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Testing Novel Patient Financial Incentives to Increase Breast Cancer Screening

Elizabeth Levy Merrick, PhD, MSW; Dominic Hodgkin, PhD; Constance M. Horgan, ScD; Laura S. Lorenz, PhD; Lee Panas, MS; Grant A. Ritter, PhD; Paul Kasuba, MD; Debra Poskanzer, MD; and Renee Altman Nef
This study tested 3 financial incentives encouraging breast cancer screening (mammograms) among women deemed overdue. None were effective overall; "person-centered" incentives worked in the most recently screened subgroup.
The percent of members receiving a mammogram during the study varied significantly by several characteristics (Table 2), including age, ranging from 10.3% of members aged 60 to 69 years to 14.3% of those aged 40 to 49 years (P <.001). Mammography rates were significantly higher for members in 3-digit zip codes where the mean annual household income was $70,000 or higher (P <.05).

Prior mammogram history was significantly associated with receipt of mammogram during the study (P <.001). About 23% of members with a mammogram claim in the most recent year prior to study-defined gap (2009) received a mammogram during the study. By contrast, the mammogram receipt rate was 17% among those with a prior mammogram recorded only in 2008 or earlier, and 7% among those with no prior mammogram claim. Product type, density of mammography facilities, and enrollment throughout study were not significantly associated with mammogram receipt.

Multivariate Analyses

Table 3 shows the results of logistic regression analysis predicting mammogram receipt, for the full sample. Incentive group variables are not significant predictors, nor are there significant differences among them, given that their 95% confidence intervals overlap. Older age group coefficients are significant and negative (odds ratios [ORs], 0.75 and 0.71; P <.01), relative to sample members aged 40 to 49 years. Having a mammogram in the most recent year (2009) before the screening gap was strongly predictive of mammogram receipt during the study (OR, 4.04; P <.0001). Having a prior mammogram, but only before 2009, was also a positive predictor (OR, 2.82; P <.0001). Longer plan enrollment was negatively associated with mammogram receipt (OR, 0.998; P <.05).

In an exploratory subgroup analysis, we estimated separate regression models for members with, versus without, prior mammogram in the most recent year (2009) before the screening gap (data not shown). This was to allow for the possibility that independent variables could have a different effect on the recently screened subgroup possibly most likely to be amenable to mammogram receipt. Among members with a recent prior mammogram (2009), those in the person-centered/choice group had a significantly higher predicted probability of mammogram receipt during the study relative to controls (26.7% vs 18.9%; P <.05). There was no significant difference in predicted probability of mammogram receipt for $15 gift card and lottery group members relative to controls. Among members without a recent prior mammogram, none of the incentive groups had significantly different predicted probabilities of mammogram receipt, compared with controls.

Incentive Preferences in Person-Centered/Choice Group

Among all members in the person-centered/choice group, only 8% completed and returned the preference forms; 6% preferred the $15 gift card and 2% preferred the lottery (data not shown). However, among the subset who actually received a mammogram during the study, a much larger percentage (38%) returned the preference form: 33% preferred the $15 gift card and 5% preferred the lottery.

DISCUSSION
This study found that none of the 3 low-cost incentive approaches were effective overall in increasing screening mammogram receipt among eligible women aged 42 to 69 years who had not had a mammogram in the prior 2.6 years. However, in exploratory analyses, person-centered (choice-based) incentives did have a positive impact on screening among the subset with a mammogram during the most recent year prior to the study-defined screening gap. The $15 gift card and the lottery approach did not have an impact even in this subgroup.

There are several possible factors contributing to the lack of overall effect. First, it may be that higher-cost incentives are required, especially in a relatively high-income population. This privately insured group lived in zip codes with relatively high income and education levels. Second, targeting incentives only to members who had not been screened in 2.6 years likely represents a harder-to-influence group than, for instance, offering the incentive to everyone eligible. Furthermore, updated and sometimes conflicting guidelines have been issued regarding mammography for breast cancer screening.20 The media has widely covered this controversy, especially relating to women in their forties. This backdrop of confusion, anxiety, and strong feelings20,21 might reduce responsiveness to incentives.

The finding that the person-centered/choice incentive did increase likelihood of screening among women with a more recent mammogram is thought-provoking, though only exploratory in nature. Members of this group may have been procrastinating or simply not getting around to being screened, rather than having strong attitudinal or other barriers. The offer of a small incentive might have gotten their attention. Indeed, several members commented along these lines in qualitative interviews. For example, one member stated, “I knew I had to get it [a mammogram] anyway, then I said, ‘Oh, okay, this is it. I’m just gonna make the appointment.’” Another member commented regarding the incentive amount, “It was negligible… It [that an incentive was offered] was more the point.” 

