TABLE 2—
Three Models Evaluating Predictors of Work-Related Traumatic Injuries by Illinois Zip Codes Based on Trauma Registry Data: 2000–2009
| Model 1a |
Model 2b |
Model 3c |
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| Variable | b (95% CI) | P | b (95% CI) | P | b (95% CI) | P |
| Fixed effects | ||||||
| Intercept | −0.67 (−1.06, −0.29) | < .001 | −1.82 (−2.23, −1.41) | < .001 | ||
| Home ownership rate | −0.05 (-0.06, −0.03) | < .001 | 0.02 (0.00, 0.04) | .023 | 0.92 (0.90, 0.95) | < .001 |
| Employed to population ratio | 0.01 (0.01, 0.02) | < .001 | 0.00 (−0.01, 0.01) | .932 | 1.01 (1.01, 1.02) | < .001 |
| Urban poverty componentd | 0.00 (−0.03, 0.02) | .764 | −0.16 (−0.23, −0.10) | < .001 | 0.64 (0.59, 0.69) | < .001 |
| Immigrants componente | 0.39 (0.33, 0.44) | < .001 | 0.04 (−0.02, 0.11) | .198 | 1.66 (1.58, 1.74) | < .001 |
| % with ≤ high school diploma | −0.01 (−0.01, 0.00) | < .001 | 0.02 (0.02, 0.02) | < .001 | 1.00 (1.00, 1.00) | .34 |
| Zero inflated model estimates | ||||||
| Intercept | −9.46 (−12.61, −6.31) | < .001 | −6.46 (−7.09, −5.83) | < .001 | . . . | . . . |
| Urban poverty componentd | −19.85 (−27.22, −12.48) | < .001 | −15.64 (−17.16, −14.13) | < .001 | . . . | . . . |
| Immigrants componente | −1.13 (−1.92, −0.34) | .005 | −1.24 (−1.53, −0.96) | < .001 | . . . | . . . |
| σ2 (random intercept for mixed model) | 1.68 (1.46, 1.90) | < .001 | 1.44 (1.28, 1.60) | < .001 | . . . | . . . |
| α (negative binomial) | 0.07 (0.06, 0.09) | < .001 | . . . | . . . | . . . | |
Note. CI = confidence interval. The parameter estimates have not been converted for models 1 and 2. To determine percent change in injuries per unit change in the independent variable, take the exponent of the parameter estimate. For example, for each 1-U change in home ownership rates, the count of injuries changes by a factor of 0.95 (exp-0.05).
Number of work-related injuries, by zip code: zero inflated negative binomial distribution model with random effects.
Rate of work-related injuries per 1000 employed, by zip code: zero inflated Poisson model with random effects.
Spatial cluster data of work-related injuries per 1000 employed, by zip code: logistic regression model, fixed effects only.
The urban poverty component was based on factor analysis weights, including violent crime rate, percentage vacant housing units, percentage below the poverty line, percentage African American.
The immigrants component was based on factor analysis weights, including percentage foreign-born, percentage veterans in the US military (inversely correlated), and percentage Hispanic.