The economic burden of mental disorders among adults in Singapore: evidence from the 2016 Singapore Mental Health Study

Abstract

Background

Little is known about the economic burden of mental disorders in multiethnic Asian populations.

Aims

The study aimed to estimate the economic cost of mental disorders in Singapore using data from the second Singapore Mental Health Study (SMHS 2016).

Method

The SMHS 2016 is a nationally representative survey of the Singapore Resident population aged 18 years and above. Data on mental disorders and healthcare resource utilization were obtained from the World Mental Health Composite International Diagnostic Interview and the adapted version of the Client Service Receipt Inventory.

Results

The costs of visits to a restructured hospital doctor, other private health workers, accident and emergency, and intermediate and long-term care services and productivity losses tend to be much higher in those with mental disorders than those without mental disorders. The average annual excess cost associated with mental disorders per person was estimated to be S$3938.9 (95% CI, S$-100.8–S$7978.7). Extrapolation of these excess costs to the population suggests that the incremental costs of mental disorders in Singapore is about S$1.7 billion per year.

Conclusion

This study provides evidence of the substantial burden of mental disorders on Singaporean society – both in terms of direct medical costs and loss of productivity costs.

Keywords:

• Mental disorders
• economic burden
• healthcare utilization
• medical costs
• productivity

Introduction

Mental disorders have been associated with loss of productivity, as well as increased utilization of health care and social services systems (Doran & Kinchin, 2019). According to the Lancet Commission report, mental disorders are expected to cost the global economy $16 trillion by 2030 (Lancet Commission, 2018). A study conducted by the Centre for Mental Health policy (UK) estimated the total cost of mental disorders to be £105.3 billion (Centre for Mental Health, 2010). In Australia, the total cost of mental disorders in 2014 was estimated to be at A$98.8 billion (6% of GDP) while in New Zealand, the total cost was estimated to be at NZ$17 billion (RANZP, 2016). In Asian countries, few studies have been conducted to determine the cost of mental disorders. In Japan, the economic burden of depression was estimated to be US$11 billion, with $6912 million attributed to workplace costs (Okumura & Higuchi, 2011). Meanwhile in Malaysia, the cost of absenteeism, presenteeism, and staff turnover due to mental disorders were estimated to be RM14.46 [USD3.48] billion in 2018 (Chua, 2020).

Singapore is an island nation with a total population of 5.70 million (Department of Statistics, 2019). The Chinese (74.4%) form the majority, followed by Malays (13.4%), Indians (9.0%), and those from other ethnic groups (3.2%). Our previous study showed that the lifetime prevalence of any mental disorder increased significantly from 12.0% in 2010 to 13.8% in 2016 (Subramaniam et al., 2019). As the prevalence of mental disorders in Singapore has increased over the past 6 years, estimation of the societal cost associated with mental disorders is important for assessing the burden of these conditions in the population. Although previous studies have examined the burden of mental disorders in terms of quality-adjusted life-years (Subramaniam et al., 2013), days out-of-role (Abdin et al., 2016a), and disability-adjusted life years (Epidemiology & Disease Control Division and IHME, 2019) in the general population, little is known about the economic burden of mental disorders in this population. Hence, the current study aimed to estimate the economic cost of mental disorders in Singapore using data from the second Singapore Mental Health Study (SMHS 2016).

Methods

Study design

The SMHS 2016 is a nationally representative survey of the Singapore resident population aged 18 years and above, which was derived using a disproportionate stratified sampling design. The institutional ethics review board of the National Healthcare Group, Domain Specific Review Board approved the study (Reference No: 2015/01035). Written informed consent was obtained from all respondents. In the case where respondents were below 21 years of age, written informed consent was taken from their legally acceptable representative/next of kin. Face-to-face interviews were conducted by trained professional survey interviewers using an online Computer Assisted Personal Interviewing application and data were collected in real-time. The study method has been described in detail elsewhere (Subramaniam et al., 2019).

Measures

Diagnosis of mental disorders was established using the World Mental Health Composite International Diagnostic Interview (WMH-CIDI) (Kessler & Ustun, 2004), a fully structured diagnostic interview using the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) criteria (American Psychiatric Association, 2000). The WMH-CIDI hierarchy rules were applied for multiple diagnoses. Given the limitations imposed by the length of the interview and the administrative burden to the respondent, only selected disorders from the WMH-CIDI modules including major depressive disorder (MDD), dysthymia, bipolar disorder, generalized anxiety disorder (GAD), obsessive-compulsive disorder (OCD) and alcohol use disorder were included in the study. The selection of disorders that were likely to have the largest impact on the Singapore population was based on a comprehensive review of the scientific literature, consultation with the relevant stakeholders, and expert consensus.

