Visualising the Optimistic, Realistic, and Pessimistic Financial Distress Outlooks for Airport Operations in Malaysia

This paper aims to visualise three financial distress outlooks using computer simulations. The financial distress exposure for airport operations in Malaysia between 1991 and 2021 is given by Altman Z”-score and modelled by the multivariate generalized linear model (MGLM). Seven determinants contributing to the financial distress from literature are examined. The determinant series are fitted individually by using linear model with time series components and autoregressive integrated moving average models to forecast values for the next 10 financial years. Future short- to long-term memory effects following COVID-19 are apparent in time series plots. In the simulations, the MGLM procedure utilised Gaussian, gamma, and Cauchy probability distributions associated with expectations and challenges of doing business as well as uncertainties in the economy. The underlying trends of realistic, optimistic, and pessimistic financial distress outlooks insinuate that the increasing risk of financial distress of airport operations in Malaysia is expected to continue for the next decade.

Investigating the impact of CO2emissions on the COVID-19 pandemic by generalized linear mixed model approach with inverse Gaussian and gamma distributions

Carbon dioxide (CO2) rate within the atmosphere has been rising for decades due to human activities especially due to usage of fuel types such as coal, cement, flaring, gas, oil, etc. Especially in 2020, COVID-19 pandemic caused major economic, production, and energy crises all around the world. As a result of this situation, there was a sharp decrease in the global CO2 emissions depending on the fuel types used during this pandemic. The aim of this study was to explore the effects of "CO2 emissions due to the fuel types"on "percentage of deaths in total cases"attributed to the COVID-19 pandemic using generalized linear model and generalized linear mixed model (GLMM) approaches with inverse Gaussian and gamma distributions, and also to obtain global statistical inferences about 169 World Health Organization member countries that will disclose the impact of the CO2 emissions due to the fuel types during this pandemic. The response variable is taken as "percentage of deaths in total cases attributed to the COVID-19 pandemic"calculated as "(total deaths/total confirmed cases attributed to the COVID-19 pandemic until December 31, 2020)∗100."The explanatory variables are taken as "production-based emissions of CO2 from different fuel types,"measured in tonnes per person, which are "coal, cement, flaring, gas, and oil."As a result of this study, according to the goodness-of-fit test statistics, "GLMM approach with gamma distribution"called "gamma mixed regression model"is determined as the most appropriate statistical model for investigating the impact of CO2 emissions on the COVID-19 pandemic. As the main findings of this study, 1 t CO2 emissions belonging to the fuel types "cement, coal, flaring, gas, and oil"per person cause increase in deaths in total cases attributed to the COVID-19 pandemic by 2.8919, 2.6151, 2.5116, 2.5774, and 2.5640%, respectively. © 2023 the author(s), published by De Gruyter.

Road crashes in Adelaide metropolitan region, the consequences of COVID-19.

Background: Many countries instituted lockdown rules as the COVID-19 pandemic progressed, however, the effects of COVID-19 on transportation safety vary widely across countries and regions. In several situations, it has been shown that although the COVID-19 closure has decreased average traffic flow, it has also led to an increase in speeding, which will indeed increase the severity of crashes and the number of fatalities and serious injuries. Methods: At the local level, Generalized linear Mixed (GLM) modelling is used to look at how often road crashes changed in the Adelaide metropolitan area before and after the COVID-19 pandemic. The Geographically Weighted Generalized Linear Model (GWGLM) is also used to explore how the association between the number of crashes and the factors that explain them varies across census blocks. Using both no-spatial and spatial models, the effects of urban structure elements like land use mix, road network design, distance to CBD, and proximity to public transit on the frequency of crashes at the local level were studied. Results: This research showed that lockdown orders led to a mild reduction (approximately 7%) in crash frequency. However, this decrease, which has occurred mostly during the first three months of the lockdown, has not systematically alleviated traffic safety risks in the Greater Adelaide Metropolitan Area. Crash hotspots shifted from areas adjacent to workplaces and education centres to green spaces and city fringes, while crash incidence periods switched from weekdays to weekends and winter to summer. Implications: The outcomes of this research provided insights into the impact of shifting driving behaviour on safety during disorderly catastrophes such as COVID-19.

