Abstract
Abstract
Objective
To determine the prevalence and factors associated with anxiety and depression and the care-seeking behaviour among Nepalese population.
Design and settings
Secondary analysis of the data from nationally representative Nepal Demographic and Health Survey 2022.
Participants
Analysed data of 12 355 participants (7442 females and 4913 males) aged 15–49 years.
Outcome measures
Depression and anxiety were assessed using Patient Health Questionnaire-9 (PHQ-9) and Generalised Anxiety Disorder Assessment (GAD-7) tools, respectively.
Statistical analysis
We performed weighted analysis to account for the complex survey design. We presented categorical variables as frequency, per cent and 95% confidence interval (CI), whereas numerical variables were presented as median, inter-quartile range (IQR) and 95% CI. We performed univariate and multivariable logistic regression to determine factors associated with anxiety and depression, and results were presented as crude OR (COR), adjusted OR (AOR) and their 95% CI.
Results
The prevalence of depression and anxiety were 4.0% (95% CI 3.5 to 4.5) and 17.7% (95% CI 16.5 to 18.9), respectively. Divorced or separated participants were found to have higher odds of developing anxiety (AOR 2.40, 95% CI 1.74 to 3.31) and depression (AOR 3.16, 95% CI 1.84 to 5.42). Among ethnic groups, Janajati had lower odds of developing anxiety (AOR 0.77, 95% CI 0.65 to 0.92) and depression (AOR 0.67, 95% CI 0.49 to 0.93) compared with Brahmin/Chhetri. Regarding disability, participants with some difficulty had higher odds of developing anxiety (AOR 1.81, 95% CI 1.56 to 2.10) and depression (AOR 1.94, 95% CI 1.51 to 2.49), and those with a lot of difficulty had higher odds of anxiety (AOR 2.09, 95% CI 1.48 to 2.96) and depression (AOR 2.04, 95% CI 1.06 to 3.90) compared with those without any disability. Among those who had symptoms of anxiety or depression, only 32.9% (95% CI 30.4 to 34.4) sought help for the conditions.
Conclusions
Marital status and disability status were positively associated with anxiety and depression, whereas Janajati ethnicity had relatively lower odds of experiencing anxiety and depression. It is essential to develop interventions and policies targeting females and divorced individuals to help reduce the burden of anxiety and depression in Nepal.
Keywords: mental health, depression & mood disorders, anxiety disorders
STRENGTHS AND LIMITATIONS OF THIS STUDY.
We analysed data from a large-scale nationally representative survey that takes into consideration the recently federalised structure of the country.
Anxiety and depression have been assessed using Patient Health Questionnaire-9 and Generalised Anxiety Disorder Assessment tools that improve the comparability of findings with other studies.
Weighed analysis was carried out to account complex survey design of the survey.
The survey was conducted during the COVID-19 pandemic period, which may have altered the prevalence of disease conditions to some extent.
Introduction
In 2019, around 970 million people globally were estimated to be living with mental disorders, with approximately 82% of these cases being from low- and middle-income countries (LMICs).1 On a global scale, one out of every eight people suffers from mental disorders, with anxiety and depressive disorders being the most common.2 3 There were 45.82 million cases of anxiety incidents with an estimated number of prevalent cases standing at 301.39 million in 2019. Anxiety disorders were responsible for approximately 28.68 million disability-adjusted life years (DALYs), with approximately 50% increase in absolute burden since 1990.4
Similarly, there were 280 million prevalent cases of depression with a prevalence rate of 3613.67 cases per 100 000 population. The number of prevalent cases increased by 63.17% since 1990. In 2019, there were 46.8 million DALYs from depression with approximately 61% increase from year 1990.3 After back and neck pain, depression was the second leading cause of years lived with disability (YLDs) in 2019, accounting for approximately 5.6% of total YLDs worldwide, while anxiety disorders ranked sixth, comprising approximately 3.4% of total YLDs.1
There were approximately 1.36 million prevalent cases of depression and 0.97 million prevalent cases of anxiety disorder in Nepal.3 Similarly, there were estimated 243 462 DALYs from depression and 91 927 DALYs from anxiety in 2019.3
Excessive fear and worry, along with behavioural disruptions, are characteristic of anxiety disorders. A wide range of anxiety disorders exist, encompassing conditions of excessive worry, panic attacks, excessive fear and worry in social situations, extreme fear or anxiety regarding separation from emotionally attached individuals and others.2 Individuals diagnosed with anxiety disorders can experience a frequently prolonged response when exposed to seemingly innocuous stimuli. This response is typically marked by sensations of tension, increased vigilance, activation of the sympathetic nervous system, subjective feelings of fear and in certain circumstances, the onset of panic.5 Depression differs from normal mood swings and transient emotional responses. It is characterised by persistent feelings of sorrow, anger or emptiness, as well as loss of interest or pleasure in activities that continue most days, for at least 2 weeks. Overwhelming guilt or poor self-worth, hopelessness, suicidal thoughts, sleep disturbances, changes in appetite or weight and exhaustion are among other symptoms of depression.2
Anxiety is associated with increased disability and diminished health and well-being. Increased disability and diminished health and well-being are linked to anxiety.6 Anxiety has been found to be associated with multiple other health conditions like heart disease, depression, asthma and gastrointestinal problems.7 Globally, individuals with poor mental state are found to bear disproportionately higher burden of mortality compared with general population.1 Calculation of mortality attributable to mental disorder including anxiety and depression is complex as mental disorders are rarely recorded as causes of deaths in death certificate. However, a report from WHO reports that people with poor mental health die 10–20 years earlier than the general population.1
Depression and anxiety are projected to cost the global economy US$1 trillion each year, mostly owing to productivity losses. Despite the importance of economic activity in healing, persons with severe mental health issues are frequently excluded from the labour force.8 Mental health, of which the major share is born by depression and anxiety, is often less prioritised in research activities. Despite serious impact on health and well-being of individuals, mental health receives approximately 7% of global health research fundings.1 9 Approximately, 99% of mental health studies are funded by high-income countries and only 5% of total mental health research funding goes to LMICs1 9 like Nepal. Although there are some studies on anxiety and depression in Nepal, they are mostly confined to specific geographic area and among specific group of people such as healthcare workers,10,13 traffic police,14 patients with specific disease conditions.15,18
Limited studies have been conducted in a nationally representative sample of population to determine prevalence and factors associated with depression and anxiety. In this study, we aimed to determine prevalence and factors associated with depression and anxiety and health-seeking patterns among the participants with anxiety and depression in Nepal. This study contributes significantly to the understanding of mental health issues within the Nepalese population by providing critical insights into the prevalence and factors associated with anxiety and depression. It notably marks the first instance of the Nepal Demographic Health Survey (NDHS) collecting data on mental health, setting a foundational benchmark for future research and interventions in this area.
Methods
Study design
We analysed data from nationally representative NDHS 2022.19
Study setting
Nepal is a landlocked country located in Southeast Asia region with 1 federal, 7 provincial and 753 local governments (6 metropolitan cities 11 submetropolitan cities, 275 urban municipalities, 460 rural municipalities). Nepal has three ecological regions: mountain, hill and terai. According to the National Population and Housing Census 2021, the total population of Nepal was 29 164 578 with 911 027 (51.1 %) females and 14 253 551 (48.9 %) males.20 Nepal has a Human Development Index (HDI) of 0.602, inequality adjusted HDI of 0.449, planetary pressure adjusted HDI of 0.584 and ranks 143 in HDI among 191 countries across the world.21
Sample and sampling
NDHS 2022 uses an updated sampling framework based on Housing and Population Census 2011. In the first stage of sampling, the 7 provinces were stratified into rural and urban settings that together formed a total of 14 sampling stratum across 7 provinces. Within each stratum, the sampling procedure included implicit stratification and proportionate allocation. The sampling frame was sorted inside each stratum based on administrative units, using a probability-proportional-to-size technique. A total of 476 primary sample units (PSUs) were chosen, with 248 from urban and 228 from rural settings. PSUs were chosen individually based on their size within each stratum. A household listing operation was carried out within each PSU and the resulting household list was considered as a sampling frame. Wards with a number of households >300 were further segmented and a segment was selected based on probability proportionate to size. From each cluster, a total of 30 households resulting in a total of 14 280 households, 7440 were in urban areas and 6840 from rural settings. A total of 14 845 women and 4913 men were successfully interviewed. Detail sampling process is elaborated elsewhere.22 We analysed data of 12 355 participants (7442 females and 4913 males) whose mental health data were collected in the NDHS 2022.