Data on other physical health conditions including hypertension, cardiovascular disorders (e.g. stroke or major paralysis, heart attack, etc.), asthma, chronic pain, cancer, ulcer and inflammatory bowel diseases, thyroid disease, neurological condition, chronic lung disease, hyperlipidaemia, and diabetes were obtained from a modified version of the CIDI checklist of chronic medical disorders. Socio-demographic data including age, gender, ethnicity, marital status, education, employment, and income were also collected. Absenteeism and presenteeism were assessed using two items from the 30-day functioning module of the CIDI. Absenteeism was defined as the number of days out of the past 30 when they were “totally unable to work or carry out normal activities due to problems with physical health, mental health, or use of alcohol or drugs”. We defined presenteeism as the number of days where they had to cut back on the type or quantity of work due to problems with physical health, mental health, or use of alcohol or drugs.

Resource utilization and costing

Information on healthcare resource utilization was obtained from the adapted version of the Client Service Receipt Inventory (CSRI) (Beecham & Knapp, 1992). The instrument asked whether respondents had accessed specific healthcare resources within the past 3-month period before the time of the interview. The costs were calculated from a broad societal perspective that consisted of four components including direct medical care, intermediate and long-term care, indirect medical care, and productivity losses. Direct medical care refers to care provided by health professionals in the public or private sector, in the outpatient or inpatient setting. This included care from the public primary care doctors (polyclinic doctor), restructured hospital doctors (a public hospital doctor which is wholly owned by the government), other restructured hospital healthcare workers (e.g. physiotherapist, nurse, medical social workers), private hospital/clinic doctors [e.g. general practitioners (GPs)], other private health workers (e.g. physiotherapist, nurse), government and private dentists, traditional healers, admissions to government/restructured or private hospitals, hospitalisation and visits to accident & emergency (A&E). Intermediate and long-term care (ILTC) services comprised care provided in day care centers, respite care, and nursing homes. The indirect medical care cost represented the time spent by family members or friends in traveling with respondents to use the services, and accompanying them during the duration of the consultation. The productivity loss comprised the costs of absenteeism and presenteeism.

We estimated the specific health care costs by multiplying each service unit (i.e. consultations per minute, visits per day) by their unit cost price. For the estimation of annual costs, the 3-month values were multiplied by four to obtain annual costs. Unlike some other countries, Singapore has not published national unit cost estimates for mental health services. Hence, we captured and examined information obtained from local sources and other relevant websites, such as government and private hospitals, polyclinics, reports from Singapore’s Ministry of Health, the Ministry of Manpower, Singapore Statistics, Singapore Silver Pages, and other relevant websites and found that there a wide variation in the unit healthcare costs. Due to wide variations in the unit healthcare costs from local sources, an alternative approach based on extrapolation through the application of UK unit costs (Liu, 2013) was used as a primer method to estimate the unit cost for selected direct medical care (consultations with the primary care doctor, restructured hospital doctor, and other restructured hospital healthcare workers) (Abdin et al., 2016b, Picco et al., 2016). A more detailed description of this approach has been reported elsewhere (Liu 2013, Abdin et al., 2016b, Picco et al., 2016). In this approach, we assumed that the relationship between the UK and Singapore unit costs for the selected direct medical services were fixed, and the ratio of costs between the two countries has remained unchanged over the years. The approach consisted of three steps: (a) identification of unit costs for the selected direct medical services from the UK as a reference country, (b) calculation of ratios for inpatient and outpatient settings between the reference country and Singapore using data from the WHO-CHOICE (WHO-CHOosing Interventions that are Cost-Effective) database (http://www.who.int/choice/en/), and (c) application of these ratios to the unit costs of each selected direct medical service in the reference country to generate country-specific unit costs for Singapore (Supplementary Table 1). Reliable sources of UK unit costs, such as the Unit Cost of Health and Social Care 2013 (Curtis, 2013) published by the Personal Social Services Research Unit (PSSRU) was used to identify and match the appropriate unit costs for selected services. For other direct and indirect medical care costs including the cost of private health care doctors, other private health care workers, dentists, traditional healers, A&E, medications and ILTC services (day care center, respite care, and nursing home); the average out-of-pocket reported expenses amount was used as they were deemed more representative of the Singapore population based on local context. For hospital admissions, we used the unit cost per bed day according to the WHO-CHOICE database. The figures were converted into local currency units (S$) and inflated to 2016 prices using the Consumer Price Index (Department of Statistics, 2013). The median national wage rates in 2016 (S$4056 per month) were used to calculate the indirect cost of carers’ time spent in accompanying the respondents to utilize the services, accounting for traveling time. The annual costs of absenteeism were calculated by multiplying the absolute number of days lost reported in the past 30 days by 12 months and S$184.4 (S$4056 per month/22 working days), which represents the median national salary per day. Presenteeism or work cut-back was assumed to be half as productive as on a normal workday (Kessler et al., 2001). Hence, the annual costs of presenteeism were calculated by multiplying 0.5 day lost by 12 months and the national median gross wage per day. The costs of absenteeism and presenteeism were calculated only among those who were employed.

Statistical analysis

Statistical analyses were carried out using the SAS software version 9.2 (SAS Institute Inc., Cary, NC, USA) and STATA version 13.0. The total societal costs attributable to mental disorders were estimated using a series of regression analyses. In the first analysis, the total societal cost (as the dependent variable) was regressed on mental disorders without controlling for covariates. In the second analysis, the effect of mental disorders on the total societal cost was controlled for socio-demographic variables and physical health conditions. In the third analysis, we tested for interaction effects between mental disorders, socio-demographic variables, and physical health conditions. Given that the distribution of costs in our sample was skewed with many zeros, generalised linear modelling (GLM) was used. As recommended by Manning and Mullahy (2001), the appropriateness of alternative error distributions (Gaussian, Poisson, gamma, or inverse Gaussian) was examined using modified Park’s test (Park, 1966). Three different sensitivity analyses were also conducted to examine the sensitivity of the results. All statistically significant differences were evaluated at the 0.05 level using two-sided tests.

Results

Socio-demographic characteristics of the sample

A total of 6126 respondents completed the study giving a response rate of 69.5%. The majority of the respondents were aged 35 and above (69.6%), of Chinese ethnicity (75.7%), and currently married (59.8%) (Supplementary Table 2). The lifetime prevalence of mental disorders in this study was 13.9%.

Annual contacts and costs per person with mental disorders

The most common healthcare utilization among everyone was private hospital/clinic doctor visits (31.8%) followed by primary care visits (polyclinic doctor) (20.9%), with an average number of 2.2 and 1.4 visits per year. In all, 14.7% had consulted restructured hospital doctors, and 15.4% had consulted a dentist (Table 1). Absenteeism was the costliest component for everyone, while the cost of visits to the restructured hospital doctor appears to be the costliest form of direct medical care, followed by hospital admissions. We found that those with mental disorders tend to incur lower costs for all forms of direct medical care except for visits to the restructured hospital doctor, other private health workers, A&E, and ILTC, as compared to those without any mental disorders. The cost of productivity losses in terms of absenteeism and presenteeism appears to be much higher in those with a mental disorder than those without a mental disorder (Table 2). We found that the unadjusted total costs incurred by a person with mental disorders were S$4619.3 (95% CI, S$2861.0–S$6377.7) per year. After adjusting for socio-demographic and other comorbid health conditions, the average annual excess cost associated with mental disorders per person was estimated to be S$2834.9 (95% CI, S$1042.0–S$4627.8) (Table 3). When interaction effects between mental disorders, socio-demographic, and other comorbid health conditions were added in the GLM model, the average annual excess cost associated with mental disorders per person was estimated to increase to S$3938.9 (95% CI, S$100.8–7978.7). That is, those diagnosed with a mental disorder would be expected to incur S$3938.9 more in total costs than those without a mental disorder. Extrapolation of these excess costs per person to the population suggests that the incremental costs of mental disorders among adults aged 18 years and above in Singapore are estimated to be S$1.7 billion per year (Table 3).

Table 1. Annual contacts of each component per person among everyone in the sample, service users and those with mental disorders, S$in 2016.

