Table 3.
Parameter estimates from the three multilevel regression models examining associations of medical expenses and length of disability with neighborhood and state socioeconomic characteristics
| Medical expenses | Length of disability | |||||
|---|---|---|---|---|---|---|
| Fixed parameter estimates | Model 1 | Model 2 | Model 3 | Model 1 | Model 2 | Model 3 |
| Intercept | 8.288*** | 8.373*** | 7.659*** | 3.835*** | 3.848*** | 3.252*** |
| Neighborhood‐level variables | ||||||
| Median household income (10 000 $) | 0.025*** (RS e < 0.001*) | −0.006** (RS < 0.001) | ||||
| Rural population (%) | −0.001*** (RS < 0.001) | <0.001 (RS < 0.001) | ||||
| White population (%) | −0.001*** (RS < 0.001) | <−0.001 (RS < 0.001) | ||||
| Educational attainment (% < some college) | 0.003*** (RS < 0.001) | <0.001 (RS < 0.001) | ||||
| State‐level variables | ||||||
| Population below 100% poverty (%) | −0.004 | −0.006 | −0.002 | −0.005 | ||
| Unemployment rate | 0.045*** | 0.038*** | 0.018* | 0.016** | ||
| Gini coefficient (%) | 0.002 | −0.007 | 0.005 | −0.001 | ||
| Disabled workers receiving SSDI a (%) | −0.092*** | −0.130*** | 0.012 | −0.012 | ||
| Variance components | ||||||
| Within‐state variability | 1.823*** | 1.819*** | 0.965*** | 1.393*** | 1.390*** | 0.899*** |
| Between‐state variability | 0.087*** | 0.092*** | 0.064*** | 0.072*** | 0.054*** | 0.041*** |
| ICC b | 5% | 5% | ||||
| Proportional reduction in between‐state variability in outcome measure | 26% | 43% | ||||
| Model fit statistic | ||||||
| −2LL c | 204 200 | 204 109*** | 167 140*** | 188 306 | 188 208*** | 162 865*** |
| BIC d | 204 208 | 204 116*** | 167 178*** | 188 310 | 188 215*** | 162 904*** |
Social Security Disability Insurance.
Intra‐class Correlation (percentage of total variability in medical expenses or work disability length that is explained by between‐state variability).
−2 log likelihood ratio.
Bayesian Information Criterion; *P < 0.05; **P < 0.01; ***P < 0.001. Parameter estimates in model 2 are also adjusted for state Workers’ Compensation policy variables (wage replacement rate, waiting period, retroactive period, medical fee schedule, treating provider choice, and treating provider change). Parameter estimates in model 3 are adjusted for age, gender, tenure, average weekly wage, industry type, injury severity, early opioid prescribing, early magnetic resonance imaging prescribing, lumbar spine surgery, and claim litigation status.
Random slope (examine if the relationship between each neighborhood‐level variable and the outcome measure vary significantly between states).