Prevalence of depression, anxiety, and stress and associated risk factors among young adults in Mathare slums, Nairobi County, Kenya

¹Department of Community Health, School of Public Health, Amref International University, Nairobi, Kenya, ²School of Medical Sciences, Amref International University, Nairobi, Kenya, ³Kenya National Human Rights Commission, Nairobi, Kenya, ⁴Department of Counselling Psychology, School of Education, Arts and Social Sciences, KCA University, Nairobi, Kenya

^&Corresponding author
Grace Wanjugu Kamau, Department of Community Health, School of Public Health, Amref International University, Nairobi, Kenya

Introduction    

Mental health is a critical global concern due to its significant contribution to morbidity, particularly affecting low- to middle-income countries [1]. The 2019 global burden of disease study highlighted mental health disorders as major contributors to years lived with disability (YLD) globally, with mood and anxiety disorders notably impacting people's well-being and productivity. Among those most impacted are adolescents and young adults, with psychological conditions accounting for 15% of their morbidity [2], prominently featuring stress, anxiety, and depression.

Depression, also known as major depressive disorder, afflicts 264 million individuals worldwide, with a higher prevalence among females [3]. Among young adults, prevalence rates range from 3.5% to 40.8% [4-6]. Anxiety disorders, affecting approximately 301 million people globally, are more prevalent in females [7]. Among young adults, the global prevalence of anxiety increased significantly, with a 52% rise between the year 1990 and 2021 [8]. Stress affects young adults globally, with a prevalence of 35.1% and higher rates in females [9].

Early adulthood, spanning ages 18 to 29 [10], is a transitional phase from adolescence to adulthood, encompassing personal, social, and professional changes. Globally, young adults represent approximately 23% of the population, totaling around 1.8 billion individuals [11]. The transition to adulthood brings challenges like changing living arrangements, social networks, and responsibilities, increasing psychological distress [12]. Academic and professional pressures, alongside financial stressors such as student debt and job insecurity, elevate stress, anxiety, and depression while limiting access to psychiatric care [13,14].

Urban slums in low- and middle-income countries (LMICs) house a growing low-income population in overcrowded, poorly constructed settlements lacking proper infrastructure [15]. Currently, about 1 billion people live in informal settlements, a figure projected to reach 3 billion by 2030 [16,17]. In South East Asia, approximately 31% of urban dwellers reside in slums [18]. India, known for its substantial urbanization, hosts a significant proportion of urban slum residents, with about 17.4% of its urban population, or approximately 65 million people, living in slums [19]. Notably, within these slum communities, young adults constitute a considerable segment of the population, representing roughly 35% of residents in LMICs [20]. In LMICs, 25% of urban dwellers reside in slums, with sub-Saharan Africa experiencing particularly high numbers [21]. Across Kenya, approximately 56% of people living in urban areas reside in informal settlements, with young individuals making up a significant portion of this population [22]. Nairobi's slums, for instance, are home to 33% of young adults aged 18-24 [23]. This demographic confronts issues such as poverty, unemployment, resource scarcity, social exclusion, contact with violent behaviors, and limited availability of community support systems [24], all of which contribute to heightened depressive symptoms [25]. An estimated 60% to 80% of people living in urban areas in Nairobi reside in informal settlements [26]. While informal settlements occupy just 5% of the total land area in Nairobi, they house over half of the city's population [27].

In Kenya´s urban informal settlements, the occurrence of anxiety and depressive disorder among young adults was 20% and 34% repectively [28]. Mathare slums, the oldest informal settlement in Nairobi, Kenya´s capital, have been confronted with various social and health challenges, including cholera outbreaks, gang violence, and building collapses, all of which can be attributed to inadequate and unregulated development [29]. A significant portion of the slum lacks access to clean drinking water, with many areas not connected to the municipal water supply. Additionally, the absence of proper sewer lines has resulted in the discharge of wastewater from tall buildings directly into nearby rivers, exacerbating the sanitation issues within the community [29]. The young adults residing in Mathare slums encounter distinctive challenges that have the potential to influence their mental well-being. These challenges encompass limited access to fundamental services such as healthcare and education, pervasive poverty, high unemployment rates, prevalent crime, and violence. As a result, these difficulties can exacerbate stress levels and contribute to heightened anxiety and depression among individuals.

