Evaluation of chronic care management (CCM) programs is necessary to determine the behavioral, clinical, and financial value of the programs. Financial outcomes of members who are exposed to interventions (treatment group) typically are compared to those not exposed (comparison group) in a quasi-experimental study design. However, because member assignment is not randomized, outcomes reported from these designs may be biased or inefficient if study groups are not comparable or balanced prior to analysis. Two matching techniques used to achieve balanced groups are Propensity Score Matching (PSM) and Coarsened Exact Matching (CEM). Unlike PSM, CEM has been shown to yield estimates of causal (program) effects that are lowest in variance and bias for any given sample size. The objective of this case study was to provide a comprehensive comparison of these 2 matching methods within an evaluation of a CCM program administered to a large health plan during a 2-year time period. Descriptive and statistical methods were used to assess the level of balance between comparison and treatment members pre matching. Compared with PSM, CEM retained more members, achieved better balance between matched members, and resulted in a statistically insignificant Wald test statistic for group aggregation. In terms of program performance, the results showed an overall higher medical cost savings among treatment members matched using CEM compared with those matched using PSM

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Hospital admissions are the source of significant health care expenses, although a large proportion of these admissions can be avoided through proper management of chronic disease. In the present study, we evaluate the impact of a proactive chronic care management program for members of a German insurance society who suffer from chronic disease. Specifically, we tested the impact of nurse-delivered care calls on hospital admission rates. Study participants were insured individuals with coronary artery disease, heart failure, diabetes, or chronic obstructive pulmonary disease who consented to participate in the chronic care management program. Intervention (n ¼ 17,319) and Comparison (n ¼ 5668) groups were defined based on records of participating (or not participating) in telephonic interactions. Changes in admission rates were calculated from the year prior to (Base) and year after program commencement. Comparative analyses were adjusted for age, sex, region of residence, and disease severity (stratification of 3 [least severe] to 1 [most severe]). Overall, the admission rate in the Intervention group decreased by 6.2% compared with a 14.9% increase in the Comparison group (P < 0.001). The overall decrease in admissions for the Intervention group was driven by risk stratification levels 2 and 1, for which admissions decreased by 8.2% and 14.2% compared to Comparison group increases of 12.1% and 7.9%, respectively. Additionally, Intervention group admissions decreased as the number of calls increased (P ¼ 0.004), indicating a dose-response relationship. These findings indicate that proactive chronic care management care calls can help reduce hospital admissions among German health insurance members with chronic disease.

An increase in chronic disease prevalence is contributing to health care cost growth and decreased quality of life in industrialized nations worldwide. Inadequate management of chronic diseases is a leading cause of hospitalizations and, thus, avoidable expenditures. In this study, we evaluated the impact of nurse-delivered care calls, the primary intervention of a proactive chronic care management (CCM) program, in a population aged 65 and older in Germany. In this analysis, hospital admission rates were evaluated among program enrollees who were diagnosed with diabetes, heart failure, coronary heart disease, or chronic obstructive pulmonary disease. The Intervention group comprised those members who participated in care calls (n=13,486), whereas the Comparison group included enrollees who did not participate in these calls (n=4,582). Changes in admission rates were calculated between the year prior to and year after program commencement. Comparative analyses were adjusted for age, sex, region of residence, and disease severity (stratification of 3 [least severe] to 1 [most severe]). Overall, a 6.0% decrease in admissions was observed among Intervention group members compared with an 18.9% increase among Comparison group members (P ≤ 0.0001). This decrease in admissions was driven by participants with the highest levels of risk. In addition, a dose-response relationship was observed in which admissions decreased with an increased number of care calls (P=0.0001). These results indicate that proactive CCM interventions are effective in reducing hospital admission rates in a senior population with chronic disease.

IntroductionCommunities are seeking to learn if and how they can improve the well-being of their residents. We therefore examined the impact of a community-led, collective-impact initiative, deployed through Blue Zones Project by Sharecare, aimed at improving health and well-being in one set of US communities.MethodsWe used data from cross-sectional surveys of the Well-Being Index (2010–2017) to assess how the Life Evaluation Index (LEI) in Hermosa Beach, Manhattan Beach and Redondo Beach in California (Beach Cities) changed over time and how this change compares with change for similar cities (Beach Cities-like) and for the USA as a whole. We examined types of interventions, perceived impacts, and relationships between intervention type and change in LEI.ResultsThe Beach Cities experienced greater increases in LEI than Beach Cities-like communities and the nation. The entire portfolio of interventions was positively associated with change in LEI in the Beach Cities (+1.12, p=0.012), with process-oriented interventions most closely associated with improvement.ConclusionsCommunity-led collective action that leverages community engagement and activation, strategic use of programming and large-scale built-environment and policy change can improve health and well-being at scale.