Browse
Submit Data
Databases
API
Help

Dataset Information

11 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

A flexible framework for intervention analysis applied to credit-card usage during the coronavirus pandemic.

ABSTRACT: We develop a variant of intervention analysis designed to measure a change in the law of motion for the distribution of individuals in a cross-section, rather than modeling the moments of the distribution. To calculate a counterfactual forecast, we discretize the distribution and employ a Markov model in which the transition probabilities are modeled as a multinomial logit distribution. Our approach is scalable and is designed to be applied to micro-level data. A wide panel often carries with it several imperfections that complicate the analysis when using traditional time-series methods; our framework accommodates these imperfections. The result is a framework rich enough to detect intervention effects that not only shift the mean, but also those that shift higher moments, while leaving lower moments unchanged. We apply this framework to document the changes in credit usage of consumers during the COVID-19 pandemic. We consider multinomial logit models of the dependence of credit card balances with categorical variables representing monthly seasonality, homeownership status, and credit scores. We find that, relative to our forecasts, consumers have greatly reduced their use of credit. This result holds for homeowners and renters as well as consumers with both high and low credit scores.

SUBMITTER: Ho ATY

PROVIDER: S-EPMC8748006 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Neural mechanisms of credit card spending.

Project description:Credit cards have often been blamed for consumer overspending and for the growth in household debt. Indeed, laboratory studies of purchase behavior have shown that credit cards can facilitate spending in ways that are difficult to justify on purely financial grounds. However, the psychological mechanisms behind this spending facilitation effect remain conjectural. A leading hypothesis is that credit cards reduce the pain of payment and so 'release the brakes' that hold expenditures in check. Alternatively, credit cards could provide a 'step on the gas,' increasing motivation to spend. Here we present the first evidence of differences in brain activation in the presence of real credit and cash purchase opportunities. In an fMRI shopping task, participants purchased items tailored to their interests, either by using a personal credit card or their own cash. Credit card purchases were associated with strong activation in the striatum, which coincided with onset of the credit card cue and was not related to product price. In contrast, reward network activation weakly predicted cash purchases, and only among relatively cheaper items. The presence of reward network activation differences highlights the potential neural impact of novel payment instruments in stimulating spending-these fundamental reward mechanisms could be exploited by new payment methods as we transition to a purely cashless society.

| S-EPMC7892835 | biostudies-literature

The COVID-19 shock and consumer credit: Evidence from credit card data.

Project description:We study the dynamic effect of the COVID-19 shock on credit card use in 2020. Local case incidence had a strong negative effect on credit card spending in the early months of the pandemic, which diminished over time. This time-varying pattern was driven by the fear of the virus, rather than government support programs, consistent with the "pandemic fatigue" of consumers. Local pandemic severity also had a strong effect on credit card repayments. These spending and repayment effects offset each other, resulting in no effect on credit card borrowing, consistent with credit-smoothing behavior. The local stringency of nonpharmaceutical interventions also had a negative effect on spending and repayments, albeit smaller in magnitude. We conclude that the pandemic itself was a more important driver of changes in credit card use than the public health policy response.

| S-EPMC10122565 | biostudies-literature

Delayed Colorectal Cancer Care During Coronavirus Disease (COVID-19) Pandemic (DECOR-19)

Project description:To understand and analyse the global impact of COVID-19 on outpatient services, inpatient care, elective surgery, and perioperative colorectal cancer care, a DElayed COloRectal cancer surgery (DECOR-19) survey was conducted in collaboration with numerous international colorectal societies with the objective of obtaining several learning points from the impact of the COVID-19 outbreak on our colorectal cancer patients which will assist us in the ongoing management of our colorectal cancer patients and to provide us safe oncological pathways for future outbreaks.

| 2346925 | ecrin-mdr-crc

Credit card fraud detection using a hierarchical behavior-knowledge space model.

