Abstract:
Background: Pneumonia is an inflammation of the lung parenchyma. The World Health
Organization estimates that 156 million cases of pneumonia, including up to 20 million cases
severe enough to require hospitalization, and 1.2 million deaths each year among under-five
children. Ethiopia is ranked sixth among top fifteen countries in terms of pneumonia related
morbidity and mortality. However, little is known about recovery time and its predictors among
under-five children admitted with severe pneumonia in the study area.
Objectives: This study aimed to assess recovery time from severe pneumonia and its predictors
among children aged 2-59 months admitted to the pediatric ward of Jimma University Medical
Center; Southwest, Ethiopia, 2023.
Methods: A facility-based retrospective cohort study was conducted among 426 children aged
between 2 and 59 months. Five years of medical records, from 2018 – 2022, were reviewed. A
simple random sampling technique was used. Data entry was done using epidata version 4.6 and
exported to and analyzed by STATA version 15. Kaplan Meier’s plot was used to compare the
survival probability of different groups and the multivariable Cox regression model was used to
investigate the independent effect of covariates on recovery time. Adjusted hazard ratio together
with a 95% confidence interval was used and a p-value of less than or equal to 0.05 was declared
as a statistically significant association.
Result: The median recovery time was 4 days (IQR: 3, 7). Incidence rate of recovery was 15.78
per 100-person day (95% CI 14.2 – 17.5). The presence of comorbidity (AHR; 0.7, 95% CI (0.54
– 0.91)), being treated with ceftazidime and vancomycin (AHR; 0.29, 95% CI (0.14 – 0.60)),
antibiotic change (AHR; 0.74, 95% CI (0.58 – 0.95)) and late presentation to the Hospital (AHR;
0.58, 95% CI (0.43 -0.78)) were statistically significant predictors that prolong recovery time.
Conclusion: The median recovery time was longer than other similar studies. Therefore, due
attention should be given concerning the identified predictors that prolong the recovery time.
