The well-being valuation model: a method for monetizing the nonmarket good of individual well-being.

The objective of this research is to advance the evaluation and monetization of well-being improvement programs, offered by population health management companies, by presenting a novel method that robustly monetizes the entirety of well-being improvement within a population. This was achieved by utilizing two employers' well-being assessments with medical and pharmacy administrative claims (2010-2011) across a large national employer (n = 50,647) and regional employer (n = 6170) data sets. This retrospective study sought to monetize both direct and indirect value of well-being improvement across a population whose medical costs are covered by an employer, insurer, and/or government entity. Logistic regression models were employed to estimate disease incidence rates and input-output modelling was used to measure indirect effects of well-being improvement. These methodological components removed the burden of specifying an exhaustive number of regression models, which would be difficult in small populations. Members who improved their well-being were less likely to become diseased. This reduction saved, per avoided occurrence, US$3060 of total annual health care costs. Of the members who were diseased, improvement in well-being equated to annual savings of US$62 while non-diseased members saved US$26. The method established here demonstrates the linkage between improved well-being and improved outcomes while maintaining applicability in varying populations.

The well-being 5: development and validation of a diagnostic instrument to improve population well-being.

Building upon extensive research from 2 validated well-being instruments, the objective of this research was to develop and validate a comprehensive and actionable well-being instrument that informs and facilitates improvement of well-being for individuals, communities, and nations. The goals of the measure were comprehensiveness, validity and reliability, significant relationships with health and performance outcomes, and diagnostic capability for intervention. For measure development and validation, questions from the Well-being Assessment and Wellbeing Finder were simultaneously administered as a test item pool to over 13,000 individuals across 3 independent samples. Exploratory factor analysis was conducted on a random selection from the first sample and confirmed in the other samples. Further evidence of validity was established through correlations to the established well-being scores from the Well-Being Assessment and Wellbeing Finder, and individual outcomes capturing health care utilization and productivity. Results showed the Well-Being 5 score comprehensively captures the known constructs within well-being, demonstrates good reliability and validity, significantly relates to health and performance outcomes, is diagnostic and informative for intervention, and can track and compare well-being over time and across groups. With this tool, well-being deficiencies within a population can be effectively identified, prioritized, and addressed, yielding the potential for substantial improvements to the health status, performance, and quality of life for individuals and cost savings for stakeholders.

Exploring robust methods for evaluating treatment and comparison groups in chronic care management programs.

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 (-$25.57 versus -$19.78, respectively). Collectively, the results suggest CEM is a viable alternative, if not the most appropriate matching method, to apply when evaluating CCM program performance.