Evaluation of methods for linking household and health care provider data to estimate effective coverage of management of child illness: results of a pilot study in Southern Province, Zambia

Abstract

BackgroundExisting population-based surveys have limited accuracy for estimating the coverage and quality of management of child illness. Linking household survey data with health care provider assessments has been proposed as a means of generating more informative population-level estimates of effective coverage, but methodological issues need to be addressed.MethodsA 2016 survey estimated effective coverage of management of child illness in Southern Province, Zambia, using multiple methods for linking temporally and geographically proximate household and health care provider data. Mothers of children <5 years were surveyed about seeking care for child illness. Information on health care providers’ capacity to manage child illness, or structural quality, was assessed using case scenarios and a tool modeled on the WHO Service Availability and Readiness Assessment (SARA). Each sick child was assigned the structural quality score of their stated (exact-match) source of care. Effective coverage was calculated as the average structural quality experienced by all sick children. Children were also ecologically linked to providers using measures of geographic proximity, with and without data on non-facility providers, to assess the effects of these linking methods on effective coverage estimates.ResultsData were collected on 83 providers and 385 children with fever, diarrhea, and/or symptoms of ARI in the preceding 2 weeks. Most children sought care from government facilities or community-based agents (CBAs). Effective coverage of management of child illness estimated through exact-match linking was approximately 15-points lower in each stratum than coverage of seeking skilled care due to providers’ limited structural quality. Estimates generated using most measures of geographic proximity were similar to the exact-match estimate, with the exception of the kernel density estimation method in the urban area. Estimates of coverage in rural areas were greatly reduced across all methods using facility-only data if seeking care from CBAs was treated as unskilled care.ConclusionsLinking household and provider data may generate more informative estimates of effective coverage of management of child illness. Ecological linking with provider data on a sample of all skilled providers may be as effective as exact-match linking in areas with low variation in structural quality within a provider category or minimal provider bypassing.

Linking household survey and health facility data for effective coverage measures: a comparison of ecological and individual linking methods using the Multiple Indicator Cluster Survey in Côte d’Ivoire

“…Unlike an earlier study in Zambia, we did not find that including CHWs improved our ecological estimates [15]. However, CHWs were a very infrequent source of care in this setting, whereas they accounted for 18% of care-seeking in rural areas in the Zambia study.…”

“…To our knowledge, this is the largest study to date to compare ecological and exact match linking methods for measuring quality-adjusted effective coverage, and the only such study to use a global survey programme (MICS) as the source of household data. The results of this study, along with those of Willey et al and Carter et al [14,15], suggest that ecological linking may be a feasible and valid approach for estimating quality-adjusted effective coverage when a census of providers is used. The findings also highlight the potential benefit in adjusting for provider type and caseload when implementing ecological linking methods The results suggest that there may be benefit in adding a question on ANC facility type to MICS (facility type is already collected for other services), and questions on caseload to facility surveys.…”

“…The proportion of women and children linked to their reported source of care using single-provider methods was lower in this study (20%-57%) than in a similar study in Zambia (77%-89%) [15]. Possible reasons for this include provider bypassing, error associated with using cluster centroid location rather than household location (the Zambia study used exact household location), error in recording exact sources of care, or limitations in the geolinking methods.…”

“…This paper is the third to describe the level of agreement between effective coverage estimates generated using ecological and exact-match linking methods [14,15]. However, this study differs from the preceding two in that in that it used a MICS survey as the source of household data and in that it compared ecological and exact match linking using structure and process quality for maternal, newborn, and child health interventions in one study.…”

“…Willey et al found that in one district of Uganda, ecological linking methods that accounted for provider type resulted in better agreement with exact match linking methods, relative to ecological linking only by administrative area [14]. Carter et al concluded that in five health facility catchment areas in Southern Zambia, excluding community health workers (CHWs) from a provider survey could result in an underestimation of effective coverage [15]. …”