Data collection
In NDHS 2022, data collection was conducted by 19 teams, each comprising a supervisor, 1 male interviewer, 3 female interviewers and 1 biomarker specialist, from 5 January to 22 June 2022.
Variables
Dependent variables
Anxiety
NDHS 2022 used Generalised Anxiety Disorder Assessment (GAD-7) tool consisting of seven items to assess anxiety.23 Each item of GAD-7 was scored on a scale of 0–3 on a 4-point Likert scale (0=not at all, 1=several days, 2=more than half the days and 3=nearly every day). The scores from seven items were summed up to determine the total GAD-7 score. The total score of GAD-7 ranges from 0 to 21 (score of 0–5 is categorised as no anxiety, 6–14 as mild-to-moderate anxiety, 15–21 as severe anxiety). In this study, we considered participants to have anxiety if GAD-7 score is >5. The GAD-7 demonstrated good sensitivity (89%) and specificity (82%).24
Depression
NDHS 2022 used Patient Health Questionnaire-9 (PHQ-9)25 tool consisting of nine items to assess depression. Each item of PHQ-9 was scored on a scale of 0–3 in a 4-point Likert scale (0=not at all, 1=several days, 2=more than a week, 3=nearly every day). Scores from each item were summed-up to determine the total PHQ-9 score. The total score of PHQ-9 ranged from 0 to 27 (score of 0–5 is classified as no depression, scores of 5–9 is classified as mild depression; 10–14 as moderate depression; 15–19 as moderately severe depression and ≥20 as severe depression). In this study, we considered participants to have depression if PHQ-9 score is ≥10.26 The tool was also previously validated in Nepal and at the cut-off of 10, the tool exhibited a sensitivity of 0.94, specificity of 0.80, a positive predictive value of 0.42, a negative predictive value of 0.99, a positive likelihood ratio of 4.62 and a negative likelihood ratio of 0.07.27
Independent variables
The independent variables assessed in this study included age (<20 years, 20–34 years, 34–49 years), sex (male, female), marital status (unmarried, married or living together, divorced or separated or not living together), ethnicity (Brahmin/Chhetri, Dalit, Janajati, Madhesi, other), religion (Hindu, other), wealth quintile (poorest, poorer, middle, richer, richest), disability (no disability, some difficulty, a lot of difficulty or cannot do at all), education (no education, basic, secondary, higher), occupation (not working, agriculture, professional or technical manager or clerical, sales and service, skilled/unskilled labour, other), smoking status (do not smoke, someday, everyday), alcohol intake (never drinker, no drink in past month, some drink in past month, everyday drink), ecological belt (mountain, hill, terai), place of residence (rural, urban) and province (Koshi, Madhesh, Bagmati, Gandaki, Lumbini, Karnali, Sudurpashchim). Selection of independent variable was based on the variables used in previous studies conducted in Nepal.28,31
Statistical analysis
We used R 4.2.0 and RStudio for data cleaning and statistical analysis. We performed weighted analysis to accommodate the complex survey design of NDHS 2022. We presented categorical variables as frequency, per cent (%) and 95% CI, whereas numerical variables were presented as mean and 95% CI. We used univariate and multivariable logistic regression to determine the association of depression and anxiety with independent variables. We included all independent variables from bivariate model (age, sex, marital status, place of residence, ecological belt, province, ethnicity, religion, wealth, education, occupation, current smoke, current alcohol and disability) into multivariable regression model. In multivariable regression analysis, we check for the presence of collinearity using variance inflation factor. The results of the logistic regression were presented as crude OR (COR) and adjusted OR (AOR) and their 95% CI. A p value of <0.05 was considered to be statistically significant .
Patient and public involvement
Patients and/or the public were not involved in the design, conduct, reporting or dissemination plans of this research.
Results
We analysed data of 12 332 (unweighted: 12355) participants accounting 7410 (unweighted: 7442) females and 4913 (unweighted: 4913) males. Slightly more than two-thirds (69.2%) of participants were from urban settings. Terai belt contributed the highest proportion of participants (55.1%), followed by hill (39.5%) while 5.4% participants were from mountain region. Among the seven provinces, 22% of participants were from Bagmati, 20.4% from Madhesh, 17.6% from Lumbini, 17.2% from Koshi, 8.9% from Gandaki, 8.1% from Sudurpashchim and 5.9% from Karnali. Janajati accounted for the highest proportion of participants (37.4%), followed by Brahmin/Chhetri (26.6%) and Madheshi (16.8%). The majority of participants (82.6%) were identified as Hindu. The median age of participants was 29 years, with 46.8% falling within the 20–34 age group. In terms of marital status, 70.1% of participants were married and living together, 27.6% were unmarried and the remaining 2.3% were divorced or separated. Approximately 19% of participants had no education, and 21.9% were not currently employed. Around 83.5% of participants were non-smokers, while 13.9% were lifetime abstainers from alcohol. Regarding disability, 17.9% of participants had some level of difficulty. Only 12.6% of participants had health insurance coverage (table 1).
Table 1. Characteristics of research participants (n=12 323).
