Exploring the effects of high temperature on mortality in four cities in the Philippines using various heat wave definitions in different mortality subgroups

Abstract

Background: Sustained high temperatures, specifically heat waves (HW), increase the risk of dying, especially among risk populations, which are highly vulnerable to its additional effect. In developing countries, there are only a few studies which focused on the magnitude of the risks attributed to HWs. Objectives: This study explored the HW effects using 15 HW definitions through the combination of duration (> 2, > 4, and > 7 consecutive days) and intensity (at the ≥ 90th, ≥ 95th, ≥ 97th, ≥ 98th, and ≥ 99th temperature percentiles). Methods: Daily mortality count data from 2006–2010 were obtained from the four tropical cities of the Philippines, and were further stratified by mortality sub-groups, such as cause of death, sex, and age. The same period of daily maximum temperature and relative humidity were also collected. We used a distributed lag non-linear model to determine the risks associated with the main temperature effects, as well as the added HW effects. Results: It has been observed that the main temperature effects comprise a substantial portion of the risks compared to the HW effects, even across the mortality sub-groups. Further stratification by the sub-groups showed significant HW effects among the young and male populations. Conclusions: Results of this study can be of use to improve (1) candidate HW definition identification/selection, and (2) risk population-specific strategies, taking into consideration the risk attributions.

Scoping Review of Climate Change and Health Research in the Philippines: A Complementary Tool in Research Agenda-Setting

“…A total of 40 articles and grey literature were found eligible for this review. Only 34 quantitative studies were presented here [14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47].…”

“…Among the time-series studies, six (22%) simply visualized patterns from time-series plots [28,31,39,40,43,47], while the rest used a variety of statistical models. Eight (30%) used general linear models (i.e., simple and multiple linear regressions) [27,32,34,35,38,41,44,45], eight (30%) used generalized linear models (e.g., quasi-poisson/poisson models and distributed lag nonlinear models) [15,19,22,25,33,36,42], two (7%) used autoregressive models (i.e., autoregressive integrated moving average models and seasonal autoregressive integrated moving average models) [37,44], two (7%) used wavelet analysis [16,20], and four (15%) used other kinds of models (i.e., general additive model [14,15], spectral analysis [18], dynamic linear model [16], and transfer entropy [46]). The temporal resolutions used in the time-series studies were daily (19%, 5/27), weekly (15%, 4/27), monthly (63%, 17/27), and annual (7%, 2/27).…”

“…The temporal resolutions used in the time-series studies were daily (19%, 5/27), weekly (15%, 4/27), monthly (63%, 17/27), and annual (7%, 2/27). Only 33% of these studies (9/27) considered temporal lags of exposure to health outcomes [14,15,18,22,25,36,37,42,44]. All analyzed short-term associations of disease patterns were on preceding and concurrent exposure, but only seven studies controlled temporal trends and seasonal patterns [14,15,22,25,36,37,42].…”

Meteorological factors affecting dengue incidence in Davao, Philippines

“…According to the Philippine Atmospheric Geophysical and Astronomical Services Administration (PAGASA), wet season occurs from the months of April to October, while dry season occurs from the months of November to March [ 37 ]. There are four existing climate types in the Philippines based on the country’s modified Coronas classification, namely Type I (dry from November to April, wet from May to October), Type II (seasonal rainfall from November to December), Type III (same as Type I, but with maximum rainfall from May to October) and Type IV (even distribution of rainfall year-round) [ 38 ]. Davao region is classified under Type IV with an annual average temperature of 28 °C; a temperature average shared with other major cities across the country [ 39 ].…”

Effects of high ambient temperature on ambulance dispatches in different age groups in Fukuoka, Japan

“…It has been suggested that younger people may be more susceptible to high temperature than older people because of a longer working day [47]. Indeed, based on the 2006 Survey on Time Use and Leisure Activities by the Japan Ministry of Internal Affairs and Communications Statistics Bureau, the mean time spent working and commuting is much longer for younger people than for the elderly.…”