Abstract
Background:
Depression is a major public health problem in India, placing a substantial burden on individuals, families, and society. Besides clinical assessment, quality of life (QoL) and out-of-pocket expenditure (OOPE) are key measures to assess the impact of this condition on a patient’s wellbeing. However, there is a dearth of published evidence on both these outcomes from India. Moreover, there is no evidence of concordance between common tools for measuring QoL in patients with depression in Indian settings.
Aim:
This study aims to assess QoL using the EQ-5D-5L and WHOQOL-BREF tools, estimate OOPE for depression care, identify factors associated with QoL and OOPE, and explore the correlation between the QoL derived from both tools.
Methods:
A cross-sectional survey was conducted at a public tertiary care facility in India, involving semistructured interviews with 259 depression patients. Mean QoL and OOPE were estimated. Multiple linear regression was employed to identify factors associated with QoL and OOPE, while Pearson’s correlation coefficient was used to assess the relationship between EQ-5D-5L and WHOQOL-BREF domain scores.
Results:
The mean EQ-5D-5L utility score was 0.731, while the QoL derived from WHOQOL-BREF ranged from 50.41 to 61.00, across the four domains. Factors like depression severity, perceived support, physical activity, and literacy were significantly associated with QoL. The annual OOPE for depression treatment was ₹35,101. Moderate to strong correlations were observed between EQ-5D-5L and WHOQOL-BREF “physical health” (r = 0.605) and “psychological” (r = 0.553) domains, with weaker correlations for “social relationships” (r = 0.255) and “environment” (r = 0.292).
Conclusion:
Depression leads to significant financial burden on the patient and the family and is associated with lower QoL. Future research should explore the addition of relevant bolt-on dimensions to the EQ-5D-5L to improve its sensitivity in depression assessment.
Keywords: Depression, EQ-5D-5L, mental disorder, out-of-pocket expenditure, quality of life, WHOQOL-BREF
INTRODUCTION
Depression has become a significant public health issue in India, affecting a large portion of the population, with one in every 20 individuals experiencing it during their lifetime.[1] The condition not only impacts individual wellbeing but also imposes a considerable economic burden on the country, projected to cost over $1.03 trillion between 2012 and 2030.[2] To address this growing burden, the Government of India has integrated mental health interventions for depression within primary care settings, aiming to provide accessible care for prevention, early detection, and management.[3] Evaluating quality of life (QoL) and out-of-pocket expenditure (OOPE) is crucial in understanding the impact of depression on individuals and families and will guide the future cost-effectiveness of interventions.[4]
While some studies in India have examined QoL in high-risk populations with comorbidities like diabetes or HIV/AIDS, a few have focused specifically on individuals diagnosed with depression.[5,6,7,8] Two studies assessed the QoL in remitted patients of depression and patients with major depressive disorder, respectively; however, the studies used different QoL instruments and relied on a small sample size.[9,10] These gaps in research are significant because findings from international studies may not be directly applicable to the Indian context, where social and environmental factors can significantly influence QoL.[11] Such research is crucial for clinicians to adopt more cost-effective management strategies from a patient’s perspective.
To measure QoL in depression, two instruments are widely used: the EuroQoL Five Dimensions Five Levels (EQ-5D-5L) and the WHO Quality of Life-BREF (WHOQOL-BREF).[12,13] The EQ-5D-5L, recommended by the Indian Health Technology Assessment Agency (HTAIn), is valuable for health technology assessment and comparisons of QoL across diseases.[12] The WHOQOL-BREF, on the other hand, is a broader tool assessing physical health, psychological health, social relationships, and the environment, making it ideal for a comprehensive evaluation of QoL in depression.[14] However, these tools are often used interchangeably, raising concerns about whether this affects the validity of results in different contexts, such as India’s healthcare setting.
Furthermore, while the National Sample Survey Organization (NSSO) provides nationally representative estimates of OOPE for outpatient and inpatient care related to mental illnesses, it remains challenging to isolate expenditure specifically for depression.[13] Additionally, the National Mental Health Survey (NMHS) presents data on OOPE for depression care but does not differentiate between types of care (inpatient/outpatient) or the components of OOPE (medical/nonmedical expenditure).[1]
To address these evidence gaps, the present study aims to assess QoL and its determinants in patients with depression using the EQ-5D-5L and WHOQOL-BREF, exploring their correlation, alongside an examination of the OOPE incurred by the patients.
METHODOLOGY
A facility-based cross-sectional study was conducted in the outpatient department in a public sector tertiary care health facility in India, which provides diagnostic and treatment services to people with psychiatric, neurological, and substance use disorders.
Sample size
Considering a standard deviation of 0.17 for depression patients,[15] we estimated a sample size of 222 for determination of QoL at 95% confidence intervals and 5% margin of error. Similarly, considering a standard deviation of 6855.74 and a standard error of 511 for OOPE,[16] the sample size was estimated to be 180. A nonresponse rate of 10% was further considered to derive a sample size of 244.
We enrolled patients aged over 18 years who had a confirmed diagnosis of major or persistent depressive disorder by a psychiatrist using the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5).[17,18] The exclusion criteria for selection of participants were patients who presented to the outpatient department with severe mental illnesses, psychotic features, suicide ideation, severe cognitive impairment, or hearing disorders, as guided by the clinicians.
The participants were recruited following a consecutive sampling approach. The participants as well as caregivers were explained about the study and written informed consents were obtained from them, prior to the initiation of the interviews.
Data collection
Trained researchers conducted face-to-face interviews using the participants eligible for the study. Data collection occurred over a 6-month period, from February 20 to July 30, 2024. The interviews were conducted in local language using a semistructured tool [Supplementary Material Section-1]. It took approximately 30 minutes to complete each interview. Clinical information about the participants was obtained through the interviews supplemented with a review of patient medical records, which included details on clinical history, diagnosis, and recommended treatment. This medical information in the data collection tools was validated by clinicians during the data collection process.
The information on nonmedical expenditure was acquired on visit basis during the face-to-face interview. The details of expenditure on medicines and diagnostics on outpatient care were elicited in a retrospective manner, if the participant had been seeking treatment for more than 1 month. The participants, who were newly diagnosed with depression, were followed up prospectively and the details of their OOPE on medicines and diagnostics were obtained after 30 days from their recruitment in the study.
Statistical analysis
Descriptive analyses were used to assess both sociodemographic and clinical characteristics of the sample. Stata13 (StataCorp LLC, College Station, TX), Microsoft Excel (Microsoft Corporation, Washington, USA), and SPSS (SPSS Inc., Chicago, IL, USA) were used to analyze the data.
The utility value corresponding to the responses of each participant was determined, using the Indian EQ-5D-5L value set.[19] The mean QoL in different domains of the WHOQOL-BREF was derived using the standard methods of analysis.[20] The OOPE on nonmedical components (travel and food) for outpatient consultation was calculated on per visit basis and multiplied with number of visits in the previous year, to calculate the total nonmedical OOPE, while the medical OOPE for outpatient care was calculated on a monthly basis and multiplied by 12 to derive the annual medical expenditure. All the components of OOPE for depression attributed hospitalization in the past 1 year were calculated on an annual basis. The per patient total annual expenditure was then calculated as the sum of annual outpatient and inpatient expenditure. Furthermore, the proportion of annual household income spent on access to outpatient care, inpatient care, and annual medical expenditure (on both outpatient and inpatient care) was calculated. The incidence of catastrophic expenditure was considered if the OOPE exceeded 25% of the household income.
Subsequently, a multiple linear regression model with parameter estimates based on the ordinary least squares method was used to assess the determinants of QoL and OOPE. For the EQ-5D-5L utility scores; D1, D2, D3, and D4 scores; and total annual OOPE, separate multivariable regression models were employed. It is assumed that the multiple linear models are
Y = b0+b1 X1+b2 X2+...+bk Xk+e (1)
where Y is the outcome variable, Xi is the value of the ith predictor, and e is the error. The clinical and sociodemographic traits of the participants were used as predictors. Prior to implementation, the multiple linear regression model’s assumptions for linearity, error term normalcy, homoscedasticity, and multicollinearity were confirmed. With insignificant P values from the “Kolmogorov Smirnov Test” and the “Breusch-Pagan Test,” the homoscedasticity and normality assumptions were satisfied. The absence of multicollinearity was indicated by the Variance Inflation Factor (VIF) values ranging from 1.49 to 4.60 in the QoL model and from 1.51 to 2.39 for OOPE model. We used the Pearson correlation coefficient (r) to assess the relationship between the D1, D2, D3, and D4 scores, based on the EQ-5D-5L index value.[21]
RESULTS
Sociodemographic and clinical characteristics of the sample
A total of 259 participants were interviewed, yielding a response rate of 94%. The mean age of the participants was 41.9 years (95% CI: 40.4, 43.4), with females comprising nearly two-thirds of the sample [Table 1]. Approximately 88% of the participants had received formal education, and 36% were employed in remunerative occupations. The mean monthly household income was ₹49,443 (95% CI: ₹42269, ₹56616), with a range from ₹1,000 to ₹350,000. Additionally, 44% of the participants reported having health insurance coverage.
Table 1.
Quality of life scores and annual out-of-pocket expenditure among patients with depression from different sociodemographic groups
| Sociodemographic variables | n (%) | Mean (Standard Deviation) |
|||||
|---|---|---|---|---|---|---|---|
| Quality of Life |
Annual OOPE (₹) | ||||||
| EQ-5D-5L | WHOQOL- BREF |
||||||
| Utility value | D1 | D2 | D3 | D4 | |||
| Age of Patient (years) | |||||||
| <40 | 122 (47.1%) | 0.772 (0.27) | 55.56 (16.18) | 52.22 (20.83) | 61.27 (20.16) | 61.02 (12.83) | 35,530 (56666) |
| 40-59 | 112 (43.2%) | 0.697 (0.32) | 50.83 (18.46) | 49.85 (20.92) | 60.57 (20.17) | 57.03 (16.01) | 29,809 (46330) |
| >59 | 25 (9.7%) | 0.684 (0.35) | 45.57 (16.44) | 44.03 (19.57) | 61.67 (12.96) | 57.5 (11.27) | 55,667 (74848) |
| Gender | |||||||
| Male | 74 (28.6%) | 0.74 (0.27) | 50.63 (16.91) | 47.13 (21.03) | 60.59 (18.01) | 58.32 (12.84) | 36,960 (58851) |
| Female | 185 (71.4%) | 0.728 (0.31) | 53.32 (17.66) | 51.72 (20.63) | 61.17 (20.15) | 59.21 (14.79) | 34,340 (53364) |
| Area of residence | |||||||
| Rural | 155 (59.8%) | 0.724 (0.31) | 53.41 (17.51) | 51.51 (19.66) | 61.18 (18.81) | 57.36 (14.63) | 34,910 (52821) |
| Urban | 104 (40.2%) | 0.742 (0.3) | 51.27 (17.38) | 48.77 (22.41) | 60.74 (20.63) | 61.33 (13.37) | 35,387 (58145) |
| Educational status | |||||||
| Illiterate | 31 (12%) | 0.665 (0.34) | 51.61 (16.47) | 53.52 (18.98) | 63.17 (14.72) | 55.95 (16.16) | 25,271 (24039) |
| Literate | 228 (88%) | 0.74 (0.30) | 52.68 (17.62) | 49.98 (21.05) | 60.71 (20.10) | 59.36 (13.95) | 36,277 (57415) |
| Employment status | |||||||
| Employed | 93 (35.9%) | 0.78 (0.25) | 53.61 (17.12) | 50.49 (21.26) | 58.6 (22.03) | 59.41 (13.49) | 27,942 (47943) |
| Unemployed | 166 (64.1%) | 0.704 (0.32) | 51.96 (17.67) | 50.36 (20.62) | 62.35 (17.90) | 58.7 (14.68) | 39,186 (58259) |
| Religion | |||||||
| Hindu | 155 (59.8%) | 0.739 (0.28) | 52.95 (17.52) | 49.58 (21.51) | 61.56 (19.17) | 60.35 (13.94) | 39,906 (62556) |
| Sikh | 90 (34.7%) | 0.713 (0.34) | 52.18 (18.22) | 52.18 (20.68) | 60.83 (20.88) | 57.5 (15.06) | 28,206 (40310) |
| Other | 14 (5.4%) | 0.762 (0.23) | 50.51 (11.62) | 48.22 (12.31) | 55.95 (14.03) | 52.9 (9.69) | 19,918 (16924) |
| Caste | |||||||
| Non-General | 93 (35.9%) | 0.706 (0.31) | 49.88 (17.21) | 47.63 (21.25) | 58.15 (20.41) | 57.26 (13.46) | 31,734 (39241) |
| General | 166 (64.1%) | 0.745 (0.3) | 54.04 (17.47) | 51.96 (20.46) | 62.6 (18.89) | 59.9 (14.62) | 37,094 (62387) |
| Marital status | |||||||
| Unmarried | 43 (16.6%) | 0.656 (0.43) | 48.84 (19.5) | 45.06 (22.65) | 52.91 (20.88) | 57.2 (13.72) | 39,174 (60109) |
| Married | 216 (83.4%) | 0.746 (0.27) | 53.29 (16.98) | 51.47 (20.31) | 62.62 (18.88) | 59.3 (14.35) | 34,312 (53961) |
| Type of living arrangement | |||||||
| Joint | 116 (44.8%) | 0.75 (0.3) | 56.47 (16.52) | 54.99 (21.03) | 64.66 (18.6) | 60.45 (14.61) | 29,625 (44836) |
| Nuclear | 143 (55.2%) | 0.716 (0.31) | 49.38 (17.61) | 46.68 (19.94) | 58.04 (19.81) | 57.74 (13.88) | 39,559 (61711) |
| Health insurance | |||||||
| None | 146 (56.4%) | 0.759 (0.29) | 56.21 (15.4) | 54.06 (19.15) | 61.7 (17.96) | 60.66 (13.11) | 32,450 (47657) |
| Public | 82 (31.7%) | 0.693 (0.3) | 46.86 (18.85) | 45.48 (21.76) | 59.35 (20.68) | 54.27 (16.05) | 40,204 (66076) |
| Private | 31 (12%) | 0.698 (0.37) | 50.35 (18.88) | 46.24 (22.91) | 62.1 (23.55) | 63.31 (11.15) | 33,315 (53507) |
| Income based wealth quintile | |||||||
| Poorest | 73 (28.2%) | 0.76 (0.21) | 53.87 (16.53) | 73 (51.37) | 73 (62.9) | 73 (55.57) | 47,951 (74375) |
| Poor | 32 (12.4%) | 0.762 (0.29) | 56.03 (15.22) | 32 (55.86) | 32 (63.28) | 32 (57.91) | 28,930 (41978) |
| Middle | 62 (23.9%) | 0.69 (0.38) | 50.86 (19.25) | 62 (49.01) | 62 (58.47) | 62 (57.66) | 31,175 (43232) |
| Rich | 48 (18.5%) | 0.722 (0.28) | 49.33 (17.08) | 48 (46.96) | 48 (58.85) | 48 (59.64) | 30,181 (44126) |
| Richest | 44 (17%) | 0.73 (0.36) | 53.73 (18.19) | 44 (50.57) | 44 (62.12) | 44 (66.41) | 27,879 (45695) |
| Substance use | |||||||
| No | 190 (73.4%) | 0.726 (0.31) | 53.08 (17.77) | 51.36 (20.87) | 61.89 (20.04) | 59.18 (14.87) | 33,698 (52689) |
| Yes | 69 (26.6%) | 0.747 (0.29) | 51.09 (16.61) | 47.77 (20.56) | 58.58 (17.96) | 58.34 (12.44) | 38,910 (60782) |
| Physical Exercise | |||||||
| Adequate | 34 (13.1%) | 0.756 (0.31) | 43.28 (16.37) | 38.11 (23.86) | 48.78 (26.28) | 56.8 (17.53) | 29,927 (58049) |
| Inadequate | 225 (86.9%) | 0.727 (0.3) | 53.95 (17.22) | 52.26 (19.71) | 62.85 (17.64) | 59.28 (13.7) | 35,805 (54567) |
Current substance use, including tobacco, alcohol, or illicit drugs, was reported by 27% of the participants, and only 13% reported engaging in adequate physical activity. Comorbidities were reported by 39% of the sample, with hypothyroidism and hypertension being the most prevalent conditions. The mean duration of depression diagnosis was 3.56 years (95% CI: 2.89, 4.22), and the mean duration of treatment in the current episode was 1.51 years (95% CI: 1.24, 1.78). The average number of depressive episodes reported was 1.83 (95% CI: 1.57, 2.09) since the time of diagnosis. Nearly all participants were on antidepressant medications, with 28% also receiving psychotherapy. The mean number of medications consumed per day by the participants was 2.88 (95% CI: 2.68, 3.07), and the mean number of psychotherapy sessions underwent was 4.42 (95% CI: 3.94, 4.90), in the past 1 year. 39% of participants reported forgetting to take medicines at the prescribed times, and 30% informed self-altering the dosage in the past month. Approximately 20% of the participants reported missing on scheduled follow-up visits in the past year. The hospitalization rate in the past year was 11% [Table 2].
Table 2.
Quality of life and annual out-of-pocket expenditure among patients with depression according to clinical subgroups
| Clinical characteristics | n (%) | Mean (Standard Deviation) |
|||||
|---|---|---|---|---|---|---|---|
| Quality of life |
Annual OOPE (₹) | ||||||
| EQ-5D-5L | WHOQOL- BREF |
||||||
| Utility value | D1 | D2 | D3 | D4 | |||
| Presence of comorbidities | |||||||
| Yes | 101 (39%) | 0.645 (0.38) | 45.15 (18.65) | 42.79 (21.09) | 55.61 (22.43) | 55.08 (14.93) | 39,081 (54354) |
| No | 158 (61%) | 0.786 (0.22) | 57.28 (14.89) | 55.27 (19.16) | 64.45 (16.6) | 61.43 (13.25) | 32,382 (55299) |
| Severity of depression | |||||||
| Currently euthymic (PHQ score<5) | 40 (15.4%) | 0.931 (0.1) | 70.54 (8.96) | 73.75 (10.3) | 70.62 (10.33) | 68.44 (10.89) | 21,696 (14463) |
| Minimal | 89 (34.4%) | 0.82 (0.19) | 59.67 (11.47) | 59.65 (13.24) | 68.45 (11.2) | 61.84 (13.11) | 36,992 (49354) |
| Mild | 59 (22.8%) | 0.733 (0.26) | 48.24 (14.83) | 42.66 (17.97) | 57.77 (21.6) | 55.77 (13.14) | 41,652 (70952) |
| Moderate | 44 (17%) | 0.578 (0.33) | 43.83 (11.01) | 38.16 (11.65) | 57.2 (19.9) | 55.19 (12.95) | 31,149 (53736) |
| Severe | 27 (10.4%) | 0.389 (0.44) | 26.06 (14) | 22.22 (15.93) | 35.49 (21.13) | 48.5 (16.25) | 40,281 (68564) |
| Years of diagnosis | |||||||
| 1-<2 | 112 (43.2%) | 0.804 (0.23) | 57.4 (14.24) | 55.03 (18.98) | 64.58 (18.12) | 60.66 (14.22) | 25,339 (35035) |
| 2-3 | 70 (27%) | 0.653 (0.36) | 46.63 (20.84) | 44.88 (22.67) | 55.24 (22.31) | 57.46 (14.53) | 29,062 (40580) |
| >3 | 77 (29.7%) | 0.696 (0.32) | 50.88 (16.56) | 48.7 (20.36) | 61.04 (17.7) | 57.84 (13.93) | 53,522 (78652) |
| Place of diagnosis | |||||||
| Public | 157 (60.6%) | 0.756 (0.28) | 55.05 (16.31) | 53 (20.15) | 62.1 (19.26) | 59.88 (14) | 28,149 (39931) |
| Private | 102 (39.4%) | 0.692 (0.33) | 48.7 (18.53) | 46.41 (21.26) | 59.31 (19.91) | 57.54 (14.57) | 45,274 (70384) |
