Table 2B.
Adjusted odds ratios (AORs) and 95% confidence intervals (CIs) in logistic regressions predicting perceived need for and receipt of general and behavioral healthcare in the 2021 NAS COVID Follow-up Survey.1
| (among those with need)2 Delayed care due to: |
(among those who received care)2 Used telehealth |
|||||||
|---|---|---|---|---|---|---|---|---|
| Lockdown/office closed/ fear |
Cost |
General / check-up (N = 864) |
Behavioral health (N = 238) |
|||||
| OR | 95% CI | OR | 95% CI | OR | 95% CI | OR | 95% CI | |
| Age | ||||||||
| 18–34 | 1.13 | (0.73, 1.75) | 3.42** | (1.38, 8.50) | 0.98 | (0.63, 1.53) | 0.47 | (0.15, 1.53) |
| 35–49 | 1.09 | (0.70, 1.70) | 2.16 | (0.74, 6.28) | 1.46ⱡ | (0.94, 2.26) | 1.00 | (0.26, 3.76) |
| 50+ | Ref | Ref | Ref | Ref | ||||
| Gender | ||||||||
| Female | 1.03 | (0.72, 1.48) | 0.65 | (0.36, 1.17) | 1.71** | (1.17, 2.50) | 2.10 | (0.78, 5.67) |
| Male | Ref | Ref | Ref | Ref | ||||
| Race | ||||||||
| White | Ref | Ref | Ref | Ref | ||||
| Black/Afr Am | 0.68 | (0.40, 1.16) | 0.81 | (0.33, 1.99) | 1.14 | (0.64, 2.03) | 0.36 | (0.11, 1.21) |
| Hispanic/Latinx | 0.80 | (0.45, 1.41) | 1.22 | (0.50, 3.02) | 1.69ⱡ | (0.92, 3.11) | 11.96*, 3 | (1.07, 133.40) |
| Other groups | 0.88 | (0.50, 1.58) | 1.01 | (0.46, 2.19) | 1.27 | (0.57, 2.81) | NA4 | |
| Insurance type, T1 | ||||||||
| Private/other | Ref | |||||||
| Public | 1.08 | (0.46, 2.54) | ||||||
| Uninsured | 4.58*** | (2.06, 10.18) | ||||||
| Household income as % of FPL at T1 | ||||||||
| ≤138% FPL | 1.40 | (0.51, 3.87) | ||||||
| 139–400% FPL | 2.20* | (1.02, 4.75) | ||||||
| >400% FPL | Ref | |||||||
| Self-rated health, T1 | ||||||||
| Fair/poor | 2.18* | (1.20, 3.96) | ||||||
| Good/excellent | Ref | |||||||
| Urbanicity, T1 | ||||||||
| Urban | Ref | Ref | ||||||
| Rural | 3.12** | (1.59, 6.13) | 0.30ⱡ | (0.09, 1.01) | ||||
| Work/pay reduced | ||||||||
| Yes | 1.40 | (0.92, 2.12) | 2.62** | (1.40, 4.92) | ||||
| No | Ref | Ref | ||||||
| Usual source of primary care, T1 | ||||||||
| Yes | Ref | Ref | ||||||
| No | 1.45 | (0.91, 2.30) | 1.36 | (0.68, 2.74) | ||||
| Region of residence, T1 | ||||||||
| Northeast | Ref | Ref | ||||||
| Midwest | 0.51* | (0.27, 0.96) | 0.21 | (0.03, 1.69) | ||||
| Pacific | 1.26 | (0.60, 2.65) | 1.51 | (0.12, 19.59) | ||||
| South | 0.55ⱡ | (0.30, 1.01) | 0.25 | (0.03, 1.91) | ||||
| Mountain | 1.26 | (0.51, 3.12) | 0.26 | (0.02, 3.34) | ||||
ⱡp < 0.10, *p < 0.05, **p < 0.01, ***p < 0.001.
FPL, US federal poverty level.
T1, the N14 (baseline) survey.
Each outcome corresponds to a logistic regression model that includes gender, age, race/ethnicity and any other predictor that was found to be at least marginally significant (p < 0.10) in Table 1 bivariate analysis; when adjusted ORs are not present for a given variable, it means the variable was not included in the model (but was used in some other model whose results are presented in this table).
See footnote 1 in Table 1 for analytic sample n's.
This high OR for Hispanic/Latinx vs. white respondents (adjusted OR = 11.96) is largely due to very high telehealth use for behavioral health among Hispanic/Latinx respondents (26 out of 27 reported that they received tele-behavioral healthcare). Given the large confidence interval, we conducted analysis of marginal effects comparing the predicted probability of telehealth use for behavioral health, adjusting for covariates. This additional analysis showed the predicted Hispanic-white difference in telehealth use is +15.9% (95% CI: 7.4%, 24.3%); that is, Hispanic/Latinx respondents had a nearly 16 percentage point greater predicted prevalence of tele-behavioral healthcare receipt relative to white respondents. Results available upon request.
OR estimate for “other” race/ethnicity is not available as all 17 respondents in this group reported using telehealth, and thus were dropped from the regression model.