Ocorrência de arboviroses a partir da perspectiva multidimensional, territorial e da determinação social da saúde em municípios da Bahia.

Abstract

Arboviruses are infectious diseases that occur in significant numbers in Brazil and pose a major challenge to Public Health. Although these diseases have been present in Brazil for a long time, control policies have proven ineffective due to their reductionist approach, which primarily focuses on biological interventions, typically involving mechanical and chemical control measures. As a result, these policies fail to address the deeply entrenched socioeconomic inequalities that characterize most Brazilian cities. In this context, the present study aimed to analyze the factors and associated vulnerabilities contributing to the occurrence of arboviruses from a multidimensional perspective, considering territorial and social determinants of health in the municipalities of the State of Bahia. To achieve this, a systematic review was initially conducted to assess the knowledge and application of spatial analysis techniques employed in investigating the spatial behavior of arboviruses. Subsequently, a quantitative and analytical study with a mixed ecological epidemiological design was carried out, focusing on the occurrence of arboviruses in municipalities within the State of Bahia. The systematic review highlighted that spatial modeling is a methodological tool that enables a deeper understanding of the current and future distribution of arboviruses. All the included studies demonstrated that arboviruses exhibited a heterogeneous spatial pattern across various geographical scales. This reflects the underlying tendency of spatial dependence in the occurrence of arboviruses, which may be associated with socioeconomic aggregation, as well as environmental and climatic effects. The epidemiological study revealed a heterogeneous distribution of arboviral incidence over the years in the municipalities of Bahia, with a significant increase and sustained incidence observed during 2019-2020, followed by a decrease in the indicator during 2017-2018 and 2021. In the Pearson matrix, a statistically significant direct correlation (p ≤ 0.05) was observed between arboviral incidence and the following variables: degree of urbanization, floods, inundations, slums, urban housing developments, urban households, piped water supply, sewerage network, income, women, White, and Indigenous populations. Through the adjustment of the classical linear model, the Spatial AutoRegressive (SAR) model, and the Conditional AutoRegressive (CAR) model, for the least and most epidemic years respectively, it was possible to relate the studied arboviruses to sociodemographic and housing variables. When comparing the three models, the classical model results were deemed unsatisfactory for not incorporating spatial dependence. The best-fitting model was the SAR, as it captured spatial dependence and allowed for the acquisition of more conclusive analytical data. This demonstrates how the inclusion of the autoregressive parameter significantly aids in explaining arboviral dynamics. In this model, the explanatory variables were: level of education, population density, household location (urban area), water supply, the presence of floods, and slums. It is concluded that arboviruses intertwine various socioeconomic and demographic conditions, making it crucial to adopt strategies that are integrated with other policies, such as economic and social policies.