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Background: Although asthma does not appear to be a risk factor for severe coronavirus disease 2019 (COVID-19), outcomes could vary for patients with different asthma subtypes. The objective of this analysis was to compare COVID-19 outcomes in real-world cohorts in the United States among patients with asthma, with or without evidence of allergy.Methods: In a retrospective analysis of the COVID-19 Optum electronic health record dataset (February 20, 2020-January 28, 2021), patients diagnosed with COVID-19 with a history of moderate-to-severe asthma were divided into 2 cohorts: those with evidence of allergic asthma and those without (nonallergic asthma). After 1:1 propensity score matching, in which covariates were balanced and potential bias was removed, COVID-19 outcomes were compared between cohorts.Results: From a COVID-19 population of 591,198 patients, 1595 patients with allergic asthma and 8204 patients with nonallergic asthma were identified. After propensity score matching (n = 1578 per cohort), risk of death from any cause after COVID-19 diagnosis was significantly lower for patients with allergic vs nonallergic asthma (hazard ratio, 0.48; 95% CI 0.28-0.83; P = 0.0087), and a smaller proportion of patients with allergic vs nonallergic asthma was hospitalized within - 7 to + 30 days of COVID-19 diagnosis (13.8% [n = 217] vs 18.3% [n = 289]; P = 0.0005). Among hospitalized patients, there were no significant differences between patients with allergic or nonallergic asthma in need for intensive care unit admission, respiratory support, or COVID-19 treatment.Conclusions: Asthma subtype may influence outcomes after COVID-19; patients with allergic asthma are at lower risk for hospitalization/death than those with nonallergic asthma. [ABSTRACT FROM AUTHOR]

To date, the primary objective of phase I trials has been to safely select the maximum tolerated dose (MTD) of a drug or drug combination for utilization in subsequent trials. Although conventional cytotoxic chemotherapy is generally more effective at the MTD than molecularly targeted agents (MTAs), recent single-institution data suggest that molecularly targeted agent may not require an MTD for efficacy.We analyzed patient outcome results in MTA phase I trials at multiple institutions throughout North America sponsored by the National Cancer Institute's CancerTherapy Evaluation Program. We retrospectively collected and analyzed data on patients treated on monotherapy phase I trials investigating novel MTAs with a defined MTD from 2000 to 2009. Logistic regression analysis was used to test whether there was an increase in the probability of a response as dose increased. A Cox proportional hazards model was used to determine if overall survival increased with increasing dose. All statistical tests were two-sided. We analyzed 1908 patients treated on 55 eligible clinical trials.The probability of both overall response (complete response plus partial response) and overall survival increased with increasing dose (odds ratio for increased response = 1.56, P= .10; hazard ratio for death = 0.37, P= .008) when controlling for study as a covariate. Patients treated in the context of phase I trials with MTAs continue to derive reasonable clinical benefit. Contrary to other single institution data, our data suggest clinical benefit in terms of increasing response and overall survival with increasing dose. [ABSTRACT FROM AUTHOR]

In this paper we describe the pass-through behavior of a major U.S. supermarket chain for 78 products across 11 categories. Our data set includes retail prices and wholesale prices for stores in 15 retail price zones for a one-year period. For the empirical model, we use a reduced-form approach that focuses directly on equilibrium prices as a function of exogenous supply-and demand-shifting variables. The reduced-form approach enables us to identify the theoretical pass-through rate without specific assumptions about the form of consumer demand or the conduct of a category-pricing manager. Thus, our measurements of pass-through are not constrained by specific structure on the underlying economic model. The empirical pricing model includes costs of all competing products in the category on the right-hand side (not only the cost of the focal brand) and yields estimates of both own-brand and cross-brand pass-through rates. Our results provide a rich picture of the retailer's pass-through behavior. We find that pass-through varies substantially across products and across categories. Own-brand pass-through rates are, on average, more than 60% for 9 of 11 categories, a finding that is at odds with the claims of manufacturers about retailers in general. Importantly, we find substantial evidence of cross-brand pass-through effects, indicating that retail prices of competing products are adjusted in response to a change in the wholesale price of any given product in the category. We find that cross-brand pass-through rates are both positive and negative. We explore determinants of own-brand and cross-brand pass-through rates and find strong evidence in multiple categories of asymmetric retailer response to trade promotions on large versus small brands. For example, brands with larger market shares, and brands that contribute more to retailer profits in the category, receive higher pass-through. We also find that trade promotions on large brands are less likely than small... [ABSTRACT FROM AUTHOR]

