Identifying prevalent multimorbidity and its correlates in a low- and middle-income country: a machine learning analysis
Abstract
Multimorbidity, the coexistence of two or more chronic conditions, presents growing challenges for health systems in low- and middle-income countries. Neighbourhood socioeconomic conditions are widely assumed to contribute to multimorbidity risk beyond individual characteristics, but this contribution has not been quantified in sub-Saharan Africa. We estimated how much neighbourhood socioeconomic context adds to the identification of prevalent multimorbidity, over and above individual characteristics and place of residence, and compared machine learning with conventional regression for this task. We analysed cross-sectional data from 6,177 adults aged 15 years and older in 691 primary sampling units in the 2016 South Africa Demographic and Health Survey. Multimorbidity was defined as two or more of twelve chronic conditions ascertained by biomarker measurement, self-reported diagnosis or reported treatment. Predictors were classified by temporal status as structural, behavioural, or illness-concurrent; the primary model excluded illness-concurrent variables because current medication use is itself a criterion for ascertaining two of the outcome conditions. We compared 18 supervised algorithms with three regression comparators using repeated ten-fold cross-validation grouped by primary sampling unit, with sampling weights applied in the evaluation of all algorithms and in the fitting of those that accept observation weights. Discrimination was estimated from pooled out-of-fold predictions with cluster-bootstrap confidence intervals. We assessed subgroup sensitivity, false-negative rate and calibration across wealth, age, sex, residence and province. Multimorbidity affected 2,628 participants (weighted prevalence 40.0%). The primary model achieved an area under the curve of 0.776 (95% CI 0.760–0.793), sensitivity 0.599 (0.568–0.631) and specificity 0.785 (0.764–0.804). Adding neighbourhood socioeconomic indicators to a model already containing individual characteristics, province and urban/rural residence changed discrimination by 0.001 (95% CI -0.001 to 0.002); the neighbourhood-level intraclass correlation was 0.027. Machine learning offered no advantage over conventional regression: multilevel logistic regression with a primary-sampling-unit random intercept achieved an identical area under the curve of 0.776 (0.761–0.793), and the total spread across the five best-performing algorithms was 0.004. Including illness-concurrent predictors inflated discrimination by 0.031 (0.025–0.036). Although accuracy was similar across subgroups, sensitivity ranged from 0.00 in adults aged 15–24 years to 0.95 in those aged 65 and over. In a weighted analytic sample drawn from a nationally representative South African survey, neighbourhood socioeconomic context contributed negligibly to identifying prevalent multimorbidity once individual characteristics and place of residence were accounted for, and flexible machine learning offered no discriminative advantage over an appropriately specified multilevel regression. Reported performance of multimorbidity models is materially inflated by predictors that reflect existing disease. Because these data are cross-sectional, the model identifies prevalent rather than incident multimorbidity, and comparable subgroup accuracy conceals near-total failure to identify affected younger adults.
Related
- Explainable machine learning and multivariable logistic regression for identifying prevalent self-reported physician-diagnosed chronic lung disease in postmenopausal women: development and multi-cohort external validation
- Development of a nomogram for identifying prevalent medication discrepancy in maintenance hemodialysis patients: a multicenter cross-sectional study
- Pregnancy loss and prevalent cardiometabolic multimorbidity: a two-stage individual participant data analysis of CHARLS, ELSA, and HRS.
- Waist wins : Waist circumference tops newer indices for identifying prevalent elevated BP and hypertension in the Saudi population
- Prevalence, patterns, and correlates of non-communicable disease multimorbidity among healthcare workers: a multicenter study from Bangladesh.
BibTeX
@article{uthman2026identifying,
title = {{Identifying prevalent multimorbidity and its correlates in a low- and middle-income country: a machine learning analysis}},
author = {Olalekan A. Uthman and Matthew Hazell and Jillian Hill and Muhammed Mubashir Babatunde Uthman and Kolawole W. Wahab and Ponnusamy Saravanan and Paramjit Gill and Andre Pascal Kengne},
journal = {Scientific Reports},
year = {2026},
month = oct,
doi = {10.1038/s41598-026-76177-6},
url = {https://www.nature.com/articles/s41598-026-76177-6},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Identifying prevalent multimorbidity and its correlates in a low- and middle-income country: a machine learning analysis
AU - Olalekan A. Uthman
AU - Matthew Hazell
AU - Jillian Hill
AU - Muhammed Mubashir Babatunde Uthman
AU - Kolawole W. Wahab
AU - Ponnusamy Saravanan
AU - Paramjit Gill
AU - Andre Pascal Kengne
PY - 2026
DA - 2026/10/21
JO - Scientific Reports
DO - 10.1038/s41598-026-76177-6
UR - https://www.nature.com/articles/s41598-026-76177-6
AB - Multimorbidity, the coexistence of two or more chronic conditions, presents growing challenges for health systems in low- and middle-income countries. Neighbourhood socioeconomic conditions are widely assumed to contribute to multimorbidity risk beyond individual characteristics, but this contribution has not been quantified in sub-Saharan Africa. We estimated how much neighbourhood socioeconomic context adds to the identification of prevalent multimorbidity, over and above individual characteristics and place of residence, and compared machine learning with conventional regression for this task. We analysed cross-sectional data from 6,177 adults aged 15 years and older in 691 primary sampling units in the 2016 South Africa Demographic and Health Survey. Multimorbidity was defined as two or more of twelve chronic conditions ascertained by biomarker measurement, self-reported diagnosis or reported treatment. Predictors were classified by temporal status as structural, behavioural, or illness-concurrent; the primary model excluded illness-concurrent variables because current medication use is itself a criterion for ascertaining two of the outcome conditions. We compared 18 supervised algorithms with three regression comparators using repeated ten-fold cross-validation grouped by primary sampling unit, with sampling weights applied in the evaluation of all algorithms and in the fitting of those that accept observation weights. Discrimination was estimated from pooled out-of-fold predictions with cluster-bootstrap confidence intervals. We assessed subgroup sensitivity, false-negative rate and calibration across wealth, age, sex, residence and province. Multimorbidity affected 2,628 participants (weighted prevalence 40.0%). The primary model achieved an area under the curve of 0.776 (95% CI 0.760–0.793), sensitivity 0.599 (0.568–0.631) and specificity 0.785 (0.764–0.804). Adding neighbourhood socioeconomic indicators to a model already containing individual characteristics, province and urban/rural residence changed discrimination by 0.001 (95% CI -0.001 to 0.002); the neighbourhood-level intraclass correlation was 0.027. Machine learning offered no advantage over conventional regression: multilevel logistic regression with a primary-sampling-unit random intercept achieved an identical area under the curve of 0.776 (0.761–0.793), and the total spread across the five best-performing algorithms was 0.004. Including illness-concurrent predictors inflated discrimination by 0.031 (0.025–0.036). Although accuracy was similar across subgroups, sensitivity ranged from 0.00 in adults aged 15–24 years to 0.95 in those aged 65 and over. In a weighted analytic sample drawn from a nationally representative South African survey, neighbourhood socioeconomic context contributed negligibly to identifying prevalent multimorbidity once individual characteristics and place of residence were accounted for, and flexible machine learning offered no discriminative advantage over an appropriately specified multilevel regression. Reported performance of multimorbidity models is materially inflated by predictors that reflect existing disease. Because these data are cross-sectional, the model identifies prevalent rather than incident multimorbidity, and comparable subgroup accuracy conceals near-total failure to identify affected younger adults.
SN - 2045-2322
ER -