Determinants of panic buying during COVID-19: causes and consequences

PurposeDespite the abundant literature on panic buying during COVID-19 pandemic, the several causes and consequences of panic buying have been enormously ignored. The purpose of this study is to emphasize the consumer's behavior during the COVID-19 pandemic and illustrate the comprehensive theoretical model of consumers' panic buying to investigate its causes and consequences in a developing country empirically to uncover this gap.Design/methodology/approachThe authors collected data from 419 households of all socioeconomic classes of Bangladesh. A hierarchical regression model analyzed the data.FindingsThis study finds that internal and external factors such as rumors, government strategies, fear and anxiety and health security significantly affect consumers' panic buying behaviors. This finding supports some theories of human behavior. This study also finds that panic buying has internal and external consequences such as price hike, shortage of supply of products, dissatisfaction of consumers and increase in utility (benefit) of the products but not on consumer's budget. This finding supports as well as contradicts some established theories of human and consumer behavior.Originality/valueThis study proves that panic buying cannot help the consumers and they are the ultimate sufferers of this. The findings of this study will help the government, media, suppliers and consumers to interact properly to maintain panic buying during a pandemic crisis. Giving a holistic explanation of the causes and consequences of panic buying by introducing some novel variables is a momentous strength of this study.

Interpretable Differential Diagnosis of Non-COVID Viral Pneumonia, Lung Opacity and COVID-19 Using Tuned Transfer Learning and Explainable AI.

The coronavirus epidemic has spread to virtually every country on the globe, inflicting enormous health, financial, and emotional devastation, as well as the collapse of healthcare systems in some countries. Any automated COVID detection system that allows for fast detection of the COVID-19 infection might be highly beneficial to the healthcare service and people around the world. Molecular or antigen testing along with radiology X-ray imaging is now utilized in clinics to diagnose COVID-19. Nonetheless, due to a spike in coronavirus and hospital doctors' overwhelming workload, developing an AI-based auto-COVID detection system with high accuracy has become imperative. On X-ray images, the diagnosis of COVID-19, non-COVID-19 non-COVID viral pneumonia, and other lung opacity can be challenging. This research utilized artificial intelligence (AI) to deliver high-accuracy automated COVID-19 detection from normal chest X-ray images. Further, this study extended to differentiate COVID-19 from normal, lung opacity and non-COVID viral pneumonia images. We have employed three distinct pre-trained models that are Xception, VGG19, and ResNet50 on a benchmark dataset of 21,165 X-ray images. Initially, we formulated the COVID-19 detection problem as a binary classification problem to classify COVID-19 from normal X-ray images and gained 97.5%, 97.5%, and 93.3% accuracy for Xception, VGG19, and ResNet50 respectively. Later we focused on developing an efficient model for multi-class classification and gained an accuracy of 75% for ResNet50, 92% for VGG19, and finally 93% for Xception. Although Xception and VGG19's performances were identical, Xception proved to be more efficient with its higher precision, recall, and f-1 scores. Finally, we have employed Explainable AI on each of our utilized model which adds interpretability to our study. Furthermore, we have conducted a comprehensive comparison of the model's explanations and the study revealed that Xception is more precise in indicating the actual features that are responsible for a model's predictions.This addition of explainable AI will benefit the medical professionals greatly as they will get to visualize how a model makes its prediction and won't have to trust our developed machine-learning models blindly.

Were hospitals with sustained high performance more successful at reducing mortality during the pandemic's second wave?

