دانلود مقاله ISI انگلیسی شماره 2014
عنوان فارسی مقاله

مدل رگرسیون چندگانه برای توضیح هزینه نام تجاری (برند) دارو ها

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
2014 2012 9 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
A multiple regression model to explain the cost of brand-drugs
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Socio-Economic Planning Sciences, Available online 15 November 2012

کلمات کلیدی
تئوری قیمت گذاری - صنعت داروسازی - نام تجاری داروها - برآورد قیمت
پیش نمایش مقاله
پیش نمایش مقاله مدل رگرسیون چندگانه برای توضیح هزینه نام تجاری (برند) دارو ها

چکیده انگلیسی

The goal of this study is to examine how four factors - level of competition, therapeutic purpose, age of the drug, and manufacturer play a role in the pricing of brand-name prescription drugs. Understanding how these factors contribute to high drug prices will allow players in this supply chain to negotiate more favorable contract terms. This can be a large benefit to society as this insight can lead to improved efficiency in pricing and increased savings, which can be passed to the consumer. We develop measures for these factors based on publicly available information. Using data on the wholesale prices of prescription drugs, we estimate a model for drug prices based on our measures of competition, therapeutic purpose, age, and manufacturer. Our analysis reveals that these factors are significant in estimating drug prices. We observe that proliferation of dosing levels tends to reduce the prices, therapeutic conditions which are both less common and more life-threatening lead to higher prices, older drugs are less expensive than newer drugs, and some manufacturers set prices systematically different from others even after controlling for other factors. These findings indicate that publicly observable factors can be used to explain drug prices.

مقدمه انگلیسی

Recently, the pharmaceutical industry has been under much public scrutiny. Consumer watchdogs and the media have raised concerns over the rising cost of health care, of which prescription drugs account for 10% [29]. U.S. health care spending has increased 2.4% faster than GDP since 1970 and is expected to exceed $4.3 trillion in 2018 [29]. In 2009, health care spending hit an unprecedented 17.6 percent of GDP [33]. Prescription drug prices have been a key contributor to the rise of health care expenditures [11]. Moreover, drug research and development (R&D) costs continue to rise, making it more difficult for manufacturers to maintain their high levels of profitability without increasing drug prices. In response to public concerns, the vice president of The Pharmaceutical Research and Manufacturers of America (PhRMA) stated that: “All companies make their own independent pricing decisions based on many factors, including patent expirations, the economy, … and huge research and development costs…” [38]. Whether or not these statements are true, identifying factors that drive prescription drug prices is in the public interest. Understanding the factors which correlate with high drug prices will allow pharmacies, hospitals, insurance providers and the government to negotiate more favorable contract terms/pricing. Competition among health care providers will result in some of these negotiated savings being passed on to consumers in the form of lower prices. As a result, healthcare costs will be lower than they otherwise would be and health outcomes have the potential to be improved as more patients are able to afford the appropriate medication. This paper offers a practical tool to assist participants down the health care supply chain to negotiate effectively with drug manufacturers, which can ultimately be of enormous benefit to society. However, one challenge of such research is data – some of the important factors, such as R&D costs, marketing efforts, and supply chain costs of individual drugs, are not observable to the public. In absence of these data, we have to take a different approach by developing measures based on publicly observable information for four classes of factors: the level of competition, the nature of the condition that the drug treats, the number of years that the drug has had FDA approval, and the manufacturer who developed the product. The objective of this paper is to test the significance of these measures in estimating prices for brand drugs. The price measure we focus on in this paper is the Wholesale Acquisition Cost (WAC) set by the manufacturer. This wholesale price, WAC, is formally defined in the Medicare Prescription Drug, Improvement, and Modernization Act of 2003 as “the manufacturer's list price for the pharmaceutical or biological to wholesalers or direct purchasers in the United States, not including prompt pay or other discounts, rebates or reductions in price, for the most recent month for which the information is available, as reported in wholesale price guides or other publications of pharmaceutical or biological pricing data [40].” WAC plays an important role in the pharmaceutical supply chain because it serves as a reference for all other prices, rebates, and reimbursements. While WAC is not the true price between players in the supply chain, it is the basis in which the actual prices are determined [50]. For example, a major manufacturer disclosed that the distributor pays between 93% and 97% of WAC. Almost all distributors also receive a prompt-pay discount that is 2% of WAC [50]. The manufactuer also revealed that the retailer obtains a discount between 1% and 3% of WAC. All other prices in the supply chain, including production costs and holding costs are also based off of WAC. The exact discount depends on the negotiations that take place between the two players (obviously larger players are able to negotiate larger discounts) and is proprietary information. However, the discount percent is consistent across drugs for each player. Thus, while WAC is almost never the price paid, it is still worth estimating because it determines the final price paid by players in the supply chain, including government drug plans. We develop a linear model to estimate WAC using covariates that are easily accessible to all market participants. While previous research has studied some of these factors, our analysis is unique in two aspects: first, we develop measures for the aforementioned four classes of factors based on information observable to the public; second, we develop a unifying framework to explain prescription drug prices by this broad range of factors. With such a reference model at hand, wholesalers and insurers can be more informed and stand at a better position to estimate and negotiate prices with the manufacturers. The rest of this paper is organized as follows: Section 2 provides a literature review. Section 3 presents the research methodology and several hypotheses regarding drug prices in the pharmaceutical industry. We also discuss the data used in the study. In Section 4, we present our findings and discuss their implications. Finally, we conclude the paper in Section 5 and discuss extensions and limitations of our study.

