Temperature Regressions

Preliminary Regressions

Overall Effect

Version 1:

Extreme Heat defined as COUNTY-YEAR in which there are 150 or more days greater than 80 degrees F Extreme Cold defined as COUNTY-YEAR in which there are 100 or more days less than 30 degrees F

## [1] "Regression summary for outcome variable: BIRTH_WT_LESS2500_PCT"
## Twoways effects Within Model
## 
## Call:
## plm(formula = formula, data = d_reg, effect = "twoways", model = "within", 
##     index = c("COUNTY", "year"))
## 
## Unbalanced Panel: n = 782, T = 1-531, N = 10492
## 
## Residuals:
##      Min.   1st Qu.    Median   3rd Qu.      Max. 
## -60.46423  -4.32770  -0.98921   1.66105  92.19344 
## 
## Coefficients:
##          Estimate Std. Error t-value Pr(>|t|)
## EXT_HEAT -1.58368    1.30966 -1.2092   0.2266
## EXT_COLD -0.63717    0.73269 -0.8696   0.3845
## 
## Total Sum of Squares:    1455000
## Residual Sum of Squares: 1454700
## R-Squared:      0.00022797
## Adj. R-Squared: -0.0813
## F-statistic: 1.1059 on 2 and 9700 DF, p-value: 0.33095
## [1] "-------------------------------------"
## Oneway (individual) effect Within Model
## 
## Call:
## plm(formula = formula, data = d_reg, effect = "individual", model = "within", 
##     index = c("COUNTY", "year"))
## 
## Unbalanced Panel: n = 782, T = 1-531, N = 10492
## 
## Residuals:
##      Min.   1st Qu.    Median   3rd Qu.      Max. 
## -60.31420  -4.35681  -0.99527   1.70345  92.17461 
## 
## Coefficients:
##          Estimate Std. Error t-value Pr(>|t|)
## EXT_HEAT -1.57098    1.29469 -1.2134   0.2250
## EXT_COLD -0.92085    0.70637 -1.3036   0.1924
## 
## Total Sum of Squares:    1458200
## Residual Sum of Squares: 1457800
## R-Squared:      0.00032662
## Adj. R-Squared: -0.080302
## F-statistic: 1.58591 on 2 and 9708 DF, p-value: 0.20481
## [1] "-------------------------------------"
## [1] "Regression summary for outcome variable: HYPERTENSION_PCT"
## Twoways effects Within Model
## 
## Call:
## plm(formula = formula, data = d_reg, effect = "twoways", model = "within", 
##     index = c("COUNTY", "year"))
## 
## Unbalanced Panel: n = 807, T = 1-593, N = 12106
## 
## Residuals:
##       Min.    1st Qu.     Median    3rd Qu.       Max. 
## -47.517435  -4.340556  -0.030785   4.349863  64.343098 
## 
## Coefficients:
##           Estimate Std. Error t-value Pr(>|t|)  
## EXT_HEAT  2.070314   0.820404  2.5235  0.01163 *
## EXT_COLD -0.027162   0.471275 -0.0576  0.95404  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Total Sum of Squares:    814050
## Residual Sum of Squares: 813590
## R-Squared:      0.00056399
## Adj. R-Squared: -0.071678
## F-statistic: 3.18523 on 2 and 11289 DF, p-value: 0.041406
## [1] "-------------------------------------"
## Oneway (individual) effect Within Model
## 
## Call:
## plm(formula = formula, data = d_reg, effect = "individual", model = "within", 
##     index = c("COUNTY", "year"))
## 
## Unbalanced Panel: n = 807, T = 1-593, N = 12106
## 
## Residuals:
##       Min.    1st Qu.     Median    3rd Qu.       Max. 
## -48.673980  -4.549021  -0.089923   4.560698  63.326637 
## 
## Coefficients:
##          Estimate Std. Error t-value Pr(>|t|)   
## EXT_HEAT  1.22045    0.83046  1.4696 0.141697   
## EXT_COLD -1.24985    0.46573 -2.6836 0.007293 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Total Sum of Squares:    857690
## Residual Sum of Squares: 856980
## R-Squared:      0.000828
## Adj. R-Squared: -0.070636
## F-statistic: 4.68082 on 2 and 11297 DF, p-value: 0.0092894
## [1] "-------------------------------------"
## [1] "Regression summary for outcome variable: DIABETES_PCT"
## Twoways effects Within Model
## 
## Call:
## plm(formula = formula, data = d_reg, effect = "twoways", model = "within", 
##     index = c("COUNTY", "year"))
## 
## Unbalanced Panel: n = 807, T = 1-593, N = 12115
## 
## Residuals:
##      Min.   1st Qu.    Median   3rd Qu.      Max. 
## -42.31025  -5.17109  -0.59758   3.84418  71.47023 
## 
## Coefficients:
##          Estimate Std. Error t-value Pr(>|t|)
## EXT_HEAT -0.56156    1.03629 -0.5419   0.5879
## EXT_COLD -0.32351    0.59325 -0.5453   0.5855
## 
## Total Sum of Squares:    1290500
## Residual Sum of Squares: 1290500
## R-Squared:      5.2495e-05
## Adj. R-Squared: -0.072169
## F-statistic: 0.296558 on 2 and 11298 DF, p-value: 0.74338
## [1] "-------------------------------------"
## Oneway (individual) effect Within Model
## 
## Call:
## plm(formula = formula, data = d_reg, effect = "individual", model = "within", 
##     index = c("COUNTY", "year"))
## 
## Unbalanced Panel: n = 807, T = 1-593, N = 12115
## 
## Residuals:
##      Min.   1st Qu.    Median   3rd Qu.      Max. 
## -42.45823  -5.27218  -0.59796   4.05615  69.91911 
## 
## Coefficients:
##          Estimate Std. Error t-value Pr(>|t|)
## EXT_HEAT -0.80156    1.03251 -0.7763   0.4376
## EXT_COLD -0.73188    0.57696 -1.2685   0.2046
## 
## Total Sum of Squares:    1316800
## Residual Sum of Squares: 1316500
## R-Squared:      0.00019559
## Adj. R-Squared: -0.071257
## F-statistic: 1.10589 on 2 and 11306 DF, p-value: 0.33095
## [1] "-------------------------------------"
## [1] "Regression summary for outcome variable: TOTAL_COST_PERCAP"
## Twoways effects Within Model
## 
## Call:
## plm(formula = formula, data = d_reg, effect = "twoways", model = "within", 
##     index = c("COUNTY", "year"))
## 
## Unbalanced Panel: n = 807, T = 1-593, N = 12123
## 
## Residuals:
##      Min.   1st Qu.    Median   3rd Qu.      Max. 
## -2617.537  -207.341   -29.286    91.610 28531.633 
## 
## Coefficients:
##          Estimate Std. Error t-value Pr(>|t|)
## EXT_HEAT  -10.499     75.943 -0.1383   0.8900
## EXT_COLD   37.440     43.584  0.8590   0.3903
## 
## Total Sum of Squares:    6983300000
## Residual Sum of Squares: 6982900000
## R-Squared:      6.6872e-05
## Adj. R-Squared: -0.072102
## F-statistic: 0.378052 on 2 and 11306 DF, p-value: 0.6852
## [1] "-------------------------------------"
## Oneway (individual) effect Within Model
## 
## Call:
## plm(formula = formula, data = d_reg, effect = "individual", model = "within", 
##     index = c("COUNTY", "year"))
## 
## Unbalanced Panel: n = 807, T = 1-593, N = 12123
## 
## Residuals:
##      Min.   1st Qu.    Median   3rd Qu.      Max. 
## -2502.827  -255.136   -55.888   135.958 28345.314 
## 
## Coefficients:
##          Estimate Std. Error t-value Pr(>|t|)
## EXT_HEAT   56.881     77.312  0.7357   0.4619
## EXT_COLD  -37.071     43.316 -0.8558   0.3921
## 
## Total Sum of Squares:    7440200000
## Residual Sum of Squares: 7439400000
## R-Squared:      0.00011257
## Adj. R-Squared: -0.071295
## F-statistic: 0.636869 on 2 and 11314 DF, p-value: 0.52897
## [1] "-------------------------------------"
## [1] "Regression summary for outcome variable: TOTAL_COST_LOG"
## Twoways effects Within Model
## 
## Call:
## plm(formula = formula, data = d_reg, effect = "twoways", model = "within", 
##     index = c("COUNTY", "year"))
## 
## Unbalanced Panel: n = 807, T = 1-593, N = 12123
## 
## Residuals:
##        Min.     1st Qu.      Median     3rd Qu.        Max. 
## -16.3232693  -0.2638019   0.0084061   0.3393383   4.1788643 
## 
## Coefficients:
##            Estimate Std. Error t-value Pr(>|t|)
## EXT_HEAT -0.0026564  0.0855501 -0.0311   0.9752
## EXT_COLD -0.0028140  0.0490974 -0.0573   0.9543
## 
## Total Sum of Squares:    8861.3
## Residual Sum of Squares: 8861.3
## R-Squared:      3.771e-07
## Adj. R-Squared: -0.072174
## F-statistic: 0.00213175 on 2 and 11306 DF, p-value: 0.99787
## [1] "-------------------------------------"
## Oneway (individual) effect Within Model
## 
## Call:
## plm(formula = formula, data = d_reg, effect = "individual", model = "within", 
##     index = c("COUNTY", "year"))
## 
## Unbalanced Panel: n = 807, T = 1-593, N = 12123
## 
## Residuals:
##       Min.    1st Qu.     Median    3rd Qu.       Max. 
## -16.751926  -0.368985   0.029699   0.440354   4.544502 
## 
## Coefficients:
##           Estimate Std. Error t-value Pr(>|t|)
## EXT_HEAT  0.131999   0.088943  1.4841   0.1378
## EXT_COLD -0.058483   0.049832 -1.1736   0.2406
## 
## Total Sum of Squares:    9849.2
## Residual Sum of Squares: 9846.1
## R-Squared:      0.0003163
## Adj. R-Squared: -0.071077
## F-statistic: 1.7899 on 2 and 11314 DF, p-value: 0.16702
## [1] "-------------------------------------"

Version 2:

Extreme Heat is the variable BIN_GT90 (the number of days a COUNTY-YEAR was greater than 90 degrees F) Extreme Cold is the variable BIN_LT30 (the number of days a COUNTY-YEAR was less than 30 degrees F)

## [1] "Regression summary for outcome variable: BIRTH_WT_LESS2500_PCT"
## Twoways effects Within Model
## 
## Call:
## plm(formula = formula, data = d_reg, effect = "twoways", model = "within", 
##     index = c("COUNTY", "year"))
## 
## Unbalanced Panel: n = 782, T = 1-531, N = 10492
## 
## Residuals:
##      Min.   1st Qu.    Median   3rd Qu.      Max. 
## -60.17874  -4.33072  -0.98565   1.65755  92.24912 
## 
## Coefficients:
##            Estimate Std. Error t-value Pr(>|t|)
## EXT_HEAT -0.0109985  0.0237674 -0.4628   0.6436
## EXT_COLD -0.0064399  0.0155098 -0.4152   0.6780
## 
## Total Sum of Squares:    1455000
## Residual Sum of Squares: 1455000
## R-Squared:      3.7896e-05
## Adj. R-Squared: -0.081505
## F-statistic: 0.183802 on 2 and 9700 DF, p-value: 0.8321
## [1] "-------------------------------------"
## Oneway (individual) effect Within Model
## 
## Call:
## plm(formula = formula, data = d_reg, effect = "individual", model = "within", 
##     index = c("COUNTY", "year"))
## 
## Unbalanced Panel: n = 782, T = 1-531, N = 10492
## 
## Residuals:
##      Min.   1st Qu.    Median   3rd Qu.      Max. 
## -60.01990  -4.34743  -0.98868   1.66721  92.02887 
## 
## Coefficients:
##            Estimate Std. Error t-value Pr(>|t|)
## EXT_HEAT  0.0048995  0.0210641  0.2326   0.8161
## EXT_COLD -0.0208539  0.0129743 -1.6073   0.1080
## 
## Total Sum of Squares:    1458200
## Residual Sum of Squares: 1457800
## R-Squared:      0.00027664
## Adj. R-Squared: -0.080356
## F-statistic: 1.3432 on 2 and 9708 DF, p-value: 0.26106
## [1] "-------------------------------------"
## [1] "Regression summary for outcome variable: HYPERTENSION_PCT"
## Twoways effects Within Model
## 
## Call:
## plm(formula = formula, data = d_reg, effect = "twoways", model = "within", 
##     index = c("COUNTY", "year"))
## 
## Unbalanced Panel: n = 807, T = 1-593, N = 12106
## 
## Residuals:
##       Min.    1st Qu.     Median    3rd Qu.       Max. 
## -47.267145  -4.367846  -0.038455   4.353826  64.365942 
## 
## Coefficients:
##           Estimate Std. Error t-value Pr(>|t|)
## EXT_HEAT 0.0120475  0.0149032  0.8084   0.4189
## EXT_COLD 0.0031021  0.0099352  0.3122   0.7549
## 
## Total Sum of Squares:    814050
## Residual Sum of Squares: 813990
## R-Squared:      6.4495e-05
## Adj. R-Squared: -0.072214
## F-statistic: 0.364067 on 2 and 11289 DF, p-value: 0.69485
## [1] "-------------------------------------"
## Oneway (individual) effect Within Model
## 
## Call:
## plm(formula = formula, data = d_reg, effect = "individual", model = "within", 
##     index = c("COUNTY", "year"))
## 
## Unbalanced Panel: n = 807, T = 1-593, N = 12106
## 
## Residuals:
##      Min.   1st Qu.    Median   3rd Qu.      Max. 
## -48.68986  -4.49132  -0.12803   4.59477  63.32664 
## 
## Coefficients:
##            Estimate Std. Error t-value Pr(>|t|)    
## EXT_HEAT -0.0029003  0.0134219 -0.2161   0.8289    
## EXT_COLD -0.0727425  0.0085046 -8.5533   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Total Sum of Squares:    857690
## Residual Sum of Squares: 852160
## R-Squared:      0.0064481
## Adj. R-Squared: -0.064614
## F-statistic: 36.6582 on 2 and 11297 DF, p-value: < 2.22e-16
## [1] "-------------------------------------"
## [1] "Regression summary for outcome variable: DIABETES_PCT"
## Twoways effects Within Model
## 
## Call:
## plm(formula = formula, data = d_reg, effect = "twoways", model = "within", 
##     index = c("COUNTY", "year"))
## 
## Unbalanced Panel: n = 807, T = 1-593, N = 12115
## 
## Residuals:
##      Min.   1st Qu.    Median   3rd Qu.      Max. 
## -42.26522  -5.18010  -0.61426   3.83906  71.71708 
## 
## Coefficients:
##           Estimate Std. Error t-value Pr(>|t|)
## EXT_HEAT -0.022814   0.018761 -1.2161   0.2240
## EXT_COLD -0.020428   0.012484 -1.6363   0.1018
## 
## Total Sum of Squares:    1290500
## Residual Sum of Squares: 1290100
## R-Squared:      0.00035119
## Adj. R-Squared: -0.071849
## F-statistic: 1.98455 on 2 and 11298 DF, p-value: 0.13749
## [1] "-------------------------------------"
## Oneway (individual) effect Within Model
## 
## Call:
## plm(formula = formula, data = d_reg, effect = "individual", model = "within", 
##     index = c("COUNTY", "year"))
## 
## Unbalanced Panel: n = 807, T = 1-593, N = 12115
## 
## Residuals:
##      Min.   1st Qu.    Median   3rd Qu.      Max. 
## -42.53259  -5.29012  -0.59006   3.98653  70.88846 
## 
## Coefficients:
##           Estimate Std. Error t-value  Pr(>|t|)    
## EXT_HEAT -0.022423   0.016658 -1.3461    0.1783    
## EXT_COLD -0.064934   0.010539 -6.1614 7.454e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Total Sum of Squares:    1316800
## Residual Sum of Squares: 1312300
## R-Squared:      0.003419
## Adj. R-Squared: -0.067803
## F-statistic: 19.3941 on 2 and 11306 DF, p-value: 3.9054e-09
## [1] "-------------------------------------"
## [1] "Regression summary for outcome variable: TOTAL_COST_PERCAP"
## Twoways effects Within Model
## 
## Call:
## plm(formula = formula, data = d_reg, effect = "twoways", model = "within", 
##     index = c("COUNTY", "year"))
## 
## Unbalanced Panel: n = 807, T = 1-593, N = 12123
## 
## Residuals:
##      Min.   1st Qu.    Median   3rd Qu.      Max. 
## -2623.675  -207.099   -29.389    91.483 28516.764 
## 
## Coefficients:
##          Estimate Std. Error t-value Pr(>|t|)
## EXT_HEAT -0.74239    1.37821 -0.5387   0.5901
## EXT_COLD  1.34679    0.91745  1.4680   0.1421
## 
## Total Sum of Squares:    6983300000
## Residual Sum of Squares: 6981800000
## R-Squared:      0.00022361
## Adj. R-Squared: -0.071934
## F-statistic: 1.26433 on 2 and 11306 DF, p-value: 0.28247
## [1] "-------------------------------------"
## Oneway (individual) effect Within Model
## 
## Call:
## plm(formula = formula, data = d_reg, effect = "individual", model = "within", 
##     index = c("COUNTY", "year"))
## 
## Unbalanced Panel: n = 807, T = 1-593, N = 12123
## 
## Residuals:
##      Min.   1st Qu.    Median   3rd Qu.      Max. 
## -2493.007  -250.831   -56.256   132.796 28383.523 
## 
## Coefficients:
##          Estimate Std. Error t-value  Pr(>|t|)    
## EXT_HEAT  6.00071    1.25073  4.7978 1.625e-06 ***
## EXT_COLD -1.46399    0.79202 -1.8484   0.06457 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Total Sum of Squares:    7440200000
## Residual Sum of Squares: 7.422e+09
## R-Squared:      0.0024547
## Adj. R-Squared: -0.068786
## F-statistic: 13.9204 on 2 and 11314 DF, p-value: 9.1597e-07
## [1] "-------------------------------------"
## [1] "Regression summary for outcome variable: TOTAL_COST_LOG"
## Twoways effects Within Model
## 
## Call:
## plm(formula = formula, data = d_reg, effect = "twoways", model = "within", 
##     index = c("COUNTY", "year"))
## 
## Unbalanced Panel: n = 807, T = 1-593, N = 12123
## 
## Residuals:
##        Min.     1st Qu.      Median     3rd Qu.        Max. 
## -16.3139059  -0.2636799   0.0091663   0.3398700   4.1762442 
## 
## Coefficients:
##             Estimate  Std. Error t-value Pr(>|t|)
## EXT_HEAT  0.00117804  0.00155263  0.7587   0.4480
## EXT_COLD -0.00035029  0.00103356 -0.3389   0.7347
## 
## Total Sum of Squares:    8861.3
## Residual Sum of Squares: 8860.8
## R-Squared:      6.3455e-05
## Adj. R-Squared: -0.072106
## F-statistic: 0.358736 on 2 and 11306 DF, p-value: 0.69857
## [1] "-------------------------------------"
## Oneway (individual) effect Within Model
## 
## Call:
## plm(formula = formula, data = d_reg, effect = "individual", model = "within", 
##     index = c("COUNTY", "year"))
## 
## Unbalanced Panel: n = 807, T = 1-593, N = 12123
## 
## Residuals:
##       Min.    1st Qu.     Median    3rd Qu.       Max. 
## -16.629284  -0.370516   0.030245   0.433590   4.544502 
## 
## Coefficients:
##             Estimate  Std. Error t-value  Pr(>|t|)    
## EXT_HEAT  0.01259791  0.00143469  8.7809 < 2.2e-16 ***
## EXT_COLD -0.00342905  0.00090851 -3.7744 0.0001612 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Total Sum of Squares:    9849.2
## Residual Sum of Squares: 9765.9
## R-Squared:      0.0084627
## Adj. R-Squared: -0.062349
## F-statistic: 48.2822 on 2 and 11314 DF, p-value: < 2.22e-16
## [1] "-------------------------------------"

