A Hybrid Bayesian Translational Epidemiology Framework for Precision Mop-Up Following Integrated Maternal, Newborn and Child Health Week in Kogi State, Nigeria

Integrated Maternal, Newborn and Child Health Week (MNCHW) campaigns improve access to essential maternal, newborn and child health interventions; however, residual populations remain unreached, particularly in geographically, socioeconomically, and health-system constrained settings. Conventional post-campaign analyses predominantly describe coverage and provide limited capacity to identify who remains unreached, explain the mechanisms generating residual gaps, or spatially prioritize precision mop-up interventions.
The objective of the study to develop and apply a Hybrid Bayesian Translational Epidemiology Framework provided the milieu for the integration of evidence synthesis, Bayesian machine learning, multilevel causal mediation, and Bayesian spatial epidemiology to identify, explain, predict, and spatially prioritize residual MNCHW service gaps in Kogi State, Nigeria.
A sequential multi-phase hybrid translational epidemiology and implementation science design was applied to linked routine MNCHW, immunization, RMNCH, health-facility, contextual and geospatial datasets covering 21 Local Government Areas, 239 political wards and n=990 health facilities. PRISMA-ScR evidence synthesis informed selection of epidemiological, socioeconomic, environmental, accessibility and health-system predictors. Data management, descriptive epidemiology, Bayesian machine learning, causal mediation and statistical modeling were performed in R version 4.3.2. Bayesian Additive Regression Trees (BART) were used to estimate individual- and facility-level probabilities of residual underperformance, with Shapley Additive Explanations (SHAP) used to quantify predictor contributions; predictive performance was assessed using repeated cross-validation, discrimination, calibration and prediction-error measures. Multilevel Bayesian causal mediation analysis estimated direct, indirect and total intervention effects through community mobilization, CBHW activities, caregiver participation, outreach implementation, commodity availability, cold-chain functionality, geographic accessibility and health workforce capacity. Bayesian spatial analysis employed the Besag–York–Mollié 2 (BYM2) model implemented through R-INLA version 23.12, with queen-contiguity spatial structures, to estimate posterior relative risks, spatial dependence, exceedance probabilities and uncertainty. QGIS version 3.34 and ArcGIS Pro version 3.2 were used for spatial visualization, hotspot characterization and operational cartographic mapping. Outputs from BART, causal mediation and BYM2 were integrated into a composite Mop-up Priority Index incorporating predicted risk, spatial risk, service gaps, accessibility and facility readiness to guide precision microplanning and targeted mop-up.
The hybrid framework demonstrated strong predictive performance, with a mean 10-fold AUROC of 0.968, mean classification accuracy of 96.6%, validated AUROC of 0.961, validated RMSE of 0.033, and Bayesian R² of 0.83. Posterior coefficients indicated that Health Equity Index (β=0.73, 95% CrI: 0.58–0.87) and flood vulnerability (β=0.42, 0.26–0.57) increased residual service-gap risk, whereas cold-chain uptime (β=−0.68, −0.82 to −0.53), outreach frequency (β=−0.54, −0.66 to −0.41), facility readiness (β=−0.46, −0.60 to −0.32), and HRH density (β=−0.39, −0.52 to −0.25) were protective, with posterior probabilities >0.99. Multilevel mediation demonstrated important indirect implementation effects through outreach (β=0.46; approximately 44.6% mediated), cold-chain uptime (β=0.31; 42.5%), caregiver participation (β=0.41; 41.6%), commodity availability (β=0.27; 37.2%), geographic accessibility (β=0.29; 35.5%), and session implementation (β=0.24; 34.7%). The integrated pathway showed a total effect of 1.25 (95% CrI: 1.09–1.41; posterior probability >0.999).
BYM2 analysis demonstrated predominantly structured spatial variation (φ=0.62) and strong geographic dependence (spatial correlation=0.71; 95% CrI: 0.63–0.79). Posterior relative risk was highest in Ankpa (RR=2.34; 95% CrI: 1.89–2.81), Dekina (2.08; 1.71–2.49), Bassa (1.96; 1.60–2.36), and Igalamela-Odolu (1.84; 1.51–2.22), identifying these LGAs as major residual zero-dose hotspots.
Bayesian policy simulation predicted improvement in programme coverage from 69.4% (95% CrI: 65.2–73.5) under the status quo to 82.6% with improved cold-chain systems, 84.8% with combined outreach and HRH strengthening, 93.7% with an integrated intervention package, and 95.2% when the integrated package was combined with Health Equity Index targeting. The posterior probability of achieving at least 90% coverage increased from 0.42 under the status quo to 0.998 under the integrated HEI-targeted strategy.
Residual MNCHW gaps were heterogeneous, spatially structured, and strongly associated with contextual, environmental, accessibility, and health-system determinants. The findings demonstrate that software-enabled integration of R-based Bayesian machine learning, SHAP explainability, multilevel causal mediation, INLA-BYM2 spatial modeling, and QGIS/ArcGIS geospatial translation can convert routine programme data into actionable implementation intelligence. The Hybrid Bayesian Translational Epidemiology Framework therefore provides a reproducible pathway from evidence synthesis → prediction → causal explanation → spatial prioritization → precision mop-up, with potential to strengthen health equity, primary healthcare performance, resource allocation, and progress toward Universal Health Coverage.

