Mental Health Care Seeking Behavior in Bangladesh: Evidence from the National Mental Health Survey 2019

doi:10.21203/rs.3.rs-2736863/v1

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Research Article

Mental Health Care Seeking Behavior in Bangladesh: Evidence from the National Mental Health Survey 2019

https://doi.org/10.21203/rs.3.rs-2736863/v1

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Objectives

The study aims to explore the existing pattern of mental healthcare-seeking behavior among the adult population in Bangladesh, and to delineate the factors that influence the mental health patients' decision to receive mental healthcare services.

Methods

We used the National Mental Health Survey Bangladesh 2019 dataset with 7270 households to identify the patterns and catalysts of seeking mental health care services. A Probit model using a standard normal cumulative distribution function (CDF) with three specifications has been applied to identify the factors influencing mental healthcare-seeking behavior and the probability of seeking mental healthcare services in Bangladesh.

Results

We found that about 19% of the total adult population have mental health disorder in the country. Among the mental health disorder patients, only 10% seek healthcare services from different sources of mental healthcare services. Patients with addictive disorder show the lowest interest in seeking healthcare services, while patients with bipolar-related disorder receive treatment from mental healthcare sources. Findings portray that 4.8% of addictive disorder patients seek healthcare services. On the other hand, 34.4% of bipolar-related disorder patients receive treatment for mental health. In addition, the findings from the Probit model show that only the existence of other mentally disordered family members is the only statistically significant determinant among the socio-economic factors, such as gender, age, religion, education level and residential status. The marginal effects analysis shows that the existence of mentally disordered family members increases the probability of seeking mental health care services by around 6%. The other socio-economic variables considered in the study are found statistically insignificant. However, a married woman has a significantly higher likelihood of seeking treatment than an unmarried woman, while the family size is the only variable that significantly influences treatment-seeking behavior for men.

Conclusion

Though mental health conditions are major public health concerns in Bangladesh, the treatment-seeking behavior among the mentally diorder people is very low, implying a large treatment gap in the mental health sector. The findings indicate the urgent need to increase mental health service coverage among mental health patients.

Mental Health

Mental Healthcare-Seeking Behavior

The Probit model

The marginal effect

Bangladesh

Mental health disorder are increasing in Bangladesh with a growing health, social and economic burden.¹ According to the recent National Mental Health Survey 2019,² about 19% of the adult population suffers from mental health disorder, compared to the 16.1% prevalence of mental illness reported in the first National Mental Health Survey 2003-2005.³ Rapid urbanization, demographic and lifestyle changes are associated with the rise in mental health disorders.⁴ However, due to limited investment in mental health service provision, health systems and services often cannot adequately meet the demand for care leading to treatment a large gap in Bangladesh. The national budget of Bangladesh for the health sector was approximately US$ 2.3 billion, of which only 0.44% was allocated for mental health, mainly to spend through mental hospitals (35.59% of allocation).⁵

People in Bangladesh are also reluctant to receive mental healthcare service. The National Mental Health Survey of Bangladesh 2019 shows that only 10% of the total mentally disordered persons have sought mental healthcare services.² Many factors can affect the decision of the treatment-seeking behavior of an individual. In Cambodia, it was found that patient’s education can influence the treatment-seeking decision, and it has been portrayed that traditional and religious medicines are the primary source of treatment for mentally disordered patients.⁶ Social stigma is also found to be an essential catalyst for being reluctant to seek mental health care services in Bangladesh.² Golberstein et al. (2008)⁷ found that stigma was higher among males, older, Asia-Pacific Islanders, and international students with lower socio-economic backgrounds. However, they did not find any causality that perceived public stigma might cause barriers to seeking mental health services.

Limited studies have been conducted on mental health treatment-seeking behavior in Bangladesh. This study aimed to identify the pattern of mental health treatment-seeking behavior among the adult population of Bangladesh and factors that may affect the patient's decision to receive mental health care.

