PM2.5短期暴露对结核病发病风险的个体精准评估

doi:10.6040/j.issn.1671-7554.0.2025.0209

摘要/Abstract

摘要： 目的探讨短期细颗粒物(2.5-micrometer particulate matter, PM_2.5)暴露对结核发病风险的影响及其人群和时空异质性,为制定精准公共卫生干预策略提供科学依据。方法收集2015年1月至2019年12月济南市1 207例新发结核病例数据,结合机器学习模型与地理信息系统,构建百米级网格化PM_2.5暴露评估模型,精准估计个体PM_2.5暴露水平;采用时间分层的病例交叉设计,通过条件logistic回归分析PM_2.5短期暴露(滞后0~3 d)与结核发病的关联,并评估年龄、性别、季节和居住地的效应差异。结果 PM_2.5的质量浓度每增加1 μg/m³,结核发病风险在滞后2 d升高0.45%(95%CI:0.12%~0.78%,P<0.05)。亚组分析显示,老年人(OR=1.14, 95%CI: 0.974~1.32)、女性(OR=1.07, 95%CI: 1.03~1.11)、寒冷季节(OR=1.11, 95%CI: 1.05~1.19)及农村地区(OR=1.05, 95%CI: 1.02~1.08)的结核发病风险更高,关联有统计学意义(P<0.05)。结论 PM_2.5短期暴露可显著增加结核发病风险,且在山东省济南市存在人群和季节异质性,需针对高风险群体及污染季节制定精准防控策略。

关键词: 空气污染, 结核病, 病例交叉设计, 机器学习, 地理信息系统

Abstract: Objective To investigate the impact of short-term exposure to 2.5-micrometer particulate matter(PM_2.5)on the risk of tuberculosis(TB)incidence and its heterogeneity across populations, space, and time, providing a scientific basis for developing targeted public health intervention strategies. Methods Data from 1,207 newly diagnosed TB cases in Jinan from from January 2015 to December 2019 were collected. By integrating machine learning models with geographic information systems, a hundred-meter grid-level PM_2.5 exposure assessment model was constructed to precisely estimate individual PM_2.5 exposure levels. A time-stratified case-crossover design was employed, and conditional logistic regression was used to analyze the association between short-term PM_2.5 exposure(0-3 days lag)and TB incidence, while evaluating effect differences by age, sex, season, and residential area. Results For each 1 μg/m³ increase in PM_2.5 concentration, the risk of TB incidence increased by 0.45%(95%CI: 0.12%-0.78%)at a 2-day lag(P<0.05). Subgroup analyses revealed significantly higher risks among the elderly(OR=1.14, 95%CI: 0.974-1.32), females(OR=1.07, 95%CI: 1.03-1.11), during cold seasons(OR=1.11, 95%CI: 1.05-1.19), and in rural areas(OR=1.05, 95%CI: 1.02-1.08)(P<0.05). Conclusion Short-term PM_2.5 exposure significantly increases the risk of TB incidence, with notable heterogeneity across populations and seasons in Jinan, Shandong Province, necessitating targeted prevention and control strategies for high-risk groups and polluted seasons.

Key words: Air pollution, Tuberculosis, Case-crossover design, Machine learning, Geographic information systems

中图分类号:

R122.7

廖原,门丹,李一帆,李怀臣,龙飞,刘铱. PM_2.5短期暴露对结核病发病风险的个体精准评估[J]. 山东大学学报 (医学版), 2026, 64(3): 116-123.

LIAO Yuan, MEN Dan, LI Yifan, LI Huaichen, LONG Fei, LIU Yi. Short-term effect of PM_2.5 on the incidence of tuberculosis based on individual precise exposure assessment[J]. Journal of Shandong University (Health Sciences), 2026, 64(3): 116-123.

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