BACKGROUND: In 2019, the COVID-19 pandemic emerged. Variation in COVID-19 patient outcomes between hospitals was later reported. PURPOSE: This study aims to determine whether sustainers-hospitals with sustained high performance on Hospital Value-Based Purchasing Total Performance Score (HVBP-TPS)-more effectively responded to the pandemic and therefore had better patient outcomes. METHODOLOGY: We calculated hospital-specific risk-standardized event rates using deidentified patient-level data from the UnitedHealth Group Clinical Discovery Database. HVBP-TPS from 2016 to 2019 were obtained from Centers for Medicare & Medicaid Services. Hospital characteristics were obtained from the American Hospital Association Annual Survey Database (2019), and county-level predictors were obtained from the Area Health Resource File. We use a repeated-measures regression model assuming an AR(1) type correlation structure to test whether sustainers had lower mortality rates than nonsustainers during the first wave (spring 2020) and the second wave (October to December 2020) of the pandemic. RESULTS: Sustainers did not have significantly lower COVID-19 mortality rates during the first wave of the pandemic, but they had lower COVID-19 mortality rates during the second wave compared to nonsustainers. Larger hospitals, teaching hospitals, and hospitals with higher occupancy rates had higher mortality rates. CONCLUSION: During the first wave of the pandemic, mortality rates did not differ between sustainers and nonsustainers. However, sustainers had lower mortality rates than nonsustainers in the second wave, most likely because of their knowledge management capabilities and existing structures and resources that enable them to develop new processes and routines to care for patients in times of crisis. Therefore, a consistently high level of performance over the years on HVBP-TPS is associated with high levels of performance on COVID-19 patient outcomes. PRACTICE IMPLICATIONS: Investing in identifying the knowledge, processes, and resources that foster the dynamic capabilities needed to achieve superior performance in HVBP might enable hospitals to utilize these capabilities to adapt more effectively to future changes and uncertainty.

Is there an association between hospital staffing levels and inpatient-COVID-19 mortality rates?

OBJECTIVE: This study aims to investigate the relationship between RNs and hospital-based medical specialties staffing levels with inpatient COVID-19 mortality rates. METHODS: We relied on data from AHA Annual Survey Database, Area Health Resource File, and UnitedHealth Group Clinical Discovery Database. In phase 1 of the analysis, we estimated the risk-standardized event rates (RSERs) based on 95,915 patients in the UnitedHealth Group Database 1,398 hospitals. We then used beta regression to analyze the association between hospital- and county- level factors with risk-standardized inpatient COVID-19 mortality rates from March 1, 2020, through December 31, 2020. RESULTS: Higher staffing levels of RNs and emergency medicine physicians were associated with lower COVID-19 mortality rates. Moreover, larger teaching hospitals located in urban settings had higher COVID-19 mortality rates. Finally, counties with greater social vulnerability, specifically in terms of housing type and transportation, and those with high infection rates had the worst patient mortality rates. CONCLUSION: Higher staffing levels are associated with lower inpatient mortality rates for COVID-19 patients. More research is needed to determine appropriate staffing levels and how staffing levels interact with other factors such as teams, leadership, and culture to impact patient care during pandemics.

Determinants of panic buying during COVID-19: causes and consequences

Purpose Despite the abundant literature on panic buying during COVID-19 pandemic, the several causes and consequences of panic buying have been enormously ignored. The purpose of this study is to emphasize the consumer's behavior during the COVID-19 pandemic and illustrate the comprehensive theoretical model of consumers' panic buying to investigate its causes and consequences in a developing country empirically to uncover this gap. Design/methodology/approach The authors collected data from 419 households of all socioeconomic classes of Bangladesh. A hierarchical regression model analyzed the data. Findings This study finds that internal and external factors such as rumors, government strategies, fear and anxiety and health security significantly affect consumers' panic buying behaviors. This finding supports some theories of human behavior. This study also finds that panic buying has internal and external consequences such as price hike, shortage of supply of products, dissatisfaction of consumers and increase in utility (benefit) of the products but not on consumer's budget. This finding supports as well as contradicts some established theories of human and consumer behavior. Originality/value This study proves that panic buying cannot help the consumers and they are the ultimate sufferers of this. The findings of this study will help the government, media, suppliers and consumers to interact properly to maintain panic buying during a pandemic crisis. Giving a holistic explanation of the causes and consequences of panic buying by introducing some novel variables is a momentous strength of this study.

Distributed learning for heterogeneous clinical data with application to integrating COVID-19 data across 230 sites.

