Mental disorders are among the leading causes of disability and disease burden worldwide, accounting for a substantial proportion of disability-adjusted life years (DALYs) and years lived with disability (YLDs), with little evidence of a reduction in this burden over the past three decades (World Health Organization [WHO], 2022; GBD 2019 Mental Disorders Collaborators, 2022). Globally, approximately 280 million people live with depression and 301 million with anxiety disorders, making them among the most prevalent mental health conditions worldwide (WHO, 2022).

These disorders substantially diminish quality of life, impair social and occupational functioning, and impose considerable social and economic burdens on individuals, families, communities, and healthcare systems (WHO, 2022; Arias et al., 2022).

The burden of mental disorders falls disproportionately on low-and middle-income countries (LMICs), where the majority of the global mental health burden is concentrated despite limited mental health resources and substantial treatment gaps (WHO, 2022; Alloh et al., 2018). Despite this considerable burden, access to mental healthcare remains critically inadequate across many LMICs. Mental health service coverage is substantially lower than in high-income countries, and in many settings fewer than 10% of individuals receive minimally adequate treatment, resulting in treatment gaps exceeding 90% (Ferrari et al., 2022; Thornicroft et al., 2022). This disparity is further reflected in the quality of care received: approximately 23% of individuals with major depressive disorder receive minimally adequate treatment in high-income countries compared with only 3% in low- and lower-middle-income countries (Ferrari et al., 2022).

Bangladesh, a rapidly urbanizing lower-middle-income country with a population exceeding 170 million (World Bank, 2023), faces considerable challenges in addressing the growing burden of mental disorders because of limited mental health resources, workforce shortages, weak governance, and increasing demand for services (WHO, 2025; Hasan et al., 2021; Koly et al., 2024). Rapid demographic change, unplanned urbanization, environmental degradation, and widening socioeconomic inequalities have transformed living conditions in Dhaka, placing considerable pressure on housing, infrastructure, environmental sustainability, and public services (United Nations Human Settlements Programme [UN-Habitat], 2020; Roy et al., 2019). These adverse urban conditions are recognized as important social and environmental determinants of psychological stress and poor mental health, particularly in rapidly urbanizing settings (WHO, 2022).

Mental disorders are also among the strongest risk factors for suicide globally, and psychiatric disorders have likewise been identified as major determinants of suicide in Bangladesh (Too et al., 2019; Arafat et al., 2020). More broadly, mental health is strongly influenced by the social determinants of health. Poverty, unemployment, housing insecurity, social exclusion, limited social support, exposure to violence, and restricted access to healthcare are well-established determinants that increase vulnerability to common mental disorders (Allen et al., 2014; Lund et al., 2018; WHO & Calouste Gulbenkian Foundation, 2014). In Bangladesh, these challenges are compounded by an under-resourced mental healthcare system, where only 0.44% of the national health budget is allocated to mental health and approximately 67% of this expenditure is directed to specialized psychiatric hospitals rather than community-based services (WHO, 2025; Hasan et al., 2021). Furthermore, shortages of trained mental health professionals, persistent stigma, and limited mental health literacy continue to delay help-seeking and restrict access to appropriate care (Hasan et al., 2021; WHO, 2025).

Although the burden of mental disorders in Bangladesh has been increasingly  recognized,  evidence  examining  mental health vulnerability across socioeconomic groups remains limited, particularly among urban populations. Previous studies have primarily focused on estimating the prevalence of mental disorders, with comparatively little attention given to the socioeconomic and structural determinants that increase vulnerability or to identifying populations at heightened risk before the onset of clinically significant mental disorders. Examining vulnerability, rather than prevalence alone, provides important insight into the contextual factors that predispose individuals to common mental disorders and supports the development of preventive public health interventions targeting high-risk populations (Lund et al., 2018; WHO, 2022). Therefore, this study aimed to examine the association between socioeconomic status and vulnerability to common mental disorders among adults residing in Dhaka City, Bangladesh. Using the World Health Organization Self-Reporting Questionnaire (SRQ-20), the study assessed mental health vulnerability across different income groups and identified socioeconomic and demographic factors associated with psychological distress. The findings are intended to inform community-based screening, early intervention strategies, and evidence-based policies aimed at reducing mental health inequalities in rapidly urbanizing settings.

