Association between expedited review designations and the US or global burden of disease for drugs approved by the US Food and Drug Administration, 2010-2019: a cross-sectional analysis.

OBJECTIVES: Pharmaceutical innovation can contribute to reducing the burden of disease in human populations. This research asks whether products approved by the US Food and Drug Administration (FDA) from 2010 to 2019 and expedited review programmes incentivising development of products for serious disease were aligned with the US or global burden of disease. DESIGN: Cross-sectional study. OUTCOME MEASURES: Association of FDA product approvals (2010-2019), first approved indications, designations for expedited review with the burden of disease (disability-adjusted life years (DALYs)), years of life lost (YLL) and years of life lived with disability (YLD) for 122 WHO Global Health Estimates (GHE) conditions in US and global (ex-US) populations. RESULTS: The FDA approved 387 drugs in 2010-2019 with lead indications associated with 59/122 GHE conditions. Conditions with at least one new drug had greater US DALYs (p=0.001), US YLL (p<0.001), global DALYs (p=0.030) and global YLL (p=0.004) but not US YLD (p=0.158) or global YLD (p=0.676). Most approvals were for conditions in the top quartile of US DALYs or YLL, but <27% were for conditions in the top quartile of global DALYs or YLL. The likelihood of a drug having one or more designations for expedited review programmes was negatively associated (OR<1) with US DALYs, US YLD and global YLD. There was a weak negative association with global DALYs and a weak positive association (OR>1) with US and global YLL. CONCLUSIONS: FDA drug approvals from 2010 to 2019 were more strongly aligned with US than global disease burden. Designations for expedited review were not aligned with either the US or global burdens of disease and may inadvertently disincentivise development of products addressing global disease burdens. These results may inform policies to better align pharmaceutical innovation with the burdens of disease.

Comparing the economic terms of biotechnology licenses from academic institutions with those between commercial firms.

Licenses of drug-related biotechnologies from academic institutions to commercial firms are intended to promote practical applications of public sector research and a return on government investments in biomedical science. This empirical study compares the economic terms of 239 biotechnology licenses from academic institutions to biotechnology companies with 916 comparable licenses between commercial firms. Academic licenses had lower effective royalty rates (median 3% versus 8%, p<0.001), deal size (median $0.9M versus $31.0M, p<0.001), and precommercial payments (median $1.1M versus $25.4M, p<0.001) than corporate licenses. Controlling for the clinical phase of the most advanced product included in the license reduced the median difference in effective royalty rate between academic and corporate licenses from 5% (95% CI 4.3-5.7) to 3% (95% C.I. 2.4-3.6) but did not change the difference in deal size or precommercial payments. Excluding licenses for co-commercialization did not change the effective royalty rate but reduced the median difference in deal size from $15.8M (95% CI 14.9-16.6) to $11.4M (95% CI 10.4-12.3) and precommercial payments from $9.0M (95% CI 8.0-10.0) to $7.6M (95% CI 6.8-8.4). Controlling for deal terms including exclusivity, equity, or R&D in multivariable regression had no substantive effect on the difference in economic terms. This analysis suggests the economic returns associated with biotechnology licenses from academic institutions are systematically lower than licenses between commercial firms and that this difference is only partially accounted for by differences in the intrinsic terms of the license agreements. These results are discussed in the context of a reasonable royalty rate, recognizing that factors extrinsic to the license agreement may reasonably impact the negotiated value of the license, as well as economic theories that view government as an early investor in innovation and technology licenses as a mechanism for achieving a return on investment.

Using natural language processing to improve suicide classification requires consideration of race.

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.

Finding a rhythm: Relating ultra-short-term heart rate variability measures in healthy young adults during rest, exercise, and recovery.

Ultra-short-term (UST; <5 min) heart rate variability (HRV) is increasingly used to indirectly assess autonomic nervous system modulation and physical health. However, UST HRV estimates may vary with measurement technique, physiological state, and data preprocessing. The purpose of this investigation was to assess the information content of UST HRV and its sensitivity to different physiological states and preprocessing techniques. 26 time, frequency, and non-linear HRV measures were determined in 80 healthy men (age: 22.1 ± 3.7 yr) and 25 women (age: 19.4 ± 2.8 yr) from 2-min ECG recordings during seated and standing rest, low-intensity exercise, and seated recovery after maximal exercise. For men, HRV measures obtained during each condition were further analyzed with principal component analysis, k-means clustering, and one-way ANCOVAs. Backward stepwise regression was used to determine the ability of UST HRV to predict aerobic fitness. The sensitivity of UST HRV estimates to different artifact correction procedures was determined with intraclass correlation coefficients. Compared with men, women displayed HRV characteristics suggestive of greater vagal modulation. Nearly 80% of HRV information content was distilled into three principal components comprised of similar measures across conditions. K-means clusters varied in composition and HRV characteristics but not aerobic fitness, which was best predicted by HRV during standing rest. HRV estimates differed depending on artifact correction procedures but were generally similar after individualized correction. Our results indicate that UST HRV measures display redundancy but convey state-specific information and do not strongly predict aerobic fitness in healthy men. Most UST HRV measures are robust to slight differences in artifact correction procedures.

