Pharmaceutical or medical device companies facing multiclaimant products liability situations will inevitably seek to evaluate the total liability exposure. Whether early in the process or later in the claim lifecycle, an effective analytical approach integrates information from various sources and provides flexibility to model alternative outcomes. The catalyst necessitating the preparation of a robust liability estimation analysis varies from case to case. Common impetus may include, among other things: evaluation of potential settlement structures with underlying claimants, assessment of overall risk for management planning, quantification of cash flow needs, financial reporting purposes, contemplated corporate transactions, and insurance coverage analysis. Each situation is different, but a general framework can guide the process to ensure consideration of the substantive issues.
The foundation of an effective analytical framework requires organization of the historical known or potential future claims associated with the product. The accuracy and reliability of any liability estimation is often shaped by the uncertainty associated with the foundational data. Uncertainty regarding the magnitude of the pending and future potentially injured population is influenced by a number of factors, including but not limited to, the nature of alleged liability, the stage in the claim lifecycle, the type of product, and the accessibility of reliable data. For example, uncertainty regarding the total number of claims is narrow when considering a design defect for implantable devices because access to data regarding the total implanted devices is likely readily available and represents a finite number of potentially injured parties. The uncertainty may increase if the liability for this same implantable device stems from a manufacturing defect linked to an unknown fraction of the total devices produced. Uncertainty also increases when the product end-users are not easily traceable or the analysis is performed in the early stages of the liability event.
The inherent uncertainty in quantifying the relevant claim population is rarely eliminated but can be narrowed or more clearly understood by seeking alternative data sources, benchmarking against other studies, or performing sensitivity testing. Fairly extensive data regarding the number of devices sold or the number of prescriptions written can be gathered. This data can be overlaid with demographic information collected from research studies or sampling the pending claimant population to develop an overall picture of demographics of the broader claimant population. A statistical analysis of demographic or other factors may also lead to discoveries about the incidence of injury or the injury severity trends among the potentially injured parties. For example, it could become apparent that a certain product impacts men at greater rates than women, the elderly at greater rates than the young, or the obese at greater rates than the slim.
Specific analysis and research related to the product at issue can help refine the estimation analysis. If registry information or other research on the impacted population exists, these can be useful tools to determine if the experience an individual company is having is similar to the industry as a whole. If so, these registries can be useful for collecting demographic information (such as age, gender, ethnicity, etc.) and identifying trends or demographic segmentation to incorporate into the liability estimation model. A flexible approach integrates data about product geographic distribution, market timing/availability, and other contributing factors, such as other ailments or medications, lifestyle factors, or even climate, that impact the rate of claims.
This information gathering and analysis effort supports a key goal of capturing data to identify a precise figure or a reasonable range of potentially injured parties in total and, if applicable, by injury severity category. The population of potentially injured parties may be further reduced based on statutes of limitations, propensity to claim, mortality rates, market withdrawal timing, or other factors. Accurately assessing the proportion of potential claims that will ultimately be compensated is fundamental to the estimation and may involve complex analyses to reflect these factors. Scenario analysis may be used to understand the sensitivity of assumptions incorporated in the estimation. In basic terms, liability estimation is a simple multiplication product of the number of compensated claims and the value of those claims. Once the number of potentially compensable claims is established, the focus turns to the value associated with each claim or group of claims.
In pharmaceutical and medical device products liability situations, the claim values can vary greatly, from claimants with no injuries or temporary discomfort to claimants that experienced severe complications or even death. Understanding the frequency of certain types of injuries and their correlation to demographic criteria is necessary in developing an accurate estimation model. If the pending claimant population is large, statistical sampling can be a useful way to understand the frequency of severe claims as well as the specific claimant attributes that lead to severity. Additionally, sampling can be used to test the accuracy of injury information provided by the claimants to any available supporting medical files. In complex matters where claimants are self-reporting their experiences on fact sheets or claim forms, incorporating data extracted from medical records can make the estimation analysis more robust and complete.
The determination of claim values often reflects subjective assessments based on resolved claims and/or similar comparable situations. To evaluate global settlement options, the liability estimation approach may establish a range of value assumptions and then assign probabilistic outcome values to each assumption. This approach provides predictive confidence intervals and expected values useful to evaluating sensitivities in the settlement context. In addition, information about the prevalence of certain injuries or claimant characteristics can be useful in creating a settlement framework that uses claim characteristic adjustments. Knowing what characteristics are prevalent and which are infrequent can be useful when negotiating discounts and escalation factors in a global settlement.
Generally, liability estimation for pharmaceutical and medical device products liability matters embodies the blessing and the curse of an abundance of data. These situations may provide mountains of data related to historical claims, market data, potential exposures, regulatory history, and other studies. Differentiating which data points are the key drivers in determining the value of the claim and which data points can be used to predict the volume of future claims is critical in creating an appropriate robust liability estimation.
Keywords: litigation, products liability, pharmaceuticals, medical devices, liability estimation, potentially injured population, demographics, statistical sampling
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