BACKGROUND: Antenatal care (ANC) is a principal component of safe motherhood and reproductive health strategies across the continuum of care. Although the coverage of antenatal care visits has increased in Ethiopia, there needs to be more evidence of effective coverage of antenatal care. The \'effective coverage\' concept can pinpoint where action is required to improve high-quality coverage in Ethiopia. Effective coverage indicates a health system\'s performance by incorporating need, utilization, and quality into a single measurement. The concept includes the number of contacts, facility readiness, interventions received, and components of services received. This study aimed to measure effective antenatal care coverage in Ethiopia.
METHODS: A two-stage cluster sampling method was used and included 2714 women aged 15-49 years and 462 health facilities from six Ethiopian regions from October 2019 to January 2020. The effective coverage cascade was analyzed among the targeted women by computing the proportion who received four or more antenatal care visits where the necessary inputs were available, received iron-folate supplementation and two doses of tetanus vaccination according to process quality components of antenatal care services.
RESULTS: Of all women, 40% (95%CI; 38, 43) had four or more visits, ranging from 3% in Afar to 74% in Addis Ababa. The overall mean health facility readiness score of the facilities serving these women was 70%, the vaccination and iron-folate supplementation coverage was 26%, and the ANC process quality was 64%. As reported by women, the least score was given to the quality component of discussing birth preparedness and complication readiness with providers. In the effective coverage cascade, the input-adjusted, intervention-adjusted, and quality-adjusted antenatal coverage estimates were 28%, 18%, and 12%, respectively.
CONCLUSIONS: The overall effective ANC coverage was low, primarily due to a considerable drop in the proportion of women who completed four or more ANC visits. Improving quality of services is crucial to increase ANC up take and completion of the recommended visits along with interventions increasing women\'s awareness.

Most existing facility assessments collect data on a sample of health facilities. Sampling of health facilities may introduce bias into estimates of effective coverage generated by ecologically linking individuals to health providers based on geographic proximity or administrative catchment.
We assessed the bias introduced to effective coverage estimates produced through two ecological linking approaches (administrative unit and Euclidean distance) applied to a sample of health facilities. Our analysis linked MICS household survey data on care-seeking for child illness and childbirth care with data on service quality collected from a census of health facilities in the Savanes region of Cote d\'Ivoire. To assess the bias introduced by sampling, we drew 20 random samples of three different sample sizes from our census of health facilities. We calculated effective coverage of sick child and childbirth care using both ecological linking methods applied to each sampled facility data set. We compared the sampled effective coverage estimates to ecologically linked census-based estimates and estimates based on true source of care. We performed sensitivity analyses with simulated preferential care-seeking from higher-quality providers and randomly generated provider quality scores.
Sampling of health facilities did not significantly bias effective coverage compared to either the ecologically linked estimates derived from a census of facilities or true effective coverage estimates using the original data or simulated random quality sensitivity analysis. However, a few estimates based on sampling in a setting where individuals preferentially sought care from higher-quality providers fell outside of the estimate bounds of true effective coverage. Those cases predominantly occurred using smaller sample sizes and the Euclidean distance linking method. None of the sample-based estimates fell outside the bounds of the ecologically linked census-derived estimates.
Our analyses suggest that current health facility sampling approaches do not significantly bias estimates of effective coverage produced through ecological linking. Choice of ecological linking methods is a greater source of bias from true effective coverage estimates, although facility sampling can exacerbate this bias in certain scenarios. Careful selection of ecological linking methods is essential to minimize the potential effect of both ecological linking and sampling error.

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. This may introduce error into analyses based on the distance between households and providers.
We 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.
Fewer 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.
Use 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.

Current methods for measuring intervention coverage for reproductive, maternal, newborn, and child health and nutrition (RMNCH+N) do not adequately capture the quality of services delivered. Without information on the quality of care, it is difficult to assess whether services provided will result in expected health improvements. We propose a six-step coverage framework, starting from a target population to (1) service contact, (2) likelihood of services, (3) crude coverage, (4) quality-adjusted coverage, (5) user-adherence-adjusted coverage and (6) outcome-adjusted coverage. We support our framework with a comprehensive review of published literature on effective coverage for RMNCH+N interventions since 2000. We screened 8103 articles and selected 36 from which we summarised current methods for measuring effective coverage and computed the gaps between \'crude\' coverage measures and quality-adjusted measures. Our review showed considerable variability in data sources, indicator definitions and analytical approaches for effective coverage measurement. Large gaps between crude coverage and quality-adjusted coverage levels were evident, ranging from an average of 10 to 38 percentage points across the RMNCH+N interventions assessed. We define effective coverage as the proportion of individuals experiencing health gains from a service among those who need the service, and distinguish this from other indicators along a coverage cascade that make quality adjustments. We propose a systematic approach for analysis along six steps in the cascade. Research to date shows substantial drops in effective delivery of care across these steps, but variation in methods limits comparability of the results. Advancement in coverage measurement will require standardisation of effective coverage terminology and improvements in data collection and methodological approaches.