The Price May Not Be Right: The Value of Comparison Shopping for Prescription Drugs | Page 2

Price shopping for medications within a small geographic area can yield considerable cost savings for uninsured and insured consumers in high-deductible health plans.
Published Online: July 28, 2017
Sanjay Arora, MD; Neeraj Sood, PhD; Sophie Terp, MD; and Geoffrey Joyce, PhD
Table 2 shows the distribution of drug prices in high- and low-socioeconomic status (SES) areas, as defined by the median household income in the zip code. Levofloxacin and azithromycin were less expensive, on average, when purchased in pharmacies located in lower-income zip codes compared with higher-income zip codes. Because high- and low-income areas are likely to have a different mix of pharmacies (eg, big-box stores may be less likely to locate in low-income areas), we also estimated the average prices controlling for pharmacy type and the results were substantively unchanged. Although the median price of levofloxacin was $26 in low-income areas, prices ranged from a low of $4 to a high of $149, depending on the pharmacy. In high-income areas, the median price of levofloxacin was $80 and ranged from $5 to $229.

Table 3 shows the extent of price variation within the same zip code, which is the implicit value of price shopping within a localized area. The average price difference between the highest and lowest-cost pharmacies in a zip code was greater than $100 for levofloxacin and $30 for azithromycin. Perhaps a more salient comparison is the average price difference between a randomly selected pharmacy and the lowest-priced pharmacy in the same zip code. In this case, consumers would save an average $52 per prescription for levofloxacin and $17 per prescription for azithromycin simply by comparison shopping within the same zip code (results not shown). We observed modestly greater price variation in high-income areas despite there being more pharmacies per zip code in low-income areas (7.7 pharmacies per zip code vs 6.5 in high-income zips).

Table 4 highlights general approaches for obtaining the lowest priced medication in an area. In more than half of the 71 unique zip codes in the study sample, independent pharmacies had the lowest price for levofloxacin (53%), followed by GoodRx (44%). In only 2 of 71 zip codes did a chain or big-box store have the lowest price for levofloxacin. We observed the same pattern when we restricted the analysis to the 39 zip codes with a big-box store. For both levofloxacin and azithromycin, the lowest price prescription was offered at a grocery, big-box, or chain drug store in just 6% of zip codes.

DISCUSSION

In our study sample of 528 pharmacies, prices found at independent pharmacies and by using online discount coupons were markedly lower, on average, than at grocery, big-box, or chain drug stores for 2 widely prescribed antibiotics. Drug prices varied dramatically within a zip code and typically were less expensive when purchased in lower-income areas. The average price difference within a zip code was $52 for levofloxacin and $17 for azithromycin, which suggests that price shopping within a small geographic area can yield considerable cost savings, particularly for uninsured and insured consumers in high-deductible health plans with high negotiated prices. A possible explanation for the greater price variation with levofloxacin is that it recently became available as a generic, so there has been less time to establish a fair market value.

There is a common perception that chain drug stores have lower prices than independent pharmacies due to economies of scale and the fact that the chains derive a smaller fraction of their revenue from the sale of prescription drugs. However, chain drug stores typically compete less on price and more on convenience, brand name, and nondrug items.8 By contrast, independent pharmacies compete largely on price and service to induce consumers to bypass chain drug stores.9 Our results suggest that cash-paying consumers often face a premium for going to chain drug stores and could save substantially by using online coupons or purchasing their medications at independent pharmacies in the same or neighboring zip codes.

Although poor adherence is endemic, it is particularly problematic for individuals of lower SES.10 An estimated 20% to 35% of patients are primary nonadherent by failing to fill an initial prescription, and an additional 20% discontinue therapy after filling the first prescription.11-13 In 2012, 22% of uninsured adults aged 18 to 64 years reported not getting needed prescription drugs due to cost compared with 5% of adults who were insured for the whole year.14 Noncompliance with antibiotics for respiratory infections can result in treatment failure, worsening severity of disease, sepsis, antibiotic resistance, and increased risk of hospitalization.