The majority of the LMICs face neglected policy attention when it comes to mental wellness, resulting in significant budgetary gaps and understaffing dedicated to mental wellness services. According to Jaguga et al. [30], Kenya confronts a critical shortage of psychiatric care facilities and professionals. For instance, there is a scarcity of psychiatric care facilities and a significant scarcity of mental health professionals. Specifically, the psychologist-to-population ratio is alarmingly high, estimated at 1 psychologist for every 4.6 million individuals. Furthermore, the accessibility of psychiatric care in the public sector in Kenya is extremely limited, with less than 1% offering such services.

With the inadequate mitigative measures, the young adults in Mathare slums are more exposed to mental disorders. A substantial body of literature on psychological health in Kenya has paid special attention to the general population. However, data on mental health prevalence in specific slums, such as Mathare, remains limited, posing challenges for tailored interventions. The absence of such crucial information hampers policymakers and healthcare providers in attending to the mental health concerns of these communities. This study, therefore, aims to assess the prevalence of depression, anxiety, and stress and examine the associated risk factors among the young adults in Mathare slums, Nairobi County, Kenya.

Methods     

Study design: a cross-sectional study was conducted to assess the prevalence of depression, anxiety, and stress along with their associated risk factors, among young adults living in the Mathare slums of Nairobi, Kenya.

Study setting: the research was undertaken in Mathare slums, positioned 5 -10 kilometers northeast of Nairobi's Central Business District. Spanning approximately 3 square kilometers, this densely populated area is home to 206,564 individuals with an almost equal ratio of males to females [31]. Mathare slum, located in the Mathare Sub-County, comprises five sub-locations: Huruma, Kiamaiko, Mlango Kubwa Mathare, Mabatini, and Hospital. As the oldest informal settlement in Nairobi City County, Mathare is characterized by its high population density. With a staggering 68,941 residents per square kilometer, it far exceeds the national average of 82 persons per square kilometer [29]. Data collection occurred from January to February 2024.

Participants: this study focused on young adults in the 18-29 age cohort residing within Mathare slums. A sample of 405 participants was recruited using a convenience sampling approach from a health center within Mathare Constituency. Eligible participants were approached in person by trained research assistants, who provided information about the study and invited them to participate through self-administered questionnaires. To be included in this study, the participants had to satisfy the following inclusion criteria: (1) belong to the 18-29 years old category; (2) be a resident of Mathare slums; (3) be able to comprehend English and Kiswahili; and (4) provide written consent indicating their willingness to participate.

Variables: the predictor variables under study include age, gender, educational attainment, employment status, income range per month, marital status, and household income affected by COVID-19, while the outcome variables are depression, anxiety, and stress.

Variables measurement: two questionnaires were used: a semi-structured questionnaire for socio-demographic data (age, gender, education, occupation, income, marital status and other household characteristics) and the 21-item Depression Anxiety and Stress Scale (DASS-21) to assess mental health [32]. DASS-21 uses a 4-point scale, with scores in each subscale doubled to match the full DASS-42. Higher scores indicate greater severity. Depression scores were categorized as normal (0-9), mild (10-13), moderate (14-20), severe (21-27) and extremely severe (28+); anxiety: normal (0-7), mild (8-9), moderate (10-14), severe (15-19) and extremely severe (20+); and stress: normal (0-14), mild (15-18), moderate (19-25), severe (26-33) and extremely severe (34+).

Sample size: sample size calculation was performed using the Daniel sample size formula [33] to calculate the adequate sample population for prevalence research.

Where N is the required sample population; Z² is 1.96 at 95% confidence interval; P is the expected prevalence, which was estimated at 0.5, and d is the estimated acceptable error margin for proportion (0.05). This gave N = 385. A sample size of 405 was obtained after adjusting the initially calculated sample size (N) to account for a 5% loss using the formula to N adj=N/(1-5% loss). This is to ensure that the potential loss of data in the calculation of the final sample size is incorporated.

Statistical analysis: data were collated in Excel and analyzed using IBM SPSS version 29.0.2.0 (20) free trial. Descriptive results were reported as percentages and means. Depression, anxiety, and stress were classified as present or absent based on cut-off scores and analyzed across demographic groups. Predictor variables (age, gender, education, employment, income, marital status and COVID-19-related income impact) were categorized. Variables such as income, which had missing responses, likely due to its sensitive nature, were retained and categorized as non-response to minimize data loss and potential bias associated with complete case analysis. Associations were assessed using Pearson´s Chi-square tests, and significant predictors underwent multivariable logistic regression to calculate adjusted odds ratios with 95% confidence intervals. A p-value < 0.05 indicated statistical significance.

