Preemption Without Borders: The Modern Conflation of Tort and Contract Liabilities

Medical device jurisprudence has taken a turn for the worse recently, turning a deaf ear to patients who have been injured or killed by devices and covertly expanding the boundaries of federal preemption in ways that threaten fundamental contractual principles. Ever since the Court\u27s holding in Riegel v. Medtronic, district and appellate courts have effectively immunized the manufacturers of certain devices from contract, as well as tort, liabilities. The lower courts\u27 rulings are not only problematic as a matter of law, but raise novel concerns about federal regulatory preemption undermining individuals\u27 contract rights. A comprehensive analysis of the Court\u27s medical device jurisprudence and the relevant statutes and regulations establishes that consumers\u27 contractual claims should not be preempted. Further, because preemption of these claims shifts the costs of device-related injuries away from the entities that are in the best position to manage losses, doing so is economically inefficient. Finally, federal preemption of contract claims poses unique threats to contractual liberty and state autonomy. Whereas statutory preemption of tort liabilities consists of the state limiting the applicability of state-imposed duties, preemption of contractual liabilities constitutes government interference in obligations that private parties have voluntarily imposed on themselves. Not only is contract law an area of state sovereignty, but the federal government lacks an interest sufficient to justify denying the parties involved in medical device sales the right to address liability concerns contractuall

Does lisdexamfetamine dimesylate (Vyvanse) reduce the weekly incidence of binge eating in adults with binge eating disorder?

Objective: The objective of this selective EBM review is to determine whether or not “Does lisdexamfetamine dimesylate (Vyvanse) reduce the weekly incidence of binge eating in adults with binge eating disorder?” Study Design: A systematic review of three double-blind, randomized, placebo-controlled trials published between 2015 and 2017. Data Sources: All articles were obtained from PubMed and were published in peer-reviewed journals, in the English language. They were selected based on their applicability to the clinical question, credibility, and if the researched outcomes were patient oriented. Outcomes Measured: The number of binge eating days per week was the outcome measured, with the data compiled from self-reported diaries and clinical interviews. The mean value of binge eating days per week ± standard deviation for both the placebo and intervention group was calculated at two time points, before the trial started and at the end of the trial. The mean change from baseline was then calculated. Results: The study conducted by McElroy et al. demonstrated a statistically significant reduction (p-value \u3c 0.001) in the number of binge eating days per week with lisdexamfetamine dimesylate 70mg, with a mean change from baseline of 4.1. In the study conducted by Hudson et al., lisdexamfetamine dimesylate 70mg led to a statistically significant reduction (p-value \u3c 0.001) in the number of binge eating days per week, indicated by a mean change from baseline of 4.72. The study conducted by Guerdjikova et al. revealed a statistically significant reduction (p-value = 0.03) in the number of binge eating days per week with lisdexamfetamine dimesylate 70mg, with a mean change from baseline of 3.4. Conclusion: The results of these three studies demonstrated that the use of lisdexamfetamine dimesylate 70mg led to a statistically significant reduction in the number of binge eating days per week in adults diagnosed with binge eating disorder

Pooling stated and revealed preference data in the presence of RP endogeneity

Pooled discrete choice models combine revealed preference (RP) data and stated preference (SP) data to exploit advantages of each. SP data is often treated with suspicion because consumers may respond differently in a hypothetical survey context than they do in the marketplace. However, models built on RP data can suffer from endogeneity bias when attributes that drive consumer choices are unobserved by the modeler and correlated with observed variables. Using a synthetic data experiment, we test the performance of pooled RP–SP models in recovering the preference parameters that generated the market data under conditions that choice modelers are likely to face, including (1) when there is potential for endogeneity problems in the RP data, such as omitted variable bias, and (2) when consumer willingness to pay for attributes may differ from the survey context to the market context. We identify situations where pooling RP and SP data does and does not mitigate each data source’s respective weaknesses. We also show that the likelihood ratio test, which has been widely used to determine whether pooling is statistically justifiable, (1) can fail to identify the case where SP context preference differences and RP endogeneity bias shift the parameter estimates of both models in the same direction and magnitude and (2) is unreliable when the product attributes are fixed within a small number of choice sets, which is typical of automotive RP data. Our findings offer new insights into when pooling data sources may or may not be advisable for accurately estimating market preference parameters, including consideration of the conditions and context under which the data were generated as well as the relative balance of information between data sources.This work was supported in part by a grant from the Link Foundation, a grant from the National Science Foundation # 1064241 , and a grant from Ford Motor Company. The opinions expressed are those of the authors and not necessarily those of the sponsors.Accepted manuscrip

logitr: Fast Estimation of Multinomial and Mixed Logit Models with Preference Space and Willingness-to-Pay Space Utility Parameterizations

