An Ecological Analysis of Predictors of Hospitalizations for Primary Care Sensitive Conditions under Brazil’s Family Health Strategy

Abstract

Background: Primary care sensitive conditions (PCSC), a classification of illnesses that includes noncommunicable diseases (NCDs) and maternal health complications, are considered preventable through appropriate care management and interventions at the primary care (PC) level. Consistent with trends in global disease burden, PCSC are a significant contributor to avoidable hospitalizations in low and middle income countries (LMIC), which carries profound social and economic consequences. Rates of hospitalizations for primary care sensitive conditions (HPCSC) have been found to be associated with the level of infrastructure of health services delivery, health system, and socioeconomic context. This study concentrates on the Brazilian state of Minas Gerais to evaluate the current profile of HPCSC and their predictors under the universal PC program, the Family Health Strategy (FHS). Methods: This is an ecological study based on: 1) data of PC infrastructure from 560 municipalities, collected from 2012-2013 through the Programa Nacional de Melhoria do Acesso e da Qualidade da Atenção Básica (PMAQ-AB), 2) data on rates of HPCSC available in the Hospital Information System of the Unified Health System, and 3) data on health system and socioeconomic indicators from the Brazilian Ministry of Health and the Brazilian Institute for Geography and Statistics, respectively. For the analysis, 7 groups of PCSC specifically targeted under the FHS were considered. 24 structure and process indicators were selected from the PMAQ-AB database and a principal component analysis with factor interpretability was performed, utilizing the theoretical rationale of the Starfield Model of Primary Care, to reduce and describe data dimensionality. Principal component scores were averaged by municipality, and assessed as predictors of HPCSC across municipalities in multiple regression models both individually and progressively adjusting for health system and socioeconomic variables as groups. Results: From January-December 2012, municipalities in our sample experienced 12,078 HPCSC due to the 7 conditions chosen, with an aggregate age-adjusted rate of 112.15 per 10,000 inhabitants. The NCDs of congestive heart failure, cerebrovascular diseases, and diabetes mellitus collectively accounted for 87.56% of all hospitalizations. The best-fitting principal component model of infrastructure data consisted of 3 components that corresponded to the level of adequacy of care comprehensiveness, continuity, and coordination. In the fully-adjusted models, the strongest predictors of HPCSC per 10,000 were continuity (β= 12.44) for heart failure, comprehensiveness (β= -3.09) for cerebrovascular diseases, continuity (β= 1.45) for diabetes, continuity (β= .92) for skin and subcutaneous tissue infections, comprehensives e (β=.99) for female pelvic inflammatory diseases, and continuity (β=.74) for prenatal and postpartum conditions. Conclusions: NCDs heavily influence incidences of avoidable hospitalizations in Minas Gerais, Brazil. Yet, our findings suggest that the community-based care models of the FHS may have the potential to mitigate the role of social vulnerability in influencing health outcomes. This project offers a model for quantifying the quality of PC infrastructure and more research is needed to validate its use in LMIC, as well as to further understand the strength and directionality of the relationship between health center, health system, and socioeconomic predictors of HPCSC.