Trends in Phenylephrine and Pseudoephedrine Sales in the US.

This study examines purchasing patterns regarding oral decongestants, concerns about their efficacy, and the need for timelier postmarket evaluation.

Hybrid deep learning models with multi-classification investor sentiment to forecast the prices of China's leading stocks.

The prediction of stock prices has long been a captivating subject in academic research. This study aims to forecast the prices of prominent stocks in five key industries of the Chinese A-share market by leveraging the synergistic power of deep learning techniques and investor sentiment analysis. To achieve this, a sentiment multi-classification dataset is for the first time constructed for China's stock market, based on four types of sentiments in modern psychology. The significant heterogeneity of sentiment changes in the sectors' leading stock markets is trained and mined using the Bi-LSTM-ATT model. The impact of multi-classification investor sentiment on stock price prediction was analyzed using the CNN-Bi-LSTM-ATT model. It finds that integrating sentiment indicators into the prediction of industry leading stock prices can enhance the accuracy of the model. Drawing upon four fundamental sentiment types derived from modern psychology, our dataset provides a comprehensive framework for analyzing investor sentiment and its impact on forecasting the stock prices of China's A-share market.

The roles of liquidity and delay in financial markets based on an optimal forecasting model.

We investigate the roles of liquidity and delay in financial markets through our proposed optimal forecasting model. The efficiency and liquidity of the financial market are examined using stochastic models that incorporate information delay. Based on machine learning, we estimate the in-sample and out-of-sample forecasting price performances of the six proposed methods using the likelihood function and Bayesian methods, and the out-of-sample prediction performance is compared with the benchmark model ARIMA-GARCH. We discover that the forecasting price performance of the proposed simplified delay stochastic model is superior to that of the benchmark methods by the test methods of a variety of loss function, superior predictive ability test (SPA), Akaike information criterion (AIC), and Bayesian information criterion (BIC). Using data from the Chinese stock market, the best forecasting model assesses the efficiency and liquidity of the financial market while accounting for information delay and trade probability. The rise in trade probability and delay time affects the stability of the return distribution and raises the risk, according to stochastic simulation. The empirical findings show that empirical and best forecasting approaches are compatible, that company size and liquidity (delay time) have an inverse relationship, and that delay time and liquidity have a nonlinear relationship. The most efficient have optimal liquidity.

Five-Year Sales for Newly Marketed Prescription Drugs With and Without Initial Orphan Drug Act Designation.

This study evaluates sales revenue earned in the first 5 years for newly marketed brand-name drugs with and without an initial orphan drug designation.

Us vehicles sales. Evidence of persistence after COVID-19.

In this paper, the sales of vehicles in the US are examined to understand if the shock caused by the current COVID-19 pandemic has had permanent or transitory effects on its subsequent evolution. Using monthly data from January 1976 until April 2021 and fractional integration methods, our results indicate that the series reverts and the shocks tend to disappear in the long run, even when they appear to be long lived. The results also indicate that the COVID-19 pandemic has not increased the degree of persistence of the series but, unexpectedly, has slightly reduced its dependence. Thus, shocks are transitory, long lived but, as time goes by, the recovery seems to be faster, which is possibly a sign of the strength of the industry.

Impacts of the COVID-19 pandemic on food prices: Evidence from storable and perishable commodities in India.

The supply chain disruptions caused by the COVID-19 outbreak have led to changes in food prices globally. The impact of COVID-19 on the price of essential and perishable food items in developing and emerging economies has been lacking. Using a recent phone survey by the World Bank, this study examines the impact of the COVID-19 pandemic on the prices of the three essential food items in India. The results indicate that price of basic food items such as atta (wheat flour) and rice increased significantly during the pandemic compared to the pre-pandemic period. In contrast, during the same period, the price of onions declined significantly. The findings may suggest panic-buying, hoarding, and storability of food items. The results further reveal that remittance income and cash transfers from the government negatively affected commodity prices. Thus, this study's findings suggest that families may have shifted the demand away from essential foods during the pandemic.

Trends in Prices of Drugs Used to Treat Metastatic Non-Small Cell Lung Cancer in the US From 2015 to 2020.

