Table 2.
Multivariable regression model of depression search intent in relation to environmental and geographic risk factors (R2=0.57).
| Variablesa | Coefficients | Standard error | t statistic | P value | Adjusted P valueb | r |
| Intercept | 94.9 | 12.2 | 7.8 | <.001 | <.001 | -c |
| Temperature | 0.0 | 0.1 | –0.3 | .74 | .99 | –0.5 |
| Humidity | 0.0 | 0.1 | 0.1 | .89 | .99 | 0.2 |
| Air Quality Index | 0.4 | 0.1 | 3.2 | .002 | .01 | 0.3 |
| Urban % | –0.1 | 0.0 | –2.7 | .01 | .06 | 0.3 |
| Sunshine % | –9.0 | 13.1 | –0.7 | .50 | .99 | –0.5 |
| Southd | –6.3 | 1.9 | –3.2 | .002 | .01 | –0.2 |
| West | –4.4 | 1.8 | –2.5 | .02 | .11 | –0.3 |
| Midwest | –3.8 | 1.6 | –2.4 | .02 | .12 | 0.1 |
aMultivariable regression model using environmental and geographic risk variables to predict depression search intent. Environmental and geographic data sets were collected as an average from 1971 to 2000 and 2008 to 2019, respectively (n=50). This model predicts depression search intent for each state based on the state's average annual temperature, humidity, air quality, urban %, sunshine %, and US census region.
bBonferroni correction for 6 independent analyses on the dependent variable (alpha=.05).
cNot applicable.
dRelative to the Northeast.