ArticleBMJ open2024
High-resolution mapping of essential maternal and child health service coverage in Nigeria: a machine learning approach.
Article in BMJ open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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2 citing papers in PubMed.
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundNational-level coverage estimates of maternal and child health (MCH) services mask district-level and community-level geographical inequities. The purpose of this study is to estimate grid-level coverage of essential MCH services in Nigeria using machine learning techniques.
methodsEssential MCH services in this study included antenatal care, facility-based delivery, childhood vaccinations and treatments of childhood illnesses. We estimated generalised additive models (GAMs) and gradient boosting regressions (GB) for each essential MCH service using data from five national representative cross-sectional surveys in Nigeria from 2003 to 2018 and geospatial socioeconomic, environmental and physical characteristics. Using the best-performed model for each service, we map predicted coverage at 1 km
resultsGAMs consistently outperformed GB models across a range of essential MCH services, demonstrating low systematic prediction errors. High-resolution maps revealed stark geographic disparities in MCH service coverage, especially between rural and urban areas and among different states and service types. Temporal trends indicated an overall increase in MCH service coverage from 2003 to 2018, although with variations by service type and location. Priority areas with lower coverage of both maternal and vaccination services were identified, mostly located in the northern parts of Nigeria.
conclusionHigh-resolution spatial estimates can guide geographic prioritisation and help develop better strategies for implementation plans, allowing limited resources to be targeted to areas with lower coverage of essential MCH services.
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