Faculty of Pharmacy, Laval University, 1050 Avenue de la Médecine, Quebec, QC G1V 0A6, Canada.
TEIAC Unity, Quebec National Institute of Public Health, 945 Avenue Wolfe, Quebec, QC G1V 5B3, Canada.
Int J Environ Res Public Health. 2021 Dec 16;18(24):13277. doi: 10.3390/ijerph182413277.
Cardiovascular morbidity and mortality are influenced by meteorological conditions, such as temperature or snowfall. Relationships between cardiovascular health and meteorological conditions are usually studied based on specific meteorological events or means. However, those studies bring little to no insight into health peaks and unusual events far from the mean, such as a day with an unusually high number of hospitalizations. Health peaks represent a heavy burden for the public health system; they are, however, usually studied specifically when they occur (e.g., the European 2003 heatwave). Specific analyses are needed, using appropriate statistical tools. Quantile regression can provide such analysis by focusing not only on the conditional median, but on different conditional quantiles of the dependent variable. In particular, high quantiles of a health issue can be treated as health peaks. In this study, quantile regression is used to model the relationships between conditional quantiles of cardiovascular variables and meteorological variables in Montreal (Canada), focusing on health peaks. Results show that meteorological impacts are not constant throughout the conditional quantiles. They are stronger in health peaks compared to quantiles around the median. Results also show that temperature is the main significant variable. This study highlights the fact that classical statistical methods are not appropriate when health peaks are of interest. Quantile regression allows for more precise estimations for health peaks, which could lead to refined public health warnings.
心血管发病率和死亡率受气象条件的影响，如温度或降雪。心血管健康与气象条件之间的关系通常是基于特定的气象事件或手段进行研究的。然而，这些研究几乎无法揭示远离平均值的健康峰值和异常事件，例如住院人数异常高的一天。健康峰值给公共卫生系统带来了沉重的负担；然而，当它们发生时，通常会专门进行研究（例如，2003 年欧洲热浪）。需要使用适当的统计工具进行具体分析。分位数回归可以通过不仅关注条件中位数，还关注因变量的不同条件分位数来提供这种分析。特别是，可以将健康问题的高分位数视为健康峰值。在这项研究中，分位数回归用于对加拿大蒙特利尔的心血管变量和气象变量的条件分位数之间的关系进行建模，重点关注健康峰值。结果表明，气象影响在条件分位数中并非是恒定的。与中位数附近的分位数相比，它们在健康峰值时更强。结果还表明，温度是主要的显著变量。本研究强调了一个事实，即当健康峰值是研究重点时，经典的统计方法并不适用。分位数回归可以更精确地估计健康峰值，这可能会导致更精细的公共卫生预警。
