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Table 3 Results on concurrent validity – linear additive model predicting the value of the health metric

From: Development of a metric for tracking and comparing population health based on the minimal generic set of domains of functioning and health

 

Number

Coefficient

SE

p-value

Intercept

 

73.61

0.44

<0.0001

Gender (female)

 

−0.56

0.34

0.0965

Education (middle)

 

3.47

0.41

<0.0001

Education (high)

 

4.67

0.41

<0.0001

Income (middle)

 

2.08

0.40

<0.0001

Income (high)

 

5.43

0.42

<0.0001

Health conditions:

High cholesterol

3546

−0.58

0.36

0.1108

Heart attack

741

−1.25

0.86

0.1459

Heart murmur

423

−1.26

0.84

0.1336

High blood pressure

4214

−2.44

0.35

<0.0001

Abnormal heart rhythm

820

−2.97

0.63

<0.0001

Angina

885

−3.31

0.80

<0.0001

Asthma

1260

−3.35

0.51

<0.0001

Cancer

571

−3.45

0.72

<0.0001

Other heart disease

303

−3.97

1.02

<0.0001

Diabetes

1063

−6.36

0.56

<0.0001

Osteoporosis

753

−7.46

0.65

<0.0001

Stroke

481

−8.44

0.81

<0.0001

Psychiatric condition

971

−9.92

0.57

<0.0001

Lung disease

544

−11.06

0.76

<0.0001

Arthritis

3816

−11.09

0.35

<0.0001

Heart failure

65

−12.38

2.17

<0.0001

Parkinson’s disease

79

−19.20

1.98

<0.0001

Dementia

154

−19.44

1.65

<0.0001

  1. Regression coefficients, standard errors (SE), and p-values resulting from the linear additive model predicting the value of the health metric for wave-4 data. For the health conditions, the number of cases with the respective health condition is additionally provided. Health conditions are sorted by increasing effect. The nonlinear effect of age is shown in Fig. 2
  2. The reference categories are male, low education, low income, and not having the respective health condition