Difference in Healthcare Resource Utilization and Health-Related Quality of Life between Patients with Major Depressive Disorder (MDD) and Treatment-Resistant Depression (TRD) in a Public Hospital in Malaysia – A Retrospective Case-Note Review and Cross-Sectional Survey

doi:10.21203/rs.3.rs-4929054/v1

Major depressive disorder (MDD) is emerging as a common disease globally, therefore it is important to assess its economic impact on the national budget. A major contributing factor with cost implication is due to non-responsiveness towards initial treatment for MDD, resulting in progression to treatment resistant depression (TRD). Recently, National Health and Morbidity Survey 2023 had reported an increasing prevalence of depression within the Malaysian population. A cohort of 286 patients diagnosed with MDD or TRD was recruited from Psychiatry and Mental Health Clinic of Hospital Kuala Lumpur (HKL) and was retrospectively studied for 18 months. This study adopted a combined retrospective chart review and cross-sectional survey. The study was conducted from both a government and societal perspective. Results show that MDD patients were more likely to be in the lowest income range. Patients with TRD have significantly higher odds of outpatient resource utilization, including a 5-fold increase in the likelihood of having more than 10 consultation sessions (p = 0.035), and increased use of second-generation antipsychotics (p < 0.001). The average healthcare cost per patient is higher for those with TRD than for MDD (RM1,845 vs RM839 respectively, 1 Ringgit Malaysia (RM) = USD 0.22 approx.) over a period of 18 months suggesting that a 55% (or RM1,006) reduction in healthcare spending is possible if an MDD patient is prevented from progressing to TRD by proper treatment. In conclusion, appropriate care is of utmost importance to reduce the progression of MDD to TRD which would otherwise require significantly higher healthcare resource utilisation. The study highlights the need for adequate resource allocation for the early management of MDD and prevent progression to TRD.

Disease burden

major depressive disorder

treatment resistant depression

healthcare resource utilisation

Malaysia

An estimated 3.8% of the world population had been diagnosed or experienced depression and this figure includes 5% of adults (4% among men and 6% among women), and 5.7% of adults aged 60 and above [1]. Based on the statistics by World Health Organisation (WHO) in their “Depression and other Common Mental Disorders Global Health Estimates” report published in 2017, about 27% of the global cases of depression were residing in South-East Asian region [2]. National Health and Morbidity Survey 2023 reported that about 1 million or 4.6% of people in Malaysia aged 16 and above suffer from depression and the number of people with depression was doubled from 2019 to 2023 [3].

With the high number of world population suffering from major depressive disorder (MDD), the economic impact of this condition is therefore significant for the community worldwide. In 2011, the World Economic Forum (WEF) had described three different approaches to quantify the burden of economic diseases, namely cost of illness, value of lost output and value of statistical life [4, 5]. The first suggested approach of assessing the human capital costs can be broadly divided into the direct and indirect costs measurement. The direct costs are financial expenses related to the treatment care and recovery such as outpatient and inpatient expenditures whilst indirect costs are related to revenue loss, reduced work productivity, personal injury or death attributed to the mental health condition and etc [4]. The WEF report had estimated the global cost of mental conditions in 2010 was US$ 2.5 trillion with the projected future costs to be approximately US$ 6.0 trillion by 2030, 1 Ringgit Malaysia (RM) = USD 0.22 approx.) [6].

