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Within the last decade, clustering of comorbidities has become an increasing health problem on a global scale and will continue to challenge healthcare professionals in the coming years. People with multiple diseases find difficulties in managing their daily lives due to the implications each disease brings; attending and keeping up to date with hospital appointments, being prescribed and taking various medications, the effects of mental health and quality of life, and the impact it has on their families. Most research in clinical trials often exclude individuals with multimorbidity and observational studies mainly focus on single disease outcomes, therefore there is an opportunity to encourage future research in an area which could help prevent further cases and improve the lives of those already living with multimorbidity. This review aims to summarise the rising prevalence and most common clusters, highlight the challenges faced in healthcare, and explore ways to improve future research.

Background Although age, obesity and pre-existing chronic diseases are established risk factors for COVID-19 outcomes, their interactions have not been well researched. Methods We used data from the Clinical Characterisation Protocol UK (CCP-UK) for Severe Emerging Infection developed by the International Severe Acute Respiratory and emerging Infections Consortium (ISARIC). Patients admitted to hospital with COVID-19 from 6th February to 12th October 2020 were included where there was a coded outcome following hospital admission. Obesity was determined by an assessment from a clinician and chronic disease by medical records. Chronic diseases included: chronic cardiac disease, hypertension, chronic kidney disease, chronic pulmonary disease, diabetes and cancer. Mutually exclusive categories of obesity, with or without chronic disease, were created. Associations with in-hospital mortality were examined across sex and age categories. Results The analysis included 27,624 women with 6407 (23.2%) in-hospital deaths and 35,065 men with 10,001 (28.5%) in-hospital deaths. The prevalence of chronic disease in women and men was 66.3 and 68.5%, respectively, while that of obesity was 12.9 and 11.1%, respectively. Association of obesity and chronic disease status varied by age (p Conclusion Obesity and chronic disease are important risk factors for in-hospital mortality in younger age groups, with the combination of chronic disease and obesity being particularly important in those under 50 years of age. These findings have implications for targeted public health interventions, vaccination strategies and in-hospital clinical decision making.

BackgroundLife expectancy is a simple measure of assessing health differences between two or more populations but current life expectancy calculations are not reliable for small populations. A potential solution to this is to borrow strength from larger populations from the same source but this has not formally been investigated.MethodsUsing data on 451,222 individuals from the Clinical Practice Research Datalink (CPRD) on the presence/absence of intellectual disability and type 2 diabetes mellitus (T2DM), we compared stratified and combined flexible parametric models, and Chiang’s methods, for calculating life expectancy. Confidence intervals were calculated using the Delta method, Chiang’s adjusted life table approach and bootstrapping.ResultsThe flexible parametric models allowed calculation of life expectancy by exact age and beyond traditional life expectancy age thresholds. The combined model that fit age interaction effects as a spline term provided greater statistical precision for small covariate subgroups by borrowing strength from the larger subgroups. However, careful consideration of the distribution of events in the smallest group was needed.ConclusionsLife expectancy is a simple measure to compare health differences between populations. The use of combined flexible parametric methods to calculate life expectancy in small samples has shown promising results by allowing life expectancy to be modelled by exact age, greater statistical precision and prediction of different covariate patterns without stratification. We recommend further investigation of their application for both policymakers and researchers.

Summary Objective To assess the association between household size and risk of non-severe or severe COVID-19. Design A longitudinal observational study. Setting This study utilised UK Biobank linked to national SARS-CoV-2 laboratory test data. Participants 401,910 individuals with available data on household size in UK Biobank. Main outcome measures Household size was categorised as single occupancy, two-person households and households of three or more. Severe COVID-19 was defined as a positive SARS-CoV-2 test on hospital admission or death with COVID-19 recorded as the underlying cause; and non-severe COVID-19 as a positive test from a community setting. Logistic regression models were fitted to assess associations, adjusting for potential confounders. Results Of 401,910 individuals, 3612 (1%) were identified as having suffered from a severe COVID-19 infection and 11,264 (2.8%) from a non-severe infection, between 16 March 2020 and 16 March 2021. Overall, the odds of severe COVID-19 was significantly higher among individuals living alone (adjusted odds ratio: 1.24 [95% confidence interval: 1.14 to 1.36], or living in a household of three or more individuals (adjusted odds ratio: 1.28 [1.17 to 1.39], when compared to individuals living in a household of two. For non-severe COVID-19 infection, individuals living in a single-occupancy household had lower odds compared to those living in a household of two (adjusted odds ratio: 0.88 [0.82 to 0.93]. Conclusions Odds of severe or non-severe COVID-19 infection were associated with household size. Increasing understanding of why certain households are more at risk is important for limiting spread of the infection.

IntroductionType 2 diabetes and cardiovascular disease are highly prevalent conditions, for which health education represents a significant component of management. A wide variety of health education formats are available and preferences of people with diabetes or cardiovascular disease for certain formats may vary between sociodemographic groups.MethodsA questionnaire was deployed to collect information about the influence of six sociodemographic factors of interest on the preference for health education format in type 2 diabetes and/or cardiovascular disease. Chi-squared tests were used to examine the distribution of preferences between groups. The characteristics of the population preferring the online format were then examined in more detail using logistic regression.ResultsResponses were received from 1559 participants. Overall the preferred modality was one to one learning from a doctor or nurse (67%). Age, gender, diagnosis and educational level all affected the preferences expressed. The characteristics showing most consistent and significant influence were age and educational level. Overall, 29% ranked the online modality highly (1 or 2). This group were more likely to be aged under 65 (p