Components	Everyone		Service users	Mental disorders
Healthcare cost				No (N=5280)	Yes (N=846)
	N (%)^a	Mean (SE)	Mean (SE)	Mean (SE)	Mean (SE)
Direct medical care
Government primary care (polyclinic doctor)	1519 (20.9)	1.4 (0.1)	6.7 (0.6)	1.4 (0.1)	1.4 (0.2)
Restructured hospital doctor	1017 (14.7)	1.3 (0.1)	8.5 (0.4)	1.2 (0.1)	1.8 (0.3)
Other restructured hospital health worker	159 (2.4)	0.4 (0.1)	17.0 (4.4)	0.4 (0.1)	0.5 (0.2)
Private hospital/clinic doctor	1795 (31.8)	2.2 (0.1)	6.8 (0.2)	2.1 (0.1)	2.7 (0.3)
Other private health worker	46 (0.9)	0.1 (0.04)	12.3 (3.1)	0.1 (0.04)	0.2 (0.1)
Dentist	764 (15.4)	0.9 (0.05)	5.5 (0.2)	0.8 (0.05)	1.0 (0.1)
Traditional healer	140 (3.6)	0.6 (0.2)	16.8 (5.0)	0.6 (0.2)	0.3 (0.1)
Hospital admissions	210 (2.3)	0.5(0.1)	21.4 (2.6)	0.5 (0.1)	0.3 (0.1)
A&E^c	246 (3.1)	–	–	–	–
Intermediate and long-term care
Day care centre, respite care, and nursing home	11 (0.2)	0.12 (0.05)	51.6 (20.1)	0.1 (0.1)	0.05 (0.03)
Indirect medical care
Accompanying and travelling^d	1088 (15.0)	–	–
Productivity loss days
Absenteeism	4055 (72.0)^b	5.0(0.7)	–	4.6 (0.7)	7.6 (2.0)
Presenteeism	4055 (72.0)^b	5.9 (0.6)	–	4.6 (0.6)	12.9 (2.7)

–: Not applicable.

^aPercentage of people using services in the population.

^bPercentage of employed people in the population.

^cOnly data on the total cost of visits instead of the number of visits were available in the study.

^dData was captured based on the caregiver’s time is spent on accompanying and travelling instead of the number of visits.

Table 2. Annual costs of each component per person among everyone in the sample, service users and those with mental disorders, S$in 2016.

Components	Everyone	Service users	Mental disorders
	S$ costs	S$ costs	No	Yes
Healthcare cost	Mean (SE)	Mean (SE)	S$ costs Mean (SE)	S$ costs Mean (SE)
Direct medical care
Primary care (polyclinic doctor)	114.3 (6.6)	546.78 (26.9)	313.7 (199.6)	120.6 (15.2)
Restructured hospital doctor	568.0 (113.4)	4338.4 (787.8)	417.9 (38.5)	1497.5 (779.1)
Other restructured hospital health worker	35.2 (13.2)	1445.6 (525.2)	37.6 (15.3)	20.0 (6.2)
Private hospital/clinic doctor	168.7 (37.3)	530.5 (116.3)	168.7 (43.0)	168.8 (30.5)
Other private health worker	9.0 (3.5)	1050.7 (0.0)	7.7 (3.8)	16.9 (8.5)
Dentist	303.5 (87.1)	1976.6 (561.3)	304.4 (99.3)	297.8 (119.1)
Traditional healer	27.3 (11.7)	766.8 (0.0)	30.2 (13.6)	9.2 (2.8)
Hospital admissions	522.2(80.0)	22,844.7(0.0)	557.2 (92.0)	305.9 (84.2)
A&E	63.1 (19.9)	2060.1(637.2)	58.0 (22.3)	94.9 (37.4)
Intermediate and long-term care
Day care centre, respite care, and nursing home	0.8 (0.7)	318.9 (0.0)	0.1 (0.1)	5.1(5.1)
Indirect medical care
Accompanying and travelling	65.5 (10.5)	437.9 (67.6)	65.9 (11.9)	62.8 (15.5)
Productivity loss*
Absenteeism	666.6 (88.6)	926.0 (122.9)	597.9 (92.0)	1091.6(288.1)
Presenteeism	390.3 (43.0)	542.2 (59.5)	302.9 (38.2)	931.0 (197.8)

*Cost of productivity losses were estimated only among those who were employed.

