Modelling Under-Five Mortality among Hospitalized Pneumonia Patients in Hawassa City, Ethiopia: A Cross-Classified Multilevel Analysis

Abstract

Community acquired pneumonia refers to pneumonia acquired outside of hospitals or extended health facilities and it is a leading infectious disease. This study aims to model mortality of hospitalized under-5 year child pneumonia patients and investigate potential risk factors associated with child mortality due to pneumonia. The study was a retrospective study on 305 sampled under-five hospitalized patients of community acquired pneumonia. A cross-classified multilevel logistic regression was employed with resident and hospital classified at the second level. Bayesian estimation method was applied in which the posterior distribution was simulated via Markov Chain Monte Carlo. The variability attributable to hospital was found to be larger than variability attributable to residence. The odds of dying from the community acquired pneumonia was higher among patients who were; diagnosed in spring season, complicated with malaria, AGE and AFI, in a neonatal age group, diagnosed late (more than a week). The risk of mortality was also found high for lower nurse: patient and physician: patients' ratios.

The Bayesian Parametric Survival Approach to Predicting Time to Death of Children Under Five year with Acute Pneumonia in Jigjiga University Referal Hospital

“…Timely intervention and appropriate management are crucial in reducing the mortality rate associated with this condition. Predicting the time to death for children with acute pneumonia can aid in allocating resources and providing targeted medical interventions [2].…”

The effect of coronavirus infection on QT and QTc intervals of hospitalized patients in Qazvin, Iran

“…Rescue and recovery of a patient with COVID-19 can be predicted by analyzing the relationships between parameters such as demographic characteristics (age and gender), clinical symptoms, medical examinations, and paraclinical findings. Using different approaches that may explore the important determinants of mortality due to pneumonia (COVID-19 pneumonia) is important at patient’s and hospital levels [ 18 ]. The growing emphasis on data mining in medical fields can provide the right environment for change and improvement [ 19 ].…”