Background Updatable estimates of COVID-19 onset, progression, and trajectories underpin pandemic mitigation efforts. To identify and characterise disease trajectories, we aimed to define and validate ten COVID-19 phenotypes from nationwide linked electronic health records (EHR) using an extensible framework. Methods In this cohort study, we used eight linked National Health Service (NHS) datasets for people in England alive on Jan 23, 2020. Data on COVID-19 testing, vaccination, primary and secondary care records, and death registrations were collected until Nov 30, 2021. We defined ten COVID-19 phenotypes reflecting clinically relevant stages of disease severity and encompassing five categories: positive SARS-CoV-2 test, primary care diagnosis, hospital admission, ventilation modality (four phenotypes), and death (three phenotypes). We constructed patient trajectories illustrating transition frequency and duration between phenotypes. Analyses were stratified by pandemic waves and vaccination status. Findings Among 57 032 174 individuals included in the cohort, 13 990 423 COVID-19 events were identified in 7 244 925 individuals, equating to an infection rate of 12·7% during the study period. Of 7 244 925 individuals, 460 737 (6·4%) were admitted to hospital and 158 020 (2·2%) died. Of 460 737 individuals who were admitted to hospital, 48 847 (10·6%) were admitted to the intensive care unit (ICU), 69 090 (15·0%) received non-invasive ventilation, and 25 928 (5·6%) received invasive ventilation. Among 384 135 patients who were admitted to hospital but did not require ventilation, mortality was higher in wave 1 (23 485 [30·4%] of 77 202 patients) than wave 2 (44 220 [23·1%] of 191 528 patients), but remained unchanged for patients admitted to the ICU. Mortality was highest among patients who received ventilatory support outside of the ICU in wave 1 (2569 [50·7%] of 5063 patients). 15 486 (9·8%) of 158 020 COVID-19-related deaths occurred within 28 days of the first COVID-19 event without a COVID-19 diagnoses on the death certificate. 10 884 (6·9%) of 158 020 deaths were identified exclusively from mortality data with no previous COVID-19 phenotype recorded. We observed longer patient trajectories in wave 2 than wave 1. Interpretation Our analyses illustrate the wide spectrum of disease trajectories as shown by differences in incidence, survival, and clinical pathways. We have provided a modular analytical framework that can be used to monitor the impact of the pandemic and generate evidence of clinical and policy relevance using multiple EHR sources. Funding British Heart Foundation Data Science Centre, led by Health Data Research UK.

Funder: National Institute for Health Research; doi: http://dx.doi.org/10.13039/501100000272; Grant(s): NIHR Applied Research Collaboration East Midlands (ARC EM), NIHR Leicester Biomedical Research Centre, Biomedical Research Centre Cambridge: Nutrition, Diet, and Lifestyle Research Theme (IS-BRC-1215-20014), Background: Pre-existing comorbidities have been linked to SARS-CoV-2 infection but evidence is sparse on the importance and pattern of multimorbidity (2 or more conditions) and severity of infection indicated by hospitalisation or mortality. We aimed to use a multimorbidity index developed specifically for COVID-19 to investigate the association between multimorbidity and risk of severe SARS-CoV-2 infection. Methods: We used data from the UK Biobank linked to laboratory confirmed test results for SARS-CoV-2 infection and mortality data from Public Health England between March 16 and July 26, 2020. By reviewing the current literature on COVID-19 we derived a multimorbidity index including: (1) angina; (2) asthma; (3) atrial fibrillation; (4) cancer; (5) chronic kidney disease; (6) chronic obstructive pulmonary disease; (7) diabetes mellitus; (8) heart failure; (9) hypertension; (10) myocardial infarction; (11) peripheral vascular disease; (12) stroke. Adjusted logistic regression models were used to assess the association between multimorbidity and risk of severe SARS-CoV-2 infection (hospitalisation/death). Potential effect modifiers of the association were assessed: age, sex, ethnicity, deprivation, smoking status, body mass index, air pollution, 25‐hydroxyvitamin D, cardiorespiratory fitness, high sensitivity C-reactive protein. Results: Among 360,283 participants, the median age was 68 [range 48–85] years, most were White (94.5%), and 1706 had severe SARS-CoV-2 infection. The prevalence of multimorbidity was more than double in those with severe SARS-CoV-2 infection (25%) compared to those without (11%), and clusters of several multimorbidities were more common in those with severe SARS-CoV-2 infection. The most common clusters with severe SARS-CoV-2 infection were stroke with hypertension (79% of those with stroke had hypertension); diabetes and hypertension (72%); and chronic kidney disease and hypertension (68%). Multimorbidity was independently associated with a greater risk of severe SARS-CoV-2 infection (adjusted odds ratio 1.91 [95% confidence interval 1.70, 2.15] compared to no multimorbidity). The risk remained consistent across potential effect modifiers, except for greater risk among older age. The highest risk of severe infection was strongly evidenced in those with CKD and diabetes (4.93 [95% CI 3.36, 7.22]). Conclusion: The multimorbidity index may help identify individuals at higher risk for severe COVID-19 outcomes and provide guidance for tailoring effective treatment.

