Association of the medication protocols and longitudinal change of COVID-19 symptoms: a hospital-based mixed-statistical methods study

The objective of this study was to identify the relationship between hospitalization treatment strategies leading to change in symptoms during 12-week follow-up among hospitalized patients during the COVID-19 outbreak. In this article, data from a prospective cohort study on COVID-19 patients admitted to Khorshid Hospital, Isfahan, Iran, from February 2020 to February 2021, were analyzed and reported. Patient characteristics, including socio-demographics, comorbidities, signs and symptoms, and treatments during hospitalization, were investigated. Also, to investigate the treatment effects adjusted by other confounding factors that lead to symptom change during follow-up, the binary classification trees, generalized linear mixed model, machine learning, and joint generalized estimating equation methods were applied. This research scrutinized the effects of various medications on COVID-19 patients in a prospective hospital-based cohort study, and found that heparin, methylprednisolone, ceftriaxone, and hydroxychloroquine were the most frequently prescribed medications. The results indicate that of patients under 65 years of age, 76% had a cough at the time of admission, while of patients with Cr levels of 1.1 or more, 80% had not lost weight at the time of admission. The results of fitted models showed that, during the follow-up, women are more likely to have shortness of breath (OR = 1.25; P-value: 0.039), fatigue (OR = 1.31; P-value: 0.013) and cough (OR = 1.29; P-value: 0.019) compared to men. Additionally, patients with symptoms of chest pain, fatigue and decreased appetite during admission are at a higher risk of experiencing fatigue during follow-up. Each day increase in the duration of ceftriaxone multiplies the odds of shortness of breath by 1.15 (P-value: 0.012). With each passing week, the odds of losing weight increase by 1.41 (P-value: 0.038), while the odds of shortness of breath and cough decrease by 0.84 (P-value: 0.005) and 0.56 (P-value: 0.000), respectively. In addition, each day increase in the duration of meropenem or methylprednisolone decreased the odds of weight loss at follow-up by 0.88 (P-value: 0.026) and 0.91 (P-value: 0.023), respectively (among those who took these medications). Identified prognostic factors can help clinicians and policymakers adapt management strategies for patients in any pandemic like COVID-19, which ultimately leads to better hospital decision-making and improved patient quality of life outcomes.

Traffic violation analysis using time series, clustering and panel zero-truncated one-inflated mixed model

Traffic rules violations in urban areas, which can cause traffic crashes and unsafe situations, are a major issue nowadays. The present paper aims to analyze the frequency of traffic violations in Tehran city, Iran, over a five-year period (March 2016- March 2021). The data is obtained via road traffic violation monitoring system which can capture and process various traffic violations. This database, containing about 97 million violations committed by about 16 million drivers, is explored applying three statistical approaches. In the first approach, some multiplicative SARIMA and Bayesian Spatio-temporal models are fitted to the monthly violations. Also, in the second approach, the K-means clustering algorithm is applied to discover homogeneous districts of Tehran Municipality regarding their number of violations and their number of violations per camera towers meter during the study. Finally, in the third approach, a random-effect zero-truncated one-inflated Poisson model is proposed to study factors affecting driver's number of violations over time.

Assessing the Effects of Important Factors and Province Heterogeneity on Different Quantiles of Hospitalization Cost

Objectives: The aim of the study was to investigate the effects of some covariates on different quantiles of the cost of hospitalization. The effect of the province that the individual belongs to on these quantiles will be also examined.Methods: We employed a linear quantile-mixed model (LQMM) for analyzing the cost of hospitalization in Iranians Utilization of Health Services (IUHS) survey considering the province effect, the effects of some important covariates, and also the effect of the choice of the random-effects distribution. For this, both classical and Bayesian approaches are used for parameter estimation.Results: The results of data analysis show that ward, type of hospital, and duration of hospitalization are significant factors on quantiles of the cost of hospitalization, of course with different impacts on different quantiles. Our findings reveal significant discrepancies in the cost of hospitalization in different provinces and significant heterogeneity among provinces.Conclusion: More works must be done related to hospitalization cost and its consequences since it is a matter of social life. To be exact, one should notice that provinces with hospitals involving high hospitalization costs may have households dealing with poverty.