Apples and Oranges: Four Definitions of Multiple Chronic Conditions and their Relationship to 30‐Day Hospital Readmission

Abstract

BACKGROUND Despite the importance of identifying populations with multiple chronic conditions (MCC) for policy-makers, researchers, and clinicians, definitions of MCC vary considerably. OBJECTIVES To 1) determine the extent of agreement among four commonly used definitions of MCC, and 2) compare each definition's ability to predict 30-day hospital readmissions. DESIGN Retrospective cohort study. SETTING National Medicare claims data. PARTICIPANTS Random sample of Medicare beneficiaries experiencing hospital discharge from 2005-2009 (n=710,609). MEASUREMENTS We measured baseline chronic conditions for each participant using four definitions of MCC. The primary outcome was all-cause 30-day hospital readmission. We measured agreement between MCC definitions and calculated sensitivities and specificities for each definition's ability to identify patients experiencing a future readmission. We used logistic regression to assess the ability of each MCC definition to predict 30-day hospital readmission. RESULTS The sample prevalence of hospitalized Medicare beneficiaries with ≥2 chronic conditions ranged from 18.6% (ACG) to 92.9% (CCW) (n=710,609). There was slight to moderate agreement (kappa 0.03-0.44) between pair-wise combinations of MCC definitions. CCW-defined MCC was the most sensitive (Se 95.4%, Sp 7.4%) and ACG-defined MCC was the most specific (Se 32.7%, Sp 83.2%) predictor of having a hospital readmission. In the fully adjusted model, the risk of readmission was higher for those with c-SNP-defined MCC (OR 1.50, 95% CI 1.47-1.52), CCI-defined MCC (OR 1.45, 95% CI 1.42-1.47), ACG-defined MCC (OR 1.22, 95% CI 1.19-1.25), and CCW-defined MCC (OR 1.15, 95% CI 1.11-1.19) compared to those without MCC. CONCLUSION MCC definitions demonstrate poor agreement and should not be used interchangeably. The two definitions with the greatest agreement (CCI and c-SNP) were also the best predictors of 30-day hospital readmissions.

Measuring multimorbidity in hospitalised patients using linked hospital episode data: comparison of two measures

“…Prevalence did not seem to increase with the number of conditions included, but there were methodological differences in how the measures were operationalised which may explain this. Using the Charlson measure, Dattalo et al [23] reported a multimorbidity prevalence of 36.8%, which was higher than we found in patients of a similar age (≥60 years, 23.8%). However, we used only inpatient admission data, whereas Dattalo et al [23] included inpatient and outpatient data to identify Charlson conditions [23].…”

“…Although several studies have compared weighted measures [33][34][35][36], to our knowledge only two previous studies compared count-based multimorbidity measures in hospitalised patients [15,23]. Neither of these studies used the Tonelli measure (based on the landmark Barnett study [24]), although both included a count of Charlson conditions.…”

“…Neither of these studies used the Tonelli measure (based on the landmark Barnett study [24]), although both included a count of Charlson conditions. Dattalo et al [23] compared four count-based measures in patients aged 65 and older, based on ICD coding. Comparing Charlson to the other three measures, they reported Kappa values of 0.07, 0.36 and 0.44 -lower agreement than we found when comparing Charlson with Tonelli (Kappa 0.57).…”

“…We are aware of only two studies, however, that have directly compared any simple count-based measures in hospitalised patients [15,23]. Dattalo et al [23] compared four count-based measures based on ICD coding, but focussed on patients aged 65 years and older. Prevalence estimates varied depending on measure, and there was an increased risk of 30-day readmission for patients with multimorbidity for each of the four measures.…”

Self‐care of patient and caregiver DyAds in multiple chronic conditions: A LongITudinal studY (SODALITY) protocol

“…Currently, 20% of the European population is over 65 years old and this percentage is expected to increase to 25% by 2030 (Christensen, Doblhammer, Rau, & Vaupel, 2009;Lunenfeld & Stratton, 2013). This older population is more likely to suffer from multiple chronic conditions (MCCs)-the coexistence of two or more chronic illnesses in the same person, at the same time (Dattalo et al, 2017). In Europe, 48.7% of older adults live with at least two chronic illnesses (Leadley, Armstrong, Lee, Allen, & Kleijnen, 2012).…”