Comparison of the Utility of Two Assessments for Explaining and Predicting Productivity Change: Well-Being Versus an HRA.

OBJECTIVE: To compare utility of employee well-being to health risk assessment (HRA) as predictors of productivity change. METHODS: Panel data from 2189 employees who completed surveys 2 years apart were used in hierarchical models comparing the influence of well-being and health risk on longitudinal changes in presenteeism and job performance. Absenteeism change was evaluated in a nonexempt subsample. RESULTS: Change in well-being was the most significant independent predictor of productivity change across all three measures. Comparing hierarchical models, well-being models performed significantly better than HRA models. The HRA added no incremental explanatory power over well-being in combined models. Alone, nonphysical health well-being components outperformed the HRA for all productivity measures. CONCLUSIONS: Well-being offers a more comprehensive measure of factors that influence productivity and can be considered preferential to HRA in understanding and addressing suboptimal productivity.

Comparing the contributions of well-being and disease status to employee productivity.

OBJECTIVE: To compare employee overall well-being to chronic disease status, which has a long-established relationship to productivity, as relative contributors to on-the-job productivity. METHODS: Data from two annual surveys of three companies were used in longitudinal analyses of well-being as a predictor of productivity level and productivity change among 2629 employees with diabetes or without any chronic conditions. RESULTS: Well-being was the most significant predictor of productivity cross-sectionally in a model that included disease status and demographic characteristics. Longitudinally, changes in well-being contributed to changes in productivity above and beyond what could be explained by the presence of chronic disease or other fixed characteristics. CONCLUSIONS: These findings support the use of well-being as the broader framework for understanding, explaining, and improving employee productivity in both the healthy and those with disease.

Well-being and employee health-how employees' well-being scores interact with demographic factors to influence risk of hospitalization or an emergency room visit.

The goal of this study was to determine the relationship between individual well-being and risk of a hospital event in the subsequent year. The authors hypothesized an inverse relationship in which low well-being predicts higher likelihood of hospital use. The study specifically sought to understand how well-being segments and demographic variables interact in defining risk of a hospital event (inpatient admission or emergency room visit) in an employed population. A retrospective study design was conducted with data from 8835 employees who completed a Well-Being Assessment questionnaire based on the Gallup-Healthways Well-Being Index. Cox proportional hazards models were used to examine the impact of Individual Well-Being Score (IWBS) segments and member demographics on hazard ratios (HRs) for a hospital event during the 12 months following assessment completion. Significant main effects were found for the influence of IWBS segments, sex, education, and relationship status on HRs of a hospital event, but not for age. However, further analysis revealed significant interactions between age and IWBS segments (P=0.005) and between age and sex (P<0.0001), indicating that the effects for IWBS segments and sex on HRs of a hospital event are mediated through their relationship with age. Overall, the strong relationship between low well-being and higher risk of an event in employees ages 44 years and older is mitigated in younger age groups. These results suggest that youth attenuates the risk engendered in poor well-being; therefore, methods to maintain or improve well-being as individuals age presents a strong opportunity for reducing hospital events.

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.

Remote physiological monitoring: clinical, financial, and behavioral outcomes in a heart failure population.

This article reports on the outcomes associated with remote physiological monitoring (RPM) conducted as part of a heart failure disease management program. Claims data, medical records, data transmission records, and survey results for 91 individuals ages 50-92 (mean 74 years) successfully completing a heart failure RPM program were analyzed for time periods before, during, and after the monitoring intervention. The program was associated with significant reductions in per member per month costs and emergency room and hospital utilization. More detailed analyses were performed for specific gender and age subgroups. Participant surveys indicated high levels of satisfaction, and improvements in self-perceived health status, self-efficacy, and self-management behaviors. This study is the first to assess the impact of a RPM program following removal of the monitoring equipment. The results indicate that RPM, as a component of a traditional disease management program, has a sustained, beneficial effect on participants' lifestyles after the monitoring period has ended.