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The American Journal of Managed Care October 2017
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Boosting Workplace Wellness Programs With Financial Incentives
Alison Cuellar, PhD; Amelia M. Haviland, PhD; Seth Richards-Shubik, PhD; Anthony T. LoSasso, PhD; Alicia Atwood, MPH; Hilary Wolfendale, MA; Mona Shah, MS; and Kevin G. Volpp, MD, PhD
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Jeffrey D. Clough, MD, MBA; Michaela A. Dinan, PhD; and Kevin A. Schulman, MD
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Julia Thornton Snider, PhD; Katharine Batt, MD, MSc; Yanyu Wu, PhD; Mahlet Gizaw Tebeka, MS; and Seth Seabury, PhD
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Craig Jones, MD; Mary Kate Mohlman, PhD; David Jorgenson, MS; Karl Finison, MA; Katie McGee, MS; and Hans Kastensmith
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Xiao Xiang, PhD; Wendy Yi Xu, PhD, MS; and Randi E. Foraker, PhD, MA
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Tristan Cordier, MPH; S. Lane Slabaugh, PharmD; Eric Havens, MA; Jonathan Pena, MS; Gil Haugh, MS; Vipin Gopal, PhD; Andrew Renda, MD; Mona Shah, PhD; and Matthew Zack, MD

Boosting Workplace Wellness Programs With Financial Incentives

Alison Cuellar, PhD; Amelia M. Haviland, PhD; Seth Richards-Shubik, PhD; Anthony T. LoSasso, PhD; Alicia Atwood, MPH; Hilary Wolfendale, MA; Mona Shah, MS; and Kevin G. Volpp, MD, PhD
Financial incentives created under the Affordable Care Act can help promote employer wellness programs and support preventive services utilization.
In order to identify whether the program effect was greater for nonregular users of services versus users who had recently received the service, we repeated our models adding an indicator for receipt of the service in the prior year and its interaction with the incentive indicator. Although we used all years of data for each employer, we restricted the models to individuals present in the data for 24 continuous months. Introducing a lagged dependent variable (ie, prior receipt) could lead to bias if there is serial correlation in unobserved individual-level factors and it is greater or lesser at employers offering financial incentives.

We addressed observable differences between treatment and comparison employers with treatment-on-the-treated propensity score weighting. Our outcome models were weighted using inverse probability weights obtained from a series of propensity score models. The weighting models were estimated using boosted regression, as implemented in the “Toolkit for Weighting and Analysis of Nonequivalent Groups” package in R,15 which predicted the probability of being a treated observation in the year before those employers added financial incentives based on on age, gender, race and ethnicity, Census region, urban location, chronic conditions, and offer of high-deductible coverage with a health savings or health reimbursement account. Because the study used a DID comparison design, we weighted both treatment and control observations for each year to match the baseline year for the treatment group in order to balance the distribution of covariates both over time for each treatment group and between the treatment groups.16 This allowed us to control for any compositional changes over time in the treatment or comparison group as well as provide appropriate weights to observations in the control group. Separate weighting models were run for each treatment group by year combination. The high-deductible health plan variable was not balanced by weighting in all years and was removed from 6 of the 11 propensity score models, but was included in all outcome models. Weighting models were re-estimated for each of the cancer screening eligibility subgroups defined by age and gender. The propensity score weights were applied in calculating all reported results except sample sizes.

RESULTS

Study Population and Covariate Balance

The characteristics of individuals in the treated employers were substantively similar to those in the control employers in the baseline year after weighting (Table 1). Baseline rates for receiving a full biometric tests and breast cancer screening were 3 to 5 percentage points lower in the employers that did not introduce incentives. Thus, introducing financial incentives does not appear to be a selected response by employers to low baseline rates of targeted services. Covariate balance from propensity score weighting is typically measured by standardized mean differences (eAppendix Figure 2). The balance was successful; among 365 comparisons, all were below 0.12 and only 4 were greater than 0.10.

Preventive Visits and Blood Tests for Disease Screening

Baseline rates for preventive visits were an average 36.1% in the treatment group. In years when employers offered a wellness program with financial incentives, members were 7.7 percentage points more likely to have wellness visits (P <.05) (Table 2), a 21.3% increase. They were also 7.9 percentage points more likely to have cholesterol screenings and 7.1 percentage points more likely to have blood sugar tests for diabetes (P <.05). Results for the blood sugar and LDL-C tests were similar, and 95% of members who received the LDL-C test also received a blood sugar screening test. When considered as a set, the financial incentives resulted in 8.1% more individuals having all 3 biometric components (ie, a preventive visit and the 2 blood screenings) over a baseline rate of 19.0%, a 42.6% increase over baseline. Full regression results are shown in eAppendix Table 2.

