The Causes and Solutions of Chronic Drug Shortages in the United States: The Important Role of Better Analytics A Commentary

The Causes and Solutions of Chronic Drug Shortages in the United States: The Important Role of Better Analytics – A Commentary
George A. Chressanthis, Ph.D., Principal Scientist, Axtria Inc.

Abstract: A vexing public health policy issue plaguing the United States (US) pharmaceutical industry has been the existence of chronic drug shortages since 2000 with no end in sight. Chronic drug shortages persist despite attempts by the Food and Drug Administration (FDA) and federal legislation to remedy this problem. Drug shortages are severe enough to potentially cause adverse effects on patient health outcomes, changes from optimal treatment, and added costs to the healthcare system. Prior research shows drug shortages being caused by a lack of economic incentives, supply chain factors, manufacturing-quality problems, and managing regulatory expectations. However, the application of better analytics has recently been cited as needed to account for these effects and changes in market forces, business continuity planning, supply chain management, and improved insights into future demands through better forecasting to reduce the number and severity of drug shortages. This paper is a call to action to Pharmaceutical Management Science Association (PMSA) members to address this important health policy issue.

Keywords: chronic drug shortages, public health and business policy, pharmaceutical decision science analytics
1. Existence of Chronic Drug Shortages
Since 2000, one of the more vexing and troubling public health policy issues that has plagued the US pharmaceutical industry, yet receiving less deserving public news coverage than other industry stories, has been the existence of chronic drug shortages. A drug shortage is defined in which the “total supply of all clinically interchangeable versions of an FDA-regulated drug is inadequate to meet the current or projected demand at the patient level.”1 The peak year was 2011, with 251 drug shortages, 73% being generic sterile injectable drugs used to treat cancer, sepsis, and many other life-threatening conditions.1 While the annual number has dropped, the issue of chronic drug shortages still persists, despite attempts by the FDA and federal legislation to remedy the problem.2-3 The 2011-2014 period saw 456 situations of drug shortages severe enough to potentially cause adverse effects on patients and changes in treatment.4 Health practitioners in office-based and hospital settings, as well as policymakers, have been alarmed at the adverse consequences to patients and higher costs to the healthcare system caused by persistent and prolonged drug shortages. The issue of chronic drug shortages in the US recently came to the forefront of health policymakers with discussions announced between the FDA and Pfizer regarding the shortage of numerous injectable medicines, including emergency syringes of epinephrine.5 According to the FDA, manufacturing, distribution, and third-party delays were cited by Pfizer for the shortages.5 Moreover, in October 2017, the FDA announced an initiative (with more long-term changes planned) to provide guidance to generic manufacturers on the most efficient way to develop complex difficult-to-manufacture medicines (e.g., injectable medications and drug-device combination medicines) that are often singled-sourced even after patient expiration.6

