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Impact of a Patient Incentive Program on Receipt of Preventive Care
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Impact of a Patient Incentive Program on Receipt of Preventive Care

Ateev Mehrotra, MD; Ruopeng An, PhD; Deepak N. Patel, MBBS; and Roland Sturm, PhD
Patient financial incentives are a potential mechanism to improve health. In a South African health plan, a patient incentive program is associated with increased prevention.
In sensitivity analyses, we limited our study population to those continuously enrolled in the health plan over the 7-year period. The goal of this sensitivity analysis was to address the possibility that the changing composition of the population might bias the results because those who were not screened tended to drop out of the program at higher rates.

Standard errors are estimated using the Eicker-Huber- White sandwich estimator.15 All statistical analyses are conducted in STATA 12.0 (StataCorp, College Station, Texas).


Of the 4,186,047 unique individuals enrolled in the health plan between 2005 to 2011, 65.5% (2,742,268) of them enrolled in the incentive program at some point. Enrollment in the health plan and the incentive program steadily increased during the study period. In 2005 there were 1.83 and 1.26 million people in the health plan and incentive programs, respectively. Enrollees in the incentive program were younger and healthier (Table 2). For example, among enrollees in the incentive program and those not in the incentive program, the fraction that was 71 years and older was 1.0% and 7.0%, respectively. Among enrollees in the incentive program and those not in the incentive program, the fraction with no chronic illness was 81.5% and 74.7%, respectively. The fraction of the eligible population that received a given preventive service was generally low in a given year (Table 1). For example, only 13.8% of women 35 years and older received a mammogram.

Becoming a member of the incentive program was associated with an increased likelihood of receiving all 10 of the preventive services e tracked (Table 3). Among the 8 preventive care services associated with a financial incentive, the odds ratio (OR) for receipt of cholesterol testing was 2.70; glucose testing, 1.51; glaucoma screening, 1.34; dental check-up, 1.64; HIV testing, 3.47; PSA testing, 1.39; Pap test, 2.17; and mammogram, 1.90 (P <.001 for all 8 services). Among the 2 preventive care services not associated with a financial incentive, the OR for receipt of colon cancer screening was 1.30 and bone density scan was 1.25 (both P <.001).

From our models, we estimated the marginal effect of the incentive program on receipt of the preventive services (Figure). The percentage-point improvement in receipt of preventive services for cholesterol testing was 8.9%; glucose testing, 4.7%; glaucoma screening, 3.9%; dental exam, 6.3%; HIV test, 2.6%; PSA test, 5.6%; Pap test, 7.0%; and mammogram, 3.1%. This translated into a relative percentage increase from 19.3% (PSA test) to 97.4% (HIV test) increase in preventive care use.

We looked at the impact of the incentive program among subgroups of the population. Compared with enrollees older than 50 years, those 50 years or younger in the incentive program had higher ORs for most of the preventive services applicable to both age groups (Table 3). Compared with enrollees with a chronic illness, the association of the incentive program and preventive care services among those without a chronic illness was larger for 4 of the 8 preventive services. Across these subgroups, the differences were generally quite small.

We conducted several sensitivity analyses (results in eAppendix B) using individual fixed effects (versus individual random effects in our main analyses), dropping year 2005 because patient co-payments in that year were different, limiting the analyses to those continuously enrolled for 7 years (to address whether selective drop out might bias results), and stratifying by prior history of hospitalizations as a marker of illness. Though the ORs vary across these analyses, across all the sensitivity analyses, entry into the incentive program is associated with increasing use of preventive care.


Across the several million individuals in the health plan who enrolled in the incentive program, we find that enrollment into the program was associated with increases in preventive care use across a wide range of preventive care measures. The OR for receiving a preventive service ranged from 1.34 to 3.47 across the 8 preventive services associated with incentives. We believe this is the first study that demonstrates that a health plan financial incentive programs increases use of preventive care. The results support the theory that monetary rewards might help overcome barriers to receipt of preventive care.

