Currently Viewing:
The American Journal of Managed Care October 2015
Scalable Hospital at Home With Virtual Physician Visits: Pilot Study
Wm. Thomas Summerfelt, PhD; Suela Sulo, PhD; Adriane Robinson, RN; David Chess, MD; and Kate Catanzano, ACNP-BC
Health Coaching by Medical Assistants Improves Patients' Chronic Care Experience
David H. Thom, MD, PhD, MPH; Danielle Hessler, PhD; Rachel Willard-Grace, MPH; Denise DeVore, BA; Camille Prado, BA; Thomas Bodenheimer, MD, MPH; and Ellen H. Chen, MD
The Path to Value Through the Use of Holistic Care
Roy A. Beveridge, MD, Chief Medical Officer, Humana
Delivering Value by Focusing on Patient Experience
Paula Chatterjee, MD, MPH; Thomas C. Tsai, MD, MPH; and Ashish K. Jha, MD, MPH
Currently Reading
Medication Adherence and Healthcare Disparities: Impact of Statin Co-Payment Reduction
Jennifer Lewey, MD; William H. Shrank, MD, MSHS; Jerry Avorn, MD; Jun Liu, MD, MPH; and Niteesh K. Choudhry, MD, PhD
Solutions for Filling Gaps in Accountable Care Measure Sets
Tom Valuck, MD, JD, MHSA; Donna Dugan, PhD, MS; Robert W. Dubois, MD, PhD; Kimberly Westrich, MA; Jerry Penso, MD, MBA; and Mark McClellan, MD, PhD
The Impact of Kaua'i Care Transition Intervention on Hospital Readmission Rates
Fenfang Li, PhD; Jing Guo, PhD; Audrey Suga-Nakagawa, MPH; Ludvina K. Takahashi, BA; and June Renaud, BEd
Are Chronically Ill Patients High Users of Homecare Services in Canada?
Donna M. Wilson, PhD, RN; Corrine D. Truman, PhD, RN; Jessica A. Hewitt, BScKin; and Charl Els, MBChB, FCPsych, MMedPsych, ABAM, MROCC
Request of Acute Phase Markers in Primary Care in Spain
Maria Salinas, PhD; Maite López-Garrigós, MD; Emilio Flores, PhD; Joaquin Uris, PhD; and Carlos Leiva-Salinas, MD
Antibiotic Use for Viral Acute Respiratory Tract Infections Remains Common
Mark H. Ebell, MD, MS; and Taylor Radke, MPH
Clinician Considerations When Selecting High-Risk Patients for Care Management
Vivian Haime, BS; Clemens Hong, MD, MPH; Laura Mandel, BA; Namita Mohta, MD; Lisa I. Iezzoni, MD, MSc; Timothy G. Ferris, MD, MPH; and Christine Vogeli, PhD
"Meaningful" Clinical Quality Measures for Primary Care Physicians
Cara B. Litvin, MD, MS; Steven M. Ornstein, MD; Andrea M. Wessell, PharmD; and Lynne S. Nemeth, RN, PhD

Medication Adherence and Healthcare Disparities: Impact of Statin Co-Payment Reduction

Jennifer Lewey, MD; William H. Shrank, MD, MSHS; Jerry Avorn, MD; Jun Liu, MD, MPH; and Niteesh K. Choudhry, MD, PhD
This study examined patterns of medication adherence after a reduction in medication co-payment amount among privately insured patients living in racially diverse neighborhoods.
Statistical Analysis
We first plotted monthly adherence proportions for the intervention and control cohorts before and after co-payments were reduced, stratified by neighborhood racial composition. We then compared the impact of the co-payment reduction on patients living in communities with different proportions of black residents, using a difference-in-differences design. Our regression models included a constant term, a binary indicator for exposure (ie, intervention vs control), a binary indicator for the post intervention time period, and an interaction term between exposure and post intervention period indicator. We fit separate models, comparing intervention and control, for each tertile of percentage of black residents. We controlled for correlated error terms using generalized estimating equations assuming normally distributed errors. We repeated this analysis adjusting for the covariates listed above, and we repeated our analysis by evaluating the proportion of patients who were fully adherent—defined as a proportion of days covered greater than or equal to 80%.27
 
Collinearity between zip code–level race and income was addressed by conducting the primary analysis with and without income and then looking for changes in standard error. The Pearson correlation coefficient between these 2 continuous variables was also measured. Effect modification was examined by introducing an interaction term between the main effect in the primary analysis with income in each black race tertile. Because we were most interested in testing the interaction between low income and neighborhood racial composition, we initially divided zip code–median income into tertiles for low, middle, and high income. We then re-ran the analysis combining middle and high income into 1 group (compared with low income). These results were consistent with the analysis conducted with income included as a 3-group categorical variable, and are thus presented here. We also analyzed the impact of co-payment reduction on 6 groups stratified by zip code–level race (3 groups) and zip code–level median income (2 groups).
 
Sensitivity Analyses
We performed several additional analyses to test the robustness of our results. First, we repeated our analysis, dividing the cohort into deciles based on percentage of black residents in each zip code, and compared patients in the decile with the greatest proportion of black residents to the remaining 90% of patients. We then divided the cohort into 4 groups corresponding to the 10th, 75th, and 90th percentiles of black residents in each zip code: less than 1%, 1% to 14%, 14% to 32%, and more than 32%. Second, we re-ran our models including both an indicator variable for the post intervention level and slope and interaction terms between group membership (intervention or control) and the post intervention level and slope parameters.19 This allowed us to evaluate whether the policy intervention influenced medication adherence immediately or over the longer term, by changing the level or slope of the trend in adherence, respectively. Third, we re-ran our models using 3-way interaction terms; the results were similar to the above models. Finally, we conducted the analysis among prevalent users at the time of co-payment reduction so as to exclude patients who initiated therapy in response to the policy change.
 
RESULTS
Patient Characteristics
Our primary cohort consisted of 1961 individuals with vascular disease or diabetes who were eligible for co-payment reductions along with 37,320 comparable control subjects who also met the inclusion criteria. Their baseline characteristics are presented in Table 1. Compared with controls, members of the intervention cohort were older, more likely to have completed high school, and less likely to have hypertension. They were similarly matched with regard to other comorbidities, medication use, and hospitalizations prior to cohort entry (Table 1).
 
Baseline characteristics differed significantly by race. Individuals living in zip codes with the highest proportion of black residents had lower incomes, were less likely to have graduated from high school, were younger, and more likely to be female. Zip code–level black race and median household income were negatively correlated (correlation coefficient, –0.41; 95% CI, –0.42 to –0.40; P <.0001). Rates of comorbid conditions were also different. Patients living in areas with the highest proportion of black residents had lower rates of coronary artery disease, higher rates of diabetes and hypertension, and were prescribed more medications than the other cohorts.
 
Co-Payment Changes

Prior to the new co-payment policy, statin co-payments were higher in the intervention cohort than among controls in all tertiles based on neighborhood racial composition (Table 1). The new policy brought about a substantial reduction in monthly statin co-payment in the intervention cohort ($23.18 vs $0.47); there was virtually no decrease in co-payment in the control cohort during this time period ($10.89 vs $10.63). While the new co-payment policy should have eliminated co-payments for statins, small co-payments were still charged on 3% of eligible statin claims because of incorrect claims processing.19
 


 
Copyright AJMC 2006-2017 Clinical Care Targeted Communications Group, LLC. All Rights Reserved.
x
Welcome the the new and improved AJMC.com, the premier managed market network. Tell us about yourself so that we can serve you better.
Sign Up
×

Sign In

Not a member? Sign up now!