Variation of Air Quality in Four Districts of Lima in the Period of the COVID-19 Pandemic

The purpose of the study was to evaluate the variation of air quality parameters: PM10, PM2.5, NO2, and O3 in four districts of Lima-Peru (Carabayllo, San Juan de Lurigancho, Villa María del Triunfo, and Jesús María) in the period 2015-2019 and 2020-2021. Likewise, the ozone variability in the Carabayllo district was modelled. Pollutant concentration data were collected from the National Service of Meteorology and Hydrology of Peru (SENAMHI) from the 4 stations located in the aforementioned districts. The data was processed with the IBM SPSS Statistics v.25 software. A statistically significant decrease was observed between the 2015-2019 and 2020-2021 periods in pollutants PM10, PM2.5, and NO2, in the four monitoring stations, mainly because the country entered in a state of emergency (quarantine due to COVID -19). However, an increase in O3 was observed, attributed to the decrease in NOX concentrations. Finally, the gamma generalized linear model represented 87.6% of the ozone variability in the Carabayllo district, showing a good fit for the field data. © 2022 IEEE.

Factors Associated with Mortality in Patients Hospitalized with COVID-19 Using Generalized Linear Model (Ordinal Logistic Regression) in North of Iran

Background and purpose: Controlling the severity and death of the COVID-19 disease is still a major challenge. This research aimed at identifying the factors associated with mortality in hospitalized patients with COVID-19 applying generalized linear model. Materials and methods: In this cross-sectional study, demographic and clinical data of COVID-19 patients hospitalized with positive RT-PCR test results (n=6759) in Mazandaran hospitals (August 2019) were obtained from the national registration system for COVID-19. SPSS V27 and R V4.0 were used for data analyses and multivariate generalized linear model test with an ordinal logistic scale was applied. Results: Findings showed that full recovery and relative recovery occurred in 5888 (87.11%) and 400 (5.92%) patients, respectively. Mortality rate was 6.97%. The chance of death in patients with relative recovery (49.55%) compared with those with full recovery (21.61%) was almost 2.3 times higher. Predictors of mortality in these patients included age over 60 years (OR: 1.60), ICU admission (OR: 5.09), intubation (OR: 4.10), SpO2≤ 93% (OR: 2.41), cancer (OR: 1.74), diabetes (OR: 1.29), heart disease (OR: 1.41), and chronic kidney disease (OR: 2.17). Conclusion: Specific and timely medical care, considering the mentioned risk factors, should be introduced and provided to prevent mortality in patients hospitalized with COVID-19. © 2022, Mazandaran University of Medical Sciences. All rights reserved.

Forecasting Model Based on Lifestyle Risk and Health Factors to Predict COVID-19 Severity.

The COVID-19 pandemic has now spread worldwide, becoming a real global health emergency. The main goal of this work is to present a framework for studying the impact of COVID-19 on Italian territory during the first year of the pandemic. Our study was based on different kinds of health features and lifestyle risk factors and exploited the capabilities of machine learning techniques. Furthermore, we verified through our model how these factors influenced the severity of the pandemics. Using publicly available datasets provided by the Italian Civil Protection, Italian Ministry of Health and Italian National Statistical Institute, we cross-validated the regression performance of a Random Forest model over 21 Italian regions. The robustness of the predictions was assessed by comparison with two other state-of-the-art regression tools. Our results showed that the proposed models reached a good agreement with data. We found that the features strongly associated with the severity of COVID-19 in Italy are the people aged over 65 flu vaccinated (24.6%) together with individual lifestyle behaviors. These findings could shed more light on the clinical and physiological aspects of the disease.

CellDrift: inferring perturbation responses in temporally sampled single-cell data.

Cells and tissues respond to perturbations in multiple ways that can be sensitively reflected in the alterations of gene expression. Current approaches to finding and quantifying the effects of perturbations on cell-level responses over time disregard the temporal consistency of identifiable gene programs. To leverage the occurrence of these patterns for perturbation analyses, we developed CellDrift (https://github.com/KANG-BIOINFO/CellDrift), a generalized linear model-based functional data analysis method that is capable of identifying covarying temporal patterns of various cell types in response to perturbations. As compared to several other approaches, CellDrift demonstrated superior performance in the identification of temporally varied perturbation patterns and the ability to impute missing time points. We applied CellDrift to multiple longitudinal datasets, including COVID-19 disease progression and gastrointestinal tract development, and demonstrated its ability to identify specific gene programs associated with sequential biological processes, trajectories and outcomes.