| Characteristic | Overall | Female | Male | |||
| N | % (95% CI) | N | % (95% CI) | N | % (95% CI) | |
| Place of residence | ||||||
| Urban | 8525 | 69.2 (67.6 to 70.7) | 5064 | 68.3 (66.7 to 69.9) | 3462 | 70.5 (68.6 to 72.3) |
| Rural | 3798 | 30.8 (29.3 to 32.4) | 2347 | 31.7 (30.1 to 33.3) | 1451 | 29.5 (27.7 to 31.4) |
| Ecological belt | ||||||
| Mountain | 663 | 5.4 (3.75 to 7.66) | 408 | 5.5 (3.86 to 7.82) | 255 | 5.2 (3.55 to 7.53) |
| Hill | 4870 | 39.5 (35.6 to 43.5) | 2896 | 39.1 (35.2 to 43.1) | 1973 | 40.2 (36.0 to 44.5) |
| Terai | 6791 | 55.1 (51.2 to 58.9) | 4106 | 55.4 (51.5 to 59.2) | 2685 | 54.6 (50.4 to 58.8) |
| Province | ||||||
| Koshi | 2123 | 17.2 (15.9 to 18.6) | 1241 | 16.7 (15.4 to 18.2) | 882 | 18 (16.4 to 19.6) |
| Madhesh | 2509 | 20.4 (19.1 to 21.7) | 1512 | 20.4 (19.1 to 21.8) | 997 | 20.3 (18.7 to 22.0) |
| Bagmati | 2707 | 22.0 (20.2 to 23.8) | 1493 | 20.2 (18.5 to 21.9) | 1214 | 24.7 (22.3 to 27.2) |
| Gandaki | 1092 | 8.9 (7.83 to 10.0) | 704 | 9.5 (8.35 to 10.8) | 387 | 7.9 (6.9 to 9.0) |
| Lumbini | 2172 | 17.6 (16.3 to 19.0) | 1360 | 18.4 (17.0 to 19.8) | 812 | 16.5 (15.0 to 18.2) |
| Karnali | 724 | 5.9 (5.31 to 6.50) | 458 | 6.2 (5.62 to 6.79) | 266 | 5.4 (4.72 to 6.21) |
| Sudurpashchim | 996 | 8.1 (7.37 to 8.85) | 641 | 8.6 (7.96 to 9.39) | 355 | 7.2 (6.32 to 8.24) |
| Ethnicity | ||||||
| Brahmin/Chhetri | 3281 | 26.6 (24.3 to 29.1) | 2049 | 27.7 (25.3 to 30.1) | 1232 | 25.1 (22.5 to 27.8) |
| Dalit | 1773 | 14.4 (12.7 to 16.2) | 1115 | 15.0 (13.3 to 16.9) | 658 | 13.4 (11.6 to 15.4) |
| Janajati | 4605 | 37.4 (34.6 to 40.2) | 2735 | 36.9 (34.1 to 39.8) | 1869 | 38.0 (35.0 to 41.2) |
| Madheshi | 2066 | 16.8 (14.6 to 19.2) | 1149 | 15.5 (13.3 to 18.0) | 917 | 18.7 (16.3 to 21.3) |
| Others | 599 | 4.9 (3.48 to 6.75) | 3624 | 4.9 (3.44 to 6.90) | 236 | 4.8 (3.41 to 6.76) |
| Religion | ||||||
| Hindu | 1017 | 82.6 (80.4 to 84.6) | 6151 | 83.0 (80.8 to 85.0) | 4025 | 81.9 (79.5 to 84.1) |
| Other | 2147 | 17.4 (15.4 to 19.6) | 1259 | 17.0 (15.0 to 19.2) | 888 | 18.1 (15.9 to 20.5) |
| Age (years), mean±SD | 29.8±9.8 | 29.8±9.7 | 29.8±10.1 | |||
| <20 | 2307 | 18.7 (17.9 to 19.6) | 1322 | 17.8 (16.9 to 18.8) | 985 | 20.0 (18.7 to 21.5) |
| 20–34 | 5771 | 46.8 (45.7 to 48.0) | 3581 | 48.3 (47.0 to 49.6) | 2190 | 44.6 (42.9 to 46.3) |
| 35–49 | 4246 | 34.5 (33.4 to 35.5) | 2507 | 33.8 (32.7 to 35.0) | 1739 | 35.4 (33.7 to 37.1) |
| Marital status | ||||||
| Unmarried | 3400 | 27.6 (26.3 to 28.9) | 1632 | 22.0 (20.8 to 23.4) | 1768 | 36 (34.0 to 38.0) |
| Married or living together | 8634 | 70.1 (68.7 to 71.4) | 5533 | 74.7 (73.3 to 76.0) | 3101 | 63.1 (61.1 to 65.1) |
| Divorced or not living together | 289 | 2.3 (2.07 to 2.66) | 2453 | 3.3 (2.89 to 3.80) | 44 | 0.9 (0.65 to 1.21) |
| Wealth | ||||||
| Poorest | 2095 | 17.0 (15.1 to 19.0) | 1344 | 18.1 (16.1 to 20.3) | 751 | 15.3 (13.5 to 17.3) |
| Poorer | 2304 | 18.7 (16.9 to 20.7) | 1372 | 18.5 (16.7 to 20.5) | 933 | 19.0 (17.0 to 21.2) |
| Middle | 2469 | 20.0 (18.4 to 21.8) | 1512 | 20.4 (18.7 to 22.3) | 957 | 19.5 (17.7 to 21.3) |
| Richer | 2839 | 23.0 (21.1 to 25.0) | 1704 | 23.0 (21.1 to 25.0) | 1135 | 23.1 (21.0 to 25.4) |
| Richest | 2616 | 21.2 (18.7 to 24.0) | 1479 | 20.0 (17.5 to 22.7) | 1137 | 23.1 (20.4 to 26.1) |
| Education | ||||||
| No education | 2337 | 19.0 (17.6 to 20.4) | 1944 | 26.2 (24.6 to 28.0) | 393 | 8.0 (6.85 to 9.34) |
| Basic | 4155 | 33.7 (32.2 to 35.2) | 2256 | 30.4 (29.0 to 31.9) | 1898 | 38.6 (36.5 to 40.8) |
| Secondary | 5175 | 42.0 (40.3 to 43.7) | 2931 | 39.6 (37.7 to 41.4) | 2244 | 45.7 (43.7 to 47.7) |
| Higher | 657 | 5.3 (4.56 to 6.22) | 280 | 3.8 (3.11 to 4.58) | 377 | 7.7 (6.50 to 9.05) |
| Occupation | ||||||
| Not working | 2705 | 21.9 (20.5 to 23.4) | 2033 | 27.4 (25.5 to 29.4) | 672 | 13.7 (12.2 to 15.3) |
| Agriculture | 4747 | 38.5 (36.2 to 40.9) | 3591 | 48.5 (45.6 to 51.3) | 1156 | 23.5 (21.4 to 25.8) |
| Professional/Technical/Managerial/Clerical | 1096 | 8.9 (8.07 to 9.80) | 533 | 7.2 (6.36 to 8.13) | 563 | 11.5 (10.1 to 12.9) |
| Sales and service | 1311 | 10.6 (9.70 to 11.6) | 606 | 8.2 (7.22 to 9.24) | 705 | 14.3 (12.9 to 15.9) |
| Skilled/Unskilled labour | 2451 | 19.9 (18.6 to 21.2) | 639 | 8.6 (7.58 to 9.80) | 1812 | 36.9 (34.7 to 39.1) |
| Other | 13 | 0.1 (0.05 to 0.22) | 8 | 0.1 (0.04 to 0.28) | 6 | 0.1 (0.05 to 0.29) |
| Smoking status | ||||||
| Do not smoke | 1028 | 83.5 (82.5 to 84.4) | 7091 | 95.7 (94.9 to 96.3) | 3198 | 65.1 (63.0 to 67.1) |
| Every day | 1624 | 13.2 (12.4 to 14.0) | 221 | 3.0 (2.50 to 3.55) | 1403 | 28.6 (26.7 to 30.4) |
| Some days | 410 | 3.3 (2.93 to 3.78) | 98 | 1.3 (1.00 to 1.75) | 312 | 6.3 (5.51 to 7.30) |
| Alcohol intake | ||||||
| Never drinker | 1716 | 13.9 (12.9 to 15.0) | 940 | 12.7 (11.5 to 13.9) | 776 | 15.8 (14.1 to 17.6) |
| No drink in past month | 7732 | 62.7 (61.0 to 64.5) | 5672 | 76.5 (74.8 to 78.2) | 2061 | 41.9 (39.2 to 44.8) |
| Some drink in past month | 2571 | 20.9 (19.7 to 22.1) | 746 | 10.1 (9.0 to 11.2) | 1825 | 37.2 (35.0 to 39.3) |
| Everyday drink | 304 | 2.5 (2.0 to 3.0) | 53 | 0.7 (0.5 to 1.0) | 252 | 5.1 (4.2 to 6.3) |
| Disability | ||||||
| No difficulty | 9923 | 80.5 (79.5 to 81.5) | 5828 | 78.7 (77.4 to 79.9) | 4094 | 83.4 (82.1 to 84.6) |
| Some difficulty | 2200 | 17.9 (17.0 to 18.8) | 1455 | 19.6 (18.5 to 20.9) | 745 | 15.2 (14.0 to 16.4) |
| A lot of difficulty to cannot do at all | 197 | 1.6 (1.4 to 1.9) | 126 | 1.7 (1.4 to 2.0) | 8 | 0.2 (0.1 to 0.3) |
| Missing | 3 | 1 | 2 | |||
| Health insurance | 1556 | 12.6 (11.3 to 14.1) | 904 | 12.2 (10.8 to 13.7) | 652 | 13.3 (11.7 to 15.0) |
%, weighted percentage; CI, CI; N, weighted frequencySD, standard deviation
The prevalence of mild, moderate, severely moderate and severe depression was 13.2% (95% CI 12.3 to 14.1), 2.9% (95% CI 2.5 to 3.2), 0.9% (95% CI 0.7 to 1.1) and 0.20% (95% CI 0.16 to 0.36), respectively, with the 4.0% (95% CI 3.5 to 4.5) overall prevalence of depression. The prevalence of mild-to-moderate anxiety and severe anxiety were 16.7% (95% CI 15.6 to 17.9) and 1.0% (95% CI 0.8 to 1.2), respectively, with the 17.7% (95% CI 16.5 to 18.9) overall prevalence of anxiety. Of total participants, 18.0% (95% CI 16.8 to 19.2) had depression or anxiety (figure 1).