Funder: UKRI Science and Technology Facilities Council, OBJECTIVE: To quantify the association between accelerometer-assessed physical activity and coronavirus disease 2019 (COVID-19) outcomes. METHODS: Data from 82,253 UK Biobank participants with accelerometer data (measured 2013-2015), complete covariate data, and linked COVID-19 data from March 16, 2020, to March 16, 2021, were included. Two outcomes were investigated: severe COVID-19 (positive test result from in-hospital setting or COVID-19 as primary cause of death) and nonsevere COVID-19 (positive test result from community setting). Logistic regressions were used to assess associations with moderate to vigorous physical activity (MVPA), total activity, and intensity gradient. A higher intensity gradient indicates a higher proportion of vigorous activity. RESULTS: Average MVPA was 48.1 (32.7) min/d. Physical activity was associated with lower odds of severe COVID-19 (adjusted odds ratio per standard deviation increase: MVPA, 0.75 [95% CI, 0.67 to 0.85]; total, 0.83 [0.74 to 0.92]; intensity, 0.77 [0.70 to 0.86]), with stronger associations in women (MVPA, 0.63 [0.52 to 0.77]; total, 0.76 [0.64 to 0.90]; intensity, 0.63 [0.53 to 0.74]) than in men (MVPA, 0.84 [0.73 to 0.97]; total, 0.88 [0.77 to 1.01]; intensity, 0.88 [0.77 to 1.00]). In contrast, when mutually adjusted, total activity was associated with higher odds of a nonsevere infection (1.10 [1.04 to 1.16]), whereas the intensity gradient was associated with lower odds (0.91 [0.86 to 0.97]). CONCLUSION: Odds of severe COVID-19 were approximately 25% lower per standard deviation (∼30 min/d) MVPA. A greater proportion of vigorous activity was associated with lower odds of severe and nonsevere infections. The association between total activity and higher odds of a nonsevere infection may be through greater community engagement and thus more exposure to the virus. Results support calls for public health messaging highlighting the potential of MVPA for reducing the odds of severe COVID-19.

ImportanceObesity and ethnicity are well characterised risk factors for severe COVID-19 outcomes, but the differential effects of obesity on COVID-19 outcomes by race/ethnicity has not been examined robustly in the general population.ObjectiveTo investigate the association between body mass index (BMI) and COVID-19 mortality across different ethnic groups.Design, Setting, and ParticipantsThis is a retrospective cohort study using linked national Census, electronic health records and mortality data for English adults aged 40 years or older who were alive at the start of pandemic (24th January 2020).ExposuresBMI obtained from electronic health records. Self-reported ethnicity (white, black, South Asian, other) was the effect-modifying variable.Main Outcomes and MeasuresCOVID-19 related death identified by ICD-10 codes U07.1 or U07.2 mentioned on the death certificate from 24th January 2020 until December 28th 2020.ResultsThe analysis included white (n = 11,074,708; mean age 61.9 [±13.4] years; 54% women), black (n = 416,542; 56.4 [±11.7] years; 57% women), South Asian (621,691; 55.7 [±12.4] years; 51% women) and other (n = 478,196; 55.3 [±11.6] years; 55% women) ethnicities with linked BMI data. The association between BMI and COVID-19 mortality was stronger in ethnic minority groups. Compared to a BMI of 22.5 kg/m2 in white ethnicities, the adjusted HR for COVID-19 mortality at a BMI of 30 kg/m2 in white, black, South Asian and other ethnicities was 0.95 (95% CI: 0.87-1.03), 1.72 (1.52-1.94), 2.00 (1.78-2.25) and 1.39 (1.21-1.61), respectively. The estimated risk of COVID-19 mortality at a BMI of 40 kg/m2 in white ethnicities (HR = 1.73) was equivalent to the risk observed at a BMI of 30.1 kg/m2, 27.0 kg/m2, and 32.2 kg/m2 in black, South Asian and other ethnic groups, respectively.ConclusionsThis population-based study using linked Census and electronic health care records demonstrates that the risk of COVID-19 mortality associated with obesity is greater in ethnic minority groups compared to white populations.QuestionDoes the association between BMI and COVID-19 mortality vary by ethnicity?FindingsIn this study of 12.6 million adults, BMI was associated with COVID-19 in all ethnicities, but with stronger associations in ethnic minority populations such that the risk of COVID-19 mortality for a BMI of 40 kg/m2 in white ethnicities was observed at a BMI of 30.1 kg/m2, 27.0 kg/m2, and 32.2 kg/m2 in black, South Asian and other ethnicities, respectively.MeaningBMI is a stronger risk factor for COVID-19 mortality in ethnic minorities. Obesity management is therefore a priority in these populations.

AIM To investigate factors associated with delays in receiving glucose-lowering therapy in patients newly diagnosed with type 2 diabetes mellitus (T2DM), and explore the preferential order and time of intensifications. MATERIALS AND METHODS Retrospective cohort study including 120 409 adults with T2DM initiating first- to fourth-line glucose-lowering therapy in primary care between 2000 and 2018, using the UK Clinical Practice Research Datalink linked to Hospital Episode Statistics, Office of National Statistics death registration, and 2007 Index of Multiple Deprivation data. Associations were investigated using time-to-event analysis. RESULTS The longest delays to prescription of first-line therapy were observed in older patients, of black or other ethnicities, and with multimorbidity. People from the most deprived areas received earlier first-line treatment than those from the least deprived areas. The majority were treated with metformin (82.4%) as the first-line prescription, sulphonylurea (50.4%) as second-line, dipeptidyl peptidase-4 inhibitor (27.7%) as third-line, and insulin (28.0%) as fourth-line. In the past 5 years, there was an increase in prescriptions of dipeptidyl peptidase-4-inhibitor and sodium-glucose transport protein-2 inhibitor. The median time was 0.5 years for first-line prescription, 4.1 for second-line, 4.6 for third-line and 4.7 for fourth-line. After T2DM diagnosis, 25% of patients developed cardiovascular disease and non-cardiovascular disease complications within a median time of 12-14 years, and received intensification 5-6 years later. CONCLUSIONS Within the complex challenges of managing blood glucose levels and risk of additional comorbidities, future health care research and guidelines should focus on overcoming therapeutic inertia particularly at an earlier stage for older patients, from ethnic minorities and with multimorbidities.