Among those within the incentive program, the estimated increase in receipt of prevention in a year was 3% to 9%, or a relative 19% to 97% increase. This increase in prevention is consistent with the impact of other interventions on preventive services. For example, women who faced an increased co-payment (median $20) or coinsurance of 20% were approximately 8% less likely to receive a mammogram.4 Trials in which physicians are notified of missing preventive care among their patients increased relative delivery of preventive services by 13%.16

Though the incentive program substantially increased the receipt of preventive care, it is clearly not a panacea for low rates of prevention. The majority of patients did not receive all evidence-based preventive care. There are several potential reasons why the program was not associated with greater increases in use of preventive care. The financial incentive associated with a given preventive service is relatively small. For example, receiving a mammogram earns a person 1500 points and an enrollee needs 15,000 points to move from the lowest tier (Blue) to the next tier (Bronze). In contrast, Volpp and colleagues used a $750 incentive to encourage patients to quit smoking.13 Also, the relatively complex structure of the incentives makes it difficult for the average participant to translate the receipt of points to an immediate and tangible award.17

It is possible that a simpler program, larger incentives, or other means of making the incentive more attractive would improve the effectiveness of the program.7 Lastly, and perhaps most importantly, there are many barriers to receiving prevention, such as access to care and patient perceptions. The incentive program is only addressing 1 potential barrier. It is notable that the amount the average enrollee pays to be in the incentive program (approximately $240) is close to what the average enrollee receives in incentives (approximately $275). Given this relatively weak incentive, it may be surprising that the program is associated with any increase in preventive care. It is possible that the potential benefits (ie, discounts on lower air fares for vacation or movie tickets) drive behavior change rather than the actual benefits received. If the incentives were larger, then it might also be more difficult for the health plan to financially sustain the program.

Our results do not support the concern that enrollees in the incentive program would concentrate their energy on just the preventive care rewarded, and thereby ignore other important care. Entry into the incentive program was associated with an increased likelihood of receiving preventive care services that are not included in the incentive program, though the ORs for nonincentivized preventive services were smaller than for the incentivized services. This positive spillover could be due to a greater sense of wellness among those in the incentive program, which translated into more preventive care. Another possibility is that when a patient sees a physician for 1 preventive service, the physician may order other necessary care.

Among younger health plan enrollees, we see a slightly larger increase in preventive services among those enrolled in the incentive program. Older patients may have more contact with the healthcare system, which leads to more opportunities for the patient to receive the preventive care.18 Given this baseline contact with the healthcare system, the marginal benefit of the incentive program might therefore be smaller. Also, the complex design of the program might deter older patients, and the incentives themselves (ie, flights, gym memberships) might be relatively less attractive among older patients.

The key concern with our analyses is selection bias. In our analyses, we compared each individual to themselves. We looked at the receipt of preventive care before and after the individual entered the program. This analytic design helps control for patient factors that make someone likely to enter a program. Nonetheless, given that patients were not randomized into the incentive program, we do not know whether the incentive program was itself driving the increases in preventive care. For example, a person might decide to engage in healthier behavior and therefore join the incentive program and obtain preventive services. In such a scenario, the incentive program did not drive a response. Such selection issues might also explain why we see increase in preventive care services not associated with incentives.

There are several other important limitations of this analysis. The data we analyzed is limited to South Africa, and it is therefore unclear how it translates to other countries in other parts of the world. In eAppendix A, we compare the patient population of South Africans in the health plan and the US population. In general, the 2 groups are reasonably similar, but the rate of preventive care services at baseline among South Africans is lower than what is observed in other nations.2 Whether the incentive program would have the same effect given a higher baseline of preventive care is unclear. Given our analytic design, we tracked yearly receipt, and many preventive care measures are only due at less regular intervals. This means the numbers we present underestimate the number of people who are up-to-date on their receipt of preventive services. Some services included in the program (ie, PSA testing) are not recommended by some organization such as the United States Preventive Services Task Force. Lastly, it is important to highlight that enrollees had to pay to join the program, and it is therefore somewhat similar to a deposit or pre-commitment contract, which might be a more effective way to drive behavior change. It is therefore not clear whether our results would generalize to a program in which patients did not have to pay to enter.

In summary, among enrollees in a private health plan in South Africa, enrollment into a patient incentive program is associated with a higher likelihood of receiving preventive services. Patient incentive programs might be another mechanism to increase rates of preventive care.

