2022
DOI: 10.1186/s12889-022-13532-y
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Analysis of risk factors associated with acute respiratory infections among under-five children in Uganda

Abstract: Background Globally, infectious diseases are the major cause of death in children under the age of 5 years. Sub-Saharan Africa and South Asia account for 95% of global child mortalities every year, where acute respiratory infections (ARI) remain the leading cause of child morbidity and mortality. The aim of this study is to analyze the risk factors of ARI disease symptoms among children under the age of 5 years in Uganda. Methods A cross-sectional … Show more

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Cited by 19 publications
(14 citation statements)
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References 31 publications
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“…ARI symptoms in young children (0–35 months) were common in our study than in older children (36–59 months). These results are in line with other studied [ 6 , 13 , 30 ]. The increased risk for ARI in this age group might be explained by the children’s low immunity.…”
Section: Discussionsupporting
confidence: 94%
See 1 more Smart Citation
“…ARI symptoms in young children (0–35 months) were common in our study than in older children (36–59 months). These results are in line with other studied [ 6 , 13 , 30 ]. The increased risk for ARI in this age group might be explained by the children’s low immunity.…”
Section: Discussionsupporting
confidence: 94%
“…The immune system appeared to be stronger at a later stage in older children after vaccination. In particularly in countries in Southeast Asia and sub-Saharan African, where health facilities and maternal healthcare education need to be improved, the factors were low rates of immunization in young children, low maternal literacy, and young mothers engaged in farming activities that prevent the care of young children [ 30 , 31 ]. Additionally, compared to children living households with improved toilet facilities, children from households with unimproved toilet facilities were more likely to experience ARI symptoms.…”
Section: Discussionmentioning
confidence: 99%
“…The reviewed studies showed that children above the age of two years are at higher risk of ALRTIs [6, 25,27,28,30,35,[39][40][41]. However, this evidence is inconclusive because under two years seem highly susceptible to ALRTIs compared to the rest of children under ve [30,39,40]. Moreover, children under ve of teenage mothers [21,30,40] and children from mothers above age 35 [42] are more susceptible to ALRTIs.…”
Section: Risk Factors Of Alrtis Of Children Under Ve In Ssamentioning
confidence: 99%
“…However, this evidence is inconclusive because under two years seem highly susceptible to ALRTIs compared to the rest of children under ve [30,39,40]. Moreover, children under ve of teenage mothers [21,30,40] and children from mothers above age 35 [42] are more susceptible to ALRTIs. Evidence strongly establishes that children of mothers with low education [14, 15, 19, 21, 23-25, 37, 38, 43] and lack of employment [20,42] are more vulnerable to developing ALRTIs.…”
Section: Risk Factors Of Alrtis Of Children Under Ve In Ssamentioning
confidence: 99%
“…Nareeba et al [30] Machine Learning Machine learning algorithm for identifying the predictors of childhood immunization in rural Uganda. Bisaso et al [31] Machine Learning A comparative study of logistic regression based machine learning techniques for prediction of early virological suppression in antiretroviral initiating HIV patients Nshimiyimana and Zhou [32] Machine Learning Analysis of risk factors associated with acute respiratory infections among under-five children in Uganda. Katushabe et al [33] Fuzzy Logic Fuzzy Based Prediction Model for Air Quality Monitoring for Kampala City in East Africa Finnegan et al [34] Machine Learning Deploying machine learning with messy, real-world data in low-and middle-income countries: Developing a global health use case Ouma et al [35] Statistical Modelling Model-based small area estimation methods and precise district-level HIV prevalence estimates in Uganda Mafigiri et al [36] Statistical Modelling HIV prevalence and uptake of HIV/AIDS services among youths (15-24 Years) in fishing and neighbouring communities of Kasensero, Rakai District, South Western Uganda Igulot [37] Statistical Modelling Sexual and Gender-Based Violence and Vulnerability to HIV Infection in Uganda: Evidence from Multilevel Modelling of Population-Level HIV/AIDS Data Kabukye et al [38] Statistical Modelling Assessment of organizational readiness to implement an electronic health record system in a low-resource settings cancer hospital: A cross-sectional survey Bbosa et al [39] Machine Learning On the goodness of fit of parametric and nonparametric data mining techniques: the case of malaria incidence thresholds in Uganda Roberts and Matthews [40] Statistical Modelling Risk factors of malaria in children under the age of five years old in Uganda Nabyonga et al [41] Statistical Modelling Health care seeking patterns and determinants of outof-pocket expenditure for Malaria for the children under-five in Uganda Baik et al [42] Statistical Modelling A clinical score for identifying active tuberculosis while awaiting microbiological results: Development and validation of a multivariable prediction model in sub-Saharan Africa Becker et al [17] Deep Learning Detection of tuberculosis patterns in digital photographs of chest X-ray images using Deep Learning: feasibility study Coker et al [15] Machine Learning A land use regression model using machine learning and locally developed low-cost particulate matter sensors in Uganda.…”
Section: Authormentioning
confidence: 99%