The usefulness of a technology product for an end-user often depends on the availability of complementary software products and services. Computers require software, cameras require film, and DVD players require movie programming in order for customers to value the whole product. This phenomenon, where the demand for hardware products is mediated by the supply of complementary software products, is called an indirect network externality. Indirect network externalities create a two-way contingency between the demand for the hardware product and the supply of software products, and result in a strategic interdependence between the actions of hardware manufacturers and the actions of software providers. Indirect network externalities are gaining economic significance in technology markets, because hardware and software are typically provided by independent firms, and both sets of firms have an incentive to free- ride on each others' demand creation efforts. Despite the ubiquity of this phenomenon, it has largely been ignored in the marketing science literature. We present a conceptual and operational model for the evolution of markets with indirect network externalities. The key feature of our framework is to model the market-mediated dependence between the actions of hardware manufacturers and software complementors, created by the direct dependence of consumer demand for the whole product on the actions of manufacturers as well as complementors. In addition, we incorporate marketing-mix effects on consumer response, as well as heterogeneity in consumer preferences for hardware and software attributes. We model consumer response using a latent-class choice model. To estimate the complement or response functions, we use a modified Delphi technique that allows us to convert qualitative response data into quantitative response functions. We integrate the consumer and complement or response models to create a simulation model that generates forecasts of market shares and sales volumes for competing technologies, as a function of marketing-mix effects and exogenously specified regulatory scenarios. The modeling framework is of interest to new product modelers interested in creating empirical models and decision-support systems for forecasting demand in technology markets characterized by indirect network externalities. The decision-support aspects of the modeling framework should appeal to managers interested in understanding and quantifying the complex interplay between hardware manufacturers and software complementors in the evolution of markets with indirect network externalities. We present an application of the modeling framework to the U.S. digital television industry, and use the framework to characterize the competition among analog and digital TV technologies. Our results suggest that complementor actions play an important role in the acceptance of digital TV technologies in general, and high definition television (HDTV) in particular. We find that forecasts that ignore the influence of indirect network externalities would be seriously biased in favor of HDTV. We illustrate how the modeling framework can be used to identify and profile customer segments in the digital TV market based on their utility for hardware-related features as well as programming-related features. We also illustrate the decision-support capabilities of the modeling framework by evaluating the sensitivity of the forecasts to varying marketing, regulatory, and complementor response scenarios. We derive implications for marketing and public affairs policies of the hardware manufacturers. The developments in the digital TV industry generally support our finding that HDTV will be a niche product, and will diffuse slower than originally expected due in part to the lack of programming. The delays in the introduction of digital TV to the marketplace also suggest that most forecasts for infrastructure-intensive technologies like digital TV may be too optimistic simply because they underestimate the delays in agreeing upon technology standards and resolving regulatory debates. [ABSTRACT FROM AUTHOR]

Introduction: Evidence-based tobacco cessation interventions increase quit rates, yet most smokers do not use them. Every primary care visit offers the potential to discuss such options, but communication can be tricky for patients and provider alike. We explored smokers' personal interactions with health care providers to better understand what it is like to be a smoker in an increasingly smoke-free era and the resources needed to support quit attempts and to better define important patient-centered outcomes., Methods: Three 90-minute focus groups, involving 33 patients from 3 primary care clinics, were conducted. Participants were current or recent (having quit within 6 months) smokers. Topics included tobacco use, quit attempts, and interactions with providers, followed by more pointed questions exploring actions patients want from providers and outcome measures that would be meaningful to patients., Results: Four themes were identified through inductive coding techniques: 1) the experience of being a tobacco user (inconvenience, shame, isolation, risks, and benefits), 2) the medical encounter (expectations of providers, trust and respect, and positive, targeted messaging), 3) high-value actions (consistent dialogue, the addiction model, point-of-care nicotine patches, educational materials, carbon monoxide monitoring, and infrastructure), and 4) patient-centered outcomes., Conclusion: Engaged patient-centered smoking cessation counseling requires seeking the patient voice early in the process. Participants desired honest, consistent, and pro-active discussions and actions. Participants also suggested creative patient-centered outcome measures to consider in future research.