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Malaria Risk Perception and Preventive Behaviors Among Elementary School Students, Southwest Ethiopia. Generalized Structural Equation Model

BACKGROUND: In 2020, more than three billion of the world’s population were the risk of being infected with malaria and four out of five deaths were from the African population. However, information is scarce on the association between risk perceptions and malaria prevention behaviors in resource-li...

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Detalles Bibliográficos
Autores principales: Deressa, Alemayehu, Gamachu, Mulugeta, Birhanu, Abdi, Mamo Ayana, Galana, Raru, Temam Beshir, Negash, Belay, Merga, Bedasa Taye, Regassa, Lemma Demissei, Ababulgu, Fira Abamecha
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10351682/
https://www.ncbi.nlm.nih.gov/pubmed/37465183
http://dx.doi.org/10.2147/IDR.S415376
Descripción
Sumario:BACKGROUND: In 2020, more than three billion of the world’s population were the risk of being infected with malaria and four out of five deaths were from the African population. However, information is scarce on the association between risk perceptions and malaria prevention behaviors in resource-limited countries, particularly Ethiopia. Therefore, this study aimed to assess malaria risk perceptions and preventive behaviors. METHODS: A cross-sectional study design was conducted among 401 elementary school students in Jimma zone, Oromia, Ethiopia, from April 2 to June 8, 2020. Data were collected through interviews using a semi-structured questionnaire. The data were entered into Epi-data 4.6 and analyzed using STATA version 14.2. The descriptive statistics were presented using frequency and percentages. A Cronbach’s α coefficient of 0.7 or higher was used to assess the reliability of each domain. The Generalized Structural Equation Model (GSEM) was employed to examine the relationships and prediction of explanatory variables with risk perception and preventive behaviors of malaria. The model with a lower information criterion was taken as a better-fitting model. Finally, the statistically significant model effects were declared at a P-value of less than 0.05 at a confidence interval of 95%. RESULTS: This study showed that having knowledge about malaria had an indirect positive effect on malaria preventive behavior (β = 1.29, 95% CI 0.11 to 2.47), and had a positive total effect on the preventive behavior (β = 2.99, 95% CI 0.08 to 2.67). Besides, an increased knowledge level had a direct positive effect on malaria risk perceptions (β = 0.08, 95% CI 0.01 to 0.14), and malaria risk perception had a direct positive effect on malaria preventive behavior (β = 1.21, 95% CI 0.10 to 2.31). CONCLUSION AND RECOMMENDATION: This study demonstrated that having knowledge about malaria had a direct and indirect association with malaria preventive behavior. An increased level of knowledge had a direct positive effect on malaria risk perceptions. Moreover, malaria risk perception had a direct positive effect on malaria preventive behavior. Therefore, malaria prevention-targeted interventions, behavior change, and knowledge enhancing communication should be enhanced or scaled up to contribute to prompt treatment and progress toward the elimination of malaria.