Understanding Mental Health Among University Students in Kenya: What Role Do Family Support and Age Play?

Abstract

While mental health conditions play a significant role in the global disease burden, their determinants and predictors are still not well understood in Kenya. This study examined the prevalence of mental health conditions among university students and the factors associated with them. This cross-sectional study evaluated 1,424 students at Pwani University in Kenya, assessing anxiety, depression, and psychosis, using validated screening tools: the Generalized Anxiety Disorder-7 (GAD-7), Patient Health Questionnaire-9 (PHQ-9), and Psychosis Screening Questionnaire (PSQ). The Chi-square tests analyzed associations, while binary logistic regression identified predictors. Confounders were controlled using multivariable adjustments, with model selection based on both clinical relevance and statistical significance of the variables.

The prevalence of mental health conditions among students was 30.9%. Those from unsupportive families exhibited the highest prevalence at 35.2% (χ2 = 94.91, p < 0.001), while first-year students reported the highest rate among academic levels at 40.7% (χ2 = 24.38, p < 0.001). Students aged 25–29 years were 2.6 times more likely to experience mental health conditions (OR = 2.6, 95% CI: 1.67–3.98, p < 0.001). Access to mental health services (χ2 = 4.62, p = 0.032) and mental health insurance (χ2 = 4.11, p = 0.043) were associated with lower odds of mental health conditions, thereby reducing the risk by 34 and 33%, respectively.

The findings highlight the urgent need for age-sensitive, student-centered mental health interventions in Kenyan universities. Specifically, universities should implement targeted support programs for first-year and final-year students who face unique mental health risks due to transitional and graduation-related stressors. Additionally, integrating family engagement initiatives to strengthen family support structures can serve as a protective factor against mental health challenges. Policies aimed at expanding access to mental health insurance and services should also be prioritized. Given the use of non-probabilistic sampling, findings should be interpreted with caution. Future research should investigate longitudinal trends to establish causal relationships and inform the development of evidence-based policies.