Variation in Prescription Use and Spending for Lipid-Lowering and Diabetes Medications in the Veterans Affairs Healthcare System | Page 2

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.

Published Online: October 15, 2010
Walid F. Gellad, MD, MPH; Chester B. Good, MD, MPH; John C. Lowe, RPh, MBA; and Julie M. Donohue, PhD
To examine whether patient-level and facility-level differences might explain variation in outpatient prescription use and spending, we obtained facility-level data on organization characteristics from VA administrative sources such as the percentage of patients older than 65 years, the total number of outpatient visits, and the percentage of uninsured patients seen as outpatients at each VAMC in FY2008. In addition, we stratified facilities by “complexity” rating (1a or 1b on the VA Allocation Resource Center Complexity Scale). The VA rates each VAMC on this 5-level scale, with high-complexity facilities seeing the largest volume of patients and having the highest patient risk. Patient risk is based on all VA patient diagnoses and uses the same diagnostic cost group risk scores as Medicare. In addition, high-complexity facilities employ a greater relative number and breadth of specialists, offer the highest level of intensive care unit healthcare (based on the type of services provided and the availability of subspecialty services), train more medical residents, and engage in more research activities. These variables are combined in a weighted mean using established methods to create these ratings.27 More complex VAMCs have been shown to be more likely to have year-round emergency department access, academic physicians, formal residency training in primary care, local clinical champions, and designated nurses for quality improvement.28 Facility characteristics included in the VA complexity ratings have previously been used to measure facility complexity in VA research.29

Statistical Analysis

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.

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