Figure 1. Gender-wise prevalence of anxiety and depression among Nepalese population aged 15–49 years.
Sex, marital status, province, ethnicity, wealth quintile, alcohol use and disability status were associated with anxiety among participants. Variables like age, place of residence, ecological belt, religion, education, occupation and smoking status did not show any statistically significant association with anxiety in multivariable regression model. With female as reference, males had lower odds of developing anxiety (AOR 0.42, 95% CI 0.36 to 0.50). Participants who were divorced or not living together were found to have two-fold higher odds (AOR 2.40, 95% CI 1.74 to 3.31) of developing anxiety. Among provinces, considering Koshi as reference, residents of Madhesh province had lower odds of having anxiety (AOR 0.63; 95% CI 0.45 to 0.87), while no significant association was found with other provinces. Although residents of Gandaki province had lower odds of having anxiety in bivariate analysis (COR 0.67, 95% CI 0.49 to 0.93), multivariable analysis did not reveal any statistically significant association. Among different ethnic groups, Janajati had lower odds (AOR 0.77. 95% CI 0.65 to 0.92) of developing anxiety compared with Brahmin/Chhetri. Participants belonging to Dalit ethnic group were found to have higher odds (AOR 1.29, 95% CI 1.05 to 1.58) of developing anxiety in multivariable regression model. Compared with participants in the poorest wealth quintile, participants in poorer wealth quintile had higher odds (AOR 1.21, 95% CI 1.02 to 1.43) of developing anxiety. Compared with those who never drink, participants who had ever drank but not in last 1 month were found to have lower odds (AOR 0.70, 95% CI 0.59 to 0.84) of developing anxiety. Regarding disability, compared with participants who did not have any difficulty, participants who had some difficulty (AOR 1.81, 95% CI 1.56 to 2.10) and had lot of difficulty/cannot do at all (AOR 2.09, 95% CI 1.48 to 2.96) had higher odds of having anxiety (table 2).
Table 2. Prevalence and factors associated with anxiety.
| Characteristics | % (95% CI) | COR (95% CI) | P value | AOR (95% CI) | P value |
| Age (years) | |||||
| <20 | 14.8 (13.0 to 16.7) | Ref | Ref | ||
| 20–34 | 18.3 (16.8 to 19.9) | 1.29 (1.11 to 1.51) | 0.001 | 1.12 (0.90 to 1.41) | 0.311 |
| 35–49 | 18.5 (16.9 to 20.1) | 1.31 (1.11 to 1.54) | 0.001 | 0.94 (0.72 to 1.23) | 0.653 |
| Sex | |||||
| Female | 21.9 (20.4 to 23.6) | Ref | Ref | ||
| Male | 11.3 (10.0 to 12.8) | 0.45 (0.39 to 0.53) | <0.001 | 0.42 (0.36 to 0.50) | <0.001 |
| Marital status | |||||
| Unmarried | 13.9 (12.5 to 15.5) | Ref | Ref | ||
| Married/Living together | 18.5 (17.2 to 19.8) | 1.40 (1.23 to 1.60) | <0.001 | 1.13 (0.94 to 1.36) | 0.206 |
| Divorced/Separated | 39.2 (33.1 to 45.6) | 3.99 (3.01 to 5.27) | <0.001 | 2.40 (1.74 to 3.31) | <0.001 |
| Place of residence | |||||
| Urban | 17.6 (16.1 to 19.2) | Ref | Ref | ||
| Rural | 18.0 (16.4 to 19.6) | 1.03 (0.88 to 1.20) | 0.719 | 0.97 (0.82 to 1.14) | 0.700 |
| Ecological belt | |||||
| Mountain | 20.3 (16.3 to 24.9) | Ref | Ref | ||
| Hill | 16.2 (14.8 to 17.7) | 0.76 (0.57 to 1.01) | 0.061 | 0.91 (0.71 to 1.18) | 0.476 |
| Terai | 18.5 (16.8 to 20.4) | 0.89 (0.67 to 1.19) | 0.440 | 1.25 (0.92 to 1.69) | 0.157 |
| Province | |||||
| Koshi | 20.0 (17.1 to 23.1) | Ref | Ref | ||
| Madhesh | 15.9 (13.0 to 19.2) | 0.76 (0.56 to 1.02) | 0.065 | 0.63 (0.45 to 0.87) | 0.005 |
| Bagmati | 16.6 (14.5 to 18.9) | 0.8 (0.62 to 1.02) | 0.070 | 1.01 (0.78 to 1.30) | 0.963 |
| Gandaki | 14.4 (11.4 to 18.0) | 0.67 (0.49 to 0.93) | 0.017 | 0.76 (0.55 to 1.05) | 0.092 |
| Lumbini | 18.1 (15.3 to 21.3) | 0.89 (0.67 to 1.17) | 0.393 | 0.77 (0.58 to 1.02) | 0.072 |
| Karnali | 24.1 (20.9 to 27.7) | 1.28 (0.98 to 1.67) | 0.071 | 1.28 (0.97 to 1.70) | 0.079 |
| Sudurpashchim | 18.6 (15.9 to 21.7) | 0.92 (0.70 to 1.20) | 0.531 | 0.81 (0.61 to 1.08) | 0.149 |
| Ethnicity | |||||
| Brahmin/Chhetri | 18.1 (16.4 to 20.0) | Ref | Ref | ||
| Dalit | 23.5 (20.5 to 26.8) | 1.39 (1.14 to 1.70) | 0.001 | 1.29 (1.05 to 1.58) | 0.014 |
| Janajati | 15.9 (14.6 to 17.3) | 0.86 (0.74 to 0.99) | 0.041 | 0.77 (0.65 to 0.92) | 0.003 |
| Madheshi | 16.6 (14.0 to 19.6) | 0.90 (0.71 to 1.14) | 0.375 | 1.06 (0.81 to 1.39) | 0.680 |
| Others | 15.3 (11.2 to 20.5) | 0.81 (0.56 to 1.19) | 0.289 | 0.99 (0.64 to 1.53) | 0.949 |
| Religion | |||||
| Hindu | 18.0 (16.8 to 19.3) | Ref | Ref | ||
| Other | 16.3 (14.3 to 18.4) | 0.88 (0.75 to 1.04) | 0.136 | 0.99 (0.80 to 1.22) | 0.914 |
| Wealth | |||||
| Poorest | 19.0 (17.1 to 21.0) | Ref | Ref | ||
