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Investigating the impact of extreme weather events and related indicators on cardiometabolic multimorbidity.
Wu, Di; Shi, Yu; Wang, ChenChen; Li, Cheng; Lu, Yaoqin; Wang, Chunfang; Zhu, Weidong; Sun, Tingting; Han, Junjie; Zheng, Yanling; Zhang, Liping.
Affiliation
  • Wu D; School of Public Health, Xinjiang Medical University, Urumqi, China.
  • Shi Y; School of Public Health, Xinjiang Medical University, Urumqi, China.
  • Wang C; Center for Disease Control and Prevention of Xinjiang Uygur Autonomous Region, Urumqi, China.
  • Li C; The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
  • Lu Y; Center for Disease Control and Prevention of Urumqi, Urumqi, China.
  • Wang C; School of Public Health, Nanjing Medical University, Nanjing, China.
  • Zhu W; School of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi, China.
  • Sun T; School of Agriculture, Xinjiang Agricultural University, Urumqi, China.
  • Han J; School of Nursing and Public Health, Yangzhou University, Yangzhou, China.
  • Zheng Y; School of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, China.
  • Zhang L; School of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, China. zhanglp1219@163.com.
Arch Public Health ; 82(1): 128, 2024 Aug 19.
Article in En | MEDLINE | ID: mdl-39160599
ABSTRACT

BACKGROUND:

The impact of weather on human health has been proven, but the impact of extreme weather events on cardiometabolic multimorbidity (CMM) needs to be urgently explored.

OBJECTIVES:

Investigating the impact of extreme temperature, relative humidity (RH), and laboratory testing parameters at admission on adverse events in CMM hospitalizations. DESIGNS Time-stratified case-crossover design.

METHODS:

A distributional lag nonlinear model with a time-stratified case-crossover design was used to explore the nonlinear lagged association between environmental factors and CMM. Subsequently, unbalanced data were processed by 12 propensity score matching (PSM) and conditional logistic regression was employed to analyze the association between laboratory indicators and unplanned readmissions for CMM. Finally, the previously identified environmental factors and relevant laboratory indicators were incorporated into different machine learning models to predict the risk of unplanned readmission for CMM.

RESULTS:

There are nonlinear associations and hysteresis effects between temperature, RH and hospital admissions for a variety of CMM. In addition, the risk of admission is higher under low temperature and high RH conditions with the addition of particulate matter (PM, PM2.5 and PM10) and O3_8h. The risk is greater for females and adults aged 65 and older. Compared with first quartile (Q1), the fourth quartile (Q4) had a higher association between serum calcium (HR = 1.3632, 95% CI 1.0732 ~ 1.7334), serum creatinine (HR = 1.7987, 95% CI 1.3528 ~ 2.3958), fasting plasma glucose (HR = 1.2579, 95% CI 1.0839 ~ 1.4770), aspartate aminotransferase/ alanine aminotransferase ratio (HR = 2.3131, 95% CI 1.9844 ~ 2.6418), alanine aminotransferase (HR = 1.7687, 95% CI 1.2388 ~ 2.2986), and gamma-glutamyltransferase (HR = 1.4951, 95% CI 1.2551 ~ 1.7351) were independently and positively associated with unplanned readmission for CMM. However, serum total bilirubin and High-Density Lipoprotein (HDL) showed negative correlations. After incorporating environmental factors and their lagged terms, eXtreme Gradient Boosting (XGBoost) demonstrated a more prominent predictive performance for unplanned readmission of CMM patients, with an average area under the receiver operating characteristic curve (AUC) of 0.767 (95% CI0.7486 ~ 0.7854).

CONCLUSIONS:

Extreme cold or wet weather is linked to worsened adverse health effects in female patients with CMM and in individuals aged 65 years and older. Moreover, meteorologic factors and environmental pollutants may elevate the likelihood of unplanned readmissions for CMM.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Arch Public Health Year: 2024 Document type: Article Affiliation country: Country of publication:

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Arch Public Health Year: 2024 Document type: Article Affiliation country: Country of publication: