Assessing the Importance of Factors Associated with Cost-Related Nonadherence to Medication for Older US Medicare Beneficiaries

Abstract

Background

Prescription drug costs have been rising rapidly in the USA, contributing to the persistent problem of cost-related medication nonadherence (CRN) among older Medicare beneficiaries. Given the importance of CRN and the negative outcomes associated with it, it is important to examine the factors that affect CRN. This study aims to estimate the factors influencing CRN among older Medicare beneficiaries and to rank their relative contribution in explaining CRN.

Methods

We used a 2015 Medicare Current Beneficiary Survey linked to Medicare claims data to identify older Medicare beneficiaries aged 65 years and over. Multivariate logistic regression was performed to identify factors associated with CRN. Factors included in the regression analyses were based on a conceptual framework adapted from Piette et al., including main effects (financial factors and regimen complexity) and contextual factors (sociodemographic, lifestyle and health factors). Dominance analysis was conducted to determine their relative importance in predicting CRN.

Results

Our study sample included 4427 older Medicare beneficiaries, 13.43% of whom reported CRN. For main effects, drug coverage and regimen complexity were significantly associated with CRN. Compared to beneficiaries with public coverage, those with private drug coverage were less likely to report CRN while those without drug coverage were more likely to report CRN. Having more than two monthly prescriptions was also associated with higher CRN. Significant contextual factors included age, activities of daily living limitations, perceived health status, cancer, rheumatoid arthritis, non-rheumatoid arthritis, depression, and lung disease. Dominance analysis showed drug coverage was the most influential factor in explaining CRN, after which age, ADL limitations, and depression ranked in sequence.

Conclusions

These findings can help policy makers understand the relative importance of factors affecting CRN and identify the most important areas for intervention to improve CRN.

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Fig. 1

Data Availability

The data source used for this study is the Medicare Current Beneficiary Survey (MCBS) linked with Medicare claims. It is available upon request from the MCBS program.

References

  1. 1.

    He W, Goodkind D, Kowal PR. An aging world: 2015. Washington, DC: United States Census Bureau; 2016.

    Google Scholar 

  2. 2.

    Nobili A, Garattini S, Mannucci PM. Multiple diseases and polypharmacy in the elderly: challenges for the internist of the third millennium. J Comorbidities. 2011;1:28–44.

    Google Scholar 

  3. 3.

    Schumock GT, Stubbings J, Wiest MD, Li EC, Suda KJ, Matusiak LM, et al. National trends in prescription drug expenditures and projections for 2018. Am J Health Syst Pharm. 2018;75:1023–38.

    PubMed  Google Scholar 

  4. 4.

    Kesselheim AS, Avorn J, Sarpatwari A. The high cost of prescription drugs in the United States: origins and prospects for reform. JAMA. 2016;316:858–71.

    PubMed  Google Scholar 

  5. 5.

    Madden JM, Graves AJ, Zhang F, Adams AS, Briesacher BA, Ross-Degnan D, et al. Cost-related medication nonadherence and spending on basic needs following implementation of Medicare Part D. JAMA. 2008;299:1922–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Piette JD, Heisler M, Wagner TH. Cost-related medication underuse among chronically III adults: the treatments people forgo, how often, and who is at risk. Am J Public Health. 2004;94:1782–7.

    PubMed  PubMed Central  Google Scholar 

  7. 7.

    Tamblyn R, Laprise R, Hanley JA, Abrahamowicz M, Scott S, Mayo N, et al. Adverse events associated with prescription drug cost-sharing among poor and elderly persons. JAMA. 2001;285:421–9.

    CAS  PubMed  Google Scholar 

  8. 8.

    Heisler M, Langa KM, Eby EL, Fendrick AM, Kabeto MU, Piette JD. The health effects of restricting prescription medication use because of cost. Med Care. 2004;42:626–34.

    PubMed  Google Scholar 

  9. 9.

    Zivin K, Ratliff S, Heisler MM, Langa KM, Piette JD. Factors influencing cost-related nonadherence to medication in older adults: a conceptually based approach. Value Health. 2010;13:338–45.

    PubMed  PubMed Central  Google Scholar 

  10. 10.

    Piette JD, Heisler M, Horne R, Alexander GC. A conceptually based approach to understanding chronically ill patients’ responses to medication cost pressures. Soc Sci Med. 2006;62:846–57.

    PubMed  Google Scholar 

  11. 11.

    Briesacher BA, Gurwitz JH, Soumerai SB. Patients at-risk for cost-related medication nonadherence: a review of the literature. J Gen Intern Med. 2007;22:864–71.

    PubMed  PubMed Central  Google Scholar 

  12. 12.

    Zhang JX, Lee JU, Meltzer DO. Risk factors for cost-related medication non-adherence among older patients with diabetes. World J Diabetes. 2014;5:945–50.

    PubMed  PubMed Central  Google Scholar 

  13. 13.

    Zhang JX, Meltzer DO. Risk factors for cost-related medication non-adherence among older patients with cancer. Integr Cancer Sci Ther. 2015;2:300–4.

