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

Abstract

Abstract Introduction Pneumonia is one of the most common causes of death and serious illness among children below the age of five in underdeveloped nations, including Ethiopia. However, there is a scarcity of data on this dangerous condition among particularly sensitive and vulnerable children living in peri-urban environments. Establishing the proportion of cases of pneumonia as well as understanding the risk factors can be crucial for successful prevention and reaction. Methods: A retrospective cohort analysis was carried out, with 451 child pneumonia patients from Jigjiga University reference hospital provided as samples. The Cox proportional hazard model, the parametric AFT model, and the Bayesian parametric survival model have been applied, each of which employed a different model selection approach. Result: among covariates, Female pneumonia patients had an acceleration factor of 1.48 with [95% CI: 0.556, 0.741], co-morbidity had an acceleration factor of 1.26 with [95% CI: 0.740, 1.416], severe acute malnutrition had an acceleration factor of 0.26 with [95% CI: 1.609, 6.890], and anemia status had an acceleration factor of 0.89 with [95% CI: -1.1, -0.5357]. On the basis of DIC, the Bayesian lognormal parametric model was selected. Conclusion: At a 5% level of significance, sex, residency, comorbidities, Severe Acute Malnutrition (SAM), age, anemia status, delivery palace, season, and weight were all strongly associated with the patient's overall survival time. In all health facilities, physicians, clinicians, and health extension workers need to concentrate on preventing anemia, severe acute malnutrition, morbidity, and mortality from pneumonia through the delivery of health promotion to the community based on the understood risk factors.