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Climatic conditions and human mortality: spatial and regional variation in the United States

Abstract

Previous research on climatic conditions and human mortality in the United States has three gaps: largely ignoring social conditions, lack of nationwide focus, and overlooking potential spatial variations. Our goal is to understand whether climatic conditions contribute to mortality after considering social conditions and to investigate whether spatial non-stationarity exists in these factors. Applying geographically weighted regression to a unique nationwide county-level dataset, we found that (1) net of other factors, average July temperatures are positively (detrimentally) associated with mortality, while January temperatures mainly have a curvilinear relationship, (2) the mortality-climatic condition associations are spatially non-stationary, (3) the relationships between social conditions (e.g., social capital) and mortality are stable geographically, and (4) without a spatial approach to understanding the environment-mortality relationship, important spatial variations are overlooked. Our findings suggest that a universal approach to coping with the relationships between rapid climate changes and health may not be appropriate nor effective.

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Notes

  1. The US decadal censuses aim to include all populations. As such, the US Census Bureau has sought to minimize potential errors when collecting data by using specialized procedures to count people without conventional housing (e.g., homeless).

  2. The total number of parameter estimates is the product of total number of observations and number of variables (including the intercept). It is more feasible to summarize these estimates into maps than in tables (Fotheringham et al. 2003).

  3. We tested an interaction term between July temperature and humidity in the analysis, but it was not statistically significant.

  4. The marginal effect of January temperature (based on Model V) is 1.374-0.11*January temperature. We use the term “marginal effect” without any causal implication.

  5. More importantly, our sensitivity analysis considered an interaction term between January temperature and south but this interaction term was not significant (results available upon request) and other estimates were comparable to the model in “Appendix 1”.

  6. The region in the US surrounding the geographic point where Colorado, Utah, Arizona, and New Mexico come together.

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Acknowledgments

We acknowledge the support from the Center for Social and Demographic Analysis at the University at Albany, SUNY [R24-HD044943] and from the Population Research Institute at Penn State [R24-HD041025]. Both receive core funding from The Eunice Kennedy Shriver National Institute of Child Health and Human Development. Leif Jensen was supported by a USDA-funded Hatch Multistate Project W-3001, “The Great Recession, Its Aftermath, and Patterns of Rural and Small Town Demographic Change,” administered through Penn State College of Agricultural Sciences Experiment Station Project Number PEN04504. We thank Karen Fisher-Vanden and anonymous reviewers for helpful comments. Remaining errors in logic or analysis are ours alone.

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Correspondence to Tse-Chuan Yang.

Appendix 1

Appendix 1

See Table 4.

Table 4 Sensitivity analysis results of the model with the dummy variable “South.”

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Yang, TC., Jensen, L. Climatic conditions and human mortality: spatial and regional variation in the United States. Popul Environ 38, 261–285 (2017). https://doi.org/10.1007/s11111-016-0262-y

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Keywords

  • Environment
  • Health and mortality
  • Regional variation
  • US south
  • Climate
  • Geographically weighted regression