ArticlePloS one2017
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.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Socio-geographical disparities in cardiometabolic multimorbidity in Sweden: an Intersectional Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (I-MAIHDA).International journal for equity in health · 2025Article
- Geographic and socioeconomic differences in potentially inappropriate medication among older adults - applying a simplified analysis of individual heterogeneity and discriminatory accuracy (AIHDA) for basic comparisons of healthcare quality.BMC health services research · 2025Article
- Article
- Geographical and sociodemographic differences in discontinuation of medication for Chronic Obstructive Pulmonary Disease - A Cross-Classified Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA).Clinical epidemiology · 2020Article
- Observational
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- Albuminuria measurement in diabetic care: a multilevel analysis measuring the influence of accreditation on institutional performance.BMJ open quality · 2019Article
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Abstract
purposeTo evaluate the general contextual effect (GCE) of the hospital department on one-year mortality in Swedish and Danish patients with heart failure (HF) by applying a multilevel analysis of individual heterogeneity.
methodsUsing the Swedish patient register, we obtained data on 36,943 patients who were 45-80 years old and admitted for HF to the hospital between 2007 and 2009. From the Danish Heart Failure Database (DHFD), we obtained data on 12,001 patients with incident HF who were 18 years or older and treated at hospitals between June 2010 and June2013. For each year, we applied two-step single and multilevel logistic regression models. We evaluated the general effects of the department by quantifying the intra-class correlation coefficient (ICC) and the increment in the area under the receiver operating characteristic curve (AUC) obtained by adding the random effects of the department in a multilevel logistic regression analysis.
resultsOne-year mortality for Danish incident HF patients was low in the three audit years (around 11.1% -13.1%) and departments performed homogeneously (ICC ≈1.5% - 3.5%). The discriminatory accuracy of a model including age and gender was rather high (AUC≈ 0.71-0.73) but the increment in AUC after adding the department random effects into these models was only about 0.011-0.022 units in the three years. One-year mortality in Swedish patients with first hospitalization for heart failure, was relatively higher for 2007-2009 (≈21.3% - 22%) and departments performed homogeneously (ICC ≈ 1.5% - 3%). The discriminatory accuracy of a model including age, gender and patient risk score was rather high (AUC≈ 0.726-0.728) but the increment in AUC after adding the department random effects was only about 0.010-0.017 units in the three years.
conclusionUsing the DHFD standard benchmark for one-year mortality, Danish departments had a good, homogeneous performance. In reference to literature, Swedish departments had a homogeneous performance and the mortality rates for patients with first hospitalization for heart failure were similar to those reported since 2000. Considering this, if health authorities decide to further reduce mortality rates, a comprehensive quality strategy should focus on all Swedish hospitals. Yet, a complementary assessment for the period after the study period is required to confirm whether department performance is still homogeneous or not to determine the most appropriate action.
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