Cancer Screening Tests

The wellness program with financial incentives was associated with smaller increases in cancer screening rates (Table 2). Financial incentives were associated with a 2.7 percentage-point increase in mammography (P <.05) and a 2.2 percentage-point increase in colorectal cancer screening (P <.01). Relative to baseline rates in the treatment group, these changes represent 5% to 7% improvements in screening rates. No differences were detected for cervical cancer screening rates over time in employers who offered incentives compared with those who did not.

Differences by Prior Service Use

Individuals who sought services in a given year were 17% to 30% more likely than others to receive them in the following year, controlling for other characteristics (Table 3). Estimates of the incentive program’s effect by prior receipt of service are mixed, but 2 results emerge. The program had a significantly greater impact on receiving the full biometric screen for those who had previously had one, by 6.1 percentage points (P <.05). In contrast, for the cervical and breast cancer screening tests, the program had a greater effect on individuals who did not receive screening in the prior year, meaning that here the incentives had a stronger effect on nonregular service users. The effect of the incentive on nonregular users was 3.1% (P <.01) for cervical cancer and 4.6% (P <.01) for breast cancer. We did not detect different impacts of incentives on colorectal cancer screening by prior year receipt. 

For all preventive tests, we found a strong association between having the test in the prior year and repeating it, independent of the incentive. Although federal screening recommendations do not support annual cancer screens for all individuals, we note that our data include a mix of individuals for whom an annual cancer screen would not be recommended and others who may have had positive results in the past and for whom annual exams are recommended.17-19

DISCUSSION

Within a single national insurer, workplace wellness programs paired with financial incentives were associated with increased utilization of targeted preventive services relative to worksite wellness programs without incentives. Increases ranged from 3% to 42% over baseline rates. The largest impacts were seen for receiving a full biometric screen (ie, preventive visit and 2 lab tests), as our study found that adding financial incentives to wellness programs nearly doubled the number of individuals receiving a full biometric screening exam. These results represent the first national data on the impact of adding financial incentives to wellness programs affecting all employees within a set of employers. Although data from randomized trials show significant impacts of financial incentives on health behaviors for self-selected populations of participants, these data speak to the impact across entire employer populations of implementing wellness programs with financial incentives geared toward increasing prevention and screening.

Efficiency and equity are potential challenges in wellness programs with financial incentives. Rewarding individuals who would receive services regardless of a financial reward is not an efficient use of resources, yet under the ACA, wellness programs implemented in employer settings are required to apply to all “similarly situated” employees and therefore do not allow programs to target just those individuals who would otherwise be unlikely to get a given program. Given this, it is interesting that we found that the incentive effect was similar for individuals who did or did not receive preventive visits, screening blood tests, and colorectal cancer screens in the past year and that it was more effective for individuals receiving prior-year services for the full biometric test. Thus, the financial incentives were not more systematically effective at bringing in “new” or infrequent users than “prior” or more frequent users for these services. This was not the case for cervical and breast cancer, where those who had not received screening in the prior year were more likely to be impacted by the reward.

Limitations

Our study has several limitations. First, we evaluated the incentive programs as they were implemented, which resulted in a limited range of incentive values. We cannot assess the impact of larger incentives. Second, we have taken several steps to mitigate potential selection bias on observed characteristics, yet we cannot rule out bias on unobserved characteristics that would occur if the trends in targeted service use systematically differed for the treatment versus comparison groups for reasons other than the addition of financial incentives. We were, however, able to rule out that employers with lower baseline use of targeted services were more likely to add financial incentives to their wellness programs. Third, because our analysis required at least 12 months of continuous enrollment, we lost members to attrition. If sicker employees were more likely to leave one set of employers than the other, this could bias our results. We addressed this issue partially through the use of propensity score weighting. Fourth, claims data have been found to underreport preventive services relative to medical records.20 Because our sample was composed of privately insured, continuously enrolled individuals, we believe this problem is attenuated. Moreover, any measurement error caused by our claims data should not differ systematically across the treatment and comparison employers or bias our results. Despite being the largest of its kind, the current study is limited to 15 intervention and 24 comparison employers, limiting the generalizability of the results. Finally, we were not able to assess impacts on health outcomes, as these were not included in the claims data.

CONCLUSIONS

We find that the addition of financial incentives to workplace wellness programs has a statistically significant impact on the receipt of targeted preventive care services. Although the magnitude of the incentives evaluated in this study were well below the federally determined maximum of 30% of premiums for outcomes-based incentives, the effectiveness of these programs signals that modest financial incentive programs in workplace settings can drive health behavior in desired directions. Because targeting financial incentives to particular groups, such as individuals who have not had preventive services in the past year, is challenging within the ACA framework, wellness programs may need to rely on other outreach efforts. Overall, our results highlight the potential promise of ACA-induced movement toward greater use of wellness programs in employment-based settings when they are paired with financial incentives.

Acknowledgments 

 
Copyright AJMC 2006-2017 Clinical Care Targeted Communications Group, LLC. All Rights Reserved.
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