[Proposal for a methodology to estimate the potential coverage of primary care teamsPropuesta de una metodología para estimar el área de cobertura potencial de los equipos de atención primaria]

[Factors associated with hospitalizations for primary care-sensitive conditions in Brazil: an ecological studyFactores asociados a las hospitalizaciones infantiles por afecciones que podrían tratarse en la atención primaria en Brasil: estudio ecológico]

Objective

To investigate whether structural aspects of primary care units (PCUs) and the work processes of primary care teams are associated with the rate of hospitalizations for primary care-sensitive conditions (HPCSC) in children younger than 5 years of age in Brazil.

Method

For this longitudinal ecological study, secondary data were obtained from the Brazilian Hospital Information System and from three cycles of the National Program for Access and Quality Improvement in Primary Care (PMAQ-AB) (2012, 2014, 2017/2018). The analysis included 42 916 PCUs. A multilevel random intercept model with fixed slope was used. In the first level, the outcome (HPCSC rates) and explanatory variables (structure and process indicators) aggregated by PCU were analyzed. Social determinants (represented by a stratification criterion combining municipality population and health care management indicators) were entered in the second level. The t test with Bonferroni correction was used to compare indicator means between regions, and multilevel linear regression was used to estimate the correlation coefficients.

Results

The HPCSC rate in children younger than 5 years was 62.78/100 thousand population per estimated PCU coverage area. A direct association with the outcome was observed for: participation in one or more PMAQ-AB cycles; team planning; special hours; dedicated pediatric care area; and availability of vaccines. Equipment, materials, supplies, and being a small or medium-size municipality were inversely associated with HPCSC.

Conclusions

HPCSC rates in children below 5 years of age may potentially be reduced through improvements in PCU structure and process indicators and in municipal social determinants.

Data-driven risk stratification for preterm birth in Brazil: a population-based study to develop of a machine learning risk assessment approach

Background

Preterm birth (PTB) is a growing health issue worldwide, currently considered the leading cause of newborn deaths. To address this challenge, the present work aims to develop an algorithm capable of accurately predicting the week of delivery supporting the identification of a PTB in Brazil.

Methods

This a population-based study analyzing data from 3,876,666 mothers with live births distributed across the 3,929 Brazilian municipalities. Using indicators comprising delivery characteristics, primary care work processes, and physical infrastructure, and sociodemographic data we applied a machine learning-based approach to estimate the week of delivery at the point of care level. We tested six algorithms: eXtreme Gradient Boosting, Elastic Net, Quantile Ordinal Regression - LASSO, Linear Regression, Ridge Regression and Decision Tree. We used the root-mean-square error (RMSE) as a precision.

Findings

All models obtained RMSE indexes close to each other. The lower levels of RMSE were obtained using the eXtreme Gradient Boosting approach which was able to estimate the week of delivery within a 2.09 window 95%IC (2.090-2.097). The five most important variables to predict the week of delivery were: number of previous deliveries through Cesarean-Section, number of prenatal consultations, age of the mother, existence of ultrasound exam available in the care network, and proportion of primary care teams in the municipality registering the oral care consultation.

Interpretation

Using simple data describing the prenatal care offered, as well as minimal characteristics of the pregnant, our approach was capable of achieving a relevant predictive performance regarding the week of delivery.

Funding

Bill and Melinda Gates Foundation, and National Council for Scientific and Technological Development - Brazil, (Conselho Nacional de Desenvolvimento Científico e Tecnológico - CNPQ acronym in portuguese) Support of the research project named: Data-Driven Risk Stratification for Preterm Birth in Brazil: Development of a Machine Learning-Based Innovation for Health Care- Grant: OPP1202186.