Identifying unobserved heterogeneity in mobile wallet adoption – A FIMIX-PLS approach for user segmentation

Purpose>The purpose of this study is to segment mobile wallet users using a finite mixture partial least squares (FIMIX-PLS) approach and evaluate the unobserved heterogeneity across segments.Design/methodology/approach>Partial least square structural equation modeling (PLS-SEM) using a convenience sample of 744 responses was used to analyze the measurement, structural model and hypotheses testing. To examine unobserved heterogeneity and identify user segments, FIMIX-PLS technique was employed. To generate more precise recommendations, importance-performance map analysis (IPMA) was performed with attitude as the target variable.Findings>A structural equation model revealed that except perceived ease of use (PEOU) all other dimensions, namely perceived usefulness (PU), lifestyle compatibility (LC), facilitating conditions (FC), trust and security significantly influences attitude which, in turn, determines intention. The FIMIX-PLS technique resulted in four segments – The Rationalist, Early Adopters, Late Adopters and The Innovators.Practical implications>The paper provides segment specific and between segment differences to derive implications. Identification of relevant predictors and segments will help academicians, marketing researchers and practitioners in gaining further understanding of the mobile wallet adoption. The findings of the paper can guide mobile wallet providers to frame appropriate strategies and offerings pertaining to the obtained segments.Originality/value>The paper builds upon Technology Acceptance Model (TAM) to propose an integrated model to explain adoption behaviors associated with mobile wallet. To the best of the authors' knowledge, this is one of the first empirical attempts using FIMIX-PLS technique to assess precursors of adoption and substantiates the perceived value-attitude-intention linkage to identify heterogeneity among mobile wallet users.

Do Precautionary Corporate Cash Holdings Help During the Economic Shocks? Evidence from the Covid-19 Pandemic

The present study examines the validity of precautionary motive for cash holdings during various phases of the Covid-19 pandemic by establishing a causal relationship between firms? cash holdings and their abnormal returns. Considering the Covid-19 outbreak as an exogenous event, listed Indian firms? cumulative abnormal returns regressed on their pre-pandemic cash holdings and relevant firm-level control variables. Results indicate that during the early outbreak period of the pandemic, investors continue to value firms? growth opportunities over the cash holdings. However, during the lockdown period which had a severe overall negative impact on the firms, firms with higher cash holdings generate significant positive abnormal returns. This supports the precautionary motive behind the cash holdings. During the economic recovery phase, firms with volatile earnings struggle to rebound. The agency cost perspective of cash holdings was found irrelevant during the severe economic crisis. From the risk-management perspective, findings also highlight the difficulty in estimating the optimal cash levels incorporating the black swan events like the Covid-19 pandemic.

The Impact of Androgen Deprivation Therapy on COVID-19 Illness in Men With Prostate Cancer.

BACKGROUND: TMPRSS2, a cell surface protease regulated by androgens and commonly upregulated in prostate cancer (PCa), is a necessary component for SARS-CoV-2 viral entry into respiratory epithelial cells. Previous reports suggested a lower risk of SARS-CoV-2 among PCa patients on androgen deprivation therapy (ADT). However, the impact of ADT on severe COVID-19 illness is poorly understood. METHODS: We performed a multicenter study across 7 US medical centers and evaluated patients with PCa and SARS-CoV-2 detected by polymerase-chain-reaction between March 1, 2020, and May 31, 2020. PCa patients were considered on ADT if they had received appropriate ADT treatment within 6 months of COVID-19 diagnosis. We used multivariable logistic and Cox proportional-hazard regression models for analysis. All statistical tests were 2-sided. RESULTS: We identified 465 PCa patients (median age = 71 years) with a median follow-up of 60 days. Age, body mass index, cardiovascular comorbidity, and PCa clinical disease state adjusted overall survival (hazard ratio [HR] = 1.16, 95% confidence interval [CI] = 0.68 to 1.98, P = .59), hospitalization status (HR = 0.96, 95% CI = 0.52 to 1.77, P = .90), supplemental oxygenation (HR 1.14, 95% CI = 0.66 to 1.99, P = .64), and use of mechanical ventilation (HR = 0.81, 95% CI = 0.25 to 2.66, P = .73) were similar between ADT and non-ADT cohorts. Similarly, the addition of androgen receptor-directed therapy within 30 days of COVID-19 diagnosis to ADT vs ADT alone did not statistically significantly affect overall survival (androgen receptor-directed therapy: HR = 1.27, 95% CI = 0.69 to 2.32, P = .44). CONCLUSIONS: In this retrospective cohort of PCa patients, the use of ADT was not demonstrated to influence severe COVID-19 outcomes, as defined by hospitalization, supplemental oxygen use, or death. Age 70 years and older was statistically significantly associated with a higher risk of developing severe COVID-19 disease.

Post-anticoagulant D-dimer is a highly prognostic biomarker of COVID-19 mortality.

