{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T07:40:46Z","timestamp":1792136446948,"version":"4.4.0"},"reference-count":40,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2017,3,30]],"date-time":"2017-03-30T00:00:00Z","timestamp":1490832000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"RAND Corporation, Asia-Pacific Policy Research Funding Initiative"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJERPH"],"abstract":"<jats:p>Assessing geographic variability in heat wave vulnerability forms the basis for planning appropriate targeted adaptation strategies. Given several recent deadly heatwaves in India, heat is increasingly being recognized as a public health problem. However, to date there has not been a country-wide assessment of heat vulnerability in India. We evaluated demographic, socioeconomic, and environmental vulnerability factors and combined district level data from several sources including the most recent census, health reports, and satellite remote sensing data. We then applied principal component analysis (PCA) on 17 normalized variables for each of the 640 districts to create a composite Heat Vulnerability Index (HVI) for India. Of the total 640 districts, our analysis identified 10 and 97 districts in the very high and high risk categories (&gt; 2SD and 2-1SD HVI) respectively. Mapping showed that the districts with higher heat vulnerability are located in the central parts of the country. On examination, these are less urbanized and have low rates of literacy, access to water and sanitation, and presence of household amenities. Therefore, we concluded that creating and mapping a heat vulnerability index is a useful first step in protecting the public from the health burden of heat. Future work should incorporate heat exposure and health outcome data to validate the index, as well as examine sub-district levels of vulnerability.<\/jats:p>","DOI":"10.3390\/ijerph14040357","type":"journal-article","created":{"date-parts":[[2017,3,30]],"date-time":"2017-03-30T09:49:54Z","timestamp":1490867394000},"page":"357","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":101,"title":["Heat Wave Vulnerability Mapping for India"],"prefix":"10.3390","volume":"14","author":[{"given":"Gulrez","family":"Azhar","sequence":"first","affiliation":[{"name":"The RAND Corporation, 1776 Main Street, Santa Monica, CA 90401, USA"},{"name":"Pardee RAND Graduate School, 1776 Main Street, Santa Monica, CA 90401, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shubhayu","family":"Saha","sequence":"additional","affiliation":[{"name":"Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Partha","family":"Ganguly","sequence":"additional","affiliation":[{"name":"Indian Institute of Public Health, Gandhinagar, Gujarat 382042, India"},{"name":"Public Health Foundation of India, New Delhi 110070, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dileep","family":"Mavalankar","sequence":"additional","affiliation":[{"name":"Indian Institute of Public Health, Gandhinagar, Gujarat 382042, India"},{"name":"Public Health Foundation of India, New Delhi 110070, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jaime","family":"Madrigano","sequence":"additional","affiliation":[{"name":"The RAND Corporation, 1776 Main Street, Santa Monica, CA 90401, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,3,30]]},"reference":[{"key":"ref_1","unstructured":"Parry, M.L. 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