Participant characteristics and exclusion from phase 3/4 industry funded trials of chronic medical conditions: meta-analysis of individual participant level data.

Objectives: To assess whether age, sex, comorbidity count, and race and ethnic group are associated with the likelihood of trial participants not being enrolled in a trial for any reason (ie, screen failure). Design: Bayesian meta-analysis of individual participant level data. Setting: Industry funded phase 3/4 trials of chronic medical conditions. Participants: Participants were identified using individual participant level data to be in either the enrolled group or screen failure group. Data were available for 52 trials involving 72 178 screened individuals of whom 24 733 (34%) were excluded from the trial at the screening stage. Main outcome measures: For each trial, logistic regression models were constructed to assess likelihood of screen failure in people who had been invited to screening, and were regressed on age (per 10 year increment), sex (male v female), comorbidity count (per one additional comorbidity), and race or ethnic group. Trial level analyses were combined in Bayesian hierarchical models with pooling across condition. Results: In age and sex adjusted models across all trials, neither age nor sex was associated with increased odds of screen failure, although weak associations were detected after additionally adjusting for comorbidity (odds ratio of age, per 10 year increment was 1.02 (95% credibility interval 1.01 to 1.04) and male sex (0.95 (0.91 to 1.00)). Comorbidity count was weakly associated with screen failure, but in an unexpected direction (0.97 per additional comorbidity (0.94 to 1.00), adjusted for age and sex). People who self-reported as black seemed to be slightly more likely to fail screening than people reporting as white (1.04 (0.99 to 1.09)); a weak effect that seemed to persist after adjustment for age, sex, and comorbidity count (1.05 (0.98 to 1.12)). The between-trial heterogeneity was generally low, evidence of heterogeneity by sex was noted across conditions (variation in odds ratios on log scale of 0.01-0.13). Conclusions: Although the conclusions are limited by uncertainty about the completeness or accuracy of data collection among participants who were not randomised, we identified mostly weak associations with an increased likelihood of screen failure for age, sex, comorbidity count, and black race or ethnic group. Proportionate increases in screening these underserved populations may improve representation in trials. Trial registration number: PROSPERO CRD42018048202.

Is Austerity Responsible for the Stalled Mortality Trends Across Many High-Income Countries? A Systematic Review.

This article systematically reviews evidence evaluating whether macroeconomic austerity policies impact mortality, reviewing high-income country data compiled through systematic searches of nine databases and gray literature using pre-specified methods (PROSPERO registration: CRD42020226609). Eligible studies were quantitatively assessed to determine austerity's impact on mortality. Two reviewers independently assessed eligibility and risk of bias using ROBINS-I. Synthesis without meta-analysis was conducted due to heterogeneity. Certainty of evidence was assessed using the GRADE framework. Of 5,720 studies screened, seven were included, with harmful effects of austerity policies demonstrated in six, and no effect in one. Consistent harmful impacts of austerity were demonstrated for all-cause mortality, life expectancy, and cause-specific mortality across studies and different austerity measures. Excess mortality was higher in countries with greater exposure to austerity. Certainty of evidence was low. Risk of bias was moderate to critical. A typical austerity dose was associated with 74,090 [-40,632, 188,792] and 115,385 [26,324, 204,446] additional deaths per year. Austerity policies are consistently associated with adverse mortality outcomes, but the magnitude of this effect remains uncertain and may depend on how austerity is implemented (e.g., balance between public spending reductions or tax rises, and distributional consequences). Policymakers should be aware of potential harmful health effects of austerity policies.

Wildfire, deforestation and health in tropical rainforest areas: a scoping review protocol.

