ABSTRACT
Monitoring food retail stock-outs or the unplanned unavailability of certain food items for purchase assists policymakers in responding to food supply chain disruptions. This study focuses on identifying food stock-outs using store-level scanner data on US grocery store sales during the COVID-19 pandemic in 2020. The total median stock-out rates of fixed-weight items increased by approximately 130% after March 15, 2020. Categories such as meat and poultry products, some convenience and frozen foods, baby formula, and carbonated beverages had the highest stock-out rates. The analysis also explores the relationship between stock-out rates, sales increases, and food prices during the pandemic. Published 2023. This article is a U.S. Government work and is in the public domain in the USA.
ABSTRACT
Aim: In this study, we aimed to show the contribution of the chest computed tomography (CT)-based histogram analysis method, which will enable us to make quick decisions for patients who are clinically suspected of having COVID-19 infection and whose diagnoses cannot be confirmed by polymerase chain reaction (PCR) tests. Material(s) and Method(s): A total of 84 patients, 40 in the PCR-positive group (age range: 17-90 years) and 44 in the PCR-negative group (age range: 15-75 years), were included in the study. A total of 154 lesions with ground-glass density, 78 in the PCR-positive group and 76 in the PCR-negative group, were detected in these patients' thorax CT scans. The region of interest was placed on the ground-glass opacities from the images and numerical data were obtained by histogram analysis. Numerical data were uploaded to the MATLAB program. Result(s): The localizations of ground-glass densities in the CT findings of patients with probable and definite COVID-19 diagnoses were similar;74.7% of the ground-glass areas in both groups showed peripheral distribution. Lesions were frequently observed in right lungs and lower lobes. In histogram analysis, standard deviation, variance, size %L, size %M, and kurtosis values were higher in the PCR-positive than the PCR-negative group. When receiver operating characteristic curve analysis was performed for standard deviation values, the area under the curve was 0.640, and when the threshold value was selected as 123.4821, the two groups could be differentiated with 62.8% sensitivity and 61.8% specificity. Discussion(s): The use of histogram-based tissue analysis, which is a subdivision of artificial intelligence, for clinically highly suspicious patients increases the diagnostic accuracy of CT. Therefore, performing CT analysis with the histogram method will significantly aid healthcare professionals, especially in clinics where rapid decisions are required, such as in emergency services.Copyright © 2022, Derman Medical Publishing. All rights reserved.
ABSTRACT
Background: COVID-19 has become a serious health problem worldwide. Objective(s): The current study investigated the prognostic factors associated with demographical parameters, clinical and vital signs, and laboratory results for predicting severity and mortality in patients infected with COVID-19. Method(s): This retrospective analysis was conducted on the medical records of 372 COVID-19-positive patients hospitalized at the Khatam al-Anbiya Hospital, Shoushtar, Iran, from Sep 2020 to Sep 2021. The association of demographic parameters, clinical and vital signs, and laboratory results with severity and patients' outcomes (survival/mortality) was studied. The patients were divided into the non-severe group (n = 275) and the severe group (n = 97). COVID-19 disease severity was determined based on the severity of pulmonary involvement using CT chest images. The collected data were analyzed using IBM SPSS software for Windows (version 18). Logistic regression analysis was employed using the Forward LR method to predict COVID-19 severity and mortality. Result(s): The rates of mortality and the severe form of the disease were 87.1% (n = 324) and 12.9% (n = 48), respectively. A prognostic value was observed in predicting COVID-19 severity and mortality for some clinical and vital signs (diabetes (P < 0.001, P = 0.019), hypertension (P = 0.024, P = 0.012), pulmonary diseases (P = 0.038, P < 0.001), and anosmia (P = 0.043, P = 0.044) and paraclinical parameters (FBS (P = 0.014, P = 0.045), BUN (P = 0.045, 0.001), Cr (P = 0.027, P = 0.047), Neut (P = 0.002, P = 0.005), and SpO2 (P = 0.014, P = 0.001)). Cardiovascular disorders (P = 0.037), fever (P = 0.008), and dyspnea (P = 0.020) were also effective at predicting disease-related mortality. Multiple logistic regression analyses showed that diabetes disease, the place of residence, PCO2, and BUN with R2 = 0.18, and age, pulmonary diseases, and BUN with R2 = 0.21 were involved in predicting the severity and mortality, respectively. Conclusion(s): It seems that in addition to the BUN, diabetes and pulmonary diseases play a more significant role in predicting the severity and mortality due to COVID-19, respectively.Copyright © 2023, Author(s).
