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Background Nationwide data on the prevalence of atrioventricular (AV) block are currently unavailable in China. Thus, we aimed to assess the prevalence and risk factors of AV block among Chinese health examination adults. Methods A total of 15,181,402 participants aged ≥ 18 years (mean age 41.5 ± 13.4 years, 53.2% men) who underwent an electrocardiogram as a part of routine health examination in 2018 were analyzed. AV block was diagnosed by physicians using 12-lead electrocardiogram. Overall and stratified prevalence (by age, sex, and city size) of all, first-, second- and third-degree AV block were calculated. Multivariable logistic regression analyses were performed to explore risk factors associated with AV block. Results AV block was observed in 88,842 participants, including 86,153 with first-degree, 2249 with second-degree and 440 with third-degree AV block. The age- and sex-standardized prevalence rate [95% confidence interval (CI)] of all, first-, second- and third-degree AV block were 7.06‰ (7.01–7.11), 6.84‰ (6.79–6.89), 0.18‰ (0.17–0.18) and 0.04‰ (0.03–0.04) respectively. After multivariable adjustment, the risk of AV block was positively associated with older age, being male, lower heart rate, higher body mass index, hypertension, diabetes and low high-density lipoprotein cholesterol. High total cholesterol was associated with a lower risk of AV block. Conclusion First-degree AV block is relatively common while severe AV block is rare in health examination adults. Besides, AV block was highly prevalent among the elderly. The risk of AV block was associated with older age, being male and metabolic factors.

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Background Air pollution health studies have been increasingly using prediction models for exposure assessment even in areas without monitoring stations. To date, most studies have assumed that a single exposure model is correct, but estimated effects may be sensitive to the choice of exposure model. Methods We obtained county-level daily cardiovascular (CVD) admissions from the New York (NY) Statewide Planning and Resources Cooperative System (SPARCS) and four sets of fine particulate matter (PM2.5) spatio-temporal predictions (2002–2012). We employed overdispersed Poisson models to investigate the relationship between daily PM2.5 and CVD, adjusting for potential confounders, separately for each state-wide PM2.5 dataset. Results For all PM2.5 datasets, we observed positive associations between PM2.5 and CVD. Across the modeled exposure estimates, effect estimates ranged from 0.23% (95%CI: -0.06, 0.53%) to 0.88% (95%CI: 0.68, 1.08%) per 10 µg/m3 increase in daily PM2.5. We observed the highest estimates using monitored concentrations 0.96% (95%CI: 0.62, 1.30%) for the subset of counties where these data were available. Conclusions Effect estimates varied by a factor of almost four across methods to model exposures, likely due to varying degrees of exposure measurement error. Nonetheless, we observed a consistently harmful association between PM2.5 and CVD admissions, regardless of model choice.