INTRODUCTION: Wildfires and deforestation potentially have direct effects on multiple health outcomes as well as indirect consequences for climate change. Tropical rainforest areas are characterised by high rainfall, humidity and temperature, and they are predominantly found in low-income and middle-income countries. This study aims to synthesise the methods, data and health outcomes reported in scientific papers on wildfires and deforestation in these locations. METHODS AND ANALYSIS: We will carry out a scoping review according to the Joanna Briggs Institute's (JBI) manual for scoping reviews and the framework proposed by Arksey and O'Malley, and Levac et al. The search for articles was performed on 18 August 2023, in 16 electronic databases using Medical Subject Headings terms and adaptations for each database from database inception. The search for local studies will be complemented by the manual search in the list of references of the studies selected to compose this review. We screened studies written in English, French, Portuguese and Spanish. We included quantitative studies assessing any human disease outcome, hospitalisation and vital statistics in regions of tropical rainforest. We exclude qualitative studies and quantitative studies whose outcomes do not cover those of interest. The text screening was done by two independent reviewers. Subsequently, we will tabulate the data by the origin of the data source used, the methods and the main findings on health impacts of the extracted data. The results will provide descriptive statistics, along with visual representations in diagrams and tables, complemented by narrative summaries as detailed in the JBI guidelines. ETHICS AND DISSEMINATION: The study does not require an ethical review as it is meta-research and uses published, deidentified secondary data sources. The submission of results for publication in a peer-reviewed journal and presentation at scientific and policymakers' conferences is expected. STUDY REGISTRATION: Open Science Framework (https://osf.io/pnqc7/).

The prevalence of common mental disorders across 18 ethnic groups in Britain during the COVID-19 pandemic: Evidence for Equality National Survey (EVENS).

BACKGROUND: The COVID-19 pandemic negatively impacted mental health in the general population in Britain. Ethnic minority people suffered disproportionately, in terms of health and economic outcomes, which may contribute to poorer mental health. We compare the prevalence of depression and anxiety across 18 ethnic groups in Britain during the COVID-19 pandemic. METHODS: Secondary analysis of cross-sectional data (February-November 2021) from 12,161 participants aged 18-60 years old (N with data on outcomes = 11,540 for depression & 11,825 for anxiety), obtained from the Evidence for Equality National Survey (EVENS). Data were weighted to account for selection bias and coverage bias. Weighted regression models examined ethnic differences in depression (Centre for Epidemiologic Studies Depression Scale) and anxiety (Generalised Anxiety Disorder-7). Effect modification analyses explored whether ethnic differences in outcomes were consistent within age and sex sub-groups. RESULTS: Compared to White British people, greater odds of anxiety caseness (and greater anxiety symptoms) were observed for Arab (OR = 2.57; 95 % CI = 1.35-4.91), Mixed White and Black Caribbean (1.57; 1.07-2.30), any other Black (2.22, 1.28-3.87) and any other Mixed (1.58; 1.08-2.31) ethnic groups. Lower odds of depression caseness (and lower depressive symptoms) were identified for Chinese (0.63; 0.46-0.85), Black African (0.60; 0.46-0.79), and any other Asian (0.55; 0.42-0.72) ethnic groups. LIMITATIONS: Cross-sectional data limits the opportunity to identify changes in ethnic inequalities in mental health over time. CONCLUSIONS: We have identified certain ethnic groups who may require more targeted mental health support to ensure equitable recovery post-pandemic. Despite finding lower levels of depression for some ethnic groups, approximately one third of people within each ethnic group met criteria for depression.

Evaluating the influence of taxation and social security policies on psychological distress: A microsimulation study of the UK during the COVID-19 economic crisis.

Economic determinants are important for population health, but actionable evidence of how policies can utilise these pathways remains scarce. This study employs a microsimulation framework to evaluate the effects of taxation and social security policies on population mental health. The UK economic crisis caused by the COVID-19 pandemic provides an informative context involving an economic shock accompanied by one of the strongest discretionary fiscal responses amongst OECD countries. The analytical setup involves a dynamic, stochastic, discrete-time microsimulation model (SimPaths) projecting changes in psychological distress given predicted economic outcomes from a static tax-benefit microsimulation model (UKMOD) based on different policy scenarios. We contrast projections of psychological distress for the working-age population from 2017 to 2025 given the observed policy environment against a counterfactual scenario where pre-crisis policies remained in place. Levels of psychological distress and potential cases of common mental disorders (CMDs) were assessed with the 12-item General Health Questionnaire (GHQ-12). The UK policy response to the economic crisis is estimated to have prevented a substantial fall (over 12 percentage points, %pt) in the employment rate in 2020 and 2021. In 2020, projected psychological distress increased substantially (CMD prevalence increase >10%pt) under both the observed and the counterfactual policy scenarios. Through economic pathways, the policy response is estimated to have prevented a further 3.4%pt [95%UI 2.8%pt, 4.0%pt] increase in the prevalence of CMDs, approximately 1.2 million cases. Beyond 2021, as employment levels rapidly recovered, psychological distress returned to the pre-pandemic trend. Sustained preventative effects on poverty are estimated, with projected levels 2.1%pt [95%UI 1.8%pt, 2.5%pt] lower in 2025 than in the absence of the observed policy response. The study shows that policies protecting employment during an economic crisis are effective in preventing short-term mental health losses and have lasting effects on poverty levels. This preventative effect has substantial public health benefits.