The short-term effect of the government of Ghana's decision to open borders at the early-onset of the COVID-19 pandemic.

Non-Pharmaceutical Interventions (NPI) are used in public health to mitigate the risk and impact of epidemics or pandemics in the absence of medical or pharmaceutical solutions. Prior to the release of vaccines, COVID-19 control solely depended on NPIs. The Government of Ghana after assessing early NPIs introduced at the early stage of the pandemic began to ease some restrictions by the opening of international borders with isolation and quarantine measures enforced. It was argued by some experts that this was a hasty decision. In this study, we assessed the impact of the opening of borders to ascertain if this action caused a surge or otherwise in cases in the country. Using data from the database on Africa's records of COVID-19 from the John Hopkins University, the Generalized Linear Model (GLM) time-series regression model for count data was applied to study effects in Ghana during a 4-month and 8-month period post-opening of borders. The study showed that after the decision of the government to open international borders, Ghana's expected case count declined by 72.01 % in the 4-month period and 54.44 % in the 8-month period. This gives an indication of the gradual reversal of the gains made due to the early implementation of NPIs. Notably, this may not only be attributed to the opening of borders but the relaxation of the strict enforcement measures that were put in place at the onset of the pandemic in Ghana. There is therefore the need for continuous enforcement of intervention measures to reduce case counts, particularly with the emergence of new COVID-19 virus strains. The study provides some recommendations for policy and improvements in model building such as developing better data collection system in Ghana, investigating more control variables, estimating the decaying effect of interventions, and ensuring better preparations prior to easing of public health restrictions.

Autoregressive count data modeling on mobility patterns to predict cases of COVID-19 infection.

At the beginning of 2022 the global daily count of new cases of COVID-19 exceeded 3.2 million, a tripling of the historical peak value reported between the initial outbreak of the pandemic and the end of 2021. Aerosol transmission through interpersonal contact is the main cause of the disease's spread, although control measures have been put in place to reduce contact opportunities. Mobility pattern is a basic mechanism for understanding how people gather at a location and how long they stay there. Due to the inherent dependencies in disease transmission, models for associating mobility data with confirmed cases need to be individually designed for different regions and time periods. In this paper, we propose an autoregressive count data model under the framework of a generalized linear model to illustrate a process of model specification and selection. By evaluating a 14-day-ahead prediction from Sweden, the results showed that for a dense population region, using mobility data with a lag of 8 days is the most reliable way of predicting the number of confirmed cases in relative numbers at a high coverage rate. It is sufficient for both of the autoregressive terms, studied variable and conditional expectation, to take one day back. For sparsely populated regions, a lag of 10 days produced the lowest error in absolute value for the predictions, where weekly periodicity on the studied variable is recommended for use. Interventions were further included to identify the most relevant mobility categories. Statistical features were also presented to verify the model assumptions.

Impact of the COVID-19 pandemic on management of children and adolescents with Type 1 diabetes.

BACKGROUND: The coronavirus disease-2019 (COVID-19) pandemic had widespread impacts on the lives of parents and children. We determined how the pandemic affected Type 1 diabetes patients at a large urban pediatric teaching hospital. METHODS: We compared patient characteristics, glycemic control, PHQ-9 depression screen, in person and virtual outpatient encounters, hospitalizations and continuous glucose monitor (CGM) utilization in approximately 1600 patients in 1 year periods preceding and following the local imposition of COVID-related restrictions on 3/15/2020 ("2019" and "2020" groups, respectively). RESULTS: In a generalized linear model, increasing age, non-commercial insurance, Black and Hispanic race/ethnicity, and non-utilization of CGMs were all associated with higher hemoglobin A1c (HbA1c), but there was no difference between the 2019 and 2020 groups. The time in range in CGM users was lower with non-commercial insurance and in Black and Hispanic patients; it improved slightly from 2019 to 2020. CGM utilization by patients with non-commercial insurance (93% of such patients were in government programs, 7% uninsured or "other") increased markedly. In 2020, patients with commercial insurance (i.e., private-pay or provided by an employer) had fewer office visits, but insurance status did not influence utilization of the virtual visit platform. There was no change in hospitalization frequency from 2019 to 2020 in either commercially or non-commercially insured patients, but patients with non-commercial insurance were hospitalized at markedly higher frequencies in both years. PHQ-9 scores were unchanged. CONCLUSIONS: Hospitalization frequency, glycemic control and depression screening were unchanged in our large urban pediatric teaching hospital during the COVID pandemic. Increased utilization of CGM and rapid adoption of telemedicine may have ameliorated the impact of the pandemic on disease management.

