Estimating hospital catchments from in-patient admission records: a spatial statistical approach applied to malaria

Victor A Alegana,Cynthia Khazenzi,Robert W Snow,Samuel O Akech

doi:10.1038/s41598-020-58284-0

Abstract

Admission records are seldom used in sub-Saharan Africa to delineate hospital catchments for the spatial description of hospitalised disease events. We set out to investigate spatial hospital accessibility for severe malarial anaemia (SMA) and cerebral malaria (CM). Malaria admissions for children between 1 month and 14 years old were identified from prospective clinical surveillance data recorded routinely at four referral hospitals covering two complete years between December 2015 to November 2016 and November 2017 to October 2018. These were linked to census enumeration areas (EAs) with an age-structured population. A novel mathematical-statistical framework that included EAs with zero observations was used to predict hospital catchment for malaria admissions adjusting for spatial distance. From 5766 malaria admissions, 5486 (95.14%) were linked to specific EA address, of which 272 (5%) were classified as cerebral malaria while 1001 (10%) were severe malaria anaemia. Further, results suggest a marked geographic catchment of malaria admission around the four sentinel hospitals although the extent varied. The relative rate-ratio of hospitalisation was highest at <1-hour travel time for SMA and CM although this was lower outside the predicted hospital catchments. Delineation of catchments is important for planning emergency care delivery and in the use of hospital data to define epidemiological disease burdens. Further hospital and community-based studies on treatment-seeking pathways to hospitals for severe disease would improve our understanding of catchments.

Highlights

Admission records are seldom used in sub-Saharan Africa to delineate hospital catchments for the spatial description of hospitalised disease events
4972 (90.6%) had complete information for cerebral malaria (CM) classification while all the 5486 (100%) had information on severe malarial anaemia (SMA). 272 were CM based on children who were unconscious (U) or could only respond to a painful stimulus (P) (AVPU = P or U) while 1001 were SMA based on the combination of haemoglobin ≤5 g/dL and clinical features
This study provides an example of how in-patient malaria admission records can be used in a mathematical-statistical framework to delineate catchments

Summary

Introduction

Admission records are seldom used in sub-Saharan Africa to delineate hospital catchments for the spatial description of hospitalised disease events. It is unlikely that children who develop these two syndromes would survive in the absence of emergency treatment and supportive interventions provided through in-patient hospital care For those who reach the hospital, case-fatalities for cerebral malaria remain over 20%7,8, and for severe anaemia without blood transfusion over 50%8,9. Gravity based models combine aspects of distance-decay and health system characteristics (e.g. hospital size) but do not always include all aspects of population density and demand[17] These approaches remain mechanistic and a robust statistical approach improving on these descriptions and integrating disease-specific admission records, mechanistic estimates on distance, population and health system factors to characterize hospital use is required

Methods

Results

Conclusion