Abstract

BackgroundGeographic proximity is often used to link household and health provider data to estimate effective coverage of health interventions. Existing household surveys often provide displaced data on the central point within household clusters rather than household location. This may introduce error into analyses based on the distance between households and providers.MethodsWe assessed the effect of imprecise household location on quality-adjusted effective coverage of child curative services estimated by linking sick children to providers based on geographic proximity. We used data on care-seeking for child illness and health provider quality in Southern Province, Zambia. The dataset included the location of respondent households, a census of providers, and data on the exact outlets utilized by sick children included in the study. We displaced the central point of each household cluster point five times. We calculated quality-adjusted coverage by assigning each sick child to a provider’s care based on three measures of geographic proximity (Euclidean distance, travel time, and geographic radius) from the household location, cluster point, and displaced cluster locations. We compared the estimates of quality-adjusted coverage to each other and estimates using each sick child’s true source of care. We performed sensitivity analyses with simulated preferential care-seeking from higher-quality providers and randomly generated provider quality scores.ResultsFewer children were linked to their true source of care using cluster locations than household locations. Effective coverage estimates produced using undisplaced or displaced cluster points did not vary significantly from estimates produced using household location data or each sick child’s true source of care. However, the sensitivity analyses simulating greater variability in provider quality showed bias in effective coverage estimates produced with the geographic radius and travel time method using imprecise location data in some scenarios.ConclusionsUse of undisplaced or displaced cluster location reduced the proportion of children that linked to their true source of care. In settings with minimal variability in quality within provider categories, the impact on effective coverage estimates is limited. However, use of imprecise household location and choice of geographic linking method can bias estimates in areas with high variability in provider quality or preferential care-seeking.

Highlights

  • Geographic proximity is often used to link household and health provider data to estimate effective coverage of health interventions

  • We developed an automated script in QGIS comparable to the process outlined for application in ArcGIS in a previous paper [6]

  • Among the 1084 children included in the household care-seeking survey, 35% of urban children and 36% of rural children experienced at least one illness meeting Demographic and Health Survey (DHS) criteria in the 2 weeks preceding the survey, primarily fever (Table 1)

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Summary

Introduction

Geographic proximity is often used to link household and health provider data to estimate effective coverage of health interventions. Existing household surveys often provide displaced data on the central point within household clusters rather than household location. Data from household surveys [such as the Demographic and Health Survey (DHS) and Multiple Indicator Cluster Survey (MICS)] provide a population-based denominator of intervention need and care-seeking for services. Health provider assessments [such as the Service Provision Assessment (SPA) and Service Availability and Readiness Assessment (SARA)] offer information on provider quality, including structural quality and potentially provision of care. Linking these two data sources can be used to estimate effective coverage, or the proportion of the population in need of a service that received it with sufficient quality to achieve a health benefit. The methods used for combining data sets and aspects of the data sources can influence results

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