Table 3. Annual costs per person attributable to any mental disorders, S$in 2016.

	People with at least one mental disorder		Average annual excess cost per person		Total annual cost due to mental disorder in Singapore in 2016
	Yes	No
Components	Mean S$ cost (95% CI)	Mean S$ cost (95% CI)	Adjusted excess S$ cost (Model 1) (95% CI)*	Adjusted excess S$ cost (Model 2) (95% CI)**	Adjusted excess S$ cost (Model 1) (95% CI)*	Adjusted excess S$ cost (Model 2) (95% CI)**
Direct medical care	2531.7 (969.7–4093.7)	1895.6 (1375.8–2415.5)	1354.3 (275.5–2433.0)	1854.8 (−790.0–4499.7)	586,733,760 (119,374,112–1,054,093,376)	803,604,507.21 (−342,282,319.36–1,949,491,333.79)
Intermediate and long-term care	5.1 (−4.9–15.0)	0.1 (−0.1–0.2)	5.1^# (−5.1–15.4)	3.9^# (−4.2–12.1)	2,228,480 (−2,206,184–6,663,145)	1,697,546.89 (−1,835,758.44–5,230,852.22)
Indirect medical care	59.9 (29.9–89.9)	63.9 (40.7–87.1)	0.8 (−31.9–33.5)	8.0^# (−41.3–57.4)	352,615 (−13,816,686–14,521,917)	3,484,466.76 (−17,879,445.61–24,848,379.14)
Productivity loss	2022.6 (1281–2764.3)	900.8 (686.5–1115.1)	983.8^# (203.1–1764.4)	823.3^# (−59.8–1706.4)	426,232,992 (88,007,456–764,458,560)	356,700,323.56 (−25,891,525.73–739,292,172.85)
Total	4619.3 (2861.0–6377.7)	2860.5 (2247.5–3473.5)	2834.9 (1042.0–4627.8)	3938.9 (−100.8–7978.7)	1,228,245,248 (451,468,640–2,005,021,824)	1,706,542,403.01 (−43,691,527.86–3,456,776,333.89)

*Regression analysis after controlling for age, gender, ethnicity, marital status, education and employment, income, and physical health conditions.

**Regression analysis after controlling for age, gender, ethnicity, marital status, education and employment, income, physical health conditions, and interaction terms between these variables and mental disorders.

^#Due to non-convergence in gamma regression, coefficients were estimated using a multiple linear regression model.

Note. Cost of each component was not added up to total costs due to different regression models used to estimate regression coefficients.

In this GLM model (Supplementary Table 3: Model 2), other covariates that were significantly associated with the higher total cost included those aged 65 years and over (vs. aged 18–34 years), Malay ethnicity (vs. Chinese), employed (vs. economically inactive) and those who reported that a doctor had ever diagnosed them with any chronic physical condition. A significant interaction was also found between mental disorders and chronic physical conditions, Indian/Others ethnicity, widowed, and economically inactive.

We explored the sensitivity of our findings by using the 95% CI range of excess cost estimates per person as a base estimate in comparison to three different scenarios: to model the effects of mental disorders on costs using a two-part model based on the combination of logit and gamma models (Scenario 1), which assumed that the costs of healthcare utilization in the past 3-month period is equivalent to the 1-year period, the costs of productivity losses in the past 30 days is equivalent to the 1-year period (Scenario 2) and compared the base estimate and the new estimates using the unit costs derived from local cost data (Scenario 3) (Table 4). We found that the adjusted excess cost produced in scenario 1 (S$3938.9), scenario 2 (S$984.7), and scenario 3 (S$3641.4) are within the range of 95% CI estimates (S$−100.8–S$7978.7). However, although we found that the excess cost is much lower (S$984.7) in scenario 2 than the base estimates (S$3938.9), this analysis should be interpreted with caution because the assumption is reflecting the best scenario in the population which might not be applicable for certain groups in the population, including the elderly and those with chronic conditions.

Table 4. Sensitivity analysis.

	Adjusted	95% CI
Model	Excess S$ cost	Lower	Upper
GLM model (base estimates)	3938.9	−100.8	7978.7
Sensitivity analyses
Scenario 1: Two-part model (logit + gamma)*	3468.5	78.9	6858.2
Scenario 2: Assumed healthcare utilization in the past 3 months and productivity losses in the past 30 days are fixed	984.7	−25.2	1994.7
Scenario 3: Compared the base estimate and the new estimates using unit costs derived from local sources	3641.4	−327.9	7610.8

*Details of regression outputs derived from two-part model (logit + gamma) are available upon request.

