them .” ( Nurse ).

Mental health.

Staff reported feeling overwhelmed, experiencing anxiety , and having sleep problems with vivid dreams and nightmares, for example, “ I have been woken most nights with nightmares and/or vivid dreams which does not normally happen .” ( Nurse ), with uncertainty being a key contributing factor:

“ Uncertainty , changes happening so frequently with no consultation to staff on the floor until things are actually happening . Uncertainty of the future for our dept .” ( AHP ).

Participants working in the mental health services noted that they felt that their services were “ completely forgotten ” ( AHP ) relative to physical health services. The significant work-based pressures in mental health service were reported as impacting negatively on their own mental health, and therefore their ability to care for others:

“ The number of referrals for patients with serious mental health difficulties have increased significantly causing a huge strain on the service and my own mental health .” ( AHP ).

Positively, some participants highlighted the support from their organisation and senior leaders such as Chief Executives. This was felt to be protective, which is echoed in the ‘Workplace culture’ theme below:

“ The positive messages from our chief executive have been key to maintaining mental health against the onslaught of negativity and speculation from the media .” ( Non - clinical staff ).

With regards to the wider impact on life , a number of participants experienced loneliness and isolation during the pandemic, as noted above. This was particularly related to separation from family, friends, romantic partners or being single—” Being a single person has led to increased loneliness and self - doubt about all aspects of my life ” ( Non - clinical staff ).

On the other hand, some participants noted personal benefits during the pandemic in being able to spend more time with family and developing a supportive social support network:

“ We as a family have actually been happier during this period once we’d got over the infection . The pace of life has slowed down and given us more quality time together . For us this has been the silver lining to this sad situation .” ( Non - clinical staff ).

Participants expressed frustration with being labelled a hero, and wished for better working conditions, pay, and support instead:

“ The whole ’heroes’ thing was awful , and the clapping mawkish and largely insincere . Hero statues means we don’t need normal basic human needs . It will also be forgotten immediately and we will be bullied into doing more to catch up without recognition , reward , and with threats and bullying . Working in the NHS will be worse than before , despite the superficial societal praise .” ( Doctor ).

For some HCWs, these challenges resulted in diagnosis of personal mental health conditions and the need for treatment from health services, for example:

“ I worked in ITU [Intensive Care Unit] during the first surge and have since been diagnosed with PTSD , depression , anxiety and binge eating disorder . Started on anti - depressants and awaiting intense CBT therapy for the PTSD .” ( AHP ).

In addition, some staff members with pre-existing mental health conditions faced exacerbation during the pandemic, “ I am Bipolar and had an episode of depression at this time , largely precipitated by having to do things ’virtually’ “ ( Non - clinical staff ). This was related to work and social stressors, as well as disruption in access to support services.

Professional

Workplace challenges..

A major challenge reported by staff was the pressure of workload . Respondents described the stress of increasing workloads and understaffing, in part due to staff sickness with COVID-19, but also due to increased bed capacity or caseloads without commensurate increases in staffing:

Any stress I experience comes about due to my staff having very high caseloads and increasing referrals with staff not having caseload space to take these people on and be able to do a good job for them . ( Non - clinical staff ).

Staff report a ‘vicious cycle’ of increased workloads leading to stress and burnout , which in turn lead to staff off sick, with fewer to pick up the resulting work, and difficulties in retaining staff. For example:

We have been massively understaffed throughout COVID due to staff sickness—mostly down to stress rather than having to isolate . This has impacted on the stress levels of the remaining staff who are firefighting trying to stay afloat . ( Non - clinical staff ).

These issues were apparent for clinical and non-clinical staff, and are summed up by one respondent’s comment, that, “ The pressure on staff is unsustainable .” ( Nurse ).

Respondents reported the adverse effects of shortages of PPE , as well as feeling that where PPE was available it was not of high quality or sufficient to protect workers:

The lack of PPE equipment has been terrifying . The visors appropriate to operate on are being kept hidden because there aren’t enough and the PPE visors ( not on a surgical mask ) don’t protect from upward blood splashes . It is becoming dangerous to do our job , even more so than usual due to failings of ordering and providing correct equipment . ( Doctor ).

Even for those with adequate access to PPE there were problems, with some reporting that equipment did not fit properly, “ There are no FFP3 masks that fit me so I feel unsafe .” ( AHP ), and a lack of training: “ I do not think the training for PPE ( donning and discarding ) was adequate , especially for community staff .” ( Doctor ).

There were multiple challenges to clinical practice , with the unpredictability exacerbating existing stress, and potentially reducing the quality of patient care:

“ The main stressor for me is the unpredictability at work . You never know if you are going to be moved or if the ward you’re on is going to swing from green to red , or back . Resulting in massive patient movement and extra work .” ( Nurse ).

Lack of consultation left staff feeling undervalued, and changes in the types of work being undertaken also caused stress, as staff felt unprepared and not able to provide adequate levels of care:

“ We were redeployed in the first wave without consultation as a testing unit… . we were an older peoples medicine ward and we were without warning taking patients from A+E who were quite unwell and outside of our normal expertise… we have had constant trouble sourcing equipment for unwell patients . Again , we were not consulted … which felt like the Trust saying they did not value us .” ( Nurse ).

Although some staff expressed satisfaction with aspects of their work, for example, “ Been really happy that I have been able to continue offering a service to our patients ” ( AHP ), others reported deep dissatisfaction and intentions to leave their roles: “ It’s made me decide to retire .” ( Non - clinical staff ). Some respondents felt that management were indecisive and unsupportive:

“ Management seem unable to decide how we should offer psychotherapy to clients , whether we should go into work or not . there is a feeling we are being ’scrutinised’ rather than supported .” ( AHP ).

Staff reported lack of access to employment rights, wellbeing resources, and equipment to allow homeworking, and some experienced frustration and ethical dilemmas at not being able to offer high quality care:

“ Many of us in the NHS have been unable to deliver services to our patients because of services being stopped . For me this has led to feelings of guilt , frustration and moral injury .” ( AHP ).

Home working.

Participants often mentioned working from home due to shielding : “ I’m shielding so I’ve been working at home and felt very disconnected from work .” ( Non - clinical staff ). For some, working from home has been helpful in providing control and autonomy, whereas for others, the blurred boundaries with other family members and lack of in-person support from colleagues has been challenging:

“ I find working from home has helped A LOT with my mood , life and with work . I feel more in control and when I have bad mental health days being at home is the best thing for me and I don’t need to take a day off . I can still work and get my tasks done which has a positive effect on my mood .” ( Non - clinical staff ). “ The impact from working at home in when you work in mental health is negative . Conversations about family abuse , rape , incest and violence now happen in my home . At times whilst my children are in the next room . These conversations have happened in my bedroom . The boundaries between home and work were extremely important for mental health . The little bits of informal supervision you get in the office and ways you can debrief after a traumatic session are all lessened by working from home .” ( AHP ).

An issue often raised by participants was managing work alongside home-schooling , and the challenge of these competing demands:

“ Home - schooling primary school age children is not compatible with a full - time job and the provisions offered by schools were very varied .” ( Non - clinical staff ).

Participants reported that an additional challenge to homeworking was the physical health impact , commonly noting musculoskeletal difficulties resulting from not having the correct seating/desk/computer equipment to work in a comfortable position, and the eye strain/headaches caused by long hours looking at screens:

“ Working from home has been extremely difficult . Not the correct equipment . I’ve had to self - refer to physiotherapy for back , shoulder and neck problems .” ( AHP ).

New roles during the pandemic.

A number of participants described starting or ending roles during the pandemic unrelated to redeployment. A small number of participants told us that they had been made redundant, that they had difficulty finding a new role, or that beginning their new role had presented additional challenges given remote working/workload pressure:

“ Started my role in February just before so this has affected on how I have adapted the way I work before really even getting to grips with the role .” ( Nurse ).

Others referenced temporary redeployment and the deep anxiety around this, in particular the fear of providing inadequate care to patients:

“ I found being sent to adult ITU extremely difficult , it made me anxious and terrified to come to work […] I am a PICU [Pediatric Intensive Care Unit] nurse not an adult nurse and was left alone to take full responsibility for extremely ill COVID adult . I felt out of my depth , scared and it has made me and my colleagues to worried about a second peak and being sent again .” ( Nurse ).

For others however, redeployment was a positive experience:

“ I felt the time before redeployment was more stressful than actually being redeployed . My time at the Nightingale was very positive because everyone was very supportive .” ( Nurse ).

This suggests that the experience was very individual and often related to the team, colleagues and other work-based support systems, and this is echoed in the ‘Workplace culture’ theme below.

Workplace culture.

In contrast to some of the challenges outlined above, many respondents reported how well supported they felt by colleagues, highlighting the importance of teamwork . For example, staff found it helpful to continue working with existing colleagues, “ Being able to work with my same colleagues has been really helpful and I feel has made us stronger as a team .” ( Nurse ). Others also noted the fact that well-supported staff function better, “ It has shown that teamwork , support and compassion are needed , when that is in place people function better .” ( Non - clinical staff ).

Examples were given of the practical steps team leaders have put in place to support staff, for example:

“ As a single parent with a young child I have been really well supported by my team leader and rest of my team…My team leader set up a buddy system and we each had a buddy who we contacted each day for a video chat , this was such a help and I am extremely grateful to have such supportive colleagues in the NHS .” ( AHP ).

While those with good leadership/managerial support coped well, others reported poor relationships, and the negative impacts of these. Senior management in some areas were criticised for lack of communication:

“ I did not feel supported by senior managers . My direct manager was great but was not supported herself . The trust did a terrible job of communicating and appeared panicked and out of control .” ( Nurse ).

Honesty from senior managers, as well as regular, clear communication, and addressing problems in practical, visible ways were all appreciated by staff, summarised neatly by this participant, “ Compassionate leadership , genuine compassionate leadership is key .” ( Non - clinical staff ).

The support provided by Trusts was perceived as key to helping staff to feel valued, and to perform well. Free parking was mentioned repeatedly as being one-way Trusts can support staff, for example, “ The Trust has been very supportive to staff—allowing free parking has been monumental in my wellbeing during this time .” ( AHP ). However, pressure for services to see as many patients as possible was seen as unhelpful, especially given most staff value providing quality care: “ Pushes on flow rather than getting things right has been a feature pushed through by Director of Operations which has been unhelpful and quite frankly–frustrating .” ( Non - clinical staff ).

