count regression

 The sudden spread of COVID-19 raised significant concerns about the potential impact of the pandemic on healthcare systems in low- and middle-income nations. The length of hospital stay is a critical health metric with far-reaching implications in healthcare systems and a matter of resource allocation. Hospital beds, ventilators, medications, and the invaluable time and expertise of medical professionals are all finite resources. Understanding the factors influencing the length of hospitalization is essential for the efficient allocation of these resources and the provision of high-quality care.

 To explore the demographic features and clinical presentations of COVID-19 patients and investigate the relationship between patient characteristics and hospitalization length by employing advanced statistical models.

 This was a retrospective study in Bam City, Kerman province, on 2096 patients who were admitted to the Pastor Hospital of Bam City with the diagnosis of COVID-19 between March and September 2021. The criterion for inclusion was either laboratory confirmation of SARS-CoV-2 infection by swab sampling or clinical diagnosis. No exclusion criteria were applied. Several variables, including age, gender, history of drug abuse, comorbid diseases, chronic illnesses, and clinical symptoms were gathered. A univariate analysis was performed, and significant parameters were subsequently included in the final multivariable model. We used SPSS 22, STATA 13, and R 4.1.3 software for statistical analysis.

 A total of 2096 patients admitted to the Pastor Hospital of Bam City from March to September 2021 diagnosed with COVID-19 were included in this study. The results of a negative binomial regression model showed that the factors affecting hospitalization length in COVID-19 patients were advanced age, male gender, contact with COVID-19 patients, PO2, body temperature, cancer, fever, heart disease, coughing severity, and respiratory distress.

 Elder COVID-19 patients had relatively longer hospitalization periods. Moreover, hospitalization duration was longer in males, those who had contact with a COVID-19 patient, and patients with PO2 below 93 %, higher body temperature and fever, coughing, respiratory distress, heart diseases, and cancer compared to others. Therefore, it is recommended to monitor COVID-19 patients with heart disease and cancer more cautiously. Investigating the factors associated with long hospitalization is important for the suitable management of available resources and demands for hospital beds.

Congenital malformations are one of the most important and common types of anomalies in infants, which are one of the main causes of disability and mortality in children.

This study aimed to investigate the risk factors affecting the incidence of congenital malformations, as well as the number of different infant anomalies recorded in neonatal health data in Khoy, Iran, during 2017.

In this study, all neonates born in the maternity wards of hospitals in Khoy, Iran, during 2017 were evaluated in terms of gender, weight, and parental consanguinity. Hurdle and Zero-inflation approaches were utilized for the double Poisson model. Moreover, the data werecollected using some checklists, and the analyses were performed in R-3-6-1 software.

According to the results of the present study, the Hurdle approach was better than Zero-inflation. The birth weight and parental consanguinity affected the incidence of congenital malformations in infants.

Given that a significant proportion of infants are born without any congenital malformations, it is important to use count regression models based on excess zero approaches to assess congenital malformations. It is also necessary to take steps to reduce consanguineous marriages and the number of infants with low-birth-weight to prevent congenital malformations.