| History of depression episodes | |||||||
| First | 69 (56.6%) | 0.773 (0.22) | 52.07 (17.19) | 46.14 (21.58) | 57.49 (22.56) | 56.84 (14.79) | 35,243 (65633) |
| 2-3 | 40 (32.8%) | 0.613 (0.39) | 43.13 (20.29) | 37.5 (19.59) | 58.96 (24.27) | 58.83 (16.7) | 38,503 (64875) |
| >3 | 13 (10.7%) | 0.599 (0.38) | 35.99 (23.62) | 34.94 (25.49) | 48.08 (23.85) | 50.48 (13.43) | 19,226 (8978) |
| Years of treatment | |||||||
| <1 | 82 (31.7%) | 0.729 (0.31) | 56.53 (14.21) | 54.17 (17.29) | 65.75 (15.55) | 62.12 (13.62) | 18,883 (15697) |
| 1-2 | 146 (56.4%) | 0.734 (0.3) | 50.12 (19.08) | 47.81 (22.54) | 58.5 (21.34) | 57.99 (14.62) | 41,814 (62576) |
| >2 | 31 (12%) | 0.722 (0.29) | 53.46 (15.61) | 52.69 (19.62) | 60.22 (18.22) | 55.14 (12.8) | 47,744 (73704) |
| Type of current treatment | |||||||
| Antidepressant medicines | 168 (64.9%) | 0.755 (0.3) | 53.59 (17.84) | 52.21 (19.91) | 61.01 (20.3) | 59.01 (14.34) | 24,844 (39344) |
| Antidepressant medicines and psychotherapy | 73 (28.2%) | 0.726 (0.27) | 51.91 (16.36) | 49.94 (21.02) | 60.62 (18.49) | 58.78 (13.46) | 38,026 (46568) |
| Others | 18 (6.9%) | 0.532 (0.38) | 45.44 (17.36) | 35.42 (23.14) | 62.5 (16.97) | 59.2 (17.1) | 128,506 (118450) |
| Number of medicines | |||||||
| None | 4 (1.5%) | 0.505 (0.48) | 46.43 (24.91) | 43.75 (24.65) | 68.75 (22.95) | 67.97 (31.7) | 120 (0) |
| 1-2 | 110 (42.5%) | 0.816 (0.19) | 58.02 (15.89) | 56.33 (18.82) | 65 (16.47) | 61.54 (13.22) | 24,401 (35983) |
| >2 | 145 (56%) | 0.673 (0.35) | 48.57 (17.38) | 46.09 (21.17) | 57.76 (21.03) | 56.75 (14.07) | 43,410 (64679) |
| Number of psychotherapy sessions | |||||||
| None | 129 (49.8%) | 0.689 (0.35) | 48.75 (18.61) | 46.29 (20.5) | 58.46 (22.02) | 57.8 (15.54) | 28,311 (46814) |
| 1-5 | 80 (30.9%) | 0.757 (0.24) | 54.33 (16.95) | 51.41 (22.43) | 61.15 (16.87) | 58.56 (14.15) | 41,786 (66133) |
| >5 | 50 (19.3%) | 0.798 (0.22) | 59.5 (12.02) | 59.42 (15.56) | 67.33 (14.95) | 62.56 (9.87) | 38,740 (49946) |
| Patient reported side effects | |||||||
| No | 198 (76.4%) | 0.761 (0.28) | 54.4 (16.63) | 52.42 (20.01) | 62.16 (19.03) | 59.79 (13.9) | 30,573 (48424) |
| Yes | 61 (23.6%) | 0.636 (0.36) | 46.55 (18.83) | 43.85 (22.13) | 57.24 (20.78) | 56.25 (15.12) | 49,162 (70032) |
| Hospitalization in past 1 year | |||||||
| Yes | 30 (11.6%) | 0.718 (0.24) | 51.43 (13.28) | 46.53 (21.16) | 60.83 (15.5) | 58.23 (11.93) | 117,193 (112716) |
| No | 229 (88.4%) | 0.733 (0.31) | 52.7 (17.95) | 50.91 (20.76) | 61.03 (20.02) | 59.05 (14.54) | 23,029 (22404) |
| Adherence to treatment | |||||||
| Yes | 148 (57.1%) | 0.696 (0.34) | 49.88 (19.25) | 47.1 (21.45) | 58.73 (21.41) | 57.75 (15.57) | 32,820 (52024) |
| No | 111 (42.9%) | 0.779 (0.24) | 56.11 (14.05) | 54.81 (19.14) | 64.04 (16.28) | 60.56 (12.13) | 38,004 (58481) |
| Perceived support | |||||||
| Not at All | 15 (5.8%) | 0.706 (0.38) | 43.81 (14.44) | 38.89 (22.31) | 36.11 (25.91) | 49.17 (16.43) | 16,777 (8057) |
| Not Much Support | 27 (10.4%) | 0.659 (0.35) | 41.8 (17.35) | 35.99 (17.37) | 41.05 (18.77) | 49.31 (13.44) | 34,994 (59704) |
| Moderate Support | 41 (15.8%) | 0.682 (0.32) | 46.86 (16.64) | 44.61 (19.97) | 58.33 (19.18) | 54.88 (12.83) | 30,176 (39253) |
| Great Support | 79 (30.5%) | 0.719 (0.28) | 52.4 (16.59) | 49.42 (19.04) | 63.5 (15.58) | 59.22 (13.63) | 43,388 (69946) |
| Complete Support | 97 (37.5%) | 0.785 (0.28) | 59.43 (16.19) | 59.45 (19.28) | 69.5 (13.81) | 64.66 (12.5) | 32,949 (47907) |
Quality of life
The mean EQ-5D-5L utility value for patients with depression was estimated as 0.731 (95% CI: 0.694–0.768). The mean QoL derived from WHOQOL-BREF ranged from 50.41 to 61.00. Specifically, the mean scores for physical health (D1), psychological health (D2), social relationships (D3), and environment (D4) were 52.55 (95% CI: 50.41–54.69), 50.41 (95% CI: 47.86–52.95), 61.00 (95% CI: 58.62–63.39), and 58.96 (95% CI: 57.21–60.70), respectively.
According to the EQ-5D-5L instrument, the most reported problems by the patients with depression were depression/anxiety (86%) and pain/discomfort (66%), respectively, followed by difficulties in usual activities (54%). Approximately two-fifth of the participants reported difficulties with mobility (37%) and self-care (39%).
Factors associated with QoL
After adjusting for all dependent variables, the severity of depression was found to be significantly associated with QoL in individuals with depression [Table 3]. A one-point increase in the PHQ-9 score resulted in a significant reduction of 0.024 in the EQ-5D-5L score, as well as a decline in the D1, D2, D3, and D4 scores by 1.87, 2.17, 0.86, and 0.72, respectively.
Table 3.
