Heat and cold-related morbidity risk in north-east of Iran: a time-stratified case crossover design

Abstract

Background: This study aimed to estimate hospitalization risk/number attributed to air extreme temperatures using time-stratified case crossover study and distributed lag non-linear model in a region of Iran during 2015-2019. Methods:A time-stratified case crossover design based on aggregated exposure data was used in this study. In order to have no overlap bias in the estimations, a fixed and disjointed window by using one-month strata was used in the design. A conditional Poisson regression model allowing for over dispersion (Quasi-Poisson) was applied into Distributed Lag Non-linear Model (DLNM).Different approaches were applied to estimate Optimum Temperature (OT). In the model, the interaction effect between temperature and humidity was assessed to see if the impact of heat or cold on Hospital Admissions (HAs) are different between different levels of humidity. Results:The cumulative effect of heat during 21 days was not significant and it was the cold that had significant cumulative adverse effect on all groups. While the number of HAs attributed to any ranges of heat, including medium, high, extreme and even all values were negligible, but a large number was attributable to cold values; about 10000 HAs were attributable to all values of cold temperature, of which about 9000 were attributed to medium range and about 1000 and less than 500 were attributed to high and extreme values of cold, respectively. Conclusion:This study highlights the need for interventions in cold seasons by policymakers. The results inform researchers as well as policy makers to address both men and women and elderly when any plan or preventive program is developed in the area under study.

Preterm‐born individuals: a vulnerable population at risk of cardiovascular morbidity and mortality during thermal extremes?

“…Given rising global temperatures continually setting new temperature records, and a four‐ to five‐fold increase in heat waves, the threshold for vulnerability is reducing (World Meteorological Organization, 2019 ). Excess hospital admissions and death due to climatic extremes are now routinely being documented (Kim et al., 2017 ; Robine et al., 2008 ; Shoraka et al., 2022 ; K. R. Smith et al., 2014 ). This necessitates wider investigation of potentially vulnerable populations, such as those born preterm, in the climate change context.…”

“…CVD is a leading cause of morbidity and mortality worldwide, but symptoms frequently remain concealed until the cardiovascular system is stressed, as commonly occurs during thermal extremes (Casas et al., 2016 ). This perhaps explains the dramatic increase in CVD‐related hospital admissions during heat waves (Kim et al., 2017 ; Wang & Lin, 2014 ) and cold snaps (Hess et al., 2009 ; Shoraka et al., 2022 ), both acutely and in the days following exposure (Kang et al., 2016 ; Wang & Lin, 2014 ). Other factors, such as ageing (>60 years; Gravel et al., 2021 ; Holowatz & Kenney, 2010 ) and metabolic disease (Kenny et al., 2010 ; Wang & Lin, 2014 ) further enhance this cardiovascular risk in vulnerable populations.…”

“…Other factors, such as ageing (>60 years; Gravel et al., 2021 ; Holowatz & Kenney, 2010 ) and metabolic disease (Kenny et al., 2010 ; Wang & Lin, 2014 ) further enhance this cardiovascular risk in vulnerable populations. Among vulnerable population subgroups presenting with limited adaptive capacity, such as the elderly, heat‐ and cold‐stress may favour the occurrence of disease with early death (Liu et al., 2015 ; Shoraka et al., 2022 ). For instance, excess mortality during the 2003 European heat wave exceeded 10% in most countries across Europe (Robine et al., 2008 ), and heat illness during the North American heat wave rose 69 times above the same period in 2019 (Philip et al., 2021 ).…”

Air pollution and stroke hospitalization in the Beibu Gulf Region of China: A case-crossover analysis

“…For example, a given exposure for a case occurring on Tuesday, 8 June would be compared to the exposure occurring on all other Tuesdays in June (i.e. 1 June, 15 June, 22 June, and 29 June) ( Shoraka et al, 2022 ). Therefore, the estimates represent the acute triggering effect of air pollution on the stroke hospitalization ( Cai et al, 2023c , Liu et al, 2022 ).…”