Prevalence and risk factors for long COVID among adults in Scotland using electronic health records: a national, retrospective, observational cohort study.

Background: Long COVID is a debilitating multisystem condition. The objective of this study was to estimate the prevalence of long COVID in the adult population of Scotland, and to identify risk factors associated with its development. Methods: In this national, retrospective, observational cohort study, we analysed electronic health records (EHRs) for all adults (≥18 years) registered with a general medical practice and resident in Scotland between March 1, 2020, and October 26, 2022 (98-99% of the population). We linked data from primary care, secondary care, laboratory testing and prescribing. Four outcome measures were used to identify long COVID: clinical codes, free text in primary care records, free text on sick notes, and a novel operational definition. The operational definition was developed using Poisson regression to identify clinical encounters indicative of long COVID from a sample of negative and positive COVID-19 cases matched on time-varying propensity to test positive for SARS-CoV-2. Possible risk factors for long COVID were identified by stratifying descriptive statistics by long COVID status. Findings: Of 4,676,390 participants, 81,219 (1.7%) were identified as having long COVID. Clinical codes identified the fewest cases (n = 1,092, 0.02%), followed by free text (n = 8,368, 0.2%), sick notes (n = 14,469, 0.3%), and the operational definition (n = 64,193, 1.4%). There was limited overlap in cases identified by the measures; however, temporal trends and patient characteristics were consistent across measures. Compared with the general population, a higher proportion of people with long COVID were female (65.1% versus 50.4%), aged 38-67 (63.7% versus 48.9%), overweight or obese (45.7% versus 29.4%), had one or more comorbidities (52.7% versus 36.0%), were immunosuppressed (6.9% versus 3.2%), shielding (7.9% versus 3.4%), or hospitalised within 28 days of testing positive (8.8% versus 3.3%%), and had tested positive before Omicron became the dominant variant (44.9% versus 35.9%). The operational definition identified long COVID cases with combinations of clinical encounters (from four symptoms, six investigation types, and seven management strategies) recorded in EHRs within 4-26 weeks of a positive SARS-CoV-2 test. These combinations were significantly (p < 0.0001) more prevalent in positive COVID-19 patients than in matched negative controls. In a case-crossover analysis, 16.4% of those identified by the operational definition had similar healthcare patterns recorded before testing positive. Interpretation: The prevalence of long COVID presenting in general practice was estimated to be 0.02-1.7%, depending on the measure used. Due to challenges in diagnosing long COVID and inconsistent recording of information in EHRs, the true prevalence of long COVID is likely to be higher. The operational definition provided a novel approach but relied on a restricted set of symptoms and may misclassify individuals with pre-existing health conditions. Further research is needed to refine and validate this approach. Funding: Chief Scientist Office (Scotland), Medical Research Council, and BREATHE.

Pharmacological treatments for alcohol dependence: Evidence on uptake, inequalities and comparative effectiveness from a UK population-based cohort.

