Heat illness data strengthens vulnerability maps

Abstract Background Previous extreme heat and human health studies have investigated associations either over time (e.g. case-crossover or time series analysis) or across geographic areas (e.g. spatial models), which may limit the study scope and regional variation. Our study combines a case-crossov...

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Autores principales:	Jihoon Jung, Christopher K. Uejio, Kristina W. Kintziger, Chris Duclos, Keshia Reid, Melissa Jordan, June T. Spector
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Heat vulnerability Case-crossover analysis Spatial lag model Social determinants of health Public aspects of medicine RA1-1270
Acceso en línea:	https://doaj.org/article/8ff8e41843094038a68a800a34ac3a99
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spelling	oai:doaj.org-article:8ff8e41843094038a68a800a34ac3a992021-11-08T10:43:52ZHeat illness data strengthens vulnerability maps10.1186/s12889-021-12097-61471-2458https://doaj.org/article/8ff8e41843094038a68a800a34ac3a992021-11-01T00:00:00Zhttps://doi.org/10.1186/s12889-021-12097-6https://doaj.org/toc/1471-2458Abstract Background Previous extreme heat and human health studies have investigated associations either over time (e.g. case-crossover or time series analysis) or across geographic areas (e.g. spatial models), which may limit the study scope and regional variation. Our study combines a case-crossover design and spatial analysis to identify: 1) the most vulnerable counties to extreme heat; and 2) demographic and socioeconomic variables that are most strongly and consistently related to heat-sensitive health outcomes (cardiovascular disease, dehydration, heat-related illness, acute renal disease, and respiratory disease) across 67 counties in the state of Florida, U. S over 2008–2012. Methods We first used a case-crossover design to examine the effects of air temperature on daily counts of health outcomes. We employed a time-stratified design with a 28-day comparison window. Referent periods were extracted from ±7, ±14, or ± 21 days to address seasonality. The results are expressed as odds ratios, or the change in the likelihood of each health outcome for a unit change in heat exposure. We then spatially examined the case-crossover extreme heat and health odds ratios and county level demographic and socioeconomic variables with multiple linear regression or spatial lag models. Results Results indicated that southwest Florida has the highest risks of cardiovascular disease, dehydration, acute renal disease, and respiratory disease. Results also suggested demographic and socioeconomic variables were significantly associated with the magnitude of heat-related health risk. The counties with larger populations working in farming, fishing, mining, forestry, construction, and extraction tended to have higher risks of dehydration and acute renal disease, whereas counties with larger populations working in installation, maintenance, and repair workers tended to have lower risks of cardiovascular, dehydration, acute renal disease, and respiratory disease. Finally, our results showed that high income counties consistently have lower health risks of dehydration, heat-related illness, acute renal disease, and respiratory disease. Conclusions Our study identified different relationships with demographic/socioeconomic variables for each heat-sensitive health outcome. Results should be incorporated into vulnerability or risk indices for each health outcome.Jihoon JungChristopher K. UejioKristina W. KintzigerChris DuclosKeshia ReidMelissa JordanJune T. SpectorBMCarticleHeat vulnerabilityCase-crossover analysisSpatial lag modelSocial determinants of healthPublic aspects of medicineRA1-1270ENBMC Public Health, Vol 21, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Heat vulnerability Case-crossover analysis Spatial lag model Social determinants of health Public aspects of medicine RA1-1270
spellingShingle	Heat vulnerability Case-crossover analysis Spatial lag model Social determinants of health Public aspects of medicine RA1-1270 Jihoon Jung Christopher K. Uejio Kristina W. Kintziger Chris Duclos Keshia Reid Melissa Jordan June T. Spector Heat illness data strengthens vulnerability maps
description	Abstract Background Previous extreme heat and human health studies have investigated associations either over time (e.g. case-crossover or time series analysis) or across geographic areas (e.g. spatial models), which may limit the study scope and regional variation. Our study combines a case-crossover design and spatial analysis to identify: 1) the most vulnerable counties to extreme heat; and 2) demographic and socioeconomic variables that are most strongly and consistently related to heat-sensitive health outcomes (cardiovascular disease, dehydration, heat-related illness, acute renal disease, and respiratory disease) across 67 counties in the state of Florida, U. S over 2008–2012. Methods We first used a case-crossover design to examine the effects of air temperature on daily counts of health outcomes. We employed a time-stratified design with a 28-day comparison window. Referent periods were extracted from ±7, ±14, or ± 21 days to address seasonality. The results are expressed as odds ratios, or the change in the likelihood of each health outcome for a unit change in heat exposure. We then spatially examined the case-crossover extreme heat and health odds ratios and county level demographic and socioeconomic variables with multiple linear regression or spatial lag models. Results Results indicated that southwest Florida has the highest risks of cardiovascular disease, dehydration, acute renal disease, and respiratory disease. Results also suggested demographic and socioeconomic variables were significantly associated with the magnitude of heat-related health risk. The counties with larger populations working in farming, fishing, mining, forestry, construction, and extraction tended to have higher risks of dehydration and acute renal disease, whereas counties with larger populations working in installation, maintenance, and repair workers tended to have lower risks of cardiovascular, dehydration, acute renal disease, and respiratory disease. Finally, our results showed that high income counties consistently have lower health risks of dehydration, heat-related illness, acute renal disease, and respiratory disease. Conclusions Our study identified different relationships with demographic/socioeconomic variables for each heat-sensitive health outcome. Results should be incorporated into vulnerability or risk indices for each health outcome.
format	article
author	Jihoon Jung Christopher K. Uejio Kristina W. Kintziger Chris Duclos Keshia Reid Melissa Jordan June T. Spector
author_facet	Jihoon Jung Christopher K. Uejio Kristina W. Kintziger Chris Duclos Keshia Reid Melissa Jordan June T. Spector
author_sort	Jihoon Jung
title	Heat illness data strengthens vulnerability maps
title_short	Heat illness data strengthens vulnerability maps
title_full	Heat illness data strengthens vulnerability maps
title_fullStr	Heat illness data strengthens vulnerability maps
title_full_unstemmed	Heat illness data strengthens vulnerability maps
title_sort	heat illness data strengthens vulnerability maps
publisher	BMC
publishDate	2021
url	https://doaj.org/article/8ff8e41843094038a68a800a34ac3a99
work_keys_str_mv	AT jihoonjung heatillnessdatastrengthensvulnerabilitymaps AT christopherkuejio heatillnessdatastrengthensvulnerabilitymaps AT kristinawkintziger heatillnessdatastrengthensvulnerabilitymaps AT chrisduclos heatillnessdatastrengthensvulnerabilitymaps AT keshiareid heatillnessdatastrengthensvulnerabilitymaps AT melissajordan heatillnessdatastrengthensvulnerabilitymaps AT junetspector heatillnessdatastrengthensvulnerabilitymaps
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Heat illness data strengthens vulnerability maps

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