Abstract
BACKGROUND: Short-term cold spells and heat events are commonly considered risk factors for cardiovascular disease (CVD). This study quantitatively examined the effects of country-specific "climate-patterned temperature" (T(MP)), measured as long-term mean temperature, on global CVD incidence. METHODS: Recently published country-specific data on CVD incidence and T(MP) were analysed for statistical correlations at the population level using Microsoft Excel and SPSS. Confounding effects of humidity, aging, GDP PPP, obesity prevalence, and urbanization were controlled. Fisher r-to-z transformation compared correlation coefficients. RESULTS: Pearson's r and nonparametric analyses revealed a significant inverse correlation between T(MP) and CVD incidence worldwide (r = -0.646 and -0.574, respectively, p < 0.001). This relationship remained significant after controlling for confounders in a partial correlation model (r = -0.584, p < 0.001). Multiple linear regression showed T(MP) as a significant and independent predictor of CVD incidence (Beta = -0.384, p < 0.001). Stepwise regression identified aging as the most influential factor (R(2) = 0.591), with T(MP) and GDP PPP following, increasing R(2) to 0.731 and 0.747, respectively. Humidity, obesity prevalence, and urbanization were not significant predictors. T(MP) had a stronger predictive effect on CVD incidence in high-income countries compared to low- and middle-income countries (z = 1.96 and 2.28 in Pearson's r and nonparametric models, respectively, p < 0.05). CONCLUSIONS: Long-term lower mean temperature (T(MP)) is a significant and independent risk factor for CVD worldwide, particularly in developed countries. T(MP) should be considered in epidemiological studies of CVD.