Version 3:

Extreme Heat is the variable BIN_GT80 (the number of days a COUNTY-YEAR was greater than 80 degrees F) Extreme Cold is the variable BIN_LT30 (the number of days a COUNTY-YEAR was less than 30 degrees F)

## [1] "Regression summary for outcome variable: BIRTH_WT_LESS2500_PCT"
## Twoways effects Within Model
## 
## Call:
## plm(formula = formula, data = d_reg, effect = "twoways", model = "within", 
##     index = c("COUNTY", "year"))
## 
## Unbalanced Panel: n = 782, T = 1-531, N = 10492
## 
## Residuals:
##     Min.  1st Qu.   Median  3rd Qu.     Max. 
## -60.2265  -4.3235  -0.9786   1.6566  92.1378 
## 
## Coefficients:
##            Estimate Std. Error t-value Pr(>|t|)
## EXT_HEAT  0.0076854  0.0171124  0.4491   0.6534
## EXT_COLD -0.0052446  0.0155969 -0.3363   0.7367
## 
## Total Sum of Squares:    1455000
## Residual Sum of Squares: 1455000
## R-Squared:      3.6614e-05
## Adj. R-Squared: -0.081507
## F-statistic: 0.177583 on 2 and 9700 DF, p-value: 0.83729
## [1] "-------------------------------------"
## Oneway (individual) effect Within Model
## 
## Call:
## plm(formula = formula, data = d_reg, effect = "individual", model = "within", 
##     index = c("COUNTY", "year"))
## 
## Unbalanced Panel: n = 782, T = 1-531, N = 10492
## 
## Residuals:
##      Min.   1st Qu.    Median   3rd Qu.      Max. 
## -60.03828  -4.35511  -0.98022   1.66918  91.95612 
## 
## Coefficients:
##           Estimate Std. Error t-value Pr(>|t|)
## EXT_HEAT  0.011367   0.013660  0.8321   0.4054
## EXT_COLD -0.020005   0.013012 -1.5375   0.1242
## 
## Total Sum of Squares:    1458200
## Residual Sum of Squares: 1457700
## R-Squared:      0.00034238
## Adj. R-Squared: -0.080285
## F-statistic: 1.66246 on 2 and 9708 DF, p-value: 0.18973
## [1] "-------------------------------------"
## [1] "Regression summary for outcome variable: HYPERTENSION_PCT"
## Twoways effects Within Model
## 
## Call:
## plm(formula = formula, data = d_reg, effect = "twoways", model = "within", 
##     index = c("COUNTY", "year"))
## 
## Unbalanced Panel: n = 807, T = 1-593, N = 12106
## 
## Residuals:
##       Min.    1st Qu.     Median    3rd Qu.       Max. 
## -47.522856  -4.382828  -0.043908   4.380248  64.398518 
## 
## Coefficients:
##          Estimate Std. Error t-value Pr(>|t|)
## EXT_HEAT 0.017749   0.010912  1.6265   0.1039
## EXT_COLD 0.004849   0.010009  0.4845   0.6281
## 
## Total Sum of Squares:    814050
## Residual Sum of Squares: 813850
## R-Squared:      0.00024091
## Adj. R-Squared: -0.072024
## F-statistic: 1.36013 on 2 and 11289 DF, p-value: 0.25667
## [1] "-------------------------------------"
## Oneway (individual) effect Within Model
## 
## Call:
## plm(formula = formula, data = d_reg, effect = "individual", model = "within", 
##     index = c("COUNTY", "year"))
## 
## Unbalanced Panel: n = 807, T = 1-593, N = 12106
## 
## Residuals:
##      Min.   1st Qu.    Median   3rd Qu.      Max. 
## -48.71088  -4.48990  -0.13663   4.57173  63.46440 
## 
## Coefficients:
##            Estimate Std. Error t-value Pr(>|t|)    
## EXT_HEAT  0.0169945  0.0088327  1.9240  0.05437 .  
## EXT_COLD -0.0706405  0.0085431 -8.2688  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Total Sum of Squares:    857690
## Residual Sum of Squares: 851880
## R-Squared:      0.0067694
## Adj. R-Squared: -0.06427
## F-statistic: 38.4977 on 2 and 11297 DF, p-value: < 2.22e-16
## [1] "-------------------------------------"
## [1] "Regression summary for outcome variable: DIABETES_PCT"
## Twoways effects Within Model
## 
## Call:
## plm(formula = formula, data = d_reg, effect = "twoways", model = "within", 
##     index = c("COUNTY", "year"))
## 
## Unbalanced Panel: n = 807, T = 1-593, N = 12115
## 
## Residuals:
##      Min.   1st Qu.    Median   3rd Qu.      Max. 
## -42.23027  -5.18645  -0.61477   3.84592  71.81167 
## 
## Coefficients:
##           Estimate Std. Error t-value Pr(>|t|)
## EXT_HEAT  0.010120   0.013729  0.7371   0.4611
## EXT_COLD -0.018453   0.012580 -1.4669   0.1424
## 
## Total Sum of Squares:    1290500
## Residual Sum of Squares: 1290200
## R-Squared:      0.00026842
## Adj. R-Squared: -0.071937
## F-statistic: 1.51673 on 2 and 11298 DF, p-value: 0.21947
## [1] "-------------------------------------"
## Oneway (individual) effect Within Model
## 
## Call:
## plm(formula = formula, data = d_reg, effect = "individual", model = "within", 
##     index = c("COUNTY", "year"))
## 
## Unbalanced Panel: n = 807, T = 1-593, N = 12115
## 
## Residuals:
##     Min.  1st Qu.   Median  3rd Qu.     Max. 
## -42.6220  -5.2935  -0.6199   3.9846  71.0638 
## 
## Coefficients:
##           Estimate Std. Error t-value  Pr(>|t|)    
## EXT_HEAT  0.014787   0.010959  1.3493    0.1773    
## EXT_COLD -0.062210   0.010588 -5.8755 4.334e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Total Sum of Squares:    1316800
## Residual Sum of Squares: 1312300
## R-Squared:      0.0034198
## Adj. R-Squared: -0.067802
## F-statistic: 19.3983 on 2 and 11306 DF, p-value: 3.889e-09
## [1] "-------------------------------------"
## [1] "Regression summary for outcome variable: TOTAL_COST_PERCAP"
## Twoways effects Within Model
## 
## Call:
## plm(formula = formula, data = d_reg, effect = "twoways", model = "within", 
##     index = c("COUNTY", "year"))
## 
## Unbalanced Panel: n = 807, T = 1-593, N = 12123
## 
## Residuals:
##      Min.   1st Qu.    Median   3rd Qu.      Max. 
## -2619.367  -207.653   -29.140    91.236 28518.400 
## 
## Coefficients:
##          Estimate Std. Error t-value Pr(>|t|)
## EXT_HEAT  0.31652    1.00788  0.3140   0.7535
## EXT_COLD  1.40921    0.92440  1.5245   0.1274
## 
## Total Sum of Squares:    6983300000
## Residual Sum of Squares: 6981900000
## R-Squared:      0.00020667
## Adj. R-Squared: -0.071952
## F-statistic: 1.16854 on 2 and 11306 DF, p-value: 0.31086
## [1] "-------------------------------------"
## Oneway (individual) effect Within Model
## 
## Call:
## plm(formula = formula, data = d_reg, effect = "individual", model = "within", 
##     index = c("COUNTY", "year"))
## 
## Unbalanced Panel: n = 807, T = 1-593, N = 12123
## 
## Residuals:
##      Min.   1st Qu.    Median   3rd Qu.      Max. 
## -2457.607  -247.237   -59.062   133.732 28398.380 
## 
## Coefficients:
##          Estimate Std. Error t-value  Pr(>|t|)    
## EXT_HEAT  5.12326    0.82223  6.2309 4.801e-10 ***
## EXT_COLD -1.14181    0.79517 -1.4359     0.151    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Total Sum of Squares:    7440200000
## Residual Sum of Squares: 7411600000
## R-Squared:      0.0038435
## Adj. R-Squared: -0.067298
## F-statistic: 21.8267 on 2 and 11314 DF, p-value: 3.4595e-10
## [1] "-------------------------------------"
## [1] "Regression summary for outcome variable: TOTAL_COST_LOG"
## Twoways effects Within Model
## 
## Call:
## plm(formula = formula, data = d_reg, effect = "twoways", model = "within", 
##     index = c("COUNTY", "year"))
## 
## Unbalanced Panel: n = 807, T = 1-593, N = 12123
## 
## Residuals:
##        Min.     1st Qu.      Median     3rd Qu.        Max. 
## -16.3187149  -0.2631419   0.0084538   0.3387943   4.1797476 
## 
## Coefficients:
##             Estimate  Std. Error t-value Pr(>|t|)
## EXT_HEAT -0.00076665  0.00113543 -0.6752   0.4996
## EXT_COLD -0.00048126  0.00104138 -0.4621   0.6440
## 
## Total Sum of Squares:    8861.3
## Residual Sum of Squares: 8860.9
## R-Squared:      5.2862e-05
## Adj. R-Squared: -0.072117
## F-statistic: 0.298844 on 2 and 11306 DF, p-value: 0.74168
## [1] "-------------------------------------"
## Oneway (individual) effect Within Model
## 
## Call:
## plm(formula = formula, data = d_reg, effect = "individual", model = "within", 
##     index = c("COUNTY", "year"))
## 
## Unbalanced Panel: n = 807, T = 1-593, N = 12123
## 
## Residuals:
##       Min.    1st Qu.     Median    3rd Qu.       Max. 
## -16.651207  -0.362120   0.015565   0.439903   4.536772 
## 
## Coefficients:
##             Estimate  Std. Error t-value  Pr(>|t|)    
## EXT_HEAT  0.00864929  0.00094354  9.1669 < 2.2e-16 ***
## EXT_COLD -0.00299647  0.00091248 -3.2839  0.001027 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Total Sum of Squares:    9849.2
## Residual Sum of Squares: 9760
## R-Squared:      0.0090653
## Adj. R-Squared: -0.061703
## F-statistic: 51.7515 on 2 and 11314 DF, p-value: < 2.22e-16
## [1] "-------------------------------------"

Community Vulnerability

Version 1:

Extreme Heat defined as COUNTY-YEAR in which there are 150 or more days greater than 80 degrees F Extreme Cold defined as COUNTY-YEAR in which there are 100 or more days less than 30 degrees F