BackgroundPhysical activity (PA) levels among adolescent girls in the UK are low. ‘Girls Active’, developed by the Youth Sport Trust (YST), has been designed to increase girls’ PA levels.ObjectiveTo understand the effectiveness and cost-effectiveness of the Girls Active programme.DesignA two-arm cluster randomised controlled trial.SettingState secondary schools in the Midlands, UK.ParticipantsGirls aged between 11 and 14 years.InterventionGirls Active involves teachers reviewing PA, sport and physical education provision, culture and practices in their school; attending training; creating action plans; and effectively working with girls as peer leaders to influence decision-making and to promote PA to their peers. Support from a hub school and the YST is offered.Main outcome measuresThe change in objectively measured moderate to vigorous intensity PA (MVPA) levels at 14 months. Secondary outcomes included changes in overall PA level (mean acceleration), light PA levels, sedentary time, body composition and psychosocial outcomes. Cost-effectiveness and process evaluation (qualitative and quantitative) data were collected.ResultsTwenty schools and 1752 pupils were recruited; 1211 participants provided complete primary outcome data at 14 months. No difference was found in mean MVPA level between groups at 14 months [1.7 minutes/day, 95% confidence interval (CI) –0.8 to 4.3 minutes/day], but there was a small difference in mean MVPA level at 7 months (2.4 minutes/day, 95% CI 0.1 to 4.7 minutes/day). Significant differences between groups were found at 7 months, but not at 14 months, in some of the objective secondary outcomes: overall PA level represented by average acceleration (1.39 mg, 95% CI 0.1 to 2.2 mg), after-school sedentary time (–4.7 minutes/day, 95% CI –8.9 to –0.6 minutes/day), overall light PA level (5.7 minutes/day, 95% CI 1.0 to 10.5 minutes/day) and light PA level on school days (4.5 minutes/day, 95% CI 0.25 to 8.75 minutes/day). Minor, yet statistically significant, differences in psychosocial measures at 7 months were found in favour of control schools. Significant differences in self-esteem and identified motivation in favour of intervention schools were found at 7 and 14 months, respectively. Subgroup analyses showed a significant effect of the intervention for those schools with higher numbers of pupils at 14 months. Girls Active was well received by teachers, and they reported that implemented strategies and activities were having a positive impact in schools. Barriers to implementation progress included lack of time, competing priorities and the programme flexibility. Implementation costs ranged from £2054 (£23/pupil) to £8545 (£95/pupil) per school. No differences were found between groups for health-related quality-of-life scores or frequencies, or for costs associated with general practitioner, school nurse and school counsellor use.ConclusionsGirls Active may not have had an effect on the random 90 girls per school included in the evaluation. Although we included a diverse sample of schools, the results may not be generalisable to all schools. Girls Active was viewed positively but teachers did not implement as many aspects of the programme as they wanted. The intervention was unlikely to have a wide impact and did not have an impact on MVPA level at 14 months. Capitalising on the opportunities of a flexible programme like this, while also learning from the stated barriers to and challenges of long-term implementation that teachers face, is a priority for research and practice.Trial registrationCurrent Controlled Trials ISRCTN10688342.FundingThis project was funded by the National Institute for Health Research (NIHR) Public Health Research programme and will be published in full inPublic Health Research; Vol. 7, No. 5. See the NIHR Journals Library website for further project information. The YST funded the intervention. This study was undertaken in collaboration with the Leicester Clinical Trials Unit, a UK Clinical Research Collaboration-registered clinical trials unit in receipt of NIHR Clinical Trials Unit support funding. Neither the YST nor the NIHR Clinical Trials Unit had any involvement in the Trial Steering Committee, data analysis, data interpretation, data collection or writing of the report. The University of Leicester authors are supported by the NIHR Leicester–Loughborough Biomedical Research Unit (2012–17), the NIHR Leicester Biomedical Research Centre (2017–22) and the Collaboration for Leadership in Applied Health Research and Care East Midlands. These funders had no involvement in the Trial Steering Committee, the data analysis, data interpretation, data collection or writing of the report.

Background People from South Asian and black minority ethnic groups are disproportionately affected by the COVID-19 pandemic. It is unknown whether deprivation mediates this excess ethnic risk. Methods We used UK Biobank with linked COVID-19 outcomes occurring between 16th March 2020 and 24th August 2020. A four-way decomposition mediation analysis was used to model the extent to which the excess risk of testing positive, severe disease and mortality for COVID-19 in South Asian and black individuals, relative to white individuals, would be eliminated if levels of high material deprivation were reduced within the population. Results We included 15 044 (53.0% women) South Asian and black and 392 786 (55.2% women) white individuals. There were 151 (1.0%) positive tests, 91 (0.6%) severe cases and 31 (0.2%) deaths due to COVID-19 in South Asian and black individuals compared with 1471 (0.4%), 895 (0.2%) and 313 (0.1%), respectively, in white individuals. Compared with white individuals, the relative risk of testing positive for COVID-19, developing severe disease and COVID-19 mortality in South Asian and black individuals were 2.73 (95% CI: 2.26, 3.19), 2.96 (2.31, 3.61) and 4.04 (2.54, 5.55), respectively. A hypothetical intervention moving the 25% most deprived in the population out of deprivation was modelled to eliminate between 40 and 50% of the excess risk of all COVID-19 outcomes in South Asian and black populations, whereas moving the 50% most deprived out of deprivation would eliminate over 80% of the excess risk of COVID-19 outcomes. Conclusions The excess risk of COVID-19 outcomes in South Asian and black communities could be substantially reduced with population level policies targeting material deprivation.