Methods

Sampling and Sample Characteristics

In this community-based cross-sectional study, the minimum required sample size was calculated to be 288 participants. To improve the statistical power of the study and increase the precision of the estimates, the sample size was doubled, resulting in a final sample of 576 participants.

Participants were adults aged 18 years and above residing in selected slum and urban areas of Dhaka City, Bangladesh. The study examined the association between socioeconomic status and vulnerability to developing mental health disorders among adults from different socioeconomic backgrounds. Socioeconomic status comprised monthly household income together with other demographic characteristics, including education, employment, marital status, family type, age, and sex.

A systematic random sampling technique was used for participant selection. Households were randomly selected based on a household census obtained from the Directorate General of Health Services (DGHS), with a sampling frame of 2,312 households. One eligible adult from every fourth household, following the selection of a random starting point, was interviewed. If more than one eligible adult was present in a household, one respondent was selected randomly. Individuals who refused to participate or were physically unable to complete the interview were excluded from the study.

For descriptive analyses, participants were classified into three socioeconomic groups according to monthly household income: low income (≤15,000 BDT); lower-middle income (15,001–30,000 BDT); and upper-middle income (>30,000 BDT). Participants representing the low-income group were recruited from the slum areas of Kamrangirchar and Mirpur, whereas participants from the lower-middle- and upper-middle-income groups were recruited from the residential areas of Dhanmondi, Bashundhara, and Banasree.

Data Collection Procedure

The study was conducted between June 2019 and February 2022, with data collection carried out between 2019 and 2020. Research ethics approval was obtained from the Ethical Review Committee of the Centre for Injury Prevention and Research, Bangladesh. Written informed consent was obtained from all participants. De-identified data were used, and participants were free to decline participation or withdraw from the study at any time without consequence.

Face-to-face interviews were conducted using a pre-tested structured questionnaire and the World Health Organization Self-Reporting Questionnaire-20 (SRQ-20), a widely used screening instrument for common mental disorders in community settings (Harpham et al., 2003; Sartorius & Janca, 1996). The Bangla version of the SRQ-20, validated by the National Institute of Mental Health, Bangladesh, was used in this study (Netsereab et al., 2018). Before each interview, participants were informed about the purpose and significance of the study. During the COVID-19 lockdown, data collection was temporarily suspended and subsequently resumed using appropriate personal protective equipment, including face masks.

Variables and Instruments Mental Health Assessment

Mental health status was assessed using the World Health Organization Self-Reporting Questionnaire-20 (SRQ-20). The SRQ-20 consists of 20 dichotomous (Yes/No) items assessing symptoms experienced during the preceding 30 days. Responses were scored as 1 ("Yes") or 0 ("No"), yielding a total score ranging from 0 to 20, with higher scores indicating poorer mental health status.

Participants with a total SRQ-20 score of ≥7 were classified as vulnerable to developing a mental health disorder, whereas those scoring <7 were classified as not vulnerable. In addition to the overall SRQ-20 score, symptom-specific analyses were conducted using predefined domains comprising cognitive symptoms (items 8, 12, and 13); anxiety/depression symptoms (items 4, 6, 9, and 10); and somatic symptoms (items 1, 2, 3, and 7).

Sociodemographic and Socioeconomic Variables

Sociodemographic information was collected using a structured questionnaire. Variables included age, sex, educational attainment, marital status, employment status, family type, and place of residence. Household characteristics, including access to electricity, cooking gas, telephone services, ownership of vehicles, and household furniture, were also recorded.

Socioeconomic status was assessed using monthly household income together with other socioeconomic characteristics. Monthly household income was categorized into three groups: low income (≤15,000 BDT); lower-middle income (15,001 - 30,000 BDT); and upper-middle income (>30,000 BDT).

Statistical Analysis

Data were analyzed using the Statistical Package for the Social Sciences (SPSS) version 24. Completed questionnaires were reviewed daily for completeness and accuracy before data entry and analysis.

