Predictors of Optimal Uptake of Intermittent Preventive Treatment in Pregnancy (IPTp3+) in Nigeria: Survey-Weighted Regression and Exploratory Machine-Learning Analysis of the 2021 Nigeria Malaria Indicator Survey

Background: Malaria in pregnancy remains a public health challenge in Nigeria despite the effectiveness of intermittent preventive treatment with sulfadoxine-pyrimethamine (IPTp-SP). Uptake of the WHO-recommended minimum of three doses (IPTp3+) remains below national targets. This study examined factors associated with optimal uptake of IPTp and explored machine-learning-based prediction.

Methodology: We analysed 2021 Nigeria Malaria Indicator Survey data for 3,103 women aged 15–49 years with a live birth, guided by Andersen’s Behavioural Model. Descriptive and regression analyses were conducted in R. Descriptive summaries were unweighted, whereas the primary regression incorporated sampling weights, strata, and primary sampling units. Following a survey-adjusted nonlinearity test, ANC visits were categorised as 0–3, 4–7, and ≥8. Random Forest and XGBoost were fitted in Python as unweighted exploratory models, with calibration, permutation importance, and SHAP assessed on held-out data.

Results: Overall, 52.4% of women in the unweighted analytic sample reported IPTp3+ uptake. The association with ANC was nonlinear (F(2,84)=18.12; p<0.001). Compared with 0–3 visits, adjusted odds ratios were 2.10 (95% CI:1.64–2.68) for 4–7 visits and 2.05 (95% CI:1.49–2.83) for ≥8 visits; both p<0.001. Geopolitical zone was associated with uptake. XGBoost and Random Forest showed modest discrimination (ROC-AUC:0.661 and 0.640) and Brier scores of 0.225 and 0.228.

Conclusion: Only about half of women achieved optimal IPTp uptake. ANC attendance was strongly associated with IPTp3+ uptake, while geographic disparities persisted after adjustment. The exploratory machine-learning models showed modest predictive performance. Strengthening ANC access and improving IPTp delivery remain important priorities.