Abstract
Background
Healthcare professionals pursuing higher education experience psychological distress due to combined professional and academic pressures. However, evidence regarding their mental health status in Bangladesh is limited. This study aimed to assess depression, anxiety, and stress among healthcare professionals enrolled in a Master of Public Health program in Bangladesh.
Methods
This cross-sectional study involved 280 healthcare professionals engaged as public health students at a private university in Dhaka, Bangladesh. Demographic, clinical, organizational and mental health related data was collected using an online self-administered questionnaire. Mental health status was assessed by DASS-21 Scale. Regression analyses were performed to examine relationships between outcome and exposure variables.
Results
The results showed that 26.4% of respondents were depressed, 35.4% were anxious, and 16.4% were stressed. Depression was significantly higher among single individuals (AOR, 4.06; 95% CI 1.69-9.69; P=0.01), younger respondents (AOR, 2.48; 95% CI 1.23-5.04; P=0.01), those with smaller families (AOR, 2.91; 95% CI 1.51-5.59; P=0.01), and those with comorbidities (AOR, 2.49; 95% CI 1.34-4.62; P=0.01). Anxiety was more prevalent in females (AOR, 3.12; 95% CI 1.29-7.52; P=0.01), singles (AOR, 2.64; 95% CI 1.10-6.35; P=0.03), and had ≤1 child (AOR, 1.90; 95% CI 1.06-3.41; P=0.03) and those comorbidities (AOR, 2.49; 95% CI 1.34-4.62; P=0.01). Stress was higher in younger individuals (AOR, 2.89; 95% CI 1.27-6.63; P = 0.01), those with smaller families (AOR, 2.23; 95% CI 1.09-4.59; P=0.03), and those with comorbidities (AOR, 2.46; 95% CI 1.26-4.79; P=0.01).
Conclusion
A substantial prevalence of depression, anxiety, and stress was observed among healthcare professionals. Depression was associated with single, younger age, smaller families, and comorbidities. Anxiety was linked to female, single, having ≤1 child, and comorbidities, while stress related to younger age, smaller families, and comorbid conditions. The findings highlight the need for mental health screening, counseling, and awareness programs for healthcare professionals and public health students.
Keywords: mental health, university, healthcare professionals, depression, anxiety, stress
Introduction
Mental health is a condition of well-being that allows individuals to cope with life stressors, recognize their potential, work productively, and contribute to their society. 1 Healthcare professionals are more susceptible to mental health problems than the general population, which may negatively affect their personal well-being, quality of healthcare delivery, and workforce stability.2,3 Extensive research has highlighted the mental health challenges faced by individuals in healthcare training and academic environments, with studies reporting elevated levels of depression, anxiety, and burnout associated with academic pressure, workload demands, and limited institutional support. 4 From 1990 to 2013, the worldwide prevalence of mental disorders surged by 50%, growing from 416 million to 615 million cases. 5
Mental health is also an important public health concern in Bangladesh. A nationwide community survey reported that 18.7% of Bangladeshi adults had psychiatric disorders, with higher prevalence among women than men. 6 Previous studies among Bangladeshi students have also shown substantial psychological distress. More than 60% of nursing students reported moderate to extremely severe depressive symptoms, while family pressure and academic factors were significant predictors of poor mental health. 7 Among university students, anxiety and depression were common, with lifestyle and socioeconomic factors influencing outcomes. 8 Recent evidence among Bangladeshi medical students further reported high prevalence of depression (55.8%), anxiety (58.7%), and stress (38.6%). 9 These findings suggest that psychological distress is a growing concern in both the general population and academic environments in Bangladesh.
The mental health and overall wellness of healthcare professionals, especially those employed within academic institutions, is a critical issue that demands comprehensive investigation. 10 Accumulating evidence suggests that mental health challenges, including disorders and work-related stress, have become an increasingly pressing concern for individuals working in the healthcare field, particularly those in academic settings. 11 Scientific research has demonstrated that the demands of everyday work make health professionals more vulnerable to psychological problems, including stress, anxiety, depression, and burnout syndromes. A 2024 study further states that healthcare workers are “inevitably more vulnerable” to stress, anxiety, and depression because of patient exposure and a stressful workplace environment. 12 Stress is a worldwide epidemic in the twenty first century and is linked to circumstances in which feelings of tension and/or excitement persist because the person is unable to use coping mechanisms. 13 Depression involves low self-esteem, apathy, and anhedonia, while anxiety features acceleration and anticipation. Stress and anxiety are normal but worsen when prolonged or intense. A European study found that 61% of healthcare professionals reported work-related stress, highlighting the industry’s challenges. 14 Healthcare professionals are more likely than other worker categories to cite workload/working hours as a cause of stress at work (77%), and a lack of support from coworkers or superiors to carry out their role (61%). 15 It is crucial to comprehend the hazards and impact(s) on mental health that healthcare professionals face and to discover potential countermeasures.
Lovibond and Lovibond developed the Depression, Anxiety, and Stress Scale - 21 (DASS-21) in Australia to quantify depression, anxiety, and stress through distinct subscales. This self-report measure not only assesses these states traditionally but also differentiates features associated with each. 16 As a result, the DASS-21 might be a useful tool for researchers and medical professionals to better define, comprehend, and quantify the nature, etiology, and processes of the pervasive emotional disturbance.
Although studies have separately examined mental health among healthcare workers and university students, limited evidence exists regarding healthcare professionals studying in higher education institutions, particularly in Bangladesh. Understanding the mental health status of this population is important for developing targeted support strategies, improving academic performance, strengthening workforce productivity, and ensuring sustainable healthcare systems. Therefore, this study aimed to assess the prevalence of depression, anxiety, and stress and identify associated factors among healthcare professionals enrolled in a public health academic program in Bangladesh.