Is the Way Forward to Step Back? Documenting the Frequency With Which Study Goals Are Misaligned With Study Methods and Interpretations in the Epidemiologic Literature.

In any research study, there is an underlying process that should begin with a clear articulation of the study's goal. The study's goal drives this process; it determines many study features, including the estimand of interest, the analytic approaches that can be used to estimate it, and which coefficients, if any, should be interpreted. Misalignment can occur in this process when analytic approaches and/or interpretations do not match the study's goal; misalignment is potentially more likely to arise when study goals are ambiguously framed. In this study, misalignment in the observational epidemiologic literature was documented and how the framing of study goals contributes to misalignment was explored. The following 2 misalignments were examined: use of an inappropriate variable selection approach for the goal (a "goal-methods" misalignment) and interpretation of coefficients of variables for which causal considerations were not made (e.g., Table 2 Fallacy, a "goal-interpretation" misalignment). A random sample of 100 articles published 2014-2018 in the top 5 general epidemiology journals were reviewed. Most reviewed studies were causal, with either explicitly stated (n = 13; 13%) or associational-framed (n = 71; 69%) aims. Full alignment of goal-methods-interpretations was infrequent (n = 9; 9%), although clearly causal studies (n = 5 of 13; 38%) were more often fully aligned than were seemingly causal ones (n = 3 of 71; 4%). Goal-methods misalignments were common (n = 34 of 103; 33%), but most frequently, methods were insufficiently reported to draw conclusions (n = 47; 46%). Goal-interpretations misalignments occurred in 31% (n = 32) of the studies and occurred less often when the methods were aligned (n = 2; 2%) compared with when the methods were misaligned (n = 13; 13%).

Length of Stay Analysis of COVID-19 Hospitalizations Using a Count Regression Model and Quantile Regression: A Study in Bologna, Italy.

This study aimed to identify and explore the hospital admission risk factors associated with the length of stay (LoS) by applying a relatively novel statistical method for count data using predictors among COVID-19 patients in Bologna, Italy. The second goal of this study was to model the LoS of COVID patients to understand which covariates significantly influenced it and identify the potential risk factors associated with LoS in Bolognese hospitals from 1 February 2020 to 10 May 2021. The clinical settings we focused on were the Intensive Care Unit (ICU) and ordinary hospitalization, including low-intensity stays. We used Poisson, negative binomial (NB), Hurdle-Poisson, and Hurdle-NB regression models to model the LoS. The fitted models were compared using the Akaike information criterion (AIC), Vuong's test criteria, and Rootograms. We also used quantile regression to model the effects of covariates on the quantile values of the response variable (LoS) using a Poisson distribution, and to explore a range of conditional quantile functions, thereby exposing various forms of conditional heterogeneity and controlling for unobserved individual characteristics. Based on the chosen performance criteria, Hurdle-NB provided the best fit. As an output from the model, we found significant changes in average LoS for each predictor. Compared with ordinary hospitalization and low-intensity stays, the ICU setting increased the average LoS by 1.84-fold. Being hospitalized in long-term hospitals was another contributing factor for LoS, increasing the average LoS by 1.58 compared with regular hospitals. When compared with the age group [50, 60) chosen as the reference, the average LoS decreased in the age groups [0, 10), [30, 40), and [40, 50), and increased in the oldest age group [80, 102). Compared with the second wave, which was chosen as the reference, the third wave did not significantly affect the average LoS, whereas it increased by 1.11-fold during the first wave and decreased by 0.77-fold during out-wave periods. The results of the quantile regression showed that covariates related to the ICU setting, hospitals with longer hospitalization, the first wave, and the out-waves were statistically significant for all the modeled quantiles. The results obtained from our study can help us to focus on the risk factors that lead to an increased LoS among COVID-19 patients and benchmark different models that can be adopted for these analyses.

The impact of carbon pricing, climate financing, and financial literacy on COVID-19 cases: go-for-green healthcare policies.

