Currently Viewing:
The American Journal of Managed Care October 2010
Mental Health Services Utilization Among Women Experiencing Intimate Partner Violence
Ameena T. Ahmed, MD, MPH; and Brigid R. McCaw, MD, MPH
Shared Savings Program for Accountable Care Organizations: A Bridge to Nowhere?
Robert A. Berenson, MD
Costs of Heart Failure-Related Hospitalizations in Patients Aged 18 to 64 Years
Guijing Wang, PhD; Zefeng Zhang, MD, PhD; Carma Ayala, MPH, PhD; Hilary K. Wall, MPH; and Jing Fang, MD
Scheduled and Unscheduled Hospital Readmissions Among Patients With Diabetes
Hongsoo Kim, PhD, MPH; Joseph S. Ross, MD, MHS; Gail D. Melkus, EdD, C-NP; Zhonglin Zhao, MD, MPH; and Kenneth Boockvar, MD, MS
Hospital Readmission Among Participants in a Transitional Case Management Program
Osman I. Ahmed, MD, DrPH; and David J. Rak, MPH
Retail Clinic Versus Office Setting: Do Patients Choose Appropriate Providers?
Amy R. Wilson, PhD; Xingzhou T. Zhou, MS; Wei Shi, MA; Holly Rodin, PhD; Eric P. Bargman, PhD; Nancy A. Garrett, PhD; and Thomas J. Sandberg, MGIS
Currently Reading
Variation in Prescription Use and Spending for Lipid-Lowering and Diabetes Medications in the Veterans Affairs Healthcare System
Walid F. Gellad, MD, MPH; Chester B. Good, MD, MPH; John C. Lowe, RPh, MBA; and Julie M. Donohue, PhD
Assessing the Accuracy of Drug Profiles in an Electronic Medical Record System of a Washington State Hospital
Brett Platte, MHPA; Fevzi Akinci, PhD; and Yunus Guc, MA
Effect of a Patient Panel-Support Tool on Care Delivery
Adrianne C. Feldstein, MD, MS; Nancy A. Perrin, PhD; Robert Unitan, MD; A. Gabriela Rosales, MS; Gregory A. Nichols, PhD; David H. Smith, RPh, MHA, PhD; Jennifer Schneider, MPH; Carrie M. Davino, MD;

Variation in Prescription Use and Spending for Lipid-Lowering and Diabetes Medications in the Veterans Affairs Healthcare System

Walid F. Gellad, MD, MPH; Chester B. Good, MD, MPH; John C. Lowe, RPh, MBA; and Julie M. Donohue, PhD

Substantial variation in prescription spending and use of brand-name drugs exists across the VA healthcare system, with no apparent relationship to quality of care.

We categorized VAMCs into spending quartiles and used Kruskal-Wallis tests to assess differences in the median cost per patient across each quartile for lipid-lowering and diabetes agents. We also used Kruskal-Wallis tests and c2 tests to assess differences in VAMC characteristics across quartiles. We used analysis of variance to test for differences in the mean proportion of patients using brand-name nonformulary drugs across quartiles. We used Spearman rank correlation coefficients and scatterplots to examine the relationship between cost per patient for lipid-lowering and diabetes agents at each VAMC. We also used Spearman rank correlation coefficients and scatterplots to compare spending at each VAMC for lipidlowering and diabetes agents with HEDIS quality scores for hyperlipidemia and diabetes.

To better characterize the independent effect of facility-level cost per patient on quality outcomes, we used logistic regression analysis to model the probability of being in the highest quartile of quality for lipid-lowering and diabetes medications separately. Our primary independent variable was cost per patient at the VAMC level, used as a continuous measure, and we included all facility characteristics aforedescribed as additional covariates in the analysis. We used commercially available statistical software (SAS, version 9.2; SAS Institute, Cary, NC) for all analyses.