The fact that in the subgroup with more recent screening history, only the person-centered/choice approach had an impact, provides partial support for the hypothesis that this approach may be more effective than one-size-fits-all incentives. This would, of course, need to be confirmed in future studies. The difference in findings for the overall sample and more recently screened members underlines possible advantages of tailoring incentives. This is consistent with previous findings that screening barriers vary by prior screening status and interventions should be tailored accordingly.22 However, this must be done consistent with ACA requirements, which may restrict ability to incentivize only a subgroup of screening-eligible members based on screening history. In any case, many organizations would be concerned about “rewarding” only individuals who had not regularly obtained screening—including concerns about perverse incentives—and would prefer to incentivize all eligible members. One possibility might be to offer incentives to all eligible members (to include a broader segment of members who might be most amenable to modest incentives), while adding other types of interventions for those without a recent history of screening. These additional interventions might include personal phone calls, educational interventions, or personal physician-signed letters.23-26 This stepped approach (“incentives plus”) could be a focus of future research.

Among those offered a choice, the gift card was considerably more popular than the lottery option, and many did not express a preference. In future research, it would be valuable to examine person-centered incentives in different contexts and populations, identifying the most appealing choice of incentives within budget and administrative constraints.

Limitations

Study limitations include the fact that the HEDIS mammography measure includes some mammograms performed for diagnostic rather than screening purposes. Mammography rates could therefore be somewhat overstated, but this would be true across study arms. Also, we could only observe screening through claims data at this health plan, not elsewhere. Another limitation is that immediate incentive delivery was infeasible because screening was ascertained through claims. Behavioral economics emphasizes that incentives are more salient, thus more effective, when paid very soon after completion of targeted behavior. However, when adherence monitoring relies on administrative data, rapid payment may be infeasible. The same approach taken here might have more impact if incentives were delivered within organizations that could promptly identify screening (eg, mammography facilities, medical groups with electronic health records).

CONCLUSIONS
Study results indicate that, at least in this privately insured sample, very low-cost financial incentives may not be effective overall for health plan members overdue for mammograms. However, exploratory analyses found that for a recently screened subgroup likely to be more amenable to screening, person-centered (choice-based) incentives did have an impact. Further research is indicated regarding higher-value incentives, testing modest incentives in a lower-income population, and further exploring person-centered incentives.

Acknowledgments

The authors thank Elizabeth Goheen, formerly of Tufts Health Plan, for her work on data collection and analysis; Eve Wittenberg, MPP, PhD, for consultation; and Michele Hutcheon for manuscript preparation assistance.

Author Affiliations: Brandeis University, Institute for Behavioral Health, Heller School for Social Policy and Management (ELM [formerly at Brandeis University], DH, CMH, LL, LP, GR), Waltham, MA; Tufts Health Plan (PK, DP, RAN), Watertown, MA.

Source of Funding: The Robert Wood Johnson Foundation’s Pioneer Portfolio and the Donaghue Foundation.

Author Disclosures: Dr Poskanzer is a member of the boards of the Massachusetts Coalition for Prevention of Medical Errors and Massachusetts Health Quality Partners. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (ELM, DH, CMH, PK, RAN); acquisition of data (ELM, LL, DP, RAN); analysis and interpretation of data (ELM, DH, CMH, LL, LP, GAR, PK, DP, RAN); drafting of the manuscript (ELM, DH, CMH, GAR); critical revision of the manuscript for important intellectual content (DH, CMH, LL, LP, RAN); statistical analysis (ELM, DH, LP, GAR); provision of patients or study materials (DP); obtaining funding (ELM, CMH); administrative, technical, or logistic support (DP); and supervision (PK).

Address correspondence to: Dominic Hodgkin, PhD, Brandeis University, Institute for Behavioral Health, Heller School for Social Policy and Management, 415 South St, Mailstop 035, Waltham, MA 02454-9110. E-mail: hodgkin@brandeis.edu.
REFERENCES
 
1. Towers Watson/National Business Group on Health. Performance in an era of uncertainty [17th annual Towers Watson/National Business Group on Health Employer Survey on Purchasing Value in Health Care]. http://www.slideshare.net/TowersWatson/towers-watsonnbgh2012. Published 2012. Accessed October 20, 2015.

2. Natchek SM, Kabacinski CJ, Mrkvicka N, Held J, Bonner PA; Bonner PA, ed. Value-Based Health Care Baseline Benchmarking Survey: Multiemployer and Public Employer Plans. Brookfield, WI: International Foundation of Employee Benefit Plans; 2011.

3. Improving quality and patient experience: the state of health care quality 2013. National Committee for Quality Assurance website. https://www.ncqa.org/Portals/0/Newsroom/SOHC/2013/SOHC-web_version_report.pdf. Published October 2013. Accessed October 20, 2015.

