Leanna Silva Aquino, Ellen Mara Fernandes da Silva, Julianne Figueiredo Costa Sousa, Ednaldo Pereira Maranhão, Victoria Valentim Aguiar, Cesar Ferreira Fernandes Filho, Daliane Ferreira Marinho, Sheyla Mara Silva de Oliveira, Tatiane Costa Quaresma, Valney Mara Gomes Conde, Marcos Manoel Honorato, Veridiana Barreto do Nascimento, Guilherme Augusto Barros Conde, Franciane de Paula Fernandes, Lívia de Aguiar Valentim
Healthcare access in the Brazilian Amazon reflects intertwined territorial and social constraints. Computational approaches can complement conventional analyses by integrating multi-level information to support equity-oriented planning in remote settings.
BACKGROUND: Equitable access to healthcare services is a core principle of Brazil's Unified Health System and remains a persistent challenge in territories marked by social and geographic inequalities, such as the Brazilian Amazon. In this region, vast distances, logistical barriers, and socioeconomic disparities converge to limit the effectiveness of public health policies.
OBJECTIVE: The present study aimed to examine individual, organizational, and territorial factors associated with self-reported healthcare access among quilombola rural and riverside communities in Santarém, Brazilian Amazon, using conventional statistical and machine-learning approaches.
METHODS: This cross-sectional, community-based cross-sectional survey included 512 adults from nine Amazonian communities. Data were collected through structured interviews and analyzed using Python-based computational workflows. Supervised and unsupervised algorithms, multilevel logistic modeling, and principal component analysis (PCA) were employed. Five predictive models (Decision Tree, Random Forest, Gradient Boosting, Logistic Ridge, and Logistic Lasso) were compared using AUC, accuracy, sensitivity, and specificity, with performance reported for cross-validation in the training set and the held-out test set. A Social Vulnerability Index (SVI) was derived from PCA.
RESULTS: Access and problem-resolution rates varied among communities, ranging from 42.9% to 100% and from 54.5% to 100%, respectively. Predictive discrimination was limited in the held-out test set (AUC 0.482-0.586), indicating poor generalizability for individual-level prediction. At the community level (n ≥ 10), the SVI was negatively correlated with access (r ≈ -0.53).
CONCLUSIONS: Healthcare access in the Brazilian Amazon reflects intertwined territorial and social constraints. Computational approaches can complement conventional analyses by integrating multi-level information to support equity-oriented planning in remote settings.