Integrating real-world data (RWD) from several clinical sites offers great opportunities to improve estimation with a more general population compared to analyses based on a single clinical site. However, sharing patient-level data across sites is practically challenging due to concerns about maintaining patient privacy. We develop a distributed algorithm to integrate heterogeneous RWD from multiple clinical sites without sharing patient-level data. The proposed distributed conditional logistic regression (dCLR) algorithm can effectively account for between-site heterogeneity and requires only one round of communication. Our simulation study and data application with the data of 14,215 COVID-19 patients from 230 clinical sites in the UnitedHealth Group Clinical Research Database demonstrate that the proposed distributed algorithm provides an estimator that is robust to heterogeneity in event rates when efficiently integrating data from multiple clinical sites. Our algorithm is therefore a practical alternative to both meta-analysis and existing distributed algorithms for modeling heterogeneous multi-site binary outcomes.

DLMM as a lossless one-shot algorithm for collaborative multi-site distributed linear mixed models.

Linear mixed models are commonly used in healthcare-based association analyses for analyzing multi-site data with heterogeneous site-specific random effects. Due to regulations for protecting patients' privacy, sensitive individual patient data (IPD) typically cannot be shared across sites. We propose an algorithm for fitting distributed linear mixed models (DLMMs) without sharing IPD across sites. This algorithm achieves results identical to those achieved using pooled IPD from multiple sites (i.e., the same effect size and standard error estimates), hence demonstrating the lossless property. The algorithm requires each site to contribute minimal aggregated data in only one round of communication. We demonstrate the lossless property of the proposed DLMM algorithm by investigating the associations between demographic and clinical characteristics and length of hospital stay in COVID-19 patients using administrative claims from the UnitedHealth Group Clinical Discovery Database. We extend this association study by incorporating 120,609 COVID-19 patients from 11 collaborative data sources worldwide.

Patient and Hospital Factors Associated With Differences in Mortality Rates Among Black and White US Medicare Beneficiaries Hospitalized With COVID-19 Infection.

Importance: Black patients hospitalized with COVID-19 may have worse outcomes than White patients because of excess individual risk or because Black patients are disproportionately cared for in hospitals with worse outcomes for all. Objectives: To examine differences in COVID-19 hospital mortality rates between Black and White patients and to assess whether the mortality rates reflect differences in patient characteristics by race or by the hospitals to which Black and White patients are admitted. Design, Setting, and Participants: This cohort study assessed Medicare beneficiaries admitted with a diagnosis of COVID-19 to 1188 US hospitals from January 1, 2020, through September 21, 2020. Exposure: Hospital admission for a diagnosis of COVID-19. Main Outcomes and Measures: The primary composite outcome was inpatient death or discharge to hospice within 30 days of admission. We estimated the association of patient-level characteristics (including age, sex, zip code-level income, comorbidities, admission from a nursing facility, and days since January 1, 2020) with differences in mortality or discharge to hospice among Black and White patients. To examine the association with the hospital itself, we adjusted for the specific hospitals to which patients were admitted. We used simulation modeling to estimate the mortality among Black patients had they instead been admitted to the hospitals where White patients were admitted. Results: Of the 44 217 Medicare beneficiaries included in the study, 24 281 (55%) were women; mean (SD) age was 76.3 (10.5) years; 33 459 participants (76%) were White, and 10 758 (24%) were Black. Overall, 2634 (8%) White patients and 1100 (10%) Black patients died as inpatients, and 1670 (5%) White patients and 350 (3%) Black patients were discharged to hospice within 30 days of hospitalization, for a total mortality-equivalent rate of 12.86% for White patients and 13.48% for Black patients. Black patients had similar odds of dying or being discharged to hospice (odds ratio [OR], 1.06; 95% CI, 0.99-1.12) in an unadjusted comparison with White patients. After adjustment for clinical and sociodemographic patient characteristics, Black patients were more likely to die or be discharged to hospice (OR, 1.11; 95% CI, 1.03-1.19). This difference became indistinguishable when adjustment was made for the hospitals where care was delivered (odds ratio, 1.02; 95% CI, 0.94-1.10). In simulations, if Black patients in this sample were instead admitted to the same hospitals as White patients in the same distribution, their rate of mortality or discharge to hospice would decline from the observed rate of 13.48% to the simulated rate of 12.23% (95% CI for difference, 1.20%-1.30%). Conclusions and Relevance: This cohort study found that Black patients hospitalized with COVID-19 had higher rates of hospital mortality or discharge to hospice than White patients after adjustment for the personal characteristics of those patients. However, those differences were explained by differences in the hospitals to which Black and White patients were admitted.