Beyond the workplace, the political leadership of the country has impacted on staff wellbeing, for example, one participant was particularly blunt about their feelings:

“ The single biggest factor in my mental health around coronavirus has been the shambolic handling of the situation by the Government . I have no faith or trust in their leadership , and that is a toxic situation in a pandemic such as this .” ( Doctor ).

Similarly, the role the media played was also criticised, with a sense of resignation about whether those in positions of responsibility will be held to account:

“ The fear mongering media and politicians have not come out of this looking good . Stats have been manipulated and lies told . Will it all ever come out and accountability made … I doubt it .” ( Non - clinical staff ).

Regression analyses

The results of the linear regression models are displayed below. There were some differences in themes raised by age ( Fig 2 ). Notably, older participants were more likely to discuss challenges related to physical health and to home working, while younger participants were more likely to discuss new roles.

Derived from linear regression models including adjustment for age, date, sex, marital status, ethnicity and NHS trust. Source: Drawn by the authors from data analysed for this paper.

https://doi.org/10.1371/journal.pone.0275720.g002

There were also differences according to the date free-text responses were recorded ( Fig 3 ). As may be expected, discussion of home working decreased across the pandemic, while discussion of workplace challenges increased. Discussion of mental health was lowest between September-November 2020, the period between the first and second waves of COVID-19 in the UK.

https://doi.org/10.1371/journal.pone.0275720.g003

Differences according to sex, marital status and ethnic group were also observed ( Fig 4 ). Notably, women were more likely to discuss home working, new roles and social impact, and were less likely to discuss restrictions and rules. There were also differences by NHS trust, most notably regarding positive and negative experiences of home working and physical health (results not shown for privacy reasons).

https://doi.org/10.1371/journal.pone.0275720.g004

Summary of results

Responses from the 7,412 participants fell broadly into two domains, personal and professional. though there was overlap in the content of several themes, with important connections between home working, isolation, and HCW mental health. Furthermore, we found evidence that demographic factors affected the way the respondents reported their experiences of the pandemic, and that experiences also varied depending on the nature of national picture of the pandemic at the time the data was collected. This paper presents qualitative data from the largest UK survey of HCW wellbeing, with demographics broadly representative of the wider NHS workforce [ 20 ]. It is likely that participants reported the most salient experiences for them, and thus the responses provide insight into important factors that may not have been picked up by other research using pre-defined survey questions or fixed interview schedules.

One, perhaps unsurprising, finding was that the personal challenges most commonly reported by HCWs overlapped considerably with those faced by the general population (e.g. bereavement, social isolation, caring responsibilities). However, we also identified specific workplace challenges for HCWs that are less relevant for many other types of workers (e.g. facing significant risk of infection, redeployment), and these in turn impacted home life. That said, our sample was not homogenous, and variations between participants’ experiences within the workplace suggest that guidance around infection control, vulnerable staff, and redeployment differed between Trusts and teams. This also demonstrates important differences in personal preferences for ways of working, and underlines the challenges for Trusts in offering support that is appropriate for all members of staff.

Results in relation to previous research

Some of our findings echo the results of previous quantitative work in this area especially in that increased workloads were a key concern for HCWs [ 2 ]. Many of our participants reported increased workloads, with a vicious cycle of this leading to burnout and sickness absence, which in turn led to further increased workloads for those remaining; this has been noted in other qualitative research [ 21 , 22 ]. As our regression analyses showed, discussion of workplace challenges increased over time, indicating the growing importance of these as the pandemic progressed. However, concerns about heavy workloads would not have been solely due to the pandemic, though it has clearly exacerbated them. The 2018 NHS Staff Survey found that 40% of HCWs reported work-related stress, burnout, and dissatisfaction due to increased workload, which was caused by staff shortages [ 23 ]. In our findings and in other research, inconsistency and uncertainty at work are repeatedly mentioned as stressful, and causes of negative wellbeing [ 24 – 27 ], as were redeployment and changes to working patterns. In contrast, and also found in quantitative studies, peer support and effective leadership help to reduce negative outcomes [ 21 , 26 , 28 ].

Lack of PPE was a concern for our participants, and has also been found to negatively impact of employee mental health in non-healthcare settings [ 29 ]. A number of qualitative studies have found similar results, that access to appropriate PPE is an important factor for HCWs’ mental and physical wellbeing [ 26 , 27 , 30 ], though our evidence suggests a temporal trend, with more concerns about this in the early months of the pandemic. There is evidence that access to PPE has been an issue internationally [ 28 ] and in previous pandemics [ 22 ]. Lack of PPE, or access to inadequate PPE, as well as changing guidance about its use, have impacted HCW trust in leaders and politicians, and there have been particular concerns about differential access to PPE, e.g. by women and Black, Asian and minority ethnic HCWs [ 30 ].

Fear of infecting loved ones was frequently reported as an important source of stress. This has been reported previously [ 21 , 22 ], as have the challenges of balancing personal and professional stressors. For example, home working was experienced positively by some, providing time to slow down and connect with family, but others reported this to have been a negative experience, with social isolation, home schooling while trying to work, and physical health impacts all issues of concern [ 31 , 32 ]. These findings were somewhat gendered, with women more likely to discuss home working than men, and this echoes previous qualitative research looking at female HCWs’ experiences of the pandemic [ 33 ].

Implications for policy and practice

Whilst the provision of clear and appropriate guidance on working practices is needed, employers of HCWs need to provide flexibility to cater to differing social needs and preferences. We identified the highly detrimental effects of uncertainty on staff wellbeing, and we suggest that proactive, clear communication from senior managers, and Government, is likely to positively impact on the mental health of staff.

We also identified that peer support and compassionate leaders were viewed as mitigating stress, and may prevent the onset of formal mental health problems. These views correlate well with findings from quantitative studies that have examined other trauma-prone occupational groups, which show that supportive junior leadership and effective peer support are associated with better employee mental health [ 34 ]. Training for managers may help to reduce the highly variable support participants reported in this study, and could be particularly beneficial given the fact that HCWs seem to want immediate support from those around them, rather than from well-being counsellors on a help line. Brief training for managers to improve their confidence to speak to their staff about mental health has been shown to be effective [ 35 ]. Alongside strong employee social bonds, our results also suggest Trusts should invest in ensuring that staff are able to access both wellbeing support measures (e.g. wellbeing spaces) as well as access to professional support from occupational health teams and, where needed, primary and secondary care services.

One aspect of our findings that has been consistently echoed in other research during the COVID-19 pandemic, and of previous pandemics, is the necessity for HCWs to be provided with appropriate and adequate PPE. While this is an issue that also needs to be addressed at the political level, healthcare leaders should be aware of the urgency in advocating for their staff on this issue. Another aspect repeatedly reported in other research is the finding that women appear to experience worse outcomes on many measures than men. Given that the NHS workforce is comprised predominantly of women (76%) [ 20 ], this finding should be considered by national and local NHS systems in order to redress inequalities. Existing qualitative research discusses the ways in which gendered organisational norms and structures can reinforce pre-pandemic inequalities (for example, greater unpaid care work undertaken by women) [ 33 ], and suggests that policies that support more equitable distribution of such labour are needed.

Strengths and limitations

Firstly, the most important strength of this study is that the data analysed are drawn from the largest UK study of HCWs, with a relatively representative sample from 18 English Trusts. Secondly, and relatedly, the STM methods used enabled us to include the views of a much larger sample than is typically possible in qualitative work, and allowed us to statistically examine how the topics raised differed according to participant characteristics. Thirdly, the open phrasing of the question allowed us to capture issues that are important to HCWs, and as such, we have generated a more holistic picture of the key issues than more tightly focussed qualitative studies can offer. This includes the interplay between the personal and the professional, such as the influences of personal circumstance, organisational culture and support, and the role of Government and wider society on respondents’ perceptions. Fourthly, the anonymity offered by the online data collection method means some participants may have felt able to be more honest about challenging topics than they would have done in a face-to-face or virtual interview.

However, the online nature of the survey highlights the first limitation of this research, that technical capability and access were required to participate, as well as English language skills, which may have excluded the most vulnerable. Secondly, as the free-text question from which the data was drawn was not compulsory, and was in addition to survey that covered a range of wellbeing topics, we might have been more likely to capture the views of participants with stronger or more extreme experiences. Conversely, the preceeding questions included measures of positive wellbeing as well as ill-health, meaning there was not a wholly negative framing. In addition, the wording of the free text question was neutral, inviting any and all perspectives. Thirdly, while the STM enabled inclusion of a large sample, the modelling is no more objective than the researchers interpreting findings; some topics covered a number of different areas meaning that researchers had to use their own judgement about which comments most clearly captured the main point of a topic, and about how to group the topics into meaningful themes. To mitigate this as far as possible, four researchers collaborated in an iterative process of individual and group interpretation. Fourthly, the data were collected over a relatively long time-period (April 2020-January 2021) so do not capture the mood of a specific era of the pandemic, though conversely this does mean we were able to map the themes identified over time.

Implications for future research

The method used in this study clearly demonstrates the benefits of large-scale qualitative data collection. Such methods can capture factors that survey designers might have missed as important to participants, and can add context, depth, and nuance to quantitative results. STM has not been widely used in health research to date, despite the frequent inclusion of free-text responses in large surveys. We advocate the use of these methods. Future research could helpfully address how to mitigate some of the limitations of these methods, such as how to capture the views of those with limited access to online surveys, or cultural differences in interpretations.

This analysis of free-text data, from 7,421 participants in the UK’s largest survey of HCWs, provides in-depth, nuanced evidence of the most salient factors affecting them during the COVID-19 pandemic. In line with previous research, increased workload, lack of PPE, uncertainty, and inconsistency in messaging and advice from leaders were experienced as negatively impacting worker wellbeing, while support from peers and managers helped staff to cope with personal and professional stressors.

Supporting information

https://doi.org/10.1371/journal.pone.0275720.s001

Acknowledgments

We wish to acknowledge the National Institute of Health Research (NIHR) Applied Research Collaboration (ARC) National NHS and Social Care Workforce Group, with the following ARCs: East Midlands, East of England, South West Peninsula, South London, West, North West Coast, Yorkshire and Humber, and North East and North Cumbria. They enabled the set-up of the national network of participating hospital sites and aided the research team to recruit effectively during the COVID-19 pandemic.