A number of studies on the economic burden on MDD had been conducted in some countries such as United States and Iran. The consensus findings include significant overall direct cost expenditures on MDD treatment due to various factors such as increasing MDD prevalence leading to increased treatment cost especially for treatment under private sector and prolonged treatment duration due to the delay of episode resolution [7–9]. One of the major contributing factors with the significant cost increment is due to non-responsiveness towards the initial lines of the recommended treatment for MDD. This would result in treatment-resistant depression (TRD) where patients are associated with higher health resource utilisation, work-loss and rate of employment status change as compared to treatment-sensitive depressive patients [10–13]. This comparison was reported in an early study that both direct and indirect costs for TRD patients were significantly higher than treatment-sensitive MDD patients. The average TRD direct and indirect 2-year costs were doubled as compared to that of treatment-sensitive MDD patients [11, 14]. Patients with TRD are also linked to a number of psychiatric and medical conditions such as anxiety disorders, cardio-neuro vascular diseases, chronic renal disease [15]. Increased work loss can also lead to substantial indirect costs resulting from depression-associated disability [16, 17]. This has provided evidence that in order to reduce the heavy financial impact arising from complications, it is important to adopt effective strategies in managing TRD patients [10, 15]. In addition, TRD poses substantial challenges to depression management and dramatically worsens patient’s prognosis and quality of life [18].

The healthcare system in Malaysia follows a national health service model where the major source of revenue is taxation. It is also a dichotomous system with a public and a private sector. The former is the main provider of healthcare services to over 90% of the public and they are heavily subsidized by the government. The current study aims to examine the disease burden of a cohort of patients with MDD and TRD in Malaysia separately. This will contribute to the understanding and planning of the clinical and economic value of both current and future therapies. The findings of this study will benefit the stakeholders including physicians, policy-makers in the government, patients, caregivers and payors in adopting a holistic approach in the management of MDD and TRD in the future. The primary objectives of this study were (i) to capture the economic burden of MDD and TRD in Malaysia (as both direct and indirect costs) by examining the difference in resource utilization between the 2 conditions, and (ii) to study the quality of life of patients with MDD and TRD through a generic questionnaire. The secondary objectives of this study were (i) to estimate the prevalence of TRD among patients diagnosed with MDD in Malaysia, (ii) to examine the difference in clinical and socio-demographic characteristics of this cohort, and (iii) to understand the prescribing pattern for antidepressant drugs for MDD and TRD in Malaysia.

2.1 Study design and study population

This study adopted a combination of retrospective chart review and cross-sectional survey in a cohort of patients diagnosed with MDD or TRD seeking outpatient treatment at the Psychiatry and Mental Health Clinic of Kuala Lumpur General Hospital between January and October 2023. Given the study site, HKL is a tertiary centre where patients are referred from other states of Malaysia and fresh cases are also treated, the research team believes that the patients studied during this period are highly representative of the local Malaysian setting from the point of view of public healthcare. The patients recruited for this study were sourced from a diverse multi-racial outpatient patient pool who had met all the inclusion criteria during the stipulated study period. The study was conducted from both a government and societal perspective. Institutional ethics as well as approval from the Medical Research Ethics Committee (MREC) of Malaysia Ministry of Health (NMRR-ID-22-02102I-RIM) and the study hospital were previously obtained.

The study population were aged 18 and above at the time of study, were medication-treated MDD or TRD patients who had been treated for a minimum of 4 months before the recruitment date, and consented to answer the questionnaire. The exclusion criteria was an uncertain MDD diagnosis at the time of recruitment based on the investigator’s judgement or; a co-existing condition of psychotic disorders; neurocognitive disorders; manic episodes or bipolar affective disorder within the past 18 months [19].

All potentially eligible MDD or TRD patients attending a routine follow-up visit at the Psychiatry and Mental Health Clinic Kuala Lumpur General Hospital were screened for suitability for participation in this study. They were referred by their attending doctors to the research team and the co-morbidities, medications, and healthcare resource utilization of eligible patients covering the 18 months prior to the recruitment date were retrieved from their medical records and reviewed retrospectively. Eligible patients were also invited to complete the cross-sectional survey. Figure 1 below illustrates the study patient recruitment and process flow.

