Predicting COVID-19 Infection Among Older Syrian Refugees in Lebanon: A Multi-Wave Survey

Abstract

Background: Older refugees, exposed to a cluster of biological and social vulnerabilities, are more susceptible to COVID-19 infection and its complications. This study developed and internally validated a predictive model estimating COVID-19 infection risk among older Syrian refugees in Lebanon. Additionally, it described the barriers to PCR testing among those who reported a COVID-19 infection. Methods: This was a cross-sectional analysis of a five-wave 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 COVID-19 infection was the outcome of interest. The predictors were identified using adaptive LASSO regression. The model performance and discrimination were presented using the calibration slope and the Area Under the Curve. Findings: 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 for 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 model had moderate discrimination and good calibration. Nearly half of the cases were diagnosed through PCR 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%]). Interpretation: High-risk individuals should be targeted based on predictive models incorporating social determinants. Implementing awareness campaigns, screening measures, and cash assistance may reduce vulnerability in future pandemics. Funding: ELRHA's Research for Health in Humanitarian Crisis Programme and AUB University Research Board.