Impact of socioeconomic factors and health determinants on preterm birth in Brazil: a register-based study.

BACKGROUND: More than 15 million children are born preterm annually. While preterm survival rates have increased in high-income countries. Low- and middle-income countries, like Brazil, continue to battle high neonatal mortality rates due to a lack of adequate postnatal care. Globally, neonatal mortality is higher for preterm infants compared to those born at term. Our study aims to map and analyze the spatial, socioeconomic, and health coverage determinants related to preterm birth in Brazil in order to understand how spatial variations in demographics and access to primary care may affect preterm birth occurrences.  METHODS: Using publicly available national-level data from the Brazilian health system for 2008-2017, we conducted an ecological study to visualize the spatial distributions of preterm birth along with socioeconomic status, the structure of health services, and primary care work process, each consisting of multiple variables reduced via principal component analysis. Regression models were created to determine predictive effects of numeric and spatial variation of these scores on preterm birth rates. RESULTS: In Brazil, preterm birth rates increased from 2008-2017, with small and rural municipalities frequently exhibiting higher rates than urban areas. Scores in socioeconomic status and work process were significant predictors of preterm birth rates, without taking into account spatial adjustment, with more positive scores in socioeconomic status predicting higher preterm birth rates (coefficient 0.001145) and higher scores in work process predicting lower preterm birth rates (coefficient -0.002416). Geographically weighted regression showed socioeconomic status to be a more significant predictor in the North, with the work process indicators being most significant in the Northeast. CONCLUSIONS: Results support that primary care work process indicators are more significant in estimating preterm birth rates than physical structures available for care. These results emphasize the importance of ensuring the presence of the minimum human resources needed, especially in the most deprived areas of Brazil. The association between social determinants of health and preterm birth rates raises questions regarding the importance of policies dedicated to foster equity in the accessibility of healthcare services, and improve income as protective proxies for preterm birth.

[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]. / Proposta de metodologia para estimar a área de cobertura potencial por equipes de atenção primária.

OBJECTIVE: To present a methodology for the empirical evaluation of primary health care (PHC) through the construction of digital representations of potential PHC coverage areas. METHODS: In this methodological study, potential areas were constructed by combinatorial analysis between census tracts and the location of basic health units with working PHC teams in Brazil. Six rules were used to parameterize the algorithm for the construction of potential areas. Thus, six restrictions were applied to enable the model: the selection of census tracts near the basic health unit; contiguous sectors; mutually exclusive sectors; sectors located in the same municipality of basic health units; sum of 4 500 users per health team in each unit; and volume of population ascribed proportional to the number of PHC teams allocated to the unit. Based on 316 594 census tracts and 39 758 basic health units, a neighborhood matrix was developed. To that matrix, a graph algorithm was applied to test combinations of sectors that simultaneously met the stipulated rules. RESULTS: A total of 1 901 114 arcs were defined, connecting 30 351 census tracts, allowing the construction of 26 907 potential areas. Based on these results, intra-municipal analyses can be performed to monitor PHC indicators. Customizable algorithm parameters can be adjusted to accommodate different sets of rules which may be adapted to different countries. CONCLUSIONS: The use of geoprocessing approaches creates conditions for the assessment of PHC impact, based on secondary databases at various levels, such as intra-municipal, basic health unit, and even at the team level.

OBJETIVO: Presentar una metodología para la evaluación empírica de la atención primaria de salud (APS) a través de la construcción de representaciones digitales de las áreas de cobertura potencial de los equipos de APS. MÉTODOS: Estudio de tipo metodológico. Las áreas potenciales se construyeron mediante un análisis combinatorio entre los sectores censales y la localización de las unidades básicas de salud con equipos de APS que trabajan en Brasil. Se utilizaron seis reglas para parametrizar el algoritmo de construcción de las áreas potenciales. Así, se estipularon seis restricciones que viabilizaron el modelo utilizado: selección de sectores censales cercanos a la unidad básica de salud; sectores contiguos; sectores mutuamente excluyentes; sectores ubicados en el mismo municipio de la unidad básica de salud; suma de 4 500 usuarios por equipo de salud en cada unidad básica de salud; y volumen de población adscrita proporcional al número de equipos de APS asignados en la unidad básica de salud. A partir de 316 574 sectores censales y 39 758 unidades básicas de salud se desarrolló una matriz de vecindad sobre la cual se aplicó un algoritmo gráfico que evaluaba las combinaciones de sectores que cumplían simultáneamente las reglas estipuladas. RESULTADOS: Se definieron en total 1 901 114 arcos, que conectaron 30 351 sectores censales, lo que permitió la construcción de 26 907 áreas potenciales. Sobre la base de estos resultados, se pueden realizar análisis intramunicipales para monitorear los indicadores de APS. Los parámetros modificables del algoritmo se pueden ajustar para adaptarse a diferentes conjuntos de reglas y a diferentes países. CONCLUSIONES: El uso de enfoques basados en geoprocesamiento puede crear condiciones para la evaluación del impacto de la APS conforme a bases de datos secundarias y a nivel intramunicipal, de la unidad básica de salud e incluso a nivel de equipo.