Climate finance and carbon pricing are regarded as sustainable policy mechanisms for mitigating negative environmental externalities via the development of green financing projects and the imposition of taxes on carbon pollution generation. Financial literacy indicates that it is beneficial to invest in cleaner technology to advance the environmental sustainability goal. The current wave of the COVID-19 epidemic has had a detrimental effect on the world economies' health and income. The pandemic crisis dwarfs previous global financial crises in terms of scope and severity, collapsing global financial markets. The study's primary contribution is constructing a climate funding index (CFI) based on four critical factors: inbound foreign direct investment, renewable energy usage, research and development spending, and carbon damages. In a cross-sectional panel of 43 nations, the research evaluates the effect of climate funding, financial literacy, and carbon pricing in lowering exposure to coronavirus cases. The study utilized Newton-Raphson and Marquardt steps to estimate the current parameter estimates while evaluating the COVID-19 prediction model with level regressors using the robust least squares regression model (S-estimator). Additionally, the innovation accounting matrix predicts estimations over a specific period. The findings indicate that climate finance significantly reduces coronavirus exposure by introducing green financing initiatives that benefit human health, which eventually strengthens the immune system's ability to fight infectious illnesses. Financial literacy and carbon pricing, on the other hand, are ineffectual in controlling coronavirus infections due to rising economic activity and densely inhabited areas that enable the transmission of coronavirus cases across countries. Similar findings were obtained using the alternative regression apparatus. The COVID-19 predicted variable was used as a "response variable," and climate financing was shown to have a favorable impact on containing coronavirus exposure. As shown by the innovation accounting matrix, carbon pricing would drastically decrease coronavirus cases' exposure over a time horizon. The study concludes that climate finance and carbon pricing were critical in improving air quality indicators, which improved countries' health and wealth, allowing them to reduce coronavirus infections via sustainable healthcare reforms.

Phylogeography Reveals Association between Swine Trade and the Spread of Porcine Epidemic Diarrhea Virus in China and across the World.

The ongoing SARS (severe acute respiratory syndrome)-CoV (coronavirus)-2 pandemic has exposed major gaps in our knowledge on the origin, ecology, evolution, and spread of animal coronaviruses. Porcine epidemic diarrhea virus (PEDV) is a member of the genus Alphacoronavirus in the family Coronaviridae that may have originated from bats and leads to significant hazards and widespread epidemics in the swine population. The role of local and global trade of live swine and swine-related products in disseminating PEDV remains unclear, especially in developing countries with complex swine production systems. Here, we undertake an in-depth phylogeographic analysis of PEDV sequence data (including 247 newly sequenced samples) and employ an extension of this inference framework that enables formally testing the contribution of a range of predictor variables to the geographic spread of PEDV. Within China, the provinces of Guangdong and Henan were identified as primary hubs for the spread of PEDV, for which we estimate live swine trade to play a very important role. On a global scale, the United States and China maintain the highest number of PEDV lineages. We estimate that, after an initial introduction out of China, the United States acted as an important source of PEDV introductions into Japan, Korea, China, and Mexico. Live swine trade also explains the dispersal of PEDV on a global scale. Given the increasingly global trade of live swine, our findings have important implications for designing prevention and containment measures to combat a wide range of livestock coronaviruses.

Impact of hydrological factors on the dynamic of COVID-19 epidemic: A multi-region study in China.

Considering the live SARS-CoV-2 was detected and isolated from the excrement and urine of infected patients, the potential public health risk of its waterborne transmission should be paid broad and close attention. The purpose of the current study is to investigate the associations between COVID-19 incidences and hydrological factors such as lake area, river length, precipitation and volume of water resources in 30 regions of China. All confirmed cases for each areas were divided into two clusters including first cases cluster driven by imported cases during the period of January 20th to January 29th, 2020 and second cases cluster driven by local cases during the period of January 30th to March 1st, 2020. Based on the results of descriptive analysis and nonlinear regression analysis, positive associations with COVID-19 confirmed numbers were observed for migration scale index (MSI), river length, precipitation and volume of water resources, but negative associations for population density. The correlation coefficient in the second stage cases cluster is apparently higher than that in the first stage cases cluster. Then, the negative binomial-generalized linear model (NB-GLM) was fitted to estimate area-specific effects of hydrological variables on relative risk (RR) with the incorporation of additional variables (e.g., MSI) and the effects of exposure-lag-response. The statistically significant associations between RR and river length, the volume of water resources, precipitation were obtained by meta-analysis as 1.24 (95% CI: 1.22, 1.27), 2.56 (95% CI: 2.50, 2.61) and 1.59 (95% CI: 1.56, 1.62), respectively. The possible water transmission routes of SARS-CoV-2 and the potential capacity of long-distance transmission of SARS-CoV-2 in water environment was also discussed. Our results could provide a better guidance for local and global authorities to broaden the mind for understanding the natural-social system or intervening measures for COVID-19 control at the current or futural stage.