نتیجه گیری انگلیسی

This paper continues a long-standing debate as to what factors contribute to the WAC for brand prescription drugs. It initiates an empirical analysis that incorporates publicly observable factors into a linear model to explain drug manufacturers' WACs. The WAC serve as a base for all other prices, discounts, and reimbursements downstream in the pharmaceutical supply chain. Such a model can be utilized by downstream players (such as insurance companies) who most likely only have the publicly observable factors available. The empirical study is based on 598 most commonly prescribed brand drugs at a major pharmacy chain store in the U.S. We show that prices are affected by factors previously not explored such as the interaction between uncommon and life-threatening variables. We also show that the WAC of a drug decreases as the number of dosing levels increases and newer drugs (measured by the years after FDA approval) have higher prices. Finally, we find that three manufacturers statistically set higher prices than the other 27 manufacturers when all other factors are held constant. Table 10 summarizes the paper. We find that neither the number of brand drugs (H1a) nor the number of generic drugs (H1b) in a therapeutic class explains the price of a brand drug. We do however find that the number of dosing levels (H1c) and the number of years since FDA approval (H3) contribute to the price of a brand drug. We also find that drugs that treat uncommon and life-threatening conditions are priced significantly higher than others, while uncommon or life threatening conditions alone do not lead to a higher or lower price. Therefore we find evidence supporting H2c but not H2b or H2a. Finally, we conclude that there are differences in manufacturers' price-setting decisions that cannot be explained by the factors we consider in this paper.We conclude this paper by identifying limitations of the study and opportunities for future research: •If supply chain participants indeed use our model to negotiate WACs down, will our model still fit in future periods? Probably not – however, the model can easily be recalibrated as the market evolves. It is important to recognize that the model results are not as important as the modeling process in influencing health outcomes. •The analysis presented in this paper is correlational. We do not have evidence of what causes drug prices to vary, only evidence of which observable variables can be used to explain the variation in brand drug prices. While a causal model would be of great interest, the correlational model presented in this paper is still extremely useful for forecasting and negotiation purposes. Consequently, the work presented in this paper contributes toward a health care supply chain which is more efficient and can better serve the needs of society. •The dataset used has WACs at a single point in time. Although these data were appropriate for this study, it would be interesting to find WACs over time. With this extension of data, a more in-depth analysis could be performed to see if the results found in this study change over multiple time periods. One could also extend this analysis by looking at the change in WACs over time. • The ratio between the number of observations and the number of variables is around 7 for Eqns. (1) and (5) for Eqn. (2). Although the ratio is not as high as we would ideally have, Eqs. (1) and (2) share many similar insights even with a different number of variables, such as the effect of competition, age of the drug, and manufacturer who produces the drug, which indicates the robustness of the study findings. With more observations, we expect to have a better understanding of the effect of manufacturers and specific therapeutic classes on drug prices. • Further analysis can be done to look at the role that the active ingredient plays in the WAC. Although it is not disputed that the amount of active ingredient plays a role in the WAC, it has been observed frequently that levels of active ingredients do not change the price of a drug. It would be interesting to see if there exists a critical level exceeding which the price increases. If doctors and patients knew this level, they could prescribe a dosage amount that minimizes the cost (i.e. prescribe a lower dose more frequently, or a higher dose less frequently). •The demand for each drug was not included in the data set. While the relative rarity and severity of the condition a drug treats is a proxy for demand, given more detailed sales data, further analysis could be done to determine relative price sensitivity of demand for each drug. • Future analysis can expand on the finding that the interaction between uncommon and life-threatening is significant. A stratified analysis can be performed by stratifying by severity of condition. Further classifications beyond rarity and life-threatening can also be examined. For example, whether a drug is eliminating (cure) or simply controlling a condition.

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