Zhang Y, Wu S, Fendrick AM, Baicker K. Variation in Medication Adherence in Heart Failure. JAMA Intern Med. 2013;173(6):468-470. doi:10.1001/jamainternmed.2013.2509
Author Affiliations: Department of Health Policy and Management, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania (Dr Zhang and Mr Wu); Department of Internal Medicine, School of Medicine, and the Department of Health Management and Policy, School of Public Health, University of Michigan, Ann Arbor (Dr Fendrick); and Department of Health Policy & Management, Harvard School of Public Health, Boston, Massachusetts (Dr Baicker).
Although recent studies have demonstrated geographic variation in pharmaceutical use and spending,1- 4 regional variation in medication adherence in Medicare has not been explored.5 Medication adherence is a critical quality measure and is especially important for Medicare beneficiaries with heart failure (HF), a common condition in which medications can save lives and reduce downstream costs.6 We used 2007-2009 national Part D data for a 5% random sample of Medicare beneficiaries to study regional variation in HF medication adherence.
Our selection criteria included the following: (1) being 18 years or older; (2) having at least 1 inpatient or 2 (nonlaboratory) outpatient claims between January 1, 2007, and December 31, 2009, with selected International Classification of Diseases, Ninth Revision (ICD-9) codes indicating HF on primary, secondary, or third diagnosis; (3) being on at least 1 drug regimen from 1 of 3 therapeutic classes: β-blockers, angiotensin-converting enzymes inhibitors (ACEs) or angiotensin receptor antagonists (ARBs), and/or diuretics7; and (4) being continuously enrolled in Medicare Parts A, B, and D during the follow-up period. The follow-up period was 1 year after the first prescription drug of interest was filled, censored at the end of the study period (December 31, 2009), or death. The resulting 178 102 beneficiaries were assigned to 306 Dartmouth hospital referral regions (HRRs) based on their zip code of residence.
The main outcome was adherence, as measured by medication possession ratio (MPR), which was defined as the ratio of total number of pills the patient had (numerator) over the total number of pills the patient should have had (denominator) during the follow-up period.8 We then defined an indicator for good adherence (1, MPR ≥0.80; 0, otherwise). The denominator for MPR can vary for a patient over time because patients may initiate different drugs at different times. For example, consider a patient who filled her first β-blocker prescription on January 1, 2008, and her first ACE prescription on March 1, 2008. Her MPR in each of the first 2 months would be the number of β-blocker pills dispensed by the pharmacy that month divided by 30, while her MPR for the third month would be the total number of β-blocker and ACE pills divided by 60 (30 days × 2 drugs). We considered drugs in the same therapeutic class substitutable, so we did not double count the overlapped pills for multiple drugs in the same class.
We defined 3 additional prescribing measures: (1) gross spending on pharmaceuticals including Part D plan payment before rebates, beneficiary out-of-pocket spending, and subsidies; (2) the number of monthly prescriptions filled (day's supply/30); and (3) intensity of medication treatment, defined as the proportion of patients receiving all 3 drug classes among those on at least 1 regimen.
We conducted individual-level linear regressions that included HRR indicators and a set of adjustment variables including patient demographics, insurance status, and clinical characteristics. We then calculated the adjusted outcomes for each HRR (thereby netting out differences between HRRs in those patient characteristics) and reported variation statistics and correlation between adjusted outcomes analysis, as previously described.9
On average, 52% of patients had good adherence (MPR ≥0.8) for HF medications, but the proportion of having good adherence varied by area, from the lowest 36% to the highest 71%. There was similar variation in the intensity of medication treatment and adherence among HRRs. Drug spending varies more across HRRs than the number of prescriptions (Table), partially owing to the mix of drugs used. For example, the area at the 90th percentile of drug spending had per-person drug spending that was 31% higher than the area at the 10th percentile of drug spending but had only 15% higher number of prescriptions. Drug spending was moderately positively correlation with intensity of treatment and the number of prescriptions (r = 0.19; P = .001) but had little correlation with adherence measures (r = 0.04; P = .44).
We found that areas with higher drug spending did not have systematically better adherence. This suggests that areas with higher drug spending are not necessarily caring for patients with HF more efficiently. There are several limitations to our study, however. First, our adherence measure is imperfect, as with most MPR-based metrics, we did not capture emerging contraindications, unfilled prescriptions, untaken pills after filling prescriptions, or changes in physicians' orders. Second, we could not completely adjust for differences in patient severity or patient preferences that differ across areas.
Nonetheless, our study provides new information on the variation in medication adherence in patients with HF using national Medicare Part D data. We found that although only 52% of patients are adherent in the average area, some areas have substantially more success in producing patient adherence than others. Areas with better adherence can provide a useful benchmark for what is achievable, and system-level quality metrics that incorporate adherence, rather than focusing solely on drug spending, could promote more efficient use of resources.
Correspondence: Dr Zhang, Department of Health Policy and Management, University of Pittsburgh, 130 De Soto St, Crabtree Hall, Room A664, Pittsburgh, PA 15261 (email@example.com).
Published Online: February 11, 2013. doi:10.1001/jamainternmed.2013.2509
Author Contributions: Dr Zhang and Mr Wu had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: Zhang, Fendrick, and Baicker. Acquisition of data: Zhang and Wu. Analysis and interpretation of data: Zhang, Wu, Fendrick, and Baicker. Drafting of the manuscript: Zhang and Wu. Critical revision of the manuscript for important intellectual content: Zhang, Fendrick, and Baicker. Statistical analysis: Zhang, Wu, and Baicker. Obtained funding: Zhang. Administrative, technical, and material support: Zhang. Study supervision: Zhang and Fendrick.
Conflict of Interest Disclosures: Dr Baicker is a Commissioner on the Medicare Payment Advisory Commission and a director of Eli Lilly.
Funding/Support: This study was supported by grant HHSP22320042509XI from the Institute of Medicine, grant RC1 MH088510 from the National Institute of Mental Health, and grant R01 HS018657 from the Agency for Healthcare Research and Quality (Dr Zhang).
Previous Presentation: Some preliminary results from this study were presented at the closed session meetings to the Institute of Medicine committee in Geographic Variation in Health Care Spending and Promotion of High-Value Care; June 4, 2012; Washington, DC.