巴西亚马逊帕拉州皮肤利什曼病的模糊和空间分析:一项生态和探索性研究。

IF 1.4 4区 医学 Q4 INFECTIOUS DISEASES
Simone Bn Costa, Claudia do Sc Miranda, Bruna C De Souza, Heloisa Maria M E S Guimarães, Camylle Mc Faria, Pedro S Da S Campos, Taiana Ma Koury, José Gabriel M Da Paixão, Alessandra L Leal, Maria de Fátima P Carrera, Silvana R De Brito, Nelson V Gonçalves
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引用次数: 0

摘要

导言:本研究旨在分析 2017 年至 2022 年期间巴西帕拉州 22 个微型地区皮肤利什曼病与流行病学、环境和社会经济条件之间的关系:在这项生态和探索性研究中,微型地区被用作空间单位,因为它们是由具有相似特征的毗连市镇组成的。所使用的流行病学、环境、社会经济和公共卫生政策数据来自卫生部、国家空间研究所和巴西地理与统计研究所的官方信息系统。使用 Python 编程语言开发了一个模糊系统来识别疾病的风险因素。分析结果采用了二元全球莫兰空间分析技术:结果表明,阿尔塔米拉微区的疾病风险百分比最高,而布雷维斯最低,其条件因素的相关性存在显著差异,主要与土地利用和覆盖模式有关,此外还与人口和生活条件指数、教育和公共卫生政策有关:与地理统计技术相结合的模糊系统在确定与森林砍伐、牧场、贫困、文盲和医疗服务覆盖率等条件变量有关的健康脆弱性梯度方面令人满意。因此,森林砍伐是该疾病的主要风险因素。该系统还可用于环境和流行病监测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fuzzy and spatial analysis of cutaneous leishmaniasis in Pará State, Brazilian Amazon: an ecological and exploratory study.

Introduction: This study sought to analyze the relationships between cutaneous leishmaniasis and its epidemiological, environmental and socioeconomic conditions, in the 22 microregions of Pará state, Brazil, for the period from 2017 to 2022.

Methodology: In this ecological and exploratory study, the microregions were used as spatial units because they are formed by contiguous municipalities with similar characteristics. The epidemiological, environmental, socioeconomic, and public health policy data employed were obtained from the official information systems at the Ministry of Health, National Institute for Space Research, and Brazilian Institute of Geography and Statistics. A fuzzy system was developed to identify risk factors for the disease, using Python programming language. The results were analyzed with the bivariate Global Moran spatial analysis technique.

Results: It was observed that the Altamira microregion had the highest risk percentage for the disease, while Breves had the lowest, with significant differences in the relevance of its conditioning factors, mainly related to land use and cover patterns, in addition to demography and living conditions index, education and public health policies.

Conclusions: The fuzzy system associated with the geostatistical technique was satisfactory for identifying areas with health vulnerability gradients related to deforestation, pasture, poverty, illiteracy, and health services coverage, as its conditioning variables. Thus, it was demonstrated that deforestation was the main risk factor for the disease. The system can also be used in environmental and epidemiological surveillance.

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来源期刊
CiteScore
3.70
自引率
5.30%
发文量
239
审稿时长
4-8 weeks
期刊介绍: The Journal of Infection in Developing Countries (JIDC) is an international journal, intended for the publication of scientific articles from Developing Countries by scientists from Developing Countries. JIDC is an independent, on-line publication with an international editorial board. JIDC is open access with no cost to view or download articles and reasonable cost for publication of research artcles, making JIDC easily availiable to scientists from resource restricted regions.
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