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The American Journal of Managed Care October 2014
Quality of Care at Retail Clinics for 3 Common Conditions
William H. Shrank, MD, MSHS; Alexis A. Krumme, MS; Angela Y. Tong, MS; Claire M. Spettell, PhD; Olga S. Matlin, PhD; Andrew Sussman, MD; Troyen A. Brennan, MD, JD; and Niteesh K. Choudhry, MD, PhD
A Comprehensive Hospital-Based Intervention to Reduce Readmissions for Chronically Ill Patients: A Randomized Controlled Trial
Ariel Linden, DrPH; and Susan W. Butterworth, PhD
Physician Compensation Strategies and Quality of Care for Medicare Beneficiaries
Bruce E. Landon, MD, MBA; A. James O'Malley, PhD; M. Richard McKellar, BA; James D. Reschovsky, PhD; and Jack Hadley, PhD
Increasing Access to Specialty Care: Patient Discharges From a Gastroenterology Clinic
Delphine S. Tuot, MDCM, MAS; Justin L. Sewell, MD, MPH; Lukejohn Day, MD; Kiren Leeds, BA; and Alice Hm Chen, MD, MPH
Increasing Preventive Health Services via Tailored Health Communications
Kathleen T. Durant, PhD; Jack Newsom, ScD; Elizabeth Rubin, MPA; Jan Berger, MD, MJ; and Glenn Pomerantz, MD
The Duration of Office Visits in the United States, 1993 to 2010
Meredith K. Shaw; Scott A. Davis, MA; Alan B. Fleischer, Jr, MD; and Steven R. Feldman, MD, PhD
Evaluation of Collaborative Therapy Review to Improve Care of Heart Failure Patients
Harleen Singh, PharmD; Jessina C. McGregor, PhD; Sarah J. Nigro, PharmD; Amy Higginson, BS; and Greg C. Larsen, MD
Extending the 5Cs: The Health Plan Tobacco Cessation Index
Victor Olaolu Kolade, MD
Caregiver Presence and Patient Completion of a Transitional Care Intervention
Gary Epstein-Lubow, MD; Rosa R. Baier, MPH; Kristen Butterfield, MPH; Rebekah Gardner, MD; Elizabeth Babalola, BA; Eric A. Coleman, MD, MPH; and Stefan Gravenstein, MD, MPH
Ninety-Day Readmission Risks, Rates, and Costs After Common Vascular Surgeries
Eleftherios S. Xenos, MD, PhD; Jessica A. Lyden, BSc; Ryan L. Korosec, MBA, CPA; and Daniel L. Davenport, PhD
Using Electronic Health Record Clinical Decision Support Is Associated With Improved Quality of Care
Rebecca G. Mishuris, MD, MS; Jeffrey A. Linder, MD, MPH; David W. Bates, MD, MSc; and Asaf Bitton, MD, MPH
The Impact of Pay-for-Performance on Quality of Care for Minority Patients
Arnold M. Epstein, MD, MA; Ashish K. Jha, MD, MPH; and E. John Orav, PhD
Healthcare Utilization and Diabetes Management Programs: Indiana 2006-2010
Tilicia L. Mayo-Gamble, MA, MPH; and Hsien-Chang Lin, PhD
Currently Reading
Predictors of High-Risk Prescribing Among Elderly Medicare Advantage Beneficiaries
Alicia L. Cooper, MPH, PhD; David D. Dore, PharmD, PhD; Lewis E. Kazis, ScD; Vincent Mor, PhD; and Amal N. Trivedi, MD, MPH

Predictors of High-Risk Prescribing Among Elderly Medicare Advantage Beneficiaries

Alicia L. Cooper, MPH, PhD; David D. Dore, PharmD, PhD; Lewis E. Kazis, ScD; Vincent Mor, PhD; and Amal N. Trivedi, MD, MPH
This study examines patterns of high-risk prescribing in the elderly Medicare Advantage population and demonstrates that the practice varies widely by geography and drug class.
ABSTRACT
Objectives

To examine patient, community, and insurance plan predictors of high-risk prescribing in the elderly Medicare Advantage population.

Study Design
Cohort study.

Methods
Using a sample of 203 Medicare Advantage plans from the 2006-2008 Health Outcomes Survey, we compared patient, community, and insurance plan characteristics of 77,247 respondents with and without new Medicare Part D claims for high-risk medications from June 2006 to May 2007.

Results
Of the Medicare Advantage enrollee respondents, 15.6% received a new prescription for a high-risk medication during 12 months of follow-up. In adjusted analyses, new users of high-risk medications were more likely to be women (OR = 1.35; 95% CI,1.28-1.42), and they reported poorer general health (Physical Component Summary score 37.3 vs 40.4, P <.05) than did individuals who never received a high-risk prescription. Being aged ≥85 years was protective against receipt of a high-risk medication (OR relative to persons aged 65-69 years = 0.69; 95% CI, 0.64-0.75). Incidence of high-risk prescribing varied by census division, with a 2-fold difference between regions with the lowest and highest rates (9% in New England vs 18% in the West South Central region). Muscle relaxants, antihistamines, and opiates accounted for over 71% of new dispensing of high-risk medications. Approximately 67% of new users of high-risk medications received only 1 dispensing.