The NHS CHECK consortium includes the following site leads: Sean Cross, Amy Dewar, Chris Dickens, Frances Farnworth, Adam Gordon, Charles Goss, Jessica Harvey, Nusrat Husain, Peter Jones, Damien Longson, Richard Morriss, Jesus Perez, Mark Pietroni, Ian Smith, Tayyeb Tahir, Peter Trigwell, Jeremy Turner, Julian Walker, Scott Weich, Ashley Wilkie.

The NHS CHECK consortium includes the following co-investigators and collaborators: Peter Aitken, Anthony David, Sarah Dorrington, Rosie Duncan, Cerisse Gunasinghe, Stephani Hatch, Daniel Leightley, Ira Madan, Isabel McMullen, Martin Parsons, Paul Moran, Dominic Murphy, Catherine Polling, Alexandra Pollitt, Danai Serfioti, Chloe Simela, Charlotte Wilson Jones.

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20. NHS Digital. NHS Workforce Statistics—September 2021. 2021. https://digital.nhs.uk/data-and-information/publications/statistical/nhs-workforce-statistics/september-2021

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Yoga: What You Need To Know

.header_greentext{color:green!important;font-size:24px!important;font-weight:500!important;}.header_bluetext{color:blue!important;font-size:18px!important;font-weight:500!important;}.header_redtext{color:red!important;font-size:28px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;font-size:28px!important;font-weight:500!important;}.header_purpletext{color:purple!important;font-size:31px!important;font-weight:500!important;}.header_yellowtext{color:yellow!important;font-size:20px!important;font-weight:500!important;}.header_blacktext{color:black!important;font-size:22px!important;font-weight:500!important;}.header_whitetext{color:white!important;font-size:22px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;}.Green_Header{color:green!important;font-size:24px!important;font-weight:500!important;}.Blue_Header{color:blue!important;font-size:18px!important;font-weight:500!important;}.Red_Header{color:red!important;font-size:28px!important;font-weight:500!important;}.Purple_Header{color:purple!important;font-size:31px!important;font-weight:500!important;}.Yellow_Header{color:yellow!important;font-size:20px!important;font-weight:500!important;}.Black_Header{color:black!important;font-size:22px!important;font-weight:500!important;}.White_Header{color:white!important;font-size:22px!important;font-weight:500!important;} What is yoga and how does it work?

Yoga is an ancient and complex practice, rooted in Indian philosophy. It began as a spiritual practice but has become popular as a way of promoting physical and mental well-being.

Although classical yoga also includes other elements, yoga as practiced in the United States typically emphasizes physical postures (asanas), breathing techniques (pranayama), and meditation (dyana).

There are many different yoga styles, ranging from gentle practices to physically demanding ones. Differences in the types of yoga used in research studies may affect study results. This makes it challenging to evaluate research on the health effects of yoga.

Yoga and two practices of Chinese origin— tai chi and qigong —are sometimes called “meditative movement” practices. All three practices include both meditative elements and physical ones.

.header_greentext{color:green!important;font-size:24px!important;font-weight:500!important;}.header_bluetext{color:blue!important;font-size:18px!important;font-weight:500!important;}.header_redtext{color:red!important;font-size:28px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;font-size:28px!important;font-weight:500!important;}.header_purpletext{color:purple!important;font-size:31px!important;font-weight:500!important;}.header_yellowtext{color:yellow!important;font-size:20px!important;font-weight:500!important;}.header_blacktext{color:black!important;font-size:22px!important;font-weight:500!important;}.header_whitetext{color:white!important;font-size:22px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;}.Green_Header{color:green!important;font-size:24px!important;font-weight:500!important;}.Blue_Header{color:blue!important;font-size:18px!important;font-weight:500!important;}.Red_Header{color:red!important;font-size:28px!important;font-weight:500!important;}.Purple_Header{color:purple!important;font-size:31px!important;font-weight:500!important;}.Yellow_Header{color:yellow!important;font-size:20px!important;font-weight:500!important;}.Black_Header{color:black!important;font-size:22px!important;font-weight:500!important;}.White_Header{color:white!important;font-size:22px!important;font-weight:500!important;} What are the health benefits of yoga?

Research suggests that yoga may:

Help improve general wellness by relieving stress, supporting good health habits, and improving mental/emotional health, sleep, and balance.
Relieve neck pain, migraine or tension-type headaches, and pain associated with knee osteoarthritis. It may also have a small benefit for low-back pain.
Help people with overweight or obesity lose weight.
Help people quit smoking.
Help people manage anxiety symptoms or depression.
Relieve menopause symptoms.
Be a helpful addition to treatment programs for substance use disorders.
Help people with chronic diseases manage their symptoms and improve their quality of life.

.header_greentext{color:green!important;font-size:24px!important;font-weight:500!important;}.header_bluetext{color:blue!important;font-size:18px!important;font-weight:500!important;}.header_redtext{color:red!important;font-size:28px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;font-size:28px!important;font-weight:500!important;}.header_purpletext{color:purple!important;font-size:31px!important;font-weight:500!important;}.header_yellowtext{color:yellow!important;font-size:20px!important;font-weight:500!important;}.header_blacktext{color:black!important;font-size:22px!important;font-weight:500!important;}.header_whitetext{color:white!important;font-size:22px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;}.Green_Header{color:green!important;font-size:24px!important;font-weight:500!important;}.Blue_Header{color:blue!important;font-size:18px!important;font-weight:500!important;}.Red_Header{color:red!important;font-size:28px!important;font-weight:500!important;}.Purple_Header{color:purple!important;font-size:31px!important;font-weight:500!important;}.Yellow_Header{color:yellow!important;font-size:20px!important;font-weight:500!important;}.Black_Header{color:black!important;font-size:22px!important;font-weight:500!important;}.White_Header{color:white!important;font-size:22px!important;font-weight:500!important;} What does research show about yoga for wellness?

Studies have suggested possible benefits of yoga for several aspects of wellness, including stress management, mental/emotional health, promoting healthy eating/activity habits, sleep, and balance.

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A 2020 review of 12 recent studies (672 total participants) of a variety of types of yoga for stress management in healthy adults found beneficial effects of yoga on measures of perceived stress in all the studies.
Of 17 older studies (1,070 total participants) of yoga for stress management included in a 2014 review, 12 showed improvements in physical or psychological measures related to stress.
Mental/emotional health. In a 2018 review of 14 studies (involving 1,084 total participants) that assessed the effects of yoga on positive aspects of mental health, most found evidence of benefits, such as improvements in resilience or general mental well-being.
In a 2021 study in which 60 women with obesity were randomly assigned to 12 yoga sessions or a waiting list, the beneficial effect of yoga on body mass index (BMI, an estimate of body fat based on height and weight) was found to depend on changes in physical activity and daily fruit and vegetable intake.
A 2018 survey of young adults (involving 1,820 participants) showed that practicing yoga regularly was associated with better eating and physical activity habits. In interviews, survey respondents said they thought yoga encouraged greater mindfulness and motivated them to participate in other forms of activity and to eat healthier. In addition, they saw the yoga community as a social circle that encourages connection, where healthy eating is commonplace.
In questionnaires and interviews, participants in a 2022 British study of a yoga intervention for people who were at risk for certain health conditions said that they had made changes in their lifestyles in response to the yoga program. They reported reducing consumption of unhealthy foods, increasing fruit and vegetable intake, and increasing their overall levels of physical activity.
Sleep. Yoga has been shown to be helpful for sleep in multiple studies of cancer patients, women with sleep problems, and older adults. Individual studies of population groups including health care workers, people with arthritis, and women with menopause symptoms have also reported improved sleep from yoga.
Balance. In a 2014 review, 11 of 15 studies (688 total participants) that looked at the effect of yoga on balance in healthy people showed improvements in at least one outcome related to balance. Several newer studies have provided additional evidence supporting a beneficial effect of yoga on balance in community-dwelling older adults.

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Research has been done on yoga for several conditions that involve pain, including low-back pain, neck pain, headaches, and knee osteoarthritis. For low-back pain, a large amount of research has been done, and the evidence suggests a slight benefit. For the other conditions, the evidence looks promising, but the amount of research is relatively small.

A 2022 review of 21 studies (2,223 total participants) of yoga interventions for low-back pain found that yoga is slightly better than no exercise, but the small difference may not be important to patients. There was evidence that participating in yoga was associated with slight improvements in physical function (ability to be active) and mental quality of life (emotional problems) in people with low-back pain. It was unclear whether there was any difference between the effects of yoga and those of other types of exercise.
A 2020 report by the Agency for Healthcare Research and Quality evaluated 10 studies of yoga for low-back pain (involving 1,520 total participants) and found that yoga improved pain and function in both the short term (1 to 6 months) and intermediate term (6 to 12 months). The effects of yoga were similar to those of exercise and massage.
A clinical practice guideline issued by the American College of Physicians in 2017 recommends using nondrug methods for the initial treatment of chronic low-back pain. Yoga is one of several suggested nondrug approaches.
Neck pain. A 2019 review of 10 studies (686 total participants) found that practicing yoga reduced the intensity of neck pain, decreased disability related to neck pain, and improved range of motion in the neck.
A 2020 review of 6 studies (240 participants) of yoga for chronic or episodic headaches (tension headaches or migraines) found evidence of reductions in headache frequency, headache duration, and pain intensity, with effects seen mostly in people with tension headaches. Because of the small numbers of studies and participants, as well as limitations in the quality of the studies, these results should be considered preliminary.
A 2022 review of 6 studies (445 participants) of yoga for migraine suggested that yoga was associated with decreases in pain intensity, headache frequency, and headache duration, and reduced the impact of migraine on daily life. However, most of the studies included small numbers of people, and the types of yoga therapy varied among studies, so the results are not conclusive. Also, most of the studies were done in Asia, and their findings might not apply to other populations.
A 2019 review of 9 studies (640 total participants) showed that yoga may be helpful for improving pain, function, and stiffness in people with osteoarthritis of the knee. However, the number of studies was small, and the research was not of high quality.
A 2019 guideline from the American College of Rheumatology and the Arthritis Foundation conditionally recommends yoga for people with knee osteoarthritis based on similarities to tai chi, which has been better studied and is strongly recommended by the same guideline.

