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Table 3 Results of the path analysis

From: What do language barriers cost? An exploratory study among asylum seekers in Switzerland

Outcome variable

R2

Predictor variable

Figure

Estimate (=β)

Standard Error

t-value

p-value

eβ

Monthly costs (*)

35%

Health care usage (*)

a

0.2489

0.0512

4.8576

<.0001

 
  

Number of visits (*)

b

0.5620

0.0674

8.3437

<.0001

 
  

Language barriers: contrast between categories

c

     
  

How much more did individuals facing language barriers using an interpreter (1) cost compared to those facing language barriers but not using an interpreter (0)

 

0.0884

0.2785

0.3175

0.7509

1.09

  

How much more did individuals using an interpreter (1) cost compared to those without language barriers (0)

 

0.1949

0.1650

1.1810

0.2376

1.22

  

How much more did individuals with language barriers who did not use an interpreter (1) cost compared to those without language barriers (0)

 

0.1065

0.2450

0.4345

0.6640

1.11

Usage of health care services and material (*)

41%

Number of ICD diagnoses

d

0.4293

0.0286

14.9971

<.0001

 
  

Language barriers: contrast between categories

e

     
  

How much more health care did individuals facing language barriers using an interpreter (1) consume compared to those with language barriers but not using an interpreter (0)

 

0.6292

0.2538

2.4794

0.0132

1.88

  

How much more health care did individuals using an interpreter (1) consume compared to those without language barriers (0)

 

1.0195

0.1443

7.0637

<.0001

2.77

  

How much more health care did individuals with language barriers who did not receive interpreter services (1) consume compared to those without language barriers (0)

 

0.3902

0.2240

1.7418

0.0815

1.48

Number of visits (*)

40%

Health care usage (*)

f

0.4604

0.0276

16.6640

<.0001

 
  

Language barriers: contrast between categories

g

     
  

How many more visits did individuals with language barriers using an interpreter (1) pay to the HMO compared to those with language barriers but not using an interpreter (0)

 

-0.2838

0.1877

-1.5122

0.1305

0.75

  

How many more visits did individuals using an interpreter (1) pay to the HMO compared to those without language barriers (0)

 

-0.0412

0.1114

-0.3696

0.7117

0.96

  

How many more visits did individuals with language barriers who did not use an interpreter (1) pay to the HMO compared to those without language barriers (0)

 

0.2426

0.1652

1.4690

0.1418

1.27

  1. Explanation to the table: Outcome variables of the model are presented in the first column. Explanatory variables are listed in column three. The column in between (R2) represents the proportion of variability explained in a particular outcome variable by its set of predictor variables. Each of the relationships between predictor and outcome variables are indicated by the letters 'a' to 'g', which refer to their related arrows in Figure 1.
  2. Language barriers are the variables of primary interest. Since these were dummy coded, the regression coefficients in column 5 represent increases in the value of the outcome variable for each 1-unit increase in the dummy variable, i.e., the increase for the language barrier category indicated with (1) as compared to the reference category indicated with (0). For instance, comparing individuals facing language barriers using an interpreter (1) to individuals facing language barriers but not using an interpreter (0) shows that the former are associated with a monthly cost score that is 0.0884 times higher than the reference. Note that monthly costs, as all variables indicated with an asterisk (*) have been logarithmically transformed to a normal distribution, which can conveniently be interpreted in terms of relative changes presented in the last column. Patients using an interpreter generated exp(0.0884) = 9% more monthly costs compared to those facing language barriers but not using an interpreter.
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BMC Health Services Research

ISSN: 1472-6963

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