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探索2021年高温穹顶期间药物与社区高温相关死亡之间的关系:一种使用机器学习的混合方法。

Exploring the relationship between medications and heat-related community deaths during the 2021 heat dome: a hybrid approach using machine learning.

作者信息

Boudreault Jeremie, McLean Kathleen E, Henderson Sarah B

机构信息

Environmental Health Services, British Columbia Centre for Disease Control, 655 West 12th Ave, Vancouver, BC, V5Z 4R4, Canada; Centre Eau Terre Environnement, Institut national de la recherche scientifique, 490 de la Couronne, Québec, QC, G1K 9A9, Canada.

Environmental Health Services, British Columbia Centre for Disease Control, 655 West 12th Ave, Vancouver, BC, V5Z 4R4, Canada.

出版信息

EBioMedicine. 2025 Jun 10;117:105788. doi: 10.1016/j.ebiom.2025.105788.

Abstract

BACKGROUND

Extreme heat events (EHEs) are a growing threat to health worldwide. To date, only a limited number of studies have evaluated medications as risk or protective factors for mortality during EHEs.

METHODS

We explored the relationship between dispensed pharmaceuticals and heat-related community deaths using linked administrative health data and both logistic regression (LR) and machine learning (ML) models. We conducted a case-control study during the 2021 EHE in British Columbia, Canada, including 504 community deaths from heat exposure as cases and 2520 similar controls who survived the EHE. We used medications dispensed 30, 60 and 90 days prior to death (or 30, 60 and 90 days before the end of the EHE for controls) as predictors, grouped by Anatomical Therapeutic Chemical (ATC) classification at level 2 for LR (28 classes) and level 4 for ML (270 subclasses). Models were adjusted for multiple covariates, including common chronic diseases.

FINDINGS

Results from LR showed increased odds of mortality associated with dispensations of antiepileptics, anti-Parkinson drugs, psycholeptics, diuretics, drugs for diabetes, beta blocking agents, analgesics, urologicals and drugs for treatment of bone diseases. We observed a protective association with dispensations of calcium channel blockers and ophthalmologicals. Results varied by sex, age, and other covariates. The ML model highlighted the most computationally important subclasses of medications within each of the ATC level 2 classes.

INTERPRETATION

This study leveraged both LR and ML to generate insights about medications and mortality during EHEs. The results add to the existing evidence on pharmaceutical risks during EHEs and provide new avenues for further research. They can be used to help develop more targeted messages to inform individuals whose medications put them at greater risk during EHEs.

FUNDING

BC Centre for Disease Control and Ministère de l'Enseignement supérieur du Québec.

摘要

背景

极端高温事件(EHEs)对全球健康构成的威胁日益增加。迄今为止,仅有有限的研究评估了药物作为极端高温事件期间死亡的风险或保护因素。

方法

我们使用关联的行政卫生数据以及逻辑回归(LR)和机器学习(ML)模型,探讨了配药与高温相关的社区死亡之间的关系。我们在加拿大不列颠哥伦比亚省2021年极端高温事件期间开展了一项病例对照研究,包括504例因高温暴露导致的社区死亡病例以及2520例在极端高温事件中存活的类似对照。我们将死亡前30天、60天和90天(或对照在极端高温事件结束前30天、60天和90天)所配药物作为预测因素,根据解剖治疗化学(ATC)分类在2级(28个类别)用于逻辑回归,在4级(270个亚类)用于机器学习进行分组。模型针对多种协变量进行了调整,包括常见慢性病。

研究结果

逻辑回归结果显示,与抗癫痫药、抗帕金森病药物、抗精神病药、利尿剂、糖尿病药物、β受体阻滞剂、镇痛药、泌尿科药物和治疗骨病药物的配药相关的死亡几率增加。我们观察到钙通道阻滞剂和眼科药物的配药具有保护作用。结果因性别、年龄和其他协变量而异。机器学习模型突出了每个ATC 2级类别中计算上最重要的药物亚类。

解读

本研究利用逻辑回归和机器学习来深入了解极端高温事件期间的药物与死亡率。研究结果补充了关于极端高温事件期间药物风险的现有证据,并为进一步研究提供了新途径。它们可用于帮助制定更具针对性的信息,以告知那些所服用药物使其在极端高温事件期间面临更高风险的个人。

资金来源

不列颠哥伦比亚省疾病控制中心和魁北克高等教育部。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e738/12182803/3989ea1f54ad/gr1.jpg

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