Ethical considerations: ethical approval for this study was granted by the Amref Ethics and Scientific Review Committee (ESRC), under approval number AMREF-ESRC P1558/2023. All participants provided written informed consent, and the study complied with ethical standards in accordance with the Helsinki Declaration.

Results     

Socio-demographic characteristics: data from 404 eligible participants (99.8% response rate) were analyzed. The mean age was 23.2 years, with a predominance of females (69.3%). On education, 99.2% of the participants had formal education, with 71.5% having secondary education. While 39.4% reported having some form of employment, the majority were either unemployed (60.4%) or in informal work, including self-employment (23.5%) and casual labor (6.7%). Only 42.2% had an income, with a majority earning less than KES 10,000 monthly. The majority were married (54.2%), and 29.7% reported that their household income had been negatively impacted by COVID-19, citing job loss, pay cuts, and reduced business (Table 1).

Prevalence and severity levels of the mental disorders: depression, anxiety and stress affected 32.2%, 43.1%, and 32.2% of participants, respectively. Moderate levels were most common-16.1% for depression and stress, while 20.0% for anxiety. Mild cases were also notable, with 10.9% for both depression and stress and 14.6% for anxiety. Severe and extremely severe forms were less frequent, each affecting fewer than 3% of participants (Table 2).

Distribution of mental disorders by their socio-demographic characteristics: depression and stress were most prevalent among individuals aged 24-26 years (36.8%) and were significantly more common in males than females (36.0% vs. 28.2%; p < 0.001). Higher education levels were linked to increased prevalence of depression and stress, with 42.7% of undergraduates and 45.5% of postgraduates affected. Those in casual employment (59.3%; p < 0.001) and earning between KES 31,000-40,000 monthly (75%; p < 0.001) were significantly associated with higher depression and stress levels. On marital status, depression and stress were highest among divorcees (100%), and individuals from households financially affected by COVID-19 also reported significantly higher rates of depression and stress (50%; p < 0.001). Bivariate analysis summarizing the factors associated with prevalence of depression and stress revealed significant associations with gender (χ² = 19.152, df = 2, p < 0.001), employment status (χ² = 19.379, df = 4, p < 0.001), income (χ² = 30.572, df = 6, p < 0.001) and COVID-19-related income disruption (χ² = 37.252, df = 2, p < 0.001). No significant associations were found with age, educational attainment, or marital status.

Anxiety was significantly higher among individuals aged 21-23 years (49.2%) and was significantly greater in males than in females (50.50% vs 38.90%; p=0.015). On educational attainment, persons with undergraduate studies were more anxious; part time or casual employees had significantly higher levels of anxiety (81.50%; p< 0.001); high income earners (those earning between KSH 31,000-40,0000 per month) showed significantly higher levels of anxiety (75%; p < 0.001); divorcees had higher levels of anxiety (100%) and households whose income was not affected by COVID-19 showed significantly higher levels of anxiety (46.10%; p < 0.001). Bivariate analysis summarizing the factors associated with prevalence of anxiety showed that age ( χ2 =11.371, df= 4, p=0.016), gender (χ2 =8.053, df= 2, p=0.015), employment status (χ2 = 32.123, df=4, p < 0.001), income range per month (χ2 = 26.377, df=6, p < 0.001) and household income affected by COVID-19 (χ2 = 17.851, df=2, p < 0.001) had significant associations with anxiety while educational attainment and marital status did not show significant associations (Table 3).

Multivariate analysis to predict depression, anxiety, and stress: multivariable logistic regression was used to determine the significant predictors of depression, anxiety, and stress among young adults residing in the Mathare slums. The findings from this analysis revealed that gender played a significant role in predicting depression and stress, but not anxiety. Specifically, males exhibited significantly lower odds of experiencing depression and stress compared to females (AOR = 0.150, 95% CI: 0.028-0.803, p = 0.027), meaning that males were approximately 85% less likely to experience these conditions than females.

