THE IMPACT OF DEMOGRAPHIC AND SOCIOECONOMIC FACTORS ON MEDICATION ADHERENCE

Karl M. Kilgore, PhD; Zulkarnain Pulungan, PhD; Christie Teigland, PhD; Alexis Parente, PhD

Avalere – An Inovalon Company, 1350 Connecticut Avenue NW, Suite 900, Washington, DC 20036 T 202.207.1300 F 202.467.4455 www.avalere.com

Presented at International Society for Pharmacoeconomics and Outcomes Research (ISPOR) 21st Annual International Meeting, May 21-25, 2016, Washington DC

Background• The Centers for Medicare & Medicaid Services (CMS) Five-Star Quality Rating System

for Medicare Advantage contracts was developed to drive quality improvement through public reporting and consumer choice. The Star Ratings are used to inform beneficiaries about the performance of health and drug plans and to determine Quality Bonus Payments (QBP).1

• The three medication adherence performance measures [Cholesterol (Statins) (MA-C), Diabetes Medications (MA-D) and Hypertension (RAS Antagonists) (MA-H)] are included in the Five-Star Quality Rating System with each having a triple weight in the overall Star Rating.

• Plans serving predominantly disadvantaged members (i.e., dual eligible and special needs populations) are rated on the same scale as other Medicare Advantage plans for quality reporting and incentive payments.2

• Lower performance rankings could penalize plans serving a higher proportion of disadvantaged members, resulting in lower quality-based payments that lead to fewer supplemental benefits for the population that is most in need and least capable of paying for their own care.3 Lower Star Ratings also have an impact on member retention.4

• Several studies have identified possible factors that may have an influence on medication adherence performance measures.5-17 However, there is a need to further assess the impact of patient demographic and socioeconomic characteristics using a more granular assignment of characteristics. Previous studies have used Census data 5-digit zip code areas that can cover disparate populations and thus result in finding little or no association of socioeconomic factors and health outcomes.

• This investigation used new data sources to assign demographic and socioeconomic factors to a member at the near neighborhood level (an average of eight households). This allows a more accurate evaluation of the impact of these factors on medication adherence.

Objective• To examine the relationship between demographic and socioeconomic factors on

medication adherence.

MethodsData Sources• The main data source for this study was member-level data extracted from a large nationally

representative and statistically de-identified administrative claims database. The database includes longitudinal patient-level data for more than 131 million individual health plan members from a broad range of sources across all payer types (Commercial, Medicare Advantage and managed Medicaid), geographic regions (capturing virtually all U.S. counties), health care settings (inpatient and outpatient services), and provider specialties.

• These member-level data were linked with socioeconomic characteristics based on Zip+4 areas which results in roughly 30 million discrete “neighborhood” data points representing an average of eight households. This allows a much more accurate assignment of characteristics compared to U.S. Census data sources used in other studies.18

• The Area Health Resource File was used to provide data on the availability of community resources (such as shortage of physicians or mental health professionals) at the county level.19

Sample Selection• Study population: 764,581 members from 44 Medicare Advantage health plans qualified

for at least one of the three medication adherence measures in 2013.

Figure 1 / Overall Medication Adherence Rate for Each Measure

Statistical Methods• A separate multivariate logistic regression model was estimated to determine factors

associated with each of the three medication adherence measures.

• Explanatory variables included an age by disability interaction term, gender, dual eligibility status, race/ethnicity, number of medications, home ownership, poverty level, education level, low income subsidy, region, metropolitan area, institutional status, and primary care shortage area.

• The final models were used to calculate predicted (i.e., “risk-adjusted”) rates. The predicted versus actual performance were compared to assess the potential impact of risk-adjusting the measures on plan rankings.

Results

Table 1 / Odds-Ratios of Statistically Significant Variables

Figure 2 / Health Plan Risk-Adjusted Rank versus Unadjusted Rank

Key Findings• For all three measures, adherence was significantly lower for members who were: younger

and had a disability (OR: 0.54-0.95); African-American (OR: 0.64-0.66) (and, for MA-C and MD-D only, Hispanic (OR: 0.79-0.90)).