## [1] "Regression summary for outcome variable: BIRTH_WT_LESS2500_PCT"
## Oneway (individual) effect Within Model
## 
## Call:
## plm(formula = formula, data = d_reg, effect = "individual", model = "within", 
##     index = c("COUNTY", "year"))
## 
## Unbalanced Panel: n = 782, T = 1-531, N = 10492
## 
## Residuals:
##      Min.   1st Qu.    Median   3rd Qu.      Max. 
## -60.11227  -4.37229  -0.98555   1.71160  92.08231 
## 
## Coefficients:
##                Estimate Std. Error t-value Pr(>|t|)  
## EXT_HEAT       -2.75858    1.54656 -1.7837  0.07451 .
## EXT_COLD       -1.36410    0.97400 -1.4005  0.16139  
## urban           0.60921    0.51359  1.1862  0.23558  
## EXT_HEAT:urban  2.19726    1.56491  1.4041  0.16033  
## EXT_COLD:urban  0.79471    1.19341  0.6659  0.50548  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Total Sum of Squares:    1458200
## Residual Sum of Squares: 1456900
## R-Squared:      0.00089787
## Adj. R-Squared: -0.080019
## F-statistic: 1.74433 on 5 and 9705 DF, p-value: 0.12081
## [1] "-------------------------------------"
## [1] "Regression summary for outcome variable: HYPERTENSION_PCT"
## Oneway (individual) effect Within Model
## 
## Call:
## plm(formula = formula, data = d_reg, effect = "individual", model = "within", 
##     index = c("COUNTY", "year"))
## 
## Unbalanced Panel: n = 807, T = 1-593, N = 12106
## 
## Residuals:
##      Min.   1st Qu.    Median   3rd Qu.      Max. 
## -46.42277  -4.53185  -0.11709   4.57948  63.27874 
## 
## Coefficients:
##                Estimate Std. Error t-value  Pr(>|t|)    
## EXT_HEAT        0.25453    0.99512  0.2558   0.79812    
## EXT_COLD       -1.15012    0.63603 -1.8083   0.07059 .  
## urban           1.49494    0.33454  4.4686 7.951e-06 ***
## EXT_HEAT:urban  1.76049    1.01296  1.7380   0.08224 .  
## EXT_COLD:urban -0.16222    0.78233 -0.2074   0.83573    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Total Sum of Squares:    857690
## Residual Sum of Squares: 854690
## R-Squared:      0.003499
## Adj. R-Squared: -0.068058
## F-statistic: 7.93121 on 5 and 11294 DF, p-value: 1.8048e-07
## [1] "-------------------------------------"
## [1] "Regression summary for outcome variable: DIABETES_PCT"
## Oneway (individual) effect Within Model
## 
## Call:
## plm(formula = formula, data = d_reg, effect = "individual", model = "within", 
##     index = c("COUNTY", "year"))
## 
## Unbalanced Panel: n = 807, T = 1-593, N = 12115
## 
## Residuals:
##     Min.  1st Qu.   Median  3rd Qu.     Max. 
## -42.5002  -5.3185  -0.6023   4.0677  69.8487 
## 
## Coefficients:
##                  Estimate Std. Error t-value Pr(>|t|)    
## EXT_HEAT       -1.3382897  1.2348810 -1.0837 0.278503    
## EXT_COLD       -0.7208410  0.7883640 -0.9144 0.360552    
## urban           1.4255334  0.4144649  3.4395 0.000585 ***
## EXT_HEAT:urban  0.9760146  1.2569609  0.7765 0.437477    
## EXT_COLD:urban -0.0029138  0.9697714 -0.0030 0.997603    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Total Sum of Squares:    1316800
## Residual Sum of Squares: 1314700
## R-Squared:      0.0015849
## Adj. R-Squared: -0.070052
## F-statistic: 3.58846 on 5 and 11303 DF, p-value: 0.0030348
## [1] "-------------------------------------"
## [1] "Regression summary for outcome variable: TOTAL_COST_PERCAP"
## Oneway (individual) effect Within Model
## 
## Call:
## plm(formula = formula, data = d_reg, effect = "individual", model = "within", 
##     index = c("COUNTY", "year"))
## 
## Unbalanced Panel: n = 807, T = 1-593, N = 12123
## 
## Residuals:
##      Min.   1st Qu.    Median   3rd Qu.      Max. 
## -2502.827  -257.734   -54.535   135.871 28345.314 
## 
## Coefficients:
##                Estimate Std. Error t-value  Pr(>|t|)    
## EXT_HEAT         35.819     92.484  0.3873   0.69854    
## EXT_COLD         41.295     59.005  0.6999   0.48403    
## urban           242.811     31.084  7.8114 6.155e-15 ***
## EXT_HEAT:urban   35.958     94.146  0.3819   0.70251    
## EXT_COLD:urban -138.894     72.649 -1.9118   0.05592 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Total Sum of Squares:    7440200000
## Residual Sum of Squares: 7394900000
## R-Squared:      0.0060911
## Adj. R-Squared: -0.065172
## F-statistic: 13.8637 on 5 and 11311 DF, p-value: 1.5649e-13
## [1] "-------------------------------------"
## [1] "Regression summary for outcome variable: TOTAL_COST_LOG"
## Oneway (individual) effect Within Model
## 
## Call:
## plm(formula = formula, data = d_reg, effect = "individual", model = "within", 
##     index = c("COUNTY", "year"))
## 
## Unbalanced Panel: n = 807, T = 1-593, N = 12123
## 
## Residuals:
##       Min.    1st Qu.     Median    3rd Qu.       Max. 
## -16.400726  -0.362721   0.032235   0.434497   4.533010 
## 
## Coefficients:
##                 Estimate Std. Error t-value  Pr(>|t|)    
## EXT_HEAT        0.171562   0.106208  1.6153   0.10627    
## EXT_COLD        0.129256   0.067760  1.9075   0.05648 .  
## urban           0.358672   0.035697 10.0477 < 2.2e-16 ***
## EXT_HEAT:urban -0.076982   0.108116 -0.7120   0.47646    
## EXT_COLD:urban -0.335407   0.083430 -4.0202 5.852e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Total Sum of Squares:    9849.2
## Residual Sum of Squares: 9752.4
## R-Squared:      0.0098277
## Adj. R-Squared: -0.061168
## F-statistic: 22.453 on 5 and 11311 DF, p-value: < 2.22e-16
## [1] "-------------------------------------"

Version 2:

Extreme Heat is the variable BIN_GT90 (the number of days a COUNTY-YEAR was greater than 90 degrees F) Extreme Cold is the variable BIN_LT30 (the number of days a COUNTY-YEAR was less than 30 degrees F)

## [1] "Regression summary for outcome variable: BIRTH_WT_LESS2500_PCT"
## Oneway (individual) effect Within Model
## 
## Call:
## plm(formula = formula, data = d_reg, effect = "individual", model = "within", 
##     index = c("COUNTY", "year"))
## 
## Unbalanced Panel: n = 782, T = 1-531, N = 10492
## 
## Residuals:
##      Min.   1st Qu.    Median   3rd Qu.      Max. 
## -59.97107  -4.39416  -0.99546   1.69327  91.91916 
## 
## Coefficients:
##                 Estimate Std. Error t-value Pr(>|t|)  
## EXT_HEAT       -0.015908   0.023507 -0.6767  0.49859  
## EXT_COLD       -0.029786   0.014475 -2.0578  0.03964 *
## urban          -0.428914   0.816156 -0.5255  0.59923  
## EXT_HEAT:urban  0.029660   0.016003  1.8534  0.06386 .
## EXT_COLD:urban  0.018351   0.012110  1.5154  0.12971  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Total Sum of Squares:    1458200
## Residual Sum of Squares: 1456800
## R-Squared:      0.0010126
## Adj. R-Squared: -0.079895
## F-statistic: 1.96737 on 5 and 9705 DF, p-value: 0.080097
## [1] "-------------------------------------"
## [1] "Regression summary for outcome variable: HYPERTENSION_PCT"
## Oneway (individual) effect Within Model
## 
## Call:
## plm(formula = formula, data = d_reg, effect = "individual", model = "within", 
##     index = c("COUNTY", "year"))
## 
## Unbalanced Panel: n = 807, T = 1-593, N = 12106
## 
## Residuals:
##      Min.   1st Qu.    Median   3rd Qu.      Max. 
## -46.90992  -4.50484  -0.14948   4.59194  63.28956 
## 
## Coefficients:
##                  Estimate Std. Error t-value  Pr(>|t|)    
## EXT_HEAT       -0.0171708  0.0149188 -1.1510    0.2498    
## EXT_COLD       -0.0722431  0.0094371 -7.6552 2.088e-14 ***
## urban           1.1572765  0.5348409  2.1638    0.0305 *  
## EXT_HEAT:urban  0.0167291  0.0103520  1.6160    0.1061    
## EXT_COLD:urban  0.0018574  0.0079137  0.2347    0.8144    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Total Sum of Squares:    857690
## Residual Sum of Squares: 850130
## R-Squared:      0.0088114
## Adj. R-Squared: -0.062364
## F-statistic: 20.0801 on 5 and 11294 DF, p-value: < 2.22e-16
## [1] "-------------------------------------"
## [1] "Regression summary for outcome variable: DIABETES_PCT"
## Oneway (individual) effect Within Model
## 
## Call:
## plm(formula = formula, data = d_reg, effect = "individual", model = "within", 
##     index = c("COUNTY", "year"))
## 
## Unbalanced Panel: n = 807, T = 1-593, N = 12115
## 
## Residuals:
##      Min.   1st Qu.    Median   3rd Qu.      Max. 
## -42.59647  -5.31562  -0.58564   4.01829  70.79503 
## 
## Coefficients:
##                  Estimate Std. Error t-value  Pr(>|t|)    
## EXT_HEAT       -0.0225976  0.0185212 -1.2201   0.22245    
## EXT_COLD       -0.0611109  0.0116982 -5.2239 1.783e-07 ***
## urban           1.7320910  0.6634818  2.6106   0.00905 ** 
## EXT_HEAT:urban -0.0054246  0.0128471 -0.4222   0.67286    
## EXT_COLD:urban -0.0045327  0.0098199 -0.4616   0.64439    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Total Sum of Squares:    1316800
## Residual Sum of Squares: 1310600
## R-Squared:      0.0046695
## Adj. R-Squared: -0.066746
## F-statistic: 10.6054 on 5 and 11303 DF, p-value: 3.5097e-10
## [1] "-------------------------------------"
## [1] "Regression summary for outcome variable: TOTAL_COST_PERCAP"
## Oneway (individual) effect Within Model
## 
## Call:
## plm(formula = formula, data = d_reg, effect = "individual", model = "within", 
##     index = c("COUNTY", "year"))
## 
## Unbalanced Panel: n = 807, T = 1-593, N = 12123
## 
## Residuals:
##      Min.   1st Qu.    Median   3rd Qu.      Max. 
## -2494.819  -253.584   -55.337   134.814 28385.680 
## 
## Coefficients:
##                  Estimate Std. Error t-value  Pr(>|t|)    
## EXT_HEAT         5.389658   1.388053  3.8829 0.0001038 ***
## EXT_COLD        -0.643942   0.877244 -0.7341 0.4629325    
## urban          270.022628  49.771780  5.4252 5.908e-08 ***
## EXT_HEAT:urban   0.056909   0.963944  0.0590 0.9529232    
## EXT_COLD:urban  -1.161014   0.736614 -1.5761 0.1150196    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Total Sum of Squares:    7440200000
## Residual Sum of Squares: 7381400000
## R-Squared:      0.0079079
## Adj. R-Squared: -0.063225
## F-statistic: 18.0319 on 5 and 11311 DF, p-value: < 2.22e-16
## [1] "-------------------------------------"
## [1] "Regression summary for outcome variable: TOTAL_COST_LOG"
## Oneway (individual) effect Within Model
## 
## Call:
## plm(formula = formula, data = d_reg, effect = "individual", model = "within", 
##     index = c("COUNTY", "year"))
## 
## Unbalanced Panel: n = 807, T = 1-593, N = 12123
## 
## Residuals:
##       Min.    1st Qu.     Median    3rd Qu.       Max. 
## -16.342686  -0.364636   0.030351   0.429715   4.528274 
## 
## Coefficients:
##                   Estimate  Std. Error t-value  Pr(>|t|)    
## EXT_HEAT        0.01321620  0.00158929  8.3158 < 2.2e-16 ***
## EXT_COLD       -0.00104259  0.00100442 -1.0380   0.29929    
## urban           0.50650811  0.05698756  8.8880 < 2.2e-16 ***
## EXT_HEAT:urban -0.00233840  0.00110369 -2.1187   0.03414 *  
## EXT_COLD:urban -0.00403491  0.00084341 -4.7841  1.74e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Total Sum of Squares:    9849.2
## Residual Sum of Squares: 9676.8
## R-Squared:      0.017506
## Adj. R-Squared: -0.052939
## F-statistic: 40.3078 on 5 and 11311 DF, p-value: < 2.22e-16
## [1] "-------------------------------------"

Version 3:

Extreme Heat is the variable BIN_GT80 (the number of days a COUNTY-YEAR was greater than 80 degrees F) Extreme Cold is the variable BIN_LT30 (the number of days a COUNTY-YEAR was less than 30 degrees F)

