Document Type

Capstone Experience

Graduation Date


Degree Name

Master of Public Health



First Committee Member

Ariane Rung

Second Committee Member

Kendra Ratnapradipa

Third Committee Member

Ishrat Kamal-Ahmed

Fourth Committee Member

Anthony Blake


Objective. To determine if insurance status can directly predict self-diagnosed Long COVID and what sociodemographic factors are significantly associated with a self-diagnosis of Long COVID among those who tested positive for COVID-19.

Methods. This cross-sectional study uses data from the 2022 Behavioral Risk Factor Surveillance System to conduct a multivariate logistic regression model analysis. Out of 445,132 participants, 110,402 participants were identified to be used in this study.

Results. After testing positive for COVID-19, 21.7% of individuals were self-diagnosed with Long COVID. After controlling for confounders, insurance status wasn't significant (p-value: 0.8458). However, in crude analysis, individuals with Medicaid (cPOR:1.44; 95%CI:[1.30-1.58]) and no health insurance (cPOR:1.37; 95%CI:[1.22-1.54]) were associated with increased odds of self-diagnosed Long COVID. Females had a 61% greater adjusted odds (95% CI: [1.44-1.80]) of self-diagnosing Long COVID than males. Individuals aged 45 to 64 had 37% greater adjusted odds (95% CI: [1.15-1.63]) of self-diagnosed Long COVID compared to those aged 65 and over.

Conclusions. Insurance status alone shouldn't be used as a direct predictor of self-diagnosed Long COVID but should be considered alongside other sociodemographic factors.

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