2.2 Data sources

Subjects who had been recruited and consented to participate in this study were asked to self-administer a three-part structured questionnaire survey with 33 questions on socio-demographics, financial profile, indirect medical costs, productivity status and intangible costs in terms of their health-related quality of life (HRQoL) status. Income was categorized according to a locally established framework, while HRQoL was assessed using the 36-Item Short Form Survey (SF-36) [14, 20, 21]. The options of either physical or online modes were offered to the respondents. Data extractions on subjects’ clinical characteristics and healthcare resource utilization namely outpatient visits, inpatient visits, emergency visits, medication prescription history, laboratory tests, psychotherapy and occupational therapy were extracted from the patients’ medical notes. The data extraction was for 18 months from the recruitment date. The date of the MDD treatment initiation served as the starting point of the data collection. Data extracted was entered into the Electronic Data Capture (EDC) via an electronic case record form. The study tools used in the study is attached in Additional File 1.

2.3 MDD and TRD criteria

The diagnosis of MDD patients who were recruited in the study must be based on the clinical judgement and be consistent with the medical record of these patients. The Diagnostic and Statistical Manual of Mental Disorders-5 (DSM-5) criteria was used to establish MDD diagnosis [22].

The MDD study respondents must be prescribed with the minimum of one or more antidepressants within the past 18 months retrospectively from the date of recruitment. They must be prescribed within a list of antidepressants with a minimum of seven daily adequate doses based on Additional File 2 [23].

Patients who had minimal clinical improvement (less than 50% improvement of symptoms), on at least 2 different antidepressant trials of adequate compliance, dose, and duration (at least 6 weeks since first date of retrospective observation) was classified as TRD [22, 24]. Data related to the patient’s prescriptions such as medication names, strength, dose frequency and quantity were used to ascertain the TRD status of the patient. Higher number of antidepressants switched or added is likely to suggest a higher degree of treatment resistance. In this study, the “degree of treatment resistance” referred to the number of anti-depressant use over the whole observation period of their MDD treatment. A TRD patient may even progress beyond using two anti-depressants if they remained unresponsive to the treatment.

2.4 Statistical analysis

Data were initially transcribed from Microsoft Forms into an Excel spreadsheet for organization and preliminary inspection. Subsequently, the dataset was imported into IBM SPSS Statistics software (Version 29.0.2.0; IBM Corp., Armonk, NY) for comprehensive statistical analysis. A cross-tabulation analysis was conducted to examine the distribution and relationships between various parameters. This preliminary step facilitated the identification of potential patterns and associations within the dataset. Following the preliminary analysis, a binary logistic regression was performed to evaluate the odds ratios (OR) for TRD across all parameters, using MDD as the reference group. This regression analysis aimed to quantify the likelihood of TRD in relation to each parameter, providing insights into the factors related to treatment resistance. The results were reported with 95% confidence intervals (95% CI) to ensure statistical robustness and reliability.

Of the 300 consented participants, 14 were excluded due to missing medical documents, resulting in a final sample of 286 patients. The comparative analysis between 242 MDD patients and 44 TRD patients were reported in Table 1–6; Additional File 3 and 4. Table 1 illustrates the sociodemographic profiles of patients with MDD and TRD and no significant statistical differences were observed across all parameters. The male:female ratio was 22:78 and the prevalence of TRD in MDD was 18.2% (44/242).

