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Table 2 Explanatory variables significantly associated with veterinary treatment (yes = 1/no = 0), given a disease event (two-level logistic model) for cows using different estimating algorithms.

From: Herd and cow characteristics affecting the odds of veterinary treatment for disease – a multilevel analysis

  

LL a

PQL b

MCMC c

Variables

Categories

Estimate

SE

Estimate

SE

Estimate

SE

Fixed part:

       

Intercept

 

1.37

0.32

1.32

0.31

1.41

0.33

Another animal with an event at the same date

Yes

2.18

0.26

2.18

0.27

2.22

0.26

 

No

0

 

0

 

0

 

Breede

Swedish Holstein

0

 

0

 

0

 
 

Swedish Red

-0.82

0.33

-0.79

0.33

-0.85

0.35

 

Other/mixed

-0.86

0.52

-0.83

0.51

-0.90

0.54

Days in milk

< 7

0

 

0

 

0

 
 

7–69

-0.52

0.23

-0.53

0.23

-0.54

0.23

 

70–168

-0.93

0.24

-0.93

0.24

-0.95

0.24

 

> 168

-1.12

0.24

-1.13

0.24

-1.15

0.24

Disease complex

Udder

0

 

0

 

0

 
 

Metabolic

0.09

0.25

0.09

0.25

0.10

0.26

 

Lameness

-0.91

0.26

-0.91

0.26

-0.92

0.26

 

Reproductive

0.37

0.39

0.36

0.39

0.39

0.39

 

Other

0.16

0.34

0.16

0.33

0.17

0.34

Study month

January

0

 

0

 

0

 
 

April

0.31

0.19

0.31

0.19

0.31

0.20

 

July

1.31

0.20

1.31

0.20

1.33

0.21

 

October

1.20

0.20

1.20

0.20

1.23

0.21

Disease complex X Another animal with an event at the same date

Metabolic X Yes

-0.88

0.51

-0.88

0.51

-0.88

0.51

 

Lameness X Yes

-1.56

0.39

-1.55

0.39

-1.59

0.39

 

Reproductive X Yes

-2.13

0.62

-2.12

0.62

-2.15

0.63

 

Other X Yes

-0.04

0.66

-0.03

0.67

0.02

0.67

Random part:

       

Herd

 

2.33

0.48

2.26

0.38

2.61

0.54

  1. a Log likelihood.
  2. b Second-order penalized quasi-likelihood (PQL) estimates with restricted iterative generalised square algorithm.
  3. c Markov-chain Monte Carlo (MCMC) estimates.
  4. d Herd-level variable.