Social and environmental determinants of Neglected infectious diseases in quilombola communities of the Brazilian Amazon: An epidemiological and machine learning analysis

Neglected infectious diseases (NIDs) remain a major public health challenge in the Amazon, particularly among quilombola populations living in rural and riverside territories marked by historical inequalities and structural limitations. This study examined the occurrence of NIDs in eight quilombola communities in the Lower Amazon, identified socioenvironmental factors associated with these conditions, and evaluated the performance of machine learning models in predicting individual risk of illness. This analytical cross-sectional study included 518 participants, with data collected through a structured questionnaire. Descriptive and bivariate analyses were conducted, followed by multivariable logistic regression, Poisson regression, cluster analysis, and predictive modeling using Random Forest, XGBoost, and Logistic Regression. Spatial analysis was performed in Google Colab. The overall prevalence of at least one NID was 34.7%. Lack of sanitation facilities, use of river or well water, precarious housing, inadequate waste disposal, low income, and residence in rural areas were significantly associated with both the occurrence and number of NIDs per individual. XGBoost and Random Forest achieved the best predictive performance (AUC-ROC 0.87 and 0.85, respectively). Cluster analysis revealed distinct vulnerability profiles, with the highest burden observed among groups characterized by multidimensional poverty and limited sanitation. The findings highlight the overlapping social and environmental determinants that sustain the persistence of NIDs in these territories, underscoring the need for structural, territorialized policies tailored to the specific realities of quilombola communities in the Amazon. The cross-sectional design and reliance on self-reported disease history should be considered when interpreting the findings.Author summary: Neglected infectious diseases (NIDs) continue to affect populations living in conditions of poverty, especially in remote regions such as the Brazilian Amazon. This study focuses on quilombola communities, Afro-descendant populations that have historically faced social exclusion and limited access to basic services. We investigated how living conditions, such as access to clean water, sanitation, housing quality, and income, are related to the occurrence of NIDs in eight quilombola communities in the Lower Amazon region. Among the 518 adults included in the study, more than one-third reported having had at least one NID during their lifetime. Diseases such as dengue, malaria, leprosy, and parasitic infections were the most frequently reported. We found that people living without a bathroom, using river or well water, residing in precarious housing, and having low income were much more likely to experience these diseases. In addition to traditional epidemiological analyses, we used machine learning models to identify patterns of vulnerability and predict disease risk. These models performed well and highlighted that social and environmental conditions act together to increase health risks. Our findings show that NIDs in quilombola communities are not only biological problems, but also reflect longstanding social and territorial inequalities. Addressing these diseases requires investments in sanitation, water access, housing, and health services tailored to the realities of Amazonian communities.