INTRODUCTION: We assessed the prevalence of prescribing of certain medications for alcohol dependence and the extent of any inequalities in receiving prescriptions for individuals with such a diagnosis. Further, we compared the effectiveness of two of the most prescribed medications (acamprosate and disulfiram) for alcohol dependence and assessed whether there is inequality in prescribing either of them. METHODS: We used a nationwide dataset on prescriptions and hospitalisations in Scotland, UK (N = 19,748). We calculated the percentage of patients receiving alcohol dependence prescriptions after discharge, both overall and by socio-economic groups. Binary logistic regressions were used to assess the odds of receiving any alcohol-dependence prescription and the comparative odds of receiving acamprosate or disulfiram. Comparative effectiveness in avoiding future alcohol-related hospitalisations (N = 11,239) was assessed using Cox modelling with statistical adjustment for potential confounding. RESULTS: Upto 7% of hospitalised individuals for alcohol use disorder received prescriptions for alcohol dependence after being discharged. Least deprived socio-economic groups had relatively more individuals receiving prescriptions. Inequalities in prescribing for alcohol dependence existed, especially across sex and comorbidities: males had 12% (odds ratio [OR] 0.88, 95% confidence interval [CI] 0.81-0.96) and those with a history of mental health hospitalisations had 10% (OR 0.90, 95% CI 0.82-0.98) lower odds of receiving prescriptions after an alcohol-related hospitalisation. Prescribing disulfiram was superior to prescribing acamprosate in preventing alcohol-related hospitalisations (hazard ratio ranged between 0.60 and 0.81 across analyses). Disulfiram was relatively less likely prescribed to those from more deprived areas. DISCUSSION AND CONCLUSIONS: Inequalities in prescribing for alcohol dependence exists in Scotland with lower prescribing to men and disulfiram prescribed more to those from least deprived areas.

Disproportionate infection, hospitalisation and death from COVID-19 in ethnic minority groups and Indigenous Peoples: an application of the Priority Public Health Conditions analytical framework.

The COVID-19 pandemic has resulted in disproportionate consequences for ethnic minority groups and Indigenous Peoples. We present an application of the Priority Public Health Conditions (PPHC) framework from the World Health Organisation (WHO), to explicitly address COVID-19 and other respiratory viruses of pandemic potential. This application is supported by evidence that ethnic minority groups were more likely to be infected, implying differential exposure (PPHC level two), be more vulnerable to severe disease once infected (PPHC level three) and have poorer health outcomes following infection (PPHC level four). These inequities are driven by various interconnected dimensions of racism, that compounds with socioeconomic context and position (PPHC level one). We show that, for respiratory viruses, it is important to stratify levels of the PPHC framework by infection status and by societal, community, and individual factors to develop optimal interventions to reduce inequity from COVID-19 and future infectious diseases outbreaks.

Estimating quality-adjusted life expectancy (QALE) for local authorities in Great Britain and its association with indicators of the inclusive economy: a cross-sectional study.

OBJECTIVES: Quantifying area-level inequalities in population health can help to inform policy responses. We describe an approach for estimating quality-adjusted life expectancy (QALE), a comprehensive health expectancy measure, for local authorities (LAs) in Great Britain (GB). To identify potential factors accounting for LA-level QALE inequalities, we examined the association between inclusive economy indicators and QALE. SETTING: 361/363 LAs in GB (lower tier/district level) within the period 2018-2020. DATA AND METHODS: We estimated life tables for LAs using official statistics and utility scores from an area-level linkage of the Understanding Society survey. Using the Sullivan method, we estimated QALE at birth in years with corresponding 80% CIs. To examine the association between inclusive economy indicators and QALE, we used an open access data set operationalising the inclusive economy, created by the System Science in Public Health and Health Economics Research consortium. RESULTS: Population-weighted QALE estimates across LAs in GB were lowest in Scotland (females/males: 65.1 years/64.9 years) and Wales (65.0 years/65.2 years), while they were highest in England (67.5 years/67.6 years). The range across LAs for females was from 56.3 years (80% CI 45.6 to 67.1) in Mansfield to 77.7 years (80% CI 65.11 to 90.2) in Runnymede. QALE for males ranged from 57.5 years (80% CI 40.2 to 74.7) in Merthyr Tydfil to 77.2 years (80% CI 65.4 to 89.1) in Runnymede. Indicators of the inclusive economy accounted for more than half of the variation in QALE at the LA level (adjusted R2 females/males: 50%/57%). Although more inclusivity was generally associated with higher levels of QALE at the LA level, this association was not consistent across all 13 inclusive economy indicators. CONCLUSIONS: QALE can be estimated for LAs in GB, enabling further research into area-level health inequalities. The associations we identified between inclusive economy indicators and QALE highlight potential policy priorities for improving population health and reducing health inequalities.