Methods
Study Design
This cross-sectional study utilized a quantitative approach to collect structured data during May, 2024 to October 2024. The objective of this study was to assess levels of depression, anxiety, and stress among healthcare professionals enrolled in a Master of Public Health program in Bangladesh.
Study Participants, Sample Size and Sampling
A total of 280 respondents were enrolled in this study. They were students from the Public Health Department of a private University in Dhaka, Bangladesh. Since 2006, the university’s Department of Public Health has offered a Master in Public Health (MPH) program in the evenings and on weekends for physicians, nurses, and allied health professionals such as nutritionists, physiotherapists, laboratory technologists, and NGO specialists. Enrollment in the program is considered part-time academic involvement, while students continue their full-time professional work in various institutions across Dhaka and other parts of Bangladesh. This population was selected because participants were actively employed healthcare professionals undertaking postgraduate education and may experience combined occupational and academic stressors. As participants were actively engaged in professional roles, the sample included respondents from diverse healthcare backgrounds.
All 398 students enrolled in the Summer 2024 semester of the MPH program were initially considered eligible for participation in this study. Initially, a potential sample size of 234 was calculated using the formula “n=Z2pq/d2” with the following parameters: Z (standard normal deviate)=1.96, p (prevalence of mental disorders among the adult population in Bangladesh) = 18.7%, 17 and margin of error=0.05. With a minimum calculated sample of 234, an additional 10% was added as a cushion to consider non-response to the self-administered data collection instrument, and 10% was added considering data cleaning and initial management, and the final sample size was 280 students. A systematic random sampling technique was employed using a numerically ordered student list (based on student identification numbers) provided by the university. A random starting point was selected using a random number generator, and students were then selected sequentially from the list. Because the total number of eligible students was close to the required sample size and recruitment was manageable, the sampling interval was approximately 1. Recruitment continued until the target sample size of 280 participants was achieved. All selected students agreed to participate and completed the survey, yielding a 100% response rate.
Inclusion and Exclusion Criteria
Eligible participants were healthcare professionals enrolled in the Master of Public Health program at a private university during the study period who were aged 18 years or older and provided online informed consent to participate. Participants were required to have a healthcare academic background or prior professional experience in the healthcare sector. Individuals without internet access or unable to complete the online survey were not included.
Data Collection
Data were collected through a self-administered, structured, and anonymous online questionnaire. Due to the regular online class format for MPH students, a physical paper-based questionnaire was not feasible. Instead, respondents were reached via email and social media platforms such as WhatsApp and Facebook Messenger. Before accessing the questionnaire, participants were presented with an information and informed consent page describing the study purpose, voluntary participation, anonymity, confidentiality, and right to withdraw at any time. Those who provided online consent were able to proceed to the main survey. The online survey took only 5 to 10 minutes to complete. The survey was conducted in English.
Questionnaire Design
Google Forms was used to create online questionnaires. The DASS-21 is a previously validated instrument and was used in its original validated form without modification [Supplemental Material]. The questionnaire was reviewed by two independent experts to ensure contextual suitability, cultural appropriateness, clarity, and applicability to the target respondents. The overall survey was pre-tested with 10 respondents assessing comprehensibility and administration procedures. Feedback from the pre-test was used to improve the overall administration and clarity of the survey. The questionnaire covered mental health, demographics, and organizational details of healthcare professionals. The mental health status was assessed using Depression, Anxiety, and Stress Scale-21 (DASS-21), a self-report instrument designed to evaluate the emotional states of depression, anxiety, and stress. Each of the 21 items is rated on a 4-point scale, from 0 (“Did not apply to me at all”) to 3 (“Applied to me very much, or most of the time”). 18 The instrument’s reliability was assessed using Cronbach’s alpha, which indicated excellent internal consistency (α= 0.92).
Data Analysis
The data quality was assessed and analyzed using the Statistical Package for the Social Sciences (SPSS) software. Descriptive statistics, including frequencies and proportions, were used to summarize the study characteristics. Relevant continuous data were categorized using mid-point values of the percentage scores as cut-off points. 19 Scores for depression, anxiety, and stress were calculated using the DASS-21 scoring guidelines by summing the scores of the relevant items for each subscale and multiplying the total scores by two. Final scores were classified as normal, mild, moderate, severe, or extremely severe according to recommended cut-off values. 18 For logistic regression analysis, the outcome variables were dichotomized as follows: normal = reference group; mild, moderate, severe, and extremely severe = one combined group. Bivariable analyses using chi-square tests were initially performed to assess associations between independent variables and mental health outcomes. Variables showing statistically significant associations in bivariable analysis, together with pre-specified confounders, were entered into multivariable logistic regression models. A backward elimination procedure was applied to obtain the final models. Adjusted odds ratios (95% CI) were calculated for mental health status (anxiety, stress, depression) based on specified exposures.
Results
Participants’ Sociodemographic Characteristics
This study included 280 respondents to yield outcomes of the objectives. The basic characteristics of the respondents indicate that the majority were from the younger age (≤40 years: 64.6%) and married (88.9%) group with mostly having one or no children (51.4%) who were belonging to a nuclear family (73.2%) with more than three family members (78.9%). From the professional characteristics, the majority were found to work in public organizations (69.6%) with more than ten years (51.1%) of working experience. Moreover, the study observed that the majority did not have any comorbidity (64.7%) or chronic clinical experiences. (Table 2). The professional composition of the study sample included nurses as the largest group (76.4%), followed by allied healthcare professionals (16.5%) and doctors (7.1%) Figure 1.
Table 2.