    PubMed  PubMed Central  Google Scholar 

  14. 14.

    Blumberg DM, Prager AJ, Liebmann JM, Cioffi GA, De Moraes CG. Cost-related medication nonadherence and cost-saving behaviors among patients with glaucoma before and after the implementation of Medicare Part D. JAMA Ophthalmol. 2015;133:985–96.

    PubMed  Google Scholar 

  15. 15.

    Harrold LR, Briesacher BA, Peterson D, Beard A, Madden J, Zhang F, et al. Cost-related medication nonadherence in older patients with rheumatoid arthritis. J Rheumatol. 2013;40:137–43.

    PubMed  Google Scholar 

  16. 16.

    Kang H, Lobo JM, Kim S, Sohn M-W. Cost-related medication non-adherence among US adults with diabetes. Diabetes Res Clin Pract. 2018;143:24–33.

    PubMed  PubMed Central  Google Scholar 

  17. 17.

    Gonzalez JS, Peyrot M, McCarl LA, Collins EM, Serpa L, Mimiaga MJ, et al. Depression and diabetes treatment nonadherence: a meta-analysis. Diabetes Care. 2008;31:2398–403.

    PubMed  PubMed Central  Google Scholar 

  18. 18.

    Centers for Medicare and Medicaid Service. Medicare Current Beneficiary Survey related files. https://www.cms.gov/MCBS. Accessed Dec 2018.

  19. 19.

    Burcu M, Alexander GC, Ng X, Harrington D. Construct validity and factor structure of survey-based assessment of cost-related medication burden. Med Care. 2015;53:199–206.

    PubMed  Google Scholar 

  20. 20.

    Piette JD, Beard A, Rosland AM, McHorney CA. Beliefs that influence cost-related medication non-adherence among the “haves” and “have nots” with chronic diseases. Patient Pref Adherence. 2011;5:389–96.

    Google Scholar 

  21. 21.

    Centers for Disease Control and Prevention. About adult BMI. http://www.cdc.gov/healthyweight/assessing/bmi/adult_bmi/index.html. Accessed Dec 2018.

  22. 22.

    Bambauer KZ, Safran DG, Ross-Degnan D, Zhang F, Adams AS, Gurwitz J, et al. Depression and cost-related medication nonadherence in Medicare beneficiaries. Arch Gen Psychiatry. 2007;64:602–8.

    PubMed  Google Scholar 

  23. 23.

    Zivin K, Madden JM, Zhang F, Soumerai SB, Graves AJ. Cost-related medication nonadherence among beneficiaries with depression following Medicare Part D. Am J Geriatr Psychiatry. 2009;17:1068–76.

    PubMed  PubMed Central  Google Scholar 

  24. 24.

    Rogers WH, Wilson IB, Bungay KM, Cynn DJ, Adler DA. Assessing the performance of a new depression screener for primary care (PC-SAD). J Clin Epidemiol. 2002;55:164–75.

    PubMed  Google Scholar 

  25. 25.

    Rogers WH, Adler DA, Bungay KM, Wilson IB. Depression screening instruments made good severity measures in a cross-sectional analysis. J Clin Epidemiol. 2005;58:370–7.

    PubMed  Google Scholar 

  26. 26.

    Azen R, Budescu DV. The dominance analysis approach for comparing predictors in multiple regression. Psychol Methods. 2003;8:129–48.

    PubMed  Google Scholar 

  27. 27.

    Azen R, Traxel N. Using dominance analysis to determine predictor importance in logistic regression. J Educ Behav Stat. 2009;34:319–47.

    Google Scholar 

  28. 28.

    Grömping U. Estimators of relative importance in linear regression based on variance decomposition. Am Statistician. 2007;261:139–47.

    Google Scholar 

  29. 29.

    Luchman JN. Determining subgroup difference importance with complex survey designs: an application of weighted dominance analysis. Surv Pract. 2015;8:1–10.

    Google Scholar 

  30. 30.

    Soumerai SB, Pierre-Jacques M, Zhang F, Ross-Degnan D, Adams AS, Gurwitz J, et al. Cost-related medication nonadherence among elderly and disabled medicare beneficiaries: a national survey 1 year before the medicare drug benefit. Arch Intern Med. 2006;166:1829–35.

    PubMed  Google Scholar 

  31. 31.

    Kaiser Family Foundation. How does prescription drug spending and use compare across large employer plans, Medicare Part D, and Medicaid? https://www.kff.org/medicare/issue-brief/how-does-prescription-drug-spending-and-use-compare-across-large-employer-plans-medicare-part-d-and medicaid/. Accessed May 2019.

  32. 32.

    Gellad WF, Grenard J, McGlynn EA, Gellad WF, Grenard JL, McGlynn EA. A review of barriers to medication adherence. In: A framework for driving policy options. Santa Monica, CA: RAND Corp; 2009.

  33. 33.

