Abstract

Cardiovascular disease (CVD), reported to relate with climate change, is the leading cause of global mortality and morbidity. Since the relevant information is quite limited from suburbs and countryside in developing and underdeveloped countries, there are no studies that focused on morbidity through diurnal temperature range (DTR) for these regions. This is the first study to evaluate the short-term effect of DTR on CVD hospital admission in suburban farmers, as well as to identify vulnerable subpopulations. Daily time series data of CVD hospital admissions on suburban farmers of Qingyang, China, and meteorological data from 2011 to 2015 were collected, and a distributed lag non-linear model (DLNM) combined with a quasi-Poisson generalized additive regression model (GAM) was used to examine the exposure-response relationship and delayed effect between DTR and CVD hospital admissions. Stratified analyses by age and gender were performed and extreme DTR effects were examined. Non-linear relation between DTR and CVD hospital admissions was observed, and whether DTR lower or higher than the reference (13 °C, 50th percentile) had adverse effect while lower DTR have slightly higher impact. Also, both extreme low and extreme high DTR had adverse effect. Besides, adults (age < 65) and males were more vulnerable to the effects of DTR compared with the old (age ≥ 65) and females, respectively. This study provides evidence that not only high DTR but also low DTR had adverse effects on CVD which should be paid attention to. Adults and males were more vulnerable among suburban farmers. The results are inconsistent with the studies from urban and indicate differences between urban and suburban residents. Multiple factors such as occupations, risk awareness, and lifestyles could have a significant influence on CVD morbidity, and further study is needed to explore more evidence.

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