Abstract Session F1: Health Policy/Advocacy/Social Justice Moderator: LeChauncy Woodard, MD, MPH, FACP THE ROLES OF COST AND QUALITY INFORMATION IN MEDICARE ADVANTAGE PLAN ENROLLMENT DECISIONS Rachel O. Reid 1 ; Partha Deb 4 ; Benjamin L. Howell 2 ; William Shrank 3 . 1 Brigham and Women's Hospital, Boston, MA; 2 Center for Medicare & Medicaid Services, Baltimore, MD; 3 Brigham and Women's Hospital, Boston, MA; 4 Hunter College, New York, NY. (Tracking ID #1933518) BACKGROUND: To facilitate informed decision making in the Medicare Advantage marketplace, the Centers for Medicare & Medicaid Services publishes information about Medicare Advantage plans via the Medicare Plan Finder website, including costs, benefits, and quality ratings provided on a 1-to-5 star scale. Little is known about how beneficiaries weigh costs versus quality when making enrollment choices. In this study, we assess the variation in Medicare Advantage enrollment attributable to plan attributes and willingness to pay for quality. METHODS: We conducted a nationwide, beneficiary-level cross-sectional analysis of the 2011 Medicare Advantage and Prescription Drug (MAPD) plan choices of beneficiaries enrolling in Medicare Advantage for the first time ever in 2011 who were not eligible for the low-income subsidy. Matching beneficiaries with their choice-sets of MAPD plans by county, we used conditional logistic regression to estimate associations between plan attributes and enrollment, to assess both the proportion of explained enrollment variation attributable to plan attributes and willingness to pay for quality. The model accounted for 5-star quality ratings, costs (premiums and average estimated out-of-pocket costs), benefits (plan structure; deductibles; coinsurance; hearing, vision, dental benefits; and prescription gap coverage), and lagged county-level sponsor organization (i.e., brand) market share. Because willingness to pay for quality may vary at different rating levels, the model included both the 5-star quality rating itself and its quadratic transform. We assessed differential willingness to pay by beneficiary characteristics (age, sex, race, and urban versus rural residence) by interacting these covariates with quality and cost covariates. RESULTS: The study cohort included 847,069 first-time Medicare Advantage enrollees who selected an eligible MAPD plan in 2011. Relative to the total variation explained by the model, market share accounted for 35.3% of variation in plan choice, premiums for 25.7%, estimated out-of-pocket costs for 11.6%, and 5-star quality ratings for 13.6%. Mean cumulative willingness to pay for a plan in total annual combined premiums and out-of-pocket costs varied from $4,154.93 for a 2.5-star plan to $5,698.66 for a 5-star plan. Increases in willingness to pay diminished at higher 5-star quality ratings: $549.27 (95% CI $541.10 to $557.44) to go from a 2.5-star plan to a 3-star plan and $68.22 (95% CI $61.44 to $75.01) to go from a 4.5-star plan to a 5-star plan. Beneficiaries aged 64-65 years were more willing to pay for plans with higher quality ratings than other age groups; black and rural beneficiaries were less willing to pay for plans with higher quality ratings. CONCLUSIONS: Medicare Advantage enrollees prefer plans with higher quality ratings and lower costs; however, market share's contribution to plan choice suggests that word-of-mouth and brand recognition are also influential. Key subgroups' differential willingness to pay for quality and market share' influence argue for continued efforts to advance communication of plan attributes to improve marketplace efficiency. If increased enrollment in plans with the highest quality ratings is a goal, the diminishing marginal utility for quality observed supports policy interventions to make achievement of the highest ratings desirable for insurers and enrollment in the highest-rated plans attractive and accessible to beneficiaries.
