Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, MA 02115.
Superintendência de Vigilância em Saúde do Amapá, Governo do Estado do Amapá, Macapá, AP 68902-865, Brazil.
Proc Natl Acad Sci U S A. 2024 Oct 29;121(44):e2409583121. doi: 10.1073/pnas.2409583121. Epub 2024 Oct 21.
Ecological change in the Brazilian Amazon is closely linked to human mobility and health. Mining, agriculture, logging, and other activities alter highly diverse ecological and demographic contexts and subsequent exposure to diseases such as malaria. Studies that have attempted to quantify the impact of deforestation on malaria in the Brazilian Amazon have produced conflicting results. However, they varied in methodology and data sources. Most importantly, all studies used annual data, neglecting the subannual seasonal dynamics of malaria. Here, we fill the knowledge gap on the subannual relationship between ecological change in the Brazilian Amazon and malaria transmission. Using the highest spatiotemporal resolution available, we estimated the effect of deforestation on malaria cases between 2003 and 2022 using a stratified Bayesian spatiotemporal hierarchical zero-inflated Poisson model fitted with the Integrated Nested Laplace Approximation. The model was also stratified by state. We found that a 1% increase in 1-mo lagged deforestation increased malaria cases in a given month and municipality by 6.3% [95% credible interval (Crl): 6.2, 6.5%]. Based on an interaction term included in the model, the effect of deforestation on malaria was even larger in areas with higher forest cover. We found that the coefficients for deforestation and mobility were highly variable when stratified by state. Our results provide detailed evidence that, on average, deforestation increases malaria transmission, but that the relationship is not spatiotemporally uniform. These results have implications for stratifying malaria control interventions based on ecological dynamics to help Brazil achieve its goal of malaria elimination by 2035.
巴西亚马逊地区的生态变化与人类流动和健康密切相关。采矿、农业、伐木和其他活动改变了高度多样化的生态和人口背景，继而导致疟疾等疾病的发生。试图量化森林砍伐对巴西亚马逊地区疟疾影响的研究得出了相互矛盾的结果。然而，这些研究在方法和数据源上存在差异。最重要的是，所有研究都使用了年度数据，忽略了疟疾的亚年度季节性动态。在这里，我们填补了关于巴西亚马逊地区生态变化与疟疾传播之间亚年度关系的知识空白。利用最高的时空分辨率，我们使用分层贝叶斯时空分层零膨胀泊松模型，结合综合嵌套拉普拉斯逼近法，估计了 2003 年至 2022 年期间森林砍伐对疟疾病例的影响。该模型还按州进行分层。我们发现，1 个月前滞后的森林砍伐增加 1%，会使给定月份和城市的疟疾病例增加 6.3%[95%可信区间（Crl）：6.2，6.5%]。根据模型中包含的一个交互项，在森林覆盖率较高的地区，森林砍伐对疟疾的影响更大。我们发现，按州分层时，森林砍伐和流动的系数变化很大。我们的研究结果提供了详细的证据，表明平均而言，森林砍伐会增加疟疾传播，但这种关系在时空上并不均匀。这些结果对于根据生态动态分层疟疾控制干预措施具有重要意义，有助于巴西实现到 2035 年消除疟疾的目标。