Clinical biomarkers that accurately predict mortality are needed for the effective management of patients with severe coronavirus disease 2019 (COVID-19) illness. In this study, we determine whether changes in D-dimer levels after anticoagulation are independently predictive of in-hospital mortality. Adult patients hospitalised for severe COVID-19 who received therapeutic anticoagulation for thromboprophylaxis were identified from a large COVID-19 database of the Mount Sinai Health System in New York City (NY, USA). We studied the ability of post-anticoagulant D-dimer levels to predict in-hospital mortality, while taking into consideration 65 other clinically important covariates including patient demographics, comorbidities, vital signs and several laboratory tests. 1835 adult patients with PCR-confirmed COVID-19 who received therapeutic anticoagulation during hospitalisation were included. Overall, 26% of patients died in the hospital. Significantly different in-hospital mortality rates were observed in patient groups based on mean D-dimer levels and trend following anticoagulation: 49% for the high mean-increase trend group; 27% for the high-decrease group; 21% for the low-increase group; and 9% for the low-decrease group (p<0.001). Using penalised logistic regression models to simultaneously analyse 67 clinical variables, the high increase (adjusted odds ratios (ORadj): 6.58, 95% CI 3.81-11.16), low increase (ORadj: 4.06, 95% CI 2.23-7.38) and high decrease (ORadj: 2.37; 95% CI 1.37-4.09) D-dimer groups (reference: low decrease group) had the highest odds for in-hospital mortality among all clinical features. Changes in D-dimer levels and trend following anticoagulation are highly predictive of in-hospital mortality and may help guide resource allocation and future studies of emerging treatments for severe COVID-19.

Pre-COVID conventional offline teaching v/s intra-COVID online teaching: A descriptive map of preference patterns among first year M.B.B.S students

Introduction: Medical education today is equipped with an armamentarium of newer online-based methods of correspondence courses, computerized virtual patient simulation, many open online courses in medical sciences, and tele-learning. The sudden, unplanned change from conventional teaching to online teaching during COVID-19 poses unique challenges and opportunities for teachers and learners, both. Many themes and principles have emerged for good online teaching learning and assessment practices (GOTLAP). Materials and Methods: The present study, involving 392 MBBS first year students from two universities, was conducted with an aim of comparing students' perception regarding online and offline teaching methodology, and online v/s offline method of assessment and to recommend the principles of GOTLAP. Data collected were analyzed by Strength, Weakness, Opportunity, and Threat (SWOT) analysis to provide a focused measure on how students perceive the program of online teaching and assessment. Results: In the present study, majority of the students (approximately 49.6%) have shown preference for offline teaching methodology, 22.9% has shown similar preference for both methods, while 27.5% has shown preference for the offline teaching method. SWOT analysis applied on qualitative data is a useful tool for assessing our present status in online learning and laying a ground work for formulating GOTLAP and a plan of future strategy. Conclusions: The GOTLAP principles can effectively pave way for the incorporation of blended learning (currently underutilized) in undergraduate medical education.

Development and validation of a machine learning-based prediction model for near-term in-hospital mortality among patients with COVID-19.

OBJECTIVES: To develop and validate a model for prediction of near-term in-hospital mortality among patients with COVID-19 by application of a machine learning (ML) algorithm on time-series inpatient data from electronic health records. METHODS: A cohort comprised of 567 patients with COVID-19 at a large acute care healthcare system between 10 February 2020 and 7 April 2020 observed until either death or discharge. Random forest (RF) model was developed on randomly drawn 70% of the cohort (training set) and its performance was evaluated on the rest of 30% (the test set). The outcome variable was in-hospital mortality within 20-84 hours from the time of prediction. Input features included patients' vital signs, laboratory data and ECG results. RESULTS: Patients had a median age of 60.2 years (IQR 26.2 years); 54.1% were men. In-hospital mortality rate was 17.0% and overall median time to death was 6.5 days (range 1.3-23.0 days). In the test set, the RF classifier yielded a sensitivity of 87.8% (95% CI: 78.2% to 94.3%), specificity of 60.6% (95% CI: 55.2% to 65.8%), accuracy of 65.5% (95% CI: 60.7% to 70.0%), area under the receiver operating characteristic curve of 85.5% (95% CI: 80.8% to 90.2%) and area under the precision recall curve of 64.4% (95% CI: 53.5% to 75.3%). CONCLUSIONS: Our ML-based approach can be used to analyse electronic health record data and reliably predict near-term mortality prediction. Using such a model in hospitals could help improve care, thereby better aligning clinical decisions with prognosis in critically ill patients with COVID-19.

Using Machine Learning to Predict ICU Transfer in Hospitalized COVID-19 Patients.

OBJECTIVES: Approximately 20-30% of patients with COVID-19 require hospitalization, and 5-12% may require critical care in an intensive care unit (ICU). A rapid surge in cases of severe COVID-19 will lead to a corresponding surge in demand for ICU care. Because of constraints on resources, frontline healthcare workers may be unable to provide the frequent monitoring and assessment required for all patients at high risk of clinical deterioration. We developed a machine learning-based risk prioritization tool that predicts ICU transfer within 24 h, seeking to facilitate efficient use of care providers' efforts and help hospitals plan their flow of operations. METHODS: A retrospective cohort was comprised of non-ICU COVID-19 admissions at a large acute care health system between 26 February and 18 April 2020. Time series data, including vital signs, nursing assessments, laboratory data, and electrocardiograms, were used as input variables for training a random forest (RF) model. The cohort was randomly split (70:30) into training and test sets. The RF model was trained using 10-fold cross-validation on the training set, and its predictive performance on the test set was then evaluated. RESULTS: The cohort consisted of 1987 unique patients diagnosed with COVID-19 and admitted to non-ICU units of the hospital. The median time to ICU transfer was 2.45 days from the time of admission. Compared to actual admissions, the tool had 72.8% (95% CI: 63.2-81.1%) sensitivity, 76.3% (95% CI: 74.7-77.9%) specificity, 76.2% (95% CI: 74.6-77.7%) accuracy, and 79.9% (95% CI: 75.2-84.6%) area under the receiver operating characteristics curve. CONCLUSIONS: A ML-based prediction model can be used as a screening tool to identify patients at risk of imminent ICU transfer within 24 h. This tool could improve the management of hospital resources and patient-throughput planning, thus delivering more effective care to patients hospitalized with COVID-19.