Global Pricing Data and Advanced Analytics for Better Decisions and Competitor Edge

This is an excerpt from our whitepaper, “Global Pricing Data and Advanced Analytics for Better Decisions and Competitor Edge”. To read the full paper, please click here.

Increasing price transparency and global competition are key challenges faced by companies across all industries. Today’s pricing decisions cannot be based on instinct or informal methods due to the high level of risk incurred through inaccurate practices.

Analytical pricing strategies are used across all global industries. Strategic decisions based on pricing data and advanced analytics have been long-established and are an integral part of price setting for the financial industry. Benchmarking competitor prices on an hourly basis and ensuring pricing decisions are optimized are practices used by the travel industry. So why not apply the same principles to pharmaceutical pricing? Basing decisions on data and analytics empowers the decision maker and allows full confidence in the choices made.

Data and analytics have become powerful tools for pharmaceutical organizations in their strive to stay ahead of competitors. Best practice shows that better data and analytical models improve decision making and thus provide better business outcomes. Within the pharma industry, data forms the basis of many decisions; clinical data defines whether or not a drug is effective; sales data defines market share; and advanced data models drive cost effectiveness decisions made by health technology authorities.

So how does pricing data play a part in strategic pricing and market access? With so many new innovative pricing schemes and tactics, pharmaceutical competitor price intelligence is crucial in optimizing prices and staying ahead of the competition. The use of data analytics can help move pricing behavior from reactive to proactive decisions based on predicted market conditions.

How can pricing data and analytics be used within pharma?

Test for Competitor Response: Competitors do not evolve in isolation—they are responding to the relevant companies in the industry, if they have the freedom, knowl­edge, and capability to do so. Knowing how the launch of a direct competitor affects the price of your product across markets is essential in knowing how to react.

Business Transformation: Data allows you to visualize what is happening in the outside world and to shape competitor actions. Modeling allows you to make the best use of the data to help predict competitor actions and optimize business strategies. Transforming business decisions based on data and models enables organizations to keep ahead of competitors and make business processes more efficient. This is particularly essential for capturing growth in emerging markets where policy and pricing is not so clear.

To read more, click here to download our whitepaper.

Return of the American Pastime – Analyzing the latest health expenditure data

Last month, AHRQ released the latest data from the Medical Expenditure Panel Survey (MEPS), which is one of the few nationally-representative, longitudinal databases with patient covariates and medical costs available to the public.  Our excitement of the recent full-year 2011 data was dimmed somewhat when realized that the current release was only preliminary without the expenditure data. Though we can still analyze utilization rates and diseases up to three digits of ICD9, we are anxiously awaiting the complete data.  In the meantime, we would like to present analysis from the latest full dataset, MEPS Panel 14 (2009-2010).

One “fact” that often gets thrown around in everything from the media to bar conversations is that a small minority of Americans account for the majority of medical expenses; coupled with the fact that the United States spends more per capita on health care, this minority is often vilified.  For this analysis, we used the MEPS dataset to analysis 1) If indeed a small population does most of the health care spending and 2) Whether we can identify that population.

Big-market vs. Small-market: How skewed is medical expenditure in the United States?

For the nationally-representative sample, we divided each observation into quintiles based on self-reported expenditure in 2009 and 2010. Then, we calculated the spending of each quintile as a percentage as total spending.   We present the 2010 results in Table 1 only because 2009 followed a very similar pattern.






Total medical spending was over 1.2 trillion USD in 2010 and the top 20% accounted for over 82% (1 trillion USD) of that total.  As quintiles, each of these groups represent roughly 60 million Americans.  For the bottom 20%, total expenditure totaled less than 80 million USD, which is roughly $1.30 per person for 2010.  The second column in Table 1 shows that people in the bottom quintile spend between $0 and $34 for the entire year versus those in the highest quintile who spent over $4000 each year.  The upshot of this analysis confirms the factoids that we often hear: 20% of Americans account for over 80% of all health spending!