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There’s evidence that yoga may help people lose weight.

A 2022 review of 22 studies (1,178 participants) of yoga interventions for people with overweight or obesity showed reductions in body weight, BMI, body fat, and waist size.
Longer and more frequent yoga sessions (at least 75 to 90 minutes, at least 3 times per week).
A longer duration of the overall program (3 months or more).
A yoga-based dietary component.
A residential component (such as a full weekend to start the program).
A larger number of elements of yoga.
Home practice.

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There’s evidence that yoga may help people stop smoking.

A 2019 NCCIH-funded study with 227 participants compared yoga classes with general wellness classes as additions to a conventional once-weekly counseling program. The people in the yoga group were 37 percent more likely to have quit smoking by the end of the 8-week program. However, 6 months after treatment, there was no difference between the groups in the proportion of people who were still not smoking.
A study published in 2020 showed a reduction in cigarette cravings after a single yoga session, as compared with a wellness education session. The study participants were people who were trying to cut back or stop smoking.

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Yoga can be a helpful addition to treatment for depression. It may also be helpful for anxiety symptoms in a variety of populations, but there’s little evidence of a benefit for people with anxiety disorders. Yoga might have benefits for people with post-traumatic stress disorder (PTSD).

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In a 2017 review of 23 studies (involving 1,272 participants) of people with depressive symptoms (although not necessarily diagnosed with depression), yoga was helpful in reducing symptoms in 14 of the studies.
A 2020 review of 7 studies (260 participants) of yoga interventions for people who had been diagnosed with major depressive disorder concluded that yoga may have small additional benefits for depression symptoms when used along with other forms of treatment.

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A 2019 review of 38 studies (2,295 participants) of yoga for anxiety symptoms found that yoga had a substantial beneficial effect, with the greatest effects seen in studies performed in India. The studies included a variety of different groups of people, including healthy people such as students and military personnel, patients with various physical or mental health conditions, and caregivers.
A 2021 review looked at the evidence on yoga for people who have been diagnosed with anxiety disorders. The reviewers identified some promising results, but they were unable to reach conclusions about whether yoga is helpful because not enough rigorous studies have been done.
A 2021 study of Kundalini yoga for generalized anxiety disorder (226 participants, 155 of whom completed the study), supported by NCCIH, found that Kundalini yoga improved symptoms but was less helpful than cognitive behavioral therapy, an established first-line treatment for this condition.

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A 2018 evaluation of 7 studies (284 participants) of yoga for people with post-traumatic stress disorder (PTSD) found only low-quality evidence of a possible benefit.

.header_greentext{color:green!important;font-size:24px!important;font-weight:500!important;}.header_bluetext{color:blue!important;font-size:18px!important;font-weight:500!important;}.header_redtext{color:red!important;font-size:28px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;font-size:28px!important;font-weight:500!important;}.header_purpletext{color:purple!important;font-size:31px!important;font-weight:500!important;}.header_yellowtext{color:yellow!important;font-size:20px!important;font-weight:500!important;}.header_blacktext{color:black!important;font-size:22px!important;font-weight:500!important;}.header_whitetext{color:white!important;font-size:22px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;}.Green_Header{color:green!important;font-size:24px!important;font-weight:500!important;}.Blue_Header{color:blue!important;font-size:18px!important;font-weight:500!important;}.Red_Header{color:red!important;font-size:28px!important;font-weight:500!important;}.Purple_Header{color:purple!important;font-size:31px!important;font-weight:500!important;}.Yellow_Header{color:yellow!important;font-size:20px!important;font-weight:500!important;}.Black_Header{color:black!important;font-size:22px!important;font-weight:500!important;}.White_Header{color:white!important;font-size:22px!important;font-weight:500!important;} Can yoga help with menopause symptoms?

Yoga seems to be at least as effective as other types of exercise in relieving menopause symptoms. A 2018 evaluation of 13 studies (more than 1,300 participants) of yoga for menopause symptoms found that yoga reduced physical symptoms such as hot flashes as well as psychological symptoms such as anxiety or depression.

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A small amount of research has looked at the possible benefits of incorporating yoga into treatment programs for various types of substance use disorders (opioid, alcohol, or tobacco use disorders or others). In a 2021 review of 8 studies (1,889 participants), 7 studies showed evidence of beneficial effects in terms of reduced use of the substance or reduction in symptoms such as pain, stress, or anxiety.

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There’s promising evidence that yoga may help people with some chronic diseases manage their symptoms and improve their quality of life. Thus, yoga could be a helpful addition to treatment programs.

In a 2018 evaluation of 138 studies on the use of yoga in patients with various types of cancer (10,660 total participants), most of the studies found that yoga improved patients’ physical and psychological symptoms and quality of life.
A 2021 review looked at 26 studies of yoga for depressive symptoms (1,486 participants) and 16 studies of yoga for anxiety symptoms (977 participants) in people with cancer. Small-to-moderate beneficial effects were seen for both types of symptoms.
Many yoga studies have focused on women who have or have had breast cancer. A 2022 review examined 23 studies that looked at the effects of yoga interventions on various symptoms in women with breast cancer during active cancer treatment. The majority of the studies showed significant benefits of yoga on quality of life, fatigue, nausea/vomiting, sleep quality, anxiety, depression, stress, or wound healing, suggesting that yoga may be helpful for symptom management.
A review of 8 studies (92 participants) suggested that yoga may have benefits for sleep, anxiety, fatigue, and quality of life in children and adolescents with cancer.
Chronic obstructive pulmonary disease (COPD). A 2019 review of 11 studies (586 participants) of breathing-focused yoga interventions for people with Parkinson’s disease found beneficial effects of these interventions on exercise capacity, lung function, and quality of life.
HIV/AIDS. A 2019 review of 7 studies (396 participants) of yoga interventions for people with HIV/AIDS found that yoga was a promising intervention for stress management.
A 2016 review of 15 studies of yoga for asthma (involving 1,048 total participants, most of whom were adults) concluded that yoga probably leads to small improvements in quality of life and symptoms.
A 2020 review of 9 studies (1,230 participants) of yoga-based interventions for children or adolescents with asthma found that the use of yoga was associated with improvements in lung function, stress/anxiety, and quality of life. However, because of wide variation in both the populations who were studied and the yoga interventions that were tested, it was unclear which components of yoga and how much yoga are needed to provide benefits.
Multiple sclerosis. Two recent reviews on yoga for people with multiple sclerosis showed little evidence of benefits. One review found a significant benefit only for fatigue (comparable to the effect of other types of exercise), and the other found no benefits for any aspect of quality of life.
Parkinson’s disease. A 2022 review (14 studies, 444 participants) suggests that yoga may have benefits for mobility, balance, and quality of life for people with mild-to-moderate Parkinson’s disease. The studies that were reviewed also suggest that yoga interventions are safe and acceptable for people with this condition.

.header_greentext{color:green!important;font-size:24px!important;font-weight:500!important;}.header_bluetext{color:blue!important;font-size:18px!important;font-weight:500!important;}.header_redtext{color:red!important;font-size:28px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;font-size:28px!important;font-weight:500!important;}.header_purpletext{color:purple!important;font-size:31px!important;font-weight:500!important;}.header_yellowtext{color:yellow!important;font-size:20px!important;font-weight:500!important;}.header_blacktext{color:black!important;font-size:22px!important;font-weight:500!important;}.header_whitetext{color:white!important;font-size:22px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;}.Green_Header{color:green!important;font-size:24px!important;font-weight:500!important;}.Blue_Header{color:blue!important;font-size:18px!important;font-weight:500!important;}.Red_Header{color:red!important;font-size:28px!important;font-weight:500!important;}.Purple_Header{color:purple!important;font-size:31px!important;font-weight:500!important;}.Yellow_Header{color:yellow!important;font-size:20px!important;font-weight:500!important;}.Black_Header{color:black!important;font-size:22px!important;font-weight:500!important;}.White_Header{color:white!important;font-size:22px!important;font-weight:500!important;} What does research show about practicing yoga during pregnancy?

Physical activities such as yoga are safe and desirable for most pregnant women as long as appropriate precautions are taken. Yoga may have health benefits for pregnant women, such as decreasing stress, anxiety, and depression.

If you are pregnant, you should be evaluated by your health care provider to make sure there’s no medical reason why you shouldn’t exercise.
You may need to modify some activities, including yoga, during pregnancy. You should avoid “hot yoga” while you are pregnant because it can cause overheating. You also need to avoid activities, including yoga poses, that involve long periods of being still or lying on your back. Talk with your health care provider about how to adjust your physical activity during pregnancy.
A 2022 analysis of 29 studies of pregnancy yoga interventions (2,217 participants) found that these programs reduced anxiety, depression, perceived stress, and duration of labor and increased the likelihood of a normal vaginal birth. However, because the yoga programs varied widely and because some of the studies had weaknesses in their methods, additional rigorous research is needed to better understand the effects of yoga during pregnancy and to find out what types of yoga programs are best in terms of both effectiveness and safety.

.header_greentext{color:green!important;font-size:24px!important;font-weight:500!important;}.header_bluetext{color:blue!important;font-size:18px!important;font-weight:500!important;}.header_redtext{color:red!important;font-size:28px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;font-size:28px!important;font-weight:500!important;}.header_purpletext{color:purple!important;font-size:31px!important;font-weight:500!important;}.header_yellowtext{color:yellow!important;font-size:20px!important;font-weight:500!important;}.header_blacktext{color:black!important;font-size:22px!important;font-weight:500!important;}.header_whitetext{color:white!important;font-size:22px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;}.Green_Header{color:green!important;font-size:24px!important;font-weight:500!important;}.Blue_Header{color:blue!important;font-size:18px!important;font-weight:500!important;}.Red_Header{color:red!important;font-size:28px!important;font-weight:500!important;}.Purple_Header{color:purple!important;font-size:31px!important;font-weight:500!important;}.Yellow_Header{color:yellow!important;font-size:20px!important;font-weight:500!important;}.Black_Header{color:black!important;font-size:22px!important;font-weight:500!important;}.White_Header{color:white!important;font-size:22px!important;font-weight:500!important;} Does yoga have benefits for children?

Research suggests that yoga may have several potential benefits for children.