Project description:With the advancement in machine learning, researchers continue to devise and implement effective intelligent methods for fraud detection in the financial sector. Indeed, credit card fraud leads to billions of dollars in losses for merchants every year. In this paper, a multi-classifier framework is designed to address the challenges of credit card fraud detections. An ensemble model with multiple machine learning classification algorithms is designed, in which the Behavior-Knowledge Space (BKS) is leveraged to combine the predictions from multiple classifiers. To ascertain the effectiveness of the developed ensemble model, publicly available data sets as well as real financial records are employed for performance evaluations. Through statistical tests, the results positively indicate the effectiveness of the developed model as compared with the commonly used majority voting method for combination of predictions from multiple classifiers in tackling noisy data classification as well as credit card fraud detection problems.

| S-EPMC8775357 | biostudies-literature

Medication adherence and complementary therapy usage in inflammatory bowel disease patients during the coronavirus disease 2019 pandemic.

Project description:Background and aimMedication nonadherence is common in patients with inflammatory bowel disease (IBD) and has been associated with worse outcomes. The coronavirus disease 2019 (COVID-19) pandemic led to significant consumer and medical concern regarding the possible risks of immunosuppressive medications during the pandemic. This study aimed to examine medication adherence and complementary and alternative medicine (CAM) usage during the COVID-19 pandemic.MethodsAn online survey was sent to patients from two tertiary IBD units. The survey included medication nonadherence attributed to the COVID-19 pandemic, complementary therapy, and IBD medication use. Validated measures of IBD disease activity, medication adherence, and beliefs about medicines were obtained.ResultsOf 262 respondents (median age of 46, 58% female) 14 (5%) patients reported self-initiated missed doses or dose reduction of IBD medications directly attributed to the COVID-19 pandemic. Positive associations with medication nonadherence included current corticosteroid requirement (P = 0.022), higher disease activity scores (P = 0.026), and higher concern about medicines score (P = 0.04). CAM usage was common, aimed at treating mental health in most cases, and infrequently attributed to the COVID-19 pandemic.ConclusionsEven in the setting of low COVID-19 prevalence, the pandemic reduced IBD medication adherence in 1 in 20 patients. This reduced adherence was co-associated with increased disease activity and corticosteroid use. Understanding the underlying beliefs driving suboptimal IBD medication adherence is critical to prevent avoidable adverse IBD outcomes.

| S-EPMC8114986 | biostudies-literature

Sequences of purchases in credit card data reveal lifestyles in urban populations.

Project description:Zipf-like distributions characterize a wide set of phenomena in physics, biology, economics, and social sciences. In human activities, Zipf's law describes, for example, the frequency of appearance of words in a text or the purchase types in shopping patterns. In the latter, the uneven distribution of transaction types is bound with the temporal sequences of purchases of individual choices. In this work, we define a framework using a text compression technique on the sequences of credit card purchases to detect ubiquitous patterns of collective behavior. Clustering the consumers by their similarity in purchase sequences, we detect five consumer groups. Remarkably, post checking, individuals in each group are also similar in their age, total expenditure, gender, and the diversity of their social and mobility networks extracted from their mobile phone records. By properly deconstructing transaction data with Zipf-like distributions, this method uncovers sets of significant sequences that reveal insights on collective human behavior.

| S-EPMC6102281 | biostudies-literature

An Adaptive Approach on Credit Card Fraud Detection Using Transaction Aggregation and Word Embeddings

Project description:Due to the surge of interest in online retailing, the use of credit cards has been rapidly expanded in recent years. Stealing the card details to perform online transactions, which is called fraud, has also seen more frequently. Preventive solutions and instant fraud detection methods are widely studied due to critical financial losses in many industries. In this work, a Gradient Boosting Tree (GBT) model for the real-time detection of credit card frauds on the streaming Card-Not-Present (CNP) transactions is investigated with the use of different attributes of card transactions. Numerical, hand-crafted numerical, categorical and textual attributes are combined to form a feature vector to be used as a training instance. One of the contributions of this work is to employ transaction aggregation for the categorical values and inclusion of vectors from a character level word embedding model which is trained on the merchant names of the transactions. The other contribution is introducing a new strategy for training dataset generation employing the sliding window approach in a given time frame to adapt to the changes on the trends of fraudulent transactions. In the experiments, the feature engineering strategy and the automated training set generation methodology are evaluated on the real credit card transactions.

| S-EPMC7256395 | biostudies-literature

A Crisis-Responsive Framework for Medical Device Development Applied to the COVID-19 Pandemic.

