Climatic Change

, Volume 136, Issue 3–4, pp 631–645 | Cite as

Vulnerability to renal, heat and respiratory hospitalizations during extreme heat among U.S. elderly

  • Carina J. Gronlund
  • Antonella Zanobetti
  • Gregory A. Wellenius
  • Joel D. Schwartz
  • Marie S. O’Neill


Extreme heat (EH) is a growing concern with climate change, and protecting human health requires knowledge of vulnerability factors. We evaluated whether associations between EH (maximum temperature > 97th percentile) and hospitalization for renal, heat and respiratory diseases among people > 65 years differed by individual and area-level characteristics. We used Medicare billing records, airport weather data, U.S. Census data and satellite land cover imagery in 109 US cities, May-September, 1992-2006, in a time-stratified case-crossover design. Interaction terms between EH and individual (> 78 years, black race, sex) and home ZIP-code (percentages of non-green space, high school education, housing built before 1940) characteristics were incorporated in a single model. Next, we pooled city-specific effect estimates or regressed them on quartiles of air conditioning prevalence (ACP) in a multivariate random effects meta-analysis. EH and combined renal/heat/respiratory hospitalization associations were stronger among blacks, the very old, in ZIP codes with lower educational attainment or older housing and in cities with lower ACP. For example, for EH versus non-heat days, we found a 15% (95% CI 11%-19%) increase in renal/heat/respiratory hospitalizations among individuals in ZIP codes with higher percent of older homes in contrast to a 9% (95% CI 6%-12%) increase in hospitalizations in ZIP codes with lower percent older homes. Vulnerability to EH-associated hospitalization may be influenced by age, educational attainment, housing age and ACP.


Extreme Heat Black Race Study City Extreme Heat Effect Build Environment Characteristic 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This research was supported by the following grants: U.S. Environmental Protection Agency Science to Achieve Results Program R832752010; a National Occupational Research Agenda Pre-Doctoral Scholarship from the University of Michigan Center for Occupational Health and Safety Engineering (a National Institute for Occupational Safety and Health-funded Education and Research Center 2T42OH008455); the National Institute on Aging Interdisciplinary Research Training in Health and Aging T32AG027708; the U.S. Centers for Disease Control and Prevention EH000348, the National Institute of Environmental Health Sciences ES015774, ES024012, R21-ES020156 and R21-ES020695 and a Dow Sustainability Fellowship from the University of Michigan Graham Sustainability Institute.

Author attributions

Each author contributed to the study design; acquisition or processing of the health and/or exposure data; analysis and manuscript preparation and editing.

Compliance with ethical standards

The research was approved by the Institutional Review Boards at Harvard University and University of Michigan. Informed consent of subjects was waived.

Conflicts of interest

The authors declare that they have no conflicts of interest.

Supplementary material

10584_2016_1638_MOESM1_ESM.docx (660 kb)
ESM 1 (DOCX 659 kb)


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Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Carina J. Gronlund
    • 1
  • Antonella Zanobetti
    • 2
  • Gregory A. Wellenius
    • 3
  • Joel D. Schwartz
    • 2
  • Marie S. O’Neill
    • 1
    • 4
  1. 1.Department of EpidemiologyUniversity of Michigan School of Public HealthAnn ArborUSA
  2. 2.Department of Environmental HealthHarvard School of Public HealthBostonUSA
  3. 3.Department of EpidemiologyBrown University School of Public HealthProvidenceUSA
  4. 4.Department of Environmental Health SciencesUniversity of Michigan School of Public HealthAnn ArborUSA

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