Predictors of employment attrition in Lebanon during multifaceted crises: The role of chronic diseases – a national cross-sectional study

The COVID-19 pandemic and Lebanon’s ongoing economic crisis exacerbated existing inequalities, including workforce disparities. This study identified predictors of employment attrition during Lebanon’s concurrent crises and examined the association between chronic conditions and employment attrition. This cross-sectional study recruited adults aged 19-64 years residing in Lebanon through random digit dialing (5 January – 9 July 2024). Data collected included socio-demographics, household characteristics, employment, and self-reported chronic conditions. The outcome was the loss of paid employment (employment attrition) during the crises. Predictors were identified through LASSO regression and model discrimination and calibration were assessed. Logistic regression models, adjusted for covariates identified through directed acyclic graphs, assessed the association between number and types of chronic conditions and employment attrition. Of 2103 participants employed prior to the onset of the concurrent crises (pre-2020), 72.7% were males, 70.1% were Lebanese, and 14.7% became unemployed during the crises. Predictors of employment attrition were: older age, females, non-Lebanese, married, no formal education, having at least one chronic condition, working in a private or non-governmental organization, and having an oral agreement with employer. The predictive model demonstrated a moderate to good discriminative ability and good calibration. Pre-existing chronic conditions, such as cardiovascular disease (aOR: 2.15; 95% CI, 1.27 to 3.64) and diabetes (aOR: 2.52; 95% CI, 1.43 to 4.45), were independently associated with employment attrition. This study underscores the need to address life-course disparities contributing to job loss and to consider proactive job protections to mitigate workforce disruptions during multiple crises, particularly in contexts where social safety nets are absent.