Importance: Oncology drug prices are a determinant of health disparities in the US and worldwide. Several new therapeutic agents for non-small cell lung cancer (NSCLC) have become available on the US market over the past decade. Although increased competition typically produces lower prices, competition among brand-name oncology drugs has not resulted in lower prices. Objective: To assess price changes in class-specific brand-name medications used to treat metastatic NSCLC in the US from 2015 to 2020. Design, Setting, and Participants: This cross-sectional study, conducted from August 13, 2015, to August 13, 2020, used data from the Micromedex Red Book and Medi-Span Price Rx databases. The study sample was limited to 17 brand-name medications used to treat metastatic NSCLC that were available for purchase before January 1, 2019. Main Outcomes and Measures: The main outcomes were trends over time in average wholesale prices and wholesale acquisition cost unit prices and the correlation in price among the multiple brand-name medications within each therapeutic class (immune checkpoint inhibitors, epidermal growth factor receptor inhibitors, anaplastic lymphoma kinase inhibitors, ROS1 inhibitors, BRAF inhibitors, and MEK inhibitors), measured using the Pearson correlation coefficient. The compounded annual growth rates of different medication costs were compared with the annual inflation rate and the consumer price index for prescription drugs. Results: For all drug classes, the Pearson correlation coefficient approached 1.0, indicating an increase in drug list prices despite within-class drug competition. The median Pearson correlation coefficient values were 0.964 (range, 0.951-0.994) for immune checkpoint inhibitors, 0.898 (range, 0.665-0.950) for epidermal growth factor receptor inhibitors, 0.999 (range, 0.982-0.999) for anaplastic lymphoma kinase inhibitors, and 0.999 for BRAF and MEK inhibitors. The median compounded annual growth rates for most drug costs were higher than the annual inflation rate and consumer price index for prescription drugs: 1.81% (range, 1.29%-2.13%) for immune checkpoint inhibitors, 2.56% (range, 2.38%-5.26%) for epidermal growth factor receptor inhibitors, 2.46% (range, 1.75%-4.66%) for anaplastic lymphoma kinase and ROS1 inhibitors, and 3.06% (range, 0%-3.06%) for BRAF and MEK inhibitors. Conclusions and Relevance: In this cross-sectional study, prices of brand-name medications for treatment of NSCLC increased in the US from 2015 to 2020 without evidence of price competition, raising concern about the affordability of promising oncology drugs. These findings suggest that drug pricing reform is needed.

Price Trends of Healthy and Less Healthy Foods and Beverages in Mexico from 2011-2018.

BACKGROUND: Cost is one of the main drivers of food selection; thus it is important to monitor food prices. Evidence from low- and middle-income countries such as Mexico is limited. OBJECTIVE: The aim of this study was to evaluate the prices and price trends of healthy and less healthy food/beverage groups in Mexico from 2011 to 2018. DESIGN: This study used a time series of the prices of foods and beverages classified by 1) healthiness, 2) processing level, and 3) pairs of healthy/less healthy substitutes. SETTING: Food and beverage prices used to estimate the Consumer Price Index were obtained. Prices were collected weekly from 46 cities (>20,000 habitants) distributed across the country. MAIN OUTCOME MEASURES: Price trend (% change/year) from 2011 to 2018 for all food/beverage groups and price/100 g in 2018 for pairs of healthy/less healthy substitutes were obtained. STATISTICAL ANALYSES: Linear regression models were used for each food/beverage group, with the logarithm of deflated price as the dependent variable and time (years) as the independent variable. RESULTS: On average, prices for less healthy foods and beverages increased more than prices of healthy foods and beverages (foods: 1.72% vs 0.70% change/year; beverages: 1.61% vs -0.19% change/year). The price change was similar for unprocessed/minimally processed foods and ultraprocessed foods (1.95% vs 1.85% change/year); however, within each processing category, the price of less healthy foods increased more. By pairs of substitutes (within food/beverage groups), the healthier option for bread, sodas, and poultry was more expensive (price/100 g) in 2018, whereas for red meat, cheese, mayonnaise, and milk, the healthier option was cheaper. CONCLUSIONS: Overall, the food prices of less healthy foods and beverages increased more than the food prices of healthy foods and beverages. However, by processing level there was no difference, and for pairs of healthy/less healthy substitutes results were mixed. Continued monitoring of food prices is warranted, and future research is needed to understand how these price changes affect dietary quality.