Oral primary care: an analysis of its impact on the incidence and mortality rates of oral cancer.

BACKGROUND: Oral cancer is a potentially fatal disease, especially when diagnosed in advanced stages. In Brazil, the primary health care (PHC) system is responsible for promoting oral health in order to prevent oral diseases. However, there is insufficient evidence to assess whether actions of the PHC system have some effect on the morbidity and mortality from oral cancer. The purpose of this study was to analyze the effect of PHC structure and work processes on the incidence and mortality rates of oral cancer after adjusting for contextual variables. METHODS: An ecological, longitudinal and analytical study was carried out. Data were obtained from different secondary data sources, including three surveys that were nationally representative of Brazilian PHC and carried out over the course of 10 years (2002-2012). Data were aggregated at the state level at different times. Oral cancer incidence and mortality rates, standardized by age and gender, served as the dependent variables. Covariables (sociodemographic, structure of basic health units, and work process in oral health) were entered in the regression models using a hierarchical approach based on a theoretical model. Analysis of mixed effects with random intercept model was also conducted (alpha = 5%). RESULTS: The oral cancer incidence rate was positively association with the proportion of of adults over 60 years (ß = 0.59; p = 0.010) and adult smokers (ß = 0.29; p = 0.010). The oral cancer related mortality rate was positively associated with the proportion of of adults over 60 years (ß = 0.24; p < 0.001) and the performance of preventative and diagnostic actions for oral cancer (ß = 0.02; p = 0.002). Mortality was inversely associated with the coverage of primary care teams (ß = -0.01; p < 0.006) and PHC financing (ß = -0.52-9; p = 0.014). CONCLUSIONS: In Brazil, the PHC structure and work processes have been shown to help reduce the mortality rate of oral cancer, but not the incidence rate of the disease. We recommend expanding investments in PHC in order to prevent oral cancer related deaths.

Addressing geographic access barriers to emergency care services: a national ecologic study of hospitals in Brazil.

BACKGROUND: Unequal distribution of emergency care services is a critical barrier to be overcome to assure access to emergency and surgical care. Considering this context it was objective of the present work analyze geographic access barriers to emergency care services in Brazil. A secondary aim of the study is to define possible roles to be assumed by small hospitals in the Brazilian healthcare network to overcome geographic access challenges. METHODS: The present work can be classified as a cross-sectional ecological study. To carry out the present study, data of all 5843 Brazilian hospitals were categorized among high complexity centers and small hospitals. The geographical access barriers were identified through the use of two-step floating catchment area method. Once concluded the previous step an evaluation using the Getis-Ord-Gi method was performed to identify spatial clusters of municipalities with limited access to high complexity centers but well covered by well-equipped small hospitals. RESULTS: The analysis of accessibility index of high complexity centers highlighted large portions of the country with nearly zero hospital beds by inhabitant. In contrast, it was possible observe a group of 1595 municipalities with high accessibility to small hospitals, simultaneously with a low coverage of high complexity centers. Among the 1595 municipalities with good accessibility to small hospitals, 74% (1183) were covered by small hospitals with at least 60% of minimum emergency service requirements. The spatial clusters analysis aggregated 589 municipalities with high values related to minimum emergency service requirements. Small hospitals in these 589 cities could promote the equity in access to emergency services benefiting more than eight million people. CONCLUSIONS: There is a spatial disequilibrium within the country with prominent gaps in the health care network for emergency services. Taking this challenge into consideration, small hospitals could be a possible solution and foster equity in access to emergency and surgical care. However more investments in are necessary to improve small hospitals capabilities to fill this gap.

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.