Data

The study used secondary data from the National Mental Health Survey of Bangladesh 2019, conducted by the National Institute of Mental Health (NIMH), Dhaka, with the technical support of the World Health Organisation (WHO). The survey was carried out for both adults and children. National Mental Health Survey 2019 was carried out for adults and children. Initially, a total of 8928 households were approached to conduct the survey. However, a total of 7270 adult individuals from the 7270 households were finally enumerated. On the other hand, among the initial selection of 2976 children, 2270 children were finally interviewed for the survey². The survey used two-stage random sampling methods across the 64 districts of the country. The programmed Android Tablet Computers (ATC) were used in the first stage, while the research psychiatrists used pens and papers to conduct the survey in the second stage. After collecting the data manually in the second stage, they were entered through the manual data entry process. Then data from both phases were transferred to a server for further statistical analysis.² However, we have only used the adult population (18 years and above) to analyze healthcare-seeking behavior and other statistical analysis. The adult population has a mean age of 41 years, ranging from 18 to 99 years. The survey covered males and females in almost equal numbers and took rural and urban samples equally.

Variables

We have graphically described the current pattern of healthcare-seeking behavior for 12 major mental health disorders. These major mental health disorders are addictive disorders, depressive disorders, personality disorders, anxiety disorders, somatic symptoms disorders, neurocognitive disorders, sleep-wake disorders, obsessive-compulsive disorders, disruptive disorders, schizophrenia-spectrum disorders, sexual dysfunction disorders, and bipolar-related disorders.

On the other hand, the treatment-seeking status (whether sought or not, a dichotomous variable) of any mental health disorder has been considered the dependent variable for the probit model.The probability of healthcare-seeking behavior has been estimated over some socio-economic variables. We have tried to estimate the probability of these variables that may influence healthcare-seeking behavior. The socio-economic variables we considered are gender identity, level of education, religious status, age, residential status, number of family members, marital status, and the existence of other mental patients in the family.

Method Estimation

The probability and marginal effect of the dichotomous or binary variable (status of receiving mental health care treatment) have been investigated in our study through the Probit model. Although Linear Probability Model (LPM) and Logit Model can be used to identify the probability and marginal effect of the dichotomous variable, a probit model is more frequently wielded to serve the purpose due to some limitations of the Linear Probability Model (LPM) and standard normal cumulative distribution function(Gujrati, 2009)⁸. Let's begin with the linear probability model,

$$z=\alpha +\beta x+e$$

where, $z$ is the dichotomous variable and presents the treatment-seeking status (0 for not seeking treatment and 1 for seeking treatment) of the respondents. The dichotomous regressand, $z$, portrays the probability of changing the status from 'does not seek mental health services' to 'seek mental healthcare services' The vector of independent variables, $x$, delineate the factors that govern the probability of making a decision for mental healthcare services. It is expected that the probability value will fall in the range of 0 to 1. However, the probability value may fall outside the range of 0 to 1 under LPM^8,,9. Besides, non-normality, heteroscedasticity of the error term, and lower value of ${R}^{2}$ of LPM lead to the selection of the cumulative distribution function (CDF)⁸. The cumulative logistic distribution is one of the CDF distributions. According to the logistic cumulative distribution function (CDF),

$$P=E\left(z=1|x\right)=\frac{1}{1+{e}^{-(\alpha +\beta x)}}$$

The cumulative distribution function captures the non-linear effect of the explanatory variable on the deponent variable. However, the most common practice is to address the normal distribution function that leads to the Probit model due to the standard normal distribution function. Incorporating the normal cumulative distribution function (CDF), the Probit model can be modeled as follows,

$$P=P\left(z=1|x\right)=P\left(N\le \alpha +\beta x\right)=F(\alpha +\beta x)$$

Where z is the binary variable representing 1 when a respondent sought mental healthcare service and 0 otherwise. P is the probability of seeking mental health services, given the independent variables. The independent variables are explained by the $x$ vector. The $x$ vector contains the socio-economic variables, for instance, gender identity, level of education, religious status, age, residential status, number of family members, marital status, and the existence of other mental patients in the family, and $N$ represents normal distribution with mean 0 and variance ${\sigma }^{2}$ i.e., $N∼N(0,{\sigma }^{2})$^{8, 9}.

