IMPORTANCE Understanding the profitability of pharmaceutical companies is essential to formulating evidence-based policies to reduce drug costs while maintaining the industry's ability to innovate and provide essential medicines. OBJECTIVE To compare the profitability of large pharmaceutical companies with other large companies. DESIGN, SETTING, AND PARTICIPANTS This cross-sectional study compared the annual profits of 35 large pharmaceutical companies with 357 companies in the S&P 500 Index from 2000 to 2018 using information from annual financial reports. A statistically significant differential profit margin favoring pharmaceutical companies was evidence of greater profitability. EXPOSURES Large pharmaceutical vs nonpharmaceutical companies. MAIN OUTCOMES AND MEASURES The main outcomes were revenue and 3 measures of annual profit: gross profit (revenue minus the cost of goods sold); earnings before interest, taxes, depreciation, and amortization (EBITDA; pretax profit from core business activities); and net income, also referred to as earnings (difference between all revenues and expenses). Profit measures are described as cumulative for all companies from 2000 to 2018 or annual profit as a fraction of revenue (margin).
Since 2000, the Government of Viet Nam has committed to provide rural communities with increased access to safe water through a variety of household water supply schemes (wells, ferrocement tanks and jars) and piped water schemes. One possible, unintended consequence of these schemes is the concomitant increase in water containers that may serve as habitats for dengue mosquito immatures, principally Aedes aegypti. To assess these possible impacts we undertook detailed household surveys of Ae. aegypti immatures, water storage containers and various socioeconomic factors in three rural communes in southern Viet Nam. Positive relationships between the numbers of household water storage containers and the prevalence and abundance of Ae. aegypti immatures were found. Overall, water storage containers accounted for 92-97% and 93-96% of the standing crops of III/IV instars and pupae, respectively. Interestingly, households with higher socioeconomic levels had significantly higher numbers of water storage containers and therefore greater risk of Ae. aegypti infestation. Even after provision of piped water to houses, householders continued to store water in containers and there was no observed decrease in water storage container abundance in these houses, compared to those that relied entirely on stored water. These findings highlight the householders' concerns about the limited availability of water and their strong behavoural patterns associated with storage of water. We conclude that household water storage container availability is a major risk factor for infestation with Ae. aegypti immatures, and that recent investment in rural water supply infrastructure are unlikely to mitigate this risk, at least in the short term.
Forward stagewise estimation is a revived slow-brewing approach for model building that is particularly attractive in dealing with complex data structures for both its computational efficiency and its intrinsic connections with penalized estimation. Under the framework of generalized estimating equations, we study general stagewise estimation approaches that can handle clustered data and non-Gaussian/non-linear models in the presence of prior variable grouping structure. As the grouping structure is often not ideal in that even the important groups may contain irrelevant variables, the key is to simultaneously conduct group selection and within-group variable selection, that is, bi-level selection. We propose two approaches to address the challenge. The first is a bi-level stagewise estimating equations (BiSEE) approach, which is shown to correspond to the sparse group lasso penalized regression. The second is a hierarchical stagewise estimating equations (HiSEE) approach to handle more general hierarchical grouping structure, in which each stagewise estimation step itself is executed as a hierarchical selection process based on the grouping structure. Simulation studies show that BiSEE and HiSEE yield competitive model selection and predictive performance compared to existing approaches. We apply the proposed approaches to study the association between the suicide-related hospitalization rates of the 15-19 age group and the characteristics of the school districts in the State of Connecticut.
Objectives: To improve the accuracy of classification of deaths of undetermined intent and to examine racial differences in misclassification. Methods:We used natural language processing and statistical text analysis on restricted-access case narratives of suicides, homicides, and undetermined deaths in 37 states collected from the National Violent Death Reporting System (NVDRS) (2017). We fit separate race-specific classification models to predict suicide among undetermined cases using data from known homicide cases (true negatives) and known suicide cases (true positives).Results: A classifier trained on an all-race dataset predicts less than half of these cases as suicide. Importantly, our analysis yields an estimated suicide rate for the Black population comparable with the typical detection rate for the White population, indicating that misclassification excess is endemic for Black suicide. This problem may be mitigated by using race-specific data. Our findings, based on the statistical text analysis, also reveal systematic differences in the phrases identified as most predictive of suicide. Conclusions:This study highlights the need to understand the reasons underlying suicide rate differences and for further testing of strategies to reduce misclassification, particularly among people of color.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.