Brenda Caroline Sampaio da Silva, Ricardo José de Paula Souza E Guimarães, Bruno Spacek Godoy, Andressa Tavares Parente, Bergson Cavalcanti de Moraes, Marcia Aparecida da Silva Pimentel, Douglas Batista da Silva Ferreira, Emilene Monteiro Furtado Serra, João de Athaydes Silva Junior, Luciano Jorge Serejo Dos Anjos, Everaldo Barreiros de Souza
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引用次数: 0
摘要
背景：亚马逊生物群落表现出复杂的虫媒病毒传播动态，受到森林砍伐加速、气候变化和社会经济不平等的影响。目的/方法：本研究将官方流行病学记录与社会经济、环境和气候变量相结合，采用先进的地质统计学方法（Moran’s I, SaTScan，核密度估计），结合主成分分析和负二项回归，评估2010年至2024年par州（巴西亚马逊东部）各城市登革热发病率的时空动态及其与社会环境决定因素的关系。结果：2010-2012年、2016年和2024年登革热发病率总体呈下降趋势，但有明显的流行高峰。空间分析结果显示，par地区具有显著的聚类性（Moran’s I = 0.221, p < 0.01），高风险热点地区持续存在。在144个城市中，104个存在严重登革热风险，58个保持持续传播。负二项回归模型确定了关键的决定因素：文盲、低城市化、GDP下降和气候变量。结论：亚马逊地区的登革热传播是由协同的社会环境破坏驱动的，因此需要跨部门政策，将公共卫生监测、可持续土地使用治理和减轻贫困联系起来。优先行动包括在高风险集群中有针对性地控制病媒，同时进行综合毁林和气候监测，以预测爆发风险。调查结果强调，迫切需要实施针对亚马逊脆弱地区领土和社会环境复杂性的多部门干预措施，以有效控制登革热。
Spatiotemporal Dynamics of Dengue in the State of Pará and the Socio-Environmental Determinants in Eastern Brazilian Amazon.
Background: The Amazon biome exhibits complex arboviral transmission dynamics influenced by accelerating deforestation, climate change, and socioeconomic inequities.
Objectives/methods: This study integrates official epidemiological records with socioeconomic, environmental, and climate variables by applying advanced geostatistical methods (Moran's I, SaTScan, kernel density estimation) combined with principal component analysis and negative binomial regression to assess the spatiotemporal dynamics of dengue incidence and its association with socio-environmental determinants across municipalities in Pará state (eastern Brazilian Amazon) from 2010 to 2024.
Results: Dengue incidence showed an overall decline but with marked epidemic peaks in 2010-2012, 2016, and 2024. The spatial analysis revealed significant clustering (Moran's I = 0.221, p < 0.01), with persistent high-risk hotspots across most of Pará. Of 144 municipalities, 104 exhibited significant dengue risk, while 58 maintained sustained transmission. Negative binomial regression model identified key determinants: illiteracy, low urbanization, reduced GDP, and climate variables.
Conclusions: Dengue transmission in the Amazon is driven by synergistic socio-environmental disruptions, necessitating intersectoral policies that bridge public health surveillance, sustainable land-use governance, and poverty alleviation. Priority actions include targeted vector control in high-risk clusters, coupled with integrated deforestation and climate monitoring to predict outbreak risks. The findings emphasize the urgency of implementing multisectoral interventions tailored to the territorial and socio-environmental complexities of vulnerable Amazonian regions for effective dengue control.
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