Long COVID and financial outcomes: evidence from four longitudinal population surveys.

BACKGROUND: Long-term sequelae of COVID-19 (long COVID) include muscle weakness, fatigue, breathing difficulties and sleep disturbance over weeks or months. Using UK longitudinal data, we assessed the relationship between long COVID and financial disruption. METHODS: We estimated associations between long COVID (derived using self-reported length of COVID-19 symptoms) and measures of financial disruption (subjective financial well-being, new benefit claims, changes in household income) by analysing data from four longitudinal population studies, gathered during the first year of the pandemic. We employed modified Poisson regression in a pooled analysis of the four cohorts adjusting for a range of potential confounders, including pre-pandemic (pre-long COVID) factors. RESULTS: Among the 20 112 observations across four population surveys, 13% reported having COVID-19 with symptoms that impeded their ability to function normally-10.7% had such symptoms for <4 weeks (acute COVID-19), 1.2% had such symptoms for 4-12 weeks (ongoing symptomatic COVID-19) and 0.6% had such symptoms for >12 weeks (post-COVID-19 syndrome). We found that post-COVID-19 syndrome was associated with worse subjective financial well-being (adjusted relative risk ratios (aRRRs)=1.57, 95% CI=1.25, 1.96) and new benefit claims (aRRR=1.79, CI=1.27, 2.53). Associations were broadly similar across sexes and education levels. These results were not meaningfully altered when scaled to represent the population by age. CONCLUSIONS: Long COVID was associated with financial disruption in the UK. If our findings reflect causal effects, extending employment protection and financial support to people with long COVID may be warranted.

Risk of winter hospitalisation and death from acute respiratory infections in Scotland: national retrospective cohort study.

OBJECTIVES: We undertook a national analysis to characterise and identify risk factors for acute respiratory infections (ARIs) resulting in hospitalisation during the winter period in Scotland. DESIGN: A population-based retrospective cohort analysis. SETTING: Scotland. PARTICIPANTS: The study involved 5.4 million residents in Scotland. MAIN OUTCOME MEASURES: Cox proportional hazard models were used to estimate adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs) for the association between risk factors and ARI hospitalisation. RESULTS: Between 1 September 2022 and 31 January 2023, there were 22,284 (10.9% of 203,549 with any emergency hospitalisation) ARI hospitalisations (1759 in children and 20,525 in adults) in Scotland. Compared with the reference group of children aged 6-17 years, the risk of ARI hospitalisation was higher in children aged 3-5 years (aHR = 4.55; 95% CI: 4.11-5.04). Compared with those aged 25-29 years, the risk of ARI hospitalisation was highest among the oldest adults aged ≥80 years (aHR = 7.86; 95% CI: 7.06-8.76). Adults from more deprived areas (most deprived vs. least deprived, aHR = 1.64; 95% CI: 1.57-1.72), with existing health conditions (≥5 vs. 0 health conditions, aHR = 4.84; 95% CI: 4.53-5.18) or with history of all-cause emergency admissions (≥6 vs. 0 previous emergency admissions, aHR = 7.53; 95% CI: 5.48-10.35) were at a higher risk of ARI hospitalisations. The risk increased by the number of existing health conditions and previous emergency admission. Similar associations were seen in children. CONCLUSIONS: Younger children, older adults, those from more deprived backgrounds and individuals with greater numbers of pre-existing conditions and previous emergency admission were at increased risk for winter hospitalisations for ARI.

Trends in inequalities in childhood overweight and obesity prevalence: a repeat cross-sectional analysis of the Health Survey for England.

