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识别巴西法定亚马逊地区呼吸疾病住院和死亡的高发区域:时空分析。

Identifying high occurrence areas of hospitalization and mortality from respiratory diseases in the Brazilian Legal Amazon: a space-time analysis.

机构信息

Escola Nacional de Saúde Pública Sergio Arouca, Fundação Oswaldo Cruz, Rio de Janeiro, Brasil.

Universidade Federal Fluminense, Niterói, Brasil.

出版信息

Cad Saude Publica. 2024 Nov 25;40(11):e00148023. doi: 10.1590/0102-311XEN148023. eCollection 2024.

DOI:10.1590/0102-311XEN148023
PMID:39607106
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11654102/
Abstract

Respiratory diseases pose a significant threat to the health of the Brazilian population, ranking among the leading causes of hospitalizations and deaths in the country. The most impacted demographics are children, adolescents, and older adults, who respectively have the highest rates of hospitalizations and deaths. An exploratory ecological study was conducted to assess the spatio-temporal distribution of hospitalizations and deaths due to respiratory diseases among children, adolescents, and older adults residing in municipalities in the Brazilian Legal Amazon. Moreover, the study aimed to identify priority municipalities within the detected clusters by employing composite synthetic municipal indices. These indices were estimated based on various socio-environmental and health indicators. The scan analysis identified clusters across various time periods but they mostly aligned with the disease trends in the region. We were able to identify clusters both near metropolitan areas and in remote locations, capturing two distinct patterns of cluster distribution. Moreover, the application of composite synthetic indices enabled a comprehensive identification of priority municipalities, considering various factors relevant to the health conditions of the population in the studied areas.

摘要

呼吸系统疾病对巴西民众的健康构成重大威胁,是导致该国住院和死亡的主要原因之一。受影响最大的人群是儿童、青少年和老年人,他们的住院率和死亡率分别最高。本研究采用探索性生态研究方法,评估了巴西法律亚马逊地区各市政当局中儿童、青少年和老年人因呼吸系统疾病住院和死亡的时空分布情况。此外,该研究旨在通过使用综合合成市政指数,确定所发现集群中的优先市政当局。这些指数是根据各种社会环境和健康指标来估计的。扫描分析在不同时期发现了集群,但它们主要与该地区的疾病趋势一致。我们能够在大都市地区附近和偏远地区识别出集群,捕捉到两种不同的集群分布模式。此外,综合合成指数的应用能够全面识别优先的市政当局,考虑到研究区域内与人群健康状况相关的各种因素。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/02d4/11654102/b20de35794c8/1678-4464-csp-40-11-EN148023-gf2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/02d4/11654102/2e0c820de444/1678-4464-csp-40-11-EN148023-gf1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/02d4/11654102/b20de35794c8/1678-4464-csp-40-11-EN148023-gf2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/02d4/11654102/2e0c820de444/1678-4464-csp-40-11-EN148023-gf1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/02d4/11654102/b20de35794c8/1678-4464-csp-40-11-EN148023-gf2.jpg

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