    Aziz H, Hatah E, Bakry MM, et al. How payment scheme affects patients’ adherence to medications? A systematic review. Patient Prefer Adherence. 2016;10:837–50.

    PubMed  PubMed Central  Google Scholar 

  34. 34.

    Cohen MJ, Shaykevich S, Cawthon C, Kripalani S, Paasche-Orlow MK, Schnipper JL. Predictors of medication adherence postdischarge: the impact of patient age, insurance status, and prior adherence. J Hosp Med. 2012;7:470–5.

    PubMed  PubMed Central  Google Scholar 

  35. 35.

    Gazmararian J, Jacobson KL, Pan Y, Schmotzer B, Kripalani S. Effect of a pharmacy-based health literacy intervention and patient characteristics on medication refill adherence in an urban health system. Ann Pharmacother. 2010;44:80–7.

    PubMed  Google Scholar 

  36. 36.

    Hinkin CH, Hardy DJ, Mason KI, Castellon SA, Durvasula RS, Lam MN, et al. Medication adherence in HIV-infected adults: effect of patient age, cognitive status, and substance abuse. AIDS. 2004;18(Suppl. 1):S19–25.

    PubMed  PubMed Central  Google Scholar 

  37. 37.

    Zhang JX, Lee JU, Pandey K, Meltzer DO. Variation in cost-related medication non-adherence with functional limitations and frequency of hospitalization in older adults. Ann Gerontol Geriatric Res. 2015;2(1):1023.

    Google Scholar 

  38. 38.

    Chan L, Beaver S, MacLehose RF, Jha A, Maciejewski M, Doctor JN. Disability and health care costs in the Medicare population. Arch Phys Med Rehab. 2002;83:1196–201.

    Google Scholar 

  39. 39.

    MacLachlan M, Mannan H, McAuliffe E. Access to health care of persons with disabilities as an indicator of equity in health systems. Open Med. 2011;5:e10.

    PubMed  PubMed Central  Google Scholar 

  40. 40.

    Pellegrino LD, Peters ME, Lyketsos CG, Marano CM. Depression in cognitive impairment. Curr Psychiatry Rep. 2013;15:384.

    PubMed  PubMed Central  Google Scholar 

  41. 41.

    Campbell NL, Boustani MA, Skopelja EN, Gao S, Unverzagt FW, Murray MD. Medication adherence in older adults with cognitive impairment: a systematic evidence-based review. Am J Geriatr Pharmacother. 2012;10:165–77.

    PubMed  Google Scholar 

  42. 42.

    Nekhlyudov L, Madden J, Graves AJ, Zhang F, Soumerai SB, Ross-Degnan D. Cost-related medication nonadherence and cost-saving strategies used by elderly Medicare cancer survivors. J Cancer Surviv. 2011;5:395–404.

    PubMed  PubMed Central  Google Scholar 

  43. 43.

    Tran G, Zafar SY. Financial toxicity and implications for cancer care in the era of molecular and immune therapies. Ann Transl Med. 2018;6(9):166.

    PubMed  PubMed Central  Google Scholar 

  44. 44.

    Peppercorn J. Financial toxicity and societal costs of cancer care: distinct problems require distinct solutions. Oncologist. 2017;22(2):123–5.

    PubMed  PubMed Central  Google Scholar 

  45. 45.

    Aikens JE, Piette JD. Diabetic patients’ medication underuse, illness outcomes, and beliefs about antihyperglycemic and antihypertensive treatments. Diabetes Care. 2009;32:19–24.

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Piette JD, Heisler M, Wagner TH. Medication characteristics beyond cost alone influence decisions to underuse pharmacotherapy in response to financial pressures. J Clin Epidemiol. 2006;59:739–46.

    PubMed  Google Scholar 

  47. 47.

    Yap AF, Thirumoorthy T, Kwan YH. Medication adherence in the elderly. J Clin Gerontol Geriatr. 2016;7:64–7.

    Google Scholar 

  48. 48.

    Chang-Quan H, Bi-Rong D, Zhen-Chan L, Yuan Z, Yu-Sheng P, Qing-Xiu L. Collaborative care interventions for depression in the elderly: a systematic review of randomized controlled trials. J Investig Med. 2009;57:446–55.

    PubMed  Google Scholar 

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Acknowledgements

We thank Gary Deyter for his editorial assistance.

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All authors contributed to the planning, conduct, and reporting of the work described in the article. Dian Gu is responsible for the overall content as the guarantor.

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Correspondence to Dian Gu.

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No funding was received for the conduct of this study or the preparation of this article.

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Dian Gu and Chan Shen have no conflicts of interest that are directly relevant to the content of this article.

Ethics Approval

This study received institutional review board exemption status from the University of Texas MD Anderson Cancer Center.

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Gu, D., Shen, C. Assessing the Importance of Factors Associated with Cost-Related Nonadherence to Medication for Older US Medicare Beneficiaries. Drugs Aging 36, 1111–1121 (2019). https://doi.org/10.1007/s40266-019-00715-3

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