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Extreme Heat Related Mortality: Spatial Patterns and Determinants in the United States, 1979–2011

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Abstract

Extreme heat has been responsible for more deaths in the United States than any other weather-related phenomenon over the past decade. The frequency and intensity of extreme heat events are projected to increase over the course of this century. In this work, we examine historical patterns of extreme heat exposure and mortality in the continental United States. We examine spatial variation in the mortality response to exposure, consider the contribution of key demographic and socio-economic factors in driving heat-related mortality, and test three different extreme heat thresholds using a national-level spatial autoregressive model and a geographically weighted regression approach. We find that the mortality response to exposure is higher in areas that do not routinely experience heat extremes, and that exposure itself is a stronger driver of heat-related mortality across the larger urban areas of the Midwest and Northeast. The importance of demographic/socio-economic factors varies substantially over space, and results are robust across alternative measures of heat extremes, suggesting that no single definition is necessarily superior. The baseline relationships established here are potentially useful for future predictions of exposure and heat-related mortality under alternative population and climate change scenarios, and may aid policy makers and planners in implementing effective adaptation and mitigation strategies.

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Notes

  1. An alternative would be to solve for the heat threshold necessary to produce a certain mortality response at different points in space (i.e., a fixed mortality response).

  2. Multicollinearity is not a significant factor in the model, and thus is not likely the source of this result. We also controlled for a potential regional effect using census region, which yielded no change in outcome.

References

  • Abatzoglou, J. T. (2013). Development of gridded surface meteorological data for ecological applications and modelling. International Journal of Climatology, 33(1), 121–131. https://doi.org/10.1002/joc.3413.

    Article  Google Scholar 

  • Abatzoglou, J. T. (2019). gridMET.

  • Alberini, A., Mastrangelo, E., & Pitcher, H. (2008). Climate change and human health: Assessing the effectiveness of adaptation to heat waves. California: Paper presented at the Association of Environmental and Resources Economists Berkeley.

    Google Scholar 

  • American Medical Association. (2012). Preparing for the ICD-10 code set: October 1, 2014 compliance date.

  • Anderson, B. G., & Bell, M. L. (2009). Weather-related mortality: How heat, cold, and heat waves affect mortality in the United States. Epidemiology, 20(2), 205–213. https://doi.org/10.1097/EDE.0b013e318190ee08.

    Article  Google Scholar 

  • Azhar, G. S., Mavalankar, D., Nori-Sarma, A., Rajiva, A., Dutta, P., Jaiswal, A., et al. (2014). Heat-related mortality in India: excess all-cause mortality associated with the 2010 Ahmedabad heat wave. PLoS ONE, 9(3), e91831–e91831. https://doi.org/10.1371/journal.pone.0091831.

    Article  Google Scholar 

  • Ballester, J., Robine, J.-M., Herrmann, F. R., & Rodó, X. (2011). Long-term projections and acclimatization scenarios of temperature-related mortality in Europe. Nature Communications, 2(1), 358. https://doi.org/10.1038/ncomms1360.

    Article  Google Scholar 

  • Barnett, A. G., Tong, S., & Clements, A. C. A. (2010). What measure of temperature is the best predictor of mortality? Environmental Research, 110(6), 604–611. https://doi.org/10.1016/j.envres.2010.05.006.

    Article  Google Scholar 

  • Barreca, A.I., Clay, K., Deschenes, O., Greenstone, M., & Shapiro, J. S. (2015). Adapting to climate change: The remarkable decline in the U.S. temperature-mortality relationship over the 20th century. In Forschungsinstitut zur Zukunft der Arbeit/Institute for the Study of Labor (Ed.). Bonn, Germany.

  • Barreca, A. I. (2012). Climate change, humidity, and mortality in the United States. Journal of Environmental Economics and Management, 63(1), 19–34. https://doi.org/10.1016/j.jeem.2011.07.004.

    Article  Google Scholar 

  • Basu, R. (2009). High ambient temperature and mortality: a review of epidemiologic studies from 2001 to 2008. Environmental Health, 8(40), 1–13.

    Google Scholar 

  • Basu, R., & Samet, J. M. (2002). Relation between elevated ambient temperature and mortality: A review of the epidemiologic evidence. Epidemiologic Reviews, 24(2), 190–202. https://doi.org/10.1093/epirev/mxf007.

