The impact of heat waves on mortality in Northwest India
Introduction
Heat waves have been shown to substantially impact human health. The 2003 French heat wave increased mortality up to 137% in Paris, with 14,800 excess deaths across 6 cities over 19 days (Filleul et al., 2006; Vandentorren et al., 2004). A 2010 heat wave in India increased all-cause mortality 41% (Azhar et al., 2014). Extreme temperatures are frequently reported in some developing countries such as India, where news reports of melting pavements during a 2015 heat wave made international headlines (Kim, 1341). Understanding how heat waves affect health is key to preparing vulnerable communities and to estimating the health impacts of climate change, which is anticipated to increase the frequency, duration, and intensity of heat waves (Climate Change and India, 2010). In addition to increasing baseline temperatures, unprecedented urbanization and the resulting urban heat island effect may pose further threats to rapidly developing nations. Scientific evidence on the impact of extreme temperatures in urban environments in developing country settings is therefore a critically important research gap.
A key challenge to the study of heat waves and health for developing country settings is a lack of health data. In such areas, standardized systems for health data collection for the purpose of conducting health research may not exist (Nori-Sarma et al., 2017). Additionally, local climate conditions in environments such as India with high baseline temperatures present challenges for extrapolating exposure-response functions from previous studies, which are primarily based in US and Europe with different populations and housing characteristics.
There exists no standard definition of a “heat wave”, in scientific research or policy. Meteorologists within the Indian Meteorological Department (IMD) use community-specific parameters to define conditions for when a “heat wave” is declared based on the historical temperature record of a given location, with forecasts limited to the temperature today and tomorrow (rather than predicting multiple days in advance). The IMD definition of a heat wave allows for a single day of high temperature to be declared a “heat wave”. (Weather Forecasting - Glo) This contrasts with more common definitions in the academic literature, in which at least two or more consecutive days of high temperature are required for a period to constitute a heat wave (National Weather Service, 2009). One impetus for our study is to understand the important ways in which the local policy-relevant definition of heat waves differs from the versions in the academic literature, and how our understanding of the health effects of heat waves may vary with these different heat wave definitions.
We estimated the mortality effects of heat waves in Northwest India for 2005–2012, as well as the heat wave effect beyond the effect of single days of high temperature. We determined how these associations changed when heat waves were more intense, longer, or later in the warm season. While previous literature in developed country settings showed that heat waves that occur earlier in the warm season are more impactful on health than later heat waves (Anderson and Bell, 2009, 2011; Hajat et al., 2006), we hypothesize that this may not be the case for our study region. In cooler climates, sensitive populations may succumb earlier to intense heat waves and other populations may have developed resilience to extreme heat. In our study population, resilience and adaptive capacity potentially differ at baseline, which may lead to differences in the impact of heat wave timing in season on health.
Section snippets
Data
Daily all-cause mortality data were obtained for five cities in Northwest India (Fig. 1) (Nori-Sarma et al., 2017).
Mortality data were collected from local municipal governments, which maintain registries for the purpose of transfer of property in accordance with rules of registration of death set forth by the Civil Registration System, in the Office of the Registrar General of India. Individual-level data were available for four cities and aggregated daily data (i.e., number of persons who
Results
We estimated the non-linear relationship between temperature and mortality risk for each community, using Equation [1]. Fig. 2 illustrates the estimated relative risk of mortality for each community for a given temperature compared to a reference temperature of 30 °C, representing the approximate average warm season daily mean temperature across all of the communities. This reference temperature is high relative to that used in prior studies, which have used values such as 15 °C (Anderson and
Discussion
Our study area has higher baseline temperatures than that of most previous studies on temperature and health (e.g., studies conducted in North America (Anderson and Bell, 2009; Anderson and Bell, 2011; Curriero et al., 2002) and Europe (Vandentorren et al., 2004), which center around 65-70 °F or 25 °C). For all our study communities, estimated mortality impact of temperature persisted at high temperatures, including temperatures higher than those observed in the prior literature. This indicates
Funding sources
This project was partially supported by a Fulbright Program grant sponsored by the Bureau of Educational and Cultural Affairs of the United States Department of State and administered by the Institute of International Education.
This publication was developed under Assistance Agreement No. RD835871 awarded by the U.S. Environmental Protection Agency to Yale University and Assistance Agreement No. FP-91782101-0 awarded by the U.S. Environmental Protection Agency to Amruta Nori-Sarma. It has not
IRB approval
This project involves analysis of human subject data and received clearance from the Yale Human Subjects Committee, Yale University Human Research Protection Program (HSC id # 1405014004).
Acknowledgments:
The authors acknowledge the Ahmedabad Heat and Climate Study Group, the Jaipur Municipal Corporation, and local governments of Idar, Himmatnagar, Churu, and Colaba Island (Mumbai) for their cooperation, and the Bhoruka Charitable Trust for support in data collection.
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