AJMC

Impact of a Patient Incentive Program on Receipt of Preventive Care

Published Online: June 25, 2014
Ateev Mehrotra, MD; Ruopeng An, PhD; Deepak N. Patel, MBBS; and Roland Sturm, PhD
Objectives

Patient financial incentives are being promoted as a mechanism to increase receipt of preventive care, encourage healthy behavior, and improve chronic disease management. However, few empirical evaluations have assessed such incentive programs.

Study Design

In South Africa, a private health plan has introduced a voluntary incentive program which costs enrollees approximately $20 per month. In the program, enrollees earn points when they receive preventive care. These points translate into discounts on retail goods such as airline tickets, movie tickets, or cell phones.

Methods

We chose 8 preventive care services over the years 2005 to 2011 and compared the change between those who entered the incentive program and those that did not. We used multivariate regression models with individual random effects to try to address selection bias.

Results

Of the 4,186,047 unique individuals enrolled in the health plan, 65.5% (2,742,268) voluntarily enrolled in the incentive program. Joining the incentive program was associated with statistically higher odds of receiving all 8 preventive care services. The odds ratio (and estimated percentage point increase) for receipt of cholesterol testing was 2.70 (8.9%); glucose testing 1.51 (4.7%); glaucoma screening 1.34 (3.9%); dental exam 1.64 (6.3%); HIV test 3.47 (2.6%); prostate specific antigen testing 1.39 (5.6%); Papanicolaou screening 2.17 (7.0%); and mammogram 1.90 (3.1%) (P <.001 for all 8 services). However, preventive care rates among those in the incentive program was still low.

Conclusions

Voluntary participation in a patient incentive program was associated with a significantly higher likelihood of receiving preventive care, though receipt of preventive care among those in the program was still lower than ideal.

Am J Manag Care. 2014;20(6):494-501
Despite widespread efforts to encourage prevention, rates of preventive care use fall well short of recommendations.1,2 Much of the focus on improving preventive care has been on decreasing financial barriers. For example, new laws in the United States have eliminated patient out-of-pocket costs for preventive health services.3 While removing out-of-pocket costs will increase the number of people who receive preventive care, the increase is likely to be modest.4,5 Employers and health plans are exploring whether patient incentive programs can spur greater use of preventive care.6,7

In a patient incentive program, a patient receives money or some other financial reward for healthy behavior.7 In theory, these programs address a fundamental problem with preventive care—when making the choice to receive preventive care, patients balance the inconvenience of receiving preventive care with distant and often intangible benefits. Humans generally discount such future benefits8,9 and therefore it may not be surprising that many patients do not seek preventive care. Incentive programs might help address this discrepancy between immediate inconvenience and future benefit by increasing the perceived immediate benefits of prevention.

There have been several randomized trials focusing on patient incentives to promote healthy behavior.10-12 For example, Volpp and colleagues found that a $750 incentive led to a three-fold increase in the number of people able to quit smoking.13 While important, this prior research has been limited to small clinical trials with a narrow focus, relatively short follow-up periods, and an incentive structure that may not be sustainable.7 In this paper, we study the impact of a patient incentive program operated by a private health plan in South Africa which has been in place for over a decade and includes almost 1.5 million enrollees. In this program, receipt of preventive care services “earns” points for enrollees, and points translate into rewards such as discounted travel or cell phone minutes. We assessed the impact of enrollment in this incentive program on receipt of preventive care services by comparing the receipt of preventive services among those who joined the program to those that did not join the program.

METHODS

Setting


We analyzed the receipt of preventive care for members of the Discovery Health Plan in South Africa between 2005 and 2011. In South Africa, approximately 15% of the population, typically the most affluent, obtain private health insurance either independently or through their employer. Those with private insurance receive care from physicians and hospitals in a system entirely separate from the larger public healthcare system. In our eAppendix (available at www.ajmc.com), we demonstrate that those with private health plan insurance in South Africa are socioeconomically comparable to the general US population.

Our study population included both health plan members in the incentives program and those not in the incentives program. Our only exclusion criteria were those in a separate low-cost insurance product. These members were not eligible for the incentive program and because this product is targeted to the poor, the enrollee population is very different.

Patient Incentives Program

The health plan’s reward program focuses on encouraging both prevention and healthy behaviors. The incentive program is offered on a voluntary opt-in basis as South African law does not permit such programs to be made mandatory in a health plan product. Members must pay $17 per month for an individual or $21 per month for a family to enroll in the incentive program (approximately 5% of the cost of health plan membership). Enrollees can drop the incentive program at any time and on average, 7.5% drop out in a year.

Members of the reward program earn points for a number health behaviors such as receiving preventive care services, visiting a gym, smoking cessation, seeing a nutritionist, and buying healthier food at the grocery store. Our focus is just on receipt of preventive care services. In Table 1, we list the allocation of points for these preventive services. The points earned by receiving preventive care did not change during study period. Points translate into increasing status levels. The default status is Blue. For an individual member, status increases as follows: Bronze (15,000-34,999 points); Silver (35,000-44,999); Gold (≥45,000); and Diamond (3 consecutive years on Gold).

Higher status level translates into increasing discounts on a range of goods and services from approximately 25 commercial partners in South Africa. These include store purchases, movie tickets, local and international flights, car rentals, and hotel booking. For example, the discount for 1 hotel chain was 30% and 50% for Blue and Diamond status, respectively. In 2009, the average annual value of incentives to members of the reward program was approximately $275 across all members and greater than $1500 in the highest Diamond tier.