Factors Associated With the Depression Status Among the Respondents (n=280)
| Characteristics | Depression status | |||||||
|---|---|---|---|---|---|---|---|---|
| Number of participants, n (%) 1 | Normal, n (%) | Depressed, n (%) | ꭓ2/p-value (≤0.05) 2 | Un-adjusted OR (95% CI) depressed Vs normal 3 | p-value | Adjusted OR (95% CI) depressed Vs normal 3 | p-value | |
| Age group (in years) | ||||||||
| ≤40 | 181 (64.6) | 121 (43.2) | 60 (21.4) | 11.89/0.01* | 3.01 (1.58-5.74) | 0.01* | 2.48 (1.23-5.04) | 0.01* |
| >40 | 99 (35.4) | 85 (30.4) | 14 (5.0) | Reference | Reference | |||
| Marital status | ||||||||
| Married | 249 (88.9) | 191 (68.2) | 58 (20.7) | 11.37/0.01* | Reference | Reference | ||
| Single | 31 (11.1) | 15 (5.4) | 16 (5.7) | 3.51 (1.64-7.54) | 0.01* | 4.06 (1.69-9.69) | 0.01* | |
| Number of children | ||||||||
| 0-1 | 144 (51.4) | 98 (35) | 46 (16.4) | 4.64/0.03* | 1.81 (1.05-3.12) | 0.03* | _ | _ |
| 2-3 | 136 (48.6) | 108 (38.6) | 28 (10.0) | Reference | Reference | |||
| Type of family | ||||||||
| Nuclear | 205 (73.2) | 144 (51.4) | 61 (21.8) | 4.36/0.04* | 2.02 (1.04-3.94) | 0.04* | _ | _ |
| Extended | 75 (26.8) | 62 (22.1) | 13 (4.6) | Reference | Reference | |||
| Family size (in number) | ||||||||
| ≤3 | 59 (21.1) | 34 (12.1) | 25 (8.9) | 9.77/0.01* | 2.58 (1.41-4.73) | 0.01* | 2.91 (1.51-5.59) | 0.01* |
| >3 | 211 (78.9) | 172 (61.4) | 49 (17.5) | Reference | Reference | |||
| Type of working organization | ||||||||
| Public | 195 (69.6) | 154 (55.0) | 41 (14.6) | 9.64/0.01* | Reference | _ | _ | |
| Private | 85 (30.4) | 52 (18.6) | 33 (11.8) | 2.38 (1.37-4.16) | 0.01* | Reference | ||
| Working experience | ||||||||
| ≤10 | 137 (48.9) | 92 (32.9) | 45 (16.1) | 5.68/0.02* | 1.92 (1.12-3.31) | 0.02* | _ | _ |
| >10 | 143 (51.1) | 114 (40.7) | 29 (10.4) | Reference | Reference | |||
| Comorbidity | ||||||||
| Yes | 98 (35.3) | 66 (23.7) | 32 (11.5) | 3.59/0.05* | 1.69 (0.98-2.94) | 0.05* | 2.49 (1.34-4.62) | 0.01* |
| No | 180 (64.7) | 140 (50.4) | 40 (14.4) | Reference | Reference | |||
*Statistical significance at p value ≤0.05.
1Data are presented as frequency (n), percentage (%).
2Chi-square test was used to observe the association.
3Logistic Regression Analysis was used to identify the predictors; reference category was considered for depression status as “Normal”.
Figure 1.
Healthcare profession distribution among respondents
Distribution of Different Mental Health Components Among the Respondents
Table 1 revealed that the status of anxiety and stress among the respondents corresponds to their depression status. In explanation, 26.4% were found to be depressed, while 35.4% and 16.4% were found to be anxious and stressed, respectively. These statistics remain concerning as they represent the forefront of healthcare services in Bangladesh. Moreover, statistical parameters revealed that the odds of depression were significantly higher among the stressed (OR/p= 10.32/0.01; 95% CI= 4.35-24.46) and anxious (OR/p= 8.61/0.01; 95% CI= 4.32-17.14) respondents compared to the normal ones (Table 1).
Table 1.
Distribution of the Status of Anxiety and Stress Among the Respondents Corresponds to Their Depression Status (n =280)
| Mental health status | Depression | Total, n (%) 1 | 3 OR/95% CI | |
|---|---|---|---|---|
| Normal | Depressed | |||
| Total | 206 (73.6%) | 74 (26.4%) | ||
| Anxiety | ||||
| Normal 2 | 164 (58.6) | 17 (6.1) | 181 (64.6) | Ref. |
| Anxious | 42 (15.0) | 57 (20.4) | 99 (35.4) | 8.61 (4.32-17.14)* |
| Stress | ||||
| Normal 2 | 196 (70.0) | 38 (13.6) | 234 (83.6) | Ref. |
| Stressed | 10 (3.6) | 36 (12.9) | 46 (16.4) | 10.32 (4.35-24.46)* |
*Statistically significant.
1Data are presented as frequency (n) and percentage (%); Logistic Regression Analysis was used to identify the predictors.
2reference category was considered for the level of Mental Health Status and behavior as “Normal”. Depressed, Anxious and Stressed refer to mild/moderate/severe/extremely severe.
3OR= Odds Ratio.