## [1] "Regression summary for outcome variable: BIRTH_WT_LESS2500_PCT"
## Oneway (individual) effect Within Model
## 
## Call:
## plm(formula = formula, data = d_reg, effect = "individual", model = "within", 
##     index = c("COUNTY", "year"))
## 
## Unbalanced Panel: n = 782, T = 1-531, N = 10492
## 
## Residuals:
##     Min.  1st Qu.   Median  3rd Qu.     Max. 
## -60.0229  -4.3842  -1.0085   1.6880  91.8312 
## 
## Coefficients:
##                  Estimate Std. Error t-value Pr(>|t|)  
## EXT_HEAT        0.0030646  0.0145241  0.2110  0.83289  
## EXT_COLD       -0.0272454  0.0144452 -1.8861  0.05931 .
## urban          -0.5461236  0.9631264 -0.5670  0.57071  
## EXT_HEAT:urban  0.0126623  0.0089809  1.4099  0.15860  
## EXT_COLD:urban  0.0162739  0.0121458  1.3399  0.18032  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Total Sum of Squares:    1458200
## Residual Sum of Squares: 1456900
## R-Squared:      0.00091529
## Adj. R-Squared: -0.08
## F-statistic: 1.77821 on 5 and 9705 DF, p-value: 0.1136
## [1] "-------------------------------------"
## [1] "Regression summary for outcome variable: HYPERTENSION_PCT"
## Oneway (individual) effect Within Model
## 
## Call:
## plm(formula = formula, data = d_reg, effect = "individual", model = "within", 
##     index = c("COUNTY", "year"))
## 
## Unbalanced Panel: n = 807, T = 1-593, N = 12106
## 
## Residuals:
##      Min.   1st Qu.    Median   3rd Qu.      Max. 
## -47.30059  -4.52151  -0.16393   4.56301  63.41907 
## 
## Coefficients:
##                   Estimate  Std. Error t-value Pr(>|t|)    
## EXT_HEAT        0.01252311  0.00936957  1.3366  0.18139    
## EXT_COLD       -0.06855049  0.00944541 -7.2575  4.2e-13 ***
## urban           1.29983784  0.64401316  2.0183  0.04358 *  
## EXT_HEAT:urban  0.00414546  0.00589368  0.7034  0.48184    
## EXT_COLD:urban -0.00094139  0.00801624 -0.1174  0.90652    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Total Sum of Squares:    857690
## Residual Sum of Squares: 850100
## R-Squared:      0.0088471
## Adj. R-Squared: -0.062326
## F-statistic: 20.1622 on 5 and 11294 DF, p-value: < 2.22e-16
## [1] "-------------------------------------"
## [1] "Regression summary for outcome variable: DIABETES_PCT"
## Oneway (individual) effect Within Model
## 
## Call:
## plm(formula = formula, data = d_reg, effect = "individual", model = "within", 
##     index = c("COUNTY", "year"))
## 
## Unbalanced Panel: n = 807, T = 1-593, N = 12115
## 
## Residuals:
##     Min.  1st Qu.   Median  3rd Qu.     Max. 
## -42.6002  -5.3151  -0.6259   4.0165  71.0103 
## 
## Coefficients:
##                  Estimate Std. Error t-value  Pr(>|t|)    
## EXT_HEAT        0.0181244  0.0116298  1.5584  0.119158    
## EXT_COLD       -0.0568507  0.0117087 -4.8554 1.217e-06 ***
## urban           2.3453578  0.7991959  2.9346  0.003346 ** 
## EXT_HEAT:urban -0.0104041  0.0073136 -1.4226  0.154890    
## EXT_COLD:urban -0.0087527  0.0099471 -0.8799  0.378919    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Total Sum of Squares:    1316800
## Residual Sum of Squares: 1310600
## R-Squared:      0.0047348
## Adj. R-Squared: -0.066676
## F-statistic: 10.7544 on 5 and 11303 DF, p-value: 2.4711e-10
## [1] "-------------------------------------"
## [1] "Regression summary for outcome variable: TOTAL_COST_PERCAP"
## Oneway (individual) effect Within Model
## 
## Call:
## plm(formula = formula, data = d_reg, effect = "individual", model = "within", 
##     index = c("COUNTY", "year"))
## 
## Unbalanced Panel: n = 807, T = 1-593, N = 12123
## 
## Residuals:
##      Min.   1st Qu.    Median   3rd Qu.      Max. 
## -2460.825  -250.088   -58.291   134.173 28400.617 
## 
## Coefficients:
##                 Estimate Std. Error t-value  Pr(>|t|)    
## EXT_HEAT         4.71638    0.87108  5.4144 6.274e-08 ***
## EXT_COLD        -0.31756    0.87747 -0.3619    0.7174    
## urban          261.98691   59.90970  4.3730 1.236e-05 ***
## EXT_HEAT:urban   0.13361    0.54840  0.2436    0.8075    
## EXT_COLD:urban  -1.18773    0.74568 -1.5928    0.1112    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Total Sum of Squares:    7440200000
## Residual Sum of Squares: 7371500000
## R-Squared:      0.009235
## Adj. R-Squared: -0.061803
## F-statistic: 21.0861 on 5 and 11311 DF, p-value: < 2.22e-16
## [1] "-------------------------------------"
## [1] "Regression summary for outcome variable: TOTAL_COST_LOG"
## Oneway (individual) effect Within Model
## 
## Call:
## plm(formula = formula, data = d_reg, effect = "individual", model = "within", 
##     index = c("COUNTY", "year"))
## 
## Unbalanced Panel: n = 807, T = 1-593, N = 12123
## 
## Residuals:
##       Min.    1st Qu.     Median    3rd Qu.       Max. 
## -16.385815  -0.356026   0.017344   0.433815   4.522258 
## 
## Coefficients:
##                   Estimate  Std. Error t-value  Pr(>|t|)    
## EXT_HEAT        0.00842340  0.00099783  8.4417 < 2.2e-16 ***
## EXT_COLD       -0.00081616  0.00100515 -0.8120    0.4168    
## urban           0.47397013  0.06862744  6.9064 5.237e-12 ***
## EXT_HEAT:urban -0.00041181  0.00062820 -0.6555    0.5121    
## EXT_COLD:urban -0.00372394  0.00085419 -4.3596 1.314e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Total Sum of Squares:    9849.2
## Residual Sum of Squares: 9672.9
## R-Squared:      0.017899
## Adj. R-Squared: -0.052518
## F-statistic: 41.2295 on 5 and 11311 DF, p-value: < 2.22e-16
## [1] "-------------------------------------"

Main Regressions (include Lookalikes)

Table 1

Raw output

## [1] "Regression summary for outcome variable: BIRTH_WT_LESS2500_PCT"
## [1] "Regression summary for outcome variable: HYPERTENSION_PCT"
## [1] "Regression summary for outcome variable: DIABETES_PCT"
## [1] "Regression summary for outcome variable: TOTAL_COST_LOG"

	Model 1: Low Birth Weight %	Model 1: Hypertension %	Model 1: Diabetes %	Model 1: Total Cost (log)	Model 2: Low Birth Weight %	Model 2: Hypertension %	Model 2: Diabetes %	Model 2: Total Cost (log)
Extreme Heat: Number of days the county registered an average temperature over 90 degrees F	0.0049	-0.0029	-0.0224	0.0126 ***	-6.31e-05	-0.00363	-0.0226	0.0128 ***
	(0.0211, p = 0.816)	(0.0134, p = 0.829)	(0.0167, p = 0.178)	(0.00143, p = 1.85e-18)	(0.0205, p = 0.998)	(0.0116, p = 0.754)	(0.0144, p = 0.118)	(0.000843, p = 1.24e-51)
Extreme Cold: Number of days the county registered an average temperature below 30 degrees F	-0.0209	-0.0727 ***	-0.0649 ***	-0.00343 ***	-0.0167	-0.0709 ***	-0.0669 ***	-0.00356 ***
	(0.013, p = 0.108)	(0.0085, p = 1.35e-17)	(0.0105, p = 7.45e-10)	(0.000909, p = 0.000161)	(0.0126, p = 0.185)	(0.00733, p = 5.28e-22)	(0.00914, p = 2.77e-13)	(0.000534, p = 2.63e-11)
nobs	10492	12106	12115	12123	10492	12106	12115	12123
r.squared	0.000277	0.00645	0.00342	0.00846	0.000194	0.00874	0.00512	0.0267
adj.r.squared	-0.0804	-0.0646	-0.0678	-0.0623	-0.154	-0.131	-0.135	-0.111
statistic	1.34	36.7	19.4	48.3	0.881	46.8	27.3	145
p.value	0.261	1.35e-16	3.91e-09	1.32e-21	0.414	6.04e-21	1.47e-12	4.91e-63
deviance	1.46e+06	8.52e+05	1.31e+06	9.77e+03	1.27e+06	5.89e+05	9.19e+05	3.14e+03
df.residual	9.71e+03	1.13e+04	1.13e+04	1.13e+04	9.09e+03	1.06e+04	1.06e+04	1.06e+04
nobs.1	1.05e+04	1.21e+04	1.21e+04	1.21e+04	1.05e+04	1.21e+04	1.21e+04	1.21e+04
*** p < 0.001; ** p < 0.01; * p < 0.05.

Clean output

Table 2A

Raw output

	Model 4: Total Cost (log)	Model 6: Total Cost (log)	Model 7: Total Cost (log)	Model 8: Total Cost (log)
Extreme Heat: Number of days the county registered an average temperature over 90 degrees F	0.0126 ***	0.0119 ***	0.0126 ***	0.00992 ***
	(0.00143, p = 1.85e-18)	(0.00143, p = 1.09e-16)	(0.0014, p = 2.76e-19)	(0.00112, p = 6.7e-19)
Extreme Cold: Number of days the county registered an average temperature below 30 degrees F	-0.00343 ***	-0.00313 ***	-0.00318 ***	-0.003 ***
	(0.000909, p = 0.000161)	(0.000906, p = 0.00056)	(0.000886, p = 0.000327)	(0.000706, p = 2.12e-05)
Urban: Whether the practice is located in an urban area (as opposed to rural)		0.301 ***
		(0.0334, p = 2.55e-19)
Look-alike: Whether the practice is a look-alike (as opposed to H80 funded)			-1.04 ***
			(0.0429, p = 7.71e-128)
Total number of patients served by the Health Center				2.98e-05 ***
				(3.46e-07, p = 0)
nobs	12123	12123	12123	12123
r.squared	0.00846	0.0155	0.0579	0.402
adj.r.squared	-0.0623	-0.0549	-0.00947	0.359
statistic	48.3	59.4	232	2.53e+03
p.value	1.32e-21	4.09e-38	6.23e-146	0
deviance	9.77e+03	9.7e+03	9.28e+03	5.89e+03
df.residual	1.13e+04	1.13e+04	1.13e+04	1.13e+04
nobs.1	1.21e+04	1.21e+04	1.21e+04	1.21e+04
*** p < 0.001; ** p < 0.01; * p < 0.05.

Clean output

Table 2B

Raw output

	Model 9: Total Cost (log)	Model 10: Total Cost (log)
Extreme Heat: Number of days the county registered an average temperature over 90 degrees F	0.00922 ***	0.00923 ***
	(0.00111, p = 1.13e-16)	(0.00109, p = 3.52e-17)
Extreme Cold: Number of days the county registered an average temperature below 30 degrees F	-0.0027 ***	-0.00254 ***
	(0.000702, p = 0.00012)	(0.000692, p = 0.000242)
Urban: Whether the practice is located in an urban area (as opposed to rural)	0.3 ***	0.326 ***
	(0.0259, p = 7.27e-31)	(0.0256, p = 4.27e-37)
Look-alike: Whether the practice is a look-alike (as opposed to H80 funded)		-0.628 ***
		(0.0339, p = 1.63e-75)
Total number of patients served by the Health Center	2.98e-05 ***	2.88e-05 ***
	(3.44e-07, p = 0)	(3.43e-07, p = 0)
nobs	12123	12123
r.squared	0.409	0.426
adj.r.squared	0.366	0.385
statistic	1.95e+03	1.68e+03
p.value	0	0
deviance	5.82e+03	5.65e+03
df.residual	1.13e+04	1.13e+04
nobs.1	1.21e+04	1.21e+04
*** p < 0.001; ** p < 0.01; * p < 0.05.

Clean output

Table 2C

Raw output

	Model 11: Total Cost (log)	Model 12: Total Cost (log)	Model 13: Total Cost (log)	Model 14: Total Cost (log)
Extreme Heat: Number of days the county registered an average temperature over 90 degrees F	0.0102 ***	0.0117 ***	0.0115 ***	0.0101 ***
	(0.00124, p = 2.07e-16)	(0.00126, p = 1.95e-20)	(0.00129, p = 4.27e-19)	(0.00121, p = 6.05e-17)
Extreme Cold: Number of days the county registered an average temperature below 30 degrees F	-0.00273 ***	-0.00268 ***	-0.00274 ***	-0.00256 ***
	(0.000782, p = 0.000473)	(0.000795, p = 0.000769)	(0.000817, p = 0.000798)	(0.000764, p = 0.00079)
Age 0-10: Percentage of population in age group 0-10	0.184 ***			0.174 ***
	(0.00336, p = 0)			(0.00341, p = 0)
Age 11-20: Percentage of population in age group 11-20	0.154 ***			0.15 ***
	(0.00366, p = 0)			(0.00372, p = 0)
Age 21-34: Percentage of population in age group 21-34	0.159 ***			0.158 ***
	(0.00357, p = 0)			(0.00363, p = 0)
Age 35-49: Percentage of population in age group 35-49	0.17 ***			0.148 ***
	(0.00418, p = 0)			(0.00455, p = 1.07e-221)
Age 50-64: Percentage of population in age group 50-64	0.13 ***			0.133 ***
	(0.00371, p = 6.9e-257)			(0.00396, p = 2.56e-235)
Age 65+: Percentage of population in age group 65 and above	0.209 ***			0.204 ***
	(0.00371, p = 0)			(0.0037, p = 0)
Race Asian: Percentage of population identifying as Asian		0.161 ***		-0.000619
		(0.0032, p = 0)		(0.00114, p = 0.586)
Race NH OPI: Percentage of population identifying as Non-Hispanic Other Pacific Islander		0.214 ***		0.0653 ***
		(0.0126, p = 1.37e-63)		(0.0119, p = 3.69e-08)
Race Black: Percentage of population identifying as Black		0.156 ***		-0.000531
		(0.00313, p = 0)		(0.000917, p = 0.562)
Race AI AN: Percentage of population identifying as American Indian or Alaska Native		0.148 ***		-0.00751 ***
		(0.00344, p = 0)		(0.00159, p = 2.28e-06)
Race White: Percentage of population identifying as White		0.161 ***		0.0025 ***
		(0.00312, p = 0)		(0.00063, p = 7.44e-05)
Race Mixed: Percentage of population identifying as Mixed		0.162 ***		0.00211
		(0.00356, p = 0)		(0.00172, p = 0.219)
Race Unknown: Percentage of population identifying as Unknown		0.158 ***
		(0.00316, p = 0)
Hispanic: Percentage of population identifying as hispanic		0.00833 ***		0.00655 ***
		(0.000709, p = 1.09e-31)		(0.000775, p = 3.07e-17)
Male: Percentage of population identifying as Male			0.154 ***	0.00701 ***
			(0.00329, p = 0)	(0.00151, p = 3.41e-06)
Female: Percentage of population identifying as Female			0.168 ***
			(0.00325, p = 0)
nobs	12123	11951	12123	11951
r.squared	0.267	0.23	0.199	0.291
adj.r.squared	0.214	0.173	0.142	0.239
statistic	515	332	703	286
p.value	0	0	0	0
deviance	7.22e+03	7.27e+03	7.89e+03	6.69e+03
df.residual	1.13e+04	1.11e+04	1.13e+04	1.11e+04
nobs.1	1.21e+04	1.2e+04	1.21e+04	1.2e+04
*** p < 0.001; ** p < 0.01; * p < 0.05.

Clean output

Table 3A

Raw output

	Model 4: Total Cost (log)	Model 6: Total Cost (log)	Model 7: Total Cost (log)	Model 8: Total Cost (log)
Extreme Heat: Number of days the county registered an average temperature over 90 degrees F	0.0128 ***	0.0119 ***	0.0128 ***	0.0101 ***
	(0.000843, p = 1.24e-51)	(0.000834, p = 1.84e-45)	(0.000838, p = 6.6e-52)	(0.000796, p = 2.03e-36)
Extreme Cold: Number of days the county registered an average temperature below 30 degrees F	-0.00356 ***	-0.00315 ***	-0.00345 ***	-0.00312 ***
	(0.000534, p = 2.63e-11)	(0.000528, p = 2.5e-09)	(0.000531, p = 8.59e-11)	(0.000502, p = 5.13e-10)
Urban: Whether the practice is located in an urban area (as opposed to rural)		0.414 ***
		(0.0255, p = 1.55e-58)
Look-alike: Whether the practice is a look-alike (as opposed to H80 funded)			-0.759 ***
			(0.0724, p = 1.31e-25)
Total number of patients served by the Health Center				2.96e-05 ***
				(7.96e-07, p = 3.96e-285)
nobs	12123	12123	12123	12123
r.squared	0.0267	0.0502	0.0366	0.139
adj.r.squared	-0.111	-0.084	-0.0995	0.0173
statistic	145	187	135	571
p.value	4.91e-63	2.88e-118	1.36e-85	0
deviance	3.14e+03	3.06e+03	3.11e+03	2.78e+03
df.residual	1.06e+04	1.06e+04	1.06e+04	1.06e+04
nobs.1	1.21e+04	1.21e+04	1.21e+04	1.21e+04
*** p < 0.001; ** p < 0.01; * p < 0.05.

