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

BackgroundOlder refugees, exposed to a cluster of biological and social vulnerabilities, are more susceptible to infectious diseases outbreaks and their complications. This study contributes to health emergency preparedness efforts by developing and internally validating a predictive model estimating COVID-19 infection risk among older Syrian refugees in Lebanon using social determinants. Additionally, it described the barriers to PCR testing among those who reported a COVID-19 infection. MethodsThis 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. ResultsOf 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%]). ConclusionsHigh-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.