Appendix Table D.2.
Regression Coefficients From Supplemental Sensitivity Models Using 2010–2014 ACS Estimates
| Model 3 | Model 4 | |||
|---|---|---|---|---|
| Variable | Est. (SE) | p-value | Est. (SE) | p-value |
| Economic environment | ||||
| Economic distress index | 12.55 (1.60) | <0.001 | 9.62 (1.47) | <0.001 |
| Housing distress | ||||
| Vacant housing units, % | 0.87 (1.20) | 0.468 | 2.79 (1.11) | 0.012 |
| Rent >30% of household income, % | 2.51 (1.11) | 0.024 | 3.31 (1.08) | 0.002 |
| Labor market dependence | ||||
| Non-specialized (ref) | 0.00 | 0.00 | ||
| Services | 4.99 (2.65) | 0.060 | 3.95 (2.36) | 0.094 |
| Public sector | –9.27 (2.56) | <0.001 | –7.37 (2.29) | 0.001 |
| Manufacturing | –3.69 (1.89) | 0.050 | –3.33 (1.68) | 0.048 |
| Mining | 13.09 (4.05) | 0.001 | 12.77 (3.69) | 0.001 |
| Farming | –9.74 (3.71) | 0.010 | –4.02 (3.24) | 0.215 |
| Social environment | ||||
| Family distress index | 7.14 (1.35) | <0.001 | 8.73 (1.22) | <0.001 |
| Residents living in different county 5 years ago, % | –1.86 (1.05) | 0.077 | –2.87 (0.95) | 0.003 |
| Social capital | ||||
| Religious establishments per 10,000 population | ||||
| Bottom 25th percentile (ref) | 0.00 | 0.00 | ||
| 25th–50th percentile | –3.47 (2.13) | 0.104 | –2.06 (1.94) | 0.288 |
| 50th–75th percentile | –7.92 (2.46) | 0.001 | –6.33 (2.27) | 0.005 |
| Top 25th percentile | –8.79 (2.93) | 0.003 | –6.08 (2.70) | 0.024 |
| Nonprofit organizations per 10,000 population | ||||
| Bottom 25th percentile (ref) | 0.00 | 0.00 | ||
| 25th–50th percentile | –0.24 (2.21) | 0.912 | –2.18 (1.98) | 0.272 |
| 50th–75th percentile | –0.62 (2.82) | 0.826 | –4.29 (2.52) | 0.089 |
| Top 25th percentile | –0.25 (3.86) | 0.949 | –5.86 (3.35) | 0.080 |
| Membership associations per 10,000 population | ||||
| Bottom 25th percentile (ref) | 0.00 | 0.00 | ||
| 25th–50th percentile | 1.21 (2.02) | 0.548 | 1.44 (1.82) | 0.430 |
| 50th–75th percentile | 3.44 (2.30) | 0.135 | 3.69 (2.15) | 0.087 |
| Top 25th percentile | 6.25 (2.74) | 0.023 | 8.39 (2.61) | 0.001 |
| Sports establishments per 10,000 population | ||||
| Bottom 25th percentile (ref) | 0.00 | 0.00 | ||
| 25th–50th percentile | 3.18 (2.06) | 0.122 | 2.10 (1.82) | 0.248 |
| 50th–75th percentile | 3.04 (2.22) | 0.172 | 1.65 (2.03) | 0.415 |
| Top 25th percentile | –1.14 (2.59) | 0.661 | –3.08 (2.41) | 0.203 |
| Healthcare environment | ||||
| Physicians per 10,000 population | 2.07 (0.87) | 0.017 | 2.26 (0.79) | 0.004 |
| Primary healthcare professional shortage area | –2.57 (1.53) | 0.093 | –2.26 (1.45) | 0.120 |
| Mental healthcare professional shortage area | 0.49 (1.87) | 0.793 | 1.84 (1.72) | 0.283 |
| Population characteristics | ||||
| Nonmetro county | –10.46 (1.92) | <0.001 | –9.58 (1.76) | <0.001 |
| Age >65 years, % | 6.12 (1.20) | <0.001 | 6.48 (1.10) | <0.001 |
| Military veterans, % | 2.23 (0.92) | 0.015 | 2.60 (0.81) | 0.001 |
| Black population, % | ||||
| Bottom 25th percentile (ref) | 0.00 | 0.00 | ||
| 25th–50th percentile | 4.41 (2.09) | 0.035 | 5.69 (2.00) | 0.004 |
| 50th–75th percentile | 0.72 (2.63) | 0.784 | 3.13 (2.38) | 0.188 |
| Top 25th percentile | –15.06 (3.57) | <0.001 | –11.64 (3.20) | <0.001 |
| Hispanic population, % | ||||
| Bottom 25th percentile (ref) | 0.00 | 0.00 | ||
| 25th–50th percentile | 2.92 (2.18) | 0.181 | 2.26 (2.10) | 0.282 |
| 50th–75th percentile | 3.15 (2.56) | 0.217 | 2.03 (2.42) | 0.401 |