A 2020 review of 27 studies (1,805 total participants) of yoga interventions in children or adolescents found reductions in anxiety or depression in 70 percent of the studies, with more promising results for anxiety. Some of the studies involved children who had or were at risk for various physical or mental health disorders and others involved groups of children in schools. The quality of the studies was relatively weak, and the results cannot be considered conclusive.
A 2021 review evaluated 9 studies (289 total participants) of yoga interventions for weight loss in children or adolescents with obesity or overweight. Some of the studies evaluated yoga alone; others evaluated yoga in combination with other interventions such as changes in diet. The majority of the yoga interventions had beneficial effects on weight loss and related behavior changes. The studies were small, and some did not use the most rigorous study designs.
A 2022 review of 21 studies (2,227 participants) of school-based yoga interventions in students age 5 to 15 showed promising results suggesting that yoga may enhance mental health among children and adolescents.
Yoga interventions in educational settings have also been studied in preschool-aged children (age 3 to 5). A 2021 review of studies of yoga and mindfulness practices in this age group suggested that these practices may have benefits for social-emotional functioning, although more research is needed before definite conclusions can be reached.
A small amount of evidence suggests that school-based yoga programs may have academic and psychological benefits for neurodiverse children.

.header_greentext{color:green!important;font-size:24px!important;font-weight:500!important;}.header_bluetext{color:blue!important;font-size:18px!important;font-weight:500!important;}.header_redtext{color:red!important;font-size:28px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;font-size:28px!important;font-weight:500!important;}.header_purpletext{color:purple!important;font-size:31px!important;font-weight:500!important;}.header_yellowtext{color:yellow!important;font-size:20px!important;font-weight:500!important;}.header_blacktext{color:black!important;font-size:22px!important;font-weight:500!important;}.header_whitetext{color:white!important;font-size:22px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;}.Green_Header{color:green!important;font-size:24px!important;font-weight:500!important;}.Blue_Header{color:blue!important;font-size:18px!important;font-weight:500!important;}.Red_Header{color:red!important;font-size:28px!important;font-weight:500!important;}.Purple_Header{color:purple!important;font-size:31px!important;font-weight:500!important;}.Yellow_Header{color:yellow!important;font-size:20px!important;font-weight:500!important;}.Black_Header{color:black!important;font-size:22px!important;font-weight:500!important;}.White_Header{color:white!important;font-size:22px!important;font-weight:500!important;} What are the risks of yoga?

Yoga is generally considered a safe form of physical activity for healthy people when performed properly, under the guidance of a qualified instructor. However, as with other forms of physical activity, injuries can occur. The most common injuries are sprains and strains, and the parts of the body most commonly injured are the knee or lower leg. Serious injuries are rare. The risk of injury associated with yoga is lower than that for higher impact physical activities.

Older adults may need to be particularly cautious when practicing yoga. The rate of yoga-related injuries treated in emergency departments is higher in people age 65 and older than in younger adults.

To reduce your chances of getting hurt while doing yoga:

Practice yoga under the guidance of a qualified instructor. Learning yoga on your own without supervision has been associated with increased risks.
If you’re new to yoga, avoid extreme practices such as headstands, shoulder stands, the lotus position, and forceful breathing.
Be aware that hot yoga has special risks related to overheating and dehydration.
Pregnant women, older adults, and people with health conditions should talk with their health care providers and the yoga instructor about their individual needs. They may need to avoid or modify some yoga poses and practices. Some of the health conditions that may call for modifications in yoga include preexisting injuries, such as knee or hip injuries, lumbar spine disease, severe high blood pressure, balance issues, and glaucoma.

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About one in seven U.S. adults practiced yoga in the past 12 months, according to a 2017 national survey. Among children age 4 to 17 years, it was about 1 in 12. The percentage of people who practice yoga grew from 2007 to 2017. This was true for both adults and children.

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National survey data from 2012 showed that 94 percent of adults who practiced yoga did it for wellness-related reasons, while 17.5 percent did it to treat a specific health condition. Some people reported doing both.

.header_greentext{color:green!important;font-size:24px!important;font-weight:500!important;}.header_bluetext{color:blue!important;font-size:18px!important;font-weight:500!important;}.header_redtext{color:red!important;font-size:28px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;font-size:28px!important;font-weight:500!important;}.header_purpletext{color:purple!important;font-size:31px!important;font-weight:500!important;}.header_yellowtext{color:yellow!important;font-size:20px!important;font-weight:500!important;}.header_blacktext{color:black!important;font-size:22px!important;font-weight:500!important;}.header_whitetext{color:white!important;font-size:22px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;}.Green_Header{color:green!important;font-size:24px!important;font-weight:500!important;}.Blue_Header{color:blue!important;font-size:18px!important;font-weight:500!important;}.Red_Header{color:red!important;font-size:28px!important;font-weight:500!important;}.Purple_Header{color:purple!important;font-size:31px!important;font-weight:500!important;}.Yellow_Header{color:yellow!important;font-size:20px!important;font-weight:500!important;}.Black_Header{color:black!important;font-size:22px!important;font-weight:500!important;}.White_Header{color:white!important;font-size:22px!important;font-weight:500!important;} Do different groups of people have different experiences with yoga?

Much of the research on yoga in the United States has been conducted in predominantly female, non-Hispanic White, well-educated people with relatively high incomes. Other people—particularly members of minority groups and those with lower incomes—have been underrepresented in yoga studies.

Different groups of people may have different yoga-related experiences, and the results of studies that did not examine a diverse population may not apply to everyone.

Differences related to age. In one survey, people age 40 to 54 were more likely to be motivated to practice yoga to increase muscle strength or lose weight, while those age 55 or older were more likely to be motivated by age-related chronic health issues. People age 65 and older may be more likely to need treatment for yoga-related injuries.
Differences related to gender. A study found evidence for differences between men and women in the effects of specific yoga poses on muscles. A study in veterans found preliminary evidence that women might benefit more than men from yoga interventions for chronic back pain.
Differences related to Hispanic ethnicity. U.S. national survey data show lower participation in yoga among Hispanic adults, compared to non-Hispanic White adults (8.0 percent vs. 17.1 percent of adults in 2017). A small 2021 survey of U.S. Hispanic adults with low incomes showed that cost was the most common barrier to participation in yoga. Other perceived barriers included concern about the need for physical flexibility (especially among men and those with no prior experience with yoga), thinking that they would feel like outsiders in a yoga class (among those with no prior experience), and considering yoga boring (among young adults).

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NCCIH is sponsoring a variety of yoga studies, including:

An evaluation of emotion regulation as a mechanism of action in yoga interventions for chronic low-back pain.
A study of yoga for chronic pain in people who are being treated for opioid use disorder.
A study of the effects of yoga postures and slow, deep breathing in people with hypertension (high blood pressure).

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Don’t use yoga to postpone seeing a health care provider about a medical problem.
Ask about the training and experience of the yoga instructor you’re considering.
Take charge of your health—talk with your health care providers about any complementary health approaches you use. Together, you can make shared, well-informed  decisions.

For More Information

Nccih clearinghouse.

The NCCIH Clearinghouse provides information on NCCIH and complementary and integrative health approaches, including publications and searches of Federal databases of scientific and medical literature. The Clearinghouse does not provide medical advice, treatment recommendations, or referrals to practitioners.

Toll-free in the U.S.: 1-888-644-6226

Telecommunications relay service (TRS): 7-1-1

Website: https://www.nccih.nih.gov

Email: [email protected] (link sends email)

Know the Science

NCCIH and the National Institutes of Health (NIH) provide tools to help you understand the basics and terminology of scientific research so you can make well-informed decisions about your health. Know the Science features a variety of materials, including interactive modules, quizzes, and videos, as well as links to informative content from Federal resources designed to help consumers make sense of health information.

Explaining How Research Works (NIH)

Know the Science: How To Make Sense of a Scientific Journal Article

Understanding Clinical Studies (NIH)

A service of the National Library of Medicine, PubMed® contains publication information and (in most cases) brief summaries of articles from scientific and medical journals. For guidance from NCCIH on using PubMed, see How To Find Information About Complementary Health Approaches on PubMed .

