Predicting cost of care using self-reported health status data.

BACKGROUND: We examined whether self-reported employee health status data can improve the performance of administrative data-based models for predicting future high health costs, and develop a predictive model for predicting new high cost individuals. METHODS: This retrospective cohort study used data from 8,917 Safeway employees self-insured by Safeway during 2008 and 2009. We created models using step-wise multivariable logistic regression starting with health services use data, then socio-demographic data, and finally adding the self-reported health status data to the model. RESULTS: Adding self-reported health data to the baseline model that included only administrative data (health services use and demographic variables; c-statistic = 0.63) increased the model" predictive power (c-statistic = 0.70). Risk factors associated with being a new high cost individual in 2009 were: 1) had one or more ED visits in 2008 (adjusted OR: 1.87, 95 % CI: 1.52, 2.30), 2) had one or more hospitalizations in 2008 (adjusted OR: 1.95, 95 % CI: 1.38, 2.77), 3) being female (adjusted OR: 1.34, 95 % CI: 1.16, 1.55), 4) increasing age (compared with age 18-35, adjusted OR for 36-49 years: 1.28; 95 % CI: 1.03, 1.60; adjusted OR for 50-64 years: 1.92, 95 % CI: 1.55, 2.39; adjusted OR for 65+ years: 3.75, 95 % CI: 2.67, 2.23), 5) the presence of self-reported depression (adjusted OR: 1.53, 95 % CI: 1.29, 1.81), 6) chronic pain (adjusted OR: 2.22, 95 % CI: 1.81, 2.72), 7) diabetes (adjusted OR: 1.73, 95 % CI: 1.35, 2.23), 8) high blood pressure (adjusted OR: 1.42, 95 % CI: 1.21, 1.67), and 9) above average BMI (adjusted OR: 1.20, 95 % CI: 1.04, 1.38). DISCUSSION: The comparison of the models between the full sample and the sample without theprevious high cost members indicated significant differences in the predictors. This has importantimplications for models using only the health service use (administrative data) given that the past high costis significantly correlated with future high cost and often drive the predictive models. CONCLUSIONS: Self-reported health data improved the ability of our model to identify individuals at risk for being high cost beyond what was possible with administrative data alone.

Toward a Contemporary Definition of Health.

Aristotle saw that the striving of humanity was toward being well - a physical, mental and spiritual state where life flourished. A contemporary definition of health recognizes that disease and disability can and often do co-exist with wellness. In this new conception, health is transformed from a state that requires the absence of disease to a state where the central theme is the fullness of life. Health becomes not a static state of being, but a dynamic quality of living where body, mind, and spirit are fully employed to make the most of each day.

The Role of Incentives in Health - Closing the Gap.

Incentives motivate individuals to act in a certain way. Incentives are everywhere and in everything; they are woven into the very fabric of our lives. To address the issue of spiraling health care costs, incentive programs must be put into place to discourage the behaviors driving the growth of these costs. With nearly 75% of all health care costs associated with chronic diseases, most of which are preventable, the value of incentives must be recognized in policy and practice. Incentives can drive behavior, help realign the system, and improve the nation's health. Behavior change incentive programs have been successful for some large organizations in dramatically controlling health care costs when incorporated into an integrated plan redesign. It is necessary to both understand the types of incentives that are impactful and integrate these incentives into the plan design, the workplace environment, retail, education, and communities in order to impact the health of our nation.

Trends in Biometric Health Indices Within an Employer-Sponsored Wellness Program With Outcome-Based Incentives.

PURPOSE: To evaluate changes in employees' biometrics over time relative to outcome-based incentive thresholds. DESIGN: Retrospective cohort analysis of biometric screening participants (n = 26 388). SETTING: Large employer primarily in Western United States. PARTICIPANTS: Office, retail, and distribution workforce. INTERVENTION: A voluntary outcome-based biometric screening program, incentivized with health insurance premium discounts. MEASURES: Body mass index (BMI), cholesterol, blood glucose, blood pressure, and nicotine. ANALYSIS: Followed were participants from their first year of participation, evaluating changes in measures. RESULTS: On average, participants who did not meet the incentive threshold at baseline decreased their BMI (1%), glucose (8%), blood pressure (systolic 9%, diastolic 8%), and total cholesterol (8%) by year 2 with improvements generally sustained or continued during each additional year of participation. CONCLUSION: On average, individuals at high health risk who participated in a financially incentivized biometric assessment program improved their health indices over time. Further research is needed to understand key determinants that drive health improvement indicated here.