Determinants of quality of life of patients with depression
| Variables | Categories | EQ-5D-5L |
WHOQOL-BREF |
||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Utility value | Physical domain | Psychological domain | Social Relationships | Environment | |||||||
|
| |||||||||||
| Coef. (SE) | P | Coef. (SE) | P | Coef. (SE) | P | Coef. (SE) | P | Coef. (SE) | P | ||
| Age Group Ref. Below 40 years |
40-59 years | -0.059 (0.066) | 0.376 | -6.348 (2.688) | 0.020 | -2.309 (2.813) | 0.414 | -2.847 (4.275) | 0.507 | -4.312 (2.892) | 0.140 |
| >59 years | 0.012 (0.105) | 0.907 | -5.6 (4.243) | 0.190 | -1.77 (4.44) | 0.691 | 4.921 (6.749) | 0.468 | 4.177 (4.565) | 0.363 | |
| Gender Ref. Male |
Female | -0.015 (0.088) | 0.864 | 4.519 (3.563) | 0.208 | 4.097 (3.728) | 0.275 | -1.349 (5.666) | 0.812 | 3.369 (3.833) | 0.382 |
| Education Ref. Illiterate |
Literate | 0.144 (0.106) | 0.180 | 9.662 (4.294) | 0.027 | 9.645 (4.493) | 0.035 | 9.161 (6.829) | 0.183 | 8.552 (4.62) | 0.068 |
| Occupation Ref. Employed |
Unemployed | -0.038 (0.069) | 0.580 | -4.217 (2.797) | 0.135 | 1.393 (2.926) | 0.635 | -0.626 (4.448) | 0.888 | -3.901 (3.009) | 0.198 |
| Health Insurance Ref. None |
Public | -0.009 (0.06) | 0.876 | -5.557 (2.435) | 0.025 | -1.078 (2.548) | 0.673 | 0.076 (3.873) | 0.984 | -3.468 (2.62) | 0.189 |
| Private | -0.066 (0.087) | 0.451 | -7.95 (3.531) | 0.027 | -2.94 (3.695) | 0.428 | 1.968 (5.616) | 0.727 | -1.536 (3.799) | 0.687 | |
| Type of Family Ref. Joint |
Nuclear | 0.075 (0.058) | 0.200 | 0.739 (2.34) | 0.753 | 0.174 (2.448) | 0.944 | -1.973 (3.721) | 0.597 | 1.334 (2.517) | 0.597 |
| Wealth Quintile Ref. Poorest |
Poor | 0.045 (0.097) | 0.640 | 3.899 (3.91) | 0.322 | 3.062 (4.092) | 0.456 | 7.855 (6.219) | 0.210 | 6.744 (4.207) | 0.113 |
| Middle | -0.023 (0.078) | 0.769 | -2.982 (3.151) | 0.347 | -5.08 (3.297) | 0.127 | -1.956 (5.011) | 0.697 | 2.269 (3.39) | 0.505 | |
| Rich | -0.03 (0.092) | 0.747 | -5.162 (3.722) | 0.169 | -0.004 (3.894) | 0.999 | -1.215 (5.919) | 0.838 | 4.524 (4.004) | 0.262 | |
| Richest | 0.02 (0.087) | 0.818 | 2.388 (3.504) | 0.497 | 0.999 (3.666) | 0.786 | -1.142 (5.573) | 0.838 | 10.135 (3.77) | 0.009 | |
| Presence of co-morbidities Ref. Yes |
No | 0.065 (0.061) | 0.288 | 5.94 (2.452) | 0.018 | 8.19 (2.566) | 0.002 | 7.6 (3.9) | 0.055 | 3.523 (2.639) | 0.185 |
| Substance Use Ref. No |
Yes | 0.046 (0.088) | 0.601 | 1.561 (3.581) | 0.664 | 3.142 (3.747) | 0.404 | -4.768 (5.696) | 0.405 | 0.177 (3.853) | 0.964 |
| Physical Exercise Ref. Adequate |
Inadequate | -0.181 (0.084) | 0.035 | 4.333 (3.411) | 0.207 | 8.868 (3.569) | 0.015 | 1.924 (5.425) | 0.724 | 2.424 (3.67) | 0.511 |
| Years of diagnosis Ref. Up to 1 |
2-3 | -0.105 (0.076) | 0.173 | -9.276 (3.084) | 0.003 | -5.089 (3.227) | 0.118 | -6.522 (4.906) | 0.187 | -1.917 (3.319) | 0.565 |
| >3 | -0.026 (0.088) | 0.773 | -2.835 (3.574) | 0.430 | 0.202 (3.739) | 0.957 | -0.84 (5.684) | 0.883 | 3.767 (3.845) | 0.330 | |
| Severity of Depression Ref. Normal |
Minimal | -0.133 (0.117) | 0.257 | -8.547 (4.73) | 0.074 | -13.575 (4.949) | 0.007 | 3.257 (7.523) | 0.666 | -6.445 (5.089) | 0.209 |
| Mild | -0.228 (0.118) | 0.057 | -20.896 (4.783) | 0.000 | -26.844 (5.004) | 0.000 | -0.539 (7.606) | 0.944 | -8.338 (5.146) | 0.109 | |
| Moderate | -0.289 (0.125) | 0.023 | -26.178 (5.053) | 0.000 | -36.878 (5.287) | 0.000 | -6.259 (8.036) | 0.438 | -15.128 (5.437) | 0.007 | |
| Severe | -0.528 (0.138) | 0.000 | -37.117 (5.605) | 0.000 | -39.267 (5.865) | 0.000 | -16.513 (8.915) | 0.067 | -13.13 (6.031) | 0.032 | |
| Number of episodes Ref. First |
2-3 | -0.033 (0.073) | 0.647 | 4.92 (2.941) | 0.098 | 3.758 (3.077) | 0.225 | 7.548 (4.677) | 0.110 | 3.009 (3.164) | 0.344 |
| >3 | 0.033 (0.108) | 0.761 | -2.107 (4.376) | 0.631 | -1.042 (4.579) | 0.821 | -5.691 (6.96) | 0.416 | -4.181 (4.708) | 0.377 | |
| Years of treatment Ref. <1 Year |
1-2 | 0.01 (0.071) | 0.892 | -1.232 (2.883) | 0.670 | 1.611 (3.016) | 0.595 | 3.389 (4.584) | 0.462 | 0.581 (3.101) | 0.852 |
| >2 | 0.23 (0.136) | 0.095 | 12.052 (5.521) | 0.032 | 3.945 (5.777) | 0.497 | 4.371 (8.781) | 0.620 | 1.406 (5.94) | 0.813 | |
| Type of Treatment Ref. Medicines |
Medicines & Psychotherapy | -0.087 (0.105) | 0.410 | 3.127 (4.24) | 0.463 | -8.356 (4.436) | 0.063 | -0.213 (6.743) | 0.975 | 0.008 (4.562) | 0.999 |
| Others | -0.275 (0.137) | 0.048 | -3.236 (5.543) | 0.561 | -24.918 (5.8) | 0.000 | -11.237 (8.815) | 0.206 | -2.752 (5.963) | 0.646 | |
| Number of medicines Ref. None |
1-2 | -0.274 (0.263) | 0.300 | -11.802 (10.651) | 0.271 | -36.908 (11.145) | 0.001 | -30.273 (16.939) | 0.077 | -34.356 (11.459) | 0.004 |
| >2 | -0.343 (0.264) | 0.197 | -14.882 (10.675) | 0.167 | -42.244 (11.17) | 0.000 | -34.95 (16.978) | 0.043 | -40.152 (11.486) | 0.001 | |
| Number of psychotherapy sessions Ref. None |
1-4 | 0.072 (0.076) | 0.348 | -1.191 (3.086) | 0.701 | 1.986 (3.229) | 0.540 | -2.542 (4.908) | 0.606 | 1.481 (3.32) | 0.657 |
| >4 | -0.027 (0.122) | 0.824 | -0.244 (4.932) | 0.961 | 10.072 (5.16) | 0.054 | 11.847 (7.843) | 0.135 | -0.501 (5.306) | 0.925 | |
| Perceived side effects Ref. No |
Yes | -0.104 (0.07) | 0.138 | -1.617 (2.827) | 0.569 | 2.172 (2.958) | 0.465 | 3.49 (4.495) | 0.440 | -2.473 (3.041) | 0.418 |
| Treatment Adherence Ref. Yes |
No | -0.078 (0.06) | 0.195 | -1.124 (2.434) | 0.646 | -2.172 (2.547) | 0.396 | 1.507 (3.871) | 0.698 | 0.566 (2.619) | 0.829 |
| Perceived Support Ref. Not Much |
Moderate Support | 0.029 (0.093) | 0.752 | 3.502 (3.749) | 0.353 | 2.965 (3.923) | 0.452 | 23.35 (5.963) | 0.000 | 7.445 (4.034) | 0.068 |
| Complete Support | -0.028 (0.077) | 0.721 | 6.652 (3.127) | 0.036 | 9.377 (3.272) | 0.005 | 30.597 (4.973) | 0.000 | 13.762 (3.364) | 0.000 | |
| Constant | 1.367 (0.33) | 0.000 | 69.575 (13.345) | 0.000 | 79.498 (13.963) | 0.000 | 56.481 (21.224) | 0.009 | 78.22 (14.358) | 0.000 | |
| R Square | 0.5151 | 0.8032 | 0.8235 | 0.6452 | 0.6275 | ||||||
| Adjusted R Square | 0.3178 | 0.7231 | 0.7516 | 0.5008 | 0.4758 | ||||||
| AIC | 42.711 | 945.656 | 956.712 | 1058.870 | 963.505 | ||||||
| BIC | 143.656 | 1046.601 | 1057.657 | 1159.816 | 1064.450 | ||||||
| Breuch Pagan Test | 0.051 | 0.535 | 0.463 | 0.051 | 0.397 | ||||||
| Normality | 0.148 | 0.724 | 0.784 | 0.751 | 0.963 | ||||||
| VIF | 1.49-4.60 | ||||||||||
Bold numbers represent significant P<0.05. Coef: Coefficient, SE: Standard error, CI: Confidence intervals
There was no significant difference in EQ-5D-5L utility values among individuals with a normal remitted state and those with minimal or mild depressive disorder [Table 3]. The severity of depression significantly affected all domains of the WHOQOL-BREF, with the exception of social relationships. Notably, WHO-QOL-BREF scores depicted significant differences only in the psychosocial domain for individuals in a normal remitted state and those with minimal depression. Additionally, perceived social support had a significant influence on WHOQOL-BREF scores across all domains.
