Introduction: During the COVID-19 pandemic, health care shifted to virtual interactions with health professionals. The aim of this study was to examine the determinants of telehealth use in a nationally representative sample of the United States adult population. Methods: The study used data from the 2020 National Health Interview Survey of 17,582 respondents aged ≥18. Andersen’s model of health services utilization was employed to examine predisposing, enabling, and needs factors associated with past-year telehealth use. Multivariable logistic regression was conducted to examine statistical associations. Results: 32.5% of adults ( n = 6402; mean age 51.6, SE = 0.4) reported telehealth use. Predisposing factors: Women and married/partnered adults and those with higher levels of education had greater odds of using telehealth. Adults living in Midwest and South and adults living in medium-small and non-metropolitan areas had decreased odds of using telehealth. Enabling factors: Income and having a usual source of care were positively associated with telehealth use. A negative association was found for those with no insurance and telehealth use, whereas a positive association was found for military insurance. Needs factors: Odds of using telehealth were increased for adults who had well-visits and ER visits in the past 12 months. Mental health services quadrupled the odds of telehealth use. Odds of using telehealth increased with each additional chronic disease, including COVID-19. Conclusion: There are disparities in telehealth use according to sex, education, rurality, access to care, and health needs. Tackling these disparities is pivotal to ensure barriers to telehealth use are not exacerbated post-pandemic.

Longitudinal change in income is crucial in explaining cardiovascular health inequalities. However, there is limited evidence for cardiovascular disease (CVD) risk associated with income dynamics over time among individuals with type 2 diabetes (T2D). Using a nationally representative sample from the Korean National Health Insurance Service database, 1 528 108 adults aged 30–64 with T2D and no history of CVD were included from 2009 to 2012 (mean follow-up of 7.3 years). Using monthly health insurance premium information, income levels were assessed annually for the baseline year and the four preceding years. Income variability was defined as the intraindividual standard deviation of the percent change in income over 5 years. The primary outcome was a composite event of incident fatal and nonfatal CVD (myocardial infarction, heart failure, and stroke) using insurance claims. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated after adjusting for potential confounders. High-income variability was associated with increased CVD risk (HRhighest vs. lowest quartile 1.25, 95% CI 1.22–1.27; Ptrend < .001). Individuals who experienced an income decline (4 years ago vs. baseline) had increased CVD risk, which was particularly notable when the income decreased to the lowest level (i.e. Medical Aid beneficiaries), regardless of their initial income status. Sustained low income (i.e. lowest income quartile) over 5 years was associated with increased CVD risk (HRn = 5 years vs. n = 0 years 1.38, 95% CI 1.35–1.41; Ptrend < .0001), whereas sustained high income (i.e. highest income quartile) was associated with decreased CVD risk (HRn = 5 years vs. n = 0 years 0.71, 95% CI 0.70–0.72; Ptrend < .0001). Sensitivity analyses, exploring potential mediators, such as lifestyle-related factors and obesity, supported the main results. Higher income variability, income declines, and sustained low income were associated with increased CVD risk. Our findings highlight the need to better understand the mechanisms by which income dynamics impact CVD risk among individuals with T2D. This study investigates the association between income dynamics over five years and incident composite cardiovascular outcomes in non-elderly individuals with type 2 diabetes. Our graphical abstract highlights significant associations between cardiovascular risk and income metrics, including income variability (standard deviation of percent change), changes in income status (rise and decline), and cumulative number of years in low- or high-income status.