Descriptive statistics, including frequencies, percentages, means, standard deviations, medians, and ranges, were used to summarize participant characteristics and SRQ-20 scores. Participants were classified as vulnerable (SRQ-20 score ≥7) or not vulnerable (SRQ-20 score <7).

Associations between socioeconomic characteristics and vulnerability to developing mental health disorders were initially examined using Pearson's chi-square test or Fisher's exact test, where appropriate. Epidemiologically relevant variables, including monthly household income, age, sex, education, employment, marital status, and family type, were subsequently entered into a multivariable logistic regression model to estimate adjusted odds ratios (AORs) and 95% confidence intervals (CIs) while controlling for potential confounding. Monthly household income was considered the primary exposure variable, whereas age, sex, education, employment, marital status, and family type were included as covariates. A two-sided p-value of <0.05 was considered statistically significant.

Results

Low-income participants had a higher vulnerability to developing mental health disorder compared to lower-middle and upper-middle-income participants. The proportion of low-income participants with a score of 7 or higher on the SRQ was 24.3%, compared to 15.7% for lower-middle-income and 8.4% for upper-middle-income participants.

The mean SRQ score was also significantly higher for low-income participants (4.27) compared to lower-middle-income (3.34) and upper-middle-income (2.57) participants. Moreover, it was reported that respondents in the low-income category had 3.7 times higher odds of vulnerability to mental disorders compared to those in the upper-middle income category [OR (95%CI): 3.73(1.47-9.42); p=0.005], and those in the lower-middle income category had 2.2 times higher odds of vulnerability to mental disorders compared to those in the upper-middle income category [OR (95%CI): 2.16(1.21-3.84); p=0.009].

Table1: Gender of the respondents (n=576)

Among the total 576 respondents, 76 participants met the SRQ-20 cut-off score (≥7), indicating vulnerability. Of these 76 vulnerable individuals, 56 (73.7%) belonged to the low and lower-middle income category, whereas only 20 (26.3%) were from the upper-middle income category. This distribution further confirms that vulnerability is concentrated among lower-income groups.

Table 2: Binary Logistic Regression Analysis of Factors Associated with Vulnerability to Developing a Mental Disorder (N = 576)

The statistical significance of this association was confirmed by both chi-square analysis (p=0.004) and logistic regression models. In the unadjusted model, participants in the low and lower-middle income group had 2.18 times higher odds of vulnerability compared to those in the upper-middle income group (95% CI:1.27–3.74; p=0.005).

After adjusting for gender, education status, employment status, marital status, and family type, income remained an independent predictor, with an adjusted odds ratio of 2.43 (95% CI: 1.38–4.29; p=0.002).

Table 3: Summary of the Association Between Sociodemographic Characteristics and Specific Mental Health Disorders (N = 576) Sociodemographic Variable Anxiety & Depression Somatic Disorder Cognitive Disorder Overall Vulnerability (*SRQ-20 ≥7)

*SRQ-20 = Self-Reporting Questionnaire-20.

The principal finding of this study is the statistically significant association between income category and vulnerability to developing mental health disorders. Participants from the low and lower-middle income group exhibited a markedly higher prevalence of vulnerability (16.6%) compared to participants from the upper-middle income group (8.4%). This nearly twofold difference clearly indicates that economically disadvantaged individuals are disproportionately affected.

Age-specific analysis showed that younger participants (≤35 years) demonstrated higher proportions of vulnerability (30.3% and 26.3%). However, the association between age and overall vulnerability was not statistically significant (p=0.833).

Cognitive disorder, however, was significantly associated with age (p<0.001), with higher prevalence in older participants (66–75 years). This suggests that certain cognitive symptoms may increase with aging, although overall vulnerability remains more strongly influenced by income. Thus, while age influences specific symptom domains, it does not independently predict overall vulnerability in this study.

Education level showed observable trends but no statistically significant association with vulnerability (p=0.884). Higher proportions of vulnerability were noted among participants with no education (26.3%) and those with higher secondary education (31.6%). However, after adjustment in regression analysis, education status was no longer a significant predictor. This suggests that although educational attainment may influence awareness and coping capacity, income disparity has a stronger and more direct impact on vulnerability.

