Antihypertensive Pharmacotherapy Gaps in Nigeria: A Predictive Machine Learning Analysis of Treatment Uptake Amid Macroeconomic Shock, Using NDHS 2023-24
- 1Marwadi University
Abstract
BackgroundHypertension is the most important modifiable cardiovascular risk factor worldwide, and the prevalence is increasing in sub-Saharan Africa. Nigerias newly released 2023-24 Demographic and Health Survey (NDHS) provides the first opportunity to explore national-level non-uptake of antihypertensive treatment using a machine-learning cascade framework, although survey fieldwork was conducted amid the unprecedented shock of fuel subsidy removal and currency devaluation in Nigeria.
ObjectivesTo identify correlates of antihypertensive treatment non-uptake (Gap 2) among the diagnosed adults in Nigeria; test if the non-uptake varied based on when the survey was conducted during this macroeconomic shock; and compare four predictive algorithms.
MethodsWe used the 2023-24 Nigeria Demographic and Health Survey (NDHS) data (2,975 women diagnosed with hypertension and 529 men diagnosed with hypertension aged 15-49) to fit survey-weighted logistic regression models separately by sex and evaluated sex-by-predictor interactions in a single pooled model. We then compared the survey-weighted logistic regression model with the elastic net, random forest, and gradient boosting (XGBoost) models, all properly weighted, using held-out test-set AUC.
ResultsThe timing of fieldwork interviews was a significant risk factor for women (OR=1.14 per month; 95% CI: 1.06-1.22; p<0.001) but not for men, and this sex difference was confirmed in the formal interaction test (p=0.016). This study tested the interaction of sex with each geopolitical zone, with the strongest result being a sex-reversed pattern in zones protective for women, showing these zones were significant risk factors for men (ORs 2.28-5.77); interaction testing (all p[≤]0.007) confirmed this. No interaction was observed between diabetes and sex (p=0.170); instead, diabetes was protective for women (OR=0.34, p<0.001). For pooled samples, wealth was associated with non-uptake (OR=0.59; p=0.012), and this appeared significant only for women (OR=0.62; p=0.026), in keeping with the richest-vs-poorest estimate in Table 2. All three ML algorithms had similar AUC for women (0.61-0.63) and lower, near-chance performance for men (0.56-0.59), reflecting the much smaller number of diagnosed men.
O_TBL View this table: [email protected]@4aba6aorg.highwire.dtl.DTLVardef@14a519org.highwire.dtl.DTLVardef@1631ab6org.highwire.dtl.DTLVardef@16eaa9e_HPS_FORMAT_FIGEXP M_TBL O_FLOATNOTable 2.C_FLOATNO O_TABLECAPTIONSurvey-weighted logistic regression, women, main model (n=2,975)
C_TABLECAPTION C_TBL ConclusionsThere is a large disparity in the rate of treatment and management of HTN across sexes, geopolitical zones, and macro-economic environments, making a single uniform national intervention model impractical. In contrast, an earlier study in Africa reported that similar algorithmic complexity was useful in predicting cardiovascular risk; this did not happen in the current study. The proposed programs are to build programs by sex and zone, integrate hypertension screenings into chronic disease and/or maternal health trigger points, and better understand the resilience of the pharmacy supply chain.
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BibTeX
@misc{nosaihaza2026antihypertensive,
title = {{Antihypertensive Pharmacotherapy Gaps in Nigeria: A Predictive Machine Learning Analysis of Treatment Uptake Amid Macroeconomic Shock, Using NDHS 2023-24}},
author = {E. A. Nosa-Ihaza and E. C. Edeh and D. N. Eze and U. N. Nosa-Ihaza},
howpublished = {medRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.11.26362863},
url = {https://www.medrxiv.org/content/10.64898/2026.09.11.26362863v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Antihypertensive Pharmacotherapy Gaps in Nigeria: A Predictive Machine Learning Analysis of Treatment Uptake Amid Macroeconomic Shock, Using NDHS 2023-24
AU - E. A. Nosa-Ihaza
AU - E. C. Edeh
AU - D. N. Eze
AU - U. N. Nosa-Ihaza
PY - 2026
DA - 2026/09/11
PB - medRxiv
DO - 10.64898/2026.09.11.26362863
UR - https://www.medrxiv.org/content/10.64898/2026.09.11.26362863v1
AB - BackgroundHypertension is the most important modifiable cardiovascular risk factor worldwide, and the prevalence is increasing in sub-Saharan Africa. Nigerias newly released 2023-24 Demographic and Health Survey (NDHS) provides the first opportunity to explore national-level non-uptake of antihypertensive treatment using a machine-learning cascade framework, although survey fieldwork was conducted amid the unprecedented shock of fuel subsidy removal and currency devaluation in Nigeria. ObjectivesTo identify correlates of antihypertensive treatment non-uptake (Gap 2) among the diagnosed adults in Nigeria; test if the non-uptake varied based on when the survey was conducted during this macroeconomic shock; and compare four predictive algorithms. MethodsWe used the 2023-24 Nigeria Demographic and Health Survey (NDHS) data (2,975 women diagnosed with hypertension and 529 men diagnosed with hypertension aged 15-49) to fit survey-weighted logistic regression models separately by sex and evaluated sex-by-predictor interactions in a single pooled model. We then compared the survey-weighted logistic regression model with the elastic net, random forest, and gradient boosting (XGBoost) models, all properly weighted, using held-out test-set AUC. ResultsThe timing of fieldwork interviews was a significant risk factor for women (OR=1.14 per month; 95% CI: 1.06-1.22; p<0.001) but not for men, and this sex difference was confirmed in the formal interaction test (p=0.016). This study tested the interaction of sex with each geopolitical zone, with the strongest result being a sex-reversed pattern in zones protective for women, showing these zones were significant risk factors for men (ORs 2.28-5.77); interaction testing (all p[≤]0.007) confirmed this. No interaction was observed between diabetes and sex (p=0.170); instead, diabetes was protective for women (OR=0.34, p<0.001). For pooled samples, wealth was associated with non-uptake (OR=0.59; p=0.012), and this appeared significant only for women (OR=0.62; p=0.026), in keeping with the richest-vs-poorest estimate in Table 2. All three ML algorithms had similar AUC for women (0.61-0.63) and lower, near-chance performance for men (0.56-0.59), reflecting the much smaller number of diagnosed men. O_TBL View this table: [email protected]@4aba6aorg.highwire.dtl.DTLVardef@14a519org.highwire.dtl.DTLVardef@1631ab6org.highwire.dtl.DTLVardef@16eaa9e_HPS_FORMAT_FIGEXP M_TBL O_FLOATNOTable 2.C_FLOATNO O_TABLECAPTIONSurvey-weighted logistic regression, women, main model (n=2,975) C_TABLECAPTION C_TBL ConclusionsThere is a large disparity in the rate of treatment and management of HTN across sexes, geopolitical zones, and macro-economic environments, making a single uniform national intervention model impractical. In contrast, an earlier study in Africa reported that similar algorithmic complexity was useful in predicting cardiovascular risk; this did not happen in the current study. The proposed programs are to build programs by sex and zone, integrate hypertension screenings into chronic disease and/or maternal health trigger points, and better understand the resilience of the pharmacy supply chain.
ER -