| Poorer | 20.6 (18.3 to 23.1) | 1.11 (0.93 to 1.32) | 0.245 | 1.21 (1.02 to 1.43) | 0.032 |
| Middle | 18.5 (16.6 to 20.6) | 0.97 (0.81 to 1.15) | 0.721 | 1.03 (0.84 to 1.25) | 0.793 |
| Richer | 17.2 (15.3 to 19.2) | 0.89 (0.74 to 1.06) | 0.177 | 0.97 (0.78 to 1.21) | 0.774 |
| Richest | 13.9 (11.8 to 16.3) | 0.69 (0.55 to 0.86) | 0.001 | 0.80 (0.60 to 1.08) | 0.140 |
| Education | |||||
| No education | 22.2 (19.7 to 24.9) | Ref | Ref | ||
| Basic | 18.2 (16.6 to 19.9) | 0.78 (0.67 to 0.91) | 0.002 | 1.01 (0.85 to 1.21) | 0.875 |
| Secondary | 15.8 (14.5 to 17.2) | 0.66 (0.56 to 0.77) | <0.001 | 1.00 (0.83 to 1.21) | 0.997 |
| Higher | 13.2 (10.1 to 17.2) | 0.54 (0.38 to 0.76) | <0.001 | 0.98 (0.67 to 1.42) | 0.911 |
| Occupation | |||||
| Not working | 17.5 (15.6 to 19.6) | Ref | Ref | ||
| Agriculture | 20.3 (18.7 to 22.0) | 1.20 (1.04 to 1.40) | 0.015 | 1.00 (0.85 to 1.18) | 0.994 |
| Professional/Technical/Managerial/Clerical | 15.4 (12.9 to 18.4) | 0.86 (0.68 to 1.09) | 0.219 | 1.01 (0.78 to 1.31) | 0.922 |
| Sales and service | 14.0 (11.9 to 16.5) | 0.77 (0.61 to 0.97) | 0.025 | 0.86 (0.67 to 1.11) | 0.252 |
| Skilled/Unskilled labour | 15.8 (13.7 to 18.1) | 0.88 (0.73 to 1.08) | 0.217 | 1.01 (0.82 to 1.25) | 0.928 |
| Other | 21.1 (5.07 to 57.3) | 1.26 (0.25 to 6.34) | 0.776 | 1.38 (0.25 to 7.47) | 0.710 |
| Smoking status | |||||
| Do not smoke | 18.1 (16.8 to 19.5) | Ref | Ref | ||
| Every day | 14.3 (12.4 to 16.5) | 0.76 (0.63 to 0.91) | 0.003 | 0.95 (0.77 to 1.19) | 0.665 |
| Some days | 20.1 (15.7 to 25.4) | 1.14 (0.84 to 1.55) | 0.400 | 1.36 (0.97 to 1.91) | 0.073 |
| Alcohol intake | |||||
| Never drinker | 19.4 (17.3 to 21.7) | Ref | Ref | ||
| No drink in past month | 17.8 (16.4 to 19.4) | 0.90 (0.77 to 1.06) | 0.201 | 0.70 (0.59 to 0.84) | <0.001 |
| Some drink in past month | 15.9 (14.0 to 17.9) | 0.78 (0.64 to 0.95) | 0.016 | 0.94 (0.77 to 1.16) | 0.586 |
| Everyday drink | 19.7 (14.5 to 26.1) | 1.02 (0.69 to 1.50) | 0.934 | 1.2 (0.81 to 1.77) | 0.357 |
| Disability | |||||
| No difficulty | 15.5 (14.3 to 16.6) | Ref | Ref | ||
| Some difficulty | 26.8 (24.3 to 29.4) | 2.00 (1.75 to 2.29) | <0.001 | 1.81 (1.56 to 2.10) | <0.001 |
| A lot of difficulty to cannot do at all | 29.1 (22.8 to 36.4) | 2.25 (1.61 to 3.14) | <0.001 | 2.09 (1.48 to 2.96) | <0.001 |
Bold represents significance at 5% level of significance.
%, weighted percentage; AOR, adjusted OR; CI, CI; COR, crude OR; n, weighted frequency; Ref, reference group
The presence of depression among participants were associated with various factors including sex, marital status, province, ethnicity, alcohol use and disability status. Individuals who were divorced or separated had three times higher odds (AOR 3.16, 95% CI 1.84 to 5.42) of developing depression. Compared with females, males were found to have 70% lower odds (AOR 0.29, 95% CI 0.21 to 0.39) of having depression. Among different ethnic groups, Janajati had lower odds (AOR 0.67, 95% CI 0.49 to 0.93) of developing depression compared with Brahmin/Chhetri. Participants who had previously consumed alcohol but not in the past month had lower odds (AOR 0.59, 95% CI 0.44 to 0.80) of developing anxiety compared with those who had never consumed alcohol. Additionally, participants who faced some difficulty had higher odds (AOR 1.94, 95% CI 1.51 to 2.49) of developing depression compared with those without any difficulty (table 3).
Table 3. Prevalence and factors associated with depression.
| Characteristic | % (95% CI) | COR (95% CI) | P value | AOR (95% CI) | P value |
| Age (years) | |||||
| <20 | 3.3 (2.41 to 4.42) | Ref | Ref | ||
| 20–34 | 4.1 (3.55 to 4.78) | 1.27 (0.91 to 1.78) | 0.154 | 1.04 (0.65 to 1.66) | 0.87 |
| 35–49 | 4.1 (3.48 to 4.90) | 1.28 (0.89 to 1.83) | 0.187 | 0.8 (0.47 to 1.37) | 0.416 |
| Sex | |||||
| Female | 5.4 (4.79 to 6.17) | Ref | Ref | ||
| Male | 1.7 (1.35 to 2.26) | 0.31 (0.23 to 0.41) | <0.001 | 0.29 (0.21 to 0.39) | <0.001 |
| Marital status | |||||
| Unmarried | 2.7 (2.06 to 3.51) | Ref | Ref | ||
| Married/Living together | 4.2 (3.65 to 4.72) | 1.57 (1.16 to 2.11) | 0.003 | 1.23 (0.82 to 1.83) | 0.32 |
| Divorced/Separated | 13.4 (9.61 to 18.4) | 5.6 (3.63 to 8.63) | <0.001 | 3.16 (1.84 to 5.42) | <0.001 |
| Place of residence | |||||
| Urban | 3.7 (3.13 to 4.26) | Ref | Ref | ||
| Rural | 4.7 (3.92 to 5.56) | 1.29 (1.01 to 1.65) | 0.04 | 1.16 (0.89 to 1.52) | 0.257 |
| Ecological belt | |||||
| Mountain | 5.6 (4.16 to 7.45) | Ref | Ref | ||
| Hill | 3.6 (3.03 to 4.31) | 0.63 (0.44 to 0.91) | 0.014 | 0.9 (0.64 to 1.26) | 0.529 |