The marginal effect depicts the probability of the dependent variable in response to the change in the explanatory variables. The probability of mental health care seeking due to change in the independent variables is delineated through the marginal effects. The marginal effect can be obtained through the derivative with respect to the independent variables.

$$\frac{d{P}_{i}}{d{x}_{i}}=(\alpha +\beta x){\beta }_{i}$$

where, $f(\alpha +\beta x)$ represents the standard normal probability density function assessed at $\alpha +\beta x$ ^{8, 9]}, normally distributed with a mean of 0 and variance 1.

Developing Wealth Index

Principal component analysis (PCA) has been used to develop the wealth index. Some household fixed assets are taken to develop the index. These assets are electricity, flush toilet, land phone, mobile phone, television, refrigerator, private car, moped/scooter/motorcycle/autorickshaw, washing machine, bicycle, sewing machine, wardrobe, table, bed, chair/bench, clock, computer/laptop/tablet computer, domestic animal (cow/ox/goat etc.), shallow machine/power tiller/tractor), rickshaw, farming land, pond for fish cultivation and roof type of the main house.² Each component has been assigned a score to make the index. The wealth index varies from 0 to 261512, with an average of 15392. The lower the value wealth index, the poorer the household is.

Descriptive Statistics

Table 1 denotes the summary statistics of the socio-economic and mental health staus variables with their definitions of the adult population. Although the number of small families is increasing day by day due to urbanization, the nature of job, and globalization, still about 20% of the families are joint in Bangladesh. The size of an average household is about 4.45, which is slightly higher than the national average¹⁰. Around 96% of the families have an excellent relationship among their members, as the mean value of the 'understanding and relationship among the members' indicator is 0.96. Since the mean valu is 0.96, it implies that a very good relationship exists in the family. Average age of the surveyd adult population is about 40 years having about six years of education. The National Mental Health Survey of Bangladesh also ensured parity in gender and geographical location (Table 1). 88% of the adult population are Muslim, supporting the national lavel¹⁰.

Table 1

Descriptive Statistics and mental health status
Variables (with definition)	Observation	Mean	Std. Dev.	Min	Max
Household Size (Number of household members)	7320	4.45	1.84	1	20
Wealth index (value of the index)	8928	11530.99	15392.43	0	261512
Age (in years)	7270	40.82	14.54	18	99
Age Squared (Square of age)	7270	1877.48	1361.09	324	9801
Gender (= 1 if male, = 0 if female)	8928	0.50	0.50	0	1
Residential Status (= 1 if respondent in urban area, = 0 if from rural area	8910	0.50	0.50	0	1
Marital status (= 1 if married, = 0 if otherwise	7270	0.90	0.31	0	1
Religion (= 1 if Islam, = 0 if otherwise)	7270	0.88	0.33	0	1
Education (years of schooling)	7270	5.98	4.43	0	20
Type of family (= 1 if joint family, = 0 if otherwise)	7270	0.20	0.40	0	1
Understanding and relationships among the members (= 1 if understanding 'very good', and 'good' =0 if 'average', 'bad' and 'very bad')	7270	0.96	0.21	0	1
Mental health prevalence among the adults (= 1 if mentally disorder,=0 if not’)	7238	0.19	0.40	0	1
Status of Mental Health treatment seeking (= 1 if desires health services, = 0 if not)	1233	0.10	0.30	0	1
Mental patients in the family (= 1 if there is another mental patient in the family, = 0 if not)	7270	0.06	0.23	0	1

Source: Authors' calculation from the National Mental Health Survey Data 2019

The above table also shows that about 19% of the adult population in the country have mental disorders, and among them, only 10% of the adults with mental disorders seekt mental healthcare services from the different mental service-providing sources. This implies that there is a large gap in mental health disorder patients and their treatment-receiving behavior. The above table also portrays that only 6% of the families have other mentally disordered patients without the respondents.

Table 2 shows the prevalence of mental health disorders disaggregated by age and sex. It portrays that about 19% of the adult population exhibits symptoms of mental disorders. The oldest people in the country (60 years and above) have the greatest proportion (28.10%) of mental illness. It also shows that 25.1% of men and 31.40% of women aged 60 years and above suffer from mental illness. The adults aged 18–29 are relatively less vulnerable to mentall illness disease compared to the(Table 2).