Enrollees can sign up for the incentive program in several ways: they can sign up when they first join the health plan, or members already with the health plan can sign up with a phone call or download and submit an application form that is available online. The process of accruing points is automated and does not require members to submit any forms. For instance, when a claim for a mammogram is received by the health plan, points are automatically allocated to the patient. Rewards (discount) are either available at the time of the purchase or are paid in a check at the end of the month. During the study period, enrollees could receive the points even if they received the service ahead of what might be recommended by different guidelines. For example, some guidelines recommend a mammogram every 2 years, but if a woman in the incentive program received a mammogram yearly, she would receive the associated points each year.

For each year, we classified whether the health plan enrollee was a member of the reward program as of January 1 of that year. We used an “intention-to-treat” analysis such that a patient who enrolls in a reward program was considered to be in the reward program in all subsequent years even if they unenrolled at some point.

Measuring Receipt of Preventive Care

For each enrollee, we tracked receipt of 10 preventive care services within each calendar year. Eight preventive care services were associated with financial incentives (cholesterol testing, fasting glucose testing, human immunodeficiency virus [HIV] test, mammogram, Papanicolaou [Pap] test, dental screening, glaucoma screening, and prostate specific antigen test). Two preventive care services were not associated with financial incentives (colon cancer screening and bone density scan for osteoporosis). We tracked the 2 nonincentivized services because there is concern that financial incentives may lead enrollees to neglect nonincentivized services.14 Although it is associated with an incentive, we did not track receipt of the influenza vaccine, because many enrollees receive the vaccine in the workplace or pay out of pocket and therefore it is not accurately tracked in the health plan claims. We also did not track human papillomavirus and pneumococcal vaccinations, because incentives for these preventive services began in 2010. Lastly, we did not track receipt of childhood vaccinations. As described below, we examined the likelihood of receiving a preventive service before and after entry into the program. Such a model cannot be used for services only offered during a narrow age period. For each service, the health plan designates age and gender eligibility for incentive program members to claim points (Table 1). Preventive care services were identified by relevant billing and diagnosis codes in health plan claims.

Health plan members must first visit a general practitioner to receive 6 of the 10 preventive services. For 3 services (cholesterol screening, glucose testing, and HIV test), health plan members can also get these tests without a physician’s order at a pharmacy or on wellness days at work sites. For dental screening, patients go directly to a dentist. As of 2006, South Africa law mandates that the 8 preventive services, except for dental screening and glaucoma screening, be provided without any patient co-payment. In 2005, coverage for preventive services varied by type of health plan. We did a sensitivity analysis in which we eliminated 2005 data.

In our analyses, we examined the receipt of a preventive service within a single year. Many preventive services such as breast or cervical cancer screening are not indicated yearly. The percentages reported are therefore not indicative of what fraction of the population is up to date with preventive care.

Measuring Utilization of Health Services by Health Plan Members

We tracked the number of visits to a general practitioner and whether the enrollee had 1 of 20 chronic illnesses. The chronic illnesses were identified by a diagnosis on an outpatient or inpatient claim or whether the patient filled a prescription for a medication related to that illness (diagnoses listed in eAppendix D). Chronic illness data was only available from the health plan from 2008 through 2011.

Study Design

We estimated the association between incentive program membership and receipt of preventive care services using an individual random-effects logistic regression. We used a separate regression for each preventive care service. The unit of analysis is each enrollee time year. The dependent variables are 10 dichotomous variables denoting the annual receipt of each preventive care service. The key explanatory variable is a dichotomous variable denoting whether a given enrollee was enrolled in the incentive program for that year. This allows us to compare those who entered the incentive program versus those that are not. The other explanatory variables are gender, age (in years), dummy variables for type of health plan product (eg, highdeductible plan), and dummy variables for each calendar year from 2005 to 2011. We included a random effect for each enrollee in the analysis. The analyses were limited to the relevant population eligible for the preventive service based on age and gender (Table 1). For example, the model for mammogram analyzes only included women aged over age 35 years in the model. We ran separate models for all enrollees, enrollees with and without a chronic illness, and enrollees younger than and older than 50 years.

Because this is an observational study, we had to address selection bias. Those who chose to enter the incentive program were different on both observed and unobserved patient traits. In our main analyses, we used random patient effects to try and address this bias. There was controversy about the use of fixed-effect versus random-effect models and therefore we use a fixed-effect model as a sensitivity analysis. While a fixed-effect model might be viewed as having greater internal validity, it might have less external generalizability. For dichotomous variables, identification using fixed-effect or conditional logistic models was restricted to the relatively small group of “switchers” whose outcome measure (ie, annual take-up of a preventive care service) changed between years.

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).

RESULTS

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.

DISCUSSION

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.

Take-Away Points

  • Discovery Health, a private South African health plan, has implemented a patient incentive program which rewards healthy behavior using discounts on retail goods and travel.

  • Over two-thirds of the health plan’s enrollees have voluntarily chosen to pay approximately $15 per month to join the program.

  • Among those within the incentive program, we found the estimated increase in receipt of preventive services to be 3% to 8%, or a relative 19% to 97% increase.

  • Our results support the idea that patient incentive programs might be a mechanism for health plans 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: mehrotra@hcp.med.harvard.edu.
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. www.ahrq.gov/research/findings/nhqrdr/ index.html.

3. Shih A, Berenson JA, Abrams M. Preventive health services under the Affordable Care Act: role of delivery system reform. Medscape website. www.medscape.com/viewarticle/761599_3. 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.
Issue: June 2014
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