A critical question is the extent to which consumers would use price information in purchasing medical services. The highly touted movement toward “consumer-directed healthcare” relies on patients having easy access to information concerning drug prices and quality. A recent survey indicates that a majority of Americans have tried to find out how much they would have to pay out of pocket—not including a co-pay—before getting care. However, the survey also reveals that most Americans are not aware that prices can vary across healthcare providers.15

Our results differ from those of a Florida study by Gellad et al that obtained drug prices for 3 chronic medications (esomeprazole, fluticasone, and clopidogrel) and a generic antibiotic (azithromycin).7 They found that mean drug prices were 9% higher in the poorest  zip codes and that independent pharmacies in the poorest areas charged the highest mean prices. We, however, found the opposite: lower prices at independent and online pharmacies and pharmacies located in low-income areas. A possible explanation for the differences across studies is that the Florida study obtained drug prices from a website whereas we collected prices by calling individual pharmacies. We also asked for any available discounts and verified concordance with in-store prices in a pilot study. The Florida study also restricted the sample to pharmacies that filled 1 of the 4 drugs to a Medicaid beneficiary in a single month (November 2006). This may have resulted in a nonrepresentative sample of pharmacies across income areas. By contrast, we collected price data over the phone from all available pharmacies. We focused on price variation for antibiotics under the assumption that consumers would have limited experience purchasing them (and thus would be less aware of price) and the consequences of not filling the prescription due to cost would have a more immediate impact on health.

Limitations

There are several limitations to our study, the most prominent of which is that we only measured prices of 2 medications for an acute condition in a single county in California (LA County). We do not know if the findings will hold in other regions or states. However, LA is an economically and culturally diverse county with a broad array of income levels and population densities. The magnitude of price variation across outlets and the savings associated with online coupons at nationwide chains suggest that we could expect similar results in other areas of the country. The extent of price variation may be lower for chronic medications, but these by their nature (length of time taken) may impose a larger financial burden on patients. Future research should focus on comparing prices across a broader spectrum of pharmaceuticals, including medications for chronic diseases.

Another limitation of our study is that we obtained drug prices via telephone rather than in person, and pharmacies may offer discounts in the store that they are unable or unwilling to provide over the phone. Also, the calls to the pharmacy were made from a doctor’s office on behalf of a hypothetical uninsured patient, and the callers asked each pharmacy for any potential discounts after an original price was provided. Over 98% of pharmacies in our sample provided prices over the phone. Patients calling on their own behalf may not receive the same discounts we received. Additionally, we only called pharmacies in the highest and lowest quartiles of median income. It is possible that we might have a better understanding of price variation if we had contacted all pharmacies regardless of income level.

Finally, we used a single website to represent discounts available online. Nonetheless, GoodRx is the largest price aggregator and coupon tool used by thousands of doctors and millions of patients every month. Further, 100% of GoodRx coupons were honored when physically presented at the pharmacy during this study.

CONCLUSIONS

Slowing the growth of healthcare costs underscores nearly every health policy initiative in the United States and is the motivation for public and private efforts to increase price transparency in healthcare markets. Price transparency initiatives face considerable obstacles, however; most prominently, how to reliably measure and convey information about quality and price for thousands of complex medical services produced by a wide array of providers and organizations. The task is less daunting for prescription drugs because quality is fixed.

The extent of price variation found in this study suggests that consumers could readily benefit from greater price transparency. If this information were widely available to consumers, large variations in drug prices across pharmacies would likely be reduced. 

Acknowledgments

The authors would like to thank Kaleigh Barnes, Brian Raffetto, Melissa Luttio, Janice Rivelle, and Erin Higginbotham for helping to collect and analyze the data.

Author Affiliations: Keck School of Medicine, (SA, ST); Sol Price School of Public Policy (NS); and School of Pharmacy (GJ), Leonard D. Schaeffer Center for Health Policy and Economics, University of Southern California, Los Angeles, CA.

Source of Funding: None.

Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (SA, GFJ, NS, ST); acquisition of data (NS, ST); analysis and interpretation of data (SA, NS, ST); drafting of the manuscript (SA, GFJ, SN, ST); critical revision of the manuscript for important intellectual content (SA, GFJ, NS, ST); statistical analysis (SN); administrative, technical, or logistic support (ST); and supervision (SA, ST).

Address Correspondence to: Geoffrey F. Joyce, PhD, Leonard D. Schaeffer Center for Health Policy and Economics and School of Pharmacy, University of Southern California, 635 Downey Way, Los Angeles, CA 90089-3333. E-mail: gjoyce@usc.edu. 
REFERENCES

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