Author Affiliations: RAND Corporation, Boston, MA and Santa Monica, CA (AM, RA, RS); Harvard Medical School, Boston, MA (AM); Discovery Health Plan, Johannesburg, South Africa (DNP).

Source of Funding: This study was supported by a grant from the US National Institutes of Health (NIH) Common Fund (R21-HD071568-01) and a career development award from the NIH (Dr. Mehrotra KL2- TR000146). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Author Disclosures: Dr Patel reports employment with Discovery Health Plan, whose data is studied in this manuscript. The other 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 (AM, RA, DNP, RS); acquisition of data (AM, DNP, RS); analysis and interpretation of data (AM, RA, DNP, RS); drafting of the manuscript (AM, RA, DNP, RS); critical revision of the manuscript for important intellectual content (AM, RA, DNP, RS); statistical analysis (AM, RA, DNP, RS); provision of study materials or patients (AM, DNP, RS); obtaining funding (AM, DNP, RS); administrative, technical, or logistic support (AM, DNP, RS).

Address correspondence to: Ateev Mehrotra, MD, 180 Longwood Avenue, Boston, MA, 617432-3905. E-mail:
1. CDC. Cancer screening - United States, 2010. MMWR. 2012;61(3):41-45.

2. National Healthcare Quality Report 2012. Agency for Healthcare Research and Quality website. index.html.

3. Shih A, Berenson JA, Abrams M. Preventive health services under the Affordable Care Act: role of delivery system reform. Medscape website. Published April 12, 2012.

4. Trivedi AN, Rakowski W, Ayanian JZ. Effect of cost sharing on screening mammography in Medicare health plans. N Engl J Med. 2008;358(4):375-383.

5. Lohr KN, Brook RH, Kamberg CJ, et al. Use of medical care in the Rand Health Insurance Experiment. Diagnosis- and service-specific analyses in a randomized controlled trial. Med Care. 1986;24(9 suppl): S1-S87.

6. Gabel JR, Whitmore H, Pickreign J, et al. Obesity and the workplace: current programs and attitudes among employers and employees. Health Aff (Millwood). 2009;28(1):46-56.

7. Volpp KG, Pauly MV, Loewenstein G, Bangsberg D. P4P4P: an agenda for research on pay-for-performance for patients. Health Aff (Millwood). 2009;28(1):206-214.

8. Loewenstein G, Brennan T, Volpp KG. Asymmetric paternalism to improve health behaviors. JAMA. 2007;298(20):2415-2417.

9. Loewenstein G, Prelec D. Anomalies in intertemporal choice: evidence and an interpretation. Quarterly J Econ. 1992;107(2):573-597.

10. Marcus AC, Kaplan CP, Crane LA, et al. Reducing loss-to-follow-up among women with abnormal Pap smears. results from a randomized trial testing an intensive follow-up protocol and economic incentives. Med Care. 1998;36(3):397-410.

11. Seal KH, Kral AH, Lorvick J, McNees A, Gee L, Edlin BR. A randomized controlled trial of monetary incentives vs. outreach to enhance adherence to the hepatitis B vaccine series among injection drug users. Drug Alcohol Depend. 2003;71(2):127-131.

12. Charness G, Gneezy U. Incentives to Exercise. Econometrica. 2009; 77(3):909-931.

13. Volpp KG, Troxel AB, Pauly MV, et al. A randomized, controlled trial of financial incentives for smoking cessation. N Engl J Med. 2009; 360(7):699-709.

14. Mehrotra A, Sorbero ME, Damberg CL. Using the lessons of behavioral economics to design more effective pay-for-performance programs. Am J Manag Care. 2010;16(7):497-503.

15. Williams RL. A note on robust variance estimation for cluster-correlated data. Biometrics. 2000;56:645-646.

16. Balas EA, Weingarten S, Garb CT, Blumenthal D, Boren SA, Brown GD. Improving preventive care by prompting physicians. Arch Intern Med. 2000;160(3):301-308.

17. Volpp KG, Asch DA, Galvin R, Loewenstein G. Redesigning employee health incentives--lessons from behavioral economics. N Engl J Med. 2011;365(5):388-390.

18. Higashi T, Wenger NS, Adams JL, et al. Relationship between number of medical conditions and quality of care. New Eng J Med. 2007;356(24):2496-2504.
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