Determinants of the Depression Status Among the Respondents
The multivariate cross-table analysis revealed that the basic criteria of the respondents were significantly associated with their depression status. For instance, depression status was found to be significant with age, marital status (p=0.01), number of children (p=0.03), type of family (p=0.04), family size, working organization (p=0.01), working experience (p=0.02), and comorbidity status (p=0.05) of the respondents. Initial screening through the regression analysis study revealed the unadjusted or crude odds ratios (COR) for the depressed conditions of the respondents. It was observed that single (COR/p=3.51/0.01) and younger (COR/p=3.01/0.01) respondents exhibited significantly higher odds of depression who belonged to nuclear families (COR/p=2.02/0.04) with fewer children (one or no children: COR/p=1.81/0.03)and fewer than or equal to three family members (≤3 members: COR/p=2.58/0.01). In addition, respondents from private organizations (COR/p=2.38/0.01) with less work experience (≤10 years: COR/p=1.92/0.02) and who experienced comorbidities (COR/p=1.69/0.05) were significantly more likely to have higher odds of depression. After the model adjustment, the study identified the final predictors of depression among the subjects by revealing the adjusted odds ratios (AOR) of depression and the backward elimination procedure of confounders. Likewise, single (AOR/p=4.06/0.01; 95% CI: 1.69-9.69) and younger (AOR/p=2.48/0.01; 95% CI: 1.23-5.04) respondents with smaller family sizes (≤3 members: AOR/p=2.91/0.01; 95% CI: 1.51-5.59) who had experienced comorbidities (AOR/p=2.49/0.01; 95% CI: 1.34-4.62) were found to have significantly higher levels of depression (Table 2).
Determinants of the Status of Anxiety Among the Respondents
Moving toward the anxiety status, multivariate cross-table analysis uncovered the key criteria of the respondents that were significantly associated with their anxiety status. Being anxious was significantly associated with age (p=0.02), gender (p=0.01), marital status (p=0.02), number of children (p=0.04), monthly family income (p=0.01), and comorbidity status (p=0.01) of the respondents. Initially, the regression analysis revealed the unadjusted or crude odds ratios (COR) for the anxiety status of the respondents. The analysis showed that younger respondents (≤40 years; COR = 1.89, p = 0.02), females (COR = 3.12, p = 0.01), and single respondents (COR = 2.47, p = 0.02) had significantly higher odds of anxiety. In addition, subjects who had fewer children (one or no children: COR/p=1.67/0.04), lower monthly family income (≤45000 BDT: COR/p=2.21/0.01), and experience of comorbidities (COR/p=2.75/0.01) had significant odds of anxiety. After the model adjustment, the study revealed the final predictors of anxiety among the subjects by eliminating the confounders. In other words, female (AOR/p=3.12/0.01; 95% CI: 1.29-7.52) and single (AOR/p=2.64/0.03; 95% CI: 1.10-6.35) respondents with lower monthly family income (≤45000 BDT: AOR/p=1.82/0.05; 95% CI: 0.96-3.43) who had experienced comorbidities (AOR/p=2.49/0.01; 95% CI: 1.34-4.62) were found to have significantly higher levels of anxiety compared to normal subjects (Table 3).
Table 3.
Factors Associated With the Status of Anxiety Among the Respondents (n=280)
| Characteristics | Status of anxiety | |||||||
|---|---|---|---|---|---|---|---|---|
| Number of participants, n (%) 1 | Normal, n (%) | Anxious, n (%) | ꭓ2/p-value (≤0.05) 2 | Un-adjusted OR (95% CI) anxious Vs normal 3 | p-value | Adjusted OR (95% CI) anxious Vs normal 3 | p-value | |
| Age group (in years) | ||||||||
| ≤40 | 181 (64.6) | 108 (38.6) | 73 (26.1) | 5.54/0.02* | 1.89 (1.11-3.25) | 0.02* | _ | _ |
| >40 | 99 (35.4) | 73 (26.1) | 26 (9.3) | Reference | Reference | |||
| Gender | ||||||||
| Male | 47 (16.8) | 39 (13.9) | 8 (2.9) | 8.31/0.01* | Reference | Reference | ||
| Female | 233 (83.2) | 142 (50.7) | 91 (92.5) | 3.12 (1.39-6.99) | 0.01* | 3.12 (1.29-7.52) | 0.01* | |
| Marital status | ||||||||
| Married | 249 (88.9) | 167 (59.6) | 82 (29.3) | 5.78/0.02* | Reference | Reference | ||
| Single | 31 (11.1) | 14 (5) | 17 (6.1) | 2.47 (1.16-5.26) | 0.02* | 2.64 (1.10-6.35) | 0.03* | |
| Number of children | ||||||||
| 0-1 | 144 (51.4) | 85 (30.4) | 59 (21.1) | 4.09/0.04* | 1.67 (1.01-2.74) | 0.04* | 1.90 (1.06-3.41) | 0.03* |
| 2-3 | 136 (48.6) | 96 (34.3) | 40 (14.3) | Reference | Reference | |||
| Monthly family income (in BDT) | ||||||||
| ≤45000 | 188 (67.1) | 111 (39.6) | 77 (27.5) | 7.85/0.01* | 2.21 (1.26-3.87) | 0.01* | 1.82 (0.96-3.43) | 0.05* |
| >45000 | 92 (32.9) | 70 (25) | 22 (7.9) | Reference | Reference | |||
| Comorbidity | ||||||||
| Yes | 98 (35.3) | 49 (17.6) | 49 (17.6) | 15.21/0.01* | 2.75 (1.64-4.61) | 0.01* | 2.49 (1.34-4.62) | 0.01* |
| No | 180 (64.7) | 132 (47.5) | 48 (17.3) | Reference | Reference | |||
*Statistical significance at p value ≤0.05.
1Data are presented as frequency (n), percentage (%).
2Chi-square test was used to observe the association.
3Logistic Regression Analysis was used to identify the predictors; reference category was considered for depression status as “Normal”.