Integrated Maternal, Newborn and Child Health Week; Translational Epidemiology; Precision Public Health; PRISMA-ScR; Bayesian Additive Regression Trees

1. Andersen, R. M. (1995). Revisiting the behavioral model and access to medical care: Does it matter? Journal of Health and Social Behavior, 36(1), 1–10. https://www.jstor.org/stable/2137284 [Google Scholar] [Crossref]

2. Besag, J., York, J., & Mollié, A. (1991). Bayesian image restoration, with two applications in spatial statistics. Annals of the Institute of Statistical Mathematics, 43(1), 1–20. https://doi.org/10.1007/BF00116466 [Google Scholar] [Crossref]

3. Blangiardo, M., & Cameletti, M. (2015). Spatial and spatio-temporal Bayesian models with R-INLA. John Wiley & Sons. https://onlinelibrary.wiley.com/doi/book/10.1002/9781118950203 [Google Scholar] [Crossref]

4. Brownson, R. C., Colditz, G. A., & Proctor, E. K. (Eds.). (2018). Dissemination and implementation research in health: Translating science to practice (2nd ed.). Oxford University Press. https://global.oup.com/academic/product/dissemination-and-implementation-research-in-health-9780190683214 [Google Scholar] [Crossref]

5. Chipman, H. A., George, E. I., & McCulloch, R. E. (2010). BART: Bayesian additive regression trees. The Annals of Applied Statistics, 4(1), 266–298. https://doi.org/10.1214/09-AOAS285 [Google Scholar] [Crossref]

6. Daniel, R. M., De Stavola, B. L., Cousens, S. N., & Vansteelandt, S. (2015). Causal mediation analysis with multiple mediators. Biometrics, 71(1), 1–14. https://doi.org/10.1111/biom.12248 [Google Scholar] [Crossref]

7. Damschroder, L. J., Aron, D. C., Keith, R. E., Kirsh, S. R., Alexander, J. A., & Lowery, J. C. (2009). Fostering implementation of health services research findings into practice: A consolidated framework for advancing implementation science. Implementation Science, 4, 50. https://doi.org/10.1186/1748-5908-4-50 [Google Scholar] [Crossref]

8. Eccles, M. P., & Mittman, B. S. (2006). Welcome to implementation science. Implementation Science, 1, 1. https://doi.org/10.1186/1748-5908-1-1 [Google Scholar] [Crossref]

9. Federal Ministry of Health Nigeria. (2023). National Strategic Health Development Plan III (2023–2027). Federal Ministry of Health. https://health.gov.ng [Google Scholar] [Crossref]

10. Federal Ministry of Health Nigeria. (2024). Nigeria Health Sector Renewal Investment Initiative (2023–2026). Federal Ministry of Health. https://health.gov.ng [Google Scholar] [Crossref]

11. Frieden, T. R. (2014). Six components necessary for effective public health program implementation. American Journal of Public Health, 104(1), 17–22. https://doi.org/10.2105/AJPH.2013.301608 [Google Scholar] [Crossref]

12. Glasgow, R. E., Vogt, T. M., & Boles, S. M. (1999). Evaluating the public health impact of health promotion interventions: The RE-AIM framework. American Journal of Public Health, 89(9), 1322–1327. https://doi.org/10.2105/AJPH.89.9.1322 [Google Scholar] [Crossref]

13. Hernán, M. A., & Robins, J. M. (2020). Causal inference: What if. Chapman & Hall/CRC. https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/ [Google Scholar] [Crossref]

14. Hill, J. L. (2011). Bayesian nonparametric modeling for causal inference. Journal of Computational and Graphical Statistics, 20(1), 217–240. https://doi.org/10.1198/jcgs.2010.08162 [Google Scholar] [Crossref]

15. Imai, K., Keele, L., & Tingley, D. (2010). A general approach to causal mediation analysis. Psychological Methods, 15(4), 309–334. https://doi.org/10.1037/a0020761 [Google Scholar] [Crossref]

16. Kapelner, A., & Bleich, J. (2016). bartMachine: Machine learning with Bayesian additive regression trees. Journal of Statistical Software, 70(4), 1–40. https://doi.org/10.18637/jss.v070.i04 [Google Scholar] [Crossref]

17. Khoury, M. J., Gwinn, M., Ioannidis, J. P. A., et al. (2010). The emergence of translational epidemiology. American Journal of Epidemiology, 172(5), 517–524. https://doi.org/10.1093/aje/kwq211 [Google Scholar] [Crossref]

18. Lawson, A. B. (2018). Bayesian disease mapping: Hierarchical modeling in spatial epidemiology (3rd ed.). CRC Press. https://www.routledge.com/Bayesian-Disease-Mapping/Lawson/p/book/9781498727242 [Google Scholar] [Crossref]

19. [19] Lindgren, F., Rue, H., & Lindström, J. (2011). An explicit link between Gaussian fields and Gaussian Markov random fields. Journal of the Royal Statistical Society: Series B, 73(4), 423–498. https://doi.org/10.1111/j.1467-9868.2011.00777.x [Google Scholar] [Crossref]

20. Marmot, M., Allen, J., Boyce, T., Goldblatt, P., & Morrison, J. (2020). Health Equity in England: The Marmot Review 10 Years On. Institute of Health Equity. [Google Scholar] [Crossref]

21. https://www.instituteofhealthequity.org/resources-reports/marmot-review-10-years-on [Google Scholar] [Crossref]

22. Moraga, P. (2023). Geospatial health data: Modeling and visualization with R-INLA and other packages (2nd ed.). CRC Press. https://doi.org/10.1201/9781003256359 [Google Scholar] [Crossref]

23. National Primary Health Care Development Agency. (2022). National guideline for Integrated Maternal, Newborn and Child Health Week implementation. National Primary Health Care Development Agency. https://nphcda.gov.ng [Google Scholar] [Crossref]

24. National Primary Health Care Development Agency. (2024). Basic Health Care Provision Fund implementation manual. National Primary Health Care Development Agency. https://nphcda.gov.ng [Google Scholar] [Crossref]

25. Nilsen, P. (2015). Making sense of implementation theories, models and frameworks. Implementation Science, 10, 53. https://doi.org/10.1186/s13012-015-0242-0 [Google Scholar] [Crossref]

26. Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., et al. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71 [Google Scholar] [Crossref]

27. Pearl, J. (2009). Causality: Models, reasoning and inference (2nd ed.). Cambridge University Press. https://doi.org/10.1017/CBO9780511803161 [Google Scholar] [Crossref]

28. Proctor, E. K., Powell, B. J., & McMillen, J. C. (2013). Implementation strategies: Recommendations for specifying and reporting. Implementation Science, 8, 139. https://doi.org/10.1186/1748-5908-8-139 [Google Scholar] [Crossref]

29. Riebler, A., Sørbye, S. H., Simpson, D., & Rue, H. (2016). An intuitive Bayesian spatial model for disease mapping that accounts for scaling. Statistics in Medicine, 35(21), 3789–3801. https://doi.org/10.1002/sim.6894 [Google Scholar] [Crossref]

30. Robins, J. M., & Greenland, S. (1992). Identifiability and exchangeability for direct and indirect effects. Epidemiology, 3(2), 143–155. https://www.jstor.org/stable/20065609 [Google Scholar] [Crossref]

31. Rue, H., Martino, S., & Chopin, N. (2009). Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. Journal of the Royal Statistical Society: Series B, 71(2), 319–392. https://doi.org/10.1111/j.1467-9868.2008.00700.x [Google Scholar] [Crossref]

32. Simpson, D., Rue, H., Riebler, A., Martins, T. G., & Sørbye, S. H. (2017). Penalising model component complexity: A principled, practical approach to constructing priors. Statistical Science, 32(1), 1–28. https://doi.org/10.1214/16-STS576 [Google Scholar] [Crossref]

33. Sørbye, S. H., & Rue, H. (2014). Scaling intrinsic Gaussian Markov random field priors in spatial modeling. Spatial Statistics, 8, 39–51. https://doi.org/10.1016/j.spasta.2013.06.004 [Google Scholar] [Crossref]

34. Tingley, D., Yamamoto, T., Hirose, K., Keele, L., & Imai, K. (2014). mediation: R package for causal mediation analysis. Journal of Statistical Software, 59(5), 1–38. https://doi.org/10.18637/jss.v059.i05 [Google Scholar] [Crossref]

35. United Nations. (2015). Transforming our world: The 2030 Agenda for Sustainable Development. United Nations. https://sdgs.un.org/2030agenda [Google Scholar] [Crossref]

36. United Nations Children's Fund. (2019). The State of the World's Children 2019: Children, food and nutrition. UNICEF. https://www.unicef.org/reports/state-of-worlds-children-2019 [Google Scholar] [Crossref]

37. United Nations Children's Fund. (2021). The State of the World's Children 2021: On my mind—Promoting, protecting and caring for children's mental health. UNICEF. https://www.unicef.org/reports/state-worlds-children-2021 [Google Scholar] [Crossref]

38. United Nations Children's Fund. (2023). The State of the World's Children 2023: For every child, vaccination. UNICEF. https://www.unicef.org/reports/state-of-worlds-children-2023 [Google Scholar] [Crossref]

39. United Nations Children's Fund. (2024). The State of the World's Children 2024. UNICEF. https://www.unicef.org/reports [Google Scholar] [Crossref]

40. United Nations Children's Fund, World Health Organization, & World Bank. (2023). Levels and trends in child malnutrition: Joint child malnutrition estimates 2023 edition. UNICEF. https://data.unicef.org/resources/jme-report-2023 [Google Scholar] [Crossref]

41. VanderWeele, T. J. (2015). Explanation in causal inference: Methods for mediation and interaction. Oxford University Press. https://global.oup.com/academic/product/explanation-in-causal-inference-9780199325870 [Google Scholar] [Crossref]

42. VanderWeele, T. J. (2016). Mediation analysis: A practitioner's guide. Annual Review of Public Health, 37, 17–32. https://doi.org/10.1146/annurev-publhealth-032315-021402 [Google Scholar] [Crossref]

43. Victora, C. G., Requejo, J. H., Barros, A. J. D., Berman, P., Bhutta, Z., Boerma, T., Chopra, M., de Francisco, A., Daelmans, B., Hazel, E., Lawn, J. E., Maliqi, B., Newby, H., Qazi, S., Rana, T. G., Requejo, J., Bryce, J., & Countdown to 2030 Collaboration. (2016). Countdown to 2030 for reproductive, maternal, newborn, child and adolescent health and nutrition. The Lancet, 387(10032), 2049–2059. https://doi.org/10.1016/S0140-6736(16)00295-4 [Google Scholar] [Crossref]

44. World Bank. (2022). Walking the talk: Reimagining primary health care after COVID-19. World Bank. https://www.worldbank.org [Google Scholar] [Crossref]

45. World Bank. (2023). World Development Indicators 2023. World Bank. https://databank.worldbank.org/source/world-development-indicators [Google Scholar] [Crossref]

46. World Bank. (2024). Improving health system resilience in low- and middle-income countries. World Bank. https://www.worldbank.org/en/topic/health [Google Scholar] [Crossref]

47. World Health Organization. (2016). Framework on integrated people-centred health services. World Health Organization. https://www.who.int/publications/i/item/framework-on-integrated-people-centred-health-services [Google Scholar] [Crossref]

48. World Health Organization. (2018). Primary health care: Closing the gap between public health and primary care through integration. World Health Organization. https://www.who.int [Google Scholar] [Crossref]

49. World Health Organization. (2018). Declaration of Astana: Global Conference on Primary Health Care. World Health Organization. https://www.who.int/publications/m/item/declaration-of-astana [Google Scholar] [Crossref]

50. World Health Organization. (2019). Primary health care on the road to universal health coverage: 2019 monitoring report. World Health Organization. https://www.who.int/publications/i/item/9789240004275 [Google Scholar] [Crossref]

51. World Health Organization. (2020). World health statistics 2020: Monitoring health for the SDGs. World Health Organization. https://www.who.int/data/gho/publications/world-health-statistics [Google Scholar] [Crossref]

52. World Health Organization. (2021). Immunization Agenda 2030: A global strategy to leave no one behind. World Health Organization. https://www.who.int/teams/immunization-vaccines-and-biologicals/strategies/ia2030 [Google Scholar] [Crossref]

53. World Health Organization. (2021). World health statistics 2021: Monitoring health for the SDGs. World Health Organization. https://www.who.int/data/gho/publications/world-health-statistics [Google Scholar] [Crossref]

54. World Health Organization. (2022). World health statistics 2022: Monitoring health for the SDGs. World Health Organization. https://www.who.int/data/gho/publications/world-health-statistics [Google Scholar] [Crossref]

55. World Health Organization. (2022). Global strategy for women's, children's and adolescents' health (2016–2030): Progress report. World Health Organization. https://www.who.int [Google Scholar] [Crossref]

56. World Health Organization. (2023). World health statistics 2023: Monitoring health for the Sustainable Development Goals (SDGs). World Health Organization. https://www.who.int/data/gho/publications/world-health-statistics [Google Scholar] [Crossref]

57. World Health Organization. (2023). Primary health care measurement framework and indicators: Monitoring health systems through a PHC approach. World Health Organization. [Google Scholar] [Crossref]

58. https://www.who.int/publications/i/item/9789240044219 [Google Scholar] [Crossref]

59. World Health Organization. (2024). World health statistics 2024: Monitoring health for the Sustainable Development Goals (SDGs). World Health Organization. https://www.who.int/data/gho/publications/world-health-statistics [Google Scholar] [Crossref]

60. World Health Organization. (2024). Trends in maternal mortality 2000–2023: Estimates by WHO, UNICEF, UNFPA, World Bank Group and UNDESA. World Health Organization. https://www.who.int/publications [Google Scholar] [Crossref]

61. World Health Organization, United Nations Children's Fund, United Nations Population Fund, World Bank Group, & United Nations Department of Economic and Social Affairs. (2024). Trends in maternal mortality 2000–2023. World Health Organization. https://www.who.int/teams/sexual-and-reproductive-health-and-research/maternal-health [Google Scholar] [Crossref]

62. World Health Organization, United Nations Children's Fund, Gavi, the Vaccine Alliance, & Bill & Melinda Gates Foundation. (2023). Immunization Agenda 2030 progress report 2023. World Health Organization. https://www.immunizationagenda2030.org [Google Scholar] [Crossref]

63. World Health Organization. (2024). Primary health care: Measurement and implementation framework. World Health Organization. https://www.who.int/publications [Google Scholar] [Crossref]

64. World Health Organization. (2024). Immunization Agenda 2030: Global progress report 2024. World Health Organization. https://www.immunizationagenda2030.org [Google Scholar] [Crossref]

65. handbook of indicators and their measurement strategies. World Health Organization. https://www.who.int [Google Scholar] [Crossref]

66. World Health Organization. (2023). Global Health Observatory (GHO): Maternal, newborn and child health indicators. World Health Organization. https://www.who.int/data/gho [Google Scholar] [Crossref]

67. United Nations Inter-agency Group for Child Mortality Estimation (UN IGME). (2024). Levels and trends in child mortality: Report 2024. United Nations Children's Fund. https://childmortality.org [Google Scholar] [Crossref]

68. Gavi, the Vaccine Alliance. (2023). Equity Goal Progress Report 2023. Gavi. https://www.gavi.org [Google Scholar] [Crossref]

69. Gavi, the Vaccine Alliance. (2024). Zero-dose children and missed communities: Annual report. Gavi. https://www.gavi.org [Google Scholar] [Crossref]

70. Alliance for Health Policy and Systems Research. (2022). Systems thinking for health systems strengthening. World Health Organization. https://ahpsr.who.int [Google Scholar] [Crossref]

71. Peters, D. H. (2018). The application of systems thinking in health: Why use systems thinking? Health Research Policy and Systems, 16, 51. https://doi.org/10.1186/s12961-018-0312-1 [Google Scholar] [Crossref]

72. Peters, D. H., Adam, T., Alonge, O., Agyepong, I. A., & Tran, N. (2013). Implementation research: What it is and how to do it. BMJ, 347, f6753. https://doi.org/10.1136/bmj.f6753 [Google Scholar] [Crossref]

73. Kruk, M. E., et al (2018). High-quality health systems in the Sustainable Development Goals era: Time for a revolution. The Lancet Global Health, 6(11), e1196–e1252. https://doi.org/10.1016/S2214-109X(18)30386-3 [Google Scholar] [Crossref]

74. Kruk, M. E., Ling, E. J., Bitton, A., Cammett, M., Cavanaugh, K., Chopra, M., et al. (2023). Building resilient and equitable health systems after COVID-19. The Lancet, 401(10376), 413–431. https://doi.org/10.1016/S0140-6736(22)02020-5 [Google Scholar] [Crossref]

75. Hanson, K., Brikci, N., Erlangga, D., Alebachew, A., De Allegri, M., Balabanova, D., et al. (2022). The Lancet Global Health Commission on financing primary health care: Putting people at the centre. The Lancet Global Health, 10(5), e715–e772. https://doi.org/10.1016/S2214-109X(22)00005-5 [Google Scholar] [Crossref]

76. Bryce, J., Victora, C. G., Black, R. E., Boerma, T., Peters, D. H., & Black, M. M. (2018). Improving coverage measurement for reproductive, maternal, newborn and child health: Gaps and opportunities. The Lancet, 391(10129), 1639–1645. https://doi.org/10.1016/S0140-6736(17)32401-4 [Google Scholar] [Crossref]

77. Requejo, J. H., Strong, K. L., Barros, A. J. D., Bhutta, Z. A., et al. (2022). Measuring progress towards the Sustainable Development Goals for women, children and adolescents. The Lancet Global Health, 10(2), e226–e238. https://doi.org/10.1016/S2214-109X(21)00519-8 [Google Scholar] [Crossref]

78. Victora, C. G., Barros, A. J. D., França, G. V. A., et al. (2021). Revisiting maternal and child health in the Sustainable Development Goal era. The Lancet, 397(10288), 1461–1472. https://doi.org/10.1016/S0140-6736(21)00314-1 [Google Scholar] [Crossref]

79. GBD 2021 Risk Factors Collaborators. (2022). Global burden of 87 risk factors in 204 countries and territories, 1990–2021: A systematic analysis for the Global Burden of Disease Study 2021. The Lancet, 400(10352), 2223–2260. https://doi.org/10.1016/S0140-6736(22)01438-6 [Google Scholar] [Crossref]

80. GBD 2021 Diseases and Injuries Collaborators. (2023). Global burden of diseases, injuries and risk factors study 2021. The Lancet, 402(10397), 2133–2161. https://doi.org/10.1016/S0140-6736(23)01275-9 [Google Scholar] [Crossref]

81. Foreman, K. J., Marquez, N., Dolgert, A., Fukutaki, K., Fullman, N., McGaughey, M., et al. (2018). Forecasting life expectancy, years of life lost, and all-cause mortality to 2040. The Lancet, 392(10159), 2052–2090. https://doi.org/10.1016/S0140-6736(18)31694-5 [Google Scholar] [Crossref]

82. Dieleman, J. L., Campbell, M., Chapin, A., Eldrenkamp, E., Fan, V. Y., Haakenstad, A., et al. (2022). Future and potential spending on health 2020–2050: Development assistance, government, out-of-pocket, and other private spending in 204 countries and territories. The Lancet, 399(10341), 2005–2030. https://doi.org/10.1016/S0140-6736(22)00429-6 [Google Scholar] [Crossref]

83. Amouzou, A., Maïga, A., Faye, C. M., et al. (2020). Health service utilization and intervention coverage in sub-Saharan Africa: Progress, challenges, and opportunities. BMJ Global Health, 5(Suppl. 1), e002406. https://doi.org/10.1136/bmjgh-2020-002406 [Google Scholar] [Crossref]

84. Arsenault, C., Gage, A. D., Kim, M. K., et al. (2020). Equity in health service delivery in low-income and middle-income countries: A systematic review. The Lancet Global Health, 8(9), e1164–e1175. https://doi.org/10.1016/S2214-109X(20)30225-9 [Google Scholar] [Crossref]

85. Boerma, T., Requejo, J. H., Victora, C. G., Amouzou, A., & George, A. (2018). Countdown to 2030: Tracking progress towards universal coverage for reproductive, maternal, newborn, and child health. The Lancet, 391(10129), 1538–1548. https://doi.org/10.1016/S0140-6736(18)30104-1 [Google Scholar] [Crossref]

86. Weiss, D. J., Nelson, A., Vargas-Ruiz, C. A., Gligorić, K., Bavadekar, S., Gabrilovich, E., et al. (2020). Global maps of travel time to healthcare facilities. Nature Medicine, 26(12), 1835–1838. https://doi.org/10.1038/s41591-020-1059-1 [Google Scholar] [Crossref]

87. [85] Bhatt, S., Weiss, D. J., Cameron, E., Bisanzio, D., Mappin, B., Dalrymple, U., et al. (2015). The effect of malaria control on Plasmodium falciparum in Africa between 2000 and 2015. Nature, 526(7572), 207–211. https://doi.org/10.1038/nature15535 [Google Scholar] [Crossref]

88. Osgood-Zimmerman, A., Millear, A. I., Stubbs, R. W., Shields, C., Pickering, B. V., Earl, L., et al. (2018). Mapping child growth failure in Africa between 2000 and 2015. Nature, 555(7694), 41–47. https://doi.org/10.1038/nature25760 [Google Scholar] [Crossref]

89. Utazi, C. E., Thorley, J., Alegana, V., Ferrari, M. J., Takahashi, S., Metcalf, C. J. E., et al. (2019). High-resolution mapping of vaccination coverage to identify underserved populations. Nature Communications, 10, 1636. https://doi.org/10.1038/s41467-019-09611-1 [Google Scholar] [Crossref]

90. Golding, N., Burstein, R., Longbottom, J., Browne, A. J., Fullman, N., Osgood-Zimmerman, A., et al. (2017). Mapping under-5 mortality in sub-Saharan Africa between 2000 and 2015. Nature, 544(7650), 471–475. https://doi.org/10.1038/nature22094 [Google Scholar] [Crossref]

91. National Population Commission (NPC) [Nigeria], & ICF. (2019). Nigeria Demographic and Health Survey 2018. NPC and ICF. https://dhsprogram.com/publications/publication-fr359-dhs-final-reports.cfm [Google Scholar] [Crossref]

92. National Population Commission (NPC) [Nigeria], Federal Ministry of Health, & ICF. (2024). Nigeria Demographic and Health Survey 2023: Key indicators report. NPC and ICF. https://dhsprogram.com [Google Scholar] [Crossref]

93. National Primary Health Care Development Agency. (2023). Annual Primary Health Care Performance Report. National Primary Health Care Development Agency. https://nphcda.gov.ng [Google Scholar] [Crossref]

94. National Primary Health Care Development Agency. (2024). National Primary Health Care Strategic Operational Plan (2024–2028). National Primary Health Care Development Agency. https://nphcda.gov.ng [Google Scholar] [Crossref]

95. National Bureau of Statistics. (2023). Nigeria Multidimensional Poverty Index Survey 2022. National Bureau of Statistics. https://nigerianstat.gov.ng [Google Scholar] [Crossref]

96. Federal Ministry of Health Nigeria. (2024). Nigeria Health Sector Renewal Investment Initiative: Implementation Roadmap. Federal Ministry of Health. https://health.gov.ng [Google Scholar] [Crossref]

97. African Union Commission. (2022). Africa Health Strategy (2016–2030): Mid-term Review. African Union Commission. https://au.int [Google Scholar] [Crossref]

98. African Union Development Agency–NEPAD. (2023). Strengthening Primary Health Care Systems in Africa. AUDA-NEPAD. https://www.nepad.org [Google Scholar] [Crossref]

99. United Nations Development Programme. (2024). Human Development Report 2023/2024: Breaking the Gridlock—Reimagining Cooperation in a Polarized World. United Nations Development Programme. https://hdr.undp.org [Google Scholar] [Crossref]

100. Institute for Health Metrics and Evaluation. (2024). Global Health Data Exchange (GHDx). Institute for Health Metrics and Evaluation. https://ghdx.healthdata.org [Google Scholar] [Crossref]

101. Countdown to 2030 Collaboration. (2023). Tracking progress for reproductive, maternal, newborn, child and adolescent health and nutrition. The Lancet Global Health, 11(4), e567–e589. https://doi.org/10.1016/S2214-109X(23)00064-8 [Google Scholar] [Crossref]

102. World Health Organization, & United Nations Children's Fund. (2024). Primary health care and universal health coverage: Operational framework for implementation. World Health Organization. https://www.who.int/publications [Google Scholar] [Crossref]

103. National Population Commission (NPC) [Nigeria], & ICF. (2026). Nigeria mini demographic and health survey 2025–26: Key indicators report. National Population Commission and ICF. https://www.dhsprogram.com/ [Google Scholar] [Crossref]

104. Curran, G. M., Bauer, M., Mittman, B., Pyne, J. M., & Stetler, C. (2012). Effectiveness-implementation hybrid designs: Combining elements of clinical effectiveness and implementation research to enhance public health impact. Medical Care, 50(3), 217–226. https://doi.org/10.1097/MLR.0b013e3182408812 [Google Scholar] [Crossref]

105. Glasgow, R. E., Harden, S. M., Gaglio, B., Rabin, B., Smith, M. L., Porter, G. C., Ory, M. G., & Estabrooks, P. A. (2019). RE-AIM planning and evaluation framework: Adapting to new science and practice with a 20-year review. Frontiers in Public Health, 7, 64. https://doi.org/10.3389/fpubh.2019.00064 [Google Scholar] [Crossref]

106. Wensing, M., Sales, A., Armstrong, R., & Wilson, P. (2020). Implementation science in times of COVID-19. Implementation Science, 15, 42. https://doi.org/10.1186/s13012-020-01006-x [Google Scholar] [Crossref]

107. Yousefi Nooraie, R., Shelton, R. C., Fiscella, K., Kwan, B. M., & McMahon, J. M. (2021). The pragmatic, rapid, and iterative dissemination and implementation (PRIDI) cycle: Adapting to the dynamic nature of public health emergencies (and beyond). Health Research Policy and Systems, 19, 110. https://doi.org/10.1186/s12961-021-00764-4 [Google Scholar] [Crossref]

108. Damschroder, L. J., Reardon, C. M., Widerquist, M. A. O., & Lowery, J. (2022). The updated Consolidated Framework for Implementation Research based on user feedback. Implementation Science, 17, 75. https://doi.org/10.1186/s13012-022-01245-0 (DOI) [Google Scholar] [Crossref]

109. Eisman, A. B., Kim, B., Salloum, R. G., Shuman, C. J., & Glasgow, R. E. (2022). Advancing rapid adaptation for urgent public health crises: Using implementation science to facilitate effective and efficient responses. Frontiers in Public Health, 10, 959567. https://doi.org/10.3389/fpubh.2022.959567 (Frontiers) [Google Scholar] [Crossref]

110. Øvretveit, J. (2025). Applying implementation science to infectious disease emergency preparedness and response. Frontiers in Public Health, 13, 1622618. https://doi.org/10.3389/fpubh.2025.1622618 (Frontiers) [Google Scholar] [Crossref]

111. National Academies of Sciences, Engineering, and Medicine. (2020). Evidence-based practice for public health emergency preparedness and response. The National Academies Press. https://doi.org/10.17226/25650 [Google Scholar] [Crossref]

112. Nelson, C., Lurie, N., Wasserman, J., & Zakowski, S. (2007). Conceptualizing and defining public health emergency preparedness. American Journal of Public Health, 97(Suppl. 1), S9–S11. https://doi.org/10.2105/AJPH.2007.114496 [Google Scholar] [Crossref]

113. Dowell, S. F., Blazes, D., & Desmond-Hellmann, S. (2016). Four steps to precision public health. Nature, 540, 189–191. https://doi.org/10.1038/540189a (Nature) [Google Scholar] [Crossref]

114. Khoury, M. J., Armstrong, G. L., Bunnell, R. E., Cyril, J., & Iademarco, M. F. (2020). The intersection of genomics and big data with public health: Opportunities for precision public health. PLoS Medicine, 17(10), e1003373. https://doi.org/10.1371/journal.pmed.1003373 (PLOS) [Google Scholar] [Crossref]

115. Roberts, M. C., Holt, K. E., Del Fiol, G., Baccarelli, A. A., & Allen, C. G. (2024). Precision public health in the era of genomics and big data. Nature Medicine, 30, 1865–1873. https://doi.org/10.1038/s41591-024-03098-0 (Nature) [Google Scholar] [Crossref]

116. Chowkwanyun, M., Bayer, R., & Galea, S. (2018). “Precision” public health—Between novelty and hype. New England Journal of Medicine, 379(15), 1398–1400. https://doi.org/10.1056/NEJMp1806634 [Google Scholar] [Crossref]

117. Taylor-Robinson, D., & Kee, F. (2019). Precision public health—the Emperor’s new clothes. International Journal of Epidemiology, 48(1), 1–6. https://doi.org/10.1093/ije/dyy184 (OUP Academic) [Google Scholar] [Crossref]

118. World Health Organization. (2023). Strengthening the global architecture for health emergency prevention, preparedness, response and resilience. World Health Organization. (World Health Organization) [Google Scholar] [Crossref]

119. World Health Organization. (2024). Emergency response framework (ERF), edition 2.1. World Health Organization. (World Health Organization) [Google Scholar] [Crossref]

120. World Health Organization. (2024). Building health system resilience to public health challenges: Guidance for implementation in countries. World Health Organization. (World Health Organization) [Google Scholar] [Crossref]

121. World Health Organization, United Nations Children’s Fund, & International Federation of Red Cross and Red Crescent Societies. (2024). Defining community protection: A core concept for strengthening the global architecture for health emergency preparedness, response and resilience. World Health Organization. (World Health Organization) [Google Scholar] [Crossref]

122. World Health Organization. (2025). International Health Regulations (2005) as amended in 2014, 2022 and 2024. World Health Organization. (WHO Apps) [Google Scholar] [Crossref]

123. World Health Organization. (2025). WHO Pandemic Agreement. World Health Organization. (World Health Organization) [Google Scholar] [Crossref]

124. World Health Organization. (2026). Emergency ready primary health care framework. World Health Organization. (World Health Organization) [Google Scholar] [Crossref]

125. World Health Organization. (2026). Emergency ready primary health care operational guide. World Health Organization. (World Health Organization) [Google Scholar] [Crossref]

126. Sirleaf, E. J., & Clark, H. (2021). Report of the Independent Panel for Pandemic Preparedness and Response: Making COVID-19 the last pandemic. The Lancet, 398(10295), 101–103. https://doi.org/10.1016/S0140-6736(21)01095-3 (PubMed Central (PMC)) [Google Scholar] [Crossref]

127. National Academies of Sciences, Engineering, and Medicine. (2020). Evidence-based practice for public health emergency preparedness and response: A consensus study report. The National Academies Press. https://doi.org/10.17226/25650 (NCBI) [Google Scholar] [Crossref]

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