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Long COVID continues to impact millions of people globally, creating a substantial burden on healthcare systems and individual productivity. Understanding the primary Long COVID risk factors is essential for clinicians to identify vulnerable populations and provide early interventions. A new cross-sectional study using the 2022 Medical Expenditure Panel Survey (MEPS) data offers critical insights into the social and behavioral drivers of this condition.
The research team applied the Andersen behavioral model to analyze data from over 256 million American adults. This model categorizes health-related variables into predisposing, enabling, and need factors. Furthermore, the study found that approximately 7% of adults met the criteria for MEPS-defined Long COVID. Consequently, this represents roughly 18.4 million individuals suffering from prolonged symptoms. Moreover, the results underscore the complexity of environmental and social influences on post-viral recovery.
The analysis identified several statistically significant Long COVID risk factors. Predisposing variables such as age, sex (specifically being female), and Asian race showed strong associations with the condition. Additionally, enabling factors like marital status and employment status played a role in symptom prevalence. For instance, individuals who were married or employed reported different likelihoods of experiencing long-term effects compared to their counterparts.
Furthermore, \"need\" variables such as the number of chronic conditions and overall health status were among the strongest predictors. Patients with pre-existing instrumental activity of daily living (IADL) limitations were more likely to report Long COVID. Interestingly, the study also found that regional factors, such as living in the southern United States, correlated with higher prevalence. Therefore, clinicians must adopt a holistic view that includes both medical history and social determinants of health.
The study also highlighted the role of personal health practices. Specifically, receiving the COVID-19 vaccine was a significant variable in the model. While the acute phase of the pandemic has passed, the persistent nature of Long COVID symptoms requires ongoing monitoring. Healthcare providers should prioritize screening in high-risk groups, such as those with multiple comorbidities. Consequently, integrated care models may be necessary to address the multisystemic nature of the disease.
Common risk factors include female sex, middle age, and the presence of pre-existing chronic conditions. Social factors like employment status and geographic location also significantly influence prevalence rates.
Research indicates that vaccination history is a key personal health practice variable. Most studies suggest that being vaccinated reduces the risk of developing severe post-acute symptoms compared to being unvaccinated.
Yes, factors like regional environment, marital status, and social support systems (enabling variables) are statistically associated with the condition's prevalence and impact on quality of life.
Disclaimer: This content is for informational and educational purposes only and does not constitute medical advice, diagnosis, or treatment. Always seek the advice of your physician or other qualified health provider with any questions you may have regarding a medical condition. Refer to the latest local and national guidelines for clinical practice.
References
Axon DR et al. Prevalence and Associations of Medical Expenditure Panel Survey-Defined Long COVID Among Adults: Cross-Sectional Study. JMIR Form Res. 2026 Jun 22. doi: 10.2196/92323. PMID: 42329675.
World Health Organization. Post COVID-19 condition (Long COVID). February 26, 2025.
Al-Aly Z et al. Long COVID science, research and policy. Nature Medicine. August 9, 2024. doi: 10.1038/s41591-024-03173-6.
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A 2022 MEPS study reveals that 7% of American adults suffer from Long COVID. Key risk factors include age, sex, pre-existing chronic conditions, and social determinants like employment and geographic region. Understanding these variables is vital for clinical risk stratification.
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