Table 4.
Multilevel logistic regression of arthritis-attributable moderate/severe joint pain on individual-level and state-level factors, among adults aged 25 to 80 years.
| Model 1 | Model 2 | Model 3 | ||||
|---|---|---|---|---|---|---|
| OR (95% CI) | OR (95% CI) | OR (95% CI) | ||||
| Individual-level factors | ||||||
| Education (ref: BA+) | ||||||
| <HS | 5.381*** | 4.923-5.881 | 5.376*** | 4.918-5.876 | ||
| HS/SC | 2.694*** | 2.565-2.829 | 2.674*** | 2.542-2.812 | ||
| Age | 1.032*** | 1.031-1.034 | 1.029*** | 1.027-1.031 | 1.029*** | 1.027-1.031 |
| Sex (ref: male) | ||||||
| Female | 1.692*** | 1.625-1.762 | 1.732*** | 1.666-1.801 | 1.732*** | 1.666-1.801 |
| Race (ref: Whites) | ||||||
| Blacks | 1.749*** | 1.621-1.887 | 1.518*** | 1.416-1.627 | 1.507*** | 1.406-1.616 |
| Hispanics | 1.201** | 1.070-1.348 | 0.785** | 0.681-0.904 | 0.783** | 0.681-0.901 |
| Other | 1.248*** | 1.131-1.378 | 1.220*** | 1.113-1.337 | 1.224*** | 1.121-1.336 |
| State-level factors | ||||||
| Percentage of immigrants | 0.864*** | 0.801-0.931 | 0.922** | 0.872-0.976 | 0.855*** | 0.800-0.914 |
| EITC | 1.008 | 0.958-1.060 | ||||
| SNAP | 0.925*** | 0.963-0.957 | ||||
| MGS | 1.022 | 0.953-1.097 | ||||
| Gini | 1.040 | 0.981-1.102 | ||||
| SCI | 0.819*** | 0.748-0.896 | ||||
| Tobacco | 1.020 | 0.956-1.090 | ||||
| Random effects | ||||||
| State (variance) | 0.078 | 0.053-0.115 | 0.067 | 0.047-0.098 | 0.021 | 0.012-0.037 |
| Education (variance) | ||||||
| <HS | 0.013 | 0.004-0.040 | 0.016 | 0.006-0.042 | ||
| HS/SC | 0.003 | 0.001-0.011 | 0.002 | 0.000-0.010 | ||
†P < 0.1. *P < 0.05. **P < 0.01. ***P < 0.001.
All models are sample weight adjusted. All state-level variables are standardized as Z-scores.
<HS, below high school; HS/SC, high school or some college; BA+, bachelor's degree or above; CI, confidence interval; EITC, Earned Income Tax Credit; Gini, Gini index; MGS, Medicaid Generosity Score; OR, odds ratio; SCI, Social Capital Index; SNAP, Supplemental Nutrition Assistance Program; Tobacco, tobacco taxes. State-level factors significantly predicting joint pain are shown in bold.