Using GIS to detect cluster and spatial disparity in maternal health indicators: a need for social health interventions

Identifying geographical inequalities of maternal care utilization in Ethiopia: a Spatio-temporal analysis from 2005 to 2019

INTRODUCTION

Inequalities in maternal care utilization pose a significant threat to maternal health programs. This study aimed to describe and explain the spatial variation in maternal care utilization among pregnant women in Ethiopia. Accordingly, this study focuses on identifying hotspots of underutilization and mapping maternal care utilization, as well as identifying predictors of spatial clustering in maternal care utilization.

METHODS

We evaluated three key indicators of maternal care utilization: pregnant women who received no antenatal care (ANC) service from a skilled provider, utilization of four or more ANC visits, and births attended in a health facility, based the Ethiopian National Demographic and Health Survey (EDHS5) to 2019. Spatial autocorrelation analysis was used to measure whether maternal care utilization was dispersed, clustered, or randomly distributed in the study area. Getis-Ord Gi statistics examined how Spatio-temporal variations differed through the study location and ordinary Kriging interpolation predicted maternal care utilization in the unsampled areas. Ordinary least squares (OLS) regression was used to identify predictors of geographic variation, and geographically weighted regression (GWR) examined the spatial variability relationships between maternal care utilization and selected predictors.

RESULT

A total of 26,702 pregnant women were included, maternal care utilization varies geographically across surveys. Overall, statistically significant low maternal care utilization hotspots were identified in the Somali region. Low hotspot areas were also identified in northern Ethiopia, stretching into the Amhara, Afar, and Beneshangul-Gumuz regions; and the southern part of Ethiopia and the Gambella region. Spatial regression analysis revealed that geographical variations in maternal care utilization indicators were commonly explained by the number of under-five children, the wealth index, and media access. In addition, the mother's educational status significantly explained pregnant women, received no ANC service and utilized ANC service four or more times. Whereas, the age of a mother at first birth was a spatial predictor of pregnant who received no ANC service from a skilled provider.

CONCLUSION

In Ethiopia, it is vital to plan to combat maternal care inequalities in a manner suitable for the district-specific variations. Predictors of geographical variation identified during spatial regression analysis can inform efforts to achieve geographical equity in maternal care utilization.

How applicable is geospatial analysis in maternal and neonatal health in sub-Saharan Africa? A systematic review

Background

Sub-Saharan Africa (SSA) has the world's highest maternal and neonatal morbidity and mortality and has shown the slowest progress in reducing them. In addition, there is substantial inequality in terms of maternal and neonatal morbidity and mortality in the region. Geospatial studies can help prioritize scarce resources by pinpointing priority areas for implementation. This systematic review was conducted to explore the application of geospatial analysis to maternal and neonatal morbidity and mortality in SSA.

Methods

A systematic search of PubMed, SCOPUS, EMBASE, and Web of Science databases was performed. All observational and qualitative studies that reported on maternal or neonatal health outcomes were included if they used a spatial analysis technique and were conducted in a SSA country. After removing duplicates, two reviewers independently reviewed each study's abstract and full text for inclusion. Furthermore, the quality of the studies was assessed using the Joanna Briggs Institute (JBI) critical appraisal checklists. Finally, due to the heterogeneity of studies, narrative synthesis was used to summarize the main findings, and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline was strictly followed to report the review results. A total of 56 studies were included in the review.

Results

We found that geospatial analysis was used to identify inequalities in maternal and neonatal morbidity, mortality, and health care utilization and to identify gaps in the availability and geographic accessibility of maternal health facilities. In addition, we identified a few studies that used geospatial analysis for modelling intervention areas. We also detected challenges and shortcomings, such as unrealistic assumptions used by geospatial models and a shortage of reliable, up-to-date, small-scale georeferenced data.

Conclusions

The use of geospatial analysis for maternal and neonatal health in SSA is still limited, and more detailed spatial data are required to exploit the potential of geospatial technologies fully.