ABSTRACT
In this paper, we examine the CPI (consumer price index) measurement errors under the first wave of the COVID-19 spread in Japan. To address this question, we construct high-frequency quality-adjusted price indices by employing daily scanner data from retail stores in Tokyo. We demonstrate the importance of using price data with the wide-ranging coverage of products and outlets by making explicit adjustments for temporary sales effects and retail service quality in examining the retail price dynamics under the COVID-19 pandemic as the voluntary lockdown constrained household purchasing behavior. Note that the sources of the CPI measurement errors under the COVID-19 pandemic differ significantly from those in the US, observed as wide-ranging and long-lasting stockouts. We show that downward bias, not upward bias generally advocated, was observed during the first wave of the COVID-19 spread in Japan. The magnitude of the downward bias is estimated at from −0.6 to −0.3 points on the CPI for food less perishables and eating out on the basis of cumulative changes from January 2020 to June. The contribution of the estimates to the overall CPI is −0.3% to −0.15% points on an annualized basis, considering that the estimation covers half-year and the weights are about a quarter of the overall CPI. The magnitude of measurement errors is deemed limited, and the overall trend of the CPI remains unchanged even after incorporating the estimated downward bias. It should be noted that this downward bias arises mainly from the "one-specification-for-one-item” policy by weakening the price representativeness in the Japanese CPI.
ABSTRACT
Interest in retail seafood sales increased dramatically with the onset of the COVID-19 pandemic. The diversity of species, types of products, product forms, and packaging of the seafood sold at retail, combined with diverse consumer preferences across the United States, requires detailed data and analysis to provide guidance and understanding of emerging trends. Weekly, store-based, Nielsen Scantrack data for the period of September 2016 through August 2021 were used to compare trends in US retail (supermarket) seafood sales across the 5-year study period, in continental regions, cities, and species categories sold. Results showed continuous increases in retail seafood sales over the study period at an average annual growth rate of 8.1%, much of which was fueled by the 21% increase in total sales (19.5% increase in quantity sold) the first year after the onset of the pandemic. The South Atlantic region was found to have the greatest total sales and sales per capita among regions. New York City had the greatest total seafood sales, followed by Los Angeles and Philadelphia. The top five most important species categories in terms of sales were, in declining order, shrimp, salmon, tuna, crab, and tilapia, although regional variability became apparent from the fourth-ranked species. The most important package sizes were 454- and 907-g packs. Frozen and refrigerated categories dominated sales (70% in 2021), with little growth in entrees and a decline in market share of shelf-stable seafood products in 2021. Retail supermarket seafood sales increased dramatically following the onset of the pandemic, with especially notable percentage increases in lobster (77%) and crab sales (70%) and the lowest percentage increases in tuna (1%) and tilapia (13%). Given that US per capita seafood consumption did not show a corresponding increase over the study period, study results likely indicate a shift to greater relative consumption at home and not an overall increase in US seafood sales.
ABSTRACT
The purpose of this study was to provide a detailed framework of wine purchases in supermarkets during the COVID-19 pandemic. The unexpected diffusion of the virus and the restrictions imposed in Italy to prevent its spread have significantly affected the food purchasing habits of consumers. By analyzing the scanner data of the wine sales in the Italian mass market retail channel, this study was intended to show whether and how the dynamics triggered by the pandemic have modified the overall value and type of wine purchases, focusing on prices, formats, and promotional sales. In particular, this study explores sales in two separate periods, namely March-April (the "lockdown", with general compulsory closing and severe restrictions) and June-July 2020 (the "post-lockdown", in which some limitations were no longer effective). The analysis of wine sales during lockdown and post-lockdown and the study of the variations compared to the sales of the previous years showed some significant changes in purchase behavior. The results could provide managers, researchers, and policy makers with extensive insights into the purchasing patterns of consumers during this unprecedented time and reveal trends that may characterize the structure of the future wine demand.