Adherence to Chronic Disease Medications among New York City Medicaid Participants

Abstract

Medication adherence is critical for cardiovascular disease prevention and control. Local health departments are well positioned to address adherence issues, however relevant baseline data and a mechanism for monitoring impact of interventions are lacking. We performed a retrospective analysis using New York State Medicaid claims from 2008 to 2009 to describe rates and predictors of adherence among New York City Medicaid participants with dyslipidemia, diabetes, or hypertension. Adherence was measured using the medication possession ratio, and multivariable logistic regression was used to assess factors related to adherence. Medication regimen adherence was 63%. Greater adherence was observed in those who were older, male, and taking medications from ≥3 drug classes. Compared with whites, blacks and Hispanics were less likely to be adherent (adjusted odds ratio [OR]=0.67, 95% confidence interval [CI]: 0.65-0.70 and adjusted OR=0.76, 95% CI: 0.73-0.78, respectively), while Asians were as likely. Medication adherence was inadequate and racial disparities were identified in NYC Medicaid participants on stable medication regimens for chronic disease. This study demonstrates a claims-based model that may be used by local health departments to monitor and evaluate efforts to improve adherence and reduce disparities.

Financial strain is associated with medication nonadherence and worse self-rated health among cardiovascular patients

“…3, 14 Female gender was associated with better adherence, which is inconsistent with a few others that have found the opposite relationship. 3, 17, 18, 31 However, this relationship did not become significant until SES indicators were entered into the model. In addition, being a racial/ethnic minority was significantly associated with being less adherent, as reported consistantly in the literature.…”

“…1-3 As research has sought to determine what factors may underlie these disparities, socioeconomic status (SES) and medication adherence are particularly interesting. For example, macro indicators of SES (e.g., education, income) and micro indicators of SES (e.g., owning a home, having a checking account) have predicted CVD outcomes and have explained racial/ethnic disparities in CVD outcomes.…”

“…Certain populations that tend to be at greater risk for non-adherence include people who are younger, 3 female, 3, 14 have health literacy limitations, 15 are racial/ethnic minorities, 16, 17 have less education, 18 or have lower incomes. 19 Among lower-income groups, having a copayment for statin therapy has distinguished between patients who are more or less adherent to their medications, 20 suggesting a more nuanced relationship may exist between indicators of SES and medication adherence, and, separately, health status among people with CVD.…”

Comorbidities and Their Management: Potential Impact on Breast Cancer Outcomes

“…African American women may be another group that is strongly impacted by comorbidities since these women are more likely to have sub-optimally managed comorbidities because of low medication adherence (Kyanko et al 2013 ;Bosworth et al 2008 ), which may decline further during breast cancer treatment (Calip et al 2013(Calip et al , 2014. African American breast cancer patients have more breast cancer recurrence/progression and worse all-cause, breast cancer specifi c and competing cause survival.…”

Effect of Physician Participation in a Multi-element Health Information and Data Exchange Program on Chronic Illness Medication Adherence

“…5,6 Patient adherence to medication regimens is a key component of effective chronic disease management, yet medication adherence for these conditions tends to be low in the United States. [7][8][9][10] Failure of patients to adhere to medication regimens can lead to adverse health outcomes, such as worsening of chronic conditions, 11,12 preventable hospitalizations, 13,14 and death, [15][16][17] as well as potentially increasing health care costs. 13,18 -20 Several primary care practice models have been hypothesized to improve health outcomes for patients with chronic conditions-this includes the adoption of an electronic health record (EHR) as a means to assess a practice's patient population and proactively intervene (eg, care coordination for and management of high-risk populations).…”