• Medication adherence was lower for those who resided in an area with a higher percent of population below the federal poverty level (OR: 0.77-0.91), and was significantly higher for members who resided in an area with higher home ownership (OR: 1.05-1.08) or higher education level (OR: 1.04-1.07).

• After controlling for socioeconomic factors and clinical characteristics (measured by number of different medications the member was taking), adherence was significantly higher for dual eligible members, with full benefit duals being more adherent (OR: 1.09-1.16) than partial duals (OR: 1.07-1.09). Sub-group analyses suggested that non-duals that were poor actually had lower adherence than dual eligibles that were poor (but have access to both Medicare and Medicaid benefits). This indicates that dual eligible members actually do better than non-dual members who may be disadvantaged, but are not eligible for dual status (e.g., live in a non-Medicaid expansion state or qualify for dual status only part of the year, but are low income).

• After risk adjustment with demographic and socioeconomic factors, plans ranked best tended to stay ranked best and plans ranked worst tended to stay ranked worst for the three medication adherence measures; there was most movement of plans in the 2nd and 3rd quartiles.

Discussion• This study provides information about the contribution of demographic and socioeconomic

characteristics on medication adherence measures.

• Results showed that adherence is significantly associated with demographic and socioeconomic factors. Income and education levels were significant predictors even after controlling for dual status, age-disability interaction and other variables.

• Medicare Advantage plans serving a high proportion of disadvantaged members may be providing better quality of care than their Star Ratings suggest. Specifically, dual members had higher adherence rates than members with similar characteristics who did not receive Medicaid benefits.

• In contrast, Medicare Advantage plans serving a lower proportion of disadvantaged members may be providing worse quality of care than their performance results suggest, leading consumers to mistakenly believing they are joining a higher quality plan than is actually the case.

• Risk adjustment does not significantly change the rankings of plans rated best under current specifications or the rankings of plans rated worst; they are still among the best and worst with risk adjustment. But it could significantly change the ranks of plans in the 2nd and 3rd quartiles resulting in more accurate performance ratings to inform consumer choice.

References1. Medicare 2016 Part C & D Star Rating Technical Notes. Center for Medicare and Medicaid Services (CMS).

Baltimore, MD, 2016. (Accessed April 25, 2016, at http://www.cms.gov/Medicare/Prescription-Drug-Coverage/PrescriptionDrugCovGenIn/PerformanceData.html.

2. Medicare’s quality incentive system does not adequately account for special needs of dual-eligible populations. Washington, D.C.: Association for Community Affiliated Plans, 2012. (Accessed April 25, 2016, at http://www.worldcongress.com/events/HR13000/PDF/ACAP_STARs_Fact_Sheet_May_2012.pdf.)

3. Estimated federal savings associated with care coordination models for Medicare-Medicaid dual eligibles. Washington, D.C.: America’s Health Insurance Plans (AHIP), 2011. (Accessed April 25, 2016 at http://www.healthlawyers.org/News/Health%20Lawyers%20Weekly/Documents/101411/Dual-Eligible-Study-September-2011.pdf.)

4. Partial enrollment data for AEP show small gains, but big ones in high star plans. Medicare Advantage News 2015; 21(1).

5. Chan DC, Shrank WH, Cutler D, et al. Patient, physician, and payment predictors of statin adherence. Med Care 2010; 48: 196-202.

6. Lemstra M, Blackburn D, Crawley A, Fung R. Proportion and risk indicators of nonadherence to statin therapy: A meta-analysis. Can J Cardiol 2012; 28: 574-580.

7. Mauskop A, Borden WB. Predictors of statin adherence. Curr Cardiol Rep 2011; 13: 553-558.

8. Mann DM, Woodward M, Muntner P. Predictors of nonadherence to statins: A systematic review and meta-analysis. Ann Pharmacother 2010; 44: 1410-21.