Occupation showed no statistically significant association with overall vulnerability (p=0.191). However, homemakers (30.3%) and participants categorized under “other” occupations (35.5%) demonstrated relatively higher proportions of vulnerability. These findings may reflect hidden economic dependence or social stressors in certain occupational categories. Nevertheless, occupation did not independently predict vulnerability after adjustment.

Marital status was not significantly associated with overall vulnerability (p=0.398). Although married participants showed higher raw numbers of vulnerability, this is likely due to their larger representation in the sample. Family type also showed no significant association with overall vulnerability(p=0.780). However, somatic disorders were significantly higher among joint families (p=0.001). This may reflect interpersonal stress or resource-sharing pressures within extended households.

The multivariate logistic regression model confirmed that income category is the only independent predictor of vulnerability in this study. After controlling for all socio-demographic variables, low-and lower-middle-income participants had 2.43 times higher odds of vulnerability. Other variables (gender, education, employment status, marital status, family type) lost significance in adjusted models, emphasizing the central role of economic disparity.

Discussion

The present study examined the relationship between socioeconomic status and vulnerability to developing common mental health disorders among adults living in selected urban and slum communities of Dhaka City, Bangladesh. The findings show a clear and consistent pattern: participants from lower socioeconomic groups were more vulnerable to mental health problems than those from the upper-middle-income group. Based on the SRQ-20 cut-off score of ≥7, vulnerability was highest among low-income participants, followed by lower-middle-income participants, and lowest among upper-middle-income participants. This pattern was also reflected in the mean SRQ scores, which were highest in the low-income group and lowest in the upper-middle-income group. These findings indicate that psychological distress is not evenly distributed across the study population but is concentrated among participants experiencing greater economic disadvantages.

Income emerged as the most important factor associated with vulnerability in this study. Low-income respondents had 3.7 times higher odds of vulnerability compared with upper-middle-income respondents, while lower-middle-income respondents had 2.2 times higher odds. When low- and lower-middle-income participants were combined, they had more than twice the odds of vulnerability compared with the upper-middle-income group. Importantly, after adjusting for sex, education, employment status, marital status, and family type, income remained statistically significant, with an adjusted odds ratio of 2.43. This confirms that income was the only independent predictor of vulnerability in the multivariable model and suggests that economic hardship has a stronger influence on mental health vulnerability than the other sociodemographic variables examined in this study.

The concentration of vulnerability among low- and lower-middle-income participants is consistent with existing evidence that poverty and socioeconomic deprivation are strongly associated with common mental disorders in low- and middle-income countries. Economic disadvantages may increase vulnerability through several pathways, including chronic financial stress, insecure employment, poor housing conditions, overcrowding, food insecurity, reduced access to healthcare, and limited opportunities for social mobility. In urban Dhaka, these stressors may be especially severe among people living in slum or resource-constrained settings, where daily living conditions can place sustained pressure on mental well-being. Therefore, the findings of this study support the view that mental health vulnerability is closely linked to broader social and economic inequalities rather than being only an individual clinical issue.

Although some differences were observed across age, education, occupation, marital status, and family type, these variables were not significantly associated with overall vulnerability after adjustment. Age-specific analysis showed that younger participants had higher proportions of overall vulnerability; however, this association was not statistically significant. In contrast, cognitive symptoms were significantly associated with age and were more common among older participants, particularly those aged 66–75 years. This suggests that while age may not independently predict overall mental health vulnerability, it may influence specific symptom domains such as cognitive complaints. These findings highlight the importance of examining both overall SRQ-20 vulnerability and symptom-specific patterns when assessing mental health risk in community populations.

Education and occupation showed observable trends but did not reach statistical significance. Participants with no education and those with higher secondary education showed relatively higher proportions of vulnerability, while homemakers and participants in the “other” occupational category also appeared more vulnerable. However, these associations did not remain significant in adjusted analyses. This may indicate that the effect of education and occupation on mental health vulnerability is partly mediated by income or broader socioeconomic disadvantage. Similarly, marital status was not significantly associated with overall vulnerability, and the higher number of vulnerable married participants likely reflected their larger representation in the sample rather than a true independent effect.