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. 2016 Apr 27;11(4):e0153778. doi: 10.1371/journal.pone.0153778

Table 3. Multilevel logistic regression analysis of choosing a private versus a public specialist in the 35–65 year-old population of Malmö, 2006, Values are odds ratios (OR) and 95% confidence interval (CI) unless stated otherwise.

Simple logistic regression analysis Multilevel logistic regression analysis
Model 1 Model 2 Model 3
Specific individual average effects
Men vs. women 0.96 (0.92–1.01) 0.94 (0.88–1.01) 0.94 (0.88–1.01)
Age groups
 35–39 Reference
 40–44 1.01 (0.93–1.09) 1.07 (0.94–1.20) 1.07 (0.94–1.20)
 45–49 1.02 (0.94–1.11) 1.22 (1.07–1.37) 1.22 (1.07–1.37)
 50–54 1.08 (1.00–1.17) 1.25 (1.10–1.41) 1.25 (1.10–1.41)
 55–59 1.21 (1.12–1.31) 1.30 (1.16–1.46) 1.30 (1.16–1.46)
 60–64 1.20 (1.10–1.30) 1.20 (1.06–1.35) 1.20 (1.06–1.35)
High vs. low income 2.13 (2.02–2.24) 1.14 (1.06–1.22) 1.13 (1.04–1.22)
Specific contextual average effects
High vs. low neighbourhood income 3.50 (2.13–5.78)
80% IOR 0.09–130.28
POOR (%) 33
General contextual effects*
Neighbourhood variance 4.479 (3.699–5.502) 3.980 (3.277–4.892)
PCV (%) 11
ICC (%) 57.8 (53.1–62.7) 54.9 (50.1–59.9)
MOR 7.53 (6.42–9.37) 6.71 (5.62–8.25)
AUC 0.600 (0.593–0.606) 0.895 (0.891–0.899) 0.895 (0.891–0.899)
AUC change* 0.295 0.000
Goodness of fit
DIC 44726 24647 24648
DIC change* -20079 1.28

IOR: interval odds ratio. POOR: proportion of opposed odds ratios. PCV: proportional change in the variance. ICC: intra-class correlation coefficient. MOR: median odds ratio. AUC: area under the receiver operating characteristic curve. DIC: Bayesian diagnostic information criterion.

*: change in relation to the previous model