Background Excess mortality from all causes combined during the COVID-19 pandemic in England and Wales in 2020 was predominantly higher for essential workers. In 2021, the vaccination programme had begun, new SARS-CoV-2 variants were identified and different policy approaches were used. We have updated our previous analyses of excess mortality in England and Wales to include trends in excess mortality by occupation for 2021. Methods We estimated excess mortality for working age adults living in England and Wales by occupational group for each month in 2021 and for the year as a whole. Results During 2021, excess mortality remained higher for most groups of essential workers than for non-essential workers. It peaked in January 2021 when all-cause mortality was 44.6% higher than expected for all occupational groups combined. Excess mortality was highest for adults working in social care (86.9% higher than expected). Conclusion Previously, we reported excess mortality in 2020, with this paper providing an update to include 2021 data. Excess mortality was predominantly higher for essential workers during 2021. However, unlike the first year of the pandemic, when healthcare workers experienced the highest mortality, the highest excess mortality during 2021 was experienced by social care workers.
Background Higher levels of education are associated with slower cognitive decline and a lower risk of dementia, with some evidence of a causal relationship. However, the mechanisms explaining these associations are not well established. Methods We collected data on dementia knowledge using a cross-sectional household survey representative of the population of Great Britain. Dementia knowledge was assessed using a self-reported measure and a question measuring the knowledge of key risk factors. We examined whether dementia knowledge varied by levels of education (as measured by the level of the highest qualification) by fitting logistic regressions adjusted for confounding factors. Findings Out of the 5036 respondents aged 25 or over (46.6% male; average age 63.8), 9.3% reported knowing a great deal about dementia, and 32.2% quite a lot. We found a strong educational gradient in dementia prevention knowledge. For people with a degree qualification compared with people with no formal qualification, the ORs of reporting having quite a lot or a great deal of knowledge about dementia were 2.54 (95% CIs 1.81 to 3.56). The ORs were 3.58 (2.61 to 4.91) for mentioning all risk factors. The difference in awareness by educational level was largest for some risk factors such as lack of physical and mental activity, alcohol consumption and poor mental health. Interpretation The protective effect of higher levels of education against the risk of dementia may partly be driven by differences in dementia prevention knowledge. Health education efforts on dementia prevention should target people with lower levels of education to reduce inequalities in dementia prevalence.
Abstract Background Little is known about the risk of long COVID following reinfection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We estimated the likelihood of new-onset, self-reported long COVID after a second SARS-CoV-2 infection, compared to a first infection. Methods We included UK COVID-19 Infection Survey participants who tested positive for SARS-CoV-2 between 1 November 2021 and 8 October 2022. The primary outcome was self-reported long COVID 12–20 weeks after each infection. Separate analyses were performed for those <16 years and ≥16 years. We estimated adjusted odds ratios (aORs) for new-onset long COVID using logistic regression, comparing second to first infections, controlling for sociodemographic characteristics and calendar date of infection, plus vaccination status in participants ≥16 years of age. Results Overall, long COVID was reported by those ≥16 years after 4.0% and 2.4% of first and second infections, respectively; the corresponding estimates among those aged <16 years were 1.0% and 0.6%. The aOR for long COVID after second compared to first infections was 0.72 (95% confidence interval [CI], .63–.81) for those ≥16 years and 0.93 (95% CI, .57–1.53) for those <16 years. Conclusions The risk of new-onset long COVID after a second SARS-CoV-2 infection is lower than that after a first infection for persons aged ≥16 years, though there is no evidence of a difference in risk for those <16 years. However, there remains some risk of new-onset long COVID after a second infection, with around 1 in 40 of those aged ≥16 years and 1 in 165 of those <16 years reporting long COVID after a second infection.
