Vaccinations, cardiovascular drugs, hospitalisation and mortality in COVID-19 and Long COVID.

OBJECTIVE: To identify highest-risk subgroups for COVID-19 and Long COVID(LC), particularly in contexts of influenza and cardiovascular disease(CVD). METHODS: Using national, linked electronic health records for England(NHS England Secure Data Environment via CVD-COVID-UK/COVID-IMPACT Consortium), we studied individuals(of all ages) with COVID-19 and LC (2020-2023). We compared all-cause hospitalisation and mortality by prior CVD, high CV risk, vaccination status(COVID-19/influenza), and CVD drugs, investigating impact of vaccination and CVD prevention using population preventable fractions. RESULTS: Hospitalisation and mortality were 15.3% and 2.0% among 17,373,850 individuals with COVID-19(LC rate 1.3%), and 16.8% and 1.4% among 301,115 with LC. Adjusted risk of mortality and hospitalisation were reduced with COVID-19 vaccination≥2 doses(COVID-19:HR 0.36 and 0.69; LC:0.44 and 0.90). With influenza vaccination, mortality was reduced, but not hospitalisation(COVID-19:0.86 and 1.01, and LC:0.72 and 1.05). Mortality and hospitalisation were reduced by CVD prevention in those with CVD, e.g. anticoagulants- COVID:19:0.69 and 0.92; LC:0.59 and 0.88; lipid lowering- COVID-19:0.69 and 0.86; LC:0.68 and 0.90. COVID-19 vaccination averted 245044 of 321383 and 7586 of 8738 preventable deaths after COVID-19 and LC, respectively. INTERPRETATION: Prior CVD and high CV risk are associated with increased hospitalisation and mortality in COVID-19 and LC. Targeted COVID-19 vaccination and CVD prevention are priority interventions. FUNDING: NIHR. HDR UK

Inter-practice variation in diagnosing hypertension and diabetes mellitus: a cross-sectional study in general practice

BACKGROUND: Previous studies of inter-practice variation of the prevalence of hypertension and diabetes mellitus showed wide variations between practices. However, in these studies inter-practice variation was calculated without controlling for clustering of patients within practices and without adjusting for patient and practice characteristics. Therefore, in the present study inter-practice variation of diagnosed hypertension and diabetes mellitus prevalence rates was calculated by 1) using a multi-level design and 2) adjusting for patient and practice characteristics. METHODS: Data were used from the Netherlands Information Network of General Practice (LINH) in 2004. Of all 168.045 registered patients, the presence of hypertension, diabetes mellitus and all available ICPC coded symptoms and diseases related to hypertension and diabetes, were determined. Also, the characteristics of practices were used in the analyses. Multilevel logistic regression analyses were performed. RESULTS: The 95% prevalence range for the practices for the prevalence of diagnosed hypertension and diabetes mellitus was 66.3 to 181.7 per 1000 patients and 22.2 to 65.8 per 1000 patients, respectively, after adjustment for patient and practice characteristics. The presence of hypertension and diabetes was best predicted by patient characteristics. The most important predictors of hypertension were obesity (OR = 3.5), presence of a lipid disorder (OR = 3.0), and diabetes mellitus (OR = 2.6), whereas the presence of diabetes mellitus was particularly predicted by retinopathy (OR = 8.5), lipid disorders (OR = 2.8) and hypertension (OR = 2.7). CONCLUSION: Although not the optimal case-mix could be used in this study, we conclude that even after adjustment for patient (demographic variables and risk factors for hypertension and diabetes mellitus) and practice characteristics (practice size and presence of a practice nurse), there is a wide difference between general practices in the prevalence rates of diagnosed hypertension and diabetes mellitu

General and abdominal adiposity and hypertension in eight world regions: a pooled analysis of 837 population-based studies with 7·5 million participants

Background Adiposity can be measured using BMI (which is based on weight and height) as well as indices of abdominal adiposity. We examined the association between BMI and waist-to-height ratio (WHtR) within and across populations of different world regions and quantified how well these two metrics discriminate between people with and without hypertension. Methods We used data from studies carried out from 1990 to 2023 on BMI, WHtR and hypertension in people aged 20–64 years in representative samples of the general population in eight world regions. We graphically compared the regional distributions of BMI and WHtR, and calculated Pearson’s correlation coefficients between BMI and WHtR within each region. We used mixed-effects linear regression to estimate the extent to which WHtR varies across regions at the same BMI. We graphically examined the prevalence of hypertension and the distribution of people who have hypertension both in relation to BMI and WHtR, and we assessed how closely BMI and WHtR discriminate between participants with and without hypertension using C-statistic and net reclassification improvement (NRI). Findings The correlation between BMI and WHtR ranged from 0·76 to 0·89 within different regions. After adjusting for age and BMI, mean WHtR was highest in south Asia for both sexes, followed by Latin America and the Caribbean and the region of central Asia, Middle East and north Africa. Mean WHtR was lowest in central and eastern Europe for both sexes, in the high-income western region for women, and in Oceania for men. Conversely, to achieve an equivalent WHtR, the BMI of the population of south Asia would need to be, on average, 2·79 kg/m² (95% CI 2·31–3·28) lower for women and 1·28 kg/m² (1·02–1·54) lower for men than in the high-income western region. In every region, hypertension prevalence increased with both BMI and WHtR. Models with either of these two adiposity metrics had virtually identical C-statistics and NRIs for every region and sex, with C-statistics ranging from 0·72 to 0·81 and NRIs ranging from 0·34 to 0·57 in different region and sex combinations. When both BMI and WHtR were used, performance improved only slightly compared with using either adiposity measure alone. Interpretation BMI can distinguish young and middle-aged adults with higher versus lower amounts of abdominal adiposity with moderate-to-high accuracy, and both BMI and WHtR distinguish people with or without hypertension. However, at the same BMI level, people in south Asia, Latin America and the Caribbean, and the region of central Asia, Middle East and north Africa, have higher WHtR than in the other regions