Table 1

Socio-demographic profile of patients with MDD and TRD
Details	Patient with MDD (N = 242) n (%)	Patient with TRD (N = 44) n (%)	p-value	Odds Ratio (95% CI)	p-value
Gender			0.952
Male	54 (22.3)	10 (22.7)		1.02 (0.48–2.21)	0.952
Female	188 (77.7)	34 (77.3)		0.98 (0.45–2.10)	0.952
Age group			0.844
0–20 years old	13 (5.4)	1 (2.3)		0.41 (0.05–3.21)	0.396
21–40 years old	161 (66.5)	30 (68.2)		1.08 (0.54–2.15)	0.830
41–60 years old	44 (18.2)	8 (18.2)		1.00 (0.44–2.30)	1.000
61–80 years old	24 (9.9)	5 (11.4)		1.17 (0.42–3.24)	0.770
Comorbidity			0.466
Yes	62 (25.6)	9 (20.5)		0.75 (0.34–1.64)	0.467
No	180 (74.4)	35 (79.5)
Transportation			0.873
Public transportation	78 (32.2)	15 (34.1)		1.09 (0.55–2.15)	0.809
Take taxi	24 (24.0)	4 (9.1)		0.91 (0.30–2.76)	0.865
Drive own vehicle	113 (46.7)	21 (47.7)		1.05 (0.55–1.98)	0.899
Walk	8 (3.3)	0		-	-
E-hailing	7 (2.9)	1 (2.3)		0.78 (0.09–6.51)	0.819
Sent by family / spouse	12 (5.0)	3 (6.8)		1.40 (0.38–5.19)	0.612
Education			0.132
No certificate, diploma or degree	38 (15.7)	9 (20.5)		1.38 (0.61–3.10)	0.435
High school certificate	87 (36.0)	11 (25.0)		0.59 (0.29–1.23)	0.162
Pre-U	17 (7.0)	1 (2.3)		0.31 (0.04–2.37)	0.258
Undergraduate (degree)	63 (26.0)	11 (25.0)		0.95 (0.45–1.99)	0.886
Graduate (master or PhD)	35 (14.5)	10 (22.7)		1.74 (0.79–3.84)	0.170
Other	2 (0.8)	2 (4.5)		5.71 (0.78–41.69)	0.086
Marital status			0.703
Single, not married	136 (56.2)	23 (52.3)		0.85 (0.45–1.63)	0.630
Married	72 (29.8)	16 (36.4)		1.35 (0.69–2.65)	0.383
Separated	6 (2.5)	2 (4.5)		1.87 (0.37–9.59)	0.452
Divorced	14 (5.8)	2 (4.5)		0.77 (0.17–3.54)	0.743
Widowed	14 (5.8)	1 (2.3)		0.38 (0.05–2.96)	0.354

TRD patients were more common in higher income groups, specifically within the ranges of RM 2500–RM 3169 (OR = 2.22, 95% CI: 1.05–4.70, p = 0.038) and RM 3970–RM 4849 (OR = 3.03, 95% CI: 1.06–8.63, p = 0.038). In contrast, the majority of MDD patients had a lower income of less than RM 2500 (OR = 0.36, 95% CI: 0.17–0.75, p = 0.006), as demonstrated in Table 2.

Table 2

Salary range of patients with MDD and TRD
Details	Patient with MDD (N = 242) n (%)	Patient with TRD (N = 44) n (%)	p-value	Odds Ratio (95% CI)	p-value
Income Category			0.104
Less than RM2500	117 (48.3)	12 (27.3)		0.36 (0.17–0.75)	0.006
RM2500 – RM3169	39 (16.1)	13 (29.5)		2.22 (1.05–4.70)	0.038
RM3170 – RM3969	17 (7.0)	3 (6.8)		0.96 (0.27–3.43)	0.945
RM3970 – RM4849	12 (5.0)	6 (13.6)		3.03 (1.06–8.63)	0.038
RM4850 – RM5879	12 (5.0)	2 (4.5)		0.90 (0.19–4.19)	0.894
RM5880 – RM7099	5 (2.1)	1 (2.3)		1.09 (0.12–9.59)	0.938
RM7110 – RM8669	4 (1.7)	0		-	-
RM8700 – RM10959	4 (1.7)	1 (2.3)		1.37 (0.15–12.58)	0.782
RM10960 – RM15039	1 (0.4)	1 (2.3)		5.55 (0.34–90.70)	0.229
More than RM15039	1 (0.4)	0		-	-
No income	30 (12.4)	5 (11.4)		0.91 (0.33–2.48)	0.848

In terms of productivity loss, as shown in Additional File 3, TRD patients who were working showed significantly higher odds of working 36–40 hours per week (OR = 3.29, 95% CI: 1.31–8.29, p = 0.012). Among students, there was a significant increase in absenteeism, with 7–8 hours of missed school time due to depression (OR = 14.00, 95% CI: 1.43–137.32, p = 0.023). Additionally, TRD patients were more likely to feel more productive at work compared to MDD patients, as the odds of 0 hours of presenteeism in the TRD group were 2.89 (95% CI: 1.08–7.76, p = 0.035). However, despite the significant p-value, the number of patients was quite small, which may limit the generalizability of the productivity-related findings.