Project description:The disruption of conventional manufacturing, supply, and distribution channels during the COVID-19 pandemic caused widespread shortages in personal protective equipment (PPE) and other medical supplies. These shortages catalyzed local efforts to use nontraditional, rapid manufacturing to meet urgent healthcare needs. Here we present a crisis-responsive design framework designed to assist with product development under pandemic conditions. The framework emphasizes stakeholder engagement, comprehensive but efficient needs assessment, rapid manufacturing, and modified product testing to enable accelerated development of healthcare products. We contrast this framework with traditional medical device manufacturing that proceeds at a more deliberate pace, discuss strengths and weakness of pandemic-responsive fabrication, and consider relevant regulatory policies. We highlight the use of the crisis-responsive framework in a case study of face shield design and production for a large US academic hospital. Finally, we make recommendations aimed at improving future resilience to pandemics and healthcare emergencies. These include continued development of open source designs suitable for rapid manufacturing, education of maker communities and hospital administrators about rapidly-manufactured medical devices, and changes in regulatory policy that help strike a balance between quality and innovation.

| S-EPMC8064560 | biostudies-literature

An integrated data framework for policy guidance during the coronavirus pandemic: Towards real-time decision support for economic policymakers.

Project description:Usually, official and survey-based statistics guide policymakers in their choice of response instruments to economic crises. However, in an early phase, after a sudden and unforeseen shock has caused unexpected and fast-changing dynamics, data from traditional statistics are only available with non-negligible time delays. This leaves policymakers uncertain about how to most effectively manage their economic countermeasures to support businesses, especially when they need to respond quickly, as in the COVID-19 pandemic. Given this information deficit, we propose a framework that guided policymakers throughout all stages of this unforeseen economic shock by providing timely and reliable sources of firm-level data as a basis to make informed policy decisions. We do so by combining early stage 'ad hoc' web analyses, 'follow-up' business surveys, and 'retrospective' analyses of firm outcomes. A particular focus of our framework is on assessing the early effects of the pandemic, using highly dynamic and large-scale data from corporate websites. Most notably, we show that textual references to the coronavirus pandemic published on a large sample of company websites and state-of-the-art text analysis methods allowed to capture the heterogeneity of the pandemic's effects at a very early stage and entailed a leading indication on later movements in firm credit ratings. While the proposed framework is specific to the COVID-19 pandemic, the integration of results obtained from real-time online sources in the design of subsequent surveys and their value in forecasting firm-level outcomes typically targeted by policy measures, is a first step towards a more timely and holistic approach for policy guidance in times of economic shocks.

| S-EPMC8843231 | biostudies-literature

Barriers to emergency department usage during the COVID-19 pandemic.

Project description:ObjectiveThe objective of this study was to determine the public's likelihood of being willing to use an emergency department (ED) for urgent/emergent illness during the coronavirus disease 2019 (COVID-19) pandemic.MethodsAn institutional review board-approved, cross-sectional survey of a non-probability sample from Amazon Mechanical Turk was administered May 24-25, 2020. Change in self-reported willingness to use an ED before and during the pandemic (primary outcome) was assessed via McNemar's test; COVID-19 knowledge and perceptions were secondary outcomes.ResultsThere were 855 survey participants (466 [54.5%] male; 699 [81.8%] White; median age 39). Proportion reporting likelihood to use the ED pre-pandemic (71% [604/855]) decreased significantly during the pandemic (49% [417/855]; P < 0.001); those unlikely to visit the ED increased significantly during the pandemic (41% [347/855] vs 22% [417/855], P < 0.001). Participants were unlikely to use the ED during the pandemic if they were unlikely to use it pre-pandemic (adjusted odds ratio, 4.55; 95% confidence interval, 3.09-6.7) or correctly answered more COVID-19 knowledge questions (adjusted odds ratio, 1.37; 95% confidence interval, 1.17-1.60). Furthermore, 23.4% (n = 200) of respondents believed the pandemic was not a serious threat to society. Respondents with higher COVID-19 knowledge scores were more likely to view the pandemic as serious (odds ratio, 1.57; 95% confidence interval, 1.36-1.82).ConclusionsThis survey study investigated the public's willingness to use the ED during the COVID-19 pandemic. Only 49% of survey respondents were willing to visit the ED during a pandemic if they felt ill compared with 71% before the pandemic.

| S-EPMC7771795 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data