Incidence of fraud and adulterations in ASEAN food/feed exports: A 20-year analysis of RASFF's notifications.

This paper explored the occurrence of food fraud and adulterations (FFA) in exports from the Association of South- East Asia Nations (ASEAN), with implications on food chain and international trade. Data from European Union Rapid Alert System for Food and Feed (EU RASFF) about FFA notifications on ASEAN exports for a period of 20 years (2000-2020) were extracted and analyzed. Results from this study revealed that of all ten ASEAN member countries, seven had cases of FFA notified in the database with Thailand (n = 47, 32%) and the Philippines (n = 37, 26%) receiving the highest frequency of notifications in the region. There was a statistical significance difference in frequency of notifications received on products from these seven countries with herbs and spices ranking highest (n = 22, 15%). Highest notifications of FFA on ASEAN exports came from the United Kingdom (n = 31, 21%). All the seven countries experienced border rejections and consequent destruction of food products especially on exports from Indonesia where 95% of product with FFA were border rejected. Border rejections on products from these countries were significantly different. Therefore, a thorough implementation system, appropriate testing and constantly updating each country's FFA database could aid actions in curtailing future events.

Linkages between share pledging, stock price risk and profitability: Evidence from the P.R. China.

Share pledging has become popular as a method of loan collateral among Chinese shareholders. Our research used a sample of Chinese listed firms between 2008-2018 and produced two main findings. Firstly, we found a negative association between stock price risk and firm profitability. Our second finding was that the interaction effect of share pledging and stock price risk is greater on firm profitability than the effect of stock price risk itself. We examined the role of share pledging by modeling pooled OLS and fixed effects using share pledging behavior, controlling shareholders' share pledging and the share pledging ratio to reinforce the robustness of our results. Furthermore, we investigated the Davis Double Play effect of share pledging to analyze how share pledging affects stock price risk. We found that higher EPS and investor expectations cannot mitigate the positive impact of share pledging on stock price risk. That is, the reduction of EPS and the deterioration of investor expectations caused by share pledging risk will not further aggravate the stock price risk, as shareholders may have taken some managerial actions to affect the transmission mechanism.

The phytosanitary risks posed by seeds for sowing trade networks.

When successful, the operation of local and international networks of crop seed distribution or "seed systems" ensures farmer access to seed and impacts rural livelihoods and food security. Farmers are both consumers and producers in seed systems and benefit from access to global markets. However, phytosanitary measures and seed purity tests are also needed to maintain seed quality and prevent the spread of costly weeds, pests and diseases, in some countries regulatory controls have been in place since the 1800s. Nevertheless, seed contaminants are internationally implicated in between 7% and 37% of the invasive plant species and many of the agricultural pests and diseases. We assess biosecurity risk across international seed trade networks of forage crops using models of contaminant spread that integrate network connectivity and trade volume. To stochastically model hypothetical contaminants through global seed trade networks, realistic dispersal probabilities were estimated from quarantine weed seed detections and incursions from border security interception data in New Zealand. For our test case we use contaminants linked to the global trade of ryegrass and clover seed. Between 2014 and 2018 only four quarantine weed species (222 species and several genera are on the quarantine schedule) warranting risk mitigation were detected at the border. Quarantine weeds were rare considering that average import volumes were over 190 tonnes for ryegrass and clover, but 105 unregulated contaminant species were allowed in. Ryegrass and clover seed imports each led to one post-border weed incursion response over 20 years. Trade reports revealed complex global seed trade networks spanning >134 (ryegrass) and >110 (clover) countries. Simulations showed contaminants could disperse to as many as 50 (clover) or 80 (ryegrass) countries within 10 time-steps. Risk assessed via network models differed 18% (ryegrass) or 48% (clover) of the time compared to risk assessed on trade volumes. We conclude that biosecurity risk is driven by network position, the number of trading connections and trade volume. Risk mitigation measures could involve the use of more comprehensive lists of regulated species, comprehensive inspection protocols, or the addition of field surveillance at farms where seed is planted.