NEW YORK CITY GREEN CARTS: IS THE PROGRAM ALLEVIATING FOOD DESERTS? Kathleen Y. Li 1,2 ; Ashley Fox 2 ; Ellen K. Cromley 3,4 ; Carol Horowitz 2 . 1 University of California, San Francisco School of Medicine, San Francisco, CA; 2 Icahn School of Medicine at Mount Sinai, New York, NY; 3 University of Connecticut School of Medicine, Farmington, CT; 4 Lund University, Lund, Sweden. (Tracking ID #1936558) BACKGROUND: As the proportion of overweight and obesity nears 70% in the United States, local public health departments are using their authority to implement programs to tackle this growing epidemic. Limited access to fresh fruits and vegetables in low- income neighborhoods is believed to contribute to high rates of obesity. To address the high rates of obesity and related illnesses in its low-income neighborhoods, in 2008, New York City established a fruit and vegetable street vendor program (NYC Green Carts) to promote healthier eating in neighborhoods with the lowest reported rates of fruit and vegetable consumption. Carts are free to move anywhere within designated neighborhoods, which can be large and often border wealthier neighborhoods. We aimed to study whether carts locate in areas that enable them to reach the low-income "food desert" populations they were designed to serve. METHODS: We obtained a list of Green Cart locations from the New York City Department of Health and information on census tract level demographic and food environment data from the census bureau and Esri Business Analyst Desktop. We identified "healthy" food stores, namely supermarkets, independent grocers, and fruit and vegetable specialty stores according to North American Industry Classification System codes as well as bodegas with evidence of selling fresh produce on Google Maps Street View. We then defined a food desert as a lack of healthy food stores within a ¼ mile. Using ArcGIS software, we mapped the existing Green Carts and generated a list of potential Green Cart locations. Within designated Green Cart areas, the intersection closest to the geographic center of each census tract without a Green Cart was coded as a candidate site. We then analyzed census tract characteristics for actual and candidate Green Carts to determine how they differed with regards to population, income, percent below the poverty level, distance to subway stops, the number of large employers, and the number of healthy food stores at both a ¼-mile and ½-mile distance from each location. RESULTS: Our team identified 265 Green Carts and 644 candidate locations without Carts. As compared with potential Green Carts sites (see table), Green Carts were significantly more likely to be within a quarter of a mile of supermarkets, grocery and fruit & vegetable stores. Over 1/3 of candidate intersections with no Green Carts were situated in food deserts compared to fewer than 1/10 of Green Carts. Green Carts were positioned in tracts with significantly larger population sizes, tended to be closer to a subway stop, and were more likely to be within a ¼ mile of large employers, though Green Cart tracts had a lower median family income compared to tracts with candidate sites. Some (22) Green Carts were located outside the officially designated Green Cart areas, and these census tracts had significantly higher income and higher access to fruits and vegetables than those within Green Cart boundaries. CONCLUSIONS: Compared with potential Green Cart locations, census tracts with Carts tended to be located in areas with large numbers of potential customers, namely population centers and areas with more pedestrian traffic (close to subway stops and large businesses), perhaps to increase market share and profitability. However, Green Carts were rarely situated in food deserts. This suggests that many already underserved neighborhoods are still not reached by this initiative. A market-driven imperative to locate near larger numbers of potential customers may be in tension with the objective of the Green Carts program to increase access to fresh fruits and vegetables in regions with lower access to supermarkets and fresh produce. However, we also identified a number of candidate locations where foot traffic is likely to be high but where Green Carts were not located. Since people usually shop close to where they live, the Green Carts program should consider introducing added incentives for Green Cart vendors to locate in high need census tracts to ensure adequate coverage of actual food deserts. Table. Average Characteristics of Green Cart vs Candidate Sites Variable Green Cart Site Candidate Site p value Tract population 5,004 3,706 <0.00001 Tract median income $37,213 $42,740 0.000284 Percent of tract below poverty level 30.1% 25.6% <0.00001 Distance to nearest subway stop (ft) 793 3,079 <0.00001 # large employers within ¼ mi 0.457 0.085 <0.00001 # supermarkets within ¼ mi 1.6 0.81 <0.00001 # other stores carrying fruits and vegetables within ¼ mi 2.09 0.84 <0.00001 # large employers within ½ mi 1.185 0.402 <0.00001 # supermarkets within ½ mi 4.72 2.86 <0.00001 # other stores carrying fruits and vegetables within ½ mi 5.4 3.1 <0.00001
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