Identification of county-level health factors associated with COVID-19 mortality in the United States.

Many studies have investigated causes of COVID-19 and explored safety measures for preventing COVID-19 infections. Unfortunately, these studies fell short to address disparities in health status and resources among decentralized communities in the United States. In this study, we utilized an advanced modeling technique to examine complex associations of county-level health factors with COVID-19 mortality for all 3141 counties in the United States. Our results indicated that counties with more uninsured people, more housing problems, more urbanized areas, and longer commute are more likely to have higher COVID-19 mortality. Based on the nationwide population-based data, this study also echoed prior research that used local data, and confirmed that county-level sociodemographic factors, such as more Black, Hispanic, and older subpopulations, are attributed to high risk of COVID-19 mortality. We hope that these findings will help set up priorities on high risk communities and subpopulations in future for fighting the novel virus.

The impact of COVID-19 on globalization.

Globalization has altered the way we live and earn a livelihood. Consequently, trade and travel have been recognized as significant determinants of the spread of disease. Additionally, the rise in urbanization and the closer integration of the world economy have facilitated global interconnectedness. Therefore, globalization has emerged as an essential mechanism of disease transmission. This paper aims to examine the potential impact of COVID-19 on globalization and global health in terms of mobility, trade, travel, and countries most impacted. The effect of globalization were operationalized in terms of mobility, economy, and healthcare systems. The mobility of individuals and its magnitude was assessed using airline and seaport trade data and travel information. The economic impact was measured based on the workforce, event cancellations, food and agriculture, academic institutions, and supply chain. The healthcare capacity was assessed by considering healthcare system indicators and preparedness of countries. Utilizing a technique for order of preference by similarity to ideal solution (TOPSIS), we calculated a pandemic vulnerability index (PVI) by creating a quantitative measure of the potential global health. The pandemic has placed an unprecedented burden on the world economy, healthcare, and globalization through travel, events cancellation, employment workforce, food chain, academia, and healthcare capacity. Based on PVI results, certain countries were more vulnerable than others. In Africa, more vulnerable countries included South Africa and Egypt; in Europe, they were Russia, Germany, and Italy; in Asia and Oceania, they were India, Iran, Pakistan, Saudi Arabia, and Turkey; and for the Americas, they were Brazil, USA, Chile, Mexico, and Peru. The impact on mobility, economy, and healthcare systems has only started to manifest. The findings of this study may help in the planning and implementation of strategies at the country level to help ease this emerging burden.

Thrombocytopenia and its association with mortality in patients with COVID-19.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes novel coronavirus disease 2019 (COVID-19), is spreading rapidly around the world. Thrombocytopenia in patients with COVID-19 has not been fully studied. OBJECTIVE: To describe thrombocytopenia in patients with COVID-19. METHODS: For each of 1476 consecutive patients with COVID-19 from Jinyintan Hospital, Wuhan, China, nadir platelet count during hospitalization was retrospectively collected and categorized into (0, 50], (50, 100], (100-150], or (150-) groups after taking the unit (×109 /L) away from the report of nadir platelet count. Nadir platelet counts and in-hospital mortality were analyzed. RESULTS: Among all patients, 238 (16.1%) patients were deceased and 306 (20.7%) had thrombocytopenia. Compared with survivors, non-survivors were older, were more likely to have thrombocytopenia, and had lower nadir platelet counts. The in-hospital mortality was 92.1%, 61.2%, 17.5%, and 4.7% for (0, 50], (50, 100], (100-150], and (150-) groups, respectively. With (150-) as the reference, nadir platelet counts of (100-150], (50, 100], and (0, 50] groups had a relative risk of 3.42 (95% confidence interval [CI] 2.36-4.96), 9.99 (95% CI 7.16-13.94), and 13.68 (95% CI 9.89-18.92), respectively. CONCLUSIONS: Thrombocytopenia is common in patients with COVID-19, and it is associated with increased risk of in-hospital mortality. The lower the platelet count, the higher the mortality becomes.