9. Yang Y, Thumula V, Pace PF, Banahan BF 3rd, Wilkin NE, Lobb WB. Predictors of medication nonadherence among patients with diabetes in Medicare part D programs: A retrospective cohort study. Clin Ther 2009; 31: 2178-88.

10. Bailey JE, Hajjar M, Shoib B, Tang J, Ray MM, Wan JY. Risk factors associated with antihypertensive medication nonadherence in a statewide Medicaid population. Am J Med Sci 2012.

11. Holmes HM, Luo R, Hanlon JT, Elting LS, Suarez-Almazor M, Goodwin JS. Ethnic disparities in adherence to antihypertensive medications of Medicare part d beneficiaries. J Am Ger Soc 2012; 60: 1298-1303.

12. Tiv M, Viel JF, Mauny F, et al. Medication adherence in type 2 diabetes: The ENTRED study 2007, a French population-based study. PLoS One 2012; 7: e32412.

13. Sharma KP, Taylor TN. Pharmacy effect on adherence to antidiabetic medications. Med Care 2012; 50: 685-91.

14. Yang Y, Thumula V, Pace PF, Banahan BF 3rd, Wilkin NE, Lobb WB. Nonadherence to angiotensin-converting enzyme inhibitors and/or angiotensin II receptor blockers among high-risk patients with diabetes in Medicare part D programs. J Am Pharm Assoc 2010; 50: 527-31.

15. Kalyango JN, Owino E, Nambuya AP. Non-adherence to diabetes treatment at Mulago hospital in Uganda: Prevalence and associated factors. Afr Health Sci 2008; 8: 67-73.

16. Adams AS, Uratsu C, Dyer W, et al. Health system factors and antihypertensive adherence in a racially and ethnically diverse cohort of new users. JAMA 2013; 173(1): 54-61.

17. Weigand P, McCombs JS, Wang JJ. Factors of hyperlipidemia medication adherence in a nationwide health plan. Am J Manag Care 2012; 18(4): 193-9.

18. Acxiom Corporation (2014). ZIP+4 InfoBase® Geo Files: Demographic, Financial and Property, Sept 2013 release; Market Indices ACS, Feb 2014 release. Acxiom Corporation. www.acxiom.com.

19. Area Health Resources Files (AHRF) 2012-2013. US Department of Health and Human Services, Health Resources and Services Administration, Bureau of Health Professions, Rockville, MD.

Odds-Ratio

Variable MA-C MA-D MA-H

Age and Disability

Mean (STD) Reference

Disabled × 18–54 0.623 0.545 0.540

Disabled × 55–64 0.766 0.755 0.726

Disabled × 65–69 0.872 0.859 0.818

Disabled × 70+ 0.953 0.850 0.868

Gender

Female Reference

Male 1.129 1.074 0.958

Race/Ethnicity

White Reference

Asian 0.920

African American 0.641 0.657 0.660

Hispanic 0.795 0.902

Other 0.833 0.899 0.848

Medicaid Status

Non-Medicaid Reference

Partial Benefit Medicaid 1.084 1.065 1.088

Full Benefit Medicaid 1.157 1.145 1.093

Number of Unique Medications

1 – 5 Reference

6 – 7 1.044 1.276 1.080

8 – 10 1.086 1.464 1.121

11 – 12 1.115 1.600 1.115

13 – 15 1.122 1.624 1.095

16 + 1.160 1.706 1.049

Percent of Households that Own Their Home

0% - 80% Reference

81%+ 1.046 1.084 1.057

Percent of Population Below Poverty Level

0% - 7% Reference

8% - 13% 0.900 0.913 0.879

14% - 23% 0.841 0.854 0.824

24% - 100% 0.785 0.804 0.774

Percent Households with Completed College or Higher

0% Reference

1% - 18% 1.036 1.053 1.052

19% - 100% 1.044 1.070 1.059

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Note / Plans below the diagonal line have better performance ranking after risk adjustment; plans above the diagonal have poorer performance ranking after risk adjustment; plans on the diagonal have the same performance ranking before and after risk adjustment.

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