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Table 4 Regression Results from Three Models, n = 6455 PCSA-level observations

From: Spatial analysis of elderly access to primary care services

 

OLS Model1

Spatial Lag Model2

IV Spatial Lag Model3

Variable

Coeff

St. Error

Coeff

St. Error

Coeff

St. Error

XMEN

-198.82*

15.636

-119.50*

12.728

-142.79*

14.530

XDUAL

-359.43*

18.393

-293.70*

12.131

-284.88*

15.777

XBLACK

-33.69*

4.601

-22.69*

3.283

-21.65*

3.948

XOTHER

-40.55

17.595

-11.49

7.397

-61.30*

12.695

XDIED

672.79*

55.498

636.97*

38.625

562.14*

47.204

XOLDER

-648.11*

23.509

-482.83*

16.024

-476.86*

21.749

RISK

12.83

5.529

0.99

5.458

5.34

4.709

HIQUINT

845.10*

31.274

678.99*

20.579

661.21*

27.717

XDIAB

50.47*

9.773

43.41*

4.857

46.42*

8.126

(1) XELDERPOV

24.45

18.154

21.58

14.155

21.25

15.224

POVRATIO

-2.36*

0.910

-0.64

0.834

-1.11

0.780

(2) XTRURELD

-4.89*

1.972

-1.13

1.870

-0.99

1.716

RURATIO

0.09

0.076

0.14

0.087

0.04

0.068

(1)*(2)

84.67*

18.235

33.17

14.061

43.29*

15.591

XLIVALONE

-3.24

10.477

-11.31

8.078

4.39

8.970

XLCOMUTE

72.25*

6.889

43.14*

5.772

56.98*

6.039

XPOORNE

-90.97*

19.722

-76.49*

9.753

-29.80

14.875

PDENSITY

0.00*

0.000

0.00*

7.557

0.00*

0.000

BEDREHAB

-0.08*

0.016

-0.08*

0.020

-0.08*

0.014

VISITS

-0.50

0.212

-0.54*

0.128

-0.63*

0.174

TOTDOCS

1.79

0.819

0.49

0.626

0.92

0.725

ALT_DOC

-87.09*

8.399

-31.05*

7.246

-38.23*

7.780

(3) IMG_RATIO

4.42*

0.993

3.68*

0.577

4.73*

0.841

(1)*(2)*(3)

-23.64*

7.593

-19.33*

4.273

-22.40*

6.270

MCPENE00

-5.98

3.289

-10.28*

3.174

-7.52*

2.909

CINCREASE

-0.62

0.786

-1.22

0.826

-1.09

0.690

XHMO00

-13.62*

3.147

-3.04

2.864

-4.91

2.748

XHMODIF

-2.36*

0.842

-2.45*

0.666

-2.97*

0.717

XPPO00

-38.77*

5.049

-22.09*

4.714

-23.20*

4.405

XPPODIF

20.50*

3.505

14.83*

3.115

11.51*

2.998

SHRLARG3

-0.07*

0.021

-0.02

0.020

-0.06*

0.018

PRICE00A

0.02*

0.004

0.01*

0.003

0.01*

0.004

ECOV97_9

-0.32*

0.052

-0.12

0.047

-0.19*

0.048

W_ACSC

  

0.42*

0.012

0.33*

0.021

N

6,475

6,475

6,475

GOF measure 4

0.7743882

0.774075

0.775249

Log Likelihood

  

-28748.9

  
  1. 1Model estimated using SYSTAT with heteroskedasticity-corrected standard errors. 2Model estimated using GeoDa. 3Model estimated using PYTHON programming in R, with heteroskedasticity-corrected standard errors. 4 To make this comparable across models, we report the correlation between observed ACSC rates and predicted values from each model. For the lag or IV model, predictions properly account for endogeneity of the lag term or for the degrees of freedom lost in instrumentation. *These coefficients are statistically significant at the 0.01 level.
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International Journal of Health Geographics

ISSN: 1476-072X