Yoga for Health—Systematic Reviews/Reviews/Meta-analyses

Yoga for Health—Randomized Controlled Trials

Website: https://pubmed.ncbi.nlm.nih.gov/

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American College of Obstetricians and Gynecologists. FAQs: Exercise During Pregnancy. Accessed at acog.org/womens-health/faqs/exercise-during-pregnancy on January 3, 2023.
Anheyer D, Klose P, Lauche R, et al. Yoga for treating headaches: a systematic review and meta-analysis. Journal of General Internal Medicine. 2020;35(3):846-854.
Batrakoulis A. Psychophysiological adaptations to yoga practice in overweight and obese individuals: a topical review . Diseases. 2022;10(4):107.
Black LI, Barnes PM, Clarke TC, Stussman BJ, Nahin RL. Use of yoga, meditation, and chiropractors among U.S. children aged 4–17 years. NCHS Data Brief, no 324. Hyattsville, MD: National Center for Health Statistics. 2018.
Bock BC, Dunsiger SI, Rosen RK, et al. Yoga as a complementary therapy for smoking cessation: results from BreathEasy, a randomized clinical trial. Nicotine and Tobacco Research. 2019;21(11):1517-1523.
Clarke TC, Barnes PM, Black LI, Stussman BJ, Nahin RL. Use of yoga, meditation, and chiropractors among U.S. adults aged 18 and older. NCHS Data Brief, no 325. Hyattsville, MD: National Center for Health Statistics. 2018.
Corrigan L, Moran P, McGrath N, et al. The characteristics and effectiveness of pregnancy yoga interventions: a systematic review and meta-analysis . BMC Pregnancy and Childbirth. 2022;22(1):250.
Cramer H, Krucoff C, Dobos G. Adverse events associated with yoga: a systematic review of published case reports and case series. PLoS One. 2013;8(10):e75515.
Cramer H, Lauche R, Klose P, et al. Yoga for improving health-related quality of life, mental health and cancer-related symptoms in women diagnosed with breast cancer. Cochrane Database of Systematic Reviews. 2017;(1):CD010802. Accessed at cochranelibrary.com on March 17, 2023.
Cramer H, Ostermann T, Dobos G. Injuries and other adverse events associated with yoga practice: a systematic review of epidemiological studies. Journal of Science and Medicine in Sport. 2018;21(2):147-154.
Cramer H, Ward L, Saper R, et al. The safety of yoga: a systematic review and meta-analysis of randomized controlled trials. American Journal of Epidemiology. 2015;182(4):281-293.
Domingues RB. Modern postural yoga as a mental health promoting tool: a systematic review. Complementary Therapies in Clinical Practice . 2018;31:248-255.
Gonzalez M, Pascoe MC, Yang G, et al. Yoga for depression and anxiety symptoms in people with cancer: a systematic review and meta-analysis . Psychooncology. 2021;30(8):1196-1208.
James-Palmer A, Anderson EZ, Zucker L, et al. Yoga as an intervention for the reduction of symptoms of anxiety and depression in children and adolescents: a systematic review. Frontiers in Pediatrics. 2020;8:78.
Khunti K, Boniface S, Norris E, et al. The effects of yoga on mental health in school-aged children: a systematic review and narrative synthesis of randomised control trials . Clinical Child Psychology and Psychiatry. 2023;28(3):1217-1238.
Kolasinski SL, Neogi T, Hochberg MC, et al. 2019 American College of Rheumatology/Arthritis Foundation guideline for the management of osteoarthritis of the hand, hip, and knee. Arthritis and Rheumatology . 2020;72(2):220-233.
Lauche R, Hunter DJ, Adams J, et al. Yoga for osteoarthritis: a systematic review and meta-analysis . Current Rheumatology Reports. 2019;21(9):47.
Li Y, Li S, Jiang J, et al. Effects of yoga on patients with chronic nonspecific neck pain: a PRISMA systematic review and meta-analysis. Medicine (Baltimore). 2019;98(8):e14649.
Long C, Ye J, Chen M, et al. Effectiveness of yoga therapy for migraine treatment: a meta-analysis of randomized controlled studies . American Journal of Emergency Medicine. 2022;58:95-99.
Qaseem A, Wilt TJ, McLean RM, et al. Noninvasive treatments for acute, subacute, and chronic low back pain: a clinical practice guideline from the American College of Physicians. Annals of Internal Medicine. 2017;166(7):514-530.
Rioux JG, Ritenbaugh C. Narrative review of yoga intervention clinical trials including weight-related outcomes. Alternative Therapies in Health and Medicine. 2013;19(3):32-46.
Selvan P, Hriso C, Mitchell J, et al. Systematic review of yoga for symptom management during conventional treatment of breast cancer patients . Complementary Therapies in Clinical Practice. 2022;48:101581.
Seshadri A, Adaji A, Orth SS, et al. Exercise, yoga, and tai chi for treatment of major depressive disorder in outpatient settings: a systematic review and meta-analysis . Primary Care Companion for CNS Disorders. 2020;23(1):20r2722.
Skelly AC, Chou R, Dettori JR, et al. Noninvasive Nonpharmacological Treatment for Chronic Pain: A Systematic Review Update. Comparative Effectiveness Review no. 227. Rockville, MD: Agency for Healthcare Research and Quality; 2020. AHRQ publication no. 20-EHC009.
Swain TA, McGwin G. Yoga-related injuries in the United States from 2001 to 2014. Orthopaedic Journal of Sports Medicine. 2016;4(11):2325967116671703.
Wang F, Szabo A. Effects of yoga on stress among healthy adults: a systematic review. Alternative Therapies in Health and Medicine. 2020;26(4):AT6214.
Wieland LS, Skoetz N, Pilkington K, et al. Yoga for chronic non-specific low back pain . Cochrane Database of Systematic Reviews. 2022;11(11):CD010671. Accessed at cochranelibrary.com on January 9, 2023.

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Agarwal RP, Maroko-Afek A. Yoga into cancer care: a review of the evidence-based research. International Journal of Yoga. 2018;11(1):3-29.
Alphonsus KB, Su Y, D’Arcy C. The effect of exercise, yoga and physiotherapy on the quality of life in people with multiple sclerosis: systematic review and meta-analysis. Complementary Therapies in Medicine. 2019;43:188-195.
Anheyer D, Koch AK, Thoms MS, et al. Yoga in women with abdominal obesity—do lifestyle factors mediate the effect? Secondary analysis of a RCT. Complementary Therapies in Medicine. 2021;60:102741.
Bandealy SS, Sheth NC, Matuella SK, et al. Mind-body interventions for anxiety disorders: a review of the evidence base for mental health practitioners . Focus (American Psychiatric Publishing). 2021;19(2):173-183.
Bolgla LA, Amodio L, Archer K, et al. Trunk and hip muscle activation during yoga poses: do sex-differences exist? Complementary Therapies in Clinical Practice. 2018;31:256-261.
Bridges L, Sharma M. The efficacy of yoga as a form of treatment for depression. Journal of Evidence-Based Complementary and Alternative Medicine. 2017;22(4):1017-1028.
Cheshire A, Richards R, Cartwright T. ‘Joining a group was inspiring’: a qualitative study of service users’ experiences of yoga on social prescription. BMC Complementary Medicine and Therapies. 2022;22(1):67.
Clarke TC, Black LI, Stussman BJ, Barnes PM, Nahin RL. Trends in the use of complementary health approaches among adults: United States, 2002–2012. National health statistics reports; no 79. Hyattsville, MD: National Center for Health Statistics. 2015.
Cocchiara RA, Peruzzo M, Mannocci A, et al. The use of yoga to manage stress and burnout in healthcare workers: a systematic review. Journal of Clinical Medicine. 2019;8(3):284.
Cramer H, Anheyer D, Saha FJ, et al. Yoga for posttraumatic stress disorder—a systematic review and meta-analysis. BMC Psychiatry. 2018;18(1):72.
Cramer H, Haller H, Klose P, et al. The risks and benefits of yoga for patients with chronic obstructive pulmonary disease: a systematic review and meta-analysis. Clinical Rehabilitation. 2019;33(12):1847-1862.
Cramer H, Peng W, Lauche R. Yoga for menopausal symptoms—a systematic review and meta-analysis. Maturitas. 2018;109:13-25.
Cramer H, Quinker D, Schumann D, et al. Adverse effects of yoga: a national cross-sectional survey. BMC Complementary and Alternative Medicine. 2019;19(1):190.
Cramer H, Ward L, Steel A, et al. Prevalence, patterns, and predictors of yoga use: results of a U.S. nationally representative survey. American Journal of Preventive Medicine. 2016;50(2):230-235.
Dai C-L, Sharma M, Chen C-C, et al. Yoga as an alternative therapy for weight management in child and adolescent obesity: a systematic review and implications for research. Alternative Therapies in Health and Medicine. 2021;27(1):48-55.
Dunne EM, Balletto BL, Donahue ML, et al. The benefits of yoga for people living with HIV/AIDS: a systematic review and meta-analysis. Complementary Therapies in Clinical Practice. 2019;34:157-164.
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Acknowledgments

NCCIH thanks Inna Belfer, M.D., Ph.D., and David Shurtleff, Ph.D., NCCIH, for their review of the 2023 update of this publication.

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Introduction
Conclusions
Article Information

County-level death counts for Florida and Ohio based on Datavant data that link mortality records to voter registration files against the CDC data. Each observation represents a single county. Additional details on the data are provided in the eMethods in Supplement 1 .

Weekly excess deaths for Florida and Ohio based on mortality records linked to voter registration files. A, Overall excess death rates in Florida and Ohio. B, Excess death rates by registered party. C, The percentage-point difference between the registered parties, after adjusting for age and state-level differences; the smooth brown curve was fit with locally estimated scatterplot smoothing. A-C, Excess death rates were calculated for each week by comparing the observed deaths in that week with expected deaths based on a Poisson model. The 95% prediction intervals (shaded areas) were determined using simulations from the Poisson coefficient and outcome distribution, with SEs clustered at the county level. Additional details on the excess death methods and statistical analyses are presented in the eMethods in Supplement 1 .

The analyses were additionally adjusted for age and state-level differences in subgroup analyses where these covariates were not used for stratification. The 95% prediction intervals (horizontal lines) were determined using simulations from the Poisson coefficient and outcome distribution, with SEs clustered at the county level. Additional details on the excess death methodology and statistical analyses are presented in the eMethods in Supplement 1 .

The diamonds are binned means; counties with similar vaccination rates were binned to form 8 equally sized bins. The curves were fit to the underlying data using locally estimated scatterplot smoothing. In the pre–COVID-19 period (before April 2020), excess death rates for both Republican and Democratic voters hover around 0. During the beginning pandemic but before open vaccine eligibility (April 2020 to March 2021), the association between excess death rates and county-level vaccination rates were generally negative and nearly identical for Republican and Democratic voters. However, in the period after open vaccine eligibility (April 2021 to December 2021), there was a clear difference between Republican and Democratic voters, with higher excess death rates for Republicans concentrated in counties with lower overall vaccination rates and minimal differences in counties with the highest vaccination rates.

eMethods . Supplemental Description of Methods

eFigure 1. Excess Death Rates by Age in Florida and Ohio: 2018-2021

eFigure 2. Excess Death Rates in Florida: 2018-2021

eFigure 3. Excess Death Rates in Ohio: 2018-2021

eFigure 4. Excess Death Rates and Vaccination Rates in Florida and Ohio During the COVID-19 Pandemic Using October 1, 2021, Vaccination Rates

eFigure 5. Excess Death Rates and Vaccination Rates in Florida and Ohio During the COVID-19 Pandemic Using March 1, 2021, Vaccination Rates

eTable 1. Summary Statistics

eTable 2. Sensitivity of Estimated Difference in Excess Death Rates Between Republican and Democratic Voters to Alterations in Excess Death Methodology and Statistical Model

Data Sharing Statement

Discrepancies in Estimating Excess Death by Political Party Affiliation—Reply JAMA Internal Medicine Comment & Response January 1, 2024 Jacob Wallace, PhD; Paul Goldsmith-Pinkham, PhD; Jason L. Schwartz, PhD
Discrepancies in Estimating Excess Death by Political Party Affiliation JAMA Internal Medicine Comment & Response January 1, 2024 Christopher Dasaro, BS; Alyson Haslam, PhD; Vinay Prasad, MD, MPH
Discrepancies in Estimating Excess Death by Political Party Affiliation JAMA Internal Medicine Comment & Response January 1, 2024 Patrick O’Mahen, PhD

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For this paper to be accurate, voting records must be accurate. Ohio has historically had inaccurate voting records, so much so that a recent Supreme Court Decision recently went against the state of Ohio, see https://www.judicialwatch.org/wp-content/uploads/2018/06/Jon-Husted-Ohio-Secretary-of-State-v.-Philip-Randolph-Institute-et-al.-decision-16-980.pdf.