    Article  Google Scholar 

  • Borden, K., & Cutter, S. (2008). Spatial patterns of natural hazards mortality in the United States. International Journal of Health Geographics, 7(1), 64.

    Article  Google Scholar 

  • Cardona, O. D., Aalst, M. K. V., Birkmann, J., Fordham, M., McGregor, G., Perez, R., et al. (2012). Determinants of risk: Exposure and vulnerability. In C. B. Field, V. Barros, T. F. Stocker, Q. Dahe, D. J. Dokken, G.-K. Plattner, et al. (Eds.), Managing the risks of extreme events and disasters to advance climate change adaptation. A special report of working groups i and ii of the intergovernmental panel on climate change (pp. 65–108). Geneva: Intergovernmental Panel on Climate Change.

  • Centers for Disease Control and Prevention. (2013). Climate change and extreme heat events.

  • Centers for Disease Control and Prevention. (2019). National Center for Health Statistics. www.cdc.gov/nchs.

  • Climate Central. (2016). U.S. faces dramatic rise in extreme heat, humidity. http://www.climatecentral.org/news/sizzling-summers-20515.

  • Coffel, E. D., Horton, R. M., & de Sherbinin, A. (2017). Temperature and humidity based projections of a rapid rise in global heat stress exposure during the 21st century. Environmental Research Letters, 13(1), 014001. https://doi.org/10.1088/1748-9326/aaa00e.

    Article  Google Scholar 

  • Corell, R. W., Liverman, D., Dow, K., Ebi, K. L., Kunkel, K., Mearns, L. O., et al. (2014). Research needs for climate and global change assessments. In J. M. Melillo, T. C. Richmond, & G. W. Yohe (Eds.), Climate change impacts in the United States: The third national climate assessment (pp. 707–718): U.S. Global Change Research Program.

  • Crimmins, A., J., Balbus, J. L., Gamble, C. B., Beard, J. E., Bell, D. D., Eisen, R. J., et al. (Eds.). (2016). The impacts of climate change on human health in the United States: A scientific assessment. Washington, DC: U.S. Global Change Research Program.

  • Curriero, F. C., Heiner, K. S., Samet, J. M., Zeger, S. L., Strug, L., & Patz, J. A. (2002). Temperature and mortality in 11 cities of the eastern United States. American Journal of Epidemiology, 155(1), 80–87. https://doi.org/10.1093/aje/155.1.80.

    Article  Google Scholar 

  • Dong, W., Liu, Z., Liao, H., Tang, Q., Li, X., & e. . (2015). New climate and socio-economic scenarios for assessing global human health challenges due to heat risk. Climatic Change, 130(4), 505–518. https://doi.org/10.1007/s10584-015-1372-8.

    Article  Google Scholar 

  • Duffy, P. B., & Tebaldi, C. (2012). Increasing prevalence of extreme summer temperatures in the US. Climatic Change, 111(2), 487–495. https://doi.org/10.1007/s10584-012-0396-6.

    Article  Google Scholar 

  • Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically weighted regression: The analysis of spatially varying relationships Chichester: Hoboken, NJ.

  • Gasparrini, A., & Armstrong, B. (2011). The impact of heat waves on mortality. Epidemiology (Cambridge, Mass), 22(1), 68–73. https://doi.org/10.1097/EDE.0b013e3181fdcd99.

    Article  Google Scholar 

  • Gasparrini, A., Guo, Y., Sera, F., Vicedo-Cabrera, A. M., Huber, V., Tong, S., et al. (2017). Projections of temperature-related excess mortality under climate change scenarios. The Lancet Planetary Health, 1(9), e360–e367. https://doi.org/10.1016/S2542-5196(17)30156-0.

    Article  Google Scholar 

  • Gover, M. (1938). Mortality during periods of excessive temperature. Public Health Reports (1896–1970), 53(27), 1122–1143.

  • Griffith, D. A. (2008). Spatial-filtering-based contributions to a critique of geographically weighted regression (GWR). Environment and Planning A, 40(11), 2751–2769.

    Article  Google Scholar 

  • Hattis, D., Ogneva-Himmelberger, Y., & Ratick, S. (2012). The spatial variability of heat-related mortality in Massachusetts. Applied Geography, 33, 45–52. https://doi.org/10.1016/j.apgeog.2011.07.008.