Hot streaks: Is high-spending persistent?

For every individual, there are years where spending is high and others where spending is low. For the top 20% in Table 1, was it just an aberration from their normal spending habits? One of the main advantages of the MEPS dataset is that it allows us to have data for individuals in subsequent years. In this section, we analyze what happened in 2010 for those who were in the top quintile in 2009.

The results for the analysis in Table 2 show over 55% (33 million) of the 61 million in the highest quintile remain in the highest quintile the following year. In fact, nearly 80% of the top spenders end up being in the top two quintiles the subsequent year.   Less than 5% move to the bottom quintile, which includes those who have zero medical expenditure in the year.  From this analysis, we conclude that high spenders consistently outspend other Americans each year.

Spending like the Yankees: who are the people in the top quintile?

Another “fact” we often hear is that the majority of the spending is done by the sick and elderly. In this section, we analyze the distribution of seniors (those aged 65 and above) and those that died during the MEPS survey.

In 2009 senior citizens represented roughly 13% of the total population; of those 40 million seniors, nearly half of them fell into the top quintile category for medical spending by Americans.  In fact nearly 80% of seniors spent more than $1200 annually.  Note that this analysis only provides associations and cannot make any statements on causality: unfortunately, we have yet to develop technologies to make people younger, so we cannot test what would happen to spending if these people became non-seniors.

Dead-ball Era: Is death costly?

In addition to calling out seniors as high spenders, the other group that receives attention for spending are those people on the last months of their lives. For this, we use the MEPS dataset to identify those that died during the survey and see how their expenditures were distributed across quintiles.

This analysis suggests that the majority of those who died during the year spent more than $4000, which put them in the top quintile. One thing to note is that the total number of deaths (nearly 4 million) is higher than commonly cited numbers.[1]  We attribute this bias to the small number of observed deaths in the survey (182), and use this to caution researchers when applying statistical weights to small numbers of observations.

From the analysis in this section, we find evidence to support the assertions than seniors and the sick spend more on medical care than others.

Postseason: Concluding Thoughts

In this post, we attempted to showcase the unique data available in MEPS.  For those in the health outcomes fields, this dataset is powerful in that it also has diagnostic codes, so it provides an easy source to see the costs associated with various diseases and conditions.  The subjects from MEPS are a subset of the National Health Interview Survey (NHIS), so researchers can actually utilize up to four years of time-series data using the publically-available linkages between the two datasets.

[1] The CDC estimates roughly 2.5 million deaths (

Current State of Government Reporting: Will We all Follow Texas?

As medical costs increase and states decide how they will handle Obama’s Affordable Care Act, they will look to the manufacturer to offset these increased costs. Expect more states to follow Texas in enacting new regulations, and revising and expanding reporting obligations.

This will mean that reporting and calculation requirements will vary for many of the states, in addition to the new Federal Government mandates.

The new Texas regulation includes:

1) That the manufacturers submit pricing data for eligible pharmacies located in Texas, if readily available, as opposed to those located in the entire US. In the event a manufacturer does not have a single price point for a product they must report the range of prices (high and low) and then calculate the weighted average for each product each period. Texas is expanding  price reporting to include:

  • Direct Price to Chain Pharmacy
  • Direct Price to Long Term Care Pharmacy
  • Direct Price to Pharmacy
  • Direct Price to Wholesaler/Distributer

2) Manufacturers should not include prices excluded from Medicaid Best Price including prices to 340b covered entities when determining price points.

3) Monthly Reporting:

  • The manufacturer is responsible for the correctness of the AWP, even though they do not play a role in the third-party price reporting compendia’s decision regarding their publication of the AWPs.
  • All price updates must be provided, except, Average Manufacturer Price (AMP) updates, to the Commission by the 10th day of each month.
  • Forecasted price concessions must be included in calculations for a product launch if the manufacturer has this information in its internal business records.
  • Manufacturers must update the Commission with change to formulation, product status, or availability.