Determinants of the Level of Stress Among the Respondents
To evaluate the stress-related factors, multivariate cross-table analysis uncovered the key criteria of the respondents that were significantly associated with their stress levels. Their stress condition was significantly associated with the age (p=0.02), number of children (p=0.04), family size (p=0.01), monthly family income (p=0.01), and comorbidity status (p=0.01) of the respondents. Initially, regression analysis indicated the crude odds ratios (COR) for the stressed respondents. It was significantly observed that younger (≤40 years: COR/p=3.02/0.01), respondents who had fewer children (one or no children: COR/p=1.97/0.04), smaller family size (COR/p=2.36/0.01), lower monthly family income (≤45000 BDT: COR/p=2.26/0.04) and suffered from any comorbidities (COR/p=2.20/0.02) were found to have significantly higher odds of stress among them. The model adjustment of the regression analysis revealed the final predictors of stress among the subjects by revealing the adjusted odds ratios (AOR) and eliminating the confounders. The final multivariable analysis identified younger respondents (≤40 years; AOR = 2.89, 95% CI: 1.27-6.63, p = 0.01), those with a smaller family size (≤3 members; AOR = 2.23, 95% CI: 1.09-4.59, p = 0.03), and those with comorbidities (AOR = 2.46, 95% CI: 1.26-4.79, p = 0.01) as having significantly higher odds of stress (Table 4).
Table 4.
Factors Associated With the Level of Stress Among the Respondents (n=280)
| Characteristics | Level of stress | |||||||
|---|---|---|---|---|---|---|---|---|
| Number of participants, n (%) 1 | Normal, n (%) | Stressed, n (%) | ꭓ2/p-value (≤0.05) 2 | Un-adjusted OR (95% CI) stressed Vs normal 3 | p-value | Adjusted OR (95% CI) stressed Vs normal 3 | p-value | |
| Age group (in years) | ||||||||
| ≤40 | 181 (64.6) | 143 (51.1) | 38 (13.6) | 7.77/0.02* | 3.02 (1.35-6.77) | 0.01* | 2.89 (1.27-6.63) | 0.01* |
| >40 | 99 (35.4) | 91 (32.5) | 8 (2.9) | Reference | Reference | |||
| Number of children | ||||||||
| 0-1 | 144 (51.4) | 114 (40.7) | 30 (10.7) | 4.19/0.04* | 1.97 (1.02-3.81) | 0.04* | _ | _ |
| 2-3 | 136 (48.6) | 120 (42.9) | 16 (5.7) | Reference | Reference | |||
| Family size (in number) | ||||||||
| ≤3 | 59 (21.1) | 43 (15.4) | 16 (5.7) | 6.22/0.01* | 2.36 (1.18-4.73) | 0.01* | 2.23 (1.09-4.59) | 0.03* |
| >3 | 211 (78.9) | 191 (68.2) | 30 (10.7) | Reference | Reference | |||
| Monthly family income (in BDT) | ||||||||
| ≤45000 | 188 (67.1) | 151 (53.9) | 37 (13.2) | 4.41/0.04* | 2.26 (1.04-4.91) | 0.04* | _ | _ |
| >45000 | 92 (32.9) | 83 (29.6) | 9 (3.2) | Reference | Reference | |||
| Comorbidity | ||||||||
| Yes | 98 (35.3) | 75 (27) | 23 (8.3) | 5.92/0.02* | 2.20 (1.16-4.20) | 0.02* | 2.46 (1.26-4.79) | 0.01* |
| No | 180 (64.7) | 158 (56.8) | 22 (7.9) | Reference | Reference | |||
*Statistical significance at p value ≤0.05.
1Data are presented as frequency (n), percentage (%).
2Chi-square test was used to observe the association.
3Logistic Regression Analysis was used to identify the predictors; reference category was considered for depression status as “Normal”.
Discussion
This study examines the mental health status of healthcare professionals in academic settings. The observed prevalence rates of depression, anxiety, and stress were 26.4%, 35.4%, and 16.4%, respectively. These findings indicate a substantial psychological burden among healthcare professionals pursuing postgraduate studies, as they often have to manage clinical responsibilities alongside academic commitments, administrative tasks, and high-performance expectations. Balancing healthcare service responsibilities with demanding work schedules may contribute to emotional exhaustion and increased psychological distress. The prevalence of psychological morbidity among healthcare professionals pursuing a postgraduate MPH degree observed in this study aligns with the substantial psychological burden reported in similar South Asian and international cohorts, though notable variations exist.20-22 Our finding of a 26.4% depression rate closely mirrors the 21.6% reported among Chinese medical postgraduates 23 and is moderately lower than the 34.8% and 37.0% reported in Nepalese and Indian cohorts, respectively.20,21 Interestingly, the anxiety prevalence in our study (35.4%) is remarkably consistent with the 32.8% observed among postgraduate medical students in Nepal, 21 underscoring the high baseline anxiety prevalent in regional training environments. However, our observed stress levels (16.4%) appear notably lower than those reported in comparable regional studies, such as 34.8% in Nepal 21 and 32.7% among Indian postgraduate residents. 24 Conversely, contrasting extremes are evident in the broader literature: a Saudi Arabian study reported significantly higher rates across all domains (Depression 49.6%, Anxiety 57.0%, Stress 39.5%), 25 whereas a Sri Lankan cohort reported remarkably lower psychological distress (Depression 11.2%, Anxiety 8.9%, Stress 1.9%). 22 The relatively high prevalence of anxiety observed in our study may be associated with demanding work schedules, performance expectations, and balancing academic and healthcare service responsibilities.
