Table 1.
Coefficient Estimates Using Ordinary Least Squares Regression.a
| Variable | Estimate | 95% CI | P Value |
|---|---|---|---|
| Spatial lag | 0.5361 | (0.4915, 0.5808) | <.0001 |
| Atmospheric pressure, hPa) | 0.0358 | (0.0267 to 0.0450) | |
| Sun, kJ/m2 | 0.0004 | (0.0001 to 0.0006) | .0043 |
| Precipitation, mm | −0.0213 | (−0.5398 to 0.4972) | .9359 |
| Male, % | −0.0721 | (−0.2162 to 0.0720) | .3268 |
| Hispanic, % | −0.0656 | (−0.1007 to −0.0305) | .0003 |
| Caucasian, % | 0.0001 | (−0.0283 to 0.0285) | .9940 |
| Poverty, % | 0.0691 | (−0.0134 to 0.1516) | .1007 |
| Population density, people per km2 | 0.0000 | (−0.0004 to 0.0004) | .9559 |
| Current smoker, % | 1.5324 | (1.4103 to 1.6545) | <.0001 |
| Obese, % | −0.3579 | (−0.5230 to −0.1928) | <.0001 |
| Diabetic, % | 0.4236 | (−0.0140 to 0.8612) | .05771 |
| PM2.5, µg/m3 | −0.0906 | (−0.2577 to 0.0765) | .28753 |
| Adjusted R 2 = .69 | |||
| AIC = 20582.80 | |||
Abbreviations: AIC, Akaike information criteria; CI, confidence interval.
aCoefficient estimates were simultaneously computed for data from 2974 counties.