OBJECTIVE: To examine trends in socio-economic and ethnic inequalities in childhood overweight and obesity in the England between 1995 and 2019 in survey data and to compare these to administrative data. DESIGN: Observational repeated cross-sectional study using the Health Survey for England (HSE) and National Child Measurement Programme (NCMP). OUTCOME: Age and sex standardised overweight, obesity and overweight including obesity. ANALYSIS: Inequalities assessed by parental education, family structure, ethnicity (binary non-white vs white) and area-level Index of Multiple Deprivation. Estimates stratified by age and sex. Trends compared against NCMP data (age 4-5 and 10-11 years). RESULTS: Prevalence of childhood overweight including obesity increased from 26.0% in 1995 to 31.7% in 2019, with the highest and fastest growing levels in those aged 11-15 years, rising from 29.7% to 38.0%. Despite a plateau in overall childhood obesity since 2004, differences between groups demonstrated widening inequalities over time. Inequalities widened by area-level deprivation, household educational attainment, household structure and ethnicity driven primarily by increased prevalence among socioeconomically disadvantaged children. For example, the gap between children from households with no qualifications versus degree-level qualifications increased from -1.1% to 13.2%, and the gap between single-parent households and couple households increased from 0.5% to 5.3%. HSE trends in prevalence of childhood overweight and obesity by deprivation quintile were consistent with those in NCMP. CONCLUSION: Overall levels of child overweight and obesity increased between 1995 and 2004. Since then, increases in prevalence among less advantaged groups have driven widening of inequalities.

Trends in inequalities in avoidable hospitalisations across the COVID-19 pandemic: a cohort study of 23.5 million people in England.

OBJECTIVE: To determine whether periods of disruption were associated with increased 'avoidable' hospital admissions and wider social inequalities in England. DESIGN: Observational repeated cross-sectional study. SETTING: England (January 2019 to March 2022). PARTICIPANTS: With the approval of NHS England we used individual-level electronic health records from OpenSAFELY, which covered ~40% of general practices in England (mean monthly population size 23.5 million people). PRIMARY AND SECONDARY OUTCOME MEASURES: We estimated crude and directly age-standardised rates for potentially preventable unplanned hospital admissions: ambulatory care sensitive conditions and urgent emergency sensitive conditions. We considered how trends in these outcomes varied by three measures of social and spatial inequality: neighbourhood socioeconomic deprivation, ethnicity and geographical region. RESULTS: There were large declines in avoidable hospitalisations during the first national lockdown (March to May 2020). Trends increased post-lockdown but never reached 2019 levels. The exception to these trends was for vaccine-preventable ambulatory care sensitive admissions which remained low throughout 2020-2021. While trends were consistent by each measure of inequality, absolute levels of inequalities narrowed across levels of neighbourhood socioeconomic deprivation, Asian ethnicity (compared with white ethnicity) and geographical region (especially in northern regions). CONCLUSIONS: We found no evidence that periods of healthcare disruption from the COVID-19 pandemic resulted in more avoidable hospitalisations. Falling avoidable hospital admissions has coincided with declining inequalities most strongly by level of deprivation, but also for Asian ethnic groups and northern regions of England.

The intersection of race/ethnicity and socioeconomic status: inequalities in breast and cervical cancer mortality in 20,665,005 adult women from the 100 Million Brazilian Cohort.

OBJECTIVES: There is limited evidence regarding the impact of race/racism and its intersection with socioeconomic status (SES) on breast and cervical cancer, the two most common female cancers globally. We investigated racial inequalities in breast and cervical cancer mortality and whether SES (education and household conditions) interacted with race/ethnicity. DESIGN: The 100 Million Brazilian Cohort data were linked to the Brazilian Mortality Database, 2004-2015 (n = 20,665,005 adult women). We analysed the association between self-reported race/ethnicity (White/'Parda'(Brown)/Black/Asian/Indigenous) and cancer mortality using Poisson regression, adjusting for age, calendar year, education, household conditions and area of residence. Additive and multiplicative interactions were assessed. RESULTS: Cervical cancer mortality rates were higher among Indigenous (adjusted Mortality rate ratio = 1.80, 95%CI 1.39-2.33), Asian (1.63, 1.20-2.22), 'Parda'(Brown) (1.27, 1.21-1.33) and Black (1.18, 1.09-1.28) women vs White women. Breast cancer mortality rates were higher among Black (1.10, 1.04-1.17) vs White women. Racial inequalities in cervical cancer mortality were larger among women of poor household conditions, and low education (P for multiplicative interaction <0.001, and 0.02, respectively). Compared to White women living in completely adequate (3-4) household conditions, the risk of cervical cancer mortality in Black women with 3-4, 1-2, and none adequate conditions was 1.10 (1.01-1.21), 1.48 (1.28-1.71), and 2.03 (1.56-2.63), respectively (Relative excess risk due to interaction-RERI = 0.78, 0.18-1.38). Among 'Parda'(Brown) women the risk was 1.18 (1.11-1.25), 1.68 (1.56-1.81), and 1.84 (1.63-2.08), respectively (RERI = 0.52, 0.16-0.87). Compared to high-educated White women, the risk in high-, middle- and low-educated Black women was 1.14 (0.83-1.55), 1.93 (1.57-2.38) and 2.75 (2.33-3.25), respectively (RERI = 0.36, -0.05-0.77). Among 'Parda'(Brown) women the risk was 1.09 (0.91-1.31), 1.99 (1.70-2.33) and 3.03 (2.61-3.52), respectively (RERI = 0.68, 0.48-0.88). No interactions were found for breast cancer. CONCLUSION: Low SES magnified racial inequalities in cervical cancer mortality. The intersection between race/ethnicity, SES and gender needs to be addressed to reduce racial health inequalities.

