Evaluating the predictive ability of temperature-related indices on the stroke morbidity in Shenzhen, China: Under cross-validation methods framework

Siyi Chen; Mengyi Dai; Jing Hu; Jinquan Cheng; Yanran Duan; Xuan Zou; Youpeng Su; Ning Liu; Maidina Jingesi; Ziwei Chen; Ping Yin; Suli Huang; Qingqing He; Peng Wang

doi:10.1016/j.scitotenv.2022.156425

Evaluating the predictive ability of temperature-related indices on the stroke morbidity in Shenzhen, China: Under cross-validation methods framework

Sci Total Environ. 2022 Sep 10;838(Pt 3):156425. doi: 10.1016/j.scitotenv.2022.156425. Epub 2022 Jun 2.

Authors

Siyi Chen¹, Mengyi Dai¹, Jing Hu¹, Jinquan Cheng², Yanran Duan¹, Xuan Zou², Youpeng Su¹, Ning Liu³, Maidina Jingesi¹, Ziwei Chen¹, Ping Yin¹, Suli Huang², Qingqing He⁴, Peng Wang⁵

Affiliations

¹ Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
² Shenzhen Center for Disease Control and Prevention, Shenzhen, China.
³ Department of Environment and Health, Shenzhen Center for Disease Control and Prevention, Shenzhen, China.
⁴ School of Resource and Environmental Engineering, Wuhan University of Technology, Wuhan, China.
⁵ Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. Electronic address: pengwang_stat@hust.edu.cn.

PMID: 35660600
DOI: 10.1016/j.scitotenv.2022.156425

Abstract

Background: Composite temperature-related indices have been utilized to comprehensively reflect the impact of multiple meteorological factors on health. We aimed to evaluate the predictive ability of temperature-related indices, choose the best predictor of stroke morbidity, and explore the association between them.

Methods: We built distributed lag nonlinear models to estimate the associations between temperature-related indices and stroke morbidity and then applied two types of cross-validation (CV) methods to choose the best predictor. The effects of this index on overall stroke, intracerebral hemorrhage (ICH), and ischemic stroke (IS) morbidity were explored and we explained how this index worked using heatmaps. Stratified analyses were conducted to identify vulnerable populations.

Results: Among 12 temperature-related indices, the alternative temperature-humidity index (THIa) had the best overall performance in terms of root mean square error when combining the results from two CVs. With the median value of THIa (25.70 °C) as the reference, the relative risks (RRs) of low THIa (10th percentile) reached a maximum at lag 0-10, with RRs of 1.20 (95%CI:1.10-1.31), 1.49 (95%CI:1.29-1.73) and 1.12 (95%CI:1.03-1.23) for total stroke, ICH and IS, respectively. According to the THIa formula, we matched the effects of THIa on stroke under various combinations of temperature and relative humidity. We found that, although the low temperature (<20 °C) had the greatest adverse effect, the modification effect of humidity on it was not evident. In contrast, lower humidity could reverse the protective effect of temperature into a harmful effect at the moderate-high temperature (24 °C-27 °C). Stratification analyses showed that the female was more vulnerable to low THIa in IS.

Conclusions: THIa is the best temperature-related predictor of stroke morbidity. In addition to the most dangerous cold weather, the government should pay more attention to days with moderate-high temperature and low humidity, which have been overlooked in the past.

Keywords: Composite temperature-related indices; Cross-validation; Distributed lag nonlinear model; Morbidity; Stroke.

MeSH terms

China / epidemiology
Female
Humans
Humidity
Morbidity
Stroke* / epidemiology
Temperature