BackgroundHealth and key workers are at an increased risk of developing severe COVID-19; it is not known, however, if this risk is exacerbated in those with irregular work patterns. We aimed to investigate the risk of severe COVID-19 in health and shift workers.MethodsWe included UK Biobank participants in employment or self-employed at baseline and with linked COVID-19 data to 31st August 2020. Participants were grouped as neither a health worker nor shift worker (reference category), health worker only, shift worker only, or both and associations with severe COVID-19 investigated in logistic regressions.FindingsOf 235,685 participants (81·5% neither health nor shift worker, 1·4% health worker only, 16·9% shift worker only, and 0·3% both), there were 580 (0·25%) cases of severe COVID-19. The risk of severe COVID-19 was higher in health workers (adjusted odds ratio: 2.32 [95% CI: 1·33, 4·05]; shift workers (2·06 [1·72, 2·47]); and in health workers who worked shifts (7·56 [3·86, 14·79]). Being both a health worker and a shift worker had a possible greater impact on the risk severe COVID-19 in South Asian and Black and African Caribbean ethnicities compared to White individuals.InterpretationBoth health and shift work were independently associated with over twice the risk of severe COVID-19; the risk was over seven times higher in health workers who work shifts. Vaccinations, therapeutic and preventative options should take into consideration not only health and key worker status but also shift worker status.FundingNational Institute for Health Research, UK Research and Innovation.Research in contextEvidence before this studyThe risk of developing severe COVID-19 is greater in occupational groups with higher levels of viral exposure, e.g. health and key workers. We searched PubMed and medRxiv up to December 8, 2020 for papers on shift work patterns, health work and incidence of COVID-19 using the keywords “COVID-19”, “SARS-CoV-2”, “shift work” “health worker”. Recent evidence suggests shift workers are also at increased risk of severe COVID-19 but it is not clear if the risk is exacerbated in those who work shifts in healthcare.Added value of this studyThis study uses data from UK Biobank, a prospective cohort of >500,000 adults aged 40-69 years with baseline assessments between March 2006 and July 2010. Participants’ occupation was categorised according to whether or not they were health workers and/or shift workers at baseline. Results showed that being a health worker, or working shifts, were similarly and independently associated with over twice the population level risk of severe COVID-19 independent of age, sex, ethnicity, deprivation and co-morbidities. The risk was seven times higher in health workers with shift working patterns. The impact of health and shift work tended to be higher in males and in minority ethnic groups, who are already at an increased risk of severe COVID-19. In people over the age of retirement, the risk of developing severe COVID-19 associated with baseline health worker status was no longer apparent, suggesting the risk is likely explained by exposure to the virus. However, the elevated risk associated with baseline shift worker status persisted, albeit attenuated.Implications of all the available evidenceShift workers are at elevated risk of developing severe COVID-19. The persistence of an elevated risk in people who are now over retirement age, but had a shift worker status at baseline, suggests the risk may not be fully explained by increased exposure to the virus. Vaccination, therapeutic and prevention programmes are being prioritised for health care workers. Our data suggests that shift workers should also be prioritised for these preventive measures.

We aim to rapidly assess public attitudes to attending hospital across the UK for research purposes and clinical appointments, and attitudes to vaccine research using an online survey and online focus groups. Online surveys offer a rapid and, during a global pandemic, safe way of collecting information about public opinion. However, they are flawed in relying on self-report, and subject to abuse by means of multiple responses from one person or even merely random responses. Sampling methods associated with online surveys require opt-in so respondents are likely to be motivated, and specifically interested in research. Thus the methodology will attract a 'warm' audience when discussing questions about research as is standard in public involvement.Online focus groups with public involvement volunteers will also experience similar issues with generalisability. All online methodologies will exclude those without access to the online world.

In order to inform clinical and research practice in secondary care in light of the COVID-19 pandemic, an online survey was used to collect public opinions on attending hospitals. The survey link was circulated via the National Institute for Health Research (NIHR) Public Involvement (PPI) Leads network and social media. 402 people completed the survey. Participants age ranged from the 18-85+, with the majority (337 (84%)) aged between 35 to 74 years. There were a higher number of women (77%) compared to men (23%); and were mainly White European (91%) compared to BAME (6%), or other (2%).Data collection included self-identified risk status due to comorbidity or age, and 100 point Likert-type scales to measures feelings of safety, factors affecting feelings of safety, intention to participate in research, comfort with new ways of working and attitudes to research.Results for feelings of safety scales indicate two distinct groups; one of respondents who felt quite safe and one of those who did not. Implementation of COVID-19 related safety measures such as social distancing, use of PPE and cleaning were strongly supported by most respondents. There was ambivalence around less certain measures such as regular staff antigen and antibody testing. Respondents were most likely to participate in research related to their own condition, COVID-19 research and vaccine research, but less likely to participate in healthy volunteer research, especially if suffering from a pre-existing comorbidity identified with increased risk or were female. There was general agreement that participants are comfortable with new ways of working, such as remote consultation, though women and BAME respondents were less comfortable. Findings raise concerns for health inequalities already impacting some groups in the pandemic. The role of clinical necessity and personal benefit support the reopening of services in line with clinical necessity. Moderate caution in respect of vaccine research relative to patient-participant research presents a challenge for pending recruitment demands, and would benefit from qualitative research to explore themes and concerns in more depth and support development and targeting of key messaging.SUMMARY BOXWhat is already known on this topic?Very little is known about public perceptions of risk of exposure to COVID-19 and engagement with clinical and research provision in secondary care.This research explores public perspectives in five key areas in order to inform health policy and both population and individual communication regarding attending secondary care sites for clinical and research activities.What this study adds?Insight into public risk perceptions specific to attending hospital during the COVID-19 pandemicThere are distinct groups of people who do feel safe and those who do notUse of personal protective equipment, social distancing measures and cleaning are essential to supporting feelings of safety and are well supportedRecruitment to vaccine and COVID-19 studies presents challenges, especially amongst women and BAME respondentsMost people are very comfortable with new ways of working (i.e. remote/digital)There is very strong support for continued health science researchInsight into the differences in perceptions and attitudes by individual risk status (due to age or comorbidity), sex and ethnicity.

