Sample
We received 2,746 complete responses to the EQ-5D-5L. Compared with the US general population, our sample was slightly older, with higher education and income; less Hispanic and Black respondents, but more individuals identifying as multi-race. There was also less chronic hypertension, diabetes, arthritis, and migraine; but more hypercholesterolemia, depression, asthma, and bronchitis (cancer). Full-time employment, gender, age, marital status, and BMI >30 were similar to the general US population (Table 1).
Most respondents reported working in management (9.6%), business/finance (11.9%), computer and mathematical industries (11.3%), and office/administrative support (10.3%). Less than 1% reported working in protective services, grounds maintenance, farming/fishing/forestry, or the military. As a result of COVID-19, 52.8% reported no change in their employment, 31.9% reported working at home, 5.8% reported losing their jobs, and 9.6% reported being temporarily laid off. 8.8% reported that COVID-19 completely prevented them from working. Most (70.4%) reported no hours of missed work due to COVID-19.
When rating fear of COVID-19’s impact on their health, 59.5% of the sample reported a score of >5 on a scale of 0-10 (mean 5.20, SD 2.95). When rating fear of COVID-19’s impact on their economic/financial well-being, 67.6% reported a score of >5 (mean 5.79, SD 3.01). 90.8% of respondents were under mandatory social distancing, and 90.6% scored >5 (mean 8.37, SD 2.5) in support of social distancing policies to prevent the spread of COVID-19.
EQ-5D-5L
Among ages 18-24 (n=198), the mean (SD) utility value was 0.752 (0.281), significantly lower compared to pre-pandemic (0.921 (0.124), p=0.01), online (0.844 (0.184), p<0.001), and face-to-face EQ-5D-5L norms (0.919 (0.127), p<0.001). Among ages 25-34 (n=817), utility was significantly worse compared to face-to-face norms (0.825 (0.235) vs. 0.911 (0.111), p<0.001); no significant differences were seen vs. online norms. Among ages 35-64 (n=1,488), utility values were higher during-pandemic but only vs. online norms; there were no significant differences compared to pre-pandemic and face-to-face samples. At age 65+ (n=248), utility values (0.827 (0.213)) were nearly identical across all samples.
For the VAS, all age groups except age 45-54 had significantly worse scores compared to face-to-face norms. Only ages 18-24 reported significantly worse mean VAS scores compared to online norms (73.1 vs. 79.9, p=0.001), and ages 25-34 reported significantly better scores compared to pre-pandemic (76.6 vs. 60.8, p=0.008). Pre-pandemic sample sizes for other age groups were too small (n<5) to draw meaningful inferences. All EQ-5D-5L and VAS comparisons between the MTurk sample and online and face-to-face samples are stratified by age group in Table 2.
Differences appear to be driven by the anxiety/depression dimension of the EQ-5D-5L, which was worse during-pandemic vs. either norm (Figure 1). This was especially pronounced among females and “other” gendered persons (Supplemental Figure 1). When stratified by BMI, those who were underweight or obese experienced the most severe/extreme anxiety/depression (Supplemental Figure 2).
Predictors of EQ-5D-5L Utility
Table 3 displays the standard OLS regression results along with E-values for the point estimates and their confidence interval limits closer to the null. Compared to males, “other” gendered persons have significantly lower utility scores, whereas females and “prefer not to say” gendered persons differ non-significantly from males. Being 25+ years old was significantly associated (p<0.025) with higher EQ-5D-5L utility relative to ages 18-24. Asian, American Indian or Alaska Native race was significantly associated with lower utility compared to being white; other race groups differed non-significantly from whites. Hispanic ethnicity was also significantly associated with lower utility, as was being married, compared to being single. Annual income levels >$35,000 were associated with significant increases in utility compared to annual incomes less than $20,000. Living alone, experiencing COVID-19-like symptoms not requiring hospitalization, and having a family member diagnosed with COVID-19 (n=187) were significantly associated with lower utility. Self-reported fear of COVID-19's impact on personal health (1-10 scale) was negatively and significantly correlated with utility.
Table 4 displays the post-lasso OLS regression median bootstrap estimates, bootstrap standard errors, bootstrap confidence intervals, and corresponding E-values. Estimates for predictors appearing both in the standard OLS and post-lasso OLS are largely similar. All additional predictors selected by lasso—arthritis (n=244), diabetes (n=181), self-reported depression (n=541), stroke (n=28) interacted with fear of COVID-19's impact on health (1-10 scale), and underweight BMI (n=120) interacted with residing in California (n=274)—are significantly associated with lower utility. As with gender, the significant interaction between underweight BMI and California residence is driven mainly by self-reported diagnosis of anxiety/depression, which is distinct from the EQ-5D question on anxiety/depression (Supplemental Figure 2).
Results of the F-test indicate the additional coefficients estimated by the post-lasso OLS significantly improve the model’s ability to predict EQ-5D utility (see technical appendix). Based on the estimated variance-covariance matrix from the bootstrap estimates, we are confident that normal approximation for the coefficients is robust and inference is normal (Supplemental Table 1, Supplemental Figure 1).33
Post-lasso OLS estimates for annual income over $150,000, arthritis, self-reported depression, and underweight BMI interacted with residing in California are most robust against bias from an unobserved confounder, all with E-value confidence limits >2.32 Relatively strong unmeasured confounding (RR>2.9) would be required to attenuate modeled effects.
Population QALY Loss
When extrapolated to the US population, we calculated an overall loss of 2.6 million QALYs compared to the pre-pandemic sample, a gain of 3.5 million QALYs compared to the online norm, and a loss of 8.4 million QALYs compared to the face-to-face norm. After dividing these values by life expectancy for each age group, we calculated an overall average gain of 18,385 lives at the expense of those aged 18-34. This was driven primarily by younger age groups, with average lives lost of 77,343 and 32,449 for 18-24 and 25-34 years old, respectively (Table 5).