This study found that depression was significantly associated (p≤0.05) with age, marital status, number of children, type of family, family size, working organization, working experience, and comorbidity status. Similar findings were observed in other studies, where depression status was significant with age and marital status. 26 However, other studies have found no significant association between depression and age or marital status. 4 Other studies indicate that marital status affects depression, with divorced or widowed individuals often reporting higher levels. Comorbid conditions also increase the likelihood of depression. Additionally, work environment, job experience, and family dynamics, including the number of children, significantly impact mental health. 12 This study showed that single (AOR=4.06) and younger (AOR=2.48) respondents with smaller family sizes (≤3 members: AOR=2.91) and comorbidities (AOR=2.49) had significantly (p≤0.01) higher levels of depression. The finding that single individuals are at a four-fold higher risk for depressionbut a multi-center survey by Zaman et al. (2014), as cited by Gogoi (2016), which reported that depression was more common among married trainees. 21 Similarly, research by Sahasrabuddhe et al., referenced by Kamble and Gaikwad (2026), found higher psychological distress among married postgraduates. 24 Psychosocially and occupationally, this can be explained by the dual-career burden; balancing rigorous clinical and academic schedules with spousal expectations and household responsibilities creates substantial role conflict and time poverty, leading to emotional exhaustion. Biologically and psychologically, the strong association between comorbidities and depression is consistent with findings by Alles et al. (2023) and Novin et al. (2023), reflecting how chronic health conditions deplete an individual’s cognitive and physical reserves, impairing their ability to recover from occupational fatigue. 27 Finally, the higher odds of depression among younger trainees align with Kamble and Gaikwad (2026), who observed higher distress in “fresh” candidates lacking the clinical coping mechanisms of older, in-service peers, 24 though this contradicts Sah (2024), who found no significant age association in a Nepalese cohort, possibly due to differences in sample age distributions or specific assessment tools utilized. 20 Additionally, smaller family sizes can lead to less social support, a known risk factor for depression. Research indicates that social isolation and lack of support significantly increase the risk of depression. Chronic health conditions also contribute to increased stress and reduced quality of life, further elevating depression risk. 28
This study reveals that being anxious was significantly associated(p≤0.05) with age, gender, marital status, number of children, monthly family income, and comorbidity status of the respondents. Similar results were found in other studies showing that anxiety is significantly influenced by these factors. 29 One study found a significant association with age and gender but not with marital status, number of children, or monthly family income. 30 Another study found age, gender, and health indices were significant predictors of anxiety, with single individuals also showing higher anxiety levels. This study found that younger respondents (≤40 years: COR=1.89), females (COR=3.12), single individuals (COR=2.47), those with fewer children (one or none: COR=1.67), lower monthly family income (≤45,000 BDT: COR=2.21), and those with comorbidities (COR=2.75) had significantly higher odds of anxiety. The finding that females are at over three times higher risk for anxiety is well-documented in the literature; Alles et al. (2023) and Tan et al. (2023) both found female gender to be significantly associated with higher levels of psychological morbidity.22,23 Similarly, Sah (2024) cited studies indicating that female medical students experience considerably more anxiety in clinical settings than males. 20 This finding aligns with other study found that lower family income is significantly associated with higher anxiety levels due to financial stress. Studies support the association between comorbidities and higher anxiety levels, indicating that managing multiple health issues can increase stress and anxiety. 29 Having fewer children can be linked to higher anxiety due to social and psychological factors. 13 This study found that stress was significantly associated (p≤0.05) with age, number of children, family size, monthly family income, and comorbidity status (p=0.01) of the respondents. Similar results were found in another study, with significant associations for gender and marital status, but not for age, number of children, or income. 30 This finding aligns with other study found that younger healthcare workers and those with pre-existing health conditions were more susceptible to anxiety, depression, and stress. 31 Increased stress was linked to balancing demanding work responsibilities with family obligations, especially for those with children or large families. Another study found similar results that significant association between monthly family income and stress levels among healthcare workers. 32 Using model adjustment, the study identified younger (≤40 years: AOR=2.89) respondents with smaller family sizes (≤3 members: AOR=2.23) and comorbidities (AOR=2.46) as having significantly higher odds of stress. These findings align with other research findings showing that smaller family sizes predict higher stress levels due to fewer social supports, and individuals with comorbidities have higher odds of stress due to managing multiple health issues. 13 Younger individuals are more likely to experience stress due to career and life pressures. 33 The heightened stress among younger professionals is supported by Tan et al. (2023), who found that younger postgraduates faced the highest levels of employment, academic, and family pressures. 23
Limitations
This study has several limitations. First, self-reported mental health data may be biased due to social desirability. Future research should include clinical assessments for accuracy. Second, although the participants were healthcare professionals, detailed occupation-specific workplace stressors such as working hours, shift duties, patient-care burden, and organizational factors were not comprehensively assessed. Third, the sample healthcare professionals studying public health at one university may not represent all Bangladeshi healthcare workers. Expanding to multiple institutions would improve generalizability. Fourth, participants were recruited from a single postgraduate degree program at one higher education institution, which may limit the representativeness of the findings. Finally, the majority of participants were nursing professionals; therefore, the findings may not fully reflect the mental health status of all healthcare professional groups in Bangladesh.
Conclusions
This study found a substantial burden of depression, anxiety, and stress among healthcare professionals engaged in academic training. Depression was significantly associated with single marital status, younger age, smaller family size, and comorbidities. Anxiety was significantly associated with female gender, single marital status, having ≤1 child, and comorbidities. Stress was significantly associated with younger age, smaller family size, and comorbidities. These findings highlight the need for higher education institutions to provide targeted mental health support, routine screening, timely counseling services, and supportive academic policies for healthcare professionals pursuing postgraduate education while balancing professional responsibilities. Strengthening their mental well-being is important for workforce productivity, quality of care, and sustainable health systems.
