Disparities in surgical care for children across Brazil: Use of geospatial analysis

Abstract

Background: Health systems for surgical care for children in low- and middle-income countries remain poorly understood. Our goal was to characterize the delivery of surgical care for children across Brazil and to identify associations between surgical resources and childhood mortality.

Methods: We performed a cross-sectional, ecological study to analyze surgical care for children in the public health system (Sistema Único de Saúde) across Brazil from 2010 to 2015. We collected data from several national databases, and used geospatial analysis (two-step floating catchment, Getis-Ord-Gi analysis, and geographically weighted regression) to explore relationships between infrastructure, workforce, access, procedure rate, under-5 mortality rate (U5MR), and perioperative mortality rate (POMR).

Results: A total of 246,769 surgical procedures were performed in 6,007 first level/ district hospitals and 491 referral hospitals across Brazil over the study period. The surgical workforce is distributed unevenly across the country, with 0.13-0.26 pediatric surgeons per 100,000 children in the poorer North, Northeast and Midwest regions, and 0.6-0.68 pediatric surgeons per 100,000 children in the wealthier South and Southeast regions. Hospital infrastructure, procedure rate, and access to care is also unequally distributed across the country, with increased resources in the South and Southeast compared to the Northeast, North, and Midwest. The U5MR varies widely across the country, although procedure-specific POMR is consistent across regions. Increased access to care is associated with lower U5MR across Brazil, and access to surgical care differs by geographic region independent of socioeconomic status.

Conclusions: There are wide disparities in surgical care for children across Brazil, with infrastructure, manpower, and resources distributed unevenly across the country. Access to surgical care is associated with improved U5MR independent of socioeconomic status. To address these disparities, policy should direct the allocation of surgical resources commensurate with local population needs.

Fig 1. Income group distribution of Brazilian municipalities.

Socioeconomic data were extracted from Brazilian Institute of Geography and Statistic (IBGE), and used with the Brazilian gross domestic product to classify municipalities according to income groups as defined by the World Bank as high income, upper-middle income, or lower-middle income. The map of Brazil was freely obtained in shapefile format (SHP) through online access to the website of the Brazilian Institute of Geography and Statistics (https://mapas.ibge.gov.br/bases-e-referenciais/bases-cartograficas/malhas-digitais.html).

Fig 2

Geospatial analysis of access to A) first level/district hospitals and B) referral-level hospitals for surgical care for children across Brazil using 2SFCA analysis. Index values reflect the number of beds/pediatric inhabitant within 120 km radius from each municipality. For example, an index of 0·00041 means that in this municipality there are 0·00041 beds per pediatric inhabitant within a 120 km radius.

Fig 3. Geographic distribution across Brazil for rates of five proxy pediatric general surgical procedures (appendectomy, colostomy, hernia repair, laparotomy, abdominal wall defect).

A) Rate of procedures/100,000 pediatric inhabitants performed at the municipality level. B) Hotspot and cold spots analysis demonstrates clustering of the rates of each procedure.

Fig 4. Geospatial distribution of pediatric mortality rate across Brazil.

A) Distribution of pediatric mortality rates by age groups; and B) Hot spot (red) and cold spot (blue) of pediatric mortality rates.

Fig 5. Geospatial representation of GWR model performance and association between variables (procedure rate, access to first/level or referral hospitals) and pediatric mortality by age group.

Outcomes for income not represented, but were included in model analysis. Regions filled with darker colors represent areas with a higher adjusted R2, emphasizing better performance of the model. Blue areas are characterized by a positive association between the variable analyzed and pediatric mortality rate. Red areas depict an inverse (negative) association between the variable and the outcome.