| Terai | 4.1 (3.42 to 4.81) | 0.72 (0.50 to 1.02) | 0.065 | 1.21 (0.76 to 1.93) | 0.413 |
| Province | |||||
| Koshi | 4.8 (3.85 to 5.87) | Ref | Ref | ||
| Madhesh | 3.2 (2.23 to 4.62) | 0.67 (0.43 to 1.03) | 0.068 | 0.54 (0.32 to 0.90) | 0.018 |
| Bagmati | 3.2 (2.28 to 4.60) | 0.67 (0.44 to 1.03) | 0.067 | 0.89 (0.54 to 1.47) | 0.644 |
| Gandaki | 3.1 (2.19 to 4.44) | 0.65 (0.42 to 0.99) | 0.045 | 0.78 (0.48 to 1.24) | 0.289 |
| Lumbini | 3.9 (2.91 to 5.13) | 0.81 (0.56 to 1.17) | 0.253 | 0.69 (0.47 to 1.02) | 0.060 |
| Karnali | 7.2 (5.79 to 8.98) | 1.56 (1.13 to 2.16) | 0.007 | 1.48 (0.97 to 2.25) | 0.069 |
| Sudurpashchim | 4.9 (3.68 to 6.46) | 1.03 (0.71 to 1.49) | 0.878 | 0.91 (0.60 to 1.38) | 0.669 |
| Ethnicity | |||||
| Brahmin/Chhetri | 4.2 (3.49 to 5.16) | Ref | Ref | ||
| Dalit | 5.5 (4.32 to 6.95) | 1.31 (0.94 to 1.82) | 0.11 | 1.15 (0.82 to 1.61) | 0.414 |
| Janajati | 3.3 (2.75 to 3.89) | 0.76 (0.58 to 1.00) | 0.047 | 0.67 (0.49 to 0.93) | 0.015 |
| Madheshi | 3.6 (2.57 to 5.04) | 0.84 (0.56 to 1.27) | 0.412 | 1.17 (0.73 to 1.87) | 0.515 |
| Others | 4.5 (2.72 to 7.28) | 1.06 (0.61 to 1.83) | 0.843 | 1.27 (0.65 to 2.48) | 0.481 |
| Religion | |||||
| Hindu | 3.9 (3.49 to 4.45) | Ref | Ref | ||
| Non-Hindu | 4.1 (3.22 to 5.19) | 1.04 (0.81 to 1.34) | 0.763 | 1.16 (0.81 to 1.65) | 0.424 |
| Wealth | |||||
| Poorest | 5.2 (4.28 to 6.30) | Ref | Ref | ||
| Poorer | 4.5 (3.54 to 5.64) | 0.85 (0.63 to 1.15) | 0.298 | 1.05 (0.74 to 1.47) | 0.795 |
| Middle | 4.4 (3.38 to 5.68) | 0.84 (0.60 to 1.18) | 0.303 | 0.96 (0.65 to 1.40) | 0.824 |
| Richer | 3.6 (2.86 to 4.62) | 0.69 (0.50 to 0.95) | 0.024 | 0.86 (0.57 to 1.29) | 0.459 |
| Richest | 2.5 (1.82 to 3.41) | 0.47 (0.32 to 0.68) | <0.001 | 0.71 (0.43 to 1.18) | 0.186 |
| Education | |||||
| No education | 5.4 (4.42 to 6.63) | Ref | Ref | ||
| Basic | 4.5 (3.83 to 5.36) | 0.83 (0.65 to 1.06) | 0.135 | 1.18 (0.90 to 1.55) | 0.234 |
| Secondary | 3.2 (2.66 to 3.76) | 0.57 (0.44 to 0.75) | <0.001 | 0.96 (0.70 to 1.32) | 0.803 |
| Higher | 1.5 (0.72 to 3.14) | 0.27 (0.12 to 0.59) | 0.001 | 0.58 (0.24 to 1.39) | 0.219 |
| Occupation | |||||
| Not working | 3.7 (2.87 to 4.68) | Ref | Ref | ||
| Agriculture | 4.9 (4.23 to 5.77) | 1.36 (1.05 to 1.77) | 0.020 | 1.05 (0.80 to 1.37) | 0.734 |
| Professional/Technical/Managerial/Clerical | 3.1 (2.02 to 4.64) | 0.83 (0.53 to 1.31) | 0.426 | 1.25 (0.75 to 2.09) | 0.395 |
| Sales and service | 2.8 (1.85 to 4.22) | 0.76 (0.46 to 1.25) | 0.278 | 0.96 (0.56 to 1.66) | 0.888 |
| Skilled/Unskilled labour | 3.4 (2.59 to 4.43) | 0.92 (0.63 to 1.36) | 0.676 | 1.21 (0.74 to 1.95) | 0.446 |
| Other | 11 (1.48 to 50.6) | 3.26 (0.38 to 27.87) | 0.279 | 4.06 (0.40 to 40.83) | 0.234 |
| Smoking | |||||
| Do not smoke | 4.1 (3.63 to 4.66) | Ref | Ref | ||
| Every day | 2.8 (2.06 to 3.83) | 0.67 (0.48 to 0.94) | 0.021 | 0.96 (0.65 to 1.41) | 0.842 |
| Some days | 4.7 (2.94 to 7.57) | 1.16 (0.71 to 1.91) | 0.557 | 1.57 (0.93 to 2.63) | 0.089 |
| Alcohol intake | |||||
| Never drinker | 4.9 (3.83 to 6.18) | Ref | Ref | ||
| No drink in past month | 4.1 (3.60 to 4.77) | 0.84 (0.64 to 1.12) | 0.238 | 0.59 (0.44 to 0.80) | <0.001 |
| Some drink in past month | 2.7 (2.03 to 3.70) | 0.55 (0.37 to 0.82) | 0.003 | 0.68 (0.46 to 1.01) | 0.059 |
| Everyday drink | 4.5 (2.22 to 9.06) | 0.93 (0.42 to 2.05) | 0.855 | 1.13 (0.52 to 2.46) | 0.751 |
| Disability | |||||
| No difficulty | 3.2 (2.82 to 3.66) | Ref | Ref | ||
| Some difficulty | 7.0 (5.77 to 8.56) | 2.28 (1.82 to 2.85) | <0.001 | 1.94 (1.51 to 2.49) | <0.001 |
| A lot of difficulty to cannot do at all | 7.3 (4.21 to 12.3) | 2.37 (1.29 to 4.36) | 0.006 | 2.04 (1.06 to 3.90) | 0.032 |
Bold represents significance at 5% level of significance.
%, weighted percentage; AOR, adjusted OR; CI, CI; COR, crude OR; n, weighted frequency; Ref, reference group
Care-seeking behaviour for anxiety and depression
Of 2217 participants with depression and/or anxiety in the past 2 weeks, 32.9% (95% CI 30.4 to 34.4) tried to seek help for the things they experienced. Care seeking among males and females were 29.2% (95% CI 24.8 to 34.2) and 34.1% (95% CI 31.3 to 37.0), respectively (not shown in table). Among those seeking care, most of the participants sought care from family member other than spouse (44.6%, 95% CI 40.5 to 48.6), from friends (37.0%; 95% CI 33.0 to 41.2), spouse (26.6%, 95% CI 22.9 to 30.7), neighbour (10.9%; 95% CI 8.8 to 13.5), healthcare providers (9.4%; 95% CI 7.3 to 12.0) and the least sought care from social workers and community health workers (<1%). (table 4).
Table 4. Care-seeking behaviour among participants with anxiety and/or depression.