Quality of ethnicity data within Scottish health records and implications of misclassification for ethnic inequalities in severe COVID-19: a national linked data study.

BACKGROUND: We compared the quality of ethnicity coding within the Public Health Scotland Ethnicity Look-up (PHS-EL) dataset, and other National Health Service datasets, with the 2011 Scottish Census. METHODS: Measures of quality included the level of missingness and misclassification. We examined the impact of misclassification using Cox proportional hazards to compare the risk of severe coronavirus disease (COVID-19) (hospitalization & death) by ethnic group. RESULTS: Misclassification within PHS-EL was higher for all minority ethnic groups [12.5 to 69.1%] compared with the White Scottish majority [5.1%] and highest in the White Gypsy/Traveller group [69.1%]. Missingness in PHS-EL was highest among the White Other British group [39%] and lowest among the Pakistani group [17%]. PHS-EL data often underestimated severe COVID-19 risk compared with Census data. e.g. in the White Gypsy/Traveller group the Hazard Ratio (HR) was 1.68 [95% Confidence Intervals (CI): 1.03, 2.74] compared with the White Scottish majority using Census ethnicity data and 0.73 [95% CI: 0.10, 5.15] using PHS-EL data; and HR was 2.03 [95% CI: 1.20, 3.44] in the Census for the Bangladeshi group versus 1.45 [95% CI: 0.75, 2.78] in PHS-EL. CONCLUSIONS: Poor quality ethnicity coding in health records can bias estimates, thereby threatening monitoring and understanding ethnic inequalities in health.

Potential contribution of vaccination uptake to occupational differences in risk of SARS-CoV-2: analysis of the ONS COVID-19 Infection Survey.

OBJECTIVES: To assess variation in vaccination uptake across occupational groups as a potential explanation for variation in risk of SARS-CoV-2 infection. DESIGN: We analysed data from the UK Office of National Statistics COVID-19 Infection Survey linked to vaccination data from the National Immunisation Management System in England from 1 December 2020 to 11 May 2022. We analysed vaccination uptake and SARS-CoV-2 infection risk by occupational group and assessed whether adjustment for vaccination reduced the variation in risk between occupational groups. RESULTS: Estimated rates of triple vaccination were high across all occupational groups (80% or above), but were lowest for food processing (80%), personal care (82%), hospitality (83%), manual occupations (84%) and retail (85%). High rates were observed for individuals working in health (95% for office based, 92% for those in patient-facing roles) and education (91%) and office-based workers not included in other categories (90%). The impact of adjusting for vaccination when estimating relative risks of infection was generally modest (ratio of hazard ratios across all occupational groups reduced from 1.37 to 1.32), but was consistent with the hypothesis that low vaccination rates contribute to elevated risk in some groups. CONCLUSIONS: Variation in vaccination coverage might account for a modest proportion of occupational differences in infection risk. Vaccination rates were uniformly very high in this cohort, which may suggest that the participants are not representative of the general population. Accordingly, these results should be considered tentative pending the accumulation of additional evidence.

Effect of trade unions on the mental health of UK workers before and during the COVID-19 pandemic: a longitudinal analysis using Understanding Society data.