Clean output

Table 3B

Raw output

	Model 9: Total Cost (log)	Model 10: Total Cost (log)
Extreme Heat: Number of days the county registered an average temperature over 90 degrees F	0.00941 ***	0.00938 ***
	(0.000791, p = 1.81e-32)	(0.000786, p = 1.22e-32)
Extreme Cold: Number of days the county registered an average temperature below 30 degrees F	-0.00281 ***	-0.0027 ***
	(0.000498, p = 1.76e-08)	(0.000496, p = 5.22e-08)
Urban: Whether the practice is located in an urban area (as opposed to rural)	0.331 ***	0.328 ***
	(0.0242, p = 2.92e-42)	(0.024, p = 4.85e-42)
Look-alike: Whether the practice is a look-alike (as opposed to H80 funded)		-0.768 ***
		(0.0674, p = 7.07e-30)
Total number of patients served by the Health Center	2.86e-05 ***	2.87e-05 ***
	(7.93e-07, p = 6.82e-269)	(7.88e-07, p = 3.05e-273)
nobs	12123	12123
r.squared	0.154	0.164
adj.r.squared	0.0342	0.0458
statistic	483	417
p.value	0	0
deviance	2.73e+03	2.7e+03
df.residual	1.06e+04	1.06e+04
nobs.1	1.21e+04	1.21e+04
*** p < 0.001; ** p < 0.01; * p < 0.05.

Clean output

Table 3C

Raw output

	Model 11: Total Cost (log)	Model 12: Total Cost (log)	Model 13: Total Cost (log)	Model 14: Total Cost (log)	Model 15: Total Cost (log)
Extreme Heat: Number of days the county registered an average temperature over 90 degrees F	0.00704 ***	0.0113 ***	0.0116 ***	0.00702 ***	0.00541 ***
	(0.000531, p = 8.6e-40)	(0.000609, p = 2.98e-75)	(0.000627, p = 2.2e-75)	(0.000519, p = 2.28e-41)	(0.000469, p = 1.32e-30)
Extreme Cold: Number of days the county registered an average temperature below 30 degrees F	-0.0022 ***	-0.00249 ***	-0.00274 ***	-0.00204 ***	-0.00165 ***
	(0.000333, p = 4.23e-11)	(0.000385, p = 1.1e-10)	(0.000397, p = 5.57e-12)	(0.000326, p = 3.62e-10)	(0.000293, p = 1.73e-08)
Age 0-10: Percentage of population in age group 0-10	0.141 ***			0.138 ***	0.141 ***
	(0.00171, p = 0)			(0.00193, p = 0)	(0.00174, p = 0)
Age 11-20: Percentage of population in age group 11-20	0.178 ***			0.173 ***	0.169 ***
	(0.00209, p = 0)			(0.00233, p = 0)	(0.00211, p = 0)
Age 21-34: Percentage of population in age group 21-34	0.138 ***			0.138 ***	0.137 ***
	(0.00192, p = 0)			(0.00208, p = 0)	(0.00187, p = 0)
Age 35-49: Percentage of population in age group 35-49	0.158 ***			0.15 ***	0.153 ***
	(0.00256, p = 0)			(0.00269, p = 0)	(0.00243, p = 0)
Age 50-64: Percentage of population in age group 50-64	0.112 ***			0.114 ***	0.122 ***
	(0.00212, p = 0)			(0.00233, p = 0)	(0.00211, p = 0)
Age 65+: Percentage of population in age group 65 and above	0.234 ***			0.231 ***	0.221 ***
	(0.00196, p = 0)			(0.00217, p = 0)	(0.00198, p = 0)
Race Asian: Percentage of population identifying as Asian		0.149 ***		-0.00693 **	-0.00855 ***
		(0.00293, p = 0)		(0.00242, p = 0.00422)	(0.00218, p = 8.88e-05)
Race NH OPI: Percentage of population identifying as Non-Hispanic Other Pacific Islander		0.162 ***		0.024 **	0.0213 **
		(0.00917, p = 1.64e-68)		(0.00765, p = 0.00169)	(0.00689, p = 0.00198)
Race Black: Percentage of population identifying as Black		0.142 ***		-0.00206	-0.000628
		(0.00191, p = 0)		(0.00108, p = 0.0562)	(0.000971, p = 0.518)
Race AI AN: Percentage of population identifying as American Indian or Alaska Native		0.148 ***		-0.000555	-0.0025
		(0.00336, p = 0)		(0.00251, p = 0.825)	(0.00226, p = 0.269)
Race White: Percentage of population identifying as White		0.147 ***		-0.0015 **	-0.00186 ***
		(0.00169, p = 0)		(0.000481, p = 0.00184)	(0.000434, p = 1.76e-05)
Race Mixed: Percentage of population identifying as Mixed		0.155 ***		0.00213 *	0.00115
		(0.00198, p = 0)		(0.000966, p = 0.0272)	(0.00087, p = 0.185)
Race Unknown: Percentage of population identifying as Unknown		0.151 ***
		(0.0017, p = 0)
Hispanic: Percentage of population identifying as hispanic		0.0151 ***		0.0082 ***	0.00846 ***
		(0.000995, p = 2.21e-51)		(0.000865, p = 3.09e-21)	(0.00078, p = 2.84e-27)
Male: Percentage of population identifying as Male			0.157 ***	0.00406 *	-0.000141
			(0.00193, p = 0)	(0.0016, p = 0.0111)	(0.00144, p = 0.922)
Female: Percentage of population identifying as Female			0.146 ***
			(0.00185, p = 0)
Urban: Whether the practice is located in an urban area (as opposed to rural)					0.0661 ***
					(0.0144, p = 4.2e-06)
Look-alike: Whether the practice is a look-alike (as opposed to H80 funded)					-0.596 ***
					(0.0418, p = 9.87e-46)
Total number of patients served by the Health Center					2.24e-05 ***
					(4.77e-07, p = 0)
nobs	12123	11951	12123	11951	11951
r.squared	0.622	0.492	0.462	0.639	0.708
adj.r.squared	0.568	0.42	0.386	0.587	0.666
statistic	2.18e+03	1.01e+03	2.28e+03	1.16e+03	1.33e+03
p.value	0	0	0	0	0
deviance	1.22e+03	1.59e+03	1.74e+03	1.13e+03	913
df.residual	1.06e+04	1.05e+04	1.06e+04	1.04e+04	1.04e+04
nobs.1	1.21e+04	1.2e+04	1.21e+04	1.2e+04	1.2e+04
*** p < 0.001; ** p < 0.01; * p < 0.05.

Clean output

Table 4

Raw output

	Model 1: Total Cost (log)	Model 1: Medical Cost (log)	Model 1: Clinical Cost (log)	Model 1: Enabling Cost (log)	Model 1: Dental + Vision + Other Professional Cost (log)	Model 1: Mental Health + Substance Use Disorders Cost (log)	Model 1: Pharmacy + Pharmaceuticals Cost (log)	Model 2: Total Cost (log)	Model 2: Medical Cost (log)	Model 2: Clinical Cost (log)	Model 2: Enabling Cost (log)	Model 2: Dental + Vision + Other Professional Cost (log)	Model 2: Mental Health + Substance Use Disorders Cost (log)	Model 2: Pharmacy + Pharmaceuticals Cost (log)
Extreme Heat: Number of days the county registered an average temperature over 90 degrees F	0.0126 ***	0.0101 ***	0.0177 ***	0.0143 ***	0.0124 ***	0.0232 ***	0.0233 ***	0.0128 ***	0.0104 ***	0.0188 ***	0.0145 ***	0.0126 ***	0.0233 ***	0.0224 ***
	(0.00143, p = 1.85e-18)	(0.00141, p = 7.55e-13)	(0.00195, p = 9.36e-20)	(0.00165, p = 5.36e-18)	(0.00248, p = 5.88e-07)	(0.00223, p = 3.36e-25)	(0.00257, p = 1.27e-19)	(0.000843, p = 1.24e-51)	(0.000781, p = 6.24e-40)	(0.00119, p = 2.86e-55)	(0.00103, p = 9.23e-45)	(0.00135, p = 7.93e-21)	(0.0016, p = 2.63e-47)	(0.00154, p = 2.13e-47)
Extreme Cold: Number of days the county registered an average temperature below 30 degrees F	-0.00343 ***	-0.00315 ***	-0.00637 ***	-0.00442 ***	-0.00241	-0.00697 ***	-0.00566 ***	-0.00356 ***	-0.00336 ***	-0.0064 ***	-0.00435 ***	-0.00291 ***	-0.00773 ***	-0.00582 ***
	(0.000909, p = 0.000161)	(0.000894, p = 0.000418)	(0.00123, p = 2.51e-07)	(0.00105, p = 2.46e-05)	(0.00152, p = 0.113)	(0.00134, p = 2.27e-07)	(0.0016, p = 0.00042)	(0.000534, p = 2.63e-11)	(0.000495, p = 1.11e-11)	(0.000757, p = 3.17e-17)	(0.00065, p = 2.38e-11)	(0.000824, p = 0.000417)	(0.000961, p = 9.62e-16)	(0.000961, p = 1.42e-09)
nobs	12123	12121	12014	12013	8265	9685	10692	12123	12121	12014	12013	8265	9685	10692
r.squared	0.00846	0.00597	0.0103	0.00871	0.0039	0.0158	0.00997	0.0267	0.0217	0.0312	0.0239	0.0153	0.0342	0.0274
adj.r.squared	-0.0623	-0.065	-0.0609	-0.0626	-0.0952	-0.0704	-0.0695	-0.111	-0.116	-0.106	-0.115	-0.17	-0.131	-0.125
statistic	48.3	34	58.4	49.2	14.7	71.7	49.8	145	118	170	129	54.2	147	130
p.value	1.32e-21	1.97e-15	5.65e-26	5.25e-22	4.15e-07	1.35e-31	2.93e-22	4.91e-63	1.96e-51	3.11e-73	5.54e-56	4.39e-24	2.55e-63	1.49e-56
deviance	9.77e+03	9.45e+03	1.76e+04	1.27e+04	1.28e+04	1.35e+04	2.35e+04	3.14e+03	2.69e+03	6.18e+03	4.57e+03	3.42e+03	6.33e+03	7.78e+03
df.residual	1.13e+04	1.13e+04	1.12e+04	1.12e+04	7.52e+03	8.9e+03	9.9e+03	1.06e+04	1.06e+04	1.05e+04	1.05e+04	6.96e+03	8.27e+03	9.24e+03
nobs.1	1.21e+04	1.21e+04	1.2e+04	1.2e+04	8.26e+03	9.68e+03	1.07e+04	1.21e+04	1.21e+04	1.2e+04	1.2e+04	8.26e+03	9.68e+03	1.07e+04
*** p < 0.001; ** p < 0.01; * p < 0.05.