RESULTS

Overall, 135 VAMCs were included in the analysis, representing 2.3 million veterans who were dispensed lipid-lowering medications and 981,031 veterans who were dispensed diabetes medications in FY2008. Table 1 lists each of the medications studied, along with the number of unique pharmacy users, the mean unit acquisition cost of the medications, and the brand-name versus generic classification of the medications. On average, generic agents were available at 10% to 20% of the acquisition cost of brandname drugs.

Table 2 lists additional characteristics of VAMCs overall and by quartile of drug spending. For lipid-lowering agents prescribed in FY2008, VAMCs in the most expensive quartile had slightly fewer outpatient visits and veterans taking the medications (P > .05), but the proportion of patients 65 years and older was the same as in the least expensive quartile. For diabetes agents, VAMCs in the most expensive quartile had fewer patients using insulin compared with VAMCs in the least expensive quartile (P = .049) and had a lower proportion  of patients overall receiving diabetes medications (20% vs 21%), although the difference was not statistically significant (P = .38).

For patients taking lipid-lowering agents in FY2008, the median cost per patient per year was $49.60 (interquartile range, $42.80-$61.00) and ranged across VAMCs from $23.50 to $125.00 (Table 2). The VAMCs in the most expensive quartile had a median cost per patient of $69.57, which was 75% higher than that of VAMCs in the least expensive quartile ($39.68). The median cost per patient per year for diabetes agents was $158.34 (interquartile range, $136.23-$182.13) and ranged across VAMCs from $106.95 to $306.58. The VAMCs in the most expensive quartile had a median cost per patient of $198.31, which was 60% higher than that of VAMCs in the least expensive quartile ($123.34). Calculations using cost per 30-day prescription showed similar variation, and the 2 measures were highly correlated (r >0.95). The percentage of patients taking brandname oral medications was greatest among VAMCs with the highest prescription spending for lipid-lowering agents and for diabetes medications (P <.001), with percentages in the highest quartile more than twice the percentage in the lowest quartile for lipid-lowering drugs and almost twice the percentage in the lowest quartile for oral diabetes medications.

There was a moderate correlation between VAMC-level cost per patient for lipid-lowering drugs and cost per patient for diabetes agents (r = 0.41, P <.001) (Figure 1). Almost half (47%) of VAMCs in the highest quartile for lipid-lowering drugs were also in the highest quartile for diabetes drugs (data not shown).

There was no statistically significant correlation between the mean prescription spending and performance on disease-specific HEDIS measures across VAMCs for lipid-lowering drugs or for diabetes agents (Figure 2). The correlation coefficient for patients with diabetes having LDL-C levels less than 100 mg/dL was r = 0.12, P = .16. The coefficient for patients with heart disease having LDL-C levels less than 100 mg/dL was r = 0.07, P = .42. The coefficient for patients with diabetes having A1C levels exceeding 9% was r = −0.10, P = .27.

When we limited the analysis to VAMCs rated as high-complexity facilities, the correlation between prescription spending and quality approached statistical significance for lipid-lowering drugs, although it remained weak (r =0.28, P = .06 for patients with diabetes having LDL-C levels <100 mg/dL; r = 0.12, P = .40 for patients with heart disease having LDL-C levels <100 mg/dL). There remained no relationship between prescription spending and performance for diabetes measures (r = 0.10, P = .49 for A1C levels >9%) (Figure 3). Similarly, no correlation was found between cost per 30-day prescription and quality of care (data not shown).

In logistic regression models controlling for facility characteristics (summarized in Table 2), there remained no relationship between prescription spending and highest-quality quartile status for diabetes drugs (odds ratio [OR], 1.00; 95% confidence interval [CI], 0.98-1.01; P = .61) or for lipid-lowering drugs (OR, 1.02; 95% CI, 0.99-1.04; P = .19 for patients with diabetes having LDL-C levels <100 mg/dL; OR, 1.01; 95% CI, 0.99-1.04; P = .27 for patients with heart disease having LDL-C levels <100 mg/dL). Complete results of the multivariate regression analysis are included in an eAppendix (available at www.ajmc.com).

