Socioeconomic inequalities of Long COVID: findings from a population-based survey in the United Kingdom

ObjectiveTo estimate the risk of Long COVID by socioeconomic deprivation and to further examine the socioeconomic inequalities in Long COVID by sex and occupational groups. DesignWe analysed data from the COVID-19 Infection Survey conducted by the Office for National Statistics between 26/04/2020 and 31/01/2022. This is the largest and nationally representative survey of COVID-19 in the UK and provides uniquely rich, contemporaneous, and longitudinal data on occupation, health status, COVID-19 exposure, and Long COVID symptoms. SettingCommunity-based longitudinal survey of COVID-19 in the UK. ParticipantsWe included 201,799 participants in our analysis who were aged between 16 and 64 years and had a confirmed SARS-CoV-2 infection. Main outcome measuresWe used multivariable logistic regression models to estimate the risk of Long COVID at least 4 weeks after acute SARS-CoV-2 infection by deciles of index of multiple deprivation (IMD) and adjusted for a range of demographic and spatiotemporal factors. We further examined the modifying effects of socioeconomic deprivation by sex and occupational groups. ResultsA total of 19,315 (9.6%) participants reported having Long COVID symptoms. Compared to the least deprived IMD decile, participants in the most deprived decile had a higher adjusted risk of Long COVID (11.4% vs 8.2%; adjusted OR: 1.45; 95% confidence interval [CI]: 1.33, 1.57). There were particularly significantly higher inequalities (most vs least deprived decile) of Long COVID in healthcare and patient facing roles (aOR: 1.76; 1.27, 2.44), and in the education sector (aOR: 1.62; 1.26, 2.08). The inequality of Long COVID was higher in females (aOR: 1.54; 1.38, 1.71) than males (OR: 1.32; 1.15, 1.51). ConclusionsParticipants living in the most socioeconomically deprived areas had a higher risk of Long COVID. The inequality gap was wider in females and certain public facing occupations (e.g., healthcare and education). These findings will help inform public health policies and interventions in adopting a social justice and health inequality lens.

Monitoring sociodemographic inequality in COVID-19 vaccination coverage in England: a national linked data study

BackgroundThe UK began an ambitious COVID-19 vaccination programme on 8th December 2020. This study describes variation in vaccination coverage by sociodemographic characteristics between December 2020 and August 2021. MethodsUsing population-level administrative records linked to the 2011 Census, we estimated monthly first dose vaccination rates by age group and sociodemographic characteristics amongst adults aged 18 years or over in England. We also present a tool to display the results interactively. FindingsOur study population included 35,223,466 adults. A lower percentage of males than females were vaccinated in the young and middle age groups (18-59 years) but not in the older age groups. Vaccination rates were highest among individuals of White British and Indian ethnic backgrounds and lowest among Black Africans (aged [≥]80 years) and Black Caribbeans (18-79 years). Differences by ethnic group emerged as soon as vaccination roll-out commenced and widened over time. Vaccination rates were also lower among individuals who identified as Muslim, lived in more deprived areas, reported having a disability, did not speak English as their main language, lived in rented housing, belonged to a lower socio-economic group, and had fewer qualifications. InterpretationWe found inequalities in COVID-19 vaccination rates by sex, ethnicity, religion, area deprivation, disability status, English language proficiency, socio-economic position, and educational attainment, but some of these differences varied by age group. Research is urgently needed to understand why these inequalities exist and how they can be addressed. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed for publications on sociodemographic inequalities in COVID-19 vaccination coverage. Several studies have reported differences in coverage by characteristics such as ethnicity and religion, however these have focused on older adults and the clinically vulnerable who were initially prioritized for vaccination. There is little evidence on sociodemographic inequalities in vaccination coverage among younger adults and evidence is also lacking on coverage by a wider range of characteristics such as sex, disability status, English language proficiency, socio-economic position, and educational attainment. Added value of this studyThis study provides the first evidence for sociodemographic inequalities in COVID-19 vaccination coverage among the entire adult population in England, using population-level administrative records linked to the 2011 Census. By disaggregating the data by age group, we were able to show that disparities in coverage by some sociodemographic characteristics differed by age group. For example, a lower proportion of males than females were vaccinated in the young and middle age groups (18-59 years) but not in the older age groups, and vaccination rates were lowest among Black Africans in those aged [≥]80 years but lowest among Black Caribbeans for all other age groups. Vaccination rates were also lower among individuals who identified as Muslim, lived in more deprived areas, reported having a disability, did not speak English as their main language, lived in rented housing, belonged to a lower socio-economic group, and had fewer qualifications. Implications of all the available evidenceMany of the groups with the lowest rates of COVID-19 vaccination are also the groups that have been disproportionately affected by the pandemic, including severe illness and mortality. Research is urgently needed to understand why these disparities exist and how they can be addressed, for example through public health or community engagement programmes. Since the relationships between sociodemographic characteristics and vaccination coverage may differ by age group, it is important for future research to disaggregate by age group when examining these inequalities.

Obesity, Ethnicity, and Covid-19 Mortality: A population-based cohort study of 12.6 Million Adults in England

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.

