The target population was youths aged 18 – 35 years being seen at the outpatient casualty in Kenya, for general medical conditions by general medical doctors. A simple random sampling technique was used to randomly select participants. A complete list of the registered outpatients was acquired at the outpatient’s casualty area in the hospital. A list of randomly generated numbers using excel was done, for each participant. A lottery method was used; participants were assigned a number, which were randomly picked until required sample size of 385 participants was achieved. The sample size was found using Cochran’s (1977) formula. The Mini International Neuropsychiatric Interview (M.I.N.I) tool Version 7.0.0 was administered together with a socio-demographic questionnaire. For participants to complete the interview, it approximately took 20 – 30 minutes. The socio-demographic data collection sheet included interviewer identification, location, age group (18 – 23, 24 – 29 and 30 – 35), gender, marital status, education level, employment status and religion. The MINI Instrument is a short organized diagnosis assessment tool whose author is Sheehan (2015), and the interview contained 17 disorders recognized as modules. Psychiatric morbidity occurs when a participant fits into one of the categories of disorders included in the MINI instrument, whose questions are derived from the Diagnostic and Statistical Manual of Mental Disorders V (DSM-5) (Kwobah et al., 2017).

Participants included in the present study were 385: 55.3% of the participants were female while 44.7% were male. The majority of the participants were 24 – 29 years old (39.2%) followed by age group 18 – 23 (31.7%) and age group 30 – 35 years (29.1%). Most participants were single (53.5%), married (41.6%), widow/widower (1.3%), and divorced/separated (3.6%). Most participants (38.7%) had completed a secondary level of education, while 22.3% had not completed tertiary, 16.4% had completed tertiary, 11.4% had not competed secondary, 7.8% had completed primary, 2.6% had not completed and 0.8% had no level of education. Almost half of the participants (45.5%) were unemployed, while self-employed comprised 35.8% and less than a quarter of the participants (18.7%) were employed. Majority of participants (91.9%) were Christians, 5.2% of Muslims, 1.6% belonged to other faiths and 1.3% identified as atheists. (Table 1)

The prevalence of psychiatric morbidity among youth patients in the hospital was 46.5%. The psychiatric illness with the highest prevalence was major depressive episode (MDE) (21.8%) systematically followed by; suicidality (8.8%), alcohol use disorder (AUD) (8.6%), suicidal behavior disorder (8.3%), post-traumatic stress disorder (PTSD) (6.2%), generalized anxiety disorder (GAD) (5.2%), social anxiety disorder (SAD) (4.9%), panic disorder (2.9%), manic and hypomanic episodes (2.3%), agoraphobia (2.3%), obsessive-compulsive disorder (OCD) (2.3%), substance use disorder-non-alcohol (SUD-NA) (2.1%), psychotic disorder lifetime (1.8%), antisocial personality disorder (ASPD) (1%), mood disorders with psychotic features lifetime (0.8%), binge eating disorder (0.8%), psychotic disorders current (0.5%), anorexia nervosa (0.5%), mood disorders with psychotic features current (0.5%) and bulimia nervosa (0.3%) (Table 2).

Marital status and employment status were found to have a statistical relationship with psychiatric morbidity. Participants who identified as single had an odds ratio (OR) higher chance of having a psychiatric morbidity as compared to the married (p = .024), OR 4.771. Participants who were a widow/widower had an OR 5.650 times of developing a mental illness compared to the married. Those who were divorced/separated had a low odds ratio (0.000) of having a psychiatric morbidity compared to those who were married (p = .016). Participants who were self-employed indicated strong odds of developing a psychiatric morbidity as compared to those who were unemployed (p = .007, OR 2.440); (p = .018).

Logistic regression analysis indicated age and marital status having a strong significant statistic link with MDE. Despite age being significant to MDE, the MDE rates in both the 24 – 29 and 30 – 35 year old age groups were lower than those in the 18 – 23 year old age group (p = .009) (OR: 0.323); (p = .024) (OR: 0.370).

Most of the participants had met a single diagnosis criterion by 26.8% (103), 9.9% (38) had 2 comorbidities, 6.8% (26) had 3 comorbidities while 3.1% (12) had 4 comorbidities (Table 3).

