A Cross-Sectional, Randomized Cluster Sample Survey of Household Vulnerability to Extreme Heat among Slum Dwellers in Ahmedabad, India

• Type of Material: Article
• Authors: Kathy V. Tran, Emory University; Gulrez S. Azhar, Indian Institute of Public Health; Rajesh Nair, Indian Institute of Public Health; Kim Knowlton, Ahmedabad Heat and Climate Study Group; Anjali Jaiswal, Ahmedabad Heat and Climate Study Group; Perry Sheffield, Icahn School of Medicine at Mount Sinai; Dileep Mavalankar, Indian Institute of Public Health; Jeremy Hess, Emory University
• Language: English
• Date: 2013-06-18
• Publisher: MDPI
• Publication Version: Final Published Version
• Copyright Statement: © 2013 Tran et al.; licensee MDPI, Basel, Switzerland.
• License: Creative Commons Attribution 3.0 Unported
• Final Published Version (URL): http://www.mdpi.com/1660-4601/10/6/2515
• Title of Journal or Parent Work: International Journal of Environmental Research and Public Health
• ISSN: 1660-4601
• Volume: 10
• Issue: 6
• Start Page: 2515
• End Page: 2543
• Grant/Funding Information: This study was financially supported by NRDC, NRDC’s Science Center, and Emory Rollins School of Public Health Travel Fund.
• Research Categories: Health Sciences, Public Health; Environmental Sciences

Abstract

Extreme heat is a significant public health concern in India; extreme heat hazards are projected to increase in frequency and severity with climate change. Few of the factors driving population heat vulnerability are documented, though poverty is a presumed risk factor. To facilitate public health preparedness, an assessment of factors affecting vulnerability among slum dwellers was conducted in summer 2011 in Ahmedabad, Gujarat, India. Indicators of heat exposure, susceptibility to heat illness, and adaptive capacity, all of which feed into heat vulnerability, was assessed through a cross-sectional household survey using randomized multistage cluster sampling. Associations between heat-related morbidity and vulnerability factors were identified using multivariate logistic regression with generalized estimating equations to account for clustering effects. Age, preexisting medical conditions, work location, and access to health information and resources were associated with self-reported heat illness. Several of these variables were unique to this study. As sociodemographics, occupational heat exposure, and access to resources were shown to increase vulnerability, future interventions (e.g., health education) might target specific populations among Ahmedabad urban slum dwellers to reduce vulnerability to extreme heat. Surveillance and evaluations of future interventions may also be worthwhile.