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.

Background: Understanding the prevalence of different suicidal ideas and context appropriate wide range of risk factors in youth in particular is important because about a third of youth with suicidal ideation develop suicidal plans and about 60% of youth with suicidal plans make suicidal attempts. This study aims to provide for the rst time wide ranging Kenyan data on the above so as to inform context appropriate practice and policy.Methods: To achieve our aim, 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 to document the prevalence of suicidal ideas and socio-demographics, univariate analysis to determine associations between suicidal ideas and mental health disorders, socio-demographic characteristics and economic indicators. All the independent variables were then estimated using bivariate logistic regression, tted to identify potential confounding factors. Variables with a pvalue of less than <.05 were entered into generalized linear models using logit links to identify independent predictors.Results: The median age was 21.3 years (range 15-43),with a majority of males (53.5%)and 93.4% single. The overall prevalence of different types of suicidal ideation was (22.6%), the commonest of which was thinking of speci c ways to take their life (19.3%) and least wanting to be dead (10.7%). 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 signi cant (p<0.05) predictors of suicidal ideation. Conclusion:We have documented the prevalence of different types of suicidal ideas and the risk factors in Kenyan students. We have therefore achieved our aims. Future studies are needed to study the progression from suicidal ideas to suicidal attempts and the factors associated with that progression.

Introduction Kenya in particular and Africa in general lack data on Binge Eating Disorder (BED). The overarching objective of this study is to fill that gap. Kenyans may not be aware that BED exists when a “very good” appetite is considered a sign of good health, especially if food is available either at home, in fast food shops or when communally eating together, a very common cultural practice. On the other hand where there is relatively insufficient food, it is not expected that one could be having a problem of eating too much. Method We administered the following tools and measurements to 9742 participants (high school, college and university students): 1) Researcher designed socio-demographic and economic indicator questionnaire; 2) An instrument documenting DSM-IV diagnostic criteria for BED and its various symptoms; 3) An instrument to determine DSM-IV psychiatric disorders and substance abuse;4) An instrument measuring high risk for psychosis ,affectivity and stress; 5) A WHO designed instrument measuring the severity of substance abuse for specific substances. We used descriptive and inferential analysis to determine the prevalence and association of the different variables. Independent predictors of BED were generated from a generalized linear model (p<0.05). Results We found a prevalence of 3.2% of BED and a wide range of prevalence for BED and BED related symptoms (8.1% to 19%). The least prevalent was "To prevent weight gain from eating binge did you force yourself to vomit, or used laxatives?”. The most common was "Did you often go on eating binges (eating a very large amount of food very quickly over a short period of time)." Major depression, obsessive compulsive disorder, panic disorder, agoraphobia, generalized anxiety disorder ,a positive stress screen and drug abuse were independent predictors of BED (p<0.05). Conclusion Our findings on the prevalence of BED and significant associations with various psychiatric disorders and substance use disorders are similar to those obtained in High Income Countries (HIC) using similar large-scale samples in non-clinical populations. Our findings suggest the need fora public health approach to enhance awareness of BED and to promote health-seeking behaviour towards management of BED.

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.

Background: Changing lifestyles in Kenya can lead to eating related behaviors and problems. The more severe problems are likely to manifest in clinical settings, but the majority and less severe forms will remain unrecognized. There is therefore the need to take a public health awareness approach to identify cases at community level and initiate appropriate intervention. This requires characterization of Eating Disorders (ED) and its associations in the local context. Our focus will be on the more common Binge Eating Disorder (BED). The overarching objective of this study is to generate Kenyan data on BED and fill a gap that exists not only in Kenya but Africa in general. The specific aims are: (1) To document the patterns and prevalence of different symptoms of BED in a student population whose age range represents a significant proportion of the population. (2) To determine associated psychiatric and substance use disorders (3) To determine independent predictors of BED. Method: We administered to a total of 9742 participants following tools: A researcher designed socio-demographic and economic indicators questionnaire; an instrument on DSM-IV diagnosis of BED and its various symptoms; instruments to determine DSM-IV psychiatric disorders, substance abuse, affectivity, psychosis and stress indicators. The participants were high school, college and university students in four out of the 47 counties in Kenya. We used descriptive and inferential analysis to determine prevalence and association of the different variables. The independent predictors of BED were generated from the generalized linear model (p<0.05). Results: We found a prevalence of 3.2% of BED and a wide range of BED symptoms varying from 8.1% to 19.0%. There were significant (p<0.05) associations between BED with various socio-demographic variables and psychiatric and substance use disorders. However, only some of these disorders were independent predictors of BED. Conclusion: Our findings on prevalence of BED and significant associations with various psychiatric disorders and substance use disorders are similar to those obtained in HICs using similar large scale samples in non-clinical populations. Economic status is not a predictor of BED. Our findings suggest a public health approach to awareness and management.