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Risk of autism spectrum disorder and association of its symptoms with psychiatric and substance use disorders in non-clinical student sample in Kenya: cross-sectional study

BACKGROUND: The prevalence and patterns of autism spectrum disorder (ASD) symptoms/traits and the associations of ASD with psychiatric and substance use disorders has not been documented in non-clinical students in Sub-Saharan Africa, and Kenya in particular. AIMS: To document the risk level of ASD...

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Detalles Bibliográficos
Autores principales: Mutiso, Victoria N., Ndetei, David M., Muia, Esther N., Masake, Monicah, Alietsi, Rita K., Onsinyo, Lydia, Musyimi, Christine, Mamah, Daniel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cambridge University Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10486240/
https://www.ncbi.nlm.nih.gov/pubmed/37605834
http://dx.doi.org/10.1192/bjo.2023.503
Descripción
Sumario:BACKGROUND: The prevalence and patterns of autism spectrum disorder (ASD) symptoms/traits and the associations of ASD with psychiatric and substance use disorders has not been documented in non-clinical students in Sub-Saharan Africa, and Kenya in particular. AIMS: To document the risk level of ASD and its traits in a Kenyan student population (high school, college and university) using the Autism-Spectrum Quotient (AQ); and to determine the associations between ASD and other psychiatric and substance use disorders. METHOD: This was a cross-sectional study among students (n = 9626). We used instruments with sufficient psychometric properties and good discriminative validity to collect data. A cut-off score of ≥32 on the AQ was used to identify those at high risk of ASD. We conducted the following statistical tests: (a) basic descriptive statistics; (b) chi-squared tests and Fisher's exact tests to analyse associations between categorical variables and ASD; (c) independent t-tests to examine two-group comparisons with ASD; (d) one-way analysis of variance to make comparisons between categorical variables with three or more groups and ASD; (e) statistically significant (P < 0.05) variables fitted into an ordinal logistic regression model to identify determinants of ASD; (f) Pearson's correlation and reliability analysis. RESULTS: Of the total sample, 54 (0.56%) were at high risk of ASD. Sociodemographic differences were found in the mean scores for the various traits, and statistically significant (P < 0.05) associations we found between ASD and various psychiatric and substance use disorders. CONCLUSIONS: Risk of ASD, gender characteristics and associations with psychiatric and substance use disorders are similar in this Kenyan sample to those found in Western settings in non-clinical populations.