Evidence to date has focused on assessing the individual prevalence and impact of comorbidities in patients with coronavirus disease (COVID-19).[1]We aimed to investigate the pattern of the most prevalent combinations of multimorbidity(i.e. presence of two or more pre-existing chronic diseases) in those infected with SARS-CoV-2.

Routine care for chronic disease is an ongoing major challenge. We aimed to evaluate the global impact of COVID-19 on routine care for chronic diseases. An online survey was posted 31 March to 23 April 2020 targeted at healthcare professionals. 202 from 47 countries responded. Most reported change in routine care to virtual communication. Diabetes, chronic obstructive pulmonary disease, and hypertension were the most impacted conditions due to reduction in access to care. 80% reported the mental health of their patients worsened during COVID-19. It is important routine care continues in spite of the pandemic, to avoid a rise in non-COVID-19-related morbidity and mortality.

Aims COVID‐19 is a global pandemic that as of the 4th May has registered over 3 585 711 confirmed cases and 248 780 deaths. This review aims to estimate the prevalence of both cardiometabolic and other co‐morbidities in patients with COVID‐19 infection, and to estimate the increased risk of severity and mortality in people with co‐morbidities. Materials and Methods Medline, Scopus and the World Health Organisation (WHO) website for Global research on COVID‐19 were searched from January 2019 up to April 23, 2020. Study inclusion was restricted to English language publications, original articles that reported prevalence of co‐morbidities in individuals with COVID‐19 disease, and case‐series >10 patients. 18 studies were selected for inclusion. Data were analysed using random effects meta‐analysis models. Results Eighteen studies with a total of 14 558 individuals were identified. The pooled prevalence for co‐morbidities in patients with COVID‐19 disease was 22.9% (95% CI: 15.8 to 29.9) for hypertension; 11.5% (9.7 to 13.4) for diabetes; and 9.7% (6.8 to 12.6) for cardiovascular disease (CVD). For chronic obstructive pulmonary disease (COPD), chronic kidney disease (CKD), cerebrovascular disease, and cancer, the pooled prevalences were all less than 4%. With the exception of cerebrovascular disease, all other co‐morbidities had a significantly increased risk for having severe COVID‐19. In addition, the risk of mortality was significantly increased in individuals with CVD, COPD, CKD, cerebrovascular disease, and cancer. Conclusions In individuals with COVID‐19, the presence of co‐morbidities (both cardiometabolic and other) is associated with a higher risk of severe COVID‐19 and mortality. These findings have important implications for the public health with regards to risk stratification and future planning. This article is protected by copyright. All rights reserved.

Background Whether a healthy lifestyle impacts longevity in the presence of multimorbidity is unclear. We investigated the associations between healthy lifestyle and life expectancy in people with and without multimorbidity. Methods and findings A total of 480,940 middle-aged adults (median age of 58 years [range 38–73], 46% male, 95% white) were analysed in the UK Biobank; this longitudinal study collected data between 2006 and 2010, and participants were followed up until 2016. We extracted 36 chronic conditions and defined multimorbidity as 2 or more conditions. Four lifestyle factors, based on national guidelines, were used: leisure-time physical activity, smoking, diet, and alcohol consumption. A combined weighted score was developed and grouped participants into 4 categories: very unhealthy, unhealthy, healthy, and very healthy. Survival models were applied to predict life expectancy, adjusting for ethnicity, working status, deprivation, body mass index, and sedentary time. A total of 93,746 (19.5%) participants had multimorbidity. During a mean follow-up of 7 (range 2–9) years, 11,006 deaths occurred. At 45 years, in men with multimorbidity an unhealthy score was associated with a gain of 1.5 (95% confidence interval [CI] −0.3 to 3.3; P = 0.102) additional life years compared to very unhealthy score, though the association was not significant, whilst a healthy score was significantly associated with a gain of 4.5 (3.3 to 5.7; P < 0.001) life years and a very healthy score with 6.3 (5.0 to 7.7; P < 0.001) years. Corresponding estimates in women were 3.5 (95% CI 0.7 to 6.3; P = 0.016), 6.4 (4.8 to 7.9; P < 0.001), and 7.6 (6.0 to 9.2; P < 0.001) years. Results were consistent in those without multimorbidity and in several sensitivity analyses. For individual lifestyle factors, no current smoking was associated with the largest survival benefit. The main limitations were that we could not explore the consistency of our results using a more restrictive definition of multimorbidity including only cardiometabolic conditions, and participants were not representative of the UK as a whole. Conclusions In this analysis of data from the UK Biobank, we found that regardless of the presence of multimorbidity, engaging in a healthier lifestyle was associated with up to 6.3 years longer life for men and 7.6 years for women; however, not all lifestyle risk factors equally correlated with life expectancy, with smoking being significantly worse than others., In a longitudinal study, Yogini Chudasama and colleagues investigate associations between life expectancy and healthy lifestyles with multimorbidity in the UK Biobank cohort., Author summary Why was this study done? People with multimorbidity (presence of 2 or more chronic conditions) have poorer health outcomes and a higher mortality risk compared to people without multimorbidity. A healthy lifestyle has been associated with a longer life expectancy. To our knowledge, no study to date has investigated this relationship in relation to the presence of multimorbidity. Most studies used a combined score that did not account for the differential impact of each lifestyle factor on the risk of death. What did the researchers do and find? We investigated the association between healthy lifestyle and individual risk factors with life expectancy in relation to the presence of multimorbidity. We found that an overall healthy lifestyle largely counterbalances the negative association between multimorbidity and life expectancy. What do these findings mean? A healthier lifestyle is consistently associated with a longer life expectancy across various individual risks and irrespective of the presence of multiple long-term medical conditions. Public health recommendations about a healthy lifestyle to reduce the risk of developing chronic long-term conditions equally apply to individuals who have already multimorbidity.