| Top 25th percentile | –1.04 (3.26) | 0.749 | –3.70 (3.07) | 0.228 |
| American Indian population, % | ||||
| Bottom 25th percentile (ref) | 0.00 | 0.00 | ||
| 25th–50th percentile | –1.09 (1.93) | 0.573 | 0.88 (1.79) | 0.624 |
| 50th–75th percentile | 1.73 (2.18) | 0.428 | 2.60 (2.05) | 0.204 |
| Top 25th percentile | 6.69 (2.67) | 0.012 | 7.12 (2.56) | 0.005 |
| Spatial autocorrelation parameter | 0.524 (0.027) | <0.001 | 0.630 (0.03) | <0.001 |
| N | 3,106 | 2,484 | ||
Notes: Boldface indicates statistical significance (p<0.05). All models control for state fixed effects. Model 3 is the same as Model 1 (N=3,106), but uses temporally proximate/overlapping demographic, economic distress, housing distress, and family distress variables from the American Community Survey 5-year estimates (2010–2014). These estimates represent more current conditions, whereas Model 1 is based on county conditions from 2000. The direction and significance (p<0.05) of coefficients are generally consistent between Model 1 and Model 3, and when there are differences in significance, it is usually because the p-value was between 0.05 and 0.10, thereby failing to meet the conventional p<0.05 threshold. There was one important difference that reflects the importance of current economic conditions on drug mortality rates. The magnitude of the economic distress coefficient is nearly twice as large in Model 3 compared to Model 1, suggesting that economic conditions from 2000, while important, may be underestimating the true impact of economic distress on county mortality rates. Importantly, this differences could also be the result of reverse causality bias; high rates of substance abuse could lead to increases in poverty, unemployment, and disability rates, for example. The only other major difference is that counties in the top quartile of membership associations had a significantly higher average AAMR than counties in the bottom quartile in this model. It is unclear why substituting economic, demographic, and family predictors from 2000 with those from 2010–2014 would result in this change. T-tests showed that counties in the top quartile of non-profit associations had lower average economic distress in both 2000 and 2010–2014, higher average percentages of vacant housing units, lower average rental stress, lower average family distress, and higher average percent aged >65 years in both 2000 and 2010–2014. The only difference between these two time periods was that percent veterans in 2000 was higher in counties in the top quartile of non-profit associations, but percent veteran in 2010–2014 was significantly lower in counties in the top quartile of nonprofit organizations than in the other 75% of counties. Model 4 is the same as Model 3, but does not use multiple imputation. This model is conducted only on complete cases (i.e., counties with at least ten drug-related deaths, N=2,484) and uses the 2010–2014 ACS estimates. Again, model results are consistent with the exception that vacant housing units is positive and significant in Model 4. This is because counties with suppressed mortality rates had a significantly higher average percentage of vacant housing units in 2010–2014 than counties without suppressed mortality rates. Excluding these counties from the analysis overestimates the relationship between vacant housing units and drug mortality rates.
Est., Estimate; AAMR, Age-Adjusted Mortality Rate