Table 3 shows that that the MDD group has a significant score of 81–100 for role limitation due to physical health (OR = 0.4, 95% CI: 0.16–0.98, p = 0.045). This suggests that patients with MDD tend to have fewer functional limitations, resulting in a better quality of life compared to the TRD patients due to milder disease severity.

Table 3

HRQoL of patients with MDD and TRD based on 36-Item Short Form Survey (SF-36)
Details	Patient with MDD (N = 242) n (%)	Patient with TRD (N = 44) n (%)	p-value	Odds Ratio (95% CI)	p-value
Role limitation due to physical health			0.283
Score 81–100	69 (28.5)	6 (13.6)		0.40 (0.16–0.98)	0.045
Score 61–80	41 (16.9)	8 (18.2)		1.09 (0.47–2.52)	0.841
Score 41–60	58 (24.0)	12 (27.3)		1.19 (0.58–2.46)	0.639
Score 21–40	47 (19.4)	10 (22.7)		1.22 (0.56–2.65)	0.614
Score 0–20	27 (11.2)	8 (18.2)		1.77 (0.75–4.20)	0.196

Patients with TRD have significantly higher odds of outpatient resource utilization, including a fivefold increase in the likelihood of having more than 10 consultation sessions (OR = 5.78, 95% CI: 1.13–29.63, p = 0.035), increased use of anticonvulsants (OR = 11.48, 95% CI: 1.02–129.41, p = 0.048), hypnotic drugs (OR = 5.20, 95% CI: 2.23–12.12, p < 0.001), and second-generation antipsychotics (OR = 18.59, 95% CI: 8.50–40.66, p < 0.001) (Tables 4 and 5). Additionally, TRD patients exhibit higher odds of inpatient resource utilization, including repeated admissions, laboratory tests, and the use of second-generation antipsychotics (OR = 14.00, 95% CI: 1.06–185.49, p = 0.045) (Additional File 4 and Table 5).

Table 4

Healthcare resource utilisation of patients with MDD and TRD
Details	Patient with MDD (N = 242) n (%)	Patient with TRD (N = 44) n (%)	p-value	Odds Ratio (95% CI)	p-value
Outpatient
Outpatient psychiatry consultation			0.001
1–3 consultation sessions	121 (50.0)	15 (34.1)		0.51 (0.26–1.00)	0.049
4–6 consultation sessions	75 (31.0)	12 (27.3)		0.83 (0.40–1.69)	0.599
7–9 consultation sessions	38 (15.7)	10 (22.7)		1.56 (0.71–3.43)	0.265
10–12 consultation sessions	3 (1.2)	3 (6.8)		5.78 (1.13–29.63)	0.035
13–15 consultation sessions	3 (1.2)	3 (6.8)		5.78 (1.13–29.63)	0.035
More than 15 consultation sessions	0	1 (2.3)		-	-