This paper will briefly address the following two questions related to chronic drug shortages in the US:
  • What are the key causes and solutions to the existence of chronic drug shortages in the US?
  • Is there a role for the application of pharmaceutical decision science analytics to help mitigate the problem of chronic drug shortages?
2. Causes and Solutions to Chronic Drug Shortages
Five factors have been identified as driving the number of drug shortages in the US,4 providing conclusions that closely align with insights also reported in the academic literature.1-3,7
  1. Market withdrawals. A high percentage of drug shortages originate from single-sourced injectable generic manufacturers. Maintaining quality controls is difficult given the low margins received for producing more complex and costly injectable drugs. The marginal cost of production is far greater for manufacturing injectable drugs than traditional small molecule pills. Moreover, given the specialized nature of producing injectable drugs, manufacturing lines are not easily transferable to the production of other drugs. Thus, when a single-sourced manufacturer is shut down due to failure to meet FDA drug quality regulations, there is insufficient supply to meet demand, thus resulting in a shortage. Financial incentives, such as instituting an investment tax credit specifically targeted to generic manufacturers for maintaining the quality of production facilities, could be employed to encourage companies. Given the social costs of higher healthcare spending caused by drug shortages, such an investment tax credit could make economic sense when comparing net marginal social benefits to costs.
  2. Supply chain design. Improvements in supply chain management by companies are necessary, especially by improved demand estimation for a product through better coordination of processes of sales, demand planning, inventory management, and production. Such process improvements would allow for more accurately estimating capacity requirements and establishing manufacturing redundancies to mitigate the effect of production breakdowns that occur in the supply chain system. The application of decision science analytics can provide greater clarity in these processes and will improve business planning.
  3. Purchaser-manufacturer incentives. As alluded to above (factor 1), insufficient financial incentives are a major factor in contributing to drug shortages. The formation of guaranteed-volume contracts, or the ability to retain contracts, would allow for lessening the risks of investments in manufacturing equipment needed to produce these specialized medicines.
  4. Limited market insights into future demands. The study referenced here and interviews conducted with pharmaceutical executives found that improvements are needed to obtain better information on expected demand.4 Internal operations improvements are needed in the areas of sales and operations planning, demand forecasting, and market environmental information that affects external systems and programs. Again, this key factor points to a role in expanding the use of decision science analytics to help reduce drug shortages.
  5. Managing regulatory expectations. Executive interview comments noted that regulations affected drug shortages given production delays and higher costs to receive approvals for expanding manufacturing capacity or improving existing equipment. Further, many of the drug shortages involve older medicines developed 10-20 years ago, where government regulations prevent product and process improvements given the risks and costs. The key takeaway is that government policy must balance the marginal cost from added regulations versus the marginal benefit of imposing such rules.
3. Role of Pharmaceutical Decision Science Analytics
The preceding five factors listed as causes of drug shortages represent an important opportunity role of pharmaceutical decision science analytics to help find and implement solutions to resolve this problem.
  1. Employ causal-based prediction models to determine the likelihood of single-sourced manufacturers from withdrawing from the market. These models can be developed and used by both public policymakers and individual companies to anticipate market disruptions due to market withdrawals.
  2. Use data mining techniques to analyze and uncover previously unknown reasons for drug shortages. This is not to suggest relying on such techniques for prediction, but rather to provide insights into potential new reasons why drug shortages are occurring. New learning can then be adopted into taking steps using causal-based models to develop business and public policy to mitigate the likelihood of drug shortages.
  3. Develop inferential-based cost and production function models to determine the effect of a lack of financial incentives in impacting manufacturing disruptions, and to estimate what changes in financial incentives are needed to minimize the likelihood of stoppages in production.
  4. Improve demand estimation through the development of more granular geographic-based models that can increase the accuracy of supply chain design and associated business planning processes. Regional and metropolitan statistical (MSA) demand estimation models should be employed to improve demand accuracy. Significant intranational geographic variations in demand exist, such as variations caused by managed care plan dynamics, healthcare system design, macroeconomic factors, etc. The previous factors mentioned justify employing more granular demand estimation models.
  5. Apply demand estimation algorithms found in (4) to machine learning to continuously update demand relative to production capacity to anticipate potential market shortages. Determinants of demand estimation can be done more quickly and regularly to assess potential conflicts with current production volume and capacity levels.
  6. Improve demand forecasting and prediction models based on results from (4) and (5) as opposed to relying on naïve-based models. Using hold-out time periods can be used to test the accuracy of causal-based forecasting and prediction models. The main advantage of using causal-based models is that one can see what factors are associated with changing forecasts and predictions. Improvements in estimating and forecasting/predicting demand can be used to enhance the accuracy of inventory management models. These developments allow for better management control and understanding what policy variables can be employed (and by how much) to mitigate adverse effects from unanticipated changes in demand.
  7. Include the effect from guaranteed-volume contracts and other means to retain contracts on purchaser-manufacturer incentives when conducting model specification in estimating cost and production functions. A similar specification addition should include effects from regulatory changes on drug cost and production. Analysis should also be conducted on specific drugs, and segmented by therapy class and drug age, to determine the existence of variations in systemic effects by key drug and market characteristics. When conjoined with demand models, these analytics can then be used to see more accurate changes in the likelihood of drug shortages and specifically what to do about it.
  8. Develop and implement dynamic tool technologies to apply all previously-stated improved demand, cost, and production estimation and forecasting/prediction models for both public policymakers and pharmaceutical companies.
  9. Enhance current data cloud information management that feed all the analytics previously stated. Adding data from non-traditional sources, such as claims and electronic health records, may be necessary to better understand the patient volume, factors associated with disease treatment, and the success of drug utilization on outcomes. This data can be analyzed to understand potential changes in future demand by measuring the success or failure of drug intervention in disease treatment.
4. Conclusions
The chronic problem of drug shortages in the US does not appear to be going away any time soon, despite numerous government policy efforts to address the problem. The social costs to our society caused by drug shortages, though not directly measured, are likely significant, in reductions in health outcomes and increased economic burden. Aside from the typical factors seen as causing drug shortages (as described above) is the growing realization how the application of improved decision science analytics could provide much needed information for manufacturers to reduce the problem through more effective planning. Typically, pharmaceutical decision science analytics address sales and marketing questions. However, there may be an expanded role for decision science analytics to help manufacturers institute more effective and efficient processes to mitigate the drug shortage problem that plagues the system.

“There’s no such thing as a free lunch.”
Milton Friedman (1912-2006); American economist and 1976 Nobel Memorial Prize in Economic Sciences
In conclusion, alleviating the problem of chronic drug shortages in the US requires resources, as implied by the above quote. Investments are needed to upgrade manufacturing/planning processes and supply chain management systems and the application of management science analytics to derive information needed to support these improvements to reduce the number and severity of drug shortages. Added incentives are also required for companies to continue bringing these life-saving medicines to the market. Lastly, companies should look to enhance capabilities in cloud information management, apply data from newer sources such as claims and electronic health records, employ machine learning and data mining, and deploy dynamic analytical tool technologies that can be leveraged with a range of pharmaceutical decision science analytics to help alleviate this national critical health problem.
About the Author
George Chressanthis is Principal Scientist at Axtria, a big data and analytics company, since July 2016. This thought leadership role involves disseminating pharmaceutical research ideas on a wide variety of topics of importance to industry practitioners. His research focuses on key trends affecting pharmaceutical commercial strategic & operational issues and their intersection to HEOR/RWE modeling associated with measuring outcomes and their implications on changes in commercial analytics, new commercial model design, payer analytics, and public policy. He also conducts numerous workshops on the pharmaceutical industry within Axtria for training and development of employees. He spent almost 15 years working in the pharmaceutical industry from 1995-2009 after a long career in academia, with the majority of his time at AstraZeneca Pharmaceuticals LP US headquarters leading teams in support of sales force strategy, sales operations, and other commercial analytical and strategic functions. Other pharma industry experiences include roles at Wyeth-Ayerst Laboratories, IMS Health, and ZS Associates.
He has had two academic careers.  His second career involved holding full-time professorships in Healthcare Management and Marketing in the Fox School of Business and a secondary professor appointment in Clinical Sciences in the School of Medicine at Temple University from 2010-2016. His first career was as an academic economist from 1982-1995, eventually becoming a tenured full professor at Mississippi State University. He received his Ph.D. in Economics from Purdue University.
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6 Fox Business. FDA acts to encourage generic competition for complex drugs. Fox Business, published online 2 October 2017, available at (accessed 9 February 2018).
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