    Article  Google Scholar 

  • Hondula, D., Davis, R., Leisten, M., Saha, M., Veazey, L., & Wegner, C. (2012). Fine-scale spatial variability of heat-related mortality in Philadelphia County, USA, from 1983–2008: a case-series analysis. Environmental Health, 11(1), 1–11. https://doi.org/10.1186/1476-069X-11-16.

    Article  Google Scholar 

  • Jones, B., O'Neill, B. C., McDaniel, L., McGinnis, S., Mearns, L. O., & Tebaldi, C. (2015). Future population exposure to US heat extremes. Nature Climate Change, 5(7), 652–655, https://doi.org/10.1038/nclimate2631. http://www.nature.com/nclimate/journal/v5/n7/abs/nclimate2631.html#supplementary-information.

  • Kharin, V. V., Zwiers, F. W., Zhang, X., & Wehner, M. (2013). Changes in temperature and precipitation extremes in the CMIP5 ensemble. Climatic Change, 119(2), 345–357. https://doi.org/10.1007/s10584-013-0705-8.

    Article  Google Scholar 

  • Kovats, R. S., & Hajat, S. (2008). Heat stress and public health: A critical review. Annual Review of Public Health, 29(1), 41–55. https://doi.org/10.1146/annurev.publhealth.29.020907.090843.

    Article  Google Scholar 

  • Lerch, M., & Oris, M. (2018). Mortality during heat episodes in Switzerland: a story of vulnerability. In P. Puschmann & T. Riswick (Eds.), Building bridges: Scholars, history and historical demography; a Festschrift in honor of Professor Theo Engelen (pp. 626–646). Nijmegen: Valkhof Pers.

    Google Scholar 

  • Luber, G., & McGeehin, M. (2008). Climate change and extreme heat events. American Journal of Preventive Medicine, 35(5), 429–435. https://doi.org/10.1016/j.amepre.2008.08.021.

    Article  Google Scholar 

  • McGeehin, M. A., & Mirabelli, M. (2001). The potential impacts of climate variability and change on temperature-related morbidity and mortality in the United States. Environmental Health Perspectives, 109(Suppl 2), 185–189.

    Article  Google Scholar 

  • Nakaya, T. (2015). GWR4. (4.09 ed.).

  • Nakaya, T. (2016). GWR4.09 user manual. (pp. 39): GWR 4 Development Team: Tomoki Nakaya, Martin Charlton, Paul Lewis, Chris Brunsdon, Jing Yao, Stewart Fotheringham.

  • National Center for Atmospheric Research. (2019). NLDAS: North American Land Data Assimilation System.

  • National Oceanic and Atmospheric Administration. (2019). Summer 2019 was hottest on record for northern hemisphere. https://www.noaa.gov/news/summer-2019-was-hottest-on-record-for-northern-hemisphere.

  • Naughton, M. P., Henderson, A., Mirabelli, M. C., Kaiser, R., Wilhelm, J. L., Kieszak, S. M., et al. (2002). Heat-related mortality during a 1999 heat wave in Chicago. American Journal of Preventive Medicine, 22(4), 221–227.

    Article  Google Scholar 

  • Petkova, E. P., Vink, J., Horton, R., Gasparrini, A., Bader, D., Francis, J., et al. (2016). Towards more comprehensive projections of urban heat-related mortality: Estimates for New York City under multiple population, adaptation, and climate scenarios. Environmental Health Perspectives. https://doi.org/10.1289/EHP166.

    Article  Google Scholar 

  • Petkova, E. P., Gasparrini, A., & Kinney, P. L. (2014). Heat and mortality in New York City since the beginning of the 20th century. Epidemiology (Cambridge, Mass), 25(4), 554–560. https://doi.org/10.1097/EDE.0000000000000123.

    Article  Google Scholar 

  • PRISM Climate Group. (2019). PRISM climate data. http://prism.oregonstate.edu.

  • Semenza, J. C., Rubin, C. H., Falter, K. H., Selanikio, J. D., Flanders, W. D., Howe, H. L., et al. (1996). Heat-related deaths during the July 1995 heat wave in Chicago. New England Journal of Medicine, 335(2), 84–90. https://doi.org/10.1056/NEJM199607113350203.

    Article  Google Scholar 

  • Sheridan, S. C., & Allen, M. J. (2018). Temporal trends in human vulnerability to excessive heat. Environmental Research Letters, 13(4), 043001. https://doi.org/10.1088/1748-9326/aab214.