Conclusions
High-risk prescribing varies widely by geography and drug class in the Medicare Advantage population. Women, persons with poorer self-reported health, and those residing in the Southern regions of the United States more frequently receive high-risk medications. Variations may highlight areas for targeted interventions to reduce high-risk prescribing to the elderly.

Am J Manag Care. 2014;20(10):e469-e478
In the elderly Medicare Advantage population, high-risk medications are prescribed not only according to patient characteristics, but also according to regional practice patterns. Such variations highlight areas for targeted interventions to reduce highrisk drug exposure among the elderly.
  • Women are significantly more likely than men to receive a high-risk prescription; plans may consider targeting female beneficiaries in efforts to reduce exposure.
  • Plans and practitioners operating in Southern regions should work together to reduce the high proportion of elderly individuals who receive high-risk prescriptions.
High-risk prescribing has been associated with adverse drug reactions in the elderly population.1,2 Among patients 65 years and older, adverse drug reactions are of particular concern because they contribute to increased healthcare utilization, increased Medicare spending, and poorer health outcomes.3,4 Medicare managed care (Medicare Advantage) plans are required to report the proportion of their enrollees receiving drugs that are considered potentially inappropriate for the elderly using Healthcare Effectiveness Data and Information Set (HEDIS) quality indicators. The HEDIS measures are based on established lists of high-risk medications that can cause adverse drug reactions in the elderly. The first list of high-risk medications was published in 1991 as the Beers criteria.5-7 Zhan and colleagues further classified a subset of these drugs into 3 categories (always inappropriate for the elderly, rarely appropriate for the elderly, and sometimes indicated for the elderly), marking distinctions that are relevant for clinical practice (eAppendix Table 1).8

Many factors influence the prescribing process, including a patient’s indication and preference for a drug, provider practice patterns, and differential prescription drug coverage by insurance plans. It is necessary to consider all such forces simultaneously to understand their relative importance in potential high-risk prescribing. Studies of high-risk prescribing in the elderly have been conducted previously in both hospitalized and community-dwelling patient populations, but have primarily focused on patient-level predictors or populations within limited geographic areas.8-11 Given previously described geographic variations in quality and appropriateness of prescribing practices, smaller localized studies may identify different predictors in different regions.12,13 In order to identify national predictors of high-risk prescribing in the elderly that can be translated into improvements in prescribing policy or practice, it is necessary to evaluate a large, nationally representative sample of elderly individuals. It is particularly relevant to examine such questions using data following the introduction of Medicare Part D, as expanded prescription drug coverage has increased access to medications among the elderly.14

We therefore examined patient, community, and insurance plan predictors of high-risk prescribing in the elderly Medicare Advantage population. Because more than 25% of the Medicare population is now enrolled in Medicare Advantage plans, characterizing high-risk prescribing within this population is increasingly important. Using a nationally representative study sample, we compared Medicare Advantage beneficiaries with new Medicare Part D claims for high-risk medications versus beneficiaries without claims for high-risk medications during the same interval.

METHODS

Sources of Data and Study Population


We conducted a cohort study that estimated the associations between baseline patient, community, and insurance plan characteristics, and subsequent initiation of a high-risk medication. Patient and insurance plan characteristics and patient mortality ascertainment were obtained from Cohort 9 of the 2006-2008 Medicare Health Outcomes Survey (HOS); the HOS features a nationally representative sample of Medicare Advantage beneficiaries. The HOS surveys a random sample of approximately 1000 beneficiaries in every participating Medicare Advantage plan during a baseline year and then again after 2 years.15 For Cohort 9 of the HOS, the survey sampled 203 Medicare Advantage plans and had a response rate of 69% for the baseline questionnaire. Prescription information was drawn from the 2006 and 2007 Medicare Part D Event files containing information about every Medicare-paid prescription filled by individuals in the study sample, including survey nonrespondents. Community characteristics were obtained from the 2006-2007 Area Resource File (ARF), which includes data on all counties in the United States. Individuals in the HOS were linked by county code to the corresponding entries in the ARF, produced annually by HHS Health Resources and Service Administration.

Cohort 9 of the Medicare HOS included 203 reporting Medicare Advantage plans, and a total sample size of 188,515 beneficiaries. From this population, the sample was further restricted to the 89,330 beneficiaries 65 years or older who completed at least 80% of the baseline survey and had at least 1 Medicare Part D claim between January 2006 and May 2007. The final study sample consisted of 77,247 individuals who had no high-risk prescriptions during a 5-month baseline period (January 2006 to May 2006) prior to the administration of the 2006 HOS (Figure 1).