Discussion

To the best of our knowledge, this is the first comprehensive study to estimate the societal cost of mental disorders in Singapore. The results show that mental disorders are associated with a significant financial burden to the society. The average annual excess costs per person associated with a mental disorder were estimated to be S$3938.9 (US$2897.83, using 2019 exchange rate of 1US$ = 1.36S$) per year. Our average annual cost per person was higher than that reported in Australia which reported that the average annual cost of treatment for people with depression, anxiety-related disorders, and substance use disorders ranged from AUD195 to AUD1058 (Lee et al., 2017). Our estimate is somewhat lower than that reported in Europe, which found that on average, the annual cost per person with mental disorders was €2670 (US$2981.57) (Gustavsson et al., 2011). Our estimates are also lower than that reported in China where the total annual average costs per patient were US$3665.4 in 2013 (Xu et al., 2016). In Singapore, the prevalence of mental disorders was 13.9%, which is estimated to be equivalent to over 433 thousand Singaporeans currently living with a mental disorder. By applying this prevalence rate, the total excess costs of mental disorders are estimated to cost the society S$1.7 billion per year. This total cost is much lower than that reported in other countries, due to the fact that the total population in Singapore is very small, with low prevalence rates of mental disorders. In the current estimation, we did not include costs of mental disorders associated with caregiver burden (Addo et al., 2018), the use of the justice system, peer support services (Lee et al., 2019), medications, supported (re)employment program, and loss of earning associated with unemployment (Wang et al., 2014). Hence, the total cost estimate of S$1.7 billion is likely to be an underestimate the true cost.

The cost of visits to primary care or polyclinic doctor, other restructured hospital health workers, and hospital admissions incurred by those with mental disorders were much lower than those without mental disorders. These differences are reflected in the higher rate of treatment gap (78.6%) in Singapore (Subramaniam et al., 2020), which is also consistently found in other countries (Kohn et al., 2004, Abramowitz et al., 2008). Hence, it is possible that the current estimate of direct medical care would be much higher if those who have mental disorders start seeking help from healthcare professionals, i.e. if there is a narrowing of the treatment gap. Moreover, in the current study, we included only select mental disorders that were considered to be common mental disorders experienced in Singapore’s local population. However, we cannot deny that there is a possibility that the population estimate of the societal costs would be greater if other mental disorders, such as schizophrenia and personality disorders were included in our estimation.

The main drivers of costs for mental disorders were direct medical care (54.8%) and loss of productivity (43.8%). These results appear to be different from other studies which tend to suggest that productivity losses are significantly greater than direct medical costs. Lower productivity losses could be because mental disorders related to work stress are not yet included in the list of occupational diseases covered by the Workplace Injury Compensation Act 2019 (WICA 2019) in Singapore, which allows workers with work-related disorders to receive compensation. It is also possible that workers are reluctant to take medical leave or get hospitalised for psychiatric care due to associated stigma and discrimination. Evidence from a systematic review (Brohan et al., 2012) has reported that stigma can prevent workers with work-related mental disorders from disclosing their conditions and asking for help because they believe that a person would be treated unfairly in the workplace or experience less favourable treatment including lack of benefits and reduced promotion prospects. Higher direct medical cost in our sample is likely due to the occurrence of comorbid chronic physical disorders among those with mental disorders (Chong et al., 2012). Consistent with our analysis, the costs of direct medical care increased with age while the cost of productivity loss declined with age. The findings of the present study suggest that the excess cost due to loss of productivity, such as absenteeism and presenteeism, remains an essential component of the economic burden of mental disorders in Singapore, especially among young working adults. Our findings are consistent with previous studies that found that there is a significant economic burden associated with loss of productivity caused by mental disorders (Stewart et al., 2003, Pares-Badell et al., 2014, Greenberg et al., 2015, Evans-Lacko & Knapp, 2016). For example, in the United States, it was estimated that the incremental costs for MDD , bipolar disorder, and dysthymia were at $210 billion in 2010, with 48–50% of the incremental costs attributable to presenteeism and absenteeism (Greenberg et al., 2015). In Spain, indirect costs (47%) which consisted of productivity losses were the main drivers of the cost of mental disorders, followed by direct healthcare costs (41%) (Pares-Badell et al., 2014). Previous studies have suggested that the impact of depression in the workplace is considerable across all countries, both in absolute monetary terms and with regards to the proportion of the country’s GDP (Centre for Mental Health, 2010; Evans-Lacko & Knapp, 2016; RANZP, 2016). It has been suggested that employed individuals experiencing mental disorders who continue to work and those reporting mental disorder-related sickness absence should be the more immediate focus of workplace mental health promotion strategies (Cocker et al., 2011).

