IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v16y2019i12p2089-d239369.html
   My bibliography  Save this article

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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Amruta Nori-Sarma & Tarik Benmarhnia & Ajit Rajiva & Gulrez Shah Azhar & Prakash Gupta & Mangesh S. Pednekar & Michelle L. Bell, 2019. "Advancing our Understanding of Heat Wave Criteria and Associated Health Impacts to Improve Heat Wave Alerts in Developing Country Settings," IJERPH, MDPI, vol. 16(12), pages 1-13, June.
  • Handle: RePEc:gam:jijerp:v:16:y:2019:i:12:p:2089-:d:239369
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/16/12/2089/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/16/12/2089/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Amruta Nori-Sarma & Anobha Gurung & Gulrez Shah Azhar & Ajit Rajiva & Dileep Mavalankar & Perry Sheffield & Michelle L. Bell, 2017. "Opportunities and Challenges in Public Health Data Collection in Southern Asia: Examples from Western India and Kathmandu Valley, Nepal," Sustainability, MDPI, vol. 9(7), pages 1-9, June.
    2. Vandentorren, S. & Suzan, F. & Medina, S. & Pascal, M. & Maulpoix, A. & Cohen, J.-C. & Ledrans, M., 2004. "Mortality in 13 French cities during the August 2003 heat wave," American Journal of Public Health, American Public Health Association, vol. 94(9), pages 1518-1520.
    3. Kaiser, R. & Le Tertre, A. & Schwartz, J. & Gotway, C.A. & Daley, W.R. & Rubin, C.H., 2007. "The effect of the 1995 heat wave in Chicago on all-cause and cause-specific mortality," American Journal of Public Health, American Public Health Association, vol. 97(S1), pages 158-162.
    4. Dianne Lowe & Kristie L. Ebi & Bertil Forsberg, 2011. "Heatwave Early Warning Systems and Adaptation Advice to Reduce Human Health Consequences of Heatwaves," IJERPH, MDPI, vol. 8(12), pages 1-26, December.
    5. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, June.
    6. Whitman, S. & Good, G. & Donoghue, E.R. & Benbow, N. & Shou, W. & Mou, S., 1997. "Mortality in Chicago attributed to the July 1995 heat wave," American Journal of Public Health, American Public Health Association, vol. 87(9), pages 1515-1518.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Fariha Hasan & Shayan Marsia & Kajal Patel & Priyanka Agrawal & Junaid Abdul Razzak, 2021. "Effective Community-Based Interventions for the Prevention and Management of Heat-Related Illnesses: A Scoping Review," IJERPH, MDPI, vol. 18(16), pages 1-14, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Simon Gosling & Jason Lowe & Glenn McGregor & Mark Pelling & Bruce Malamud, 2009. "Associations between elevated atmospheric temperature and human mortality: a critical review of the literature," Climatic Change, Springer, vol. 92(3), pages 299-341, February.
    2. John Nairn & Bertram Ostendorf & Peng Bi, 2018. "Performance of Excess Heat Factor Severity as a Global Heatwave Health Impact Index," IJERPH, MDPI, vol. 15(11), pages 1-26, November.
    3. Sue Smith & Alex J. Elliot & Shakoor Hajat & Angie Bone & Chris Bates & Gillian E. Smith & Sari Kovats, 2016. "The Impact of Heatwaves on Community Morbidity and Healthcare Usage: A Retrospective Observational Study Using Real-Time Syndromic Surveillance," IJERPH, MDPI, vol. 13(1), pages 1-12, January.
    4. Steffen Merte, 2017. "Estimating heat wave-related mortality in Europe using singular spectrum analysis," Climatic Change, Springer, vol. 142(3), pages 321-330, June.
    5. Sumi Hoshiko & Paul English & Daniel Smith & Roger Trent, 2010. "A simple method for estimating excess mortality due to heat waves, as applied to the 2006 California heat wave," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 55(2), pages 133-137, April.
    6. Dimitris Bertsimas & Agni Orfanoudaki & Rory B. Weiner, 2020. "Personalized treatment for coronary artery disease patients: a machine learning approach," Health Care Management Science, Springer, vol. 23(4), pages 482-506, December.
    7. Clément de Chaisemartin & Jaime Ramirez-Cuellar, 2024. "At What Level Should One Cluster Standard Errors in Paired and Small-Strata Experiments?," American Economic Journal: Applied Economics, American Economic Association, vol. 16(1), pages 193-212, January.
    8. Clément de Chaisemartin & Luc Behaghel, 2020. "Estimating the Effect of Treatments Allocated by Randomized Waiting Lists," Econometrica, Econometric Society, vol. 88(4), pages 1453-1477, July.
    9. Bruno Ferman & Cristine Pinto & Vitor Possebom, 2020. "Cherry Picking with Synthetic Controls," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 39(2), pages 510-532, March.
    10. Peydró, José-Luis & Jiménez, Gabriel & Kenan, Huremovic & Moral-Benito, Enrique & Vega-Redondo, Fernando, 2020. "Production and financial networks in interplay: Crisis evidence from supplier-customer and credit registers," CEPR Discussion Papers 15277, C.E.P.R. Discussion Papers.
    11. Marie Bjørneby & Annette Alstadsæter & Kjetil Telle, 2018. "Collusive tax evasion by employers and employees. Evidence from a randomized fi eld experiment in Norway," Discussion Papers 891, Statistics Norway, Research Department.
    12. Davide Viviano & Jelena Bradic, 2019. "Synthetic learner: model-free inference on treatments over time," Papers 1904.01490, arXiv.org, revised Aug 2022.
    13. Chenchuan (Mark) Li & Ulrich K. Müller, 2021. "Linear regression with many controls of limited explanatory power," Quantitative Economics, Econometric Society, vol. 12(2), pages 405-442, May.
    14. Jeon, Sung-Hee & Pohl, R. Vincent, 2019. "Medical innovation, education, and labor market outcomes of cancer patients," Journal of Health Economics, Elsevier, vol. 68(C).
    15. Johnsen, Åshild A. & Kvaløy, Ola, 2021. "Conspiracy against the public - An experiment on collusion11“People of the same trade seldom meet together, even for merriment and diversion, but the conversation ends in a conspiracy against the publ," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 94(C).
    16. Pedro H. C. Sant'Anna & Xiaojun Song & Qi Xu, 2022. "Covariate distribution balance via propensity scores," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(6), pages 1093-1120, September.
    17. Sung Jae Jun & Sokbae Lee, 2024. "Causal Inference Under Outcome-Based Sampling with Monotonicity Assumptions," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(3), pages 998-1009, July.
    18. Caloffi, Annalisa & Freo, Marzia & Ghinoi, Stefano & Mariani, Marco & Rossi, Federica, 2022. "Assessing the effects of a deliberate policy mix: The case of technology and innovation advisory services and innovation vouchers," Research Policy, Elsevier, vol. 51(6).
    19. Reizer, Balázs, 2022. "Employment and Wage Consequences of Flexible Wage Components," Labour Economics, Elsevier, vol. 78(C).
    20. Jiannan Lu & Peng Ding & Tirthankar Dasgupta, 2018. "Treatment Effects on Ordinal Outcomes: Causal Estimands and Sharp Bounds," Journal of Educational and Behavioral Statistics, , vol. 43(5), pages 540-567, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:16:y:2019:i:12:p:2089-:d:239369. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.