Effects of temperature on mortality in Chiang Mai city, Thailand: a time series study

Abstract

Background: The association between temperature and mortality has been examined mainly in North America and Europe. However, less evidence is available in developing countries, especially in Thailand. In this study, we examined the relationship between temperature and mortality in Chiang Mai city, Thailand, during 1999-2008.

Method: A time series model was used to examine the effects of temperature on cause-specific mortality (non-external, cardiopulmonary, cardiovascular, and respiratory) and age-specific non-external mortality (<=64, 65-74, 75-84, and > =85 years), while controlling for relative humidity, air pollution, day of the week, season and long-term trend. We used a distributed lag non-linear model to examine the delayed effects of temperature on mortality up to 21 days.

Results: We found non-linear effects of temperature on all mortality types and age groups. Both hot and cold temperatures resulted in immediate increase in all mortality types and age groups. Generally, the hot effects on all mortality types and age groups were short-term, while the cold effects lasted longer. The relative risk of non-external mortality associated with cold temperature (19.35°C, 1st percentile of temperature) relative to 24.7°C (25th percentile of temperature) was 1.29 (95% confidence interval (CI): 1.16, 1.44) for lags 0-21. The relative risk of non-external mortality associated with high temperature (31.7°C, 99th percentile of temperature) relative to 28°C (75th percentile of temperature) was 1.11 (95% CI: 1.00, 1.24) for lags 0-21.

Conclusion: This study indicates that exposure to both hot and cold temperatures were related to increased mortality. Both cold and hot effects occurred immediately but cold effects lasted longer than hot effects. This study provides useful data for policy makers to better prepare local responses to manage the impact of hot and cold temperatures on population health.

Figure 1

Relative risks of cause-specific mortality by mean temperature (°C) at lag 0–2 (left), lag 0–13 (middle), and lag 0–21 (right), using a “natural cubic spline-natural cubic spline” DLNM with 5 degrees of freedom natural cubic spline for temperature and 4 degrees of freedom for lag. The reference value was median temperature (26.8°C). The air pollution, relative humidity, day of the week, and season were controlled for

Figure 2

Relative risks of age-specific non-external mortality by mean temperature (°C) at lag 0–2 (left), lag 0–13 (middle), and lag 0–21 (right), using a “natural cubic spline-natural cubic spline” DLNM with 5 degrees of freedom natural cubic spline for temperature and 4 degrees of freedom for lag. The reference value was median temperature (26.8°C). The air pollution, relative humidity, day of the week, and season were controlled for

Figure 3

The estimated cold and hot effects of mean temperature on cause-specific mortality along the lag days, using 4 degrees of freedom natural cubic spline for lag. The cold effect (left) was estimated by 1^st percentile of temperature (19.35°C) relative to 25^th percentile of temperature (24.7°C)). The hot effect (right) was estimated by 99^th percentile of temperature (31.7°C) relative to 75^th percentile of temperature (28°C). The green lines are mean relative risks, and purple regions are 95% confidence intervals

Figure 4

The estimated cold and hot effects of mean temperature on age-specific non-external mortality along the lag days, using 4 degrees of freedom natural cubic spline for lag. The cold effect (left) was estimated by 1^st percentile of temperature (19.35°C) relative to 25^th percentile of temperature (24.7°C)). The hot effect (right) was estimated by 99^th percentile of temperature (31.7°C) relative to 75^th percentile of temperature (28°C). The green lines are mean relative risks, and purple regions are 95% confidence intervals