BACKGROUND: Although trade union membership rates have continuously decreased over the past 30 years, about 50% of UK employees are still represented by a union. Yet, studies on the association between collective bargaining and workers' mental health are sparse, especially in the pandemic context. This study examines differences on UK workers' mental health due to trade union presence and membership between pre-pandemic and pandemic periods. METHODS: In this longitudinal study, we analysed Understanding Society panel data in which the same participants are followed over time. The data concerned individuals aged 16 years and older and were collected biannually before COVID-19 pandemic (Waves 8-10: 2017-2020) and on a more frequent basis during pandemic (all COVID-19 surveys from 2020 [April, May, June, July, September, November] to 2021 [January, March, September] periods). The primary outcome was General Health Questionnaire-12 (GHQ-12) caseness (GHQ-12 score ≥4: probability of caseness). Two exposures were used separately: trade union presence and trade union membership, interacting with a binary variable splitting time periods between before and during the pandemic. Our analytical sample included 49 915 observations from 5988 individuals. 3341 (56%) individuals worked in unionised workplaces. We fitted mixed-effects logistic regression models adjusting for age, gender, ethnicity, UK residence, educational level, financial situation, workplace size, and survey interview date. We then replicated the analyses including a 3-way interaction with industry. All Understanding Society participants gave written informed consent. Ethics approval was not required. FINDINGS: In our sample, approximately 41% were male and 59% were female, and the mean age was 47·2 years (SD 11·4). Comparing pre-pandemic and pandemic periods, we found that the odds of GHQ-12 caseness for those in non-unionised workplaces increased by 45% (odds ratio 1·45, 95% CI 1·17-1·80), whereas in unionised workplaces odds increased by 28% (1·28, 1·05-1·57). When analysis was confined to unionised workplaces, the odds of GHQ-12 caseness for non-union members increased more (1·40, 1·07-1·83) compared with members (1·18, 0·91-1·53); however, with wide CIs. Overall, industry had no modification effect in both exposures. Sensitivity analysis using GHQ-36 as a continuous outcome demonstrated no real change in the patterns of the results. INTERPRETATION: The mental health of workers in unionised workplaces appears to have worsened less than the mental health of those in non-unionised workplaces; however, there is insufficient evidence of effect differential by type of industrial sector. Designing policies that encourage and facilitate trade union presence in workplaces should be promoted, as they are likely to mitigate adverse mental health effects in times of extreme uncertainty. FUNDING: Medical Research Council, Chief Scientist Office, Belgian National Scientific Fund.

Occupational differences in the prevalence and severity of long-COVID: analysis of the Coronavirus (COVID-19) Infection Survey.

OBJECTIVES: To establish whether prevalence and severity of long-COVID symptoms vary by industry and occupation. METHODS: We used Office for National Statistics COVID-19 Infection Survey (CIS) data (February 2021-April 2022) of working-age participants (16-65 years). Exposures were industry, occupation and major Standard Occupational Classification (SOC) group. Outcomes were self-reported: (1) long-COVID symptoms and (2) reduced function due to long-COVID. Binary (outcome 1) and ordered (outcome 2) logistic regression were used to estimate odds ratios (OR)and prevalence (marginal means). RESULTS: Public facing industries, including teaching and education, social care, healthcare, civil service, retail and transport industries and occupations, had the highest likelihood of long-COVID. By major SOC group, those in caring, leisure and other services (OR 1.44, 95% CIs 1.38 to 1.52) had substantially elevated odds than average. For almost all exposures, the pattern of ORs for long-COVID symptoms followed SARS-CoV-2 infections, except for professional occupations (eg, some healthcare, education, scientific occupations) (infection: OR<1 ; long-COVID: OR>1). The probability of reporting long-COVID for industry ranged from 7.7% (financial services) to 11.6% (teaching and education); whereas the prevalence of reduced function by 'a lot' ranged from 17.1% (arts, entertainment and recreation) to 22%-23% (teaching and education and armed forces) and to 27% (not working). CONCLUSIONS: The risk and prevalence of long-COVID differs across industries and occupations. Generally, it appears that likelihood of developing long-COVID symptoms follows likelihood of SARS-CoV-2 infection, except for professional occupations. These findings highlight sectors and occupations where further research is needed to understand the occupational factors resulting in long-COVID.