This paper introduces the logitr R package for fast maximum likelihood estimation of multinomial logit and mixed logit models with unobserved heterogeneity across individuals, which is modeled by allowing parameters to vary randomly over individuals according to a chosen distribution. The package is faster than other similar packages such as mlogit, gmnl, mixl, and apollo, and it supports utility models specified with "preference space" or "willingness-to-pay (WTP) space" parameterizations, allowing for the direct estimation of marginal WTP. The typical procedure of computing WTP post-estimation using a preference space model can lead to unreasonable distributions of WTP across the population in mixed logit models. The paper provides a discussion of some of the implications of each utility parameterization for WTP estimates. It also highlights some of the design features that enable logitr's performant estimation speed and includes a benchmarking exercise with similar packages. Finally, the paper highlights additional features that are designed specifically for WTP space models, including a consistent user interface for specifying models in either space and a parallelized multi-start optimization loop, which is particularly useful for searching the solution space for different local minima when estimating models with non-convex log-likelihood functions

Consumer Protection in the Age of Big Data

The Big Data revolution is upon us. Technological advances in the degree to which third parties can record information about individuals, along with increases in the use of predictive analytics, are transforming the way that business is conducted in practically all sectors of the economy. This is particularly true in the insurance industry, where a firm’s ability to forecast the future is the central determinant of its profitability. Scholars and the media have touted the potential benefits of Big Data analytics—it will enable businesses to tailor their practices to suit consumers’ preferences and increase the efficiency of their operations. The Big Data movement’s potential negative impacts, however, have garnered significantly less attention. Commentators have focused on privacy and data security concerns as the primary problems associated with Big Data analytics. There have been essentially no attempts to assess how these developments affect consumers’ other interests or, more broadly, the extent to which they justify additional regulation of markets. This Article fills this gap. It identifies eight societal interests that will be affected by insurers’ uses of data—actuarial fairness, loss prevention, autonomy, non-discrimination, justice, utility maximization, privacy, and good faith—and describes how regulators could act to ensure that markets generate an optimal balance of these values. While laissez-faire regulatory approaches are superior for some types of insurance, more extensive state interventions are needed for products that are sold to individual consumers. Where additional regulation is needed, community rating rules, authorization requirements for policy modifications, and claims handling standards are the mechanisms best suited to guaranteeing that insurance markets continue to advance public interests in the Big Data era

Type IV Duane Syndrome

Purpose To identify cases of synergistic divergence whose characteristics suggest that this entity is a form of Duane syndrome. Methods The records of all patients with a Duane syndrome diagnosis, including standardized eye position photographs, from the E-Consultation program of Cybersight, Orbis International were analyzed. Results A total of 350 Duane syndrome cases were identified. Of these, 19 (5%) had features consistent with synergistic divergence, or type 4 Duane syndrome. Of the 19, 16 (84%) were male, 15 (79%) had palpebral fissure narrowing, all had anomalous head posture, and 18 (95%) were exotropic. Only 9 (47%) patients were reported to have undergone surgery. Conclusions Synergistic divergence is a rare entity with features similar to those of Duane syndrome. We suggest that this entity be classified as type 4 Duane syndrome, because it has unique findings and an innervation pattern that differs from the other 3 recognized types

The Critical Role of Public Charging Infrastructure

Editors: Peter Fox-Penner, PhD, Z. Justin Ren, PhD, David O. JermainA decade after the launch of the contemporary global electric vehicle (EV) market, most cities face a major challenge preparing for rising EV demand. Some cities, and the leaders who shape them, are meeting and even leading demand for EV infrastructure. This book aggregates deep, groundbreaking research in the areas of urban EV deployment for city managers, private developers, urban planners, and utilities who want to understand and lead change

Choice at the pump: measuring preferences for lower-carbon combustion fuels

A decarbonized future will require a transition to lower carbon fuels for personal transportation. We study consumer preferences for combustion fuels including gasoline, diesel, natural gas, and E85 (85% ethanol and 15% gasoline) using consumer choice survey data from two settings: online (n = 331) and in-person at refueling stations (n = 127). Light-duty vehicle owners were asked in a series of choice tasks to choose among fuels that varied in type, price, CO2 emissions, and location of origin for a hypothetical vehicle that could accept all fuels. We find that the majority of gasoline and E85 users are willing to substitute towards other fuels at today's prices and attributes, while diesel users have a strong preference for diesel fuel. We also find that respondents are willing to pay on average $150/ton CO2 avoided from fuel consumption—more than most estimates of the social cost of carbon. Thus, communicating the climate benefits from alternative fuels may be an important strategy for decarbonizing the transportation sector