Patients reporting adequate physical activity had higher QoL scores, as measured by EQ-5D-5L and the psychological domain of the WHOQOL-BREF, compared to those with inadequate physical activity levels. In terms of clinical care, integrated treatment with more complex interventions such as ECT, RTMS (classified under other treatments), and a higher number of medications were significantly associated with poorer QoL scores in at least one of the WHOQOL-BREF domains. Furthermore, sociodemographic factors, including age, literacy, health insurance status, and socioeconomic states, were found to be associated with QoL score in at least one domain of the WHOQOL-BREF.
Correlation between EQ-5D-5L and WHOQOL-BREF
EQ-5D-5L exhibited statistically significant strong (r = 0.605) and moderate (r = 0.553) correlation with “physical health” and “psychological” domains of WHOQOL-BREF, respectively. EQ-5D-5L had weak correlation with the WHOQOL-BREF domains of “social relationships” (r = 0.255) and “environment” (r = 0.292).
Out-of-pocket expenditure and its determinants
The mean monthly OOPE on outpatient care was ₹2,314 (95% CI: ₹1,930, ₹2,699), while expenditure per hospitalization was ₹67,055 (95% CI: ₹34,975, ₹99,136). The mean length of hospital stay was 22.46 days, leading to per bed day hospitalization expenditure of ₹7,976 (95% CI: ₹3,557, ₹12,395). The nonmedical expenditure was 36% and 29% of the total expenditure on outpatient care and inpatient care, respectively. The OOPE on outpatient care majorly comprised expenditure on medicines (61%) and travel (23%), while in the case of hospitalization, bed charges (29%), medical procedures (12%), medicines (21%), and diagnostics (8%) were the main drivers of cost. The annual cost of care was ₹35,101 (95% CI: ₹28,031, ₹42,172). The OOPE across patients belonging to different sociodemographic and clinical subgroups is presented in Tables 1 and 2, respectively.
Households spent 4.7% of their annual income to seek outpatient care, and 11.3% on inpatient care. Overall, 5.9% of annual household income was spent on any form of medical care (both outpatient and inpatient care). The incidence of catastrophic expenditure was 12.9% for outpatient care, and 38.5% for hospitalization. Overall, 15% of households experienced catastrophic expenditure for any form of medical care, that is, outpatient and inpatient care in the past year.
The years of diagnosis, number of episodes, type of treatment, and number of medicines were significantly associated with the magnitude of OOPE, after controlling for all the known confounding variables [Table 4]. The patients on integrated pharmacotherapy and other treatment regimens reported significantly higher OOPE than patients only on pharmacotherapy.
Table 4.
Determinants of out-of-pocket expenditure on treatment for depression
| Variables | Categories | Coef. | SE | P | 95% CI |
|---|---|---|---|---|---|
| Wealth Quintile Ref. Poorest |
Poor | -13661 | 14574.6 | 0.351 | -42566, 15245 |
| Middle | -4349 | 12231.7 | 0.723 | -28607, 19910 | |
| Rich | -17988 | 13165.1 | 0.175 | -44098, 8122 | |
| Richest | -8482 | 12326.4 | 0.493 | -32928, 15965 | |
| Years since diagnosis Ref. Up to 1 |
2-3 | 5216 | 11720.1 | 0.657 | -18028, 28460 |
| >3 | 34750 | 13966.7 | 0.014 | 7050, 62450 | |
| Number of episodes Ref. First |
2-3 | -12831 | 10916.7 | 0.243 | -34481, 8820 |
| >3 | -34772 | 16256.3 | 0.035 | -67013, -2532 | |
| Place of Diagnosis Ref. Public |
Private | 5048 | 9246.5 | 0.586 | -13291, 23386 |
| Years of treatment Ref. <1 year |
1-2 Years | 4733 | 10339.8 | 0.648 | -15774, 25239 |
| > 2 Years | -17969 | 19680.7 | 0.363 | -57001, 21063 | |
| Type of treatment Ref. Medicines |
Medicines and psychotherapy | 42241 | 15823.8 | 0.009 | 10858, 73624 |
| Others | 160504 | 18864.9 | 0.000 | 123089, 197918 | |
| Number of medicines Ref. None |
1-2 | 163618 | 50028.2 | 0.001 | 64399, 262838 |
| >2 | 161108 | 50147.0 | 0.002 | 61653, 260563 | |
| Number of sessions Ref. None |
1-5 | -13832 | 11864.0 | 0.246 | -37361, 9698 |
| >5 | 22088 | 18278.8 | 0.230 | -14164, 58340 | |
| Constant | -139071 | 49608.3 | 0.006 | -237458, -40685 | |
| R2 | 0.585 | ||||
| Adjusted R2 | 0.517 | ||||
| Breusch Pagan | 0.051 | ||||
| VIF | 1.51-2.39 | ||||
Bold numbers represent significant P<0.05. Coef: Coefficient, SE: Standard error, CI: Confidence intervals
DISCUSSION
We assessed the QoL in patients with depression using the EQ-5D-5L and WHOQOL-BREF tools and observed a reduction in QoL among these patients. Considering the perfect QoL values of 1 (for EQ-5D-5L value) and 100 (for WHOQOL-BREF score), QoL score, as assessed by the EQ-5D-5L tool, was reduced in depression patients at 0.731, while the QoL domain scores measured with the WHOQOL-BREF ranged from 50.41 to 61.00.
