Estudo de indicadores de ambiente e saúde utilizando a metodologia de modelos multiníveis na microrregiões do Rio Grande do Sul

Abstract

Environmental modifications directly influence people's quality of life. It is appropriate to structure a system of indicators to monitor environmental conditions that may affect public health. Thus, the objective of this study was to construct and analyze environmental health indicators for the micro-regions of the State of Rio Grande do Sul (RS), called Conselho Regional de Desenvolvimento (COREDEs), verifying the relationship of morbidity and mortality with demographic, socioeconomics and coverage by health and sanitation services, which may serve as a monitoring of health-related environmental damage. The methodological procedures included: bibliographic review of the relationship between morbidity and mortality indicators (outcomes) with other environmental health indicators (predictors); research in information systems that provide data on health, environment and environmental health; multilevel modeling study; search of data in information systems; and, adjustment and interpretation of multilevel models.Multilevel regression models were used adopting the municipalities as first level analysis unit and the RS micro-regions as second level. Four models with the following outcomes were adjusted: morbidity coefficients (cases / 1,000 inhabitants) and mortality (cases / 100,000 inhabitants) for infectious and parasitic diseases (model 5 and model 6, respectively) and morbidity coefficients (cases / 10,000 inhabitants ) And mortality (cases / 100,000 inhabitants) for diarrheal diseases and gastroenteritis (model 7 and model 8, respectively).The results show that the final model 5 was composed of three predictors, being a predictor with an inverse signal of the one expected to the outcome. The final model 6, with 16 predictors, being eight predictors with signal expected to the outcome. The final model 7 consisted of five predictors with the opposite signal to the expected outcome. The final model 8 has ten predictors, with six predictors with the opposite sign of the expected outcome. As conclusion, the analyzes showed the applicability of the multilevel modeling technique to investigate and monitor relationships between health and environment indicators. It is suggested to explore other predictors that may be influencing the four adjusted models, in the sense of explaining the inverse signals found.