The present study was investigating the associations between psychiatric morbidity and being a youth. The outcome indicated that majority of the participants were women. This could be because health care-seeking tendencies are determined by various personal traits such as age and gender, whereby men are inadequately represented in medical care, and it is hard to comprehend why because it’s a continuing matter (Thompson et al., 2016). Moreover, the male gender was also noted to inadequately utilize the medical services specifically health screening and primary care (Mursa et al., 2022) and to also use medical services minimally as compared to the female gender (Australian Institute of Health and Welfare, 2019; Mursa et al., 2022). Nevertheless, more youthful patients both men and women are ready to get health care assistance as opposed to older patients (Thompson et al., 2016) as also seen in our findings.

This study indicates that the most prevalent mental illness in youth patients of age bracket 18 – 35 years was major depressive episode (21.8%) this was less in contrast to research done by Greenberg et al. (2021) that showed major depressive disorder to be at 53.7%% among those in age bracket of 18 – 34. However, age category 30 – 35 years had the highest level of major depressive episode suggesting that there is an increase of major depressive episode with increase in age. Similarly, Solmi et al. (2022) noted that average age onset of depressive disorder was 30 – 35 years. This was in contrast with Villarroel and Terlizzi (2020), who noted that those who were of age bracket of 18 – 29 years (21.0%) had the majority incidences of symptoms of depression while those in age group 30 – 44 years (16.8%) had the least symptoms.

The level of education indicated a correlation to major depressive episode; it was noted that those who were of secondary level and below had higher prevalence of major depressive episode compared to those of tertiary level and above. This was in alignment with the findings of Lee et al. (2016). In addition to that, Arias-de la Torre et al., (2021) also noted in their study that education level impacts outcome of MDD.

In this study divorced/separated respondents (64.3%) showed a high level of major depressive episode this was in alignment with Gutiérrez-Rojas et al. (2020), whose results indicated a strong correlation between major depressive disorder and being divorced/separated. Additionally, Bulloch et al.’s (2009) findings concluded that individuals who are divorced or separated have higher risk of developing major depressive disorder. Married participants (24.4%) followed in second position. This was in contrast to the findings of Islam and Adnan (2017), which indicated that married individuals 56% had a tendency to develop depression the highest.

Those who were unemployed (82.4%) had a slightly higher percentage of major depressive episode as opposed to those who were employed (81.0%) and self-employed (81.4%). This could be because unemployment is persistently linked with increased rates of depression in young adults (McGee & Thompson, 2015). This finding is also consistent with findings on depression and unemployment in young adults by McGee and Thompson (2015).

AUD had a prevalence rate of 8.6% and indicating a correlation with gender, education level and employment status. The male gender (14.0%) used more alcohol as compared to the female (4.2%), this could be because males and females are attributed with contrasting societal morals and norms (Gilbert et al., 2019; Goh et al., 2024). Similarly, Maxwell et al. (2022) and Goh et al. (2024) supported that sociocultural viewpoints, customary masculine norms particularly could be the reason for men’s high levels of alcohol consumption with possibility of having more alcohol related challenges. Those who were of secondary level of education and below indicated a higher intake of alcohol use as compared to those of tertiary level and above, this corresponded with Murakami and Hashimoto’s (2019) findings which indicated that less educational level was correlated with more risk of having a drinking challenge. Similarly, Norström and Landberg’s (2020) findings also supported this. Moreover, lesser educational level predisposes one to being a binge drinker due to occasional predisposition to social distress and poor health enlightenment about dangers of alcohol consumption (Murakami & Hashimoto, 2019).

Employment status was also linked to alcohol use disorder whereby the self-employed had 15.2% AUD while those employed had 11.1% and those unemployed had 2.4% this was similar to Kiarie (2021), who found a significant link between self-employment and alcohol use. However, this contrasted with Collins’s (2016) findings, which indicated that unemployment was what was found to be greatly linked with heavy alcohol consumption and alcohol use disorder. Although age was not statistically significant to AUD, the age group of 30 – 35 rated a higher percentage (11.6%) of AUD in contrast to other age groups which was consistent with World Health Organization (2022), and Wysokińska and Kolota (2022), who noted those of age group 25 – 45 years had a peak in alcohol usage. This differentiated with Delker et al.’s (2016) findings which indicated a heightening of alcohol use in age group 18 – 29 years.

Marital status and employment status were noted to have a statistically significant link with psychiatric morbidity. This was in contrast with Kwobah et al. (2017) who noted that there was no link found between marital status and employment status having an association with psychiatric morbidity. On the other hand, Bulloch et al. (2009) indicated an association of marital status and psychiatric illness while Eid et al. (2013) indicated an association between employment status and psychiatric illness.