Abstract Objective To estimate associations between COVID-19 vaccination and Long Covid symptoms in adults who were infected with SARS-CoV-2 prior to vaccination. Design Observational cohort study using individual-level interrupted time series analysis. Setting Random sample from the community population of the UK. Participants 28,356 COVID-19 Infection Survey participants (mean age 46 years, 56% female, 89% white) aged 18 to 69 years who received at least their first vaccination after test-confirmed infection. Main outcome measures Presence of long Covid symptoms at least 12 weeks after infection over the follow-up period 3 February to 5 September 2021. Results Median follow-up was 141 days from first vaccination (among all participants) and 67 days from second vaccination (84% of participants). First vaccination was associated with an initial 12.8% decrease (95% confidence interval: −18.6% to −6.6%) in the odds of Long Covid, but increasing by 0.3% (−0.6% to +1.2%) per week after the first dose. Second vaccination was associated with an 8.8% decrease (−14.1% to −3.1%) in the odds of Long Covid, with the odds subsequently decreasing by 0.8% (−1.2% to −0.4%) per week. There was no statistical evidence of heterogeneity in associations between vaccination and Long Covid by socio-demographic characteristics, health status, whether hospitalised with acute COVID-19, vaccine type (adenovirus vector or mRNA), or duration from infection to vaccination. Conclusions The likelihood of Long Covid symptoms reduced after COVID-19 vaccination, and the improvement was sustained over the follow-up period after the second dose. Vaccination may contribute to a reduction in the population health burden of Long Covid, though longer follow-up time is needed. Summary box What is already known on this topic COVID-19 vaccines are effective at reducing rates of SARS-CoV-2 infection, transmission, hospitalisation, and death The incidence of Long Covid may be reduced if infected after vaccination, but the relationship between vaccination and pre-existing long COVID symptoms is unclear, as published studies are generally small and with self-selected participants What this study adds The likelihood of Long Covid symptoms reduced after COVID-19 vaccination, and the improvement was sustained over the follow-up period after the second dose There was no evidence of differences in this relationship by socio-demographic characteristics, health-related factors, vaccine type, or duration from infection to vaccination Although causality cannot be inferred from this observational evidence, vaccination may contribute to a reduction in the population health burden of Long Covid; further research is needed to understand the biological mechanisms that may ultimately contribute to the development of therapeutics for Long Covid
Parental leave policies have been hypothesized to benefit mothers' mental health. We assessed the impact of a 6-week extension of parental leave in Denmark on maternal mental health.We linked individual-level data from Danish national registries on maternal sociodemographic characteristics and psychiatric diagnoses. A regression discontinuity design was applied to study the increase in parental leave duration after 26 March 1984. We included women who had given birth between 1 January 1981 and 31 December 1987. Our outcome was a first psychiatric diagnosis following the child's birth, ascertained as the first day of inpatient hospital admission for any psychiatric disorder. We presented cumulative incidences for the 30-year follow-up period and reported absolute risk differences between women eligible for the reform vs not, in 5-year intervals.In all, 291 152 women were followed up until 2017, death, emigration or date of first psychiatric diagnosis. The median follow-up time was 29.99 years, corresponding to 10 277 547 person-years at risk. The cumulative incidence of psychiatric diagnoses at 30 years of follow-up was 59.5 (95% CI: 57.4 to 61.6) per 1000 women in the ineligible group and 57.5 (95% CI: 55.6 to 59.4) in the eligible group. Eligible women took on average 32.85 additional days of parental leave (95% CI: 29.20 to 36.49) and had a lower probability of having a psychiatric diagnosis within 5 years [risk difference (RD): 2.4 fewer diagnoses per 1000 women, 95% CI: 1.5 to 3.2] and up to 20 years after the birth (RD: 2.3, 95% CI: 0.4 to 4.2). In subgroup analyses, the risk reduction was concentrated among low-educated, low-income and single women.Longer parental leave may confer mental health benefits to women, in particular to those from disadvantaged backgrounds.
Introduction Older people were at particular risk of morbidity and mortality during COVID-19. Consequently, they experienced formal (externally imposed) and informal (self-imposed) periods of social isolation and quarantine. This is hypothesised to have led to physical deconditioning, new-onset disability and frailty. Disability and frailty are not routinely collated at population level but are associated with increased risk of falls and fractures, which result in hospital admissions. First, we will examine incidence of falls and fractures during COVID-19 (January 2020–March 2022), focusing on differences between incidence over time against expected rates based on historical data, to determine whether there is evidence of new-onset disability and frailty. Second, we will examine whether those with reported SARS-CoV-2 were at higher risk of falls and fractures. Methods and analysis This study uses the Office for National Statistics (ONS) Public Health Data Asset, a linked population-level dataset combining administrative health records with sociodemographic data of the 2011 Census and National Immunisation Management System COVID-19 vaccination data for England. Administrative hospital records will be extracted based on specific fracture-centric International Classification of Diseases-10 codes in years preceding COVID-19 (2011–2020). Historical episode frequency will be used to predict expected admissions during pandemic years using time series modelling, if COVID-19 had not occurred. Those predicted admission figures will be compared with actual admissions to assess changes in hospital admissions due to public health measures comprising the pandemic response. Hospital admissions in prepandemic years will be stratified by age and geographical characteristics and averaged, then compared with pandemic year admissions to assess more granular changes. Risk modelling will assess risk of experiencing a fall, fracture or frail fall and fracture, if they have reported a positive case of COVID-19. The combination of these techniques will provide insight into changes in hospital admissions from the COVID-19 pandemic. Ethics and dissemination This study has approval from the National Statistician’s Data Ethics Advisory Committee (NSDEC(20)12). Results will be made available to other researchers via academic publication and shared via the ONS website.