Additionally, income levels were identified as a significant predictor of depression and stress. Participants earning less than KES 5,000 per month were found to have significantly elevated odds of experiencing depression and stress in comparison to those in higher income brackets (AOR = 3.69, 95% CI: 1.232-11.060, p = 0.020). This indicates that individuals in the lowest income group were 3.69 times more likely to report depression and stress than those earning more. Furthermore, economic stability during the COVID-19 pandemic emerged as a protective factor against depression and stress. Individuals whose household income remained unaffected by the pandemic had significantly lower odds of experiencing these conditions (AOR = 0.132, 95% CI: 0.038-0.458, p = 0.001) (Table 4).

Discussion     

This study aimed to investigate the prevalence of depression, anxiety, and stress among young adults in Mathare slums, Nairobi County, Kenya, and to explore associated risk factors. The prevalence rates were 32.2% for both depression and stress, and 43.1% for anxiety, figures notably higher than general population estimates, highlighting the increased vulnerability of individuals in informal settlements.

Comparative studies in LMICs reflected similar trends. For instance, a study in Ethiopia reported even higher prevalence rates of depression (41.4%), anxiety (66.7%), and stress (52.2%) [34]. Findings from North-West Tanzania showed comparable levels of depression (36%) and anxiety (31%) [35]. Similarly, research among University of Nairobi students reported moderate to high stress levels ranging from 27.6% to 37.0% [36]. A study conducted during the COVID-19 pandemic in Nairobi County found that approximately 27% of young men and 30% of young women (aged 16-25) exhibited depressive symptoms, while around 38% of both genders showed signs of anxiety [37].

The study revealed notable socio-demographic differences in mental health outcomes. Depression and stress were most prevalent among individuals aged 24-26 (36.8%), while anxiety was highest among those aged 21-23 (49.2%). These findings align with Kessler et al. [38], who found that young adults were particularly vulnerable to mental health issues due to life transitions and stress related to finances, academics, or employment. While age was significantly associated with anxiety, it was not significantly linked to depression or stress.

Gender differences were also observed, with men reporting higher levels of depression, anxiety, and stress, contrary to findings by Seedat et al. [39], who noted higher rates among women. Although women generally experience a higher lifetime prevalence of anxiety, men may experience more severe forms when they do occur [39]. Higher educational attainment was associated with increased depression and stress, particularly among undergraduates (42.7%) and postgraduates (45.5%). This aligned with the study by Eaton et al. [40], who noted that although higher education was typically linked to better physical health and longevity, it was also linked to elevated stress and depression due to academic pressures, career uncertainty and the burden of balancing education with financial or family responsibilities. Anxiety was most prevalent among undergraduates (54.7%) and those with secondary education (41.2%), though overall, educational attainment was not significantly associated with all psychological disorders.

Employment status showed notable disparities. Individuals in casual employment reported significantly high levels of depression (59.3%), anxiety (81.5%), and stress (59.3%), supporting Butterworth et al. [41], who found that precarious employment heightened mental health risks through economic instability, job dissatisfaction, and social exclusion. High-income earners (Ksh 31,000-40,000) reported significantly higher levels of depression, anxiety, and stress (75.0%; p < 0.001). As noted by Marmot et al. [42], income inequality played a key role in mental health; while higher incomes granted better access to resources, they also came with stressors like long hours, competitive work environments and pressure to maintain success, which can contribute to burnout and psychological distress. Marital status also influenced mental health, with divorcees exhibiting the highest rates of depression, anxiety, and stress (100%). Whisman et al. [43] highlighted that divorce increased vulnerability to mental health challenges due to emotional strain, financial stress, and the difficulty of adapting to a new lifestyle. Conversely, supportive marriages acted as protective buffers against mental illness. However, in this study, marital status showed no significant association with psychological disorders.

The impact of COVID-19 was also evident; individuals whose household income was affected reported significantly higher rates of depression (50%), anxiety (38.5%), and stress (50%) (p < 0.001). This aligned with findings by Xiong et al. [44], who reported elevated psychological disorders globally due to pandemic-related stressors such as social isolation, fear of infection, and economic uncertainty. Household income disruptions due to COVID-19 were significantly associated with all three mental health conditions.

