Amazon Epidemiology and Geoprocessing Laboratory, Faculty of Medicine, Pará State University, Belém, Brazil.
Cyberspace Institute, Amazon Rural Federal University, Belém, Brazil.
J Infect Dev Ctries. 2024 Jul 29;18(7):1124-1131. doi: 10.3855/jidc.18639.
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.
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.
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.
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.
本研究旨在分析 2017 年至 2022 年期间巴西帕拉州 22 个微地区皮肤利什曼病与其流行病学、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,微地区被用作空间单位,因为它们由具有相似特征的毗邻市组成。所使用的流行病学、环境、社会经济和公共卫生政策数据是从卫生部、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中获得的。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。使用二元全局 Moran 空间分析技术对结果进行分析。
观察到阿尔塔米拉微地区的疾病风险百分比最高,而布雷韦斯微地区的风险百分比最低,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以及人口统计学和生活条件指数、教育和公共卫生政策有关。
模糊系统与地统计学技术相结合,可用于识别与森林砍伐、牧场、贫困、文盲和卫生服务覆盖相关的健康脆弱性梯度的区域,这些是其条件变量。因此,表明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学监测。