DISCUSSION

To our knowledge, this is the first national study of variation in outpatient prescription spending among adults in the VA and is the first study to assess variation of this kind in a large nationwide sample. We found widespread variation in yearly drug spending for 2 commonly used categories of prescription drugs. This variation in outpatient prescription spending and use exists in the VA despite the existence of a closely managed formulary with uniform drug prices, a commitment to a uniform prescription benefit, and clinical guidance for the appropriate use of nonformulary medications.20 Moreover, VAMCs with higher spending per patient for these medication classes performed no better on quality measures.

Our findings about variation in VA prescribing are consistent with prior investigations in the VA that addressed variation in outpatient prescribing, although previous studies12,14,15

were limited to small segments of the VA or examined variation only across VISNs. Gao and Campbell13 used VA data from 2003 to look at trends in prescription costs and touched briefly on regional variation in prescription spending, although they did not separate inpatient and outpatient spendning and did not assess the relationship between spending and quality. In the only other study we are aware of that examines facility-level variation in outpatient prescription use in the entire VA, Aspinall et al9 reported regional variation in rates of antibiotic prescribing for veterans with upper respiratory tract infections. Health outcomes associated with medication treatment were not examined.

Variation in prescription drug spending within the VA cannot be attributed to differences in prices paid for drugs. Prices (ie, unit costs) are generally negotiated at the national level and vary only slightly across VAMCs, unlike prescription prices outside the VA, which vary markedly across institutions and across classes of payer (eg, uninsured, Medicaid, and private insurer).30-32 Prescription prices can also vary over short intervals (even days) in the private sector, and true prices are difficult to obtain because of undisclosed  manufacturer rebates, which would affect the measurement of prescription spending variation outside the VA.

Nor can these differences in drug use and spending be attributed to differences in VAMC formularies. We found significant differences across VAMCs in the use of brand-name nonformulary agents despite the existence of a national formulary and guidance for the use of nonformulary drugs. One explanation for this variation may be that, although the formulary is the same across the VA, the procedures for adjudicating nonformulary requests (the process by which providers ask to use nonformulary agents) may vary by VAMC. We found that VA facilities that spent more per patient for one drug class were more likely to spend more on the other drug class as well. The VA should consider disseminating throughout the system best practices in formulary management from VAMCs with low pharmacy costs that perform well on quality-of-care measures.

Patient-level factors could explain variation in prescribing if patients at certain facilities are more or less likely to require nonformulary medications because of more severe comorbid illness or because of differences in risk of adverse effects. When we repeated our analysis using only VAMCs with the highest-complexity rating, our results were similar; however, our use of aggregate data means that we cannot control for individual-level factors that might affect the need for more expensive agents. Differences in facility characteristics across quartiles of spending do not seem large enough to explain the level of variation that we observed. In fact, the lower percentage of patients using insulin in the most expensive quartile of VAMCs argues against the idea that the presence of sicker patients is leading to higher costs at these VAMCs. In addition, regression analyses adjusting for facility-level factors did not change the relationship (or lack thereof) between spending and quality.

Provider prescribing patterns differ by specialty, practice setting, level of training, age, and academic affiliation, and these factors could explain some of the documented variation. Studies show that much of the variation in medication choice and choice of generic versus brand-name drugs is related to unobserved physician factors.33 For example, it is unclear to what extent pharmaceutical promotion to physicians influences the prescribing behavior of VA physicians. The VA ethics rules prohibit acceptance of significant gifts from the pharmaceutical industry, and generally VAs do not accept free samples, although there is variation across VAMCs in the level of access to pharmaceutical sales representatives. Of note, the VA has no authority to limit interactions with industry representatives for providers who have dual affiliations with non-VA organizations.

 
Copyright AJMC 2006-2018 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!