Multivariate analysis revealed that gender significantly predicted depression and stress, with males showing lower odds compared to females. This aligned with findings from studies in informal settlements in South Africa [45] and Nakuru County, Kenya [46], which reported higher levels of depression and stress among women. Income level also emerged as a significant determinant of depression and stress. This was supported by evidence from Lund et al. [47], who found that individuals with lower socioeconomic status in LMICs were more prone to mental disorders. Similarly, research in Kenya, such as in Migori County, observed a dose-response relationship between poverty and depressive symptoms [48]. Moreover, economic stability during the COVID-19 pandemic significantly reduced the risk of depression and stress. Those whose household incomes were unaffected had lower odds of experiencing these issues, echoing findings by Kola et al. [49], who linked pandemic-related economic instability to increased mental health disorders in LMICs.

Strengths and limitations: this study has several notable strengths. The use of the DASS-21, a validated and widely applied instrument, enhances the reliability of assessing depression, anxiety, and stress. A relatively high response rate reduces the risk of non-response bias and improves sample representativeness. Additionally, the focus on a vulnerable and under-researched population provides valuable insights into an often-overlooked context.

However, some limitations should be considered. The reliance on self-reported data may introduce social desirability and recall bias, potentially affecting accuracy. The focus on young adults and the study´s restriction to a single urban informal settlement in Kenya limit generalizability. The observed gender imbalance, with a higher proportion of female participants, may introduce bias by limiting the representativeness of the study population and leading to biased effect estimates if not properly adjusted for in the analysis. Furthermore, the use of convenience sampling may have introduced selection bias and limited the generalizability of the findings, and the use of a cross-sectional design, as well as the absence of follow-up, limits the ability to assess changes over time or establish causal relationships.

Conclusion     

The study revealed a high prevalence of moderate to severe symptoms of depression, anxiety, and stress among young adults. Mental health outcomes varied significantly across socio-demographic and economic factors, including age, gender, education, employment, income, marital status, and COVID-19-related financial disruptions. Bivariate analysis showed significant associations between mental health outcomes and several factors, including age, gender, employment, income, and COVID-19 impact. However, in multivariable analysis, gender, income, and financial stability remained significant predictors. Males were less likely to experience depression and stress after adjusting for confounders, while lower income was associated with increased mental health risks. Stable income during the COVID-19 period appeared to be protective. These findings point to the need for a comprehensive approach to mental health among young adults in Mathare. Strengthening surveillance, early intervention, and access to information can help reduce psychological disorders and address group-specific disparities. Integrating mental health into existing healthcare systems and developing tailored strategies based on age, gender, education, employment, income, and marital status are essential. Providing well-paying job opportunities can alleviate economic strain. Additionally, promoting mental health literacy can empower individuals to manage their own well-being and support others.

Competing interests     

The authors declare no competing interests.

Authors' contributions     

Conceptualization: Grace Wanjugu Kamau, Lucy Njiru, Marion Mutugi, and Ann Wamathai; methodology: Grace Wanjugu Kamau, Lucy Njiru, and Marion Mutugi; data collection: Grace Wanjugu Kamau, and Ann Wamathai; formal analysis: Grace Wanjugu Kamau, Lucy Njiru, and Marion Mutugi; resources: Grace Wanjugu Kamau and Ann Wamathai, writing-original draft preparation: Grace Wanjugu Kamau and Lucy Njiru; writing-review and editing Grace Wanjugu Kamau, Lucy Njiru, and Marion Mutugi; and Ann Wamathai; supervision: Lucy Njiru and Ann Wamathai. All the authors read and approved the final version of this manuscript.

Acknowledgments     

The author would like to express sincere gratitude to the third and fourth authors for their invaluable contributions to the design of the study, data acquisition, and constructive feedback during the analysis and interpretation stages. Their insights and critical review of the manuscript significantly enriched the quality of this work. The author would also like to thank the participants for their involvement in the study and their support in facilitating the fieldwork during the research period.

Tables     

Table 1: demographic characteristics of study participants, recruited from Mathare slums (Nairobi, Kenya), aged 18-29 years, from January to February 2024 (N = 404)

Table 2: prevalence and severity levels of depression, anxiety, and stress among of study participants, recruited from Mathare slums (Nairobi, Kenya), aged 18-29 years

Table 3: distribution of mental disorders by their socio-demographic characteristics among young adults in Mathare slums (Nairobi, Kenya)

Table 4: multivariate analysis of variables associated with depression, anxiety, and stress among young adults (18-29 years) of Mathare Slums, Nairobi

References