Sociodemographic inequality in COVID-19 vaccination coverage amongst elderly adults in England: a national linked data study

ObjectiveTo examine inequalities in COVID-19 vaccination rates amongst elderly adults in England DesignCohort study SettingPeople living in private households and communal establishments in England Participants6,829,643 adults aged [≥] 70 years (mean 78.7 years, 55.2% female) who were alive on 15 March 2021. Main outcome measuresHaving received the first dose of a vaccine against COVID-19 by 15 March 2021. We calculated vaccination rates and estimated unadjusted and adjusted odds ratios using logistic regression models. ResultsBy 15 March 2021, 93.2% of people living in England aged 70 years and over had received at least one dose of a COVID-19 vaccine. While vaccination rates differed across all factors considered apart from sex, the greatest disparities were seen between ethnic and religious groups. The lowest rates were in people of Black African and Black Caribbean ethnic backgrounds, where only 67.2% and 73.9% had received a vaccine, with adjusted odds of not being vaccinated at 5.01 (95% CI 4.86 - 5.16) and 4.85 (4.75 - 4.96) times greater than the White British group. The proportion of individuals self-identifying as Muslim and Buddhist who had received a vaccine was 79.1% and 84.1%, respectively. Older age, greater area deprivation, less advantaged socio-economic position (proxied by living in a rented home), being disabled and living either alone or in a multi-generational household were also associated with higher odds of not having received the vaccine. ConclusionPeople disproportionately affected seem most hesitant to COVID-19 vaccinations. Policy Interventions to improve these disparities are urgently needed. Summary BoxO_ST_ABSWhat is already known on this subject?C_ST_ABSThe UK began an ambitious vaccination programme to combat the COVID-19 pandemic on 8th December 2020. Existing evidence suggests that COVID-19 vaccination rates differ by level of area deprivation, ethnicity and certain underlying health conditions, such as learning disability and mental health problems. What does this study add?Our study shows that first dose vaccination rates in adults aged 70 or over differed markedly by ethnic group and self-reported religious affiliation, even after adjusting for geography, socio-demographic factors and underlying health conditions. Our study also highlights differences in vaccination rates by deprivation, household composition, and disability status, factors disproportionately associated with SARS-CoV-2 infection. Public health policy and community engagement aimed at promoting vaccination uptake is these groups are urgently needed. Strengths and limitations of this studyO_LIUsing nationwide linked population-level data from clinical records and the 2011 Census, we examined a wide range of socio-demographic characteristics not available n electronic health records C_LIO_LIMost demographic and socio-economic characteristics are derived from the 2011 Census and therefore are 10 years old. However, we focus primarily on characteristics that are unlikely to change over time, such as ethnicity or religion, or likely to be stable for our population C_LIO_LIBecause the data are based on the 2011 Census, it excluded people living in England in 2011 but not taking part in the 2011 Census; respondents who could not be linked to the 2011-2013 NHS patients register; recent migrants. Consequently, we excluded 5.4% of vaccinated people who could not be linked C_LI

Association of working shifts, inside and outside of healthcare, with risk of severe COVID-19: An observational study

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{middle dot}5% neither health nor shift worker, 1{middle dot}4% health worker only, 16{middle dot}9% shift worker only, and 0{middle dot}3% both), there were 580 (0{middle dot}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{middle dot}33, 4{middle dot}05]; shift workers (2{middle dot}06 [1{middle dot}72, 2{middle dot}47]); and in health workers who worked shifts (7{middle dot}56 [3{middle dot}86, 14{middle dot}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 contextO_ST_ABSEvidence before this studyC_ST_ABSThe 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.

Patterns of Multimorbidity and Risk of Severe SARS-CoV-2 Infection: an observational study in the U.K.

BackgroundPre-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. MethodsWe 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 or 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. ResultsAmong 360,283 participants, the median age was 68 [range, 48-85] years, most were White (94.5%), and 1,706 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 men. ConclusionThe risk of severe SARS-CoV-2 infection is higher in individuals with multimorbidity, indicating the need to target research and resources in people with SARS-CoV-2 infection and multimorbidity.

Obesity, walking pace and risk of severe COVID-19: Analysis of UK Biobank

Unstructured abstractObesity is an emerging risk factor for coronavirus disease-2019 (COVID-19). Simple measures of physical fitness, such as self-reported walking pace, could also be important risk factors, but have not been well documented. This analysis includes 414,201 UK Biobank participants with complete covariate and linked COVID-19 data. We analysed the risk of severe (in-hospital) COVID-19 across categories of obesity status and walking pace. As of June 20th 2020 there were 972 cases of severe COVID-19 that had occurred within the cohort. Compared to normal weight individuals, the adjusted odds ratio (OR) for severe COVID-9 in those with obesity was 1.49 (1.24, 1.78). Compared to those with a brisk walking pace, the OR in slow walkers was 1.84 (1.49, 2.27). Slow walkers had the highest risk of severe COVID-19 regardless of obesity status. For example, compared to normal weight brisk walkers, the odds of severe COVID-19 in obese brisk walkers was 1.39 (0.99, 1.98), whereas the odds in normal weight slow walkers was 2.48 (1.56, 3.93). Self-reported walking pace, a simple measure of functional fitness, appears to be a risk factor for severe COVID-19 that is independent of obesity. This may help inform simple pragmatic public health risk stratification and preventative strategies.