Advancing our Understanding of Heat Wave Criteria and Associated Health Impacts to Improve Heat Wave Alerts in Developing Country Settings

Amruta Nori-Sarma, Tarik Benmarhnia, Ajit Rajiva, Gulrez Shah Azhar, Prakash Gupta, Mangesh S. Pednekar and Michelle L. Bell
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Amruta Nori-Sarma: Yale School of Forestry & Environmental Studies, New Haven, CT 06511, USA
Tarik Benmarhnia: Department of Family Medicine and Public Health and Scripps Institute of Oceanography, University of California at San Diego, La Jolla, CA 92093, USA
Ajit Rajiva: Yale School of Forestry & Environmental Studies, New Haven, CT 06511, USA
Gulrez Shah Azhar: Pardee RAND Graduate School, Santa Monica, CA 90401, USA
Prakash Gupta: Healis-Sekhsaria Institute for Public Health, Navi Mumbai, Maharashtra 400 701, India
Mangesh S. Pednekar: Healis-Sekhsaria Institute for Public Health, Navi Mumbai, Maharashtra 400 701, India
Michelle L. Bell: Yale School of Forestry & Environmental Studies, New Haven, CT 06511, USA

IJERPH, 2019, vol. 16, issue 12, 1-13

Abstract: Health effects of heat waves with high baseline temperatures in areas such as India remain a critical research gap. In these regions, extreme temperatures may affect the underlying population’s adaptive capacity; heat wave alerts should be optimized to avoid continuous high alert status and enhance constrained resources, especially under a changing climate. Data from registrars and meteorological departments were collected for four communities in Northwestern India. Propensity Score Matching (PSM) was used to obtain the relative risk of mortality and number of attributable deaths (i.e., absolute risk which incorporates the number of heat wave days) under a variety of heat wave definitions ( n = 13) incorporating duration and intensity. Heat waves’ timing in season was also assessed for potential effect modification. Relative risk of heat waves (risk of mortality comparing heat wave days to matched non-heat wave days) varied by heat wave definition and ranged from 1.28 [95% Confidence Interval: 1.11–1.46] in Churu (utilizing the 95th percentile of temperature for at least two consecutive days) to 1.03 [95% CI: 0.87–1.23] in Idar and Himmatnagar (utilizing the 95th percentile of temperature for at least four consecutive days). The data trended towards a higher risk for heat waves later in the season. Some heat wave definitions displayed similar attributable mortalities despite differences in the number of identified heat wave days. These findings provide opportunities to assess the “efficiency” (or number of days versus potential attributable health impacts) associated with alternative heat wave definitions. Findings on both effect modification and trade-offs between number of days identified as “heat wave” versus health effects provide tools for policy makers to determine the most important criteria for defining thresholds to trigger heat wave alerts.

Keywords: climate change; extreme temperature events; heat waves; human health; mortality; PSM; temperature-mortality relationships (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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