Trends in interventional stroke device utilization during the COVID-19 pandemic.

OBJECTIVES: The collateral effect of the COVID-19 pandemic on interventional stroke care is not well described. We studied this effect by utilizing stroke device sales data as markers of interventional stroke case volume in the United States. METHODS: Using a real-time healthcare device sales registry, this observational study examined trends in the sales of thrombectomy devices and cerebral aneurysm coiling from the same 945 reporting hospitals in the U.S. between January 22 and June 31, 2020, and for the same months in 2018 and 2019 to allow for comparison. We simultaneously reviewed daily reports of new COVID-19 cases. The strength of association between the cumulative incidence of COVID-19 and procedural device sales was measured using Spearman rank correlation coefficient (CC). RESULTS: Device sales decreased for thrombectomy (- 3.7%) and cerebral aneurysm coiling (- 8.5%) when comparing 2019-2020. In 2020, thrombectomy device sales were negatively associated with the cumulative incidence of COVID-19 (CC - 0.56, p < 0.0001), with stronger negative correlation during April (CC - 0.97, p < 0.0001). The same negative correlation was observed with aneurysm treatment devices (CC - 0.60, p < 0.001), with stronger correlation in April (CC - 0.97, p < 0.0001). CONCLUSIONS: The decline in sales of stroke interventional equipment underscores a decline in associated case volumes. Future pandemic responses should consider strategies to mitigate such negative collateral effects.

Inter-metropolitan land price characteristics and pattern in the Beijing-Tianjin-Hebei urban agglomeration, China.

Land prices are the key problem of urban land management, with prices of residential land being the most sensitive and the strongest social reflection among the different land types. Exploring spatial and temporal variation of residential land prices and the effect of land market factors on residential land prices can help the government formulate targeted regulations and policies. This study analyzes the spatial and temporal evolution of residential land prices and the factors influencing the land market in the Beijing-Tianjin-Hebei region based on land transaction data from 2014-2017 using exploratory spatial data analysis (ESDA) and a geographically weighted regression (GWR) model. The results show the following: ① Residential land prices in Beijing and Tianjin are significantly higher than those in other regions, while Zhangjiakou, Chengde, and western mountainous areas have the lowest residential land prices. Over time, a development trend of residential land price polycentricity gradually emerged, and the locational correlation has gradually increased. ② Under the influence of the land finance model of local governments in China, three factors, namely, the land stock utilization rate, revenue from residential land transfers, and the growth of residential land transaction areas, have significantly contributed to the increase in residential land prices. ③ Under the land market supply and demand mechanism and government management, four indicators, namely, the land supply rate, the per capita residential land supply area, the degree of marketization of the residential land supply, and the frequency of residential land transactions, have suppressed the rise in residential land prices. ④ The overall effect of land market factors on residential land prices in the central and northern regions of Beijing, Tianjin and Hebei is stronger than that in the southern regions, which may be related to the more active land market and stricter macromanagement policies in Beijing, Tianjin and surrounding areas.

Strangely mined bitcoins: Empirical analysis of anomalies in the bitcoin blockchain transaction network.

The blockchain technology introduced by bitcoin, with its decentralised peer-to-peer network and cryptographic protocols, provides a public and accessible database of bitcoin transactions that have attracted interest from both economics and network science as an example of a complex evolving monetary network. Despite the known cryptographic guarantees present in the blockchain, there exists significant evidence of inconsistencies and suspicious behavior in the chain. In this paper, we examine the prevalence and evolution of two types of anomalies occurring in coinbase transactions in blockchain mining, which we reported on in earlier research. We further develop our techniques for investigating the impact of these anomalies on the blockchain transaction network, by building networks induced by anomalous coinbase transactions at regular intervals and calculating a range of network measures, including degree correlation and assortativity, as well as inequality in terms of wealth and anomaly ratio using the Gini coefficient. We obtain time series of network measures calculated over the full transaction network and three sub-networks. Inspecting trends in these time series allows us to identify a period in time with particularly strange transaction behavior. We then perform a frequency analysis of this time period to reveal several blocks of highly anomalous transactions. Our technique represents a novel way of using network science to detect and investigate cryptographic anomalies.