Variation in US Hospital Mortality Rates for Patients Admitted With COVID-19 During the First 6 Months of the Pandemic.

Importance: It is unknown how much the mortality of patients with coronavirus disease 2019 (COVID-19) depends on the hospital that cares for them, and whether COVID-19 hospital mortality rates are improving. Objective: To identify variation in COVID-19 mortality rates and how those rates have changed over the first months of the pandemic. Design, Setting, and Participants: This cohort study assessed 38 517 adults who were admitted with COVID-19 to 955 US hospitals from January 1, 2020, to June 30, 2020, and a subset of 27 801 adults (72.2%) who were admitted to 398 of these hospitals that treated at least 10 patients with COVID-19 during 2 periods (January 1 to April 30, 2020, and May 1 to June 30, 2020). Exposures: Hospital characteristics, including size, the number of intensive care unit beds, academic and profit status, hospital setting, and regional characteristics, including COVID-19 case burden. Main Outcomes and Measures: The primary outcome was the hospital's risk-standardized event rate (RSER) of 30-day in-hospital mortality or referral to hospice adjusted for patient-level characteristics, including demographic data, comorbidities, community or nursing facility admission source, and time since January 1, 2020. We examined whether hospital characteristics were associated with RSERs or their change over time. Results: The mean (SD) age among participants (18 888 men [49.0%]) was 70.2 (15.5) years. The mean (SD) hospital-level RSER for the 955 hospitals was 11.8% (2.5%). The mean RSER in the worst-performing quintile of hospitals was 15.65% compared with 9.06% in the best-performing quintile (absolute difference, 6.59 percentage points; 95% CI, 6.38%-6.80%; P < .001). Mean RSERs in all but 1 of the 398 hospitals improved; 376 (94%) improved by at least 25%. The overall mean (SD) RSER declined from 16.6% (4.0%) to 9.3% (2.1%). The absolute difference in rates of mortality or referral to hospice between the worst- and best-performing quintiles of hospitals decreased from 10.54 percentage points (95% CI, 10.03%-11.05%; P < .001) to 5.59 percentage points (95% CI, 5.33%-5.86%; P < .001). Higher county-level COVID-19 case rates were associated with worse RSERs, and case rate declines were associated with improvement in RSERs. Conclusions and Relevance: Over the first months of the pandemic, COVID-19 mortality rates in this cohort of US hospitals declined. Hospitals did better when the prevalence of COVID-19 in their surrounding communities was lower.

'POLIDON' Approach-A Novel Solution for the ENT & Skull Base Surgeons in COVID-19 era.

Health care providers (HCP) of ENT and Skull base surgery are highly vulnerable and mostly infected by novel coronavirus as they have to examine and perform procedures directly in oral cavity, oropharynx, nose, nasopharynx, where coronavirus remains in plenty. ENT & Skull base surgeons need to do several aerosol generating procedures (AGP). Most of the endoscopic and microscopic ENT & skull base surgery are AGP; like-mastoid surgery, sinus surgery, surgery of pituitary, tympanomastoid paraganglioma, temporal bone malignancy, tracheostomy etc. All of we know, COVID negative by RT-PCR test is not always COVID negative. In COVID-19 pandemic-routine, even cancer surgeries are avoided or postponed for the sake of safety of HCPs. Moreover, in case of surgical emergency there's no way to refuse a patient for not having a report of COVID test. We thought about neutralizing or destroying the novel coronavirus from it's route of entry zone, as well as preventing aerosol to be transmitted in the air of OT. We designed a novel approach, i.e. 'POLIDON' (POLIDON = Polythene + Povidone Iodine), which can be the solution for these patients as well as surgeons or HCPs of above mentioned specialties. Use of Povidone Iodine as mouthwash and nasal spray or irrigation for both patient and HCPs prior to surgery is proposed. Then, use of simple polythene as barrier drape of patient or operative area for prevention of spread of aerosol in OT during surgery is the other component. With the POLIDON' approach-all these ENT & skull base surgeries can be done with more safety and confidence.