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Background Since 2008, the Improving Access to Psychological Therapies (IAPT) programme has offered adults in England evidence-based psychological treatments for common mental disorders (CMDs) such as depression and anxiety disorders. However, inequalities in access have not been explored at the national level. Methods Using a unique individual patient dataset that linked 2011 Census information of English residents to national IAPT data collected between April 2017 and March 2018, we estimated the rate of access by a wide range of socio-demographic characteristics that are not routinely available. A large household survey was used to estimate the prevalence of probable CMDs by these socio-demographic characteristics. We estimated the probability of access to IAPT amongst people with CMDs by comparing the rates of access from IAPT data and the estimates of prevalence of CMDs from the household survey. Both unadjusted and adjusted (for important patient characteristics) access rates were estimated in logistic regression models. Results As a proportion of those with a probable CMD, access to IAPT varied markedly by socio-demographic characteristics. Older adults, males, people born outside of the UK, people with religious beliefs, people from Asian ethnic backgrounds, people reporting a disability and those without any academic or professional qualifications were underrepresented in IAPT services nationally, in adjusted models. Conclusions The identification of patients who may be underrepresented in IAPT provides an opportunity for services to target outreach and engagement with these groups. Further understanding of barriers to access should help increase equity in access.

Background Although most adults infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) fully recover, a proportion have ongoing symptoms, or post-COVID conditions (PCC), after infection. The objective of this analysis was to estimate the number of United States (US) adults with activity-limiting PCC on 1 November 2021. Methods We modeled the prevalence of PCC using reported infections occurring from 1 February 2020 to 30 September 2021, and population-based, household survey data on new activity-limiting symptoms ≥1 month following SARS-CoV-2 infection. From these data sources, we estimated the number and proportion of US adults with activity-limiting PCC on 1 November 2021 as 95% uncertainty intervals, stratified by sex and age. Sensitivity analyses adjusted for underascertainment of infections and uncertainty about symptom duration. Results On 1 November 2021, at least 3.0–5.0 million US adults, or 1.2%–1.9% of the US adult population, were estimated to have activity-limiting PCC of ≥1 month’s duration. Population prevalence was higher in females (1.4%–2.2%) than males. The estimated prevalence after adjusting for underascertainment of infections was 1.7%–3.8%. Conclusions Millions of US adults were estimated to have activity-limiting PCC. These estimates can support future efforts to address the impact of PCC on the US population.

Although most adults infected with SARS-CoV-2 fully recover, a proportion have ongoing symptoms, or post-COVID conditions (PCC), after infection. The objective of this analysis was to estimate the number of US adults with activity-limiting PCC on November 1, 2021.We modeled the prevalence of PCC using reported infections occurring from February 1, 2020 - September 30, 2021, and population-based, household survey data on new activity-limiting symptoms ≥1 month following SARS-CoV-2 infection. From these data sources, we estimated the number and proportion of US adults with activity-limiting PCC on November 1, 2021, as 95% uncertainty intervals, stratified by sex and age. Sensitivity analyses adjusted for under-ascertainment of infections and uncertainty about symptom duration.On November 1, 2021, at least 3.0-5.0 million US adults were estimated to have activity-limiting PCC of ≥1 month duration, or 1.2%-1.9% of US adults. Population prevalence was higher in females (1.4%-2.2%) than males. The estimated prevalence after adjusting for under-ascertainment of infections was 1.7%-3.8%.Millions of US adults were estimated to have activity-limiting PCC. These estimates can support future efforts to address the impact of PCC on the U.S. population.

Objective: To estimate occupational differences in COVID-19 mortality, and test whether these are confounded by factors, such as regional differences, ethnicity and education or due to non-workplace factors, such as deprivation or pre-pandemic health. Design: Retrospective cohort study Setting: People living in private households England Participants: 14,295,900 people aged 40-64 years (mean age 52 years, 51% female) who were alive on 24 January 2020, living in private households in England in 2019, were employed in 2011, and completed the 2011 census. Main outcome measures: COVID-19 related death, assessed between 24 January 2020 and 28 December 2020. We estimated age-standardised mortality rates per 100,000 person-years at risk (ASMR) stratified by sex and occupations. To estimate the effect of occupation due to work-related exposures, we used Cox proportional hazard models to adjust for confounding (region, ethnicity, education), as well as non-workplace factors that are related to occupation. Results: There is wide variation between occupations in COVID-19 mortality. Several occupations, particularly those involving contact with patients or the public, show three-fold or four-fold risks. These elevated risks were greatly attenuated after adjustment for confounding and mediating non-workplace factors. For example, the hazard ratio (HR) for men working as taxi and cab drivers or chauffeurs changed from 4.60 [95%CI 3.62-5.84] to 1.47 [1.14-1.89] after adjustment. More generally, the overall HR for men working in essential occupations compared with men in nonessential occupations changed from 1.45 [1.34 - 1.56] to 1.22 [1.13 - 1.32] after adjustment. For most occupations, confounding and other mediating factors explained about 70% to 80% of the age-adjusted hazard ratios. Conclusions Working conditions are likely to play a role in COVID-19 mortality, particularly in occupations involving contact with COVID-19 patients or the public. However, there is also a substantial contribution from non-workplace factors, including regional factors, socio-demographic factors, and pre-pandemic health.