Table 5

Medication utilisation of patients with MDD and TRD
Details	Patient with MDD (N = 242) n (%)	Patient with TRD (N = 44) n (%)	p-value	Odds Ratio (95% CI)	p-value
Medication prescribed in outpatient
Anticonvulsants	1 (0.4)	2 (4.5)	0.013	11.48 (1.02–129.41)	0.048
Antimanic agents	1 (0.4)	0	0.669	-	-
Central nervous system stimulants	2 (0.8)	1 (2.3)	0.386	2.79 (0.25–31.46)	0.406
First generation antipsychotics	0	1 (2.3)	0.019	-	-
Hypnotics	122 (50.4)	37 (84.1)	0.001	5.20 (2.23–12.12)	< 0.001
Noradrenaline reuptake inhibitors (NARIs)	1 (0.4)	0	0.669	-	-
Second generation antipsychotics	16 (6.6)	25 (56.8)	< 0.001	18.59 (8.50–40.66)	< 0.001
Serotonin-Noradrenaline reuptake inhibitors (SNRIs)	3 (1.2)	2 (4.5)	0.124	3.79 (0.62–23.39)	0.151
Selective serotonin reuptake inhibitors (SSRIs)	224 (92.6)	43 (97.7)	0.206	3.46 (0.45–26.57)	0.234
Supportive therapy	5 (2.1)	2 (4.5)	0.328	2.26 (0.42–12.02)	0.340
Tricyclic antidepressants (TCAs)	5 (2.1)	1 (2.3)	0.930	1.10 (0.13–9.67)	0.930
Medication prescribed in inpatient
Hypnotics	5 (2.1)	2 (4.5)	1.000	1.00 (0.13–7.45)	1.000
Second generation antipsychotics	1 (0.4)	3 (6.8)	0.022	14.00 (1.06–185.49)	0.045
Selective serotonin reuptake inhibitors (SSRIs)	10 (4.1)	4 (9.1)	1.000	1.00 (0.13–7.45)	1.000

The average cost per patient was two-folds higher for those with TRD compared to those with MDD (RM 1,845.19 vs RM 839.43) due to higher use of outpatient consultations, pharmacy services, and medications, as illustrated in Fig. 2 and Table 6. The total medical expenses for hospitalized TRD patients, including inpatient admissions, length of stay, laboratory tests, and medications, were also found to be higher compared to those for MDD patients. The costs of each service, laboratory test, and medication were listed in Additional File 5.

Table 6

Overall healthcare resource costs of patient with MDD and TRD
		Patient with MDD (N = 242)				Patient with TRD (N = 44)
Healthcare Resource Utilisation	Cost / Unit (RM)	Number of patients, n (%)	Total Utilisation	Average cost per patient, Mean ± SD (RM)	Total cost of healthcare resource (RM)	Number of patients, n (%)	Total Utilisation	Average cost per patient, Mean ± SD (RM)	Total cost of healthcare resource (RM)
Outpatient
Outpatient consultation & pharmacy	65.40	240 (99.2)	978	264.30 ± 172.65	63,961.20	44 (100.0)	264	392.40 ± 352.05	17,265.60
Psychotherapy	250.00	28 (11.6)	62	64.05 ± 217.32	15,500.00	1 (2.3)	1	5.68 ± 37.69	250.00
Occupational therapy	60.00	4 (1.7)	4	0.99 ± 7.67	240.00	-	-	-	-
Laboratory tests	*	52 (21.5)	288	72.01 ± 148.04	17,427.00	6 (13.6)	25	54.59 ± 155.85	2,402.00
Medications	*	238 (98.3)	137,614	247.82 ± 547.70	59,973.13	44 (100.0)	43,755	900.05 ± 2299.75	39,602.12
Total HRU costs in outpatient				649.18 ± 693.78	157,101.33			1,352.72 ± 2,672.46	59,519.72
Inpatient
Inpatient admission	1500.00	15 (6.2)	16	99.17 ± 397.71	24,000.00	6 (13.6)	8	272.73 ± 742.83	12,000.00
Length of inpatient stay	120.00	15 (6.2)	60	45.97 ± 193.72	11,124.00	6 (13.6)	31	130.62 ± 342.98	5,747.40
Laboratory tests	*	10 (4.1)	72	21.81 ± 99.84	4,471.00	4 (9.1)	26	39.23 ± 130.07	1,726.00
Medications	*	11 (4.5)	980	0.79 ± 5.65	191.79	5 (11.4)	645	4.26 ± 17.94	187.47
Total HRU costs in inpatient				165.42 ± 665.84	40,031.49			446.84 ± 1,163.98	19,660.87
Emergency Department (ED)
Emergency department visitation	100.00	27 (11.2)	31	18.35 ± 49.36	3,762.00	9 (20.5)	10	28.50 ± 59.64	1,254.00
Laboratory tests	*	8 (3.3)	33	8.32 ± 48.22	2,013.00	2 (4.5)	12	15.68 ± 72.70	690.00
Medications	*	16 (6.6)	391	0.45 ± 2.19	108.62	5 (11.4)	151	1.45 ± 5.23	63.80
Total HRU costs in ED				24.83 ± 80.49	6,009.02			45.63 ± 111.31	2,007.80
TOTAL HRU COSTS				839.43 ± 948.95	203,142.14			1,845.19 ± 2802.85	81,188.39
* Cost of each laboratory test and medication was included in Additional File 5

