{
  "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.\n\nObjectivesTo 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.\n\nMethodsWe 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.\n\nResultsThe 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.\n\nO_TBL View this table: org.highwire.dtl.DTLVardef@82b92borg.highwire.dtl.DTLVardef@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)\n\nC_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.",
  "abstract_shown": true,
  "affiliations": [
    "Marwadi University"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "E. A. Nosa-Ihaza"
    },
    {
      "name": "E. C. Edeh"
    },
    {
      "name": "D. N. Eze"
    },
    {
      "affs": [
        0
      ],
      "name": "U. N. Nosa-Ihaza"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/epdmnda9jpz9a.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.11.26362863",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.09.11.26362863v1.full",
  "id": "epdmnda9jpz9a",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.09.11.26362863v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "health systems and quality improvement"
    ],
    [
      "openalex",
      "2705|Blood Pressure and Hypertension Studies"
    ]
  ],
  "openalex": "W7213456880",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.09.11.26362863v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-11",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/epdmnda9jpz9a.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:47Z",
      "native_id": "10.64898/2026.09.11.26362863",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.09.11.26362863v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:health systems and quality improvement",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Antihypertensive Pharmacotherapy Gaps in Nigeria: A Predictive Machine Learning Analysis of Treatment Uptake Amid Macroeconomic Shock, Using NDHS 2023-24",
  "title_text": "Antihypertensive Pharmacotherapy Gaps in Nigeria: A Predictive Machine Learning Analysis of Treatment Uptake Amid Macroeconomic Shock, Using NDHS 2023-24",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:47Z",
  "url": "https://research.draconitic.com/paper/epdmnda9jpz9a",
  "venue": "medRxiv",
  "versions": []
}