Utilizing Gini Coefficient To Anticipate Price Movement, Drug Shortages And Pricing Elasticity

Today’s guest post is sponsored by Elsevier and presented by Elsevier’s solution partner Todd Grover, Co-Founder of Glass Box Analytics.

Todd discusses how the Predictive Acquisition Cost (PAC) is utilizing the concept of a Gini coefficient to anticipate price movement, predict drug shortages, and pricing elasticity.

To learn more, read the brief: Predictive Acquisition Cost – a better standard.

Utilizing Gini Coefficient To Anticipate Price Movement, Drug Shortages And Pricing Elasticity
By Todd Grover, Co-Founder of Glass Box Analytics, on behalf of Elsevier

Once the province of the financial services industry, over the last decade big data has made a profound impact across many additional industries including healthcare. While data scientists leverage pharmacy claims data to gain clinical insights, so much more information remains to be extracted from within that claims data. Glass Box Analytics, as part of its PAC pricing solutions, employs advanced data science techniques to draw deep drug pricing insights from claims utilization data.

Anticipating drug price movement, better understanding price elasticity, reacting to supply and demand changes, addressing shortages and supply chain issues are fundamental business challenges for all stakeholders. Borrowing a concept from econometrics called the Gini coefficient, we can gain deep insights into price and price movement by looking at how concentrated versus widely available a given drug is across manufacturers.

GINI COEFFICIENT

The Gini coefficient is used to understand the distribution of income across a population to assess wealth inequality.

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Source: The Intelligent Economist. “The Gini Coefficient” by Prateek Agarwal, 10/22/2019

As a concrete example, below is a comparison of a country with significant income inequality (South Africa) and a country with less income inequality (Netherlands):

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Source: World Bank, Development Research Group, IndexMundi

These same principles inspire our analysis to measure, through claims utilization data, how concentrated the availability of a drug is to just a small number of manufacturers (high Gini coefficient) versus widely available across a large group of manufacturers (low Gini coefficient). The Gini coefficient measure itself, as well as movement in the measure, provide valuable pricing indications as illustrated in the following examples.

EXAMPLE: ANTICIPATE PRICE MOVEMENT

Changes in the Gini coefficient can indicate changes in availability of the drug from specific manufacturers in a drug group, and in turn helps us anticipate or explain pricing movement. For example, over the last year Olmesartan Medoxomil/Hydrochlorothiazide experienced a shift in utilization. The top two NDCs now account for nearly 75% of all utilization. The corresponding increasing Gini coefficient indicates a tighter concentration of drug availability amongst manufacturers (NDCs) and tracks with the resulting higher acquisition costs.

Olmesartan Medoxomil/Hydrochlorothiazide 40mg-25mg Tablets

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And the opposite scenario can also play out. In situations where the Gini coefficient is falling, there is a greater potential for lower drug acquisition costs.

Aripiprazole 20mg Tablet

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EXAMPLE: SHORTAGES AND SINGLE-SOURCE GENERICS

An extreme but important case of Gini coefficient movement is the identification of drugs that change from single-source to multi-source, or vice-versa. Using a Gini coefficient to systematically review thousands of drug groups enables us to better anticipate such changes. These changes can have impacts on demand for the brand versus generic products and implications on how payers/PBMs should reimburse.

Levetiracetam 500mg Extended-Release Tablets

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EXAMPLE: COMPARING SUPPLY VERSUS DEMAND FORCES

Imatinib Mesylate 400mg Tablets went generic in February 2016 and following an exclusivity period, demand quickly increased as indicated by its utilization ranking (based on adjudicated prescription counts). But beyond the increase in supply, we saw that the supply spread across more manufacturers (NDCs) resulting in a lower Gini coefficient and a significant drop in drug acquisition cost. Imatinib Mesylate 400mg Tablets

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Another high-profile example relates to COVID-19 and Hydroxychloroquine Sulfate. In spring 2020, a significant increase in demand for this drug occurred and many anticipated a spike in pricing. Instead, a broadening of availability across manufacturers occurred, as captured by the Gini coefficient, resulting in relatively stable prices.

Hydroxychloroquine Sulfate 200mg Tablets

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WRAP-UP

More than 10 years ago PAC was created to provide drug pricing transparency to the pharmacy industry, leveraging the principles of big data analytics to give insights into a drug’s true acquisition cost. We continue to refine our data science techniques to help the drug industry solve some of its biggest business challenges. To learn more, read the brief: Predictive Acquisition Cost - a better standard.

This brief will highlight the benefits and challenges of current price types, illustrate the importance of pricing standards, detail challenges with survey-based approaches to estimating a drug’s acquisition cost and explain why the PAC pricing solution can serve as the long-term drug pricing reimbursement benchmark. Elsevier, the drug price leader.

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