Also this paper contradicts more recent studies that have showed that mRNA vaccination decreases death rates from COVID, but increases deaths from other causes, so that all cause mortality is unchanged, with a relative risk of dying of 1.03 in the vaccinated group vas the unvaccinated group. https://www.cell.com/iscience/fulltext/S2589-0042(23)00810-6

This is a very interesting and informative study of public health value, but the findings are not unexpected.

It demonstrates the value of preventing disruption of health, and perhaps equally importantly emphasizes the direct and indirect economic loss to the society and the state when public health is compromised.

Political affiliation should not influence health care. Medicine is beyond politics!

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Wallace J , Goldsmith-Pinkham P , Schwartz JL. Excess Death Rates for Republican and Democratic Registered Voters in Florida and Ohio During the COVID-19 Pandemic. JAMA Intern Med. 2023;183(9):916–923. doi:10.1001/jamainternmed.2023.1154

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Excess Death Rates for Republican and Democratic Registered Voters in Florida and Ohio During the COVID-19 Pandemic

1 Yale School of Public Health, New Haven, Connecticut
2 Yale School of Management, New Haven, Connecticut
Comment & Response Discrepancies in Estimating Excess Death by Political Party Affiliation—Reply Jacob Wallace, PhD; Paul Goldsmith-Pinkham, PhD; Jason L. Schwartz, PhD JAMA Internal Medicine
Comment & Response Discrepancies in Estimating Excess Death by Political Party Affiliation Christopher Dasaro, BS; Alyson Haslam, PhD; Vinay Prasad, MD, MPH JAMA Internal Medicine
Comment & Response Discrepancies in Estimating Excess Death by Political Party Affiliation Patrick O’Mahen, PhD JAMA Internal Medicine

Question Was political party affiliation a risk factor associated with excess mortality during the COVID-19 pandemic in Florida and Ohio?

Findings In this cohort study evaluating 538 159 deaths in individuals aged 25 years and older in Florida and Ohio between March 2020 and December 2021, excess mortality was significantly higher for Republican voters than Democratic voters after COVID-19 vaccines were available to all adults, but not before. These differences were concentrated in counties with lower vaccination rates, and primarily noted in voters residing in Ohio.

Meaning The differences in excess mortality by political party affiliation after COVID-19 vaccines were available to all adults suggest that differences in vaccination attitudes and reported uptake between Republican and Democratic voters may have been a factor in the severity and trajectory of the pandemic in the US.

Importance There is evidence that Republican-leaning counties have had higher COVID-19 death rates than Democratic-leaning counties and similar evidence of an association between political party affiliation and attitudes regarding COVID-19 vaccination; further data on these rates may be useful.

Objective To assess political party affiliation and mortality rates for individuals during the initial 22 months of the COVID-19 pandemic.

Design, Setting, and Participants A cross-sectional comparison of excess mortality between registered Republican and Democratic voters between March 2020 and December 2021 adjusted for age and state of voter registration was conducted. Voter and mortality data from Florida and Ohio in 2017 linked to mortality records for January 1, 2018, to December 31, 2021, were used in data analysis.

Exposures Political party affiliation.

Main Outcomes and Measures Excess weekly deaths during the COVID-19 pandemic adjusted for age, county, party affiliation, and seasonality.

Results Between January 1, 2018, and December 31, 2021, there were 538 159 individuals in Ohio and Florida who died at age 25 years or older in the study sample. The median age at death was 78 years (IQR, 71-89 years). Overall, the excess death rate for Republican voters was 2.8 percentage points, or 15%, higher than the excess death rate for Democratic voters (95% prediction interval [PI], 1.6-3.7 percentage points). After May 1, 2021, when vaccines were available to all adults, the excess death rate gap between Republican and Democratic voters widened from −0.9 percentage point (95% PI, −2.5 to 0.3 percentage points) to 7.7 percentage points (95% PI, 6.0-9.3 percentage points) in the adjusted analysis; the excess death rate among Republican voters was 43% higher than the excess death rate among Democratic voters. The gap in excess death rates between Republican and Democratic voters was larger in counties with lower vaccination rates and was primarily noted in voters residing in Ohio.

Conclusions and Relevance In this cross-sectional study, an association was observed between political party affiliation and excess deaths in Ohio and Florida after COVID-19 vaccines were available to all adults. These findings suggest that differences in vaccination attitudes and reported uptake between Republican and Democratic voters may have been factors in the severity and trajectory of the pandemic in the US.

As of May 2023, there had been approximately 1.1 million deaths from COVID-19 in the US. 1 There is evidence that Republican-leaning counties have had higher COVID-19 death rates than Democratic-leaning counties and similar evidence of an association between political party affiliation and attitudes regarding COVID-19 vaccination, social distancing, and other mitigation strategies based on political party affiliation. 2 - 6

Prior studies 7 , 8 have found that Republican-leaning counties have had higher COVID-19 death rates than Democratic-leaning counties. It is unknown whether this county-level association persists at the individual level and whether it may be subject to the ecologic fallacy. 9 The ecologic fallacy is the incorrect assumption that associations observed at an aggregated level (eg, a county) will be the same at the individual level. Republican-leaning and Democratic-leaning counties differ in ways other than political party affiliation, 10 , 11 such as racial and ethnic composition, rurality, and educational levels, making it difficult to establish whether the differences in COVID-19 death rates are associated with political party affiliation or other differences in county-level characteristics. Research before the COVID-19 pandemic has also found evidence of higher death rates in Republican-leaning counties than Democratic-leaning counties. 12

To assess the association between political party affiliation and excess mortality for individuals during the COVID-19 pandemic, we linked voter registration data in Florida and Ohio to mortality data at the individual level to calculate excess death rates for Republican and Democratic voters and compare excess death rates before and after vaccines became available to the full adult population. 13 , 14 Because individual-level vaccination status was not included in the available data, we were able to assess excess death rates and vaccination rates only at the county level.

The eMethods in Supplement 1 provides additional details of all the methods. We obtained detailed US weekly mortality data from January 1, 2018, to December 31, 2021, from Datavant, an organization that augments the Social Security Administration Death Master File with information from newspapers, funeral homes, and other sources to construct an individual-level database containing 10 325 730 deaths in the US to individuals aged 25 or older during this period. This data set, which includes deaths reported to Datavant through March 31, 2023, covers approximately 83.5% of the Centers for Disease Control and Prevention death count for individuals who died at age 25 or older during the period from January 1, 2018, to December 31, 2021. Because the Datavant mortality data do not contain state identifiers, we are unable to assess data completeness in our individual study states of Florida and Ohio. During the COVID-19 pandemic, Datavant mortality data have been used in other peer-reviewed 15 and publicly available 16 research on excess mortality. The Yale University Institutional Review Board exempted the study from review because the data were deidentified, and reporting adheres to the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guideline.

We linked the mortality data at the individual level to 2017 Florida and Ohio voter registration files; these were the only states for which historical publicly available voter registration data were readily available. The linkage was performed from April 11 to 14, 2023. For each record, the linked data included week of death, age of deceased, county of residence, and 2017 political party affiliation. Political party affiliation in Ohio was defined by whether an individual voted in a party’s primary election within the preceding 2 calendar years; in Florida, political party affiliation was based on party registration. We excluded individuals whose political party affiliation was independent and those who were affiliated with third parties. Because COVID-19 deaths are most common at older ages, 17 we included only death records for individuals who died at age 25 years or older.

We also obtained death counts for the study period from the National Center for Health Statistics 18 and county-level vaccination rates from the Centers for Disease Control and Prevention. 19 We selected May 1, 2021, as the date for the county-level vaccination rate—1 month after eligibility for vaccines opened to all adults in the study states—because it represented the approximate date when all adults would have had the opportunity to receive at least 1 dose of a COVID-19 vaccine if they so desired, taking into account the time that states required during April 2021 to schedule and administer vaccines to newly eligible adults seeking them. As a robustness check, we assessed the sensitivity of our findings to using county-level vaccination rates on alternative dates before (March 1, 2021) and after (October 1, 2021) May 1, 2021.

We aggregated weekly death counts from January 1, 2018, to December 31, 2021, at the county-by-party-by-age level. The age ranges used were 25 to 64, 65 to 74, 75 to 84, and 85 years or older. The observed death counts included all the deaths from our mortality data that linked to Republican or Democratic voters who were registered in Florida and Ohio as of 2017.

To calculate the number of excess deaths, we estimated the number of deaths we would expect in the absence of the COVID-19 pandemic. First, we estimated expected weekly deaths at the county-by-party-by-age level by fitting a Poisson regression model to observed weekly death counts at the county-by-party-by-age-level for January 1, 2018, through December 31, 2019. 20 , 21 We then predicted expected deaths over our full sample. Excess deaths were defined as the difference between observed and expected deaths for January 1, 2018, to December 31, 2021. As a check on the model, we used predictions from the model in the weeks before the onset of COVID-19 (January 1, 2018, to March 31, 2020) to estimate excess deaths during this period.

We calculated excess death rates (the primary outcome) as the ratio of observed deaths (the numerator) to expected deaths (the denominator). To obtain estimates of excess death rates at aggregated levels, we used a weighted average of estimated excess death rates in each of the underlying cells (eg, county-by-party-by-age), weighted by their expected death counts. We estimated Poisson 95% prediction intervals (PIs), simulating from the coefficient distribution and outcome distribution, with SEs clustered by county. 22 We additionally adjusted estimated differences in excess death rates between Republican and Democratic voters—the primary estimate of interest—for differences in excess death rates by age group and state during the COVID-19 pandemic. Intuitively, this approach compared excess death rates between Democratic and Republican voters of the same age residing in the same states during the same week of the pandemic and then weighted those differences in excess death rates to either the weekly level, when plotting weekly differences in excess death rates, or to 3 broader time periods: (1) April 1, 2020, to December 31, 2021 (the part of the study period overlapping the COVID-19 pandemic); (2) April 1, 2020, to March 31, 2021 (the period during the pandemic before open vaccine eligibility for all adults); and (3) April 1, 2021, to December 31, 2021 (the period during the pandemic after open vaccine eligibility for all adults).