We also found that higher total costs were associated with certain sociodemographic characteristics. Those aged 65 years and over, of Malay ethnicity, employed, and diagnosed with any chronic physical condition were significantly associated with higher total costs. We found that older age was associated with an increase in excess cost, which is in line with other studies (Greenberg et al., 2015, Rahman et al., 2019). Those who were employed incurred higher total costs than those who were economically inactive. The higher costs incurred by employed individuals are expected because it is driven by the inclusion of absenteeism and presenteeism cost component among this group.

Those of Malay ethnicity had incurred higher total costs than those of Chinese ethnicity. These findings are in line with the recent local findings, that those of Malay ethnicity had higher healthcare expenditure and length of stay when compared to the Chinese patients (Rahman et al., 2019). It is possible that those of Malay ethnicity had higher rates of chronic conditions, such as diabetes, hypertension, dyslipidemia, asthma, chronic kidney disease, and other related risk factors like obesity and smoking as compared to those of Chinese ethnicity (Ministry of Health, 2010; Phan et al., 2014; Subramaniam et al., 2013). Moreover, even though they had higher rates of these conditions, they had the lowest rates of screening for chronic conditions, such as cancers, diabetes, hypertension, and high cholesterol (Ministry of Health, 2010). It has been postulated that when there are high rates of undetected or unaddressed chronic health problems, these could lead to greater disease severity and polypharmacy risks (Tan et al., 2019), which potentially increase the overall healthcare utilization. Our results showed several significant interaction effects of mental disorders with specific sociodemographic variables and chronic physical conditions on total costs. For example, the regression coefficient for significant interaction between mental disorders and chronic physical conditions was negative (Coefficient = −0.81), indicating that the average annual excess costs incurred by a person with comorbid mental and chronic physical conditions were higher than the annual excess costs incurred by a person with mental disorders alone or a person with chronic physical conditions alone. On the other hand, the regression coefficient for significant interaction between mental disorders and those of Indian ethnicity was positive (Coefficient = 0.62), suggesting that those of Indian ethnicity tend to incur significantly higher average annual excess costs than those of Chinese ethnicity with a mental disorder.

Study limitations

Several limitations should be considered while interpreting the results of the study. First, our estimation of healthcare utilization and costs of productivity losses relied on a self-report questionnaire, which might be influenced by recall bias. Second, our assumptions that the cost of healthcare utilization in the past 3 months and the cost of productivity losses in the past 30 days were increased over time might be overestimated. Lastly, it should be noted that due to different methodologies, our estimates are not directly comparable with previous studies; hence, the estimates from this comparison should be used with caution. For example, previous studies have estimated the costs for a much broader set of ‘brain disorders’ including other additional conditions, such as brain tumour, Parkinson’s disease, stroke trauma, epilepsy, sleep, eating and psychotic disorders, dementia, and personality disorders (Gustavsson et al., 2011). They also adopted estimates based on 12-month mental disorder instead of lifetime mental disorder timeframe in the analysis (Gustavsson et al., 2011; Lee et al., 2017), and used a much wider set of cost components including income tax forgone, welfare benefits, medications (Lee et al., 2017), length of hospitalization derived from psychiatric hospitals (Xu et al., 2016), and estimated the costs without taking into account the effects of covariates and comorbidity in their analyses.

Conclusions

In conclusion, this study provides evidence for the substantial burden of mental disorders on Singaporean society – both in terms of direct medical costs and loss of productivity costs. This study also provides a rich body of information on the health services utilization and cost of mental disorders which would be useful for future planning for mental health services in Singapore.

Disclosure statement

No potential conflict of interest was reported by authors.

Additional information

Funding

The study was funded by the Ministry of Health, Singapore and the Singapore Millennium Foundation of the Temasek Trust.