Clean output

Table 5

Raw output

	Model 11: Total Cost (log)	Model 11: Medical Cost (log)	Model 11: Clinical Cost (log)	Model 11: Enabling Cost (log)	Model 12: Total Cost (log)	Model 12: Medical Cost (log)	Model 12: Clinical Cost (log)	Model 12: Enabling Cost (log)	Model 13: Total Cost (log)	Model 13: Medical Cost (log)	Model 13: Clinical Cost (log)	Model 13: Enabling Cost (log)	Model 14: Total Cost (log)	Model 14: Medical Cost (log)	Model 14: Clinical Cost (log)	Model 14: Enabling Cost (log)	Model 15: Total Cost (log)	Model 15: Medical Cost (log)	Model 15: Clinical Cost (log)	Model 15: Enabling Cost (log)
Extreme Heat: Number of days the county registered an average temperature over 90 degrees F	0.00704 ***	0.0116 ***	0.0116 ***	0.00714 ***	0.00553 ***	0.0055 ***	0.00926 ***	0.00926 ***	0.0056 ***	0.00407 ***	0.011 ***	0.018 ***	0.0177 ***	0.0113 ***	0.00943 ***	0.00859 ***	0.0135 ***	0.0135 ***	0.00872 ***	0.0072 ***
	(0.000531, p = 8.6e-40)	(0.000615, p = 5.74e-78)	(0.000627, p = 2.2e-75)	(0.000521, p = 1.97e-42)	(0.000471, p = 1.36e-31)	(0.000508, p = 3.86e-27)	(0.000568, p = 5.32e-59)	(0.000579, p = 7.35e-57)	(0.000498, p = 3.99e-29)	(0.000451, p = 2.29e-19)	(0.000979, p = 5.58e-29)	(0.00109, p = 1.04e-60)	(0.00109, p = 5.02e-59)	(0.000974, p = 5.4e-31)	(0.000937, p = 1.09e-23)	(0.000848, p = 5.22e-24)	(0.000913, p = 2.93e-49)	(0.000919, p = 2.92e-48)	(0.000842, p = 5.56e-25)	(0.000817, p = 1.31e-18)
Extreme Cold: Number of days the county registered an average temperature below 30 degrees F	-0.0022 ***	-0.00262 ***	-0.00274 ***	-0.00214 ***	-0.00175 ***	-0.00219 ***	-0.00253 ***	-0.0026 ***	-0.00216 ***	-0.00182 ***	-0.00493 ***	-0.00536 ***	-0.00562 ***	-0.00464 ***	-0.00416 ***	-0.00317 ***	-0.00352 ***	-0.00365 ***	-0.00306 ***	-0.0027 ***
	(0.000333, p = 4.23e-11)	(0.00039, p = 1.74e-11)	(0.000397, p = 5.57e-12)	(0.000327, p = 5.88e-11)	(0.000295, p = 2.99e-09)	(0.000319, p = 7.54e-12)	(0.00036, p = 2.07e-12)	(0.000367, p = 1.44e-12)	(0.000313, p = 4.7e-12)	(0.000282, p = 1.25e-10)	(0.000615, p = 1.33e-15)	(0.000689, p = 7.55e-15)	(0.000689, p = 3.6e-16)	(0.000612, p = 3.61e-14)	(0.000588, p = 1.51e-12)	(0.000532, p = 2.67e-09)	(0.000577, p = 1.11e-09)	(0.000581, p = 3.45e-10)	(0.000528, p = 7.32e-09)	(0.000511, p = 1.3e-07)
Age 0-10: Percentage of population in age group 0-10	0.141 ***			0.141 ***	0.144 ***	0.135 ***			0.134 ***	0.137 ***	0.115 ***			0.105 ***	0.108 ***	0.107 ***			0.101 ***	0.103 ***
	(0.00171, p = 0)			(0.00191, p = 0)	(0.00172, p = 0)	(0.00163, p = 0)			(0.00182, p = 0)	(0.00165, p = 0)	(0.00317, p = 6.29e-270)			(0.00361, p = 3.08e-179)	(0.00347, p = 1.04e-202)	(0.00272, p = 2.8e-314)			(0.00308, p = 3.19e-223)	(0.00298, p = 3.94e-248)
Age 11-20: Percentage of population in age group 11-20	0.178 ***			0.178 ***	0.173 ***	0.163 ***			0.164 ***	0.159 ***	0.173 ***			0.16 ***	0.155 ***	0.15 ***			0.145 ***	0.141 ***
	(0.00209, p = 0)			(0.00229, p = 0)	(0.00207, p = 0)	(0.002, p = 0)			(0.00219, p = 0)	(0.00199, p = 0)	(0.00389, p = 0)			(0.00432, p = 1.01e-282)	(0.00417, p = 5.15e-285)	(0.00335, p = 0)			(0.0037, p = 1.94e-313)	(0.0036, p = 9.96e-314)
Age 21-34: Percentage of population in age group 21-34	0.138 ***			0.14 ***	0.139 ***	0.133 ***			0.135 ***	0.134 ***	0.118 ***			0.111 ***	0.109 ***	0.103 ***			0.102 ***	0.101 ***
	(0.00192, p = 0)			(0.00208, p = 0)	(0.00188, p = 0)	(0.00184, p = 0)			(0.00199, p = 0)	(0.0018, p = 0)	(0.00366, p = 4.35e-217)			(0.004, p = 6.14e-162)	(0.00384, p = 7.64e-171)	(0.00314, p = 4.41e-224)			(0.00343, p = 1.04e-185)	(0.00332, p = 7.91e-196)
Age 35-49: Percentage of population in age group 35-49	0.158 ***			0.156 ***	0.158 ***	0.143 ***			0.141 ***	0.144 ***	0.12 ***			0.109 ***	0.112 ***	0.143 ***			0.134 ***	0.136 ***
	(0.00256, p = 0)			(0.00264, p = 0)	(0.00239, p = 0)	(0.00245, p = 0)			(0.00253, p = 0)	(0.00229, p = 0)	(0.00495, p = 3.07e-127)			(0.00521, p = 9.27e-96)	(0.005, p = 7.46e-108)	(0.00426, p = 3.46e-234)			(0.00445, p = 4.09e-191)	(0.00431, p = 9.66e-209)
Age 50-64: Percentage of population in age group 50-64	0.112 ***			0.115 ***	0.122 ***	0.106 ***			0.108 ***	0.115 ***	0.0816 ***			0.0729 ***	0.0821 ***	0.071 ***			0.0684 ***	0.0758 ***
	(0.00212, p = 0)			(0.00234, p = 0)	(0.00212, p = 0)	(0.00203, p = 0)			(0.00224, p = 0)	(0.00203, p = 0)	(0.004, p = 7.11e-91)			(0.00444, p = 6.41e-60)	(0.00428, p = 7.7e-81)	(0.00343, p = 2.98e-93)			(0.00383, p = 2.51e-70)	(0.00371, p = 6.74e-91)
Age 65+: Percentage of population in age group 65 and above	0.234 ***			0.235 ***	0.225 ***	0.211 ***			0.212 ***	0.202 ***	0.252 ***			0.244 ***	0.231 ***	0.211 ***			0.207 ***	0.197 ***
	(0.00196, p = 0)			(0.00214, p = 0)	(0.00195, p = 0)	(0.00188, p = 0)			(0.00205, p = 0)	(0.00187, p = 0)	(0.00362, p = 0)			(0.00402, p = 0)	(0.0039, p = 0)	(0.00313, p = 0)			(0.00346, p = 0)	(0.00338, p = 0)
Race Asian: Percentage of population identifying as Asian		0.15 ***		-0.00877 ***	-0.0105 ***		0.141 ***		-0.00628 **	-0.00768 ***		0.137 ***		-0.00172	-0.00426		0.117 ***		-0.0106 **	-0.0127 ***
		(0.00296, p = 0)		(0.00243, p = 0.000298)	(0.00219, p = 1.66e-06)		(0.00273, p = 0)		(0.00232, p = 0.00673)	(0.00209, p = 0.000246)		(0.00529, p = 1.75e-143)		(0.00461, p = 0.71)	(0.00443, p = 0.335)		(0.00443, p = 4.31e-148)		(0.00398, p = 0.0076)	(0.00385, p = 0.000965)
Race NH OPI: Percentage of population identifying as Non-Hispanic Other Pacific Islander		0.159 ***		0.0208 **	0.0179 **		0.148 ***		0.0179 *	0.0152 *		0.158 ***		0.0463 **	0.0418 **		0.118 ***		0.0139	0.0111
		(0.00927, p = 1.13e-64)		(0.00768, p = 0.00685)	(0.00692, p = 0.00958)		(0.00856, p = 4.85e-66)		(0.00734, p = 0.0145)	(0.00663, p = 0.0215)		(0.0164, p = 1.03e-21)		(0.0144, p = 0.0013)	(0.0138, p = 0.00246)		(0.0137, p = 1.16e-17)		(0.0124, p = 0.261)	(0.012, p = 0.356)
Race Black: Percentage of population identifying as Black		0.143 ***		-0.00337 **	-0.00197 *		0.134 ***		-0.00237 *	-0.00122		0.117 ***		-0.00359	-0.00162		0.112 ***		-0.00299	-0.00146
		(0.00193, p = 0)		(0.00107, p = 0.00169)	(0.000969, p = 0.0416)		(0.00179, p = 0)		(0.00103, p = 0.0209)	(0.000928, p = 0.187)		(0.00342, p = 2.67e-241)		(0.00203, p = 0.0777)	(0.00195, p = 0.409)		(0.00286, p = 4.97e-310)		(0.00174, p = 0.0861)	(0.00169, p = 0.386)
Race AI AN: Percentage of population identifying as American Indian or Alaska Native		0.153 ***		-0.000395	-0.00237		0.139 ***		-0.00302	-0.00448 *		0.141 ***		0.0119 *	0.00893 *		0.131 ***		0.00759	0.00539
		(0.00338, p = 0)		(0.00252, p = 0.876)	(0.00227, p = 0.298)		(0.00312, p = 0)		(0.00241, p = 0.211)	(0.00218, p = 0.0399)		(0.00597, p = 1.62e-120)		(0.00471, p = 0.0118)	(0.00453, p = 0.0486)		(0.00501, p = 8.63e-147)		(0.00407, p = 0.0621)	(0.00394, p = 0.171)
Race White: Percentage of population identifying as White		0.152 ***		-0.000888	-0.00123 **		0.142 ***		-0.00042	-0.000703		0.131 ***		0.00179 *	0.00136		0.124 ***		0.00172 *	0.00131
		(0.00167, p = 0)		(0.000479, p = 0.0638)	(0.000432, p = 0.00443)		(0.00155, p = 0)		(0.000458, p = 0.36)	(0.000414, p = 0.0896)		(0.00295, p = 0)		(0.00091, p = 0.0497)	(0.000874, p = 0.121)		(0.00248, p = 0)		(0.00078, p = 0.0279)	(0.000755, p = 0.082)
Race Mixed: Percentage of population identifying as Mixed		0.16 ***		0.00213 *	0.00114		0.146 ***		0.000524	-0.00032		0.14 ***		0.00473 **	0.0034		0.131 ***		0.00442 **	0.0034 *
		(0.00198, p = 0)		(0.00097, p = 0.0283)	(0.000875, p = 0.193)		(0.00183, p = 0)		(0.000928, p = 0.572)	(0.000838, p = 0.703)		(0.00349, p = 0)		(0.00181, p = 0.00896)	(0.00174, p = 0.0502)		(0.00292, p = 0)		(0.00156, p = 0.00462)	(0.00151, p = 0.0242)
Race Unknown: Percentage of population identifying as Unknown		0.155 ***					0.143 ***					0.132 ***					0.124 ***
		(0.00169, p = 0)					(0.00156, p = 0)					(0.00298, p = 0)					(0.0025, p = 0)
Male: Percentage of population identifying as Male			0.157 ***	0.00252	-0.00173			0.145 ***	0.00126	-0.0026			0.146 ***	0.0193 ***	0.0139 ***			0.132 ***	0.011 ***	0.00673 **
			(0.00193, p = 0)	(0.00159, p = 0.114)	(0.00144, p = 0.23)			(0.00178, p = 0)	(0.00153, p = 0.409)	(0.00138, p = 0.0594)			(0.00337, p = 0)	(0.00309, p = 4.94e-10)	(0.00297, p = 2.74e-06)			(0.00283, p = 0)	(0.00261, p = 2.6e-05)	(0.00252, p = 0.00766)
Female: Percentage of population identifying as Female			0.146 ***					0.137 ***					0.116 ***					0.112 ***
			(0.00185, p = 0)					(0.00171, p = 0)					(0.00322, p = 6.38e-269)					(0.0027, p = 0)
Urban: Whether the practice is located in an urban area (as opposed to rural)					0.0732 ***					0.0491 ***					0.119 ***					0.0723 **
					(0.0144, p = 3.94e-07)					(0.0138, p = 0.000381)					(0.0287, p = 3.12e-05)					(0.025, p = 0.00384)
Look-alike: Whether the practice is a look-alike (as opposed to H80 funded)					-0.605 ***					-0.438 ***					-0.908 ***					-0.707 ***
					(0.042, p = 1.15e-46)					(0.0402, p = 1.58e-27)					(0.086, p = 6e-26)					(0.0745, p = 2.7e-21)
Total number of patients served by the Health Center					2.23e-05 ***					2.16e-05 ***					2.63e-05 ***					2.09e-05 ***
					(4.79e-07, p = 0)					(4.59e-07, p = 0)					(9.52e-07, p = 1.24e-161)					(8.28e-07, p = 4.82e-136)
nobs	12123	11951	12123	11951	11951	12121	11950	12121	11950	11950	12014	11850	12014	11850	11850	12013	11847	12013	11847	11847
r.squared	0.622	0.481	0.462	0.636	0.705	0.594	0.481	0.462	0.61	0.682	0.363	0.204	0.198	0.375	0.425	0.35	0.229	0.22	0.359	0.401
adj.r.squared	0.568	0.407	0.386	0.584	0.662	0.537	0.407	0.386	0.554	0.636	0.273	0.0894	0.0847	0.285	0.342	0.257	0.118	0.109	0.266	0.315
statistic	2.18e+03	1.08e+03	2.28e+03	1.22e+03	1.39e+03	1.94e+03	1.08e+03	2.28e+03	1.09e+03	1.24e+03	750	295	651	414	425	706	342	741	386	385
p.value	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
deviance	1.22e+03	1.62e+03	1.74e+03	1.14e+03	923	1.12e+03	1.38e+03	1.48e+03	1.04e+03	847	4.06e+03	4.97e+03	5.12e+03	3.9e+03	3.59e+03	3.04e+03	3.5e+03	3.65e+03	2.91e+03	2.72e+03
df.residual	1.06e+04	1.05e+04	1.06e+04	1.04e+04	1.04e+04	1.06e+04	1.05e+04	1.06e+04	1.04e+04	1.04e+04	1.05e+04	1.04e+04	1.05e+04	1.04e+04	1.04e+04	1.05e+04	1.04e+04	1.05e+04	1.04e+04	1.03e+04
nobs.1	1.21e+04	1.2e+04	1.21e+04	1.2e+04	1.2e+04	1.21e+04	1.2e+04	1.21e+04	1.2e+04	1.2e+04	1.2e+04	1.18e+04	1.2e+04	1.18e+04	1.18e+04	1.2e+04	1.18e+04	1.2e+04	1.18e+04	1.18e+04
*** p < 0.001; ** p < 0.01; * p < 0.05.

Clean output

Main Regressions (exclude Lookalikes)

Table 1

Raw output

## [1] "Regression summary for outcome variable: BIRTH_WT_LESS2500_PCT"
## [1] "Regression summary for outcome variable: HYPERTENSION_PCT"
## [1] "Regression summary for outcome variable: DIABETES_PCT"
## [1] "Regression summary for outcome variable: TOTAL_COST_LOG"

	Model 1: Low Birth Weight %	Model 1: Hypertension %	Model 1: Diabetes %	Model 1: Total Cost (log)	Model 2: Low Birth Weight %	Model 2: Hypertension %	Model 2: Diabetes %	Model 2: Total Cost (log)
Extreme Heat: Number of days the county registered an average temperature over 90 degrees F	-0.00368	-0.00986	-0.0228	0.0127 ***	-0.00751	-0.00842	-0.0239	0.0129 ***
	(0.0207, p = 0.859)	(0.0132, p = 0.454)	(0.0164, p = 0.163)	(0.00138, p = 4.68e-20)	(0.0203, p = 0.711)	(0.0114, p = 0.461)	(0.0143, p = 0.0948)	(0.000844, p = 4.13e-52)
Extreme Cold: Number of days the county registered an average temperature below 30 degrees F	-0.0161	-0.0735 ***	-0.0683 ***	-0.00314 ***	-0.0135	-0.0714 ***	-0.0671 ***	-0.00359 ***
	(0.0129, p = 0.209)	(0.00841, p = 2.61e-18)	(0.0104, p = 6.51e-11)	(0.000883, p = 0.000373)	(0.0125, p = 0.281)	(0.00729, p = 1.43e-22)	(0.00912, p = 1.96e-13)	(0.000538, p = 2.89e-11)
nobs	10016	11412	11421	11431	10016	11412	11421	11431
r.squared	0.000172	0.00714	0.00408	0.00954	0.000144	0.00945	0.00548	0.0283
adj.r.squared	-0.0828	-0.0661	-0.0693	-0.0634	-0.148	-0.123	-0.127	-0.101
statistic	0.794	38.2	21.8	51.2	0.629	48	27.7	147
p.value	0.452	2.87e-17	3.59e-10	7.09e-23	0.533	1.76e-21	9.66e-13	1.06e-63
deviance	1.31e+06	7.46e+05	1.15e+06	8.26e+03	1.17e+06	5.3e+05	8.32e+05	2.9e+03
df.residual	9.25e+03	1.06e+04	1.06e+04	1.06e+04	8.72e+03	1.01e+04	1.01e+04	1.01e+04
nobs.1	1e+04	1.14e+04	1.14e+04	1.14e+04	1e+04	1.14e+04	1.14e+04	1.14e+04
*** p < 0.001; ** p < 0.01; * p < 0.05.

Clean output

Table 2A

Raw output

	Model 4: Total Cost (log)	Model 6: Total Cost (log)	Model 8: Total Cost (log)
Extreme Heat: Number of days the county registered an average temperature over 90 degrees F	0.0127 ***	0.0118 ***	0.00998 ***
	(0.00138, p = 4.68e-20)	(0.00138, p = 9.75e-18)	(0.00108, p = 3.72e-20)
Extreme Cold: Number of days the county registered an average temperature below 30 degrees F	-0.00314 ***	-0.00273 **	-0.00287 ***
	(0.000883, p = 0.000373)	(0.000878, p = 0.00186)	(0.000692, p = 3.4e-05)
Urban: Whether the practice is located in an urban area (as opposed to rural)		0.399 ***
		(0.0332, p = 4.53e-33)
Total number of patients served by the Health Center			2.84e-05 ***
			(3.46e-07, p = 0)
nobs	11431	11431	11431
r.squared	0.00954	0.0228	0.392
adj.r.squared	-0.0634	-0.0493	0.348
statistic	51.2	82.8	2.29e+03
p.value	7.09e-23	6.07e-53	0
deviance	8.26e+03	8.15e+03	5.07e+03
df.residual	1.06e+04	1.06e+04	1.06e+04
nobs.1	1.14e+04	1.14e+04	1.14e+04
*** p < 0.001; ** p < 0.01; * p < 0.05.

Clean output

Table 2B

Raw output

	Model 9: Total Cost (log)
Extreme Heat: Number of days the county registered an average temperature over 90 degrees F	0.00922 ***
	(0.00108, p = 1.16e-17)
Extreme Cold: Number of days the county registered an average temperature below 30 degrees F	-0.00251 ***
	(0.000687, p = 0.000252)
Urban: Whether the practice is located in an urban area (as opposed to rural)	0.346 ***
	(0.026, p = 3.75e-40)
Total number of patients served by the Health Center	2.82e-05 ***
	(3.44e-07, p = 0)
nobs	11431
r.squared	0.402
adj.r.squared	0.358
statistic	1.79e+03
p.value	0
deviance	4.99e+03
df.residual	1.06e+04
nobs.1	1.14e+04
*** p < 0.001; ** p < 0.01; * p < 0.05.

