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 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.

Students stress patterns in a Kenyan socio-cultural and economic context: toward a public health intervention.

This study aimed at determining the prevalence of stress, different types of stress, their severity and their determinants in Kenyan university, college and high school students. The following tools were administered to 9741 students: (1) Researcher-designed socio-demographic tool, (2) Psychiatric Diagnostic Screening Questionnaire (PDSQ) for psychiatric disorders, (3) WERC Stress Screen for stress, (4) Washington Early Recognition Center Affectivity and Psychosis (WERCAP) screen for psychosis and affectivity, (5) Wealth Index Questionnaire for economic indicators. Descriptive analysis for the prevalence of different types of stress and inferential analysis for stress and independent variables were done. Significant variables (p < 0.05) were fitted into generalized linear model to determine independent predictors. The mean age of the respondents was 21.4 years (range 16-43). Money issues were the commonest stressors while alcohol and drug use were the least. The independent predictors of stress were females, college students and use of gas stove. In conclusion, up to 30% of the students suffer from mild to severe stress. The students experience a wide range of stressors. The most important stressors include money and finances, family related problems and concerns about their future. Our findings suggest a public health approach to create stress awareness in students.

Socio-demographic, economic and mental health problems were risk factors for suicidal ideation among Kenyan students aged 15 plus.

BACKGROUND: About a third of youth with suicidal ideation develop suicidal plans and about 60% of youth with suicidal plans make suicidal attempts. This study aimed to study different types of suicidal ideation and the risk factors in Kenyan youth. METHODS: We studied a total of 9742 high school, college and university students using following self-administered instruments: - a researcher design socio-demographic questionnaire, Psychiatric Diagnostic Screening Questionnaire (PDSQ) to document psychiatric disorders and various types of suicidal ideas in previous two weeks, Washington Early Recognition Center Affectivity and Psychosis tool to assess stress, affectivity and psychosis, Wealth index questionnaire to document economic indicators based on household items for the families of the students. We used descriptive statistics, univariate analysis, bivariate logistic regression analysis and variables with a p-value of less than <.05 were entered into generalized linear models using logit links to identify independent predictors. RESULTS: The overall prevalence of different types of suicidal ideation was (22.6%), major depression was found in 20.0%, affectivity, psychosis and stress was found in 10.4%, 8.7% and 26.0% respectively. Female gender, major depression, stress, affectivity and psychosis and being in high school were significant (p < 0.05) predictors of suicidal ideation. LIMITATIONS: This was a cross sectional study that focused only on suicidal ideas and associated economic factors and mental health disorders. It did not study suicidal behavior. CONCLUSION: Future studies are needed to study the progression from suicidal ideas to suicidal attempts and the factors associated with that progression.

Adolescent elite Kenyan runners are at risk for energy deficiency, menstrual dysfunction and disordered eating.

Limited data are available on the female athlete triad (Triad) in athletes from minority groups. We explored subclinical and clinical Triad components amongst adolescent elite Kenyan athletes (n = 61) and non-athletes (n = 49). Participants completed demographic, health, sport and menstrual history questionnaires as well as a 5-day weighed dietary record and exercise log to calculate energy availability (EA). Ultrasound assessed calcaneus bone mineral density (BMD). Eating Disorder Inventory subscales and the Three-Factor Eating Questionnaire's cognitive dietary restraint subscale measured disordered eating (DE). EA was lower in athletes than non-athletes (36.5 ± 4.5 vs. 39.5 ± 5.7 kcal ∙ kg FFM(-1) ∙ d(-1), P = 0.003). More athletes were identified with clinical low EA (17.9% vs. 2.2%, OR = 9.5, 95% CI 1.17-77, P = 0.021) and clinical menstrual dysfunction (32.7% vs. 18.3%, χ(2) = 7.1, P = 0.02). Subclinical (75.4% vs. 71.4%) and clinical DE (4.9% vs. 10.2%, P = 0.56) as well as BMD were similar between athletes and non-athletes. More athletes had two Triad components than non-athletes (8.9% vs. 0%, OR = 0.6, 95% CI 0.5-6.9, P = 0.05). Kenyan adolescent participants presented with one or more subclinical and/or clinical Triad component. It is essential that athletes and their entourage be educated on their energy needs including health and performance consequences of an energy deficiency.