The Effective Coverage Data Analysis Center, based at Johns Hopkins Bloomberg School of Public Health, develops analytical tools to permit the estimation and analysis of readiness and quality-adjusted coverage of reproductive, maternal, newborn and child health and nutrition (RMNCH-N) interventions in low and middle-income countries.
The center collaborates with other Countdown centers and countries to access and harmonize household and health facility surveys and routine health information system datasets for the analysis, and support capacity strengthening at country level for the analysis.
The coverage of an intervention is defined as the proportion of the population in need of the intervention that receives it. Effective coverage is defined as the “proportion of individuals experiencing optimal health gains from a service among those who need the service”. While it is essential to monitor coverage to assess progress toward universal health coverage, there is increasing evidence indicating that coverage defined and measured as such is insufficient to account for the full benefit on population health. There are numerous cases where high and near universal coverage coexist with high prevalence of adverse outcomes. For example, high coverage of antenatal care and skilled birth attendance occur with high levels of intrapartum stillbirth and early neonatal mortality in many countries. Therefore, understanding the structural readiness and the quality of services provided with the health system is critical to assessing whether the expected health benefits from health interventions are achieved.
Data sources for measuring effective coverage include:
- National household survey tools such as the Demographic and Health Survey (DHS) and Multiple Indicator Cluster Surveys (MICS).
- Health facility surveys tools for assessing service readiness and quality of care such as the Service Availability and Readiness Assessment (SARA) and Service Provision Assessments (SPA).
When household surveys are linked to the health facility surveys, quality-adjusted measures can be estimated.
This webinar explained the concept of effective coverage and how to conduct this analysis using available datasets and code.
Countdown published a series of articles on improving coverage measurement, including several papers that linked household surveys and health facility survey data to estimate quality adjusted coverage.
The collection of articles on Measuring and Monitoring Coverage of High Impact Health Interventions in Women, Children and Adolescents included a series of publications analyzing coverage and quality-adjusted coverage measures.
This spreadsheet was developed by the Countdown to 2030 Data & Analysis Center for Effective Coverage at Johns Hopkins University to map out availability, readiness and process quality indicators collected in Service Provision Assessments (SPA) and Service Availability and Readiness Assessment surveys (SARA). It is organized into three sheets based on type of quality of care indicators: structural quality-service availability, structural quality-service readiness, and process quality indicators. The indicators are categorized by domains, subdomains as well as area of care: family planning, sexual health, antenatal care, labor and delivery care, postnatal care, malaria, tuberculosis, immunization, child care, nutrition and adolescent health.