Clean output

Table 2C

Raw output

	Model 11: Total Cost (log)	Model 12: Total Cost (log)	Model 13: Total Cost (log)	Model 14: Total Cost (log)
Extreme Heat: Number of days the county registered an average temperature over 90 degrees F	0.00996 ***	0.0116 ***	0.0117 ***	0.00984 ***
	(0.00116, p = 8.38e-18)	(0.0012, p = 5.25e-22)	(0.00121, p = 3.36e-22)	(0.00114, p = 8.87e-18)
Extreme Cold: Number of days the county registered an average temperature below 30 degrees F	-0.00233 **	-0.00245 **	-0.00244 **	-0.00229 **
	(0.000736, p = 0.00158)	(0.000765, p = 0.00136)	(0.000772, p = 0.0016)	(0.000727, p = 0.00167)
Age 0-10: Percentage of population in age group 0-10	0.183 ***			0.177 ***
	(0.00314, p = 0)			(0.00324, p = 0)
Age 11-20: Percentage of population in age group 11-20	0.158 ***			0.155 ***
	(0.00345, p = 0)			(0.00357, p = 0)
Age 21-34: Percentage of population in age group 21-34	0.16 ***			0.163 ***
	(0.00336, p = 0)			(0.00347, p = 0)
Age 35-49: Percentage of population in age group 35-49	0.171 ***			0.152 ***
	(0.00398, p = 0)			(0.00439, p = 3.88e-249)
Age 50-64: Percentage of population in age group 50-64	0.128 ***			0.135 ***
	(0.0035, p = 2.62e-277)			(0.00379, p = 1.62e-264)
Age 65+: Percentage of population in age group 65 and above	0.216 ***			0.213 ***
	(0.00348, p = 0)			(0.00352, p = 0)
Race Asian: Percentage of population identifying as Asian		0.163 ***		-0.00235 *
		(0.00302, p = 0)		(0.00113, p = 0.0369)
Race NH OPI: Percentage of population identifying as Non-Hispanic Other Pacific Islander		0.209 ***		0.0564 ***
		(0.0122, p = 2.52e-64)		(0.0114, p = 7.53e-07)
Race Black: Percentage of population identifying as Black		0.157 ***		-0.00164
		(0.00295, p = 0)		(0.000899, p = 0.0681)
Race AI AN: Percentage of population identifying as American Indian or Alaska Native		0.144 ***		-0.014 ***
		(0.00328, p = 0)		(0.00158, p = 8.66e-19)
Race White: Percentage of population identifying as White		0.161 ***		0.00119
		(0.00294, p = 0)		(0.000624, p = 0.0559)
Race Mixed: Percentage of population identifying as Mixed		0.165 ***		0.00279
		(0.00338, p = 0)		(0.00166, p = 0.0926)
Race Unknown: Percentage of population identifying as Unknown		0.16 ***
		(0.00298, p = 0)
Hispanic: Percentage of population identifying as hispanic		0.00765 ***		0.00578 ***
		(0.000698, p = 7.6e-28)		(0.000752, p = 1.6e-14)
Male: Percentage of population identifying as Male			0.152 ***	0.00218
			(0.00304, p = 0)	(0.00151, p = 0.149)
Female: Percentage of population identifying as Female			0.172 ***
			(0.003, p = 0)
nobs	11431	11332	11431	11332
r.squared	0.313	0.262	0.244	0.335
adj.r.squared	0.262	0.207	0.188	0.284
statistic	607	375	857	331
p.value	0	0	0	0
deviance	5.73e+03	6.08e+03	6.31e+03	5.49e+03
df.residual	1.06e+04	1.05e+04	1.06e+04	1.05e+04
nobs.1	1.14e+04	1.13e+04	1.14e+04	1.13e+04
*** p < 0.001; ** p < 0.01; * p < 0.05.

Clean output

Table 3A

Raw output

	Model 4: Total Cost (log)	Model 6: Total Cost (log)	Model 8: Total Cost (log)
Extreme Heat: Number of days the county registered an average temperature over 90 degrees F	0.0129 ***	0.0119 ***	0.0102 ***
	(0.000844, p = 4.13e-52)	(0.000834, p = 5.17e-46)	(0.000801, p = 1.37e-36)
Extreme Cold: Number of days the county registered an average temperature below 30 degrees F	-0.00359 ***	-0.00315 ***	-0.00317 ***
	(0.000538, p = 2.89e-11)	(0.000532, p = 3.26e-09)	(0.000509, p = 5.1e-10)
Urban: Whether the practice is located in an urban area (as opposed to rural)		0.434 ***
		(0.0257, p = 4.15e-63)
Total number of patients served by the Health Center			2.79e-05 ***
			(8.07e-07, p = 6.97e-248)
nobs	11431	11431	11431
r.squared	0.0283	0.0551	0.131
adj.r.squared	-0.101	-0.071	0.0156
statistic	147	196	509
p.value	1.06e-63	1.77e-123	8.97e-308
deviance	2.9e+03	2.82e+03	2.6e+03
df.residual	1.01e+04	1.01e+04	1.01e+04
nobs.1	1.14e+04	1.14e+04	1.14e+04
*** p < 0.001; ** p < 0.01; * p < 0.05.

Clean output

Table 3B

Raw output

	Model 9: Total Cost (log)
Extreme Heat: Number of days the county registered an average temperature over 90 degrees F	0.00951 ***
	(0.000795, p = 9.38e-33)
Extreme Cold: Number of days the county registered an average temperature below 30 degrees F	-0.00283 ***
	(0.000505, p = 2.06e-08)
Urban: Whether the practice is located in an urban area (as opposed to rural)	0.352 ***
	(0.0245, p = 3.35e-46)
Total number of patients served by the Health Center	2.68e-05 ***
	(8.03e-07, p = 5.17e-231)
nobs	11431
r.squared	0.149
adj.r.squared	0.0351
statistic	441
p.value	0
deviance	2.54e+03
df.residual	1.01e+04
nobs.1	1.14e+04
*** p < 0.001; ** p < 0.01; * p < 0.05.

Clean output

Table 3C

Raw output

	Model 11: Total Cost (log)	Model 12: Total Cost (log)	Model 13: Total Cost (log)	Model 14: Total Cost (log)	Model 15: Total Cost (log)
Extreme Heat: Number of days the county registered an average temperature over 90 degrees F	0.00671 ***	0.0111 ***	0.0117 ***	0.00652 ***	0.00505 ***
	(0.000501, p = 1.55e-40)	(0.000598, p = 1.31e-75)	(0.000608, p = 9.2e-81)	(0.000499, p = 1.23e-38)	(0.000458, p = 4.4e-28)
Extreme Cold: Number of days the county registered an average temperature below 30 degrees F	-0.00216 ***	-0.00242 ***	-0.00274 ***	-0.002 ***	-0.00171 ***
	(0.000317, p = 9.4e-12)	(0.000381, p = 2.24e-10)	(0.000388, p = 1.6e-12)	(0.000315, p = 2.32e-10)	(0.000288, p = 3.15e-09)
Age 0-10: Percentage of population in age group 0-10	0.136 ***			0.137 ***	0.14 ***
	(0.00164, p = 0)			(0.00189, p = 0)	(0.00173, p = 0)
Age 11-20: Percentage of population in age group 11-20	0.19 ***			0.188 ***	0.179 ***
	(0.00213, p = 0)			(0.00241, p = 0)	(0.00221, p = 0)
Age 21-34: Percentage of population in age group 21-34	0.137 ***			0.139 ***	0.137 ***
	(0.00184, p = 0)			(0.00204, p = 0)	(0.00187, p = 0)
Age 35-49: Percentage of population in age group 35-49	0.155 ***			0.152 ***	0.154 ***
	(0.00246, p = 0)			(0.00262, p = 0)	(0.0024, p = 0)
Age 50-64: Percentage of population in age group 50-64	0.115 ***			0.119 ***	0.125 ***
	(0.00206, p = 0)			(0.00228, p = 0)	(0.00209, p = 0)
Age 65+: Percentage of population in age group 65 and above	0.233 ***			0.233 ***	0.224 ***
	(0.00187, p = 0)			(0.00213, p = 0)	(0.00197, p = 0)
Race Asian: Percentage of population identifying as Asian		0.15 ***		-0.00947 ***	-0.0109 ***
		(0.00295, p = 0)		(0.00243, p = 9.77e-05)	(0.00222, p = 8.77e-07)
Race NH OPI: Percentage of population identifying as Non-Hispanic Other Pacific Islander		0.169 ***		0.0314 ***	0.0281 ***
		(0.00903, p = 3.56e-77)		(0.00738, p = 2.08e-05)	(0.00675, p = 3.13e-05)
Race Black: Percentage of population identifying as Black		0.14 ***		-0.0037 ***	-0.00252 *
		(0.0019, p = 0)		(0.00109, p = 0.000713)	(0.001, p = 0.0118)
Race AI AN: Percentage of population identifying as American Indian or Alaska Native		0.149 ***		-0.000474	-0.00139
		(0.00329, p = 0)		(0.00242, p = 0.844)	(0.00221, p = 0.53)
Race White: Percentage of population identifying as White		0.147 ***		-0.00187 ***	-0.00201 ***
		(0.00165, p = 0)		(0.000483, p = 0.000114)	(0.000442, p = 5.44e-06)
Race Mixed: Percentage of population identifying as Mixed		0.155 ***		0.00186	0.00136
		(0.00196, p = 0)		(0.000955, p = 0.0516)	(0.000874, p = 0.118)
Race Unknown: Percentage of population identifying as Unknown		0.151 ***
		(0.00166, p = 0)
Hispanic: Percentage of population identifying as hispanic		0.0166 ***		0.00833 ***	0.00876 ***
		(0.00102, p = 4.23e-59)		(0.000866, p = 8.18e-22)	(0.000793, p = 3.7e-28)
Male: Percentage of population identifying as Male			0.154 ***	-0.000963	-0.00323 *
			(0.00189, p = 0)	(0.00161, p = 0.551)	(0.00148, p = 0.0287)
Female: Percentage of population identifying as Female			0.149 ***
			(0.0018, p = 0)
Urban: Whether the practice is located in an urban area (as opposed to rural)					0.0714 ***
					(0.0141, p = 4.38e-07)
Total number of patients served by the Health Center					2.04e-05 ***
					(4.7e-07, p = 0)
nobs	11431	11332	11431	11332	11332
r.squared	0.665	0.521	0.496	0.674	0.727
adj.r.squared	0.62	0.456	0.429	0.63	0.69
statistic	2.5e+03	1.09e+03	2.48e+03	1.29e+03	1.48e+03
p.value	0	0	0	0	0
deviance	1e+03	1.42e+03	1.51e+03	968	809
df.residual	1.01e+04	9.98e+03	1.01e+04	9.97e+03	9.97e+03
nobs.1	1.14e+04	1.13e+04	1.14e+04	1.13e+04	1.13e+04
*** p < 0.001; ** p < 0.01; * p < 0.05.

Clean output

Table 4

Raw output

	Model 1: Total Cost (log)	Model 1: Medical Cost (log)	Model 1: Clinical Cost (log)	Model 1: Enabling Cost (log)	Model 1: Dental + Vision + Other Professional Cost (log)	Model 1: Mental Health + Substance Use Disorders Cost (log)	Model 1: Pharmacy + Pharmaceuticals Cost (log)	Model 2: Total Cost (log)	Model 2: Medical Cost (log)	Model 2: Clinical Cost (log)	Model 2: Enabling Cost (log)	Model 2: Dental + Vision + Other Professional Cost (log)	Model 2: Mental Health + Substance Use Disorders Cost (log)	Model 2: Pharmacy + Pharmaceuticals Cost (log)
Extreme Heat: Number of days the county registered an average temperature over 90 degrees F	0.0127 ***	0.0102 ***	0.0187 ***	0.0145 ***	0.0127 ***	0.0232 ***	0.024 ***	0.0129 ***	0.0105 ***	0.019 ***	0.0145 ***	0.0128 ***	0.0235 ***	0.0225 ***
	(0.00138, p = 4.68e-20)	(0.00138, p = 1.33e-13)	(0.00182, p = 1.44e-24)	(0.00156, p = 1.57e-20)	(0.00241, p = 1.37e-07)	(0.00219, p = 5.32e-26)	(0.00249, p = 8.6e-22)	(0.000844, p = 4.13e-52)	(0.000783, p = 7.39e-41)	(0.00117, p = 1.59e-58)	(0.00101, p = 3.44e-46)	(0.00134, p = 2.11e-21)	(0.0016, p = 3.92e-48)	(0.00153, p = 4.46e-48)
Extreme Cold: Number of days the county registered an average temperature below 30 degrees F	-0.00314 ***	-0.00296 ***	-0.00592 ***	-0.00395 ***	-0.0024	-0.00706 ***	-0.00541 ***	-0.00359 ***	-0.00344 ***	-0.00631 ***	-0.00444 ***	-0.00288 ***	-0.00795 ***	-0.00574 ***
	(0.000883, p = 0.000373)	(0.000881, p = 0.000772)	(0.00116, p = 3.74e-07)	(0.000996, p = 7.48e-05)	(0.00148, p = 0.106)	(0.00133, p = 1.09e-07)	(0.00157, p = 0.000558)	(0.000538, p = 2.89e-11)	(0.0005, p = 5.89e-12)	(0.000747, p = 3.31e-17)	(0.000645, p = 6.09e-12)	(0.000826, p = 0.000494)	(0.000965, p = 2.01e-16)	(0.000962, p = 2.52e-09)
nobs	11431	11431	11379	11383	8084	9397	10245	11431	11431	11379	11383	8084	9397	10245
r.squared	0.00954	0.00656	0.013	0.0101	0.00441	0.017	0.0114	0.0283	0.0235	0.0343	0.0261	0.016	0.036	0.0287
adj.r.squared	-0.0634	-0.0666	-0.06	-0.0631	-0.0958	-0.07	-0.0695	-0.101	-0.107	-0.0948	-0.104	-0.164	-0.121	-0.116
statistic	51.2	35.2	69.7	54.1	16.3	74.5	54.7	147	122	178	134	55.4	151	132
p.value	7.09e-23	6.05e-16	8.67e-31	4.15e-24	9.02e-08	8.45e-33	2.37e-24	1.06e-63	6.87e-53	8.29e-77	2.69e-58	1.34e-24	5.31e-65	4.55e-57
deviance	8.26e+03	8.22e+03	1.42e+04	1.04e+04	1.17e+04	1.25e+04	2.08e+04	2.9e+03	2.5e+03	5.54e+03	4.13e+03	3.32e+03	6.12e+03	7.33e+03
df.residual	1.06e+04	1.06e+04	1.06e+04	1.06e+04	7.34e+03	8.63e+03	9.47e+03	1.01e+04	1.01e+04	1e+04	1e+04	6.84e+03	8.08e+03	8.92e+03
nobs.1	1.14e+04	1.14e+04	1.14e+04	1.14e+04	8.08e+03	9.4e+03	1.02e+04	1.14e+04	1.14e+04	1.14e+04	1.14e+04	8.08e+03	9.4e+03	1.02e+04
*** p < 0.001; ** p < 0.01; * p < 0.05.