| Care seeking from* | Overall, n=728 | Female, n=563 | Male, n=166 | |||
| n (%) | 95% CI | n (%) | 95% CI | n (%) | 95% CI | |
| Among participants having either anxiety or depression | ||||||
| Family members except spouse | 325 (44.6) | 40.5 to 48.6 | 256 (45.6) | 41.0 to 50.2 | 68 (41.1) | 33.1 to 49.6 |
| Friend | 270 (37.0) | 33.0 to 41.2 | 184 (32.7) | 28.1 to 37.6 | 86 (51.7) | 43.8 to 59.6 |
| Spouse | 194 (26.6) | 22.9 to 30.7 | 165 (29.3) | 25.0 to 34.1 | 29 (17.5) | 11.6 to 25.4 |
| Neighbour | 79 (10.9) | 8.8 to 13.5 | 64 (11.3) | 8.8 to 14.4 | 16 (9.6) | 6.2 to14.6 |
| Healthcare provider | 68 (9.4) | 7.3 to 12.0 | 52 (9.2) | 6.8 to 12.4 | 17 (10.0) | 6.2 to 15.7 |
| Social worker | 2 (0.3) | 0.1 to 1.4 | 0 (0.0) | 2.0 (1.3) | 0.3 to 5.9 | |
| Community health workers | 2 (0.2) | 0.08 to 0.79 | 2 (0.3) | 0.10 to 1.02 | 0 (0.0) | |
| Religious leader | 3 (0.4) | 0.13 to 1.39 | 3 (0.5) | 0.17 to 1.79 | 0 (0.0) | |
| Among participants having depression | ||||||
| Family members except spouse | 104 (50.0) | 43.0 to 57.0 | 86 (48.7) | 41.0 to 56.5 | 18 (57.1) | 39.2 to 73.4 |
| Spouse | 53 (25.4) | 18.9 to 33.1 | 48 (27.3) | 19.8 to 36.3 | 5 (15.0) | 6.51 to 31.0 |
| Friend | 57 (27.5) | 21.3 to 34.8 | 46 (26.3) | 19.6 to 34.3 | 11 (34.2) | 19.6 to 52.6 |
| Healthcare provider | 30 (14.5) | 9.68 to 21.2 | 27 (15.1) | 9.65 to 22.9 | 4 (11.2) | 4.14 to 26.8 |
| Neighbour | 25 (11.9) | 8.18 to 17.0 | 21 (11.9) | 7.69 to 17.8 | 4 (12.3) | 5.73 to 24.3 |
| Social worker | ||||||
| Religious leader | 2 (1.2) | 0.27 to 4.71 | 2 (1.4) | 0.33 to 5.53 | 0 (0.0) | |
| Community health workers | 0 (0.2) | 0.03 to 1.57 | 0 (0.3) | 0.04 to 1.86 | 0 (0.0) | |
| Among participants having anxiety | ||||||
| Family members except spouse | 319 (44.5) | 40.4 to 48.6 | 252 (45.5) | 40.9 to 50.1 | 67 (41.2) | 33.1 to 49.8 |
| Friend | 263 (36.7) | 32.8 to 40.8 | 180 (32.5) | 28.0 to 37.3 | 83 (51.0) | 43.0 to 58.9 |
| Spouse | 191 (26.7) | 22.9 to 30.8 | 162 (29.3) | 25.0 to 34.1 | 29 (17.7) | 11.8 to 25.8 |
| Neighbour | 78 (10.9) | 8.70 to 13.5 | 62 (11.2) | 8.70 to 14.3 | 16 (9.7) | 6.25 to 14.8 |
| Healthcare provider | 67 (9.4) | 7.25 to 12.0 | 50 (9.1) | 6.70 to 12.3 | 17 (10.2) | 6.34 to 15.9 |
| Social worker | 2 (0.3) | 0.07 to 1.39 | 0 (0.0) | 2 (1.4) | 0.30 to 5.98 | |
| Community health workers | 2 (0.3) | 0.08 to 0.80 | 2 (0.3) | 0.10 to 1.04 | 0 (0.0) | |
| Religious leader | 2 (0.3) | 0.07 to 1.26 | 2 (0.4) | 0.09 to 1.63 | 0 (0.0) | |
mMultiple response questions.
%, percent; %weighted percentageCIconfidence intervalCI, confidence intervaln, weighted frequency
Discussion
In our study, 17.7% of participants had anxiety while 4.0% were found to have depression which aligns with findings reported in one of the previous study in Nepal.32 Institute for Health Metrics and Evaluation estimates that there are around 1 043 324 cases of major depression with prevalence rate of 3.43%.3 Similarly, prevalence of depression was found to be 3.4% among adults and 0.7% among children in Nepal.32 33 The prevalence of depression in our study is notably lower than the pooled estimate of depression in 30 countries estimated from 68 studies using the random-effects model in which the prevalence of depression was found to be 12.9%.34 The prevalence of depression varies by continent, with South America having the highest overall rate of 20.6%. The prevalence was found to be 16.7% in Asia, 13.4% in North America, 11.9% in Europe and 11.5% in Africa. In comparison, Australia has the lowest rate of depression at 7.3%.34
Sex differences
Our study revealed that females have higher odds of developing anxiety and depression compared with males. In one of previous systematic review that computed the pooled prevalence of depression in 30 countries from 68 studies, the prevalence of depression was found to be 14.4% among females and 11.5% among males.34 Some other previous literatures have also indicated that women bear higher burden of anxiety2 and depression disorder.35 36 A multicountry study has demonstrated that females were found to have 1.6 times in prevalence and DALYs of anxiety disorder compared with males globally.4
Although the reason for higher prevalence of anxiety disorder among females is not clearly understood, some factors such as more sensitivity to criticism, rejection and separation,34 37 38 as well as more frequent encounter of adverse life events such as sexual violence and harassment and higher rates of revictimisation could be responsible for higher prevalence of anxiety and depression among females.4 37 39 40
Genetic factors, postnatal stress, cultural environment with unequal gender roles could have some role in exacerbating anxiety among females.4 5 Depression is more common during pregnancy and among women who had recently given birth affecting over 10% of women in this group, which could be one factor for higher prevalence among women.1 41 Some of the previous studies have suggested the onset of puberty may trigger a genetic susceptibility in females.37 Adolescent girls experience more stress, especially interpersonal stress, which is known to contribute to the higher rates of depression among females.37 42 The study findings indicate that women may need more specific and targeted interventions to reduce the burden of anxiety and depression at national and subnational level.
Provincial differences
Residents in Madhesh province had relatively lower odds of having anxiety and depression compared with Koshi while Karnali province reported slightly higher rates. In one of the previous studies, although anxiety and depression were not specifically assessed, Madhesh province was found to have substantially lower prevalence of lifetime mental disorder in one of the nationwide studies in 2019–2020.32 Relatively lower prevalence of anxiety, depression or other mental disorder in Madhesh province and higher prevalence in residents of Karnali province is not clearly understood. However, these differences could be because of some cultural practices, social immersion, cohesion and gathering, which can be further explored through qualitative studies.32
Wealth quintile
Although no significant difference was noted based on wealth quintile on prevalence of depression, the participants belonging to poorer wealth quintile were found to have higher odds of having anxiety compared with the participants in poorest wealth quintile. Although not every study had a statistically significant association between wealth and depression, the majority of studies included in the review exhibited a consistent pattern demonstrating inverse relation between wealth and depression.43 Multiple studies suggest that wealth provides a stronger protection against life adversities, provides a sense of financial stability, help manage family expectation and relationships, thereby reducing the likelihood of life stressors.44,46 Additionally, wealth serves as a buffer against symptoms of anxiety and depression.43 46
Disability
Our study indicates that people with disabilities have higher odds of having anxiety and depression. Depression is an independent risk factor for disability (in old age) and disability increases the risk of depression indicating bi-directional relationship.47 Multiple other studies have shown that disability is associated with anxiety48 49 and depression.50 51 Although the direct link between depression is not clearly understood, depression is linked to particular life situations that are more common among people with disabilities. Furthermore, persons with disabilities face a variety of unique concerns and challenges that may increase the chances of developing depression.52 Disability may involve difficulties in walking, performing daily tasks independently such as bathing, which can often lead to feelings of frustration and embarrassment.52 People with disabilities often face the social barriers and isolation because of difficulties in joining social functions and gathering, forming social relationship and may often receive limited social support that puts them at higher risk of depression.52 Physical or other forms of limitation often puts them on higher risk of being unemployed because of social prejudice and misconception regarding disability.52
Care-seeking behaviour
Similar to other mental disorders, there is a high undertreatment rate for anxiety and depression. In our study, 32.9% of participants with depression and/or anxiety symptoms had tried to seek help from someone. The findings resonate with low treatment-seeking behaviour reported in one of the previous studies. In a nationally representative study in Nepal, only 40% of adults with mental disorder had talked to someone about their symptoms, with 20.5% discussing symptoms with their spouse and 22.4% with other family members. Only 3.5% of individuals with symptoms had discussed about it with healthcare professionals.53 In one of the previous studies in Sweden, 40.9% of participants with depression, 36.8% of participants with anxiety and 60.9% of participants facing comorbid condition of anxiety and depression were found to have sought care.54 A systematic review analysing 149 studies from 84 countries between 2000 and 2021 estimated that 33% of patients with major depressive disorder seek care in high-income countries, while the proportion was 15% in upper middle-income countries and only 8% in LMICs. These findings highlight the notable proportion of the unmet need for mental health services.55 Apart from lower proportion of patients who seek care, delay in seeking care is other important factor that undermine the health outcomes among individuals with anxiety and depression.