IntroductionThe coronavirus pandemic has been particularly severe in the UK, with high infection and death rates, including among working age population.ObjectiveTo estimate occupational differences in COVID-19 mortality, taking into account confounding factors, such as regional differences, ethnicity, education, deprivation and pre-pandemic health.MethodsWe used data on 14,295,900 individuals who completed the UK Census in 2011, who were alive on 24 January 2020, were employed and aged 31–55 years in 2011. Data were linked to death and other health records. We examined differences between occupational groups in the risk of COVID-19 death from 24 January to 28 December 2020. We estimated age-standardised mortality rates per 100,000 person-years at risk stratified by sex and occupations. To estimate the effect of occupation due to work-related exposures, we used Cox proportional hazard models to adjust for confounding factors.ResultsThere is wide variation between occupations in COVID-19 mortality. Several occupations, particularly those involving contact with patients or the public, show three- or four-fold risks. These elevated risks were greatly attenuated after adjustment for confounding and mediating factors. For example, the hazard ratio (HR) for men working as taxi and cab drivers or chauffeurs changed from 4.60 [95%CI 3.62–5.84] to 1.47 [1.14–1.89] after adjustment. The overall HR for men working in essential occupations compared with men in non-essential occupations changed from 1.45 [1.34 - 1.56] to 1.22 [1.13 - 1.32] after adjustment. For most occupations, confounding and other mediating factors explained about 70% to 80% of the age-adjusted hazard ratios.ConclusionsWorking conditions are likely to play a role in COVID-19 mortality, particularly in occupations involving contact with COVID-19 patients or the public. However, there is also a substantial contribution from non-workplace factors, including regional factors, socio-demographic factors, and pre-pandemic health.

ObjectiveTo estimate occupational differences in COVID-19 mortality, and test whether these are confounded by factors, such as regional differences, ethnicity and education or due to non-workplace factors, such as deprivation or pre-pandemic health.DesignRetrospective cohort studySettingPeople living in private households EnglandParticipants14,295,900 people aged 40-64 years (mean age 52 years, 51% female) who were alive on 24 January 2020, living in private households in England in 2019, were employed in 2011, and completed the 2011 census.Main outcome measuresCOVID-19 related death, assessed between 24 January 2020 and 28 December 2020. We estimated age-standardised mortality rates per 100,000 person-years at risk (ASMR) stratified by sex and occupations. To estimate the effect of occupation due to work-related exposures, we used Cox proportional hazard models to adjust for confounding (region, ethnicity, education), as well as non-workplace factors that are related to occupation.ResultsThere is wide variation between occupations in COVID-19 mortality. Several occupations, particularly those involving contact with patients or the public, show three-fold or four-fold risks. These elevated risks were greatly attenuated after adjustment for confounding and mediating non-workplace factors. For example, the hazard ratio (HR) for men working as taxi and cab drivers or chauffeurs changed from 4.60 [95%CI 3.62-5.84] to 1.47 [1.14-1.89] after adjustment. More generally, the overall HR for men working in essential occupations compared with men in non-essential occupations changed from 1.45 [1.34 - 1.56] to 1.22 [1.13 - 1.32] after adjustment. For most occupations, confounding and other mediating factors explained about 70% to 80% of the age-adjusted hazard ratios.ConclusionsWorking conditions are likely to play a role in COVID-19 mortality, particularly in occupations involving contact with COVID-19 patients or the public. However, there is also a substantial contribution from non-workplace factors, including regional factors, socio-demographic factors, and pre-pandemic health.

SUMMARYBackgroundTo externally validate a risk prediction algorithm (QCovid) to estimate mortality outcomes from COVID-19 in adults in England.MethodsPopulation-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) 24th January to 30th April 2020; and (b) 1st May to 28th July 2020. We evaluated the performance of the QCovid algorithms using measures of discrimination and calibration for each validation time period.FindingsThe 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 (R2); 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.InterpretationThe 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.FundingNational Institute of Health ResearchRESEARCH IN CONTEXTEvidence before this studyPublic 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 studyCommissioned 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 evidenceQCovid 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.