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Table 4 Multilevel logistic regression analysis of frequency of ICD coded visits

From: Validity of registration of ICD codes and prescriptions in a research database in Swedish primary care: a cross-sectional study in Skaraborg primary care database

 

Model A

Model B

Fixed effects

 

OR (95% CI)

Patient age group

  

1 (-28)

-

REF

2 (29-49)

-

0.86 (0.84-0.89)

3 (50-67)

-

0.84 (0.82-0.87)

4 (68-)

-

0.75 (0.73-0.77)

Patient sex

  

Female

-

REF

Male

-

0.98 (0.96-1.00)

Type of visit

  

Planned

-

REF

Not planned

-

1.44 (1.41-1.47)

Random effects

  

HCC Variance (95% CI)

0.76 (0.40-1.54)

0.76 (0.41-1.50)

MOR (95% CI)

2.30 (1.82-3.26)

2.29 (1.84-3.22)

Physician Variance (95% CI)

2.28 (2.05-2.55)

2.25 (2.02-2.53)

MOR(95% CI)

4.22 (3.92-4.58)

4.19 (3.88-4.56)

HCC+Physician Variance

3.04

3.01

MOR

5.23

5.28

PCV

  

HCC

-

0.3%

Physician

-

1.3%

HCC+Physician

-

0.9%

 

303170.55

301079.69

DIC (MCMC)

  
  1. Outcome variable at the visit level, ICD coding (yes/no). Extracted from SPCD during 2002-2003 for the 24 Health Care Centres in Skaraborg Primary Care.
  2. OR = odds ratio, CI = credible interval, MOR = median odds ratio, DIC = Deviance Information Criterion
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BMC Medical Informatics and Decision Making

ISSN: 1472-6947

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