The unwillingness or inability to seek assistance can be attributed to a range of factors, including high expenses, poor service quality and limited resource availability. One of the previous studies in Nepal suggested that barriers such as lack of knowledge about facilities where the services are available (24.9%), inability to afford care (19.5%) and difficulty taking time off work to seek care (14%) undermine the treatment-seeking behaviour for mental disorders. Similarly, there are some attitudinal barriers in seeking care.53
Findings from other setting suggest that limited care seeking might also be due to a lack of awareness on mental health, the existence of societal stigma associated with seeking therapy and negative past experiences with seeking assistance.1 Furthermore, individuals with mental problems are prevented from receiving the essential treatment because of several reasons at the individual, provider and systemic levels. At the individual level, barriers like reluctance to disclose their issues, anxiety about stigma, time restraints, unfavourable views of treatment, cultural influences, a propensity to manage mental health issues alone and a low willingness to embrace change all contribute to impeding their desire to receive treatment.56 The proper treatment of patients with anxiety and depression is hampered at the provider level by a number of variables. These challenges include underdetection at the primary level, care professionals’ limited familiarity with mental illnesses and patients’ physical symptom presentation.56 A dearth of specialised mental health services, a shortage of health workers educated in anxiety and depression diagnosis and treatment techniques and the lack of integration of mental healthcare into primary healthcare settings are systemic issues.56
Health impacts, policy and programme implication
Anxiety disorders are consistently linked to significant impairments in both productive roles (such as work absenteeism, work performance, unemployment and underemployment) and social roles (such as social isolation, interpersonal tensions and marital disruption) in epidemiological surveys.5 Depression can have a significant impact on the economy of the country. For example, the health and economic burden of depression was estimated to represent 2.9% of gross domestic products in Singapore.57
A substantial proportion of individuals with anxiety and depression do not seek medical attention in earlier stages. Furthermore, health systems, particularly in LMICs are less prepared for delivery of mental health service and are often underfunded.5 Depression is the leading risk factor for suicide, a problem that is further exacerbated by substance use disorders.5 To transform the mental health agenda, it is critical to invest in addressing the fundamental social and economic factors that affect people’s mental well-being in addition to expanding access to high-quality services and care.1 According to WHO, expanding the provision of treatment for depression and anxiety yields a benefit-to-cost ratio of 5:1, meaning that for every US$1 spent in treatment for depression and anxiety, there would be benefits equal to US$5.1
Some of the strategies for expanding coverage of preventive and curative services include school-based social and emotional learning programmes, integrating mental health services into primary healthcare with appropriate referral network to higher-level facilities, implementing ban on the use of highly toxic pesticides to prevent suicides and improving the availability of treatment provisions outlined in the WHO Universal Health Coverage Compendium.1 Individuals with anxiety and depression can benefit from community-based mental healthcare because such services are accessible to people.1 Frequent co-occurrence of anxiety and depression and their bi-directional association with conditions such as obesity, chronic conditions like type 2 diabetes mellitus, coronary artery disease and chronic pain disorders5 also highlight the need for integrated care.
Task-sharing with primary healthcare practitioners has been shown to be successful in reducing the treatment gap and increasing coverage for priority mental health problems,1 which could be particularly relevant for countries like Nepal facing dearth of psychiatrist and specialised healthcare facilities for treatment of mental disorder. More people with depression seek support from friends than spouses for anxiety and depression. Therefore, peer support programmes, where individuals share their personal experiences to help one another, could be useful in the Nepalese context. WHO suggests that peer support programmes could help in information exchange, advocacy and increasing awareness, providing emotional support, offering practical assistance and creating social contacts.1 This suggests that family members might serve as effective mediators in providing support to individuals with mental health issues. Providing them with essential skills could aid in tackling the problem. In the Nepalese context, Female Community Health Volunteers (FCHVs) or similar peer educators can play a positive role in this regard. However, further studies on effectiveness of deploying family members, peers or FCHVs could be beneficial from policy perspective.
Strengths and limitations of the study
Most previous studies on anxiety and depression in Nepal are confined to specific groups of participants, such as healthcare workers during COVID-19 pandemic,10 11 nurses,12 13 traffic police,14 patients with type 2 diabetes,15 16 individuals living in quarantine centres during COVID-19 pandemic,58 patients with multidrug-resistant tuberculosis17 and hypertensive adults.18 These studies have typically been limited to specific localities. However, our study involves an analysis from nationally representative sample taking into consideration the current federal structure. Despite being one of the large-scale studies in nationally representative sample, our study has some limitations. The study was not primarily designed to study anxiety and depression but was a part of a more comprehensive study that included multiple other health problems such as reproductive, maternal, newborn and child health, fertility behaviour and hypertension. Consequently, important variables like stress coping skills, meditation behaviour, social capital and social support—factors potentially associated with anxiety and depression were not assessed in this study.10 11 The tools used in the study, GAD-7 and PHQ-9, are screening tools that may not truly capture the prevalence of anxiety and depressive disorder in the Nepalese population.
Conclusion
The prevalence of depression and anxiety is relatively higher among females compared with males. Similarly province, ethnicity, marital status, alcohol intake and disability status were found to be associated with depression and anxiety. The study indicates increased need to develop intervention and formulate policies to address higher prevalence of depression and anxiety in Nepal. Implementing support systems and mental health services tailored to the specific needs of the targeted groups and increasing people’s access to mental health specialists can play a crucial role in reducing the burden of anxiety and depression in Nepal.https://dhsprogram.com/data/dataset/Nepal_Standard-DHS_2022.cfm?flag=0
Footnotes
Funding: The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
Prepublication history for this paper is available online. To view these files, please visit the journal online (https://doi.org/10.1136/bmjopen-2023-078582).
Data availability free text: Data are available on request from: https://dhsprogram.com/data/dataset/Nepal_Standard-DHS_2022.cfm?flag=0.
Patient consent for publication: Consent obtained directly from patient(s).
Ethics approval: We obtained approval and access to Nepal Demographic and Health Survey 2022 (NDHS 2022) data after requesting the data from the official website of ‘The DHS programme'. The NDHS 2022 obtained ethical approval from Ethical Review Board of Nepal Health Research Council (reference number: 494/2021) and institutional review board of ICF international (reference number: 180657.0.001.NP.DHS.01). Written informed consent was obtained from every participant before enrolling them into the study.
Provenance and peer review: Not commissioned; externally peer reviewed.
Patient and public involvement: Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.
Contributor Information
Achyut Raj Pandey, Email: achyutrajpandey2014@gmail.com.
Bikram Adhikari, Email: bikram.adhikariadhitya@gmail.com.
Bihungum Bista, Email: bistabihungum@gmail.com.
Bipul Lamichhane, Email: bipul.lamichhane@herdint.com.
Deepak Joshi, Email: deepak.joshi@herdint.com.
Saugat Pratap K C, Email: saugat.kc@herdint.com.
Shreeman Sharma, Email: shreeman.sharma@herdint.com.
Sushil Baral, Email: sushil@herdint.com.
Data availability statement
Data are available in a public, open access repository.
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