Efficient interaction selection for clustered data via stagewise generalized estimating equations.

Model selection in the presence of interaction terms is challenging as the final model must maintain a hierarchy between main effects and interaction terms. This work presents two stagewise estimation approaches to appropriately select models with interaction terms that can utilize generalized estimating equations to model clustered data. The first proposed technique is a hierarchical lasso stagewise estimating equations approach, which is shown to directly correspond to the hierarchical lasso penalized regression. The second is a stagewise active set approach, which enforces the variable hierarchy by conforming the selection to a properly growing active set in each stagewise estimation step. The effectiveness in interaction selection and the superior computational efficiency of the proposed techniques are assessed in simulation studies. The new methods are applied to a study of hospitalization rates attributed to suicide attempts among 15 to 19 year old at the school district level in Connecticut.

Profitability of Large Pharmaceutical Companies Compared With Other Large Public Companies.

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). Results: From 2000 to 2018, 35 large pharmaceutical companies reported cumulative revenue of $11.5 trillion, gross profit of $8.6 trillion, EBITDA of $3.7 trillion, and net income of $1.9 trillion, while 357 S&P 500 companies reported cumulative revenue of $130.5 trillion, gross profit of $42.1 trillion, EBITDA of $22.8 trillion, and net income of $9.4 trillion. In bivariable regression models, the median annual profit margins of pharmaceutical companies were significantly greater than those of S&P 500 companies (gross profit margin: 76.5% vs 37.4%; difference, 39.1% [95% CI, 32.5%-45.7%]; P < .001; EBITDA margin: 29.4% vs 19%; difference, 10.4% [95% CI, 7.1%-13.7%]; P < .001; net income margin: 13.8% vs 7.7%; difference, 6.1% [95% CI, 2.5%-9.7%]; P < .001). The differences were smaller in regression models controlling for company size and year and when considering only companies reporting research and development expense (gross profit margin: difference, 30.5% [95% CI, 20.9%-40.1%]; P < .001; EBITDA margin: difference, 9.2% [95% CI, 5.2%-13.2%]; P < .001; net income margin: difference, 3.6% [95% CI, 0.011%-7.2%]; P = .05). Conclusions and Relevance: From 2000 to 2018, the profitability of large pharmaceutical companies was significantly greater than other large, public companies, but the difference was less pronounced when considering company size, year, or research and development expense. Data on the profitability of large pharmaceutical companies may be relevant to formulating evidence-based policies to make medicines more affordable.

Stagewise generalized estimating equations with grouped variables.

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.

Spatial, socio-economic, and ecological implications of incorporating minimum size constraints in marine protected area network design.

Marine protected areas (MPAs) are the cornerstone of most marine conservation strategies, but the effectiveness of each one partly depends on its size and distance to other MPAs in a network. Despite this, current recommendations on ideal MPA size and spacing vary widely, and data are lacking on how these constraints might influence the overall spatial characteristics, socio-economic impacts, and connectivity of the resultant MPA networks. To address this problem, we tested the impact of applying different MPA size constraints in English waters. We used the Marxan spatial prioritization software to identify a network of MPAs that met conservation feature targets, whilst minimizing impacts on fisheries; modified the Marxan outputs with the MinPatch software to ensure each MPA met a minimum size; and used existing data on the dispersal distances of a range of species found in English waters to investigate the likely impacts of such spatial constraints on the region's biodiversity. Increasing MPA size had little effect on total network area or the location of priority areas, but as MPA size increased, fishing opportunity cost to stakeholders increased. In addition, as MPA size increased, the number of closely connected sets of MPAs in networks and the average distance between neighboring MPAs decreased, which consequently increased the proportion of the planning region that was isolated from all MPAs. These results suggest networks containing large MPAs would be more viable for the majority of the region's species that have small dispersal distances, but dispersal between MPA sets and spill-over of individuals into unprotected areas would be reduced. These findings highlight the importance of testing the impact of applying different MPA size constraints because there are clear trade-offs that result from the interaction of size, number, and distribution of MPAs in a network.

Abundance and prevalence of Aedes aegypti immatures and relationships with household water storage in rural areas in southern Viet Nam.

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.

Ionic reaction of halogens with terminal alkenes: the effect of electron-withdrawing fluorine substituents on the bonding of halonium ions.

Ionic reactions of terminal alkenes with chlorine (Cl(2)), bromine (Br(2)), and iodine monochloride (ICl) are sensitive to the alkyl substituents, and the positions and number of vinyl fluorine atoms. These perturbations influence the symmetry of the halonium ion intermediates, which can be determined by the distribution of the Markovnikov to anti-Markovnikov products. A vinyl fluorine on the number-2 carbon favors an unsymmetrical intermediate with greater charge on the number-2 carbon unless the alkyl group is electron withdrawing. A vinyl fluorine on the terminal number-1 carbon favors positive charge development on that carbon unless a resonance stabilizing group is on the number-2 carbon. The symmetry of halonium ions with vinyl fluorines on both carbons-1 and -2 depends primarily on the characteristics of the alkyl substituent. Intermediates range from open-ions with the positive charge on carbon-2, to various bridged species, to open-ions on the terminal carbon.