    Article  Google Scholar 

  • Sheridan, S. C., Kalkstein, A. J., & Kalkstein, L. S. (2009). Trends in heat-related mortality in the United States, 1975–2004. Natural Hazards, 50(1), 145–160. https://doi.org/10.1007/s11069-008-9327-2.

    Article  Google Scholar 

  • Smoyer, K. E. (1998). A comparative analysis of heat waves and associated mortality in St Louis, Missouri—1980 and 1995. International Journal of Biometeorology, 42(1), 44–50. https://doi.org/10.1007/s004840050082.

    Article  Google Scholar 

  • Sparks, P. J., & Sparks, C. S. (2010). An application of spatially autoregressive models to the study of US county mortality rates. Population, Space and Place, 16(6), 465–481. https://doi.org/10.1002/psp.564.

    Article  Google Scholar 

  • United States Census Bureau. (2019). www.census.gov.

  • United States Energy Information Agency. (2009). 2009 residential energy consumption survey. www.eia.gov.

  • Wheeler, D. C. (2019). Geographically weighted regression. In Handbook of regional science, pp. 1–27.

  • Wheeler, D. C., & Calder, C. A. (2007). An assessment of coefficient accuracy in linear regression models with spatially varying coefficients. Journal of Geographical Systems, 9(2), 145–166.

    Article  Google Scholar 

  • Wheeler, D., & Tiefelsdorf, M. (2005). Multicollinearity and correlation among local regression coefficients in geographically weighted regression. Journal of Geographical Systems, 7(2), 161–187.

    Article  Google Scholar 

  • Wilder, M., Garfin, G., Eakin, H., Romero-Lankao, P., Lara-Valencia, F., Cortez-Lara, A. A., et al. (2013). Climate change and U.S.-Mexico border communities. In G. Garfin, A. Jardine, R. Merideth, M. Black, and S. LeRoy (Ed.), Assessment of climate change in the Southwest United States: A report prepared for the national climate assessment. Washington, DC: Island Press.

  • Xia, Y., Mitchell, K., Ek, M., Sheffield, J., Cosgrove, B., Wood, E., et al. (2012). Continental-scale water and energy flux analysis and validation for the North American Land Data Assimilation System project phase 2 (NLDAS-2): 1—Intercomparison and application of model products. Journal of Geophysical Research, 117, D03109.

    Google Scholar 

  • Yang, T.-C., & Jensen, L. (2017). Climatic conditions and human mortality: spatial and regional variation in the United States. Population and Environment, 38(3), 261–285. https://doi.org/10.1007/s11111-016-0262-y.

    Article  Google Scholar 

  • Yang, T.-C., Noah, A., & Shoff, C. (2015). Exploring geographic variation in US mortality rates using a spatial Durbin approach. Population, Space and Place, 21(1), 18–37. https://doi.org/10.1002/psp.1809.

    Article  Google Scholar 

  • Yang, T.-C., Shoff, C., & Matthews, S. A. (2013). Examining the spatially non-stationary associations between the second demographic transition and infant mortality: a Poisson GWR approach. Spatial Demography, 1(1), 17–40. https://doi.org/10.1007/BF03354885.

    Article  Google Scholar 

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Acknowledgements

This work was supported in part by the National Science Foundation (NSF) Science, Education, and Engineering for Sustainability (SEES) program, award CHE-1314040.

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Authors and Affiliations

  1. City University of New York Institute for Demographic Research, Marxe School of Public and International Affairs, Baruch College, 135 East 22nd Street, New York, NY, 10010, USA

    Bryan Jones, Gillian Dunn & Deborah Balk

  2. Hawaii Pacific University, 500 Ala Moana Blvd, Honolulu, HI, 96813, USA

    Gillian Dunn

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  1. Bryan Jones
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BJ and GD contributed to study design, analysis, data preparation, text and revisions, as well as figures. DB contributed to study design, analysis, text, and revisions.

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Correspondence to Gillian Dunn.

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Jones, B., Dunn, G. & Balk, D. Extreme Heat Related Mortality: Spatial Patterns and Determinants in the United States, 1979–2011. Spat Demogr 9, 107–129 (2021). https://doi.org/10.1007/s40980-021-00079-6

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