Clean output

Table 5

Raw output

	Model 11: Total Cost (log)	Model 11: Medical Cost (log)	Model 11: Clinical Cost (log)	Model 11: Enabling Cost (log)	Model 12: Total Cost (log)	Model 12: Medical Cost (log)	Model 12: Clinical Cost (log)	Model 12: Enabling Cost (log)	Model 13: Total Cost (log)	Model 13: Medical Cost (log)	Model 13: Clinical Cost (log)	Model 13: Enabling Cost (log)	Model 14: Total Cost (log)	Model 14: Medical Cost (log)	Model 14: Clinical Cost (log)	Model 14: Enabling Cost (log)	Model 15: Total Cost (log)	Model 15: Medical Cost (log)	Model 15: Clinical Cost (log)	Model 15: Enabling Cost (log)
Extreme Heat: Number of days the county registered an average temperature over 90 degrees F	0.00671 ***	0.0115 ***	0.0117 ***	0.00664 ***	0.00517 ***	0.0053 ***	0.00925 ***	0.0094 ***	0.00525 ***	0.00386 ***	0.0105 ***	0.0179 ***	0.0179 ***	0.0105 ***	0.00873 ***	0.00819 ***	0.0134 ***	0.0135 ***	0.00812 ***	0.00674 ***
	(0.000501, p = 1.55e-40)	(0.000605, p = 8.5e-79)	(0.000608, p = 9.2e-81)	(0.000501, p = 1.13e-39)	(0.000461, p = 4.78e-29)	(0.000485, p = 1.16e-27)	(0.000562, p = 3.87e-60)	(0.000563, p = 1.01e-61)	(0.000485, p = 3.55e-27)	(0.000445, p = 5.43e-18)	(0.000933, p = 3.58e-29)	(0.00106, p = 2.97e-63)	(0.00105, p = 1.38e-63)	(0.000933, p = 2.76e-29)	(0.000904, p = 5.77e-22)	(0.000816, p = 1.31e-23)	(0.000899, p = 1.9e-49)	(0.000895, p = 7.46e-51)	(0.000821, p = 5.46e-23)	(0.0008, p = 4.47e-17)
Extreme Cold: Number of days the county registered an average temperature below 30 degrees F	-0.00216 ***	-0.00258 ***	-0.00274 ***	-0.0021 ***	-0.00181 ***	-0.00221 ***	-0.00254 ***	-0.00266 ***	-0.00218 ***	-0.00192 ***	-0.00479 ***	-0.00528 ***	-0.00553 ***	-0.00459 ***	-0.00421 ***	-0.00323 ***	-0.00354 ***	-0.00374 ***	-0.00312 ***	-0.00284 ***
	(0.000317, p = 9.4e-12)	(0.000386, p = 2.42e-11)	(0.000388, p = 1.6e-12)	(0.000316, p = 3.12e-11)	(0.00029, p = 4.16e-10)	(0.000306, p = 5.32e-13)	(0.000358, p = 1.33e-12)	(0.00036, p = 1.39e-13)	(0.000306, p = 1.15e-12)	(0.00028, p = 8.06e-12)	(0.00059, p = 5.45e-16)	(0.000673, p = 4.55e-15)	(0.000673, p = 2.39e-16)	(0.000589, p = 7.56e-15)	(0.000569, p = 1.56e-13)	(0.000515, p = 3.54e-10)	(0.000572, p = 6.33e-10)	(0.00057, p = 5.58e-11)	(0.000517, p = 1.66e-09)	(0.000503, p = 1.77e-08)
Age 0-10: Percentage of population in age group 0-10	0.136 ***			0.14 ***	0.143 ***	0.129 ***			0.132 ***	0.136 ***	0.108 ***			0.105 ***	0.109 ***	0.101 ***			0.101 ***	0.104 ***
	(0.00164, p = 0)			(0.00187, p = 0)	(0.00172, p = 0)	(0.00158, p = 0)			(0.00181, p = 0)	(0.00166, p = 0)	(0.00307, p = 5.55e-255)			(0.00353, p = 5.77e-188)	(0.00341, p = 1.69e-214)	(0.00266, p = 1.06e-296)			(0.00306, p = 1.99e-225)	(0.00298, p = 5.35e-251)
Age 11-20: Percentage of population in age group 11-20	0.19 ***			0.193 ***	0.184 ***	0.174 ***			0.176 ***	0.168 ***	0.194 ***			0.19 ***	0.179 ***	0.164 ***			0.163 ***	0.154 ***
	(0.00213, p = 0)			(0.00236, p = 0)	(0.00218, p = 0)	(0.00206, p = 0)			(0.00229, p = 0)	(0.0021, p = 0)	(0.00399, p = 0)			(0.00444, p = 0)	(0.00431, p = 0)	(0.00348, p = 0)			(0.00388, p = 0)	(0.00379, p = 0)
Age 21-34: Percentage of population in age group 21-34	0.137 ***			0.141 ***	0.139 ***	0.133 ***			0.135 ***	0.134 ***	0.112 ***			0.11 ***	0.108 ***	0.102 ***			0.102 ***	0.101 ***
	(0.00184, p = 0)			(0.00204, p = 0)	(0.00187, p = 0)	(0.00178, p = 0)			(0.00197, p = 0)	(0.00181, p = 0)	(0.00353, p = 2.44e-209)			(0.00391, p = 8.14e-168)	(0.00378, p = 1.52e-172)	(0.00306, p = 1.39e-232)			(0.00342, p = 9.37e-189)	(0.00333, p = 1.5e-193)
Age 35-49: Percentage of population in age group 35-49	0.155 ***			0.157 ***	0.16 ***	0.141 ***			0.143 ***	0.145 ***	0.116 ***			0.112 ***	0.114 ***	0.14 ***			0.137 ***	0.139 ***
	(0.00246, p = 0)			(0.00257, p = 0)	(0.00236, p = 0)	(0.00238, p = 0)			(0.00249, p = 0)	(0.00228, p = 0)	(0.00481, p = 9.46e-126)			(0.00504, p = 6.6e-106)	(0.00488, p = 6.65e-117)	(0.00417, p = 7.7e-233)			(0.00439, p = 6.04e-205)	(0.00427, p = 1.37e-220)
Age 50-64: Percentage of population in age group 50-64	0.115 ***			0.12 ***	0.125 ***	0.107 ***			0.11 ***	0.116 ***	0.0899 ***			0.0885 ***	0.0952 ***	0.0716 ***			0.0717 ***	0.0771 ***
	(0.00206, p = 0)			(0.00229, p = 0)	(0.00211, p = 0)	(0.00199, p = 0)			(0.00222, p = 0)	(0.00204, p = 0)	(0.0039, p = 8.31e-115)			(0.00434, p = 1.69e-90)	(0.00421, p = 1.56e-110)	(0.00339, p = 6.57e-97)			(0.0038, p = 4.48e-78)	(0.00371, p = 4.82e-94)
Age 65+: Percentage of population in age group 65 and above	0.233 ***			0.237 ***	0.228 ***	0.21 ***			0.213 ***	0.204 ***	0.248 ***			0.246 ***	0.235 ***	0.21 ***			0.21 ***	0.201 ***
	(0.00187, p = 0)			(0.00209, p = 0)	(0.00194, p = 0)	(0.00181, p = 0)			(0.00202, p = 0)	(0.00187, p = 0)	(0.00349, p = 0)			(0.00392, p = 0)	(0.00382, p = 0)	(0.00304, p = 0)			(0.00342, p = 0)	(0.00336, p = 0)
Race Asian: Percentage of population identifying as Asian		0.151 ***		-0.0111 ***	-0.0127 ***		0.144 ***		-0.00591 *	-0.00738 ***		0.128 ***		-0.0178 ***	-0.02 ***		0.117 ***		-0.0142 ***	-0.016 ***
		(0.00299, p = 0)		(0.00243, p = 4.72e-06)	(0.00223, p = 1.23e-08)		(0.00277, p = 0)		(0.00236, p = 0.0121)	(0.00216, p = 0.000622)		(0.00528, p = 1.83e-126)		(0.00462, p = 0.000118)	(0.00446, p = 7.59e-06)		(0.00447, p = 4.49e-145)		(0.00403, p = 0.000407)	(0.00392, p = 4.51e-05)
Race NH OPI: Percentage of population identifying as Non-Hispanic Other Pacific Islander		0.165 ***		0.0279 ***	0.0244 ***		0.154 ***		0.0244 ***	0.021 **		0.169 ***		0.0557 ***	0.0509 ***		0.127 ***		0.0241 *	0.0206
		(0.00915, p = 6.15e-72)		(0.0074, p = 0.000168)	(0.00678, p = 0.000326)		(0.00849, p = 3.52e-72)		(0.00716, p = 0.000669)	(0.00655, p = 0.00133)		(0.016, p = 6.89e-26)		(0.0138, p = 5.69e-05)	(0.0134, p = 0.00014)		(0.0136, p = 6.58e-21)		(0.0121, p = 0.0465)	(0.0118, p = 0.0796)
Race Black: Percentage of population identifying as Black		0.141 ***		-0.00501 ***	-0.00391 ***		0.132 ***		-0.00379 ***	-0.00269 **		0.115 ***		-0.00624 **	-0.00489 *		0.109 ***		-0.00437 *	-0.00328
		(0.00192, p = 0)		(0.00109, p = 4.3e-06)	(0.000998, p = 9.12e-05)		(0.00178, p = 0)		(0.00105, p = 0.000324)	(0.000965, p = 0.00534)		(0.00336, p = 1.14e-241)		(0.00205, p = 0.00231)	(0.00198, p = 0.0134)		(0.00285, p = 2.61e-301)		(0.00179, p = 0.0145)	(0.00174, p = 0.0591)
Race AI AN: Percentage of population identifying as American Indian or Alaska Native		0.154 ***		-0.000451	-0.00136		0.14 ***		-0.00247	-0.00334		0.141 ***		0.0089 *	0.0078		0.132 ***		0.00728	0.00645
		(0.00332, p = 0)		(0.00243, p = 0.852)	(0.00222, p = 0.54)		(0.00308, p = 0)		(0.00235, p = 0.293)	(0.00215, p = 0.119)		(0.0058, p = 9.01e-127)		(0.00453, p = 0.0495)	(0.00437, p = 0.0746)		(0.00493, p = 3.47e-152)		(0.00397, p = 0.067)	(0.00386, p = 0.0952)
Race White: Percentage of population identifying as White		0.152 ***		-0.00129 **	-0.0014 **		0.141 ***		-0.000814	-0.000939 *		0.133 ***		0.00104	0.000929		0.125 ***		0.00134	0.00123
		(0.00163, p = 0)		(0.000482, p = 0.00741)	(0.000441, p = 0.0015)		(0.00152, p = 0)		(0.000466, p = 0.081)	(0.000427, p = 0.0277)		(0.00285, p = 0)		(0.000909, p = 0.252)	(0.000878, p = 0.29)		(0.00242, p = 0)		(0.000791, p = 0.0897)	(0.000769, p = 0.111)
Race Mixed: Percentage of population identifying as Mixed		0.16 ***		0.00193 *	0.00143		0.146 ***		0.000395	-6.48e-05		0.142 ***		0.0041 *	0.00344 *		0.132 ***		0.00405 **	0.00357 *
		(0.00196, p = 0)		(0.000959, p = 0.0446)	(0.000879, p = 0.103)		(0.00182, p = 0)		(0.000928, p = 0.671)	(0.000849, p = 0.939)		(0.00341, p = 0)		(0.00179, p = 0.0216)	(0.00173, p = 0.0463)		(0.0029, p = 0)		(0.00156, p = 0.00968)	(0.00152, p = 0.019)
Race Unknown: Percentage of population identifying as Unknown		0.156 ***					0.144 ***					0.135 ***					0.125 ***
		(0.00165, p = 0)					(0.00154, p = 0)					(0.00288, p = 0)					(0.00245, p = 0)
Male: Percentage of population identifying as Male			0.154 ***	-0.00263	-0.00498 ***			0.143 ***	-0.00232	-0.0046 **			0.144 ***	0.0108 ***	0.00767 *			0.128 ***	0.00518	0.0028
			(0.00189, p = 0)	(0.00161, p = 0.102)	(0.00148, p = 0.000747)			(0.00175, p = 0)	(0.00156, p = 0.137)	(0.00143, p = 0.00127)			(0.0033, p = 0)	(0.0031, p = 0.000504)	(0.003, p = 0.0104)			(0.00278, p = 0)	(0.00266, p = 0.0516)	(0.00259, p = 0.279)
Female: Percentage of population identifying as Female			0.149 ***					0.139 ***					0.118 ***					0.115 ***
			(0.0018, p = 0)					(0.00167, p = 0)					(0.00314, p = 9.96e-291)					(0.00265, p = 0)
Urban: Whether the practice is located in an urban area (as opposed to rural)					0.0789 ***					0.0507 ***					0.138 ***					0.0803 **
					(0.0142, p = 2.78e-08)					(0.0137, p = 0.00022)					(0.0278, p = 6.97e-07)					(0.0247, p = 0.00113)
Total number of patients served by the Health Center					2.03e-05 ***					1.99e-05 ***					2.41e-05 ***					1.91e-05 ***
					(4.72e-07, p = 0)					(4.57e-07, p = 0)					(9.27e-07, p = 2.58e-144)					(8.19e-07, p = 7.45e-118)
nobs	11431	11332	11431	11332	11332	11431	11332	11431	11332	11332	11379	11280	11379	11280	11280	11383	11284	11383	11284	11284
r.squared	0.665	0.508	0.496	0.671	0.724	0.635	0.505	0.495	0.641	0.699	0.401	0.225	0.218	0.409	0.449	0.384	0.247	0.24	0.388	0.421
adj.r.squared	0.62	0.442	0.429	0.626	0.687	0.586	0.438	0.428	0.592	0.658	0.321	0.12	0.113	0.328	0.374	0.301	0.144	0.138	0.304	0.342
statistic	2.5e+03	1.15e+03	2.48e+03	1.36e+03	1.54e+03	2.19e+03	1.13e+03	2.47e+03	1.18e+03	1.36e+03	840	320	700	458	476	781	362	791	419	425
p.value	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
deviance	1e+03	1.46e+03	1.51e+03	977	819	936	1.26e+03	1.29e+03	914	765	3.43e+03	4.4e+03	4.48e+03	3.35e+03	3.12e+03	2.61e+03	3.18e+03	3.23e+03	2.58e+03	2.44e+03
df.residual	1.01e+04	9.98e+03	1.01e+04	9.97e+03	9.97e+03	1.01e+04	9.98e+03	1.01e+04	9.97e+03	9.97e+03	1e+04	9.93e+03	1e+04	9.92e+03	9.92e+03	1e+04	9.93e+03	1e+04	9.93e+03	9.92e+03
nobs.1	1.14e+04	1.13e+04	1.14e+04	1.13e+04	1.13e+04	1.14e+04	1.13e+04	1.14e+04	1.13e+04	1.13e+04	1.14e+04	1.13e+04	1.14e+04	1.13e+04	1.13e+04	1.14e+04	1.13e+04	1.14e+04	1.13e+04	1.13e+04
*** p < 0.001; ** p < 0.01; * p < 0.05.

Clean output