The findings were in agreement with those of a similar Indian study, which reported WHOQOL-BREF scores ranging from 39.62 to 58.23 in 150 patients with depression in a symptomatic remission state.[9] Additionally, another Indian study that assessed QoL in the diabetic population with depression found domain scores between 55.04 and 63.96, which aligned with the results of our study.[7] However, a comparison with the QoL data from a study on breast cancer patients with depression could not be made due to the lack of standardization of scores on a 0 to 100 scale in that study.[6] Consistent with our study, previous research has indicated that the ‘psychological health’ domain has the lowest scores among all the WHOQOL-BREF domains, while the ‘social relationships’ and ‘environment’ domains tend to exhibit higher scores relative to the other domains in depression.[7,9] Consequently, targeted interventions aimed at improving psychological health – particularly in areas such as self-satisfaction, body image, and the pursuit of a meaningful life – through evidence-based approaches, as cognitive behavioral therapy, mindfulness techniques, could lead to improvements in QoL.[22]
It was not feasible to directly compare the EQ-5D-5L values due to limited published literature on this subject in India. However, the QoL derived through this instrument in the present study was higher than those reported in international studies from Spain, Japan, Norway, and Sweden.[23,24,25,26] While these studies shared similar age and gender distributions with the present study, the proportion of participants with moderate and severe depression was considerably higher (over 70%), compared to 27% in the current study. This contrasting factor may have contributed to the lower QoL scores observed in the international studies. Only one of these studies reported on absolute values of QoL according to severity of depression, and these values were consistent with the findings of the current study.[23] We found that the dimensions which were affected among the highest proportion of patients were ‘depression/anxiety’, followed by ‘pain/discomfort’ and ‘difficulties in usual activities’, which aligns with the findings from the existing literature.[24,25,27]
Among the clinical determinants, severity of depression was associated with variations in QoL.[28,29] Furthermore, a significant decline in WHOQOL-BREF domain scores was observed as severity increased, with exception to the ‘social relationship’ domain. This could be attributed to the social cohesion and close-knit communities in India. Reflecting the same, nearly all participants of the study were living in family arrangements, and 85% reported moderate to complete social support, regardless of the severity of their illness. This support may have mitigated the impact of severity of depression on the ‘social relationships’ domain.
Furthermore, perceived social support was found to significantly impact WHOQOL-BREF scores across all QoL domains, consistent with existing literature.[30,31] This reinforces the crucial role of family and caregivers in improving patient outcomes. Involving caregivers in treatment decisions and supporting social networks through community-level interventions could further enhance QoL for individuals with depression. In this context, the national mental health policy, which is grounded in a participatory approach and advocates for the involvement of both patients and caregivers in the planning, delivery, and monitoring of mental health services, should place a greater emphasis on strengthening the societal role for supporting the provision of care.[32]
The observation that an increase in the number of medications for depression is associated with a decrease in QoL values in the WHOQOL-BREF ‘psychological’, ‘social relationships’, and ‘environment’ domains suggests that polypharmacy may have a detrimental effect on overall wellbeing. This finding emphasizes the need for clinicians to carefully evaluate the necessity of each medication in treating depression, balancing the potential benefits of pharmacological intervention with the risk of negative effects on QoL. Conversely, the positive association between physical activity and QoL observed in this study suggests that counseling depression patients to engage in regular exercise may play a protective role in mitigating the decline of QoL.
We found that the EQ-5D-5L exhibited moderate to strong correlations with the “physical health” and “psychological” domains but weaker correlations with the “social relationships” and “environment” domains. A similar study in the Netherlands concluded that both physical and psychological health significantly contribute to EQ-5D-5L scores in schizophrenic patients, reporting moderate correlations with these domains and a weak correlation with the environment domain.[30] Previous studies have reported similar findings, suggesting that certain aspects of mental health, such as energy, drive, sleep, and concentration, may not be adequately captured by the EQ-5D-5L.[31,33,34] This highlights the need of consideration of additional QoL dimensions in multi-attribute health assessment using EQ5D5L.[35]
Our estimates of OOPE for depression care, amounting to ₹2,314 per month for outpatient care and ₹67,055 for inpatient care over the past year, were higher than the findings from ₹1,467 per month for outpatient care and ₹41,239 for IPD care based on the 76th NSS round.[36] These differences may be attributed to variations in the type and severity of mental illnesses, the type of health facilities accessed, and differences between national and subnational estimates.[36] Similarly, our estimates exceeded the median monthly expenditure of ₹1,500 reported in the 2015-16 NMHS in 12 Indian states,[1] although the survey did not provide disaggregated OOPE estimates based on severity of illness, or the type of facility accessed.
Nonetheless, our findings highlight the significant financial burden associated with depression care in terms of incidence of catastrophic expenditure, underscoring the need for more affordable mental health services. This could be achieved through subsidies and insurance coverage for both outpatient and inpatient care. Programs such as Ayushman Bharat- Pradhan Mantri Jan Arogya Yojana, which cover hospitalization costs and advanced treatments such as ECT, RTMS, and diagnostics during hospitalization, have the potential to reduce OOPE.[37] Additionally, integration of depression treatment into primary care, as envisioned under the comprehensive primary healthcare program of India, is likely to enhance the affordability of care and provide better financial protection for patients.[3]
We used Strengthening the Reporting of Observational Studies in Epidemiology checklist for cross-sectional studies to ensure comprehensive and transparent reporting of the study [Supplementary Material Section 2].[38] However, the study presents the findings with certain limitations. First, the findings are based on a population from a tertiary care hospital, which may not fully represent the broader Indian population. Despite this, the study offers QoL values disaggregated by various sociodemographic and clinical characteristics. To obtain QoL values that are more representative of the Indian population, multisite, community-based research using similar tools may be needed. Additionally, the cross-sectional design of this study limits the ability to assess the instruments’ sensitivity to detect changes in QoL over time. Future research could also explore the impact of incorporating relevant bolt-on dimensions to improve the sensitivity of the EQ-5D-5L in capturing QoL in depression. Last, the financial implications of depression currently focus solely on the OOPE incurred by households for seeking treatment in a duration of 1 year. Future research should expand this scope to include the health system costs associated with providing treatment, as well as the indirect costs borne by families, for a more comprehensive assessment of the overall financial burden of depression. Longitudinal studies may be required for estimation of financial burden of the disorder across lifetime of the individuals. Moreover, financial risk protection is better measured through other indicators of financial status, such as consumption expenditure. More studies using cutoffs of consumption expenditure should be used to measure the extent of catastrophic health expenditure.
CONCLUSION
Depression has a detrimental effect on QoL and is associated with considerable OOPE. The study highlights the beneficial effects of social support and physical activity on QoL, suggesting that involving caregivers in treatment decisions, promoting physical activity, and fostering social networks through community-based interventions could enhance QoL for individuals with depression. Moreover, the weak correlation between the EQ-5D-5L and the “social relationships” and “environment” domains of the WHOQOL-BREF underscores the need for further research on integrating relevant bolt-on dimensions into the EQ-5D-5L to improve its sensitivity in assessing QoL in depression.
Declaration regarding the use of generative AI
The authors attest that there was no use of generative artificial intelligence (AI) technology in the generation of text, figures, or other informational content of this manuscript
Patient’s consent
Written consent was acquired from the patient as well as the caregiver before initiation of interview
Ethical approval
The research study was approved by the Institutional Ethics Committee, Post Graduate Institute of Medical Education and Research, Chandigarh, India, vide reference no. PGI/IEC-INT/2024/1599.
Conflicts of interest
There are no conflicts of interest.
SUPPLEMENTARY MATERIAL
Section 1. Data collection instrument and operational definitions of variables
A semi-structured data collection instrument consisting of four sections: socio-demographic profile, clinical profile and severity of depression, QoL, and out-of-pocket expenditure was developed to meet the objectives of the study.
