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. 2024 Jun 10;14(6):e080135.
doi: 10.1136/bmjopen-2023-080135.

High-resolution mapping of essential maternal and child health service coverage in Nigeria: a machine learning approach

Yoshito Kawakatsu  1 , Jonathan F Mosser  2 , Christopher Adolph  3 , Peter Baffoe  4 , Fatima Cheshi  4 , Hirotsugu Aiga  5   6 , D A Watkins  7 , Kenneth H Sherr  8
Affiliations

High-resolution mapping of essential maternal and child health service coverage in Nigeria: a machine learning approach

Yoshito Kawakatsu et al. BMJ Open. .

Abstract

Background: National-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.

Methods: Essential 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 km2 and 5 km2 spatial resolutions in urban and rural areas, respectively.

Results: GAMs 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.

Conclusion: High-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.

Keywords: Geographical mapping; Health Services Accessibility; Maternal medicine; Paediatric infectious disease & immunisation.

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Conflict of interest statement

Competing interests: None declared.

Figures

Figure 1
Figure 1
Predicted service coverage of (A) antenatal care, (B) facility-based delivery, (C) BCG vaccination, (D), first pentavalent vaccination, (E) third pentavalent vaccination and (F) measles vaccination in 2018.
Figure 2
Figure 2
Predicted local government area (LGA)-level coverage of eight essential maternal and child health services in 2018 by state. Orange dots mark the mean coverage within each state. Grey dots show service coverage for each LGA within the state. The distribution of these grey dots is summarised by a violin plot.
Figure 3
Figure 3
Service coverage distribution of essential maternal health services (antenatal care and facility-based delivery) and immunisation services in 2018 as a bivariate choropleth map. Mixtures of the colours red and blue indicate coverage levels in each local government area. Areas with high levels of maternal service coverage but low levels of immunisation coverage are shown in red. Areas with high levels of immunisation coverage, but low levels of maternal service coverage are shown in blue. Areas with similar levels of coverage across both services are shown in shades of purple ranging from lilac (low coverage of both services) to plum (high coverage of both services).
See this image and copyright information in PMC

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References

    1. Mosser JF, Gagne-Maynard W, Rao PC, et al. . Mapping diphtheria-pertussis-tetanus vaccine coverage in Africa, 2000–2016: a spatial and temporal modelling study. Lancet 2019;393:1843–55. 10.1016/S0140-6736(19)30226-0 - DOI - PMC - PubMed
    1. Utazi CE, Thorley J, Alegana VA, et al. . High resolution age-structured mapping of childhood vaccination coverage in low and middle income countries. Vaccine 2018;36:1583–91. 10.1016/j.vaccine.2018.02.020 - DOI - PMC - PubMed
    1. Utazi CE, Thorley J, Alegana VA, et al. . A spatial regression model for the disaggregation of areal unit based data to high-resolution Grids with application to vaccination coverage mapping. Stat Methods Med Res 2019;28:3226–41. 10.1177/0962280218797362 - DOI - PMC - PubMed
    1. Bosco C, Alegana V, Bird T, et al. . Exploring the high-resolution mapping of gender-disaggregated development indicators. J R Soc Interface 2017;14:20160825. 10.1098/rsif.2016.0825 - DOI - PMC - PubMed
    1. Dwyer-Lindgren L, Cork MA, Sligar A, et al. . Mapping HIV prevalence in sub-Saharan Africa between 2000 and 2017. Nature 2019;570:189–93. 10.1038/s41586-019-1200-9 - DOI - PMC - PubMed

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