The study showed that MDD patients were more likely to be in the lowest income range of the Malaysian society. Patients with TRD have significantly higher odds of outpatient resource utilization, including a 5-fold increase in the likelihood of having more than 10 consultation sessions (p = 0.035), and increased use of second-generation antipsychotics (p < 0.001). The average healthcare cost per patient is higher for those with TRD than for MDD (RM1,845 vs RM839 respectively) over a period of 18 months.

The prevalence of TRD in MDD was 18.2% (44/242) which is in very close agreement with the recently reported rate of 19.6% in Thailand [25]. The prevalence rate of females appeared to be higher in both TRD and MDD although statistical significance was not reached. Our data shows that the patients in the lowest income bracket in the current cohort had a higher rate of MDD than TRD (p = 0.006) whereas those at the second lowest bracket seemed to have a higher rate of TRD than MDD (p = 0.038). This finding aligns with findings in other countries [7–9]. This is of significance in allocation of resources as this lowest income group may require more efficient care and more effective medications to prevent the progression to TRD from MDD [26]. In terms of resource utilization, the MDD group also showed a higher rate of using psychiatric outpatient psychiatry consultation service than the TRD group (p = 0.049) for 1–3 and 4–6 consultation sessions categories reflecting a higher demand for professional care in this group of patients (Table 4). This finding suggests that greater resource allocation is required for prevention of TRD from MDD. Medication wise, the TRD group demonstrated a significantly higher rate of using the second-generation antipsychotic agents (p < 0.001) than the MDD group, thus accounting for the single most expensive healthcare item in the study (Table 5).

In terms of productivity loss, 18.2% of TRD patients indicated that they had a relatively longer working hours in the range of 36–40 hours a week which was significantly higher than MDD patients (6.6%) (p = 0.012) suggestive of presenteeism. Similar results were also reported in other earlier studies suggesting that longer working hours could worsen the depressive symptoms [27]. From a macro-economic point of view, TRD patients who do not receive the appropriate care can ultimately affect the gross domestic productivity (GDP) of a country due to reduced productivity [28].

From our results, the average overall management cost for TRD was RM1,845 and that for MDD was RM796 over a period of 18 months suggesting that a 57% (or RM1,049) cost-saving in healthcare spending is possible if an MDD patient is prevented from progressing to TRD with proper treatment. This difference was due to the higher utilization by TRD patients in outpatient visits, outpatient medications, and hospital admissions (Tables 4 and 5). This is a strong indication that more healthcare resources are consumed for this group of patients.

The study demonstrated that for patients suffering from MDD, appropriate care is of utmost importance to avoid the progression to TRD where increased healthcare resources are required, especially if TRD patients are not treated to remission early on, which will further increase the lifetime cost of the treatment. To the best of the research group’s knowledge, this is the first study in Malaysia to compare the healthcare cost requirements and quality of life in patients with MDD and TRD.

Limitations and suggestions for future studies include firstly, due to the relatively short period of study time and limited resources, we were not able to capture the date of diagnosis and date of starting medications for the patients. This limitation prevented the research group from assessing the significance of early diagnosis and the start of treatment, as well as differentiating between patients in the acute or chronic phases of their condition, which would otherwise have provided insight into follow-up patterns and their impact on treatment outcomes. Secondly, SF-36 was used to assess patients’ quality of life and loss in productivity. Some data was captured on indirect cost due to absenteeism in patients with MDD and TRD but more in-depth future studies that include TRD/MDD patients seeking treatment in private hospitals or clinics to see if results are similar by using more specific tools that would be able to reveal more information on this very important aspect. To further address this issue, the creation of a national registry for TRD/MDD to measure effectiveness of national programs in reducing the burden of TRD may throw light on future management strategies. Apart from economic burden, further studies/analysis should also be performed on disease burden of TRD, as very often these patients receive inadequate care and treatment which could potentially lead to increased economic burden. This is due to social stigma, misunderstanding, and lack of acceptance of mental illness as a valid medical condition and therefore patients continue to suffer in silence.

