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Table 3 Regression results – OLS compared with spatial autoregressive model, Poland

From: Deaths during the first year of the COVID-19 pandemic: insights from regional patterns in Germany and Poland

 

COVID-19 deaths

Excess deaths

Difference: excess minus COVID-19 deaths

OLS

Spatial error model

Spatial lag model

OLS

Spatial error model

Spatial lag model

OLS

Spatial error model

Spatial lag model

coeff

SE

coeff

SE

coeff

SE

coeff

SE

coeff

SE

coeff

SE

coeff

SE

coeff

SE

coeff

SE

Pop. aged 50-69 (%)

0.021*

(0.012)

0.026*

(0.014)

0.016

(0.011)

0.012

(0.024)

0.020

(0.026)

0.013

(0.024)

−0.009

(0.025)

− 0.003

(0.026)

− 0.007

(0.024)

Pop. aged 70-84 (%)

0.062***

(0.022)

0.049**

(0.024)

0.052**

(0.021)

0.057

(0.044)

0.041

(0.047)

0.050

(0.044)

− 0.005

(0.045)

− 0.018

(0.047)

− 0.009

(0.044)

Pop aged 85+ (%)

−0.110*

(0.060)

−0.064

(0.064)

−0.080

(0.056)

0.022

(0.118)

0.055

(0.125)

0.033

(0.117)

0.132

(0.120)

0.152

(0.125)

0.135

(0.119)

Employed in agricul. (%)

0.004***

(0.001)

0.002*

(0.001)

0.002**

(0.001)

−0.002

(0.002)

−0.003

(0.003)

−0.002

(0.002)

−0.006**

(0.002)

−0.007**

(0.003)

−0.006**

(0.002)

Hospital beds (per 1 K)

0.021***

(0.007)

0.015**

(0.007)

0.018***

(0.006)

0.025*

(0.014)

0.022

(0.014)

0.025*

(0.013)

0.003

(0.014)

0.003

(0.014)

0.004

(0.014)

Pop. density (p/1000 sqm)

−0.063*

(0.036)

−0.057

(0.036)

−0.061*

(0.034)

−0.098

(0.072)

−0.095

(0.073)

−0.095

(0.071)

−0.035

(0.073)

−0.035

(0.074)

−0.033

(0.072)

Constant

−0.234

(0.275)

−0.258

(0.318)

−0.425

(0.260)

1.282**

(0.545)

1.207**

(0.581)

1.012*

(0.580)

1.517***

(0.553)

1.439**

(0.590)

1.291**

(0.573)

[depvar]

  

0.489***

(0.080)

   

0.137

(0.108)

  

0.142

(0.109)

e.[depvar]

 

0.523***

(0.085)

  

0.172

(0.118)

  

0.181

(0.111)

 

var(e.[depvar])

 

0.076

(0.006)

0.076

(0.006)

 

0.330

(0.024)

0.331

(0.024)

 

0.339

(0.025)

0.340

(0.025)

Diagnostics:

Measures of fit:

AIC

154.260

128.480

127.093

674.497

676.484

676.949

685.361

686.825

687.719

BIC

181.841

163.942

162.554

702.078

711.946

712.411

712.942

722.286

723.181

Tests for spatial error dependence:

Lagrange multiplier stat.

35.919

  

1.790

  

2.580

  

Lagrange multiplier p-value

0.000

  

0.181

  

0.108

  

Moran’s I z-value

6.545

  

1.692

  

1.972

  

Moran’s I p-value

0.000

  

0.091

  

0.049

  

Tests for spatial lag dependence:

Lagrange multiplier stat.

42.511

  

1.657

  

1.733

  

Lagrange multiplier p-value

0.000

  

0.198

  

0.188

  

Wald test of spatial terms:

chi2

 

38.307

37.270

 

2.112

1.587

 

2.650

1.687

Prob > chi2

 

0.000

0.000

 

0.146

0.208

 

0.104

0.194

  1. Source: own calculations based on county level data as described in Source notes for Figs. 1 and 3
  2. Notes: Number of observations: 380. *p < 0.1, **p < 0.05, ***p < 0.01. Spatial error model – with spatially lagged errors, maximum likelihood estimator. Spatial lag model – with spatially lagged dependent variable, maximum likelihood estimator. The estimated variance inflation factor for conditioning variables varies between 1.66 and 3.78
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BMC Public Health

ISSN: 1471-2458

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