Measures of association (fixed effects) obtained by two-level (patients and hospitals departments) multilevel logistic regression modelling one-year mortality for heart failure patients treated in clinical departments.

<p>Values are odds ratios (OR) and 95% confidence intervals (CI).</p

The role of the clinical departments for understanding patient heterogeneity in one-year mortality after a diagnosis of heart failure: A multilevel analysis of individual heterogeneity for profiling provider outcomes - Fig 2

<p><b>Ranking of 57 Danish departments for the three audit periods (A-C)</b> according to their one-year mortality for incident heart failure (21 June 2010–30 June 2013) using the overall average as reference. Values are logarithm odds ratios (i.e., shrunken residuals) with 95% confidence intervals (vertical lines) adjusted for age and gender (see model 2 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0189050#pone.0189050.t003" target="_blank">Table 3</a>). The figure also indicates the values of the departments intra-class correlation coefficients (ICC) for one-year mortality and the AUC.</p

Crude national mortality rates, number of heart failure patients, metrics on gender and age, and hospital departments included in the study.

<p>Crude national mortality rates, number of heart failure patients, metrics on gender and age, and hospital departments included in the study.</p

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.

<p>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.</p

Predicted prevalence of using a private physician in each neighborhood with 95% confidence intervals versus ranking.

<p>Predictions are for the reference individual in model 3, i.e., female, age 35–39, low income and living in a low income neighborhood.</p

Study Population.

<p>Flow diagram showing the selection of patients with first diagnosis of heart failure between 2007 and2009 who were included in the study population.</p

Characteristics of the population 35–65 year-olds in Malmö, 2006 by neighbourhood income.

<p>Characteristics of the population 35–65 year-olds in Malmö, 2006 by neighbourhood income.</p

Predicted prevalence of psychotropic drug use in each neighborhood with 95% confidence intervals versus ranking.

<p>Predictions are for the reference individual in model 3, i.e., female, age 35–39, high income and living in a high income neighborhood (model 3).</p

Receiver operating characteristics (ROC) curves and areas under the ROC curves (AUC) for the different models analyzed in the study.

<p>Model 1 (black line) is a simple logistic regression model including the individual risk score. Model 2 (grey line) is as model 1 but adding sex and ethnicity in categories. Model 3 is as model 2 but adding information on hospitals and wards in a multilevel logistic regression analysis. The ROC curve for model 3 is split showing the contribution of the ward level (thick dotted line) and of the hospital level (thin dotted line).</p

Measures of association (fixed effects) obtained by single (Models 1 and 2) and by three level (patients, wards and hospitals) multilevel logistic regression analysis (Models 3 and 4) modeling 30-day mortality after hospitalization from heart failure in patients who were admitted to Swedish hospitals (2007–2009).

<p>Values are odds ratios (OR) and 95% confidence intervals (CI) as well as Percentage of Opposite Odds Ratio (POOR).</p