Table 2

Prevalence of Mental Health Disorder by age (Adult)
Age	Prevalence of mental Health Disorder by age (Adult)
Age	Both Sexes (%)	Men (%)	Women (%)
Overall (18–99)	18.70	15.70	21.50
18–29	14.60	12.80	16.00
30–39	20.00	15.10	23.90
40–49	17.20	12.60	22.00
50–59	22.10	19.40	25.00
60 and above	28.10	25.10	31.40

Source: National Mental Health Survey of Bangladesh 2019²

Mental Health Service Coverage And The Source Of Service Coverage In Bangladesh

The following bar graph portrays the treatment gap of mental health care services in Bangladesh. The red bar indicates that the individuals do not seek treatment for their mental diseases or the treatment gap, whereas the green bar represents the proportion of people with mental health disorders who take healthcare services. Every category of the disease shows a large treatment gap (Fig. 1). Fiure 1 presents that addictive disorder has the largest treatment gap (around 95%), while bipolar-related disorders have the lowest treatment gap (66%). Among the most severe treatments gap, addictive, depressive, personality, anxiety, somatic symptoms disorders, and neurocognitive disorders experienced a higher treatment gap among the major mental disorders in Bangladesh. The treatment gap for the above mental disorders exceeds 90% (Fig. 1). The higher treatment gap for these disorders may reveal that these mental illnesses have a less severe impact on mental health. Therefore, patients remain reluctant to receive treatment for mental health problems.

On the other hand, sleep-wake, obsessive-compulsive, disruptive, schizophrenia spectrum, sexual dysfunction, and bipolar were found to have comparatively higher coverage rates. The greatest four coverage rates (17%, 19%, 32%, and 34%, respectively) were found for disruptive and impulse, schizophrenia spectrum, sexual dysfunctions, and bipolar disorders (Fig. 1). The lower treatment gap (hence, the larger coverage rates) indicates that patients have to seek treatment for these mental health disorders as these disorders have a severe impact on their mental health.

Table 3 portrays the sources of receiving treatment for mental health disorders. It shows that public hospitals and doctors' chambers are the primary sources of mental health treatment for patients. Public hospitals, specialized doctor's chambers, and general doctor's chambers are the sources for 2.21%, 2.49%, and 2.64% of mental health disorders patients to receive mental healthcare services, respectively. On the other hand, other sources like the private hospital, specialized hospitals, homeopathic doctors, and other sources have provided mental healthcare services for below less than 1% of the patients. The findings also portray an exciting picture of mental healthcare-seeking behavior in Bangladesh. The results show that only about 2.8% of the mental disorder patients visit specialized hospitals and doctors for treatment, and about 7% of the patients receive treatment from sources without specialization.

Table 3

Health care-seeking behavior of patients with mental illness (%)
Treatment Sources	Contribution of Sources
No Treatment	90.16
Public Hospital	2.21
Private Hospital	0.43
Specialized Mental Hospital	0.29
Chamber of Specialized Doctors of Mental Diseases	2.49
Chamber of Other Doctors	2.64
Homeopathic, and others	0.86
Others	0.86
Unknown	0.07
Total	100.00

Source: Authors' calculation from the National Mental Health Survey of Bangladesh 2019

Table 4 presents the sources of treatment-seeking for each mental disorder. Public hospitals and chambers of specialized doctors or general doctors play a vital role in providing mental healthcare treatment for different types of diseases. Most patients with sleep-awake, neurocognitive and bipolar disorders sought treatment from public hospitals, and approximately 19% of bipolar patients and around 8% of personalit disorder patients sought treatment from the chambers of specialized doctors of mental diseases (Table 4).

Table 4

Mental health care-seeking behavior by type of mental health disorders (percent)
Sources of Mental Helath Services	Anxiety disorders	Depressive disorders	Sexual dysfunctions	Neurocognitive disorders	Obsessive-compulsive and related disorder	Bipolar and related disorder	Sleep-awake disorders	Personality disorders	Somatic symptoms and related disorders	Substance-related and addictive disorder	Schizophrenia Spectrum disorder	Disruptive, impulse control, and conduct disorders
No Treatment	91.30	93.25	68.42	91.3	84.21	65.63	88.89	