4. Kahneman D, Tversky A. Prospect theory: an analysis of decision under risk. Econometrica. 1979;47(2):263-292.

5. Fair AM, Monahan PO, Russell K, Zhao Q, Champion VL. The interaction of perceived risk and benefits and the relationship to predicting mammography adherence in African American women. Oncol Nurs Forum. 2012;39(1):53-60.

6. Kane RL, Johnson PE, Town RJ, Butler M. Economic Incentives for Preventive Care [evidence report/technology assessment No. 101, AHRQ publication No. 04-E024-1]. Agency for Healthcare Research and Quality website. http://archive.ahrq.gov/downloads/pub/evidence/pdf/ecinc/ecinc.pdf. Published August 2004. Accessed October 2015.

7. Preventive services covered under the Affordable Care Act: 22 covered preventive services for women, including pregnant women. HHS website. http://www.hhs.gov/healthcare/facts/factsheets/2010/07/preventive-services-list.html#CoveredPreventiveServicesforWomenIncludingPregnantWomen. Published September 23, 2010. Reviewed September 27, 2012. Accessed October 15, 2015.

8. Sutherland K, Christianson JB, Leatherman S. Impact of targeted financial incentives on personal health behavior: a review of the literature. Med Care Res Rev. 2008;65(suppl 6):36S-78S.

9. Mehrotra A, An R, Patel DN, Sturm R. Impact of a patient incentive program on receipt of preventive care. Am J Manag Care. 2014;20(6):494-501.

10. Stone EG, Morton SC, Hulscher ME, et al. Interventions that increase use of adult immunization and cancer screening services: a meta-analysis. Ann Intern Med. 2002;136(9):641-651.

11. Slater JS, Henly GA, Ha CN, et al. Effect of direct mail as a population-based strategy to increase mammography use among low-income underinsured women ages 40 to 64 years. Cancer Epidemiol Biomarkers Prev. 2005;14(10):2346-2352.

12. Haisley E, Volpp KG, Pellathy T, Loewenstein G. The impact of alternative incentive schemes on completion of health risk assessments. Am J Health Promot. 2012;26(3):184-188.

13. Volpp KG, Loewenstein G, Troxel AB, et al. A test of financial incentives to improve warfarin adherence. BMC Health Serv Res. 2008;8:272.

14. Kullgren JT, Dicks TN, Fu X, et al. Financial incentives for completion of fecal occult blood tests among veterans: a 2-stage, pragmatic, cluster, randomized, controlled trial. Ann Intern Med. 2014;161(suppl 10):S35-S43.

15. Merrick EL, Hodgkin D, Horgan CM. Incentives to shape health behaviors: how can we make them more person-centered? J Workplace Behav Health. 2014;29(1):19-31.

16. National Committee for Quality Assurance. HEDIS Measurement, Technical Specifications. Washington, DC: 2013.

17. Quan H, Sundararajan V, Halfon P, et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care. 2005;43(11):1130-1139.

18. Ludman EJ, Ichikawa LE, Simon GE, et al. Breast and cervical cancer screening specific effects of depression and obesity. Am J Prev Med. 2010;38(3):303-310.

19. Pirraglia PA, Sanyal P, Singer DE, Ferris TG. Depressive symptom burden as a barrier to screening for breast and cervical cancers. J Women’s Health (Larchmt). 2004;13(6):731-738.

20. Harvey SC, Vegesna A, Mass S, Clarke J, Skoufalos A. Understanding patient options, utilization patterns, and burdens associated with breast cancer screening. J Women’s Health (Larchmt). 2014;23(suppl 1):S3-S9.

21. Dolan NC, Reifler DR, McDermott MM, McGaghie WC. Adherence to screening mammography recommendations in a university general medicine clinic. J Gen Intern Med. 1995;10(6):299-306.

22. Stoll CR, Roberts S, Cheng MR, Crayton EV, Jackson S, Politi MC. Barriers to mammography among inadequately screened women. Health Educ Behav. 2015;42(1):8-15.

23. Fortuna RJ, Idris A, Winters P, et al. Get screened: a randomized trial of the incremental benefits of reminders, recall, and outreach on cancer screening. J Gen Intern Med. 2014;29(1):90-97.

24. Community Preventive Services Task Force. Updated recommendations for client- and provider-oriented interventions to increase breast, cervical, and colorectal cancer screening. Am J Prev Med. 2012;43(1):92-96.

25. Chan EK, Wilson C, Tyldesley S, et al. Improving screening mammography return rates in overdue women: a randomized study of signed reminder letters from family physicians. Paper presented at: Breast Cancer Symposium 2014; September 4, 2014; San Francisco, CA.

26. Brouwers MC, De Vito C, Bahirathan L, et al. What implementation interventions increase cancer screening rates? a systematic review. Implement Sci. 2011;6:111. 
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