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使用广义相加混合模型研究墨西哥城大气中氧气(O)、颗粒物(PM)和一氧化氮(NO)水平的每日变化与因呼吸道疾病和高血压导致的医院就诊频率之间的关联。

Association between daily variations in the levels of atmospheric O, PM, and NO and the frequency of hospital visits due to respiratory diseases and hypertension in Mexico City using Generalized Additive Mixed Models.

作者信息

Vega Elizabeth, Ruiz-Olivares Alejandro, Miquelajauregui Yosune, Rentería-Campos Regina, Bramwell Lindsay, Feytmans Romain Bernard M, Namdeo Anil, Núñez-Enríquez Juan Carlos, Entwistle Jane A, Jaimes-Palomera Mónica, Torres-Rojas Nancy Minerva, Rascón-Pacheco R Alberto, Duarte-Rodríguez David A, McNally Richard J Q, García-Burgos Jimena, Resendiz-Martinez Cinthia Gabriela, Fragoso-Chino Ángel, Hayes Louise, Portas Antonio, Mejia-Arangure Juan Manuel

机构信息

Instituto de Ciencias de la Atmósfera y Cambio Climático, Universidad Nacional Autónoma de México (UNAM), Mexico City, Mexico.

Department of Soil Sciences, Colegio de Postgraduados, Montecillo, Mexico.

出版信息

Front Public Health. 2025 Sep 2;13:1593285. doi: 10.3389/fpubh.2025.1593285. eCollection 2025.

DOI:
10.3389/fpubh.2025.1593285
PMID:40963658
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12436139/
Abstract

BACKGROUND

Environmental pollution is a significant public health issue in Mexico City. Patients with respiratory or cardiovascular diseases such as asthma or hypertension often experience exacerbations triggered by environmental factors. This retrospective time series epidemiological study analyzed the association between daily air pollution levels and the numbers of medical visits for exacerbations of cardiorespiratory diseases.

METHODS

Records from primary, secondary, and tertiary hospitals of the Mexican Institute of Social Security (IMSS) were reviewed between 2017 and 2019. Air quality data, including PM, PM, O, and NO concentrations were collected hourly from air quality monitoring stations at fixed sites. To fit the models andtake into account temporal autocorrelation and the complex non-linear relationships between pollutants and cardiorespiratory diseases, Generalized Additive Mixed Models (GAMM) were applied.

RESULTS

PM, O3, and NO2 exposure showed a strong association with an increase in visits for upper respiratory diseases. Age was a relevant factor: individuals aged between 19-35 years were the most affected, whereas those aged 66-100 years were least affected. An effect on the weekdays was identified, with peaks in visits on Mondays and decreases on Saturdays. Also, seasonally, results showed an increase in October and declines in August. Regarding hypertensive diseases, only PM exhibited an association. The models confirmed the link between air pollution levels and respiratory disease exacerbations, highlighting key public health implications.

CONCLUSION

This study provides strong evidence that short term exposure to elevated concentrations of atmospheric pollutants, specifically PM, O3, and NO2, are significantly associated with an increase in medical visits for upper respiratory diseases, asthma, and hypertension in Mexico City. By employing Generalized Additive Mixed Models and analyzing health records from IMSS-affiliated hospitals between 2017 and 2019, we observed that the effects of air pollution varied by disease, age group, season, and day of the week.

摘要

背景

环境污染是墨西哥城一个重大的公共卫生问题。患有哮喘或高血压等呼吸道或心血管疾病的患者经常会因环境因素而病情加重。这项回顾性时间序列流行病学研究分析了每日空气污染水平与心肺疾病加重的就诊次数之间的关联。

方法

对墨西哥社会保障局(IMSS)的一级、二级和三级医院在2017年至2019年期间的记录进行了审查。空气质量数据,包括细颗粒物(PM)、可吸入颗粒物(PM)、臭氧(O₃)和二氧化氮(NO₂)浓度,是从固定地点的空气质量监测站每小时收集的。为了拟合模型并考虑时间自相关性以及污染物与心肺疾病之间复杂的非线性关系,应用了广义相加混合模型(GAMM)。

结果

接触细颗粒物(PM)、臭氧(O₃)和二氧化氮(NO₂)与上呼吸道疾病就诊次数增加有很强的关联。年龄是一个相关因素:19至35岁的个体受影响最大,而66至100岁的个体受影响最小。确定了工作日的影响,周一就诊人数达到峰值,周六则减少。此外,从季节性来看,结果显示10月就诊人数增加,8月减少。关于高血压疾病,只有细颗粒物(PM)显示出关联。这些模型证实了空气污染水平与呼吸道疾病加重之间的联系,突出了关键的公共卫生意义。

结论

本研究提供了有力证据,表明短期接触高浓度的大气污染物,特别是细颗粒物(PM)、臭氧(O₃)和二氧化氮(NO₂),与墨西哥城上呼吸道疾病、哮喘和高血压的就诊次数增加显著相关。通过采用广义相加混合模型并分析2017年至2019年期间IMSS附属医院的健康记录,我们观察到空气污染的影响因疾病、年龄组、季节和星期几而异。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8471/12436139/852419352f20/fpubh-13-1593285-g0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8471/12436139/8dc1a02c2a11/fpubh-13-1593285-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8471/12436139/2cf093cb836c/fpubh-13-1593285-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8471/12436139/90594912e323/fpubh-13-1593285-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8471/12436139/3814cccf87af/fpubh-13-1593285-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8471/12436139/45e735716dc4/fpubh-13-1593285-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8471/12436139/852419352f20/fpubh-13-1593285-g0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8471/12436139/8dc1a02c2a11/fpubh-13-1593285-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8471/12436139/2cf093cb836c/fpubh-13-1593285-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8471/12436139/90594912e323/fpubh-13-1593285-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8471/12436139/3814cccf87af/fpubh-13-1593285-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8471/12436139/45e735716dc4/fpubh-13-1593285-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8471/12436139/852419352f20/fpubh-13-1593285-g0006.jpg

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