The impact of temperature on mortality in a subtropical city: effects of cold, heat, and heat waves in São Paulo, Brazil

Abstract

Understanding how weather impacts health is critical, especially under a changing climate; however, relatively few studies have investigated subtropical regions. We examined how mortality in São Paulo, Brazil, is affected by cold, heat, and heat waves over 14.5 years (1996-2010). We used over-dispersed generalized linear modeling to estimate heat- and cold-related mortality, and Bayesian hierarchical modeling to estimate overall effects and modification by heat wave characteristics (intensity, duration, and timing in season). Stratified analyses were performed by cause of death and individual characteristics (sex, age, education, marital status, and place of death). Cold effects on mortality appeared higher than heat effects in this subtropical city with moderate climatic conditions. Heat was associated with respiratory mortality and cold with cardiovascular mortality. Risk of total mortality was 6.1% (95% confidence interval 4.7, 7.6%) higher at the 99th percentile of temperature than the 90th percentile (heat effect) and 8.6% (6.2, 11.1%) higher at the 1st compared to the 10th percentile (cold effect). Risks were higher for females and those with no education for heat effect, and males for cold effect. Older persons, widows, and non-hospital deaths had higher mortality risks for heat and cold. Mortality during heat waves was higher than on non-heat wave days for total, cardiovascular, and respiratory mortality. Our findings indicate that mortality in São Paulo is associated with both cold and heat and that some subpopulations are more vulnerable.

Keywords: Cold; Heat; Heat waves; Mortality; Temperature.

Figure 1

Relationship between temperature and risk of total mortality, comparing various temperature levels with a reference temperature of 20.5°C. Note: lag 0-1 days for heat effect; lag 0–20 days for cold effect. The shaded portions of the curves represent 95% confidence intervals. The bars represent the ranges of the curves measured as heat effects (dark yellow: 99th–90th percentile; light yellow: 90th–50th percentile) and cold effects (dark gray: 1st– 10th percentile; light gray: 10th–50th percentile).

Figure 2

Percentage change in total mortality risk for (A) heat (comparison of 99th vs. 90th percentile of mean temperature) and (B) cold (comparison of 1st vs. 10th percentile of mean temperature) by sex, age, education, marital status, and place of death in São Paulo. Note: The points represent the central estimates, and the vertical lines represent 95% confidence intervals. Lag0-1 for heat effect; Lag0-20 for cold effect.

Figure 2

Percentage change in total mortality risk for (A) heat (comparison of 99th vs. 90th percentile of mean temperature) and (B) cold (comparison of 1st vs. 10th percentile of mean temperature) by sex, age, education, marital status, and place of death in São Paulo. Note: The points represent the central estimates, and the vertical lines represent 95% confidence intervals. Lag0-1 for heat effect; Lag0-20 for cold effect.

Figure 3

Percentage change in total mortality risk for heat wave effect by sex, age, education, marital status, and place of death in São Paulo. Note: The points represent the central estimates, and the vertical lines represent 95% confidence intervals. Heat wave effects were estimated based on the heat wave definition of temperatures ≥96th percentile for ≥ 2 days.