{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T11:09:47Z","timestamp":1764932987011,"version":"3.46.0"},"reference-count":36,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T00:00:00Z","timestamp":1764892800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Department of Laboratory Medicine and Pathology, Faculty of Medicine and Health Sciences"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJERPH"],"abstract":"<jats:p>Background: Household contacts of smear-positive tuberculosis (TB) patients face an elevated risk of infection and disease progression, particularly young children and individuals living in overcrowded households. Despite WHO recommendations for systematic contact screening and provision of TB preventive therapy (TPT), implementation remains suboptimal in high-burden rural areas. This study aimed to develop a practical framework for identifying and prioritizing high-risk families by examining demographic predictors, household clustering, and machine learning-based risk models. Methods: A total of 437 household contacts linked to smear-positive index cases were assessed and classified as high or low risk. Statistical analyses included descriptive measures, \u03c72 tests, Z-tests for age-group differences, and multivariable logistic regression. Household-level vulnerability patterns were explored using network visualizations, clustered heatmaps, and risk-ranking charts. Three machine learning models, logistic regression, random forest, and gradient boosting, were trained using demographic and household variables with 5-fold cross-validation and an 80\/20 hold-out test split. Model performance was evaluated using the AUROC, AUPRC, accuracy, F1-score, calibration curves, and decision curve analysis. Results: Of the 437 contacts, 290 (66.4%) were classified as high risk. A younger age was strongly associated with high-risk status (\u03c72 = 16.61, p = 0.005), with children aged 0\u20134 years being significantly more likely to be in a high-risk category (Z = 2.706). Gender showed no significant association (p = 0.523). Logistic regression identified younger age (aOR = 2.41, 95% CI: 1.48\u20133.94) and larger household size (aOR = 1.12 per additional member, 95% CI: 1.01\u20131.25) as independent predictors of the outcome. Visual analytics revealed apparent clustering of high-risk individuals within \u201chotspot families,\u201d enabling prioritization through composite risk scores. Gradient boosting achieved the strongest performance (AUROC = 0.65; AUPRC = 0.76), with acceptable calibration (Brier score = 0.21) and a positive net clinical benefit in the decision curve analysis. Conclusions: TB risk is highly clustered at the household level, with large families and young children carrying disproportionate vulnerability. Combining demographic risk assessment, household-level visualization, and predictive modeling provides a practical, data-driven approach to prioritizing households during contact investigation. These findings support the WHO\u2019s family-centered strategy and underscore the need to strengthen clinical governance and community-engaged education to optimize TB prevention in resource-limited rural settings.<\/jats:p>","DOI":"10.3390\/ijerph22121823","type":"journal-article","created":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T10:50:38Z","timestamp":1764931838000},"page":"1823","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Household Clustering of High-Risk Contacts in Smear-Positive TB Patient Families: Evidence for Hotspot Households and Risk Stratification in Rural Eastern Cape"],"prefix":"10.3390","volume":"22","author":[{"given":"Hloniphani","family":"Guma","sequence":"first","affiliation":[{"name":"Department of Laboratory Medicine and Pathology, Faculty of Medicine and Health Sciences, Walter Sisulu University, Mthatha 5099, South Africa"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ntandazo","family":"Dlatu","sequence":"additional","affiliation":[{"name":"Walter Sisulu Institute for Clinical Governance, Healthcare Administration, Department of Public Health, Faculty of Medicine and Health Sciences, Walter Sisulu University, Mthatha 5099, South Africa"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wezile Wilson","family":"Chitha","sequence":"additional","affiliation":[{"name":"Walter Sisulu Institute for Clinical Governance, Healthcare Administration, Department of Public Health, Faculty of Medicine and Health Sciences, Walter Sisulu University, Mthatha 5099, South Africa"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1329-3864","authenticated-orcid":false,"given":"Teke","family":"Apalata","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine and Pathology, Faculty of Medicine and Health Sciences, Walter Sisulu University, Mthatha 5099, South Africa"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7308-4239","authenticated-orcid":false,"given":"Lindiwe Modest","family":"Faye","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine and Pathology, Faculty of Medicine and Health Sciences, Walter Sisulu University, Mthatha 5099, South Africa"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Li, X., Li, Y., Guo, L., Chen, Y., Wang, G., and Zhang, H. 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