The exhaustive information about non-communicable diseases associated with COVID-19 and severe acute respiratory syndrome corona virus-2 (SARS-CoV-2) are getting easier to find in the literature. H...

Background: Accessibility to the immense collection of studies on noncommunicable diseases related to coronavirus disease of 2019 (COVID‐ 19) and severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) is an immediate focus of researchers. However, there is a scarcity of information about chronic obstructed pulmonary disease (COPD), which is associated with a high rate of infection in COVID‐19 patients. Moreover, by combining the effects of the SARS‐CoV‐2 on COPD patients, we may be able to overcome formidable obstacles factors, and diagnosis influencers. Materials and Methods: A retrospective study of 280 patients was conducted at DHQ Hospital Muzaffargarh in Punjab, Pakistan. Negative binomial regression describes the risk of fixed successive variables. The association is described by the Cox proportional hazard model and the model coefficient is determined through log‐likelihood observation. Patients with COPD had their survival and mortality plotted on Kaplan–Meier curves. Results: The increased risk of death in COPD patients was due to the effects of variables such as cough, lower respiratory tract infection (LRTI), tuberculosis (TB), and body‐aches being 1.369, 0.693, 0.170, and 0.217 times higher at (95% confidence interval [CI]: 0.747–1.992), (95% CI: 0.231–1.156), (95% CI: 0.008–0.332), and (95% CI: −0.07 to 0.440) while it decreased 0.396 in normal condition. Conclusion: We found that the symptoms of COPD (cough, LRTI, TB, and bodyaches) are statistically significant in patients who were most infected by SARS‐CoV‐2. KEYWORDS COPD, COVID‐19, Cox proportional hazard model and negative binomial distribution, SARS‐CoV‐2

Research was conducted employing the extended socio-ecological model (ESEM) to examine factors associated with cesarean sections (CSs) and vaginal deliveries (VDs) among the women in our study population. Using the ESEM to determine South-Punjab and Pakistani women's mode of delivery predictors. The cross-sectional study with a sample size of 908 patients was conducted through private and public obstetrics and gynecology departments in South Punjab, Pakistan, from 12 June 2022 to 29 May 2023. The Chi-square analysis compared baseline variables, delivery outcomes and delivery methods. Logistic regression analysis is used to determine predictive factors. The study revealed that 75.66% of women had CS, while 24.34% had VD. Education level, age, residential area and fetal presentation all had significant associations with mode of delivery. Logistic regression analysis suggested that gynecologist recommendation, belief in CS safety, abnormal fetal presentation, maternal anemia and meconium aspiration syndrome were all significant predictors of CS. The ESEM model concluded that age, fatigue and women's preference for CS due to its reliability for her and her fetus were predictive factors for mode of delivery choices among South-Punjab Pakistani women.

This study developed a new family of distributions, the Marshall-Olkin generalized distribution. A novel model, the Marshall-Olkin Lomax (NMOL) distribution, was introduced into this family. The NMOL distribution exhibited a variety of density forms, including symmetry, right-skewness, reversed-J shape, upside-down bathtub curves, and scenarios with growing, decreasing, and mixed trends. From a practical perspective, the NMOL model was used to assess the failure and insurance data. Furthermore, a group-acceptance sampling plan (GASP) was developed based on the NMOL distribution, with the median serving as a quality criterion. This analysis also included numerical and graphical representations of the expected shortfalls and values at risk. Simulation studies were conducted to evaluate the effectiveness of the proposed estimation methods, and the findings significantly validated the NMOL model. However, this new distribution family and model has wide-ranging applications in sectors such as engineering, survival analysis, hydrology, and economics.