Abstract

Acute respiratory tract infections are among the leading causes of child morbidity and mortality worldwide. Although the diagnosis of acute respiratory tract infections requires simple outpatient medical techniques and care, it is still misdiagnosed among primary care physicians, leading to delayed treatment and increased mortality. This study described the prevalence of common acute respiratory tract infections and simple techniques that effectively detect and diagnose children presenting with acute respiratory symptoms to primary healthcare physicians in remote settings. This descriptive cross-sectional study was conducted at the pediatric outpatient clinic of a tertiary hospital in western Uganda in April, May and June 2019. A total of 896 children aged 2-59 months attending the clinic were recruited consecutively into the study and examined for the presence of acute respiratory infection. Participants' sociodemographic and clinical data were collected through history taking and clinical examination using a validated Uganda Ministry of Health Uganda outpatient clinical checklist (FORM 5). The outcome variable was the presence of an acute upper or lower respiratory condition. Data was analyzed using STATA version 13.0 (StataCorp, College Station, USA) and summarized using descriptive statistics. The overall period prevalence of acute respiratory tract infections among children aged 2 to 59 months was 36.9% (36,942 per 100,000 population). Upper respiratory tract infections with a prevalence of 24.8% were more common than lower respiratory tract infections. The most frequent upper respiratory tract infection in this setting was common cold (52%), followed by tonsillopharyngitis (10.7%), while pneumonia (26%) was the most frequent lower respiratory tract infection. Acute respiratory tract infections contribute to the high burden of disease in pediatric outpatient clinics. Simple, affordable, and approved diagnostic clinical techniques that involve physical examination of the upper and lower respiratory systems can precisely diagnose acute respiratory tract infections in resource-limited settings where there is no access to sophisticated diagnostic equipment.

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