SUMMARY Background To externally validate a risk prediction algorithm (QCovid) to estimate mortality outcomes from COVID-19 in adults in England. Methods Population-based cohort study using the ONS Public Health Linked Data Asset, a cohort based on the 2011 Census linked to Hospital Episode Statistics, the General Practice Extraction Service Data for pandemic planning and research, radiotherapy and systemic chemotherapy records. The primary outcome was time to COVID-19 death, defined as confirmed or suspected COVID-19 death as per death certification. Two time periods were used: (a) 24 th January to 30 th April 2020; and (b) 1 st May to 28 th July 2020. We evaluated the performance of the QCovid algorithms using measures of discrimination and calibration for each validation time period. Findings The study comprises 34,897,648 adults aged 19-100 years resident in England. There were 26,985 COVID-19 deaths during the first time-period and 13,177 during the second. The algorithms had good calibration in the validation cohort in both time periods with close correspondence of observed and predicted risks. They explained 77.1% (95% CI: 76.9% to 77.4%) of the variation in time to death in men in the first time-period (R 2 ); the D statistic was 3.76 (95% CI: 3.73 to 3.79); Harrell’s C was 0.935 (0.933 to 0.937). Similar results were obtained for women, and in the second time-period. In the top 5% of patients with the highest predicted risks of death, the sensitivity for identifying deaths in the first time period was 65.9% for men and 71.7% for women. People in the top 20% of predicted risks of death accounted for 90.8% of all COVID-19 deaths for men and 93.0% for women. Interpretation The QCovid population-based risk algorithm performed well, showing very high levels of discrimination for COVID-19 deaths in men and women for both time periods. It has the potential to be dynamically updated as the pandemic evolves and therefore, has potential use in guiding national policy. Funding National Institute of Health Research RESEARCH IN CONTEXT Evidence before this study Public policy measures and clinical risk assessment relevant to COVID-19 need to be aided by rigorously developed and validated risk prediction models. A recent living systematic review of published risk prediction models for COVID-19 found most models are subject to a high risk of bias with optimistic reported performance, raising concern that these models may be unreliable when applied in practice. A population-based risk prediction model, QCovid risk prediction algorithm, has recently been developed to identify adults at high risk of serious COVID-19 outcomes, which overcome many of the limitations of previous tools. Added value of this study Commissioned by the Chief Medical Officer for England, we validated the novel clinical risk prediction model (QCovid) to identify risks of short-term severe outcomes due to COVID-19. We used national linked datasets from general practice, death registry and hospital episode data for a population-representative sample of over 34 million adults. The risk models have excellent discrimination in men and women (Harrell’s C statistic>0.9) and are well calibrated. QCovid represents a new, evidence-based opportunity for population risk-stratification. Implications of all the available evidence QCovid has the potential to support public health policy, from enabling shared decision making between clinicians and patients in relation to health and work risks, to targeted recruitment for clinical trials, and prioritisation of vaccination, for example.
Abstract Background Risk of suicide is complex and often a result of multiple interacting factors. It is vital research identifies predictors of suicide to provide a strong evidence base for targeted interventions. Methods Using linked Census and population level mortality data we estimated rates of suicide across different groups in England and Wales and examine which factors are independently associated with the risk of suicide. Findings The highest rates of suicide were amongst those who reported an impairment affecting their day-to-day activities, those who were long term unemployed or never had worked, or those who were single or separated. Rates of suicide were highest in the White and Mixed/multiple ethnic groups compared to other ethnicities, and in people who reported a religious affiliation compared with those who had no religion. Comparison of minimally adjusted models (predictor, sex and age) with fully-adjusted models (sex, age, ethnicity, region, partnership status, religious affiliation, day-to-day impairments, armed forces membership and socioeconomic status) identified key predictors which remain important risk factors after accounting for other characteristics; day-to-day impairments were still found to increase the incidence of suicide relative to those whose activities were not impaired after adjusting for employment status. Overall, rates of suicide were higher in men compared to females across all ages, with the highest rates in 40-to-50-year-olds. Interpretation The findings of this work provide novel population level insights into the risk of suicide by sociodemographic characteristics. Understanding the interaction between key risk factors for suicide has important implications for national suicide prevention strategies. Funding This study received no specific funding. Research in context Evidence before this study Previous studies have identified key risk factors for suicide; being male and being aged 40 to 50 years of age have the highest rates of suicide. Suicide is a major public health concern, with prevention strategies imperative to minimising events. Added value of this study For the first time we make population level estimates of suicide rates in England and Wales using death registration data linked to 2011 Census. Furthermore, we calculate incidence rate ratios for fully adjusted models which provide novel insights into the interplay between different risk factors. For instance, we see that people who report having day-to-day impairments risk of suicide is 2- to 3-times higher for men and women respectively compared to people who do not report day-to-day impairments, after adjusting for other characteristics such as socioeconomic status which are likely associated with impairments. Implications of this study Understanding the groups most at risk of suicide is imperative for national suicide prevention strategies. This work provides novel population level insights into the risk of suicide by sociodemographic characteristics.
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