We also assessed county-level vaccination rates (as of May 1, 2021) and excess death rates by plotting average excess death rates for Republican and Democratic voters against the county-level vaccination rate during (1) the pre–COVID-19 pandemic period, (2) the period during the pandemic before open vaccine eligibility for all adults, and (3) the period during the pandemic after open vaccine eligibility for all adults.

In sensitivity analyses, we altered the Poisson model used to predict baseline death counts by including a linear time trend (and in one analysis allowing it to vary by state) and additional seasonality terms to capture higher frequency season-of-the-year trends. 23 For transparency, we calculated differences in the excess death rates between Republican and Democratic voters with no adjustments (removing our state and age group adjustments) and, separately, with a model that included our primary adjustments (state and age group) and additional adjustments for county-by-age differences in excess death rates during the pandemic.

We performed all calculations using R, version 4.1.3 (R Foundation for Statistical Computing). Statistical analyses report 95% PIs using simulations from the coefficient distribution and outcome distribution, with SEs clustered by county. Significance testing was 2-sided, and a P < .05 was considered statistically significant.

Our study included 538 159 deaths for individuals aged 25 years and older in Florida and Ohio between January 2018 and December 2021 linked to their 2017 voter data (eTable 1 in Supplement 1 ). The median age at death was 78 years (IQR, 71-89 years). The pattern of death counts in our linked data and in the National Center for Health Statistics data was similar ( Figure 1 ).

Using these data, we found a 20.5 percentage-point (95% PI, 15.6-25.6 percentage points) increase in weekly death counts in Florida and Ohio in the March 2020 to December 2021 period relative to the expected death counts for those weeks ( Figure 2 A and Table ). By comparison, for the time periods before the pandemic, we found only small fluctuations in excess death rates around 0.

Before the pandemic, excess death rates for Republican and Democratic voters were centered around 0 ( Figure 2 B). In the winter of 2021, both groups experienced sharp increases of similar magnitude in excess death rates. However, in the summer of 2021, after vaccines were available to all adults, the excess death rate among Republican voters began to increase relative to the excess death rate among Democratic voters; in the fall of 2021, the gap widened further. Between March 2020 and December 2021, excess death rates were 2.8 percentage points (15%) higher for Republican voters compared with Democratic voters (95% PI, 1.6-3.7 percentage points) ( Table ). After April 1, 2021, when all adults were eligible for vaccines in Florida and Ohio, this gap widened from −0.9 percentage point (95% PI, −2.5 to 0.3 percentage points) between March 2020 and March 2021, to 7.7 percentage points (95% PI, 6.0-9.3 percentage points) in the adjusted analysis, or a 43% difference ( Table ).

The estimates of differences in excess death rates between Republican and Democratic voters (adjusted for age, time, and state) were small until the summer of 2021, when excess death rates among Republican voters began to increase compared with excess death rates among Democratic voters ( Figure 2 C). The analyses stratified by age showed that Republican voters had significantly higher excess death rates compared with Democratic voters for 2 of the 4 age groups in the study, the differences for the age group 25 to 64 years were not significant ( Figure 3 ; eFigure 1 in Supplement 1 ). Democratic voters had significantly higher excess death rates compared with Republican voters for the age group 65 to 74 years. The analyses stratified by state showed that differences in excess death rates between Republican and Democratic voters were primarily seen in voters residing in Ohio, with smaller, and generally nonsignificant, differences in weekly excess death rates between Republican and Democratic voters in Florida (eFigure 2 and eFigure 3 in Supplement 1 ). In analyses that pooled data from March 2020 to December 2021, Republican voters in Florida did not have a statistically significantly higher excess death rate than Democratic voters in Florida ( Figure 3 ). Additional sensitivity analyses supported our main conclusions (eTable 2 in Supplement 1 ).

Before the COVID-19 pandemic, there was no association between county-level excess death rates, which hovered around 0, and the county-level vaccination rates ( Figure 4 A). During the pandemic, there was generally a negative association between county-level excess death rates and the share of the county population administered at least 1 dose of the vaccine as of May 1, 2021 ( Figure 4 B and C). In the period before open vaccine eligibility for adults (April 2020 to March 2021), the association between excess death rates and county-level vaccination rates was nearly identical for Republican and Democratic voters ( Figure 4 B). In the period after open vaccine eligibility (April to December 2021), there was a clear difference between Republican and Democratic voters, with higher excess death rates for Republicans in counties with lower overall vaccination rates ( Figure 4 C). Sensitivity analyses supported our main conclusions (eFigure 4 and eFigure 5 in Supplement 1 ).

During the initial years of the COVID-19 pandemic, political party affiliation in the US was associated with excess death rates in Florida and Ohio at the individual level. Republican voters had higher excess death rates than Democratic voters, as noted in a large mortality gap in the period after, but not before, all adults were eligible for vaccines in Florida and Ohio. With adjustments for differences in age and state of residence between Republican and Democratic voters, our findings suggest that, among individuals in the same age groups living in the same states, there were significant differences in excess death rates during the COVID-19 pandemic associated with political party affiliation. The results were robust to alterations in the methods used to estimate excess mortality as well as the statistical model used to estimate the difference in excess death rates between Republican and Democratic voters.

Our findings suggest that political party affiliation became a substantial factor only after COVID-19 vaccines were available to all adults in the US. Although the lack of individual-level vaccination status limited our ability to note further associations, the results suggest that well-documented differences in vaccination attitudes and reported uptake between Republican and Democratic voters 24 , 25 may have been factors in the severity and trajectory of the pandemic. However, one alternative explanation is that political party affiliation is a proxy for other risk factors (beyond age, which we adjusted for) for excess mortality during the COVID-19 pandemic, such as rates of underlying medical conditions, race and ethnicity, socioeconomic status, or health insurance coverage, 26 - 29 and these risk factors may be associated with differences in excess mortality by political party, even though we only observed differences in excess mortality after vaccines were available to all adults. It is also possible that specific risk factors for excess mortality interact with the emergence of COVID-19 variants (eg, Delta) or changes in vaccine-associated protection over time to be more consequential at different stages of the pandemic. Because data limitations prevented us from directly adjusting for these factors, their potential influence remains an important question for future research.

In addition to vaccines, nonpharmaceutical interventions, including facial masks and restrictions on large gatherings, have been reported to contribute to reductions in transmission of COVID-19 or its severe outcomes, including death, in experimental, quasi-experimental, and modeling studies. 30 - 33 However, differences in support for these measures by political party affiliation emerged early in the pandemic, 34 and the gradual loosening of the strictest government policies regarding the use of facial masks and restrictions on large gatherings predated April 2021, when vaccines became available to all adults in the study states. The extent of public adherence to these and other interventions at various stages of the pandemic, associations between individual political party affiliation and the adoption over time of these interventions in specific geographic areas, and their relative contribution to trends in individual and community COVID-19 mortality over time are also worthwhile areas for further investigation.

Since the fall of 2022, the focus of the US COVID-19 vaccination program has turned to the administration of updated, bivalent booster doses to those who have already received a primary vaccine series and, in many cases, 1 or more prior booster dose. Federal health officials have also begun considering future strategies for COVID-19 vaccination, including annual revaccination campaigns using vaccines reformulated to match circulating variants. 35 Yet more than 2 years into the vaccination effort, more than 50 million adults in the US have not completed a primary series, and these individuals remain at a substantially increased risk of hospitalization and death. 36 The causes of this vaccine hesitancy and refusal are varied and extend beyond political beliefs or party affiliation alone. 37 It therefore remains imperative for public health officials to continue and enhance activities intended to improve initial vaccination coverage, in tandem with current or future booster campaigns. To be most effective, these efforts—and corresponding messages—should be tailored to their intended audiences, address the particular sources of vaccine hesitancy among those groups, and seek to include direct participation from members of those communities as trusted ambassadors of provaccine messages. 38 As part of this work, engagement with conservative and Republican leaders, in particular, has been identified as a promising approach to promoting COVID-19 vaccine acceptance. 38

Our study has several limitations. First, there are plausible alternative explanations for the difference in excess death rates by political party affiliation beyond the explanatory role of vaccines discussed herein. Second, our mortality data, although detailed and recent, only included approximately 83.5% of deaths in the US and did not include cause of death. Although overall excess death patterns in our data are similar to those in other reliable sources, such as the Centers for Disease Control and Prevention National Center for Health Statistics data, it is possible that the deaths that our study data did not include may disproportionately occur among individuals registered with a particular political party, potentially biasing our results. In addition, the completeness of our mortality data may vary across states or time, potentially biasing our estimates of excess death rates. Third, all excess death models rely on fundamentally untestable assumptions to construct the baseline number of deaths we would expect in the absence of the COVID-19 pandemic. Fourth, because we did not have information on individual vaccination status, analyses of the association between vaccination rates and excess deaths relied on county-level vaccination rates. Fifth, our study was based on data from 2 states with readily obtainable historical voter registration information (Florida and Ohio); hence, our results may not generalize to other states.

Our study found evidence of higher excess mortality for Republican voters compared with Democratic voters in Florida and Ohio after, but not before, COVID-19 vaccines were available to all adults in the US. These differences in excess death rates were larger in counties with lower vaccination rates. If differences in COVID-19 vaccination by political party affiliation persist, particularly in the absence of other pandemic mitigation strategies, the higher excess death rate observed among Republican voters may continue through subsequent stages of the pandemic.

Accepted for Publication: March 4, 2023.

Published Online: July 24, 2023. doi:10.1001/jamainternmed.2023.1154

Corresponding Author: Jacob Wallace, PhD, Department of Health Policy and Management, Yale School of Public Health, 60 College St, New Haven, CT 06510 ( [email protected] ).

Author Contributions: Drs Wallace and Goldsmith-Pinkham had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: All authors.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: All authors.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Goldsmith-Pinkham.

Obtained funding: Wallace, Schwartz.

Supervision: Wallace.

Conflict of Interest Disclosures: None reported.

Funding/Support: The Tobin Center for Economic Policy at Yale University and the Yale School of Public Health COVID-19 Rapid Response Research Fund funded this study.

Role of the Funder/Sponsor: The funding organizations had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: See Supplement 2 .

Disclaimer: The content is solely the responsibility of the authors and does not necessary reflect the official views of the supporting organizations.

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