Background Whether and to what extent leisure-time physical activity at the recommended levels of 150-min moderate activity is associated with survival in people with cardiometabolic multimorbidity and depression is unknown. Methods UK Biobank participants were classified into groups: (i) no disease; (ii) diabetes; (iii) cardiovascular disease (CVD); (iv) depression; (v) diabetes and CVD; (vi) diabetes and depression; (vii) CVD and depression; (viii) diabetes, CVD and depression. Leisure-time physical activity was categorized as active (meeting recommendations) or inactive. Survival models were applied to estimate life expectancy. Results A total of 480 940 participants were included (median age, 58 years; 46% men; 95% white), of whom 74% with cardiometabolic multimorbidity and depression were inactive. During a mean follow-up of 7 years, 11 006 deaths occurred. At age of 45 years, being physically active was associated with 2.34 (95% confidence interval: 0.93, 3.54) additional years of life compared with being inactive in participants with diabetes; corresponding estimates were 2.28 (1.40, 3.16) for CVD; 2.15 (0.05, 4.26) for diabetes and CVD; and 1.58 (1.27, 1.89) for no disease. Participants with a combination of diabetes, CVD and depression, being active was associated with 6.81 (-1.50, 15.31) additional years compared with being inactive; corresponding estimates were 3.07 (-2.46, 8.59) for diabetes and depression; 2.34 (-1.24, 5.91) for CVD and depression; and 0.80 (-0.46, 2.05) for depression. A similar pattern was found at 65 years. Conclusions Meeting the recommended level of physical activity was associated with a longer life expectancy in people with cardiometabolic multimorbidity but not in those with depression.

Objective To evaluate the effectiveness of a structured education programmes in women with polycystic ovary syndrome (PCOS). Methods Single-centre, randomised controlled trial, testing a single exposure to a group-based, face-to-face, structured education programme. Inclusion criteria were women with PCOS, aged 18–49 years inclusive and body mass index ≥23 kg/m2 for black and minority ethnicities or ≥25 kg/m2 for white Europeans. Primary outcome was step-count/day at 12 months. Secondary outcomes included indices of physical activity, cardiovascular risk factors, quality of life (QoL) and illness perception (IP). Results 161 women were included (78 control, 83 intervention); 69% white; mean age 33.4 (s.d. 7.6) years, of whom 100 (48 intervention; 52 control) attended their 12-month visit (38% attrition). 77% of the intervention arm attended the education programme. No significant change in step-count was observed at 12 months (mean difference: +351 steps/day (95% confidence interval −481, +1183); P = 0.40). No differences were found in biochemical or anthropometric outcomes. The education programme improved participants’ IP in 2 dimensions: understanding their PCOS (P P P P P P Discussion A single exposure to structured education programme did not increase physical activity or improve biochemical markers in overweight and obese women with PCOS. However, providing a structured education in parallel to routine medical treatment can be beneficial for participants’ understanding of their condition, reducing their anxiety and improving their QoL.

Background Multimorbidity is an emerging public health priority. Physical activity (PA) is recommended as one of the main lifestyle behaviours, yet the benefits of PA for people with multimorbidity are unclear. We assessed the benefits of PA on mortality and life expectancy in people with and without multimorbidity. Methods Using the UK Biobank dataset, we extracted data on 36 chronic conditions and defined multimorbidity as (a) 2 or more conditions, (b) 2 or more conditions combined with self-reported overall health, and (c) 2 or more top-10 most common comorbidities. Leisure-time PA (LTPA) and total PA (TPA) were measured by questionnaire and categorised as low (

Table S1. Number of participants with comorbidity. Table S2. Number of participants by the total number of chronic conditions. Table S3. Most to least prevalent chronic conditions by overall health rating and multimorbidity status. Table S4. Participant characteristics by overall health status and multimorbidity. Table S5. Number of participants with the top-10 comorbidity. Table S6. Characteristics by participants with and without top-10 comorbidity. Table S7. Participant characteristics who undertook the objective physical activity by multimorbidity status. Table S8. Participants characteristics by completion of the objective physical activity measurements. Table S9. Self-reported physical activity in tertiles and years of life gained at the age of 45 years by multimorbidity status. Table S10. Association between continuously measured (log-transformed) physical activity and mortality. Table S11. Association between physical activity and mortality for participants with good or poor overall health rating and multimorbidity status, and years of life gained at the age of 45 years. Table S12. Association between physical activity and mortality for participants with and without top-10 comorbidity, and years of life gained at the age of 45 years. Table S13. Association between objective physical activity in tertiles of time accumulated above 250 mg of total acceleration and mortality. Figure S1. Most to least prevalent chronic conditions used to define multimorbidity (n = 491,939). Figure S2. Association between self-reported physical activity in tertiles and mortality. Figure S3. Association between objective physical activity and mortality, taking the time at objective PA assessment as the start of the follow-up in participants with and without multimorbidity. (DOCX 2092 kb)