In summary, the present study provides evidence that for patients suffering from MDD, appropriate care is of utmost importance to avoid the progression into TRD where increased healthcare resources will be required. It is therefore suggested that the evidences presented in this study could be used by the policy makers to adopt a holistic approach in managing MDD and TRD. This would facilitate evidence-based and cost-effective resource allocation, emphasizing the importance of investing more resources in preventing the development of MDD and its progression to TRD.

WHO	:	World Health Organisation
MDD	:	Major depressive disorder
WEF	:	World Economic Forum
RM	:	Ringgit Malaysia
TRD	:	Treatment-resistant depression
MREC	:	Medical Research Ethics Committee
HRQoL	:	Health-related quality of life
IEQ	:	Involvement Evaluation Questionnaire
EDC	:	Electronic data capture
DSM-5	:	Diagnostic and Statistical Manual of Mental Disorders-5
OR	:	Odds ratio
95% CI	:	95% Confidence intervals
SF-36	:	36-Item Short Form Survey
NARI	:	Noradrenaline reuptake inhibitors
SNRI	:	Serotonin-noradrenaline reuptake inhibitors
SSRI	:	Selective serotonin reuptake inhibitors
TCA	:	Tricyclic antidepressants
HRU	:	Healthcare resources utilisation
GDP	:	Gross domestic product

Ethics approval and consent to participate: Institutional ethics as well as approval from the Medical Research Ethics Committee (MREC) of Malaysia Ministry of Health (NMRR-ID-22-02102I-RIM) and the study hospital were obtained. A written consent document had be provided to all potential study subjects so that they can have the information they need to make an informed decision to volunteering in this study. Written informed consent had been obtained from all study subjects prior to the conduct of any study-related activities and data collection.

Consent for publication: Not applicable.

Availability of data and materials: All data generated or analysed during this study are included in this published article and its supplementary information files.

Competing interest: The authors declare no competing financial interest and personal relationships that could have appeared to influence the work reported in this paper.

Funding: Johnson & Johnson Malaysia

Authors’ contributions: KC contributed to the concept and design of the study, as well as the preparation and review of the manuscript. NE, SA, DN and AZ was involved in data acquisition and manuscript preparation. JS handled data cleaning, data analysis, performed statistical analysis, and was involved in manuscript editing and review. RA contributed to data analysis and statistical analysis. ST contributed to the design, data collection tool, and research registration. MS was responsible for data management and cleaning. RS contributed to manuscript preparation and review.

Acknowledgements: The authors would like to express their gratitude to researchers and staffs from Monash University Malaysia, Taylor’s University, Hospital Kuala Lumpur and Universiti Malaya, in providing resources, support and assistance for the present study. The authors would like to also thank the Director General of Health for the permission to publish this article. The publication is funded by Johnson & Johnson Malaysia.

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No competing interests reported.

Difference in Healthcare Resource Utilization and Health-Related Quality of Life between Patients with Major Depressive Disorder (MDD) and Treatment-Resistant Depression (TRD) in a Public Hospital in Malaysia – A Retrospective Case-Note Review and Cross-Sectional Survey

Status:

Version 1

Abstract

Figures

1.0 BACKGROUND

2.0 METHODS

2.1 Study design and study population

2.2 Data sources

2.3 MDD and TRD criteria

2.4 Statistical analysis

3.0 Results

4.0 Discussion

5.0 CONCLUSION

6.0 LIST OF ABBREVIATIONS

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1