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. 2014 Sep 3;4(9):e005768. doi: 10.1136/bmjopen-2014-005768

Table 5.

Variance and ICC for hospital-level random effects from multilevel logistic regression, all public and private hospitals in New South Wales, Australia (n=313)

Hypertension (N=15 279) Diabetes (N=4794) Heart disease (N=8307) Stroke (N=2480) Smoking (N=2099) Obesity (N=8162)
Hospital-level variance (SE)*
 Model 0 Patient factors 0.80 (0.10) 0.27 (0.06) 0.91 (0.12) 0.38 (0.10) 0.35 (0.09) 0.68 (0.14)
 Model 1 Model 0+hospital type (public/private) 0.65 (0.08) 0.27 (0.06) 0.71 (0.10) 0.16 (0.06) 0.35 (0.09) 0.69 (0.14)
 Model 2 Model 0+hospital remoteness 0.77 (0.09) 0.25 (0.05) 0.92 (0.12) 0.37 (0.10) 0.33 (0.08) 0.68 (0.14)
 Model 3 Model 0+hospital depth of coding 0.46 (0.06) 0.20 (0.05) 0.56 (0.08) 0.26 (0.08) 0.29 (0.08) 0.68 (0.14)
 Model 4 Model 0+hospital peer group 0.72 (0.09) 0.21 (0.05) 0.75 (0.10) 0.34 (0.09) 0.31 (0.08) 0.67 (0.14)
(ICC (%)† 19.5 7.6 21.6 10.4 9.6 17.1
(MOR† 2.34 1.64 2.48 1.80 1.76 2.19

*Patient-level variance in a logistic regression is set at π2/3=3.29.31

†ICC and MOR calculated from model 0 (ICC=hospital-level variance divided by total variance (hospital-level+patient-level); MOR is calculated as Inline graphic).30

ICC, intraclass correlation coefficient; MOR, median OR; N, number of patients who self-reported condition.