Methods S1 List of the 36 chronic conditions included within the definition of multimorbidity. Methods S2 Physical activity measurements. Methods S3 Additional methods for sociodemographic and lifestyle factors. Methods S4 Summary of main and sensitivity analyses. (DOCX 31 kb)

The global coronavirus pandemic has precipitated a rapid unprecedented research response, including investigations into risk factors for COVID-19 infection, severity, or death. However, results from this research have produced heterogeneous findings, including articles published in Wellcome Open Research. Here, we use ethnicity, obesity, and smoking as illustrative examples to demonstrate how a research question can produce very different answers depending on how it is framed. For example, these factors can be both strongly associated or have a null association with death due to COVID-19, even when using the same dataset and statistical modelling. Highlighting the reasons underpinning this apparent paradox provides an important framework for reporting and interpreting ongoing COVID-19 research.

Background Globally, adolescent girls’ physical activity (PA) levels are low. The ‘Girls Active’ secondary school-based programme, developed by the Youth Sport Trust, aims to increase PA in adolescent girls. This paper explores the effectiveness of the ‘Girls Active’ school-based PA programme. Methods A random sample of girls aged 11–14 from 20 secondary schools (Midlands, UK) participated in a two-arm cluster randomised controlled trial. Ten schools received Girls Active and 10 continued with usual practice. Measurements were taken at baseline, seven- and 14-month follow-up. Primary outcome: wrist-worn accelerometer measured moderate- to vigorous-intensity PA (MVPA). Secondary outcomes: overall PA, light PA, sedentary time, body composition, and psychosocial outcomes. Generalised estimating equations, adjusted for school cluster and potential confounders, were used and A priori subgroup analysis was undertaken. Micro-costing and cost-consequence analyses were conducted using bespoke collection methods on programme delivery information. Outcomes for the cost-consequence analysis were health related quality of life measured by the Child Health Utility-9D and service use. Results Overall, 1752 pupils participated, 1211 (69.1%) provided valid 14-month accelerometer data. No difference in MVPA (mins/day; 95% confidence intervals) was found at 14 months (1.7; -0.8 to 4.3), there was at seven months (2.4; 0.1 to 4.7). Subgroup analyses showed significant intervention effects on 14-month in larger schools (3.9; 1.39 to 6.09) and in White Europeans (3.1; 0.60 to 6.02) and in early maturers (5.1; 1.69 to 8.48) at seven months. The control group did better in smaller schools at 14-months (-4.38; -7.34 to -1.41). Significant group differences were found in 14-month identified motivation (-0.09; -0.18 to -0.01) and at seven months in: overall PA (1.39 mg/day; 0.1 to 2.2), after-school sedentary time (-4.7; -8.9 to -0.6), whole day (5.7; 1.0 to 10.5) and school day (4.5; 0.25 to 8.75) light PA, self-esteem. Small, statistically significant, differences in some psychosocial variables favoured control schools. Micro-costing demonstrated that delivering the programme resulted in a range of time and financial costs at each school. Cost-consequence analysis demonstrated no effect of the programme for health related quality of life or service use. Conclusions Compared with usual practice, ‘Girls Active’ did not affect 14-month MVPA. Trial registration ISRCTN10688342

Background: Observational evidence suggests there is an association between air pollution and type 2 diabetes; however, there is high risk of bias. Objective: To investigate the association between air pollution and type 2 diabetes, while reducing bias due to exposure assessment, outcome assessment, and confounder assessment. Methods: Data were collected from 10,443 participants in three diabetes screening studies in Leicestershire, UK. Exposure assessment included standard, prevailing estimates of outdoor nitrogen dioxide and particulate matter concentrations in a 1×1km area at the participant's home postcode. Three-year exposure was investigated in the primary analysis and one-year exposure in a sensitivity analysis. Outcome assessment included the oral glucose tolerance test for type 2 diabetes. Confounder assessment included demographic factors (age, sex, ethnicity, smoking, area social deprivation, urban or rural location), lifestyle factors (body mass index and physical activity), and neighbourhood green space. Results: Nitrogen dioxide and particulate matter concentrations were associated with type 2 diabetes in unadjusted models. There was no statistically significant association between nitrogen dioxide concentration and type 2 diabetes after adjustment for demographic factors (odds: 1.08; 95% CI: 0.91, 1.29). The odds of type 2 diabetes was 1.10 (95% CI: 0.92, 1.32) after further adjustment for lifestyle factors and 0.91 (95% CI: 0.72, 1.16) after yet further adjustment for neighbourhood green space. The associations between particulate matter concentrations and type 2 diabetes were also explained away by demographic factors. There was no evidence of exposure definition bias. Conclusions: Demographic factors seemed to explain the association between air pollution and type 2 diabetes in this cross-sectional study. High-quality longitudinal studies are needed to improve our understanding of the association. Keywords: Air pollutants, Diabetes mellitus, type 2, Cross-sectional studies