» Articles » PMID: 38157021

Identifying Heat Thresholds for South Africa Towards the Development of a Heat-health Warning System

Overview
Specialty Biophysics
Date 2023 Dec 29
PMID 38157021
Authors
Affiliations
Soon will be listed here.
Abstract

Exposure to heatwaves may result in adverse human health impacts. Heat alerts in South Africa are currently based on defined temperature-fixed threshold values for large towns and cities. However, heat-health warning systems (HHWS) should incorporate metrics that have been shown to be effective predictors of negative heat-related health outcomes. This study contributes to the development of a HHWS for South Africa that can potentially minimize heat-related mortality. Distributed lag nonlinear models (DLNM) were used to assess the association between maximum and minimum temperature and diurnal temperature range (DTR) and population-adjusted mortality during summer months, and the effects were presented as incidence rate ratios (IRR). District-level thresholds for the best predictor from these three metrics were estimated with threshold regression. The mortality dataset contained records of daily registered deaths (n = 8,476,532) from 1997 to 2013 and data for the temperature indices were for the same period. Maximum temperature appeared to be the most statistically significant predictor of all-cause mortality with strong associations observed in 40 out of 52 districts. Maximum temperature was associated with increased risk of mortality in all but three of the districts. Our results also found that heat-related mortality was influenced by regional climate because the spatial distribution of the thresholds varied according to the climate zones across the country. On average, districts located in the hot, arid interior provinces of the Northern Cape and North West experienced some of the highest thresholds compared to districts located in temperate interior or coastal provinces. As the effects of climate change become more significant, population exposure to heat is increasing. Therefore, evidence-based HHWS are required to reduce heat-related mortality and morbidity. The exceedance of the maximum temperature thresholds provided in this study could be used to issue heat alerts as part of effective heat health action plans.

Citing Articles

The graded heat-health risk forecast and early warning with full-season coverage across China: a predicting model development and evaluation study.

Wang Q, Chen C, Xu H, Liu Y, Zhong Y, Liu J Lancet Reg Health West Pac. 2025; 54:101266.

PMID: 39877409 PMC: 11772993. DOI: 10.1016/j.lanwpc.2024.101266.


Developing a Healthy Environment Assessment Tool (HEAT) to Address Heat-Health Vulnerability in South African Towns in a Warming World.

Wright C, Mathee A, Goldstone C, Naidoo N, Kapwata T, Wernecke B Int J Environ Res Public Health. 2023; 20(4).

PMID: 36833550 PMC: 9957206. DOI: 10.3390/ijerph20042852.

References
1.
Fong Y, Huang Y, B Gilbert P, Permar S . chngpt: threshold regression model estimation and inference. BMC Bioinformatics. 2017; 18(1):454. PMC: 5644082. DOI: 10.1186/s12859-017-1863-x. View

2.
Knowlton K, Rotkin-Ellman M, King G, Margolis H, Smith D, Solomon G . The 2006 California heat wave: impacts on hospitalizations and emergency department visits. Environ Health Perspect. 2009; 117(1):61-7. PMC: 2627866. DOI: 10.1289/ehp.11594. View

3.
Wu Y, Wang X, Wu J, Wang R, Yang S . Performance of heat-health warning systems in Shanghai evaluated by using local heat-related illness data. Sci Total Environ. 2020; 715:136883. DOI: 10.1016/j.scitotenv.2020.136883. View

4.
Gasparrini A . Distributed Lag Linear and Non-Linear Models in R: The Package dlnm. J Stat Softw. 2011; 43(8):1-20. PMC: 3191524. View

5.
Vardoulakis S, Dear K, Hajat S, Heaviside C, Eggen B, McMichael A . Comparative assessment of the effects of climate change on heat- and cold-related mortality in the United Kingdom and Australia. Environ Health Perspect. 2014; 122(12):1285-92. PMC: 4256046. DOI: 10.1289/ehp.1307524. View