-
Socio-demographic profile
This section of the data collection tool comprised of questions regarding the participant’s demographic and socio-economic characteristics, such as current age, gender, area of residence, highest educational qualification, employment status, religion, caste, marital status, living arrangement, health insurance status, and annual household income. Age was recorded in completed years, while gender and area of residence were categorized into binary options: male/female and urban/rural, respectively. The response categories for educational qualification, employment status, religion, caste, marital status, and health insurance status were aligned with the data collection formats used in national surveys in India [1].
Economic status was assessed based on the annual household income, with participants being grouped into five income quintiles according to the income data collected. Additionally, the tool included standardized questions to evaluate lifetime and current patterns of substance use, as well as the sufficiency of physical activity[2,3,4].
-
Clinical characteristics
The severity of depression was evaluated using the Patient Health Questionnaire-9 (PHQ-9), with scores categorized as minimal (5-9), mild (10-14), moderate (15-19), and severe (20 or higher)[5]. We collected information on the treatment-seeking behaviour of patients, including the duration of diagnosis and treatment, as well as the number of outpatient visits in the past year. Additionally, we recorded details of hospitalizations over the past year, such as the number of hospitalizations, the type of healthcare facility accessed, and the duration of hospitalization. The type of current treatment was validated from the clinical records, and later categorized into ‘pharmacotherapy’, ‘pharmacotherapy and psychotherapy’, and others, which included treatments as ‘pharmacotherapy along with electroconvulsive therapy (ECT), or repetitive transcranial magnetic stimulation (RTMS)’.
The tool further included questions regarding the current treatment regime and any perceived side effects. Non-adherence to treatment was determined if participants reported forgetting or failure to take medicines at the prescribed times or self-altering the dosage in the past month or conveyed forgetting or missing scheduled follow-up visits in the past year.
-
Quality of life tools
WHOQOL-BREF
WHOQOL-BREF is a structured generic QoL tool, consisting of 26 questions. The questions are divided into four domains of physical health (D1), psychological health (D2), social relationship (D3), and environment (D4)[6]. Two questions of the tool reflect general wellbeing, which are not considered for scoring under any specific domain. The responses to the questions are measured on a scale from one to five, with the scores for each domain ranging from 0 to 100. A higher score indicates a higher level of QoL attributed to that particular domain. The WHOQOL-BREF has been validated across multiple countries, including India, demonstrating strong discriminant validity, content validity, internal consistency, and test-retest reliability[7].
EQ-5D-5L
The EQ-5D-5L is a standardized instrument used to assess QoL, comprising five dimensions: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression[8]. Each dimension is evaluated on a five-level scale, ranging from no problems (level 1) to extreme problems (level 5). This multiparametric tool generates an index value based on the five responses, utilizing predefined value sets[9]. The index value ranges from -0.923 to 1.0 in the Indian value set, with higher values indicating better QoL.
Additionally, the EQ-5D-5L includes a Visual Analogue Scale (EQ-VAS), where individuals rate their overall health on a scale from 0 (representing the worst health) to 100 (representing the best imaginable health)[8].
Out-of-pocket expenditure
This section had questions on frequency of outpatient visits, and OOPE across various categories, including registration fees, medicines, psychotherapy sessions, diagnostics, food, travel, as well as boarding and lodging. Expenditures related to treatment therapies, medicines, and diagnostics were assessed in terms of monthly costs, while non-medical expenses (such as food, travel, and accommodation) were evaluated on a per-visit basis.
For hospitalizations in last one year, the OOPE included expenditure on registration, bed charges, therapy costs, as well as expenses for medicines, diagnostics, food, travel, and accommodation.
Section 2: STROBE Statement (Checklist of items that should be included in reports of cross-sectional studies)[10]
| Item No | Recommendation | Page number | |
|---|---|---|---|
| Title and abstract | 1 | (a) Indicate the study’s design with a commonly used term in the title or the abstract | Page 1 |
| (b) Provide in the abstract an informative and balanced summary of what was done and what was found | Page 1 | ||
| Introduction | |||
| Background/rationale | 2 | Explain the scientific background and rationale for the investigation being reported | Page 2 |
| Objectives | 3 | State specific objectives, including any prespecified hypotheses | Page 3 |
| Methods | |||
| Study design | 4 | Present key elements of study design early in the paper | Page 3 |
| Setting | 5 | Describe the setting, locations, and relevant dates, including periods of recruitment, exposure, follow-up, and data collection | Page 3 |
| Participants | 6 | (a) Give the eligibility criteria, and the sources and methods of selection of participants | Page 3 |
| Variables | 7 | Clearly define all outcomes, exposures, predictors, potential confounders, and effect modifiers. Give diagnostic criteria, if applicable | Page 3,4 |
| Data sources/measurement | 8 | For each variable of interest, give sources of data and details of methods of assessment (measurement). Describe comparability of assessment methods if there is more than one group | Supplementary material (Section 1), Page 4 (manuscript) |
| Bias | 9 | Describe any efforts to address potential sources of bias | |
| Study size | 10 | Explain how the study size was arrived at | Page 3 |
| Quantitative variables | 11 | Explain how quantitative variables were handled in the analyses. If applicable, describe which groupings were chosen and why | Page 4 |
| Statistical methods | 12 | (a) Describe all statistical methods, including those used to control for confounding | Page 4 |
| (b) Describe any methods used to examine subgroups and interactions | Page 4 | ||
| (c) Explain how missing data were addressed | NA | ||
| (d) If applicable, describe analytical methods taking account of sampling strategy | |||
| (e) Describe any sensitivity analyses | NA | ||
| Results | |||
| Participants | 13 | (a) Report numbers of individuals at each stage of study—eg numbers potentially eligible, examined for eligibility, confirmed eligible, included in the study, completing follow-up, and analysed | Page 5,6 |
| (b) Give reasons for non-participation at each stage | NA | ||
| (c) Consider use of a flow diagram | Page 5-7 | ||
| Descriptive data | 14 | (a) Give characteristics of study participants (eg demographic, clinical, social) and information on exposures and potential confounders | Page 5,6 |
| (b) Indicate number of participants with missing data for each variable of interest | NA | ||
| Outcome data | 15 | Report numbers of outcome events or summary measures | Pages 5-7 |
| Main results | 16 | (a) Give unadjusted estimates and, if applicable, confounder-adjusted estimates and their precision (eg, 95% confidence interval). Make clear which confounders were adjusted for and why they were included | NA |
| (b) Report category boundaries when continuous variables were categorized | Pages 5-7 | ||
| (c) If relevant, consider translating estimates of relative risk into absolute risk for a meaningful time period | NA | ||
| Other analyses | 17 | Report other analyses done—eg analyses of subgroups and interactions, and sensitivity analyses | NA |
| Discussion | |||
| Key results | 18 | Summarise key results with reference to study objectives | Page 8 |
| Limitations | 19 | Discuss limitations of the study, taking into account sources of potential bias or imprecision. Discuss both direction and magnitude of any potential bias | Pages 10-11 |
| Interpretation | 20 | Give a cautious overall interpretation of results considering objectives, limitations, multiplicity of analyses, results from similar studies, and other relevant evidence | Page 8-11 |
| Generalisability | 21 | Discuss the generalisability (external validity) of the study results | Page 11 |
| Other information | |||
| Funding | 22 | Give the source of funding and the role of the funders for the present study and, if applicable, for the original study on which the present article is based | Title Page |
NA: Not applicable
Funding Statement
Nil.
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