Geographic inequalities in health intervention coverage - mapping the composite coverage index in Peru using...

Area of Science:

• Geospatial analysis
• Public health
• Health equity

Background:

• The Composite Coverage Index (CCI) integrates reproductive, maternal, newborn, and child health indicators for universal health coverage assessment.
• Traditional household surveys lack granular estimates below the first administrative level, necessitating advanced methodologies.
• Geostatistical approaches are crucial for detailed health coverage mapping in diverse regions.

Purpose of the Study:

• To estimate the CCI at multiple resolutions in Peru using a model-based geostatistical approach.
• To identify geographic disparities and areas with high inequality in health coverage.
• To inform targeted public health interventions for underserved populations.

Main Methods:

• Utilized data from two national household surveys (2018-2019) and geospatial covariates.
• Employed Bayesian geostatistical models with the INLA-SPDE approach for estimation.
• Validated model fit through cross-validation and comparison with direct survey estimates.

Main Results:

• CCI coverage was higher in coastal provinces compared to other regions.
• Northern and eastern jungle areas exhibited the lowest CCI and significant intra-provincial gaps.
• Largest inequalities were observed in expansive, remote jungle provinces with scattered populations.

Conclusions:

• High-resolution CCI estimates highlight provinces with substantial inequality, particularly in low-populated jungle areas.
• Identified regions, like the Bolivian border, require increased health coverage efforts.
• Granular geographic estimates are essential for uncovering and addressing health inequities often masked by traditional survey representativeness.