Abstract
BackgroundRoutinely collected health facility data usually captured and stored in Health Management Information Systems (HMIS) are potential sources of data for frequent and local disaggregated estimation of the coverage of reproductive, maternal, newborn, and child health interventions (RMNCH), but have been under-utilized due to concerns over data quality. We reviewed methods for estimation of national or subnational coverage of RMNCH interventions using HMIS data exclusively or in conjunction with survey data from low- and middle-income countries (LMICs).MethodsWe conducted a comprehensive review of studies indexed in PubMed and Scopus to identify potential papers based on predefined search terms. Two reviewers screened the papers using defined inclusion and exclusion criteria. Following sequences of title, abstract and full paper reviews, we retained 18 relevant papers.Results12 papers used only HMIS data and 6 used both HMIS and survey data. There is enormous lack of standards in the existing methods for estimating RMNCH intervention coverage; all appearing to be highly author dependent. The denominators for coverage measures were estimated using census, non-census and combined projection-based methods. No satisfactory methods were found for treatment-based coverage indicators for which the estimation of target population requires the population prevalence of underlying conditions. The estimates of numerators for the coverage measures were obtained from the count of users or visits and in some cases correction for completeness of reporting in the HMIS following an assessment of data quality.ConclusionsStandard methods for correcting numerators from HMIS data for accurate estimation of coverage of RMNCH interventions are needed to expand the use of these data. More research and investments are required to improve denominators for health facility-derived statistics. Improvement in routine data quality and analytical methods would allow for timely estimation of RMNCH intervention coverage at the national and subnational levels.
Highlights
Collected health facility data usually captured and stored in Health Management Information Systems (HMIS) are potential sources of data for frequent and local disaggregated estimation of the coverage of reproductive, maternal, newborn, and child health interventions (RMNCH), but have been under-utilized due to concerns over data quality
Of the 668 articles 621 (93 %) were excluded after title and abstract review because they were not related to RMNCH or coverage estimation
This review provides a comprehensive overview of all the available analytical methods, their strengths and limitations which can be used by health departments across low- and middle-income countries (LMICs)
Summary
Collected health facility data usually captured and stored in Health Management Information Systems (HMIS) are potential sources of data for frequent and local disaggregated estimation of the coverage of reproductive, maternal, newborn, and child health interventions (RMNCH), but have been under-utilized due to concerns over data quality. We reviewed methods for estimation of national or subnational coverage of RMNCH interventions using HMIS data exclusively or in conjunction with survey data from low- and middle-income countries (LMICs). As low- and middle-income countries (LMICs) strive to maintain the gains and progress towards achieving the Sustainable Development Goal 3 (SDG 3), there is a need to use rigorous analytical methods to analyze readily available routine health facility data to track coverage of key health indicators both at the national and subnational levels. The coverage of a reproductive, maternal, newborn, and child health (RMNCH) intervention is defined as the proportion of the population in need of the intervention or service that receives it [3]. RMNCH intervention coverage indicators are used to determine countries eligibility for global support programs such as performance-based financing [6,7,8], support from the GAVI Alliance for the introduction of new vaccines [9], the Millennium Challenge Account assistance [10] and other international support programs [11]
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