Conceptual Model

We employed the model of health services use developed by Andersen, Aday, and Newman, in which health service use is driven by predisposing (eg, age, sex, education), enabling (eg, income, insurance), and need-based (eg, disease-burden, disability) factors.16-18 Phillips and colleagues built upon this model to also include community characteristics as determinants of healthcare use.19 These models informed variable selection for these analyses, seeking to assess the relative importance of a variety of determinants in predicting the receipt of a high-risk prescription among the elderly.

Study Variables

The primary outcome of interest was receipt of a new dispensing for a high-risk medication. New users were defined as individuals with no high-risk prescriptions prior to completion of the baseline survey (January 2006 to May 2006) who received a new prescription for a high-risk medication during a 12-month period after the survey (June 2006 to May 2007) eAppendix Figure 1). In the analyses, we used dichotomous indicators of receipt of high-risk medications according to the Zhan criteria as the dependent variables of interest. These variables included an indicator for receipt of any high-risk medication and indicators for each Zhan subclass reflecting medications that are always inappropriate for the elderly, rarely appropriate for the elderly, and sometimes indicated for the elderly. We conducted additional analyses using dichotomous indicators of receipt of any high-risk medications in the most commonly prescribed drug classes (antihistamines, opiates, skeletal muscle relaxants; eAppendix Table 2) as the outcome. We also stratified our main analysis by US census division.

The primary independent variables were patient sociodemographic characteristics, including age, sex, race, marital status, highest educational attainment, and annual household income, measured prior to the receipt of any high-risk medication. Baseline measures of patient health included self-reported general health (excellent/very good or good/fair/poor); limitation in moderate activity (any limitation or no limitation); total number of chronic conditions reported in the HOS; total number of unique drugs received; and Physical Component Summary (PCS) and Mental Component Summary (MCS) scores. The PCS and MCS scores range from 0 to 100 and are calculated based on individuals’ responses to survey items included in the HOS that are derived from the Veterans RAND 12-Item Health Survey (VR-12), a validated instrument that spans 8 dimensions of physical and mental health. The scores are used to measure disease burden and health-related quality of life.20,21 The VR-12 is scored using a t score transformation with a norm of 50 and an SD of 10, where higher scores denote better health. Self-reports of physical health and functional status have been shown to predict mortality, healthcare costs, and use of healthcare services, even after adjustment for the presence of coexisting conditions.22-24

The following community and insurance plan characteristics were also included as independent variables: percent of county population under the poverty line, percent of county population that is white, and percent of county population 65 years and older, as well as the per capita supply of physicians for each county, the US census division in which each Medicare Advantage plan operates, plan model type (staff/group, non-staff/non-group), plan profit status, the number of years a plan has participated in Medicare Advantage, and the number of beneficiaries served by a plan.

Analyses

We identified prevalent and new users of high-risk medications among the full HOS population (both respondents and nonrespondents) and among those who completed at least 80% of the baseline survey. We conducted X2 and t tests to compare the distribution of potential predictors across exposure categories. We employed generalized logistic regression models to estimate adjusted odds ratios for potential predictors of receiving new prescriptions for high-risk medications, using generalized estimating equations to account for patient clustering within Medicare Advantage plans. We also estimated adjusted odds ratios for potential predictors of receiving Zhan criteria drugs classified as antihistamines, opiates, and skeletal muscle relaxants. We repeated regression analyses, stratifying by US census division. All analyses were performed with SAS software, version 9.2 (SAS Institute, Cary, North Carolina). Results are reported with 95% confidence intervals. The study was approved by the institutional review board of Brown University; the requirement for informed consent was waived.

RESULTS

In the full study population 65 years or older with at least 1 Medicare Part D claim between January 2006 and May 2007 (N = 143,684), 13.5% had at least 1 dispensing of a high-risk medication during the first 5 months of 2006, and an additional 13.5% had at least 1 high-risk medication dispensing between June 2006 and May 2007. Of the beneficiaries 65 years or older who had at least 1 Medicare Part D claim between January 2006 and May 2007 and completed at least 80% of the baseline survey (n = 89,330), 13.5% had at least 1 dispensing of a high-risk medication during the first 5 months of 2006. These individuals, classified as prevalent users, were excluded from subsequent analyses. The final study sample consisted of 77,247 individuals not using high-risk medications for the first 5 months of 2006. Of these, 15.6% received a new dispensing of a high-risk medication according to the Zhan criteria from June 2006 through May 2007 (Table 1).

New users of high-risk medications were more likely to be women than those without a prescription for a high-risk medication (66% vs 58%), were more likely to have annual income <$10,000 (16% vs 13%), and were less likely tohave any college education (33% vs 37%) (all P values <.01). New users also reported poorer general health, greater activity limitation, and more chronic conditions than individuals who never received a high-risk medication. On average, they received approximately 4 more unique prescriptions in 2006-2007 than did individuals without a prescription for a high-risk medication (11.9 prescriptions vs 7.6 prescriptions). Individuals newly receiving drugs classified as “rarely appropriate” and “always inappropriate” had poorer health at baseline than those receiving drugs classified as “sometimes indicated.”

 
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