Antimalarial Pharmacotherapy Gaps in Nigerian Children Under Five: A Predictive Machine Learning Analysis of Care-Seeking, Testing, and ACT Treatment Using NDHS 2023-24
- 1Marwadi University
Abstract
BackgroundNigerias malaria burden is the highest in the world, both in terms of incidence and fatalities. Case management uses the WHOs Test, Treat, Track (T3) approach, where failures can occur at three stages (care-seeking, diagnostic testing, and treatment appropriateness), which are often combined into a single coverage measure. In this study, we split the cascade into two outcomes and compared four predictive modeling approaches to identify determinants.
MethodsFrom the Childrens Recode file of the 2023-24 Nigeria DHS, we identified 3,962 children under 5 years of age who reported having a fever in the two weeks before the survey. Gap A (access) was defined as: seeking care from any source and receiving a diagnostic blood test. Gap B (quality) was limited to the 1,776 children who had received any antimalarial, and specifically defined as receipt of an artemisinin-based combination therapy (ACT) only. To predict both outcomes, we tested and compared survey-weighted logistic regression, elastic-net regression, random forest, and gradient boosting (XGBoost) using a single held-out test partition and the area under the receiver operating characteristic curve (AUC) for all four models across both outcomes.
ResultsThe proportion of febrile children with combined access threshold was 16.0%, and 60.2% of children treated with antimalarials received an ACT. The Gap A logistic model did not reach overall statistical significance (p = 0.189); the Gap B model did (p = 0.003), with caregiver-reported financial barriers to care significantly associated with lower odds of ACT receipt ({beta} = -0.97, p = 0.013), a finding that replicated across three of the four modeling approaches. AUCs were between 0.48 and 0.62 for the eight models, and all 95% CIs contained 0.50, meaning that none of the models could be confidently ruled out as being no better at discrimination than chance. For both outcomes, gradient boosting had the lowest AUC of the four methods, similar to what has been previously reported for a comparable treatment-cascade outcome in Mexicos ENSANUT survey.
ConclusionLimited variation in Nigerias malaria care-seeking, testing, and treatment cascade can be explained by household and maternal characteristics captured in standard survey data. The consistent association between financial access barriers and reduced likelihood of ACT-specific treatment suggests point-of-sale cost, rather than caregiver knowledge alone, as a target for intervention within Nigerias pharmacy and patent/proprietary medicine vendor distribution channels.
Related
- Antihypertensive Pharmacotherapy Gaps in Nigeria: A Predictive Machine Learning Analysis of Treatment Uptake Amid Macroeconomic Shock, Using NDHS 2023-24
- Patterns and determinants of antimalarial drug use and treatment adherence in Osun State, Southwestern Nigeria: a cross-sectional study.
- Corticosteroids in septic shock: mechanisms, heterogeneous treatment effects, and trajectory-guided precision pharmacotherapy
- Prevalence and predictors of undernutrition among preschool children (3 to 5 years) in Nigerian semi-urban community: conventional anthropometric indices versus composite index of anthropometric failure approach.
- Risk factors for plastic bronchitis complicating severe Mycoplasma pneumoniae pneumonia in children and development of a machine learning-based predictive model
BibTeX
@misc{nosaihaza2026antimalarial,
title = {{Antimalarial Pharmacotherapy Gaps in Nigerian Children Under Five: A Predictive Machine Learning Analysis of Care-Seeking, Testing, and ACT Treatment Using NDHS 2023-24}},
author = {E. A. Nosa-Ihaza and G. Shepherd-Moses and E. D. Okenwa and U. N. Nosa-Ihaza},
howpublished = {medRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.10.26362761},
url = {https://www.medrxiv.org/content/10.64898/2026.09.10.26362761v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Antimalarial Pharmacotherapy Gaps in Nigerian Children Under Five: A Predictive Machine Learning Analysis of Care-Seeking, Testing, and ACT Treatment Using NDHS 2023-24
AU - E. A. Nosa-Ihaza
AU - G. Shepherd-Moses
AU - E. D. Okenwa
AU - U. N. Nosa-Ihaza
PY - 2026
DA - 2026/09/10
PB - medRxiv
DO - 10.64898/2026.09.10.26362761
UR - https://www.medrxiv.org/content/10.64898/2026.09.10.26362761v1
AB - BackgroundNigerias malaria burden is the highest in the world, both in terms of incidence and fatalities. Case management uses the WHOs Test, Treat, Track (T3) approach, where failures can occur at three stages (care-seeking, diagnostic testing, and treatment appropriateness), which are often combined into a single coverage measure. In this study, we split the cascade into two outcomes and compared four predictive modeling approaches to identify determinants. MethodsFrom the Childrens Recode file of the 2023-24 Nigeria DHS, we identified 3,962 children under 5 years of age who reported having a fever in the two weeks before the survey. Gap A (access) was defined as: seeking care from any source and receiving a diagnostic blood test. Gap B (quality) was limited to the 1,776 children who had received any antimalarial, and specifically defined as receipt of an artemisinin-based combination therapy (ACT) only. To predict both outcomes, we tested and compared survey-weighted logistic regression, elastic-net regression, random forest, and gradient boosting (XGBoost) using a single held-out test partition and the area under the receiver operating characteristic curve (AUC) for all four models across both outcomes. ResultsThe proportion of febrile children with combined access threshold was 16.0%, and 60.2% of children treated with antimalarials received an ACT. The Gap A logistic model did not reach overall statistical significance (p = 0.189); the Gap B model did (p = 0.003), with caregiver-reported financial barriers to care significantly associated with lower odds of ACT receipt ({beta} = -0.97, p = 0.013), a finding that replicated across three of the four modeling approaches. AUCs were between 0.48 and 0.62 for the eight models, and all 95% CIs contained 0.50, meaning that none of the models could be confidently ruled out as being no better at discrimination than chance. For both outcomes, gradient boosting had the lowest AUC of the four methods, similar to what has been previously reported for a comparable treatment-cascade outcome in Mexicos ENSANUT survey. ConclusionLimited variation in Nigerias malaria care-seeking, testing, and treatment cascade can be explained by household and maternal characteristics captured in standard survey data. The consistent association between financial access barriers and reduced likelihood of ACT-specific treatment suggests point-of-sale cost, rather than caregiver knowledge alone, as a target for intervention within Nigerias pharmacy and patent/proprietary medicine vendor distribution channels.
ER -