Clinical Implications
A significant proportion of healthcare professionals in academic settings experience depression, anxiety, and stress, highlighting a critical need for mental health interventions. Younger age, smaller family size, marital status, gender, low income, and comorbidities are significant factors for poor mental health, emphasizing the need for targeted support strategies. These findings may assist postgraduate public health programs in identifying students at greater risk of psychological distress and in developing tailored support mechanisms to promote their mental well-being and academic performance.
Supplemental Material
Supplemental Material for Mental Health Status Among Healthcare Professionals in a Master of Public Health Program at a Higher Education Institution in Bangladesh: A Cross-Sectional Study by Bilkis Banu, Nasrin Akter, Nusrat Hossain Sheba, Md. Allama Faysal, Nigar Sultana, Md Jiaur Rahman in Health Services Insights.
Acknowledgments
The authors express their sincere gratitude to the healthcare professionals who generously contributed their time and shared their information for this study. Their insights were crucial in shaping and enhancing the research outcomes. The authors also acknowledge with appreciation the Northern University whose cooperation and support facilitated the successful execution of this study.
Author Contributions: Bilkis Banu was responsible for leading the study’s design, overseeing data collection, and preparing the manuscript. Nasrin Akter and Nusrat Hossain Sheba played roles in gathering data, conducting analysis, and revising the manuscript. Md. Allama Faysal provided support with statistical analysis and interpreting the data. Nigar Sultana provided logistic support during data collection and critically reviewed the manuscript. Md Jiaur Rahman was involved in conceptualizing the study, developing the methodology, and interpreting data. All authors participated in reviewing and approving the final version of the manuscript.
Funding: The authors received no financial support for the research, authorship, and/or publication of this article.
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Supplemental Material: Supplemental material for this article is available online.
ORCID iDs
Nasrin Akter https://orcid.org/0000-0003-2199-5353
Md Jiaur Rahman https://orcid.org/0000-0002-2027-2893
Ethical Considerations
This study was approved by the Ethical Review Committee of the Department of Public Health, Northern University Bangladesh (NUB/DPH/EC/2024/30) and adhered to the Declaration of Helsinki. The date of ethical approval was 25/5/2024.
Consent to Participate
Participation was both anonymous and voluntary, with online informed consent obtained from all respondents at the beginning of the survey. Participants had the option to withdraw from the survey at any point.
Data Availability Statement
The data supporting the findings of this study are not accessible to the public due to limitations set by the Institutional Ethical Review Board to ensure participant confidentiality. Nonetheless, the data can be obtained from the corresponding author if a reasonable request is made and approved in line with the guidelines of the Ethical Review Board.*
References
- 1.World Health Organization . Mental health: strengthening our response. WHO. https://www.who.int/news-room/fact-sheets/detail/mental-health-strengthening-our-response. Accessed July 8, 2024. [Google Scholar]
- 2.Jiang F, Liu H, Tang YL. Editorial: mental health of healthcare professionals. Front Psychol. 2022;13:1093569. doi: 10.3389/fpsyg.2022.1093569. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Brooks SK, Gerada C, Chalder T. Review of literature on the mental health of doctors: are specialist services needed? J Ment Health. 2011;20(2):146-156. [DOI] [PubMed] [Google Scholar]
- 4.Obeagu EI. Strategies to Safeguard Mental Health Among Medical and Allied Health Trainees: A Narrative Review. Adv Med Educ Pract. 2025;16:2219-2227. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Kerstetter B. Global mental health statistics. Children’s Hope Chest. 2022. https://www.hopechest.org/global-mental-health-statistics/. Accessed July 8, 2024.
- 6.Alam MF, Ahmed HU, Alam MT, et al. Community prevalence of psychiatric disorders: Findings from a nationwide survey in Bangladesh. Asian Journal of Psychiatry. 2024;92:103897. [DOI] [PubMed] [Google Scholar]
- 7.Roy AK, Hossain MI, Chowdhury SA, et al. Depression symptoms and mental health well-being among Bangladeshi nursing students. Discover Social Science and Health. 2025. [Google Scholar]
- 8.Al-Amin M, Rinky F, Nizamul HBM, et al. Factors influencing mental health outcomes among university students: a cross-sectional study in Bangladesh. BMJ Open. 2025;15(3):e097745. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Muyeed A, Rahman R, Suchi SI, Ahmed K, Tithi TA. Sleep quality and psychological distress among Bangladeshi medical students: Prevalence, predictors, and gender-institutional differences. Glob Epidemiol. 2026;11:100243. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Søvold LE, Naslund JA, Kousoulis AA, et al. Prioritizing the mental health and well-being of healthcare workers: an urgent global public health priority. Front Public Health. 2021;9:679397. doi: 10.3389/fpubh.2021.679397. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Nuri NN, Sarker M, Ahmed HU, Hossain MD, Beiersmann C, Jahn A. Pathways to care of patients with mental health problems in Bangladesh. Int J Ment Health Syst. 2018;12(1):1-10. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Marković S, Kostić O, Terzić-Supic Z, et al. Exposure to Stress and Burnout Syndrome in Healthcare Workers, Expert Workers, Professional Associates, and Associates in Social Service Institutions. Medicina (Kaunas). 2024;60(3):499. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Odonkor ST, Adams S. Predictors of stress and associated factors among healthcare workers in Western Ghana. Heliyon. 2021;7(6):e07223. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.European Agency for Safety and Health at Wo . European opinion poll on occupational safety and health: May 2013. [Internet] Available from: https://op.europa.eu/mt/publication-detail/-/publication/1bafff62-073b-4536-9ba4-99e76d9b0422/language-en. 2013. doi: 10.2802/55505 [DOI] [Google Scholar]
- 15.Janssen M, Van der Heijden B, Engels J, Korzilius H, Peters P, Heerkens Y. Effects of mindfulness-based stress reduction training on healthcare professionals’ mental health: results from a pilot study testing its predictive validity in a specialized hospital setting. Int J Environ Res Public Health. 2020;17(24):9420. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Lovibond SH, Lovibond PF. Manual for the Depression Anxiety & Stress Scales. 2nd ed. Sydney: Psychology Foundation; 1995. [Google Scholar]
- 17.Hasan MT, Anwar T, Christopher E, et al. The current state of mental healthcare in Bangladesh: part 1 an updated country profile. BJPsych Int. 2021;18(4):78-82. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Osman A, Wong JL, Bagge CL, Freedenthal S, Gutierrez PM, Lozano G. The Depression Anxiety Stress Scales-21 (DASS-21): further examination of dimensions, scale reliability, and correlates. J Clin Psychol. 2012;68(12):1322-1338. [DOI] [PubMed] [Google Scholar]
- 19.Banu B, Khan MH, Ali L, Barnighausen T, Sauerborn R, Souares A. Pattern and predictors of non-adherence to diabetes self-management recommendations among patients in a peripheral district of Bangladesh. Trop Med Int Health. 2024;29(3):233-242. [DOI] [PubMed] [Google Scholar]
- 20.Sah R. Stress, anxiety and depression and associated risk factors among postgraduate medical students in tertiary hospital in Kathmandu. J Community Med Public Health Rep. 2024;5(5). doi: 10.38207/JCMPHR/2024/MAR05050541. [DOI] [Google Scholar]
- 21.Gogoi HS. A cross sectional study of the prevalence of depression among the male post-graduate medical students of Gauhati Medical College and Hospital, India. Eur Sci J. 2016;12(14):262. [Internet] Available from. doi: 10.19044/esj.2016.v12n14p262. [DOI] [Google Scholar]
- 22.Alles PS, Amarakoon L, Rohanachandra YM. Symptoms of depression and anxiety and stress among postgraduate trainees in Sri Lanka: Risk factors and implications for practice. Indian J Psychiatry. 2023;65(11):1190-1194. [Internet] Available from. doi: 10.4103/indianjpsychiatry.indianjpsychiatry_701_22. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Tan T, Liu W, Zhao Q, et al. Anxiety, depression, psychological stress and coping style in medical postgraduates in southeastern China when restricted to commuting between the campus and hospital during the COVID-19 pandemic. Front Psychiatry. 2022;13:1035075. [Internet] Available from. doi: 10.3389/fpsyt.2022.1035075. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Kamble VA, Gaikwad VA. Prevalence of stress amongst postgraduate residents by using DASS-21 and perceived stress scale: a cross-sectional study at a tertiary care centre. Int J Community Med Public Health. 2026;13(6):2782-2786. [Internet] Available from. doi: 10.18203/2394-6040.ijcmph20261754. [DOI] [Google Scholar]
- 25.Alrasheedi J, Rabbani U. Prevalence of Anxiety and Depression among postgraduate trainees in Qassim, Saudi Arabia. World Family Medicine Journal /Middle East Journal of Family Medicine. 2022;20(4). [Internet] Available from. doi: 10.5742/mewfm.2022.9525020. [DOI] [Google Scholar]
- 26.Freeman A, Tyrovolas S, Koyanagi A, et al. The role of socio-economic status in depression: results from the COURAGE (aging survey in Europe). BMC Public Health. 2016;16:1098. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Novin MH, Shokri S, Eslamdoust-Siahestalkhi F, Pourramzani A. Frequency of smoking, alcohol consumption, and substance use in relation to general health indicators in Guilan University of Medical Sciences. Addict Health. 2023;15(4):266-274. [Internet] Available from. doi: 10.34172/ahj.2023.1432. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Pedersen T, Arthur S. Risk factors for depression: genetics, trauma, and more. Psych Central; 2016. Published. https://psychcentral.com/depression/what-are-the-risk-factors-for-depression. Accessed August 4, 2024. [Google Scholar]
- 29.Williams K, Frech A, Carlson DL. Marital status and mental health. In: Aneshensel CS, Phelan JC, Bierman A, eds. A Handbook for the Study of Mental Health: Social Contexts, Theories, and Systems. 2nd ed. Cambridge University Press; 2010:306-320. [Google Scholar]
- 30.Morales-Montoya M, Córdova-Limaylla N, Briceño-Vergel G, et al. Psychological impact on dental students and professionals in a Lima population during COVID-19’s wave: a study with predictive models. Sci Rep. 2022;12(1):14752. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Huang J, Huang ZT, Sun XC, Chen TT, Wu XT. Mental health status and related factors influencing healthcare workers during the COVID-19 pandemic: a systematic review and meta-analysis. PLoS One. 2024;19(1):e0289454. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Grinde B, Tambs K. Effect of household size on mental problems in children: results from the Norwegian Mother and Child Cohort study. BMC Psychol. 2016;4(1):1-11. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Mason KE, Maudsley G, McHale P, Pennington A, Day J, Barr B. Age-adjusted associations between comorbidity and outcomes of COVID-19: a review of the evidence from the early stages of the pandemic. Front Public Health. 2021;9:584182. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplemental Material for Mental Health Status Among Healthcare Professionals in a Master of Public Health Program at a Higher Education Institution in Bangladesh: A Cross-Sectional Study by Bilkis Banu, Nasrin Akter, Nusrat Hossain Sheba, Md. Allama Faysal, Nigar Sultana, Md Jiaur Rahman in Health Services Insights.
Data Availability Statement
The data supporting the findings of this study are not accessible to the public due to limitations set by the Institutional Ethical Review Board to ensure participant confidentiality. Nonetheless, the data can be obtained from the corresponding author if a reasonable request is made and approved in line with the guidelines of the Ethical Review Board.*