Sociodemographic and health-related differences in undiagnosed hypertension in the health survey for England 2015-2019: a cross-sectional cohort study.

Background: Hypertension is a leading cause of morbidity and mortality worldwide, yet a substantial proportion of cases are undiagnosed. Understanding the scale of undiagnosed hypertension and identifying groups most at risk is important to inform approaches to detection. Methods: In this cross-sectional cohort study, we used data from the 2015 to 2019 Health Survey for England, an annual, cross-sectional, nationally representative survey. The survey follows a multi-stage stratified probability sampling design, involving a random sample of primary sampling units based on postcode sectors, followed by a random sample of postal addresses within these units. Within each selected household, all adults (aged ≥16 years) and up to four children, were eligible for participation. For the current study, individuals aged 16 years and over who were not pregnant and had valid blood pressure data were included in the analysis. The primary outcome was undiagnosed hypertension, defined by a measured blood pressure of 140/90 mmHg or above but no history of diagnosis. Age-adjusted prevalence of undiagnosed hypertension was estimated across sociodemographic and health-related characteristics, including ethnicity, region, rural-urban classification, relationship status, highest educational qualification, National Statistics Socio-Economic Classification (NS-SEC), Body Mass Index (BMI), self-reported general health, and smoking status. To assess the independent association between undiagnosed hypertension and each characteristic, we fitted a logistic regression model adjusted for sociodemographic factors. Findings: The sample included 21,476 individuals, of whom 55.8% were female and 89.3% reported a White ethnic background. An estimated 30.7% (95% confidence interval 29.0-32.4) of men with hypertension and 27.6% (26.1-29.1) of women with hypertension were undiagnosed. Younger age, lower BMI, and better self-reported general health were associated with an increased likelihood of hypertension being undiagnosed for men and women. Living in rural areas and in regions outside of London and the East of England were also associated with an increased likelihood of hypertension being undiagnosed for men, as were being married or in a civil partnership and having higher educational qualifications for women. Interpretation: Hypertension is commonly undiagnosed, and some of the groups that are at the lowest risk of hypertension are the most likely to be undiagnosed. Given the high lifetime risk of hypertension and its strong links with morbidity and mortality, our findings suggest a need for greater awareness of the potential for undiagnosed hypertension, including among those typically considered 'low risk'. Further research is needed to assess the impact of extending hypertension screening to lower-risk groups. Funding: None.

Socio-demographic differences in access to psychological treatment services: evidence from a national cohort study.

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.

Point Prevalence Estimates of Activity-Limiting Long-term Symptoms Among United States Adults ≥1 Month After Reported Severe Acute Respiratory Syndrome Coronavirus 2 Infection, 1 November 2021.

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.

Occupation and COVID-19 mortality in England: a national linked data study of 14.3 million adults.

OBJECTIVES: 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 prepandemic health. METHODS: Using a cohort study of over 14 million people aged 40-64 years living in England, we analysed occupational differences in death involving COVID-19, assessed between 24 January 2020 and 28 December 2020.We estimated age-standardised mortality rates (ASMRs) per 100 000 person-years at risk stratified by sex and occupation. We estimated the effect of occupation on COVID-19 mortality using Cox proportional hazard models adjusted for confounding factors. We further adjusted for non-workplace factors and interpreted the residual effects of occupation as being due to workplace exposures to SARS-CoV-2. RESULTS: In men, the ASMRs were highest among those working as taxi and cab drivers or chauffeurs at 119.7 deaths per 100 000 (95% CI 98.0 to 141.4), followed by other elementary occupations at 106.5 (84.5 to 132.4) and care workers and home carers at 99.2 (74.5 to 129.4). Adjusting for confounding factors strongly attenuated the HRs for many occupations, but many remained at elevated risk. Adjusting for living conditions reduced further the HRs, and many occupations were no longer at excess risk. For most occupations, confounding factors and mediators other than workplace exposure to SARS-CoV-2 explained 70%-80% of the excess age-adjusted occupational differences. CONCLUSIONS: Working conditions play a role in COVID-19 mortality, particularly in occupations involving contact with patients or the public. However, there is also a substantial contribution from non-workplace factors.

An external validation of the QCovid risk prediction algorithm for risk of mortality from COVID-19 in adults: a national validation cohort study in England.

BACKGROUND: Public policy measures and clinical risk assessments relevant to COVID-19 need to be aided by risk prediction models that are rigorously developed and validated. We aimed to externally validate a risk prediction algorithm (QCovid) to estimate mortality outcomes from COVID-19 in adults in England. METHODS: We did a population-based cohort study using the UK Office for National Statistics Public Health Linked Data Asset, a cohort of individuals aged 19-100 years, based on the 2011 census and linked to Hospital Episode Statistics, the General Practice Extraction Service data for pandemic planning and research, and 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 periods were used: (1) Jan 24 to April 30, 2020, and (2) May 1 to July 28, 2020. We assessed the performance of the QCovid algorithms using measures of discrimination and calibration. Using predicted 90-day risk of COVID-19 death, we calculated r2 values, Brier scores, and measures of discrimination and calibration with corresponding 95% CIs over the two time periods. FINDINGS: We included 34 897 648 adults aged 19-100 years resident in England. 26 985 (0·08%) COVID-19 deaths occurred during the first period and 13 177 (0·04%) during the second. The algorithms had good discrimination and calibration in both periods. In the first period, they explained 77·1% (95% CI 76·9-77·4) of the variation in time to death in men and 76·3% (76·0-76·6) in women. The D statistic was 3·761 (3·732-3·789) for men and 3·671 (3·640-3·702) for women and Harrell's C was 0·935 (0·933-0·937) for men and 0·945 (0·943-0·947) for women. Similar results were obtained for 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 period was 65·94% for men and 71·67% for women. INTERPRETATION: The QCovid population-based risk algorithm performed well, showing high levels of discrimination for COVID-19 deaths in men and women for both time periods. QCovid has the potential to be dynamically updated as the pandemic evolves and, therefore, has potential use in guiding national policy. FUNDING: UK National Institute for Health Research.