Predicting COVID-19 infection among older Syrian refugees in Lebanon: A multi-wave survey
- 1Department of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, United States of America
- 2Center for Research on Population and Health, Faculty of Health Sciences, American University of Beirut, Beirut, Lebanon
- 3Department of Health Promotion and Community Health, Faculty of Health Sciences, American University ofBeirut, Beirut, Lebanon
Abstract
Objectives: Older refugees, exposed to a cluster of biological and social vulnerabilities, are more susceptible to infectious disease outbreaks and their complications. This study developed a predictive model estimating self-reported COVID-19 infection risk among older Syrian refugees in Lebanon using social determinants of health. Additionally, it described the barriers to diagnostic testing among those who reported having had COVID-19 infection at least once. Methods: This prediction modeling analysis uses data from a longitudinal study conducted between September 2020 and March 2022. Syrian refugees aged 50 years or older living in households that received assistance from a humanitarian organization were interviewed by phone. Self-reported occurrence at least one COVID-19 infection was the outcome of interest. The predictors were identified using adaptive lasso regression. The apparent model performance and discrimination were presented using the calibration slope and the Area Under the Curve (AUC). Results: Of 2,886 participants (median [IQR] age: 56 [52 –62]; 52.9% males), 283 individuals (9.8%) reported a COVID-19 infection at least once. Six predictors of at least one COVID-19 infection were identified: living outside informal tented settlements, having elementary and preparatory education or above, having chronic conditions, not receiving cash assistance, being water insecure and having unmet waste management needs. The apparent model had modest discrimination (AUC = 0.621 [95%CI: 0.587 to 0.655]) and good calibration (c-Slope = 1.004 [95%CI: 0.704 to 1.304]). Nearly half of the cases were diagnosed through testing. The main reasons for not testing were perception that the tests were unnecessary (n = 91 [63.6%]) or inability to afford them (n = 46 [32.2%]). Conclusions: The identified predictors provide insight into characteristics associated with higher predicted risk of COVID-19 infection among Syrian refugees. However, given the model’s modest predictive performance and reliance on lasso tuning, further validation and refinement in other populations and settings are needed before implementation. Awareness campaigns, screening measures, and cash assistance may be associated with reduced risk of infection in future pandemics.
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BibTeX
@article{zeid2026predicting,
title = {{Predicting COVID-19 infection among older Syrian refugees in Lebanon: A multi-wave survey}},
author = {Berthe Abi Zeid and Tanya El Khoury and Sawsan Abdulrahim and Hala Ghattas and Stephen J. McCall},
journal = {PLOS One},
year = {2026},
month = oct,
doi = {10.1371/journal.pone.0359272},
url = {https://doi.org/10.1371/journal.pone.0359272},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Predicting COVID-19 infection among older Syrian refugees in Lebanon: A multi-wave survey
AU - Berthe Abi Zeid
AU - Tanya El Khoury
AU - Sawsan Abdulrahim
AU - Hala Ghattas
AU - Stephen J. McCall
PY - 2026
DA - 2026/10/01
JO - PLOS One
DO - 10.1371/journal.pone.0359272
UR - https://doi.org/10.1371/journal.pone.0359272
AB - Objectives: Older refugees, exposed to a cluster of biological and social vulnerabilities, are more susceptible to infectious disease outbreaks and their complications. This study developed a predictive model estimating self-reported COVID-19 infection risk among older Syrian refugees in Lebanon using social determinants of health. Additionally, it described the barriers to diagnostic testing among those who reported having had COVID-19 infection at least once. Methods: This prediction modeling analysis uses data from a longitudinal study conducted between September 2020 and March 2022. Syrian refugees aged 50 years or older living in households that received assistance from a humanitarian organization were interviewed by phone. Self-reported occurrence at least one COVID-19 infection was the outcome of interest. The predictors were identified using adaptive lasso regression. The apparent model performance and discrimination were presented using the calibration slope and the Area Under the Curve (AUC). Results: Of 2,886 participants (median [IQR] age: 56 [52 –62]; 52.9% males), 283 individuals (9.8%) reported a COVID-19 infection at least once. Six predictors of at least one COVID-19 infection were identified: living outside informal tented settlements, having elementary and preparatory education or above, having chronic conditions, not receiving cash assistance, being water insecure and having unmet waste management needs. The apparent model had modest discrimination (AUC = 0.621 [95%CI: 0.587 to 0.655]) and good calibration (c-Slope = 1.004 [95%CI: 0.704 to 1.304]). Nearly half of the cases were diagnosed through testing. The main reasons for not testing were perception that the tests were unnecessary (n = 91 [63.6%]) or inability to afford them (n = 46 [32.2%]). Conclusions: The identified predictors provide insight into characteristics associated with higher predicted risk of COVID-19 infection among Syrian refugees. However, given the model’s modest predictive performance and reliance on lasso tuning, further validation and refinement in other populations and settings are needed before implementation. Awareness campaigns, screening measures, and cash assistance may be associated with reduced risk of infection in future pandemics.
SN - 1932-6203
ER -