Zero-Dose and Dropout Children in Nigeria: A Comparative Machine Learning Analysis of Childhood Immunization Cascade Failure Using the 2023-24 Nigeria Demographic and Health Survey
- 1Marwadi University
Abstract
BackgroundThere are two distinct failure points in childhood immunization: children who receive no immunization ("zero-dose"); and children who start but do not complete the immunization series ("dropout"). Two failure points to address are possible: zero-dose, where children receive no first doses; and dropout, where children begin but do not complete the immunization series. These are plausibly different failure points that require different interventions. Nigeria has the largest number of children with zero doses in absolute numbers in the world. The newly released Nigeria Demographic and Health Survey (NDHS) 2023-24 is used to describe failure cases and to compare the performance of machine learning techniques with standard regression in predicting them.
MethodsWe analyzed 4,937 children aged 12-23 months (the age range covered by the WHO/UNICEF childrens recode) in the NDHS. Survey-weighted logistic regression was used to model ZDS and dropout (Penta1 received vs. Penta3 not received) and to compare them with the WHO/UNICEF national coverage estimates (WUENIC). With the same held-out test data, each of three additional algorithms - elastic net, random forest, and XGBoost - was compared to logistic regression, and sampling weights were added to each. Geographic clustering, after adjusting for individual-level effects, was quantified using a mixed-effects model with a state-level random intercept.
ResultsZero-dose prevalence was 37.3% (implied Penta1 coverage 62.7%), compared with the WUENIC national estimate of 71% Penta1 coverage; incomplete series coverage was at 9.1% (14.5% of Penta1 recipients), and full vaccination coverage was at 53.6%, which is lower than the administratively weighted global coverage estimates (67% Penta3). The associations of zero-dose status were strong and independent for maternal education, facility delivery, attending ANC, and household wealth, whereas these factors were surprisingly weak, albeit statistically significant for Maternal education and Facility delivery, and not significant for the other factors in the dropout group. After full adjustment, religious affiliation (among Muslims) remained an independent predictor of zero dose, even though clustering by state was adjusted for. Survey-weighted logistic regression performed best for both outcomes (zero-dose AUC=0.825; dropout AUC=0.613), whereas XGBoost performed worst for both. Only 4.7% of the residual variation in dropout could be accounted for by state of residence, but 11.2% could be accounted for by zero-dose status.
ConclusionsZero-dose status as well as dropout are characterized by very different predictor profiles and are, therefore, separate policy problems and should not be treated as a single "coverage gap". There was no improvement in prediction performance in terms of algorithmic complexity compared to a correctly specified logistic regression for either outcome.
Related
- Socioeconomic and health system determinants of zero-dose and under-immunization among children in Zambia: Evidence from the 2024 Demographic and Health Survey
- High-confidence predictors of childhood anemia in Ghana: a parallel multilevel and machine learning analysis of the 2022 demographic and health survey
- Early childhood teachers’ practices and perspectives on the integration of smart devices in children’s learning environments
- Impact of COVID-19 pandemic on childhood immunization coverage in Indonesia: lesson learned from a nationwide analysis of the Expanded Programme on Immunization
- A systematic review of barriers associated with immunization activities among under-5 children in Nigeria
BibTeX
@misc{nosaihaza2026zerodose,
title = {{Zero-Dose and Dropout Children in Nigeria: A Comparative Machine Learning Analysis of Childhood Immunization Cascade Failure Using the 2023-24 Nigeria Demographic and Health Survey}},
author = {E. A. Nosa-Ihaza and M. C. Ogidi and S. O. Odeke and U. N. Nosa-Ihaza},
howpublished = {medRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.05.26362351},
url = {https://www.medrxiv.org/content/10.64898/2026.09.05.26362351v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Zero-Dose and Dropout Children in Nigeria: A Comparative Machine Learning Analysis of Childhood Immunization Cascade Failure Using the 2023-24 Nigeria Demographic and Health Survey
AU - E. A. Nosa-Ihaza
AU - M. C. Ogidi
AU - S. O. Odeke
AU - U. N. Nosa-Ihaza
PY - 2026
DA - 2026/09/05
PB - medRxiv
DO - 10.64898/2026.09.05.26362351
UR - https://www.medrxiv.org/content/10.64898/2026.09.05.26362351v1
AB - BackgroundThere are two distinct failure points in childhood immunization: children who receive no immunization ("zero-dose"); and children who start but do not complete the immunization series ("dropout"). Two failure points to address are possible: zero-dose, where children receive no first doses; and dropout, where children begin but do not complete the immunization series. These are plausibly different failure points that require different interventions. Nigeria has the largest number of children with zero doses in absolute numbers in the world. The newly released Nigeria Demographic and Health Survey (NDHS) 2023-24 is used to describe failure cases and to compare the performance of machine learning techniques with standard regression in predicting them. MethodsWe analyzed 4,937 children aged 12-23 months (the age range covered by the WHO/UNICEF childrens recode) in the NDHS. Survey-weighted logistic regression was used to model ZDS and dropout (Penta1 received vs. Penta3 not received) and to compare them with the WHO/UNICEF national coverage estimates (WUENIC). With the same held-out test data, each of three additional algorithms - elastic net, random forest, and XGBoost - was compared to logistic regression, and sampling weights were added to each. Geographic clustering, after adjusting for individual-level effects, was quantified using a mixed-effects model with a state-level random intercept. ResultsZero-dose prevalence was 37.3% (implied Penta1 coverage 62.7%), compared with the WUENIC national estimate of 71% Penta1 coverage; incomplete series coverage was at 9.1% (14.5% of Penta1 recipients), and full vaccination coverage was at 53.6%, which is lower than the administratively weighted global coverage estimates (67% Penta3). The associations of zero-dose status were strong and independent for maternal education, facility delivery, attending ANC, and household wealth, whereas these factors were surprisingly weak, albeit statistically significant for Maternal education and Facility delivery, and not significant for the other factors in the dropout group. After full adjustment, religious affiliation (among Muslims) remained an independent predictor of zero dose, even though clustering by state was adjusted for. Survey-weighted logistic regression performed best for both outcomes (zero-dose AUC=0.825; dropout AUC=0.613), whereas XGBoost performed worst for both. Only 4.7% of the residual variation in dropout could be accounted for by state of residence, but 11.2% could be accounted for by zero-dose status. ConclusionsZero-dose status as well as dropout are characterized by very different predictor profiles and are, therefore, separate policy problems and should not be treated as a single "coverage gap". There was no improvement in prediction performance in terms of algorithmic complexity compared to a correctly specified logistic regression for either outcome.
ER -