Heat-mortality relationship in North Carolina: Comparison using different exposure methods.

Hayon Michelle Choi, Michelle L Bell
Author Information
  1. Hayon Michelle Choi: School of the Environment, Yale University, New Haven, CT, USA. hayonmichelle.choi@yale.edu.
  2. Michelle L Bell: School of the Environment, Yale University, New Haven, CT, USA.

Abstract

BACKGROUND: Many studies have explored the heat-mortality relationship; however, comparability of results is hindered by the studies' use of different exposure methods.
OBJECTIVE: This study evaluated different methods for estimating exposure to temperature using individual-level data and examined the impacts on the heat-mortality relationship.
METHODS: We calculated different temperature exposures for each individual death by using a modeled, gridded temperature dataset and a monitoring station dataset in North Carolina for 2000-2016. We considered individual-level vs. county-level averages and measured vs. modeled temperature data. A case-crossover analysis was conducted to examine the heat-mortality risk under different exposure methods.
RESULTS: The minimum mortality temperature (MMT) (i.e., the temperature with the lowest mortality rate) for the monitoring station dataset was 23.87 °C and 22.67 °C (individual monitor and county average, respectively), whereas for the modeled temperature dataset the MMT was 19.46 °C and 19.61 °C (individual and county, respectively). We found higher heat-mortality risk while using temperature exposure estimated from monitoring stations compared to risk based on exposure using the modeled temperature dataset. Individual-aggregated monitoring station temperature exposure resulted in higher heat mortality risk (odds ratio (95% CI): 2.24 (95% CI: 2.21, 2.27)) for a relative temperature change comparing the 99th and 90th temperature percentiles, while modeled temperature exposure resulted in lower odds ratio of 1.27 (95% CI: 1.25, 1.29).
SIGNIFICANCE: Our findings indicate that using different temperature exposure methods can result in different temperature-mortality risk. The impact of using various exposure methods should be considered in planning health policies related to high temperatures, including under climate change. IMPACT STATEMENT: (1) We estimated the heat-mortality association using different methods to estimate exposure to temperature. (2) The mean temperature value among different exposure methods were similar although lower for the modeled data, however, use of the monitoring station temperature dataset resulted in higher heat-mortality risk than the modeled temperature dataset. (3) Differences in mortality risk from heat by urbanicity varies depending on the method used to estimate temperature exposure.

Keywords

References

  1. Environ Int. 2015 Aug;81:80-6 [PMID: 25965185]
  2. Crit Rev Toxicol. 2011 Sep;41(8):651-71 [PMID: 21823979]
  3. Environ Health Perspect. 2014 Aug;122(8):811-6 [PMID: 24780880]
  4. Environ Health Perspect. 2000 May;108(5):419-26 [PMID: 10811568]
  5. Int J Biometeorol. 2016 Jan;60(1):73-83 [PMID: 25972307]
  6. Sci Total Environ. 2021 Sep 15;787:147672 [PMID: 34000533]
  7. Sci Total Environ. 2021 Oct 10;790:147958 [PMID: 34098271]
  8. Environ Res. 2017 Jul;156:845-853 [PMID: 28499499]
  9. PLoS One. 2012;7(6):e38551 [PMID: 22761684]
  10. Arch Environ Occup Health. 2006 Nov-Dec;61(6):265-70 [PMID: 17967749]
  11. Clim Change. 2018 Oct;150(3-4):391-402 [PMID: 30405277]
  12. Epidemiology. 2009 Mar;20(2):205-13 [PMID: 19194300]
  13. Environ Epidemiol. 2019 Oct 14;3(5):e072 [PMID: 33195965]
  14. Sci Rep. 2022 Mar 25;12(1):5178 [PMID: 35338191]
  15. Indoor Air. 2014 Feb;24(1):103-12 [PMID: 23710826]
  16. Glob Environ Change. 2013 Apr;23(2):475-484 [PMID: 29375195]
  17. Environ Health. 2016 Mar 08;15 Suppl 1:27 [PMID: 26961286]
  18. Environ Health Perspect. 2013 Oct;121(10):1111-9 [PMID: 23934704]
  19. Sci Total Environ. 2013 Jan 1;442:275-81 [PMID: 23178831]
  20. Paediatr Perinat Epidemiol. 2015 Sep;29(5):407-15 [PMID: 26154414]
  21. Biostatistics. 2011 Oct;12(4):610-23 [PMID: 21252080]
  22. Sci Total Environ. 2014 Aug 15;490:686-93 [PMID: 24893319]
  23. N Engl J Med. 2003 Feb 13;348(7):666-7 [PMID: 12584383]
  24. Environ Res. 2015 Jan;136:449-61 [PMID: 25460667]
  25. Environ Res. 2012 Jan;112:20-7 [PMID: 22071034]
  26. Environ Res. 2016 Nov;151:728-733 [PMID: 27644031]
  27. Epidemiology. 2015 Nov;26(6):781-93 [PMID: 26332052]
  28. Environ Health Perspect. 2020 Dec;128(12):127007 [PMID: 33300819]
  29. Health Aff (Millwood). 2020 Dec;39(12):2056-2062 [PMID: 33284705]
  30. Environ Res. 2013 Jan;120:55-62 [PMID: 23026801]
  31. Am J Epidemiol. 2010 Aug 1;172(3):344-52 [PMID: 20573838]
  32. Natl Health Stat Report. 2014 Jul 30;(76):1-15 [PMID: 25073563]
  33. J Epidemiol Community Health. 2011 Apr;65(4):340-5 [PMID: 20439353]
  34. Environ Res. 2016 Nov;151:610-617 [PMID: 27611992]

Grants

  1. R01 MD012769/NIMHD NIH HHS
  2. UL1 TR001863/NCATS NIH HHS

MeSH Term

Humans
Hot Temperature
North Carolina
Temperature
Climate Change
Mortality

Word Cloud

Created with Highcharts 10.0.0temperatureexposuredifferentmethodsusingmodeleddatasetriskheat-mortalitymonitoringstationmortality21relationshipdataindividualhigherresulted95%howeveruseindividual-levelNorthconsideredvsMMTcountyrespectively19estimatedheatoddsratioCI:27changelowerestimateExposureBACKGROUND:Manystudiesexploredcomparabilityresultshinderedstudies'OBJECTIVE:studyevaluatedestimatingexaminedimpactsMETHODS:calculatedexposuresdeathgriddedCarolina2000-2016county-levelaveragesmeasuredcase-crossoveranalysisconductedexamineRESULTS:minimumielowestrate2387 °C2267 °Cmonitoraveragewhereas46 °C61 °CfoundstationscomparedbasedIndividual-aggregatedCI:2421relativecomparing99th90thpercentiles2529SIGNIFICANCE:findingsindicatecanresulttemperature-mortalityimpactvariousplanninghealthpoliciesrelatedhightemperaturesincludingclimateIMPACTSTATEMENT:associationmeanvalueamongsimilaralthough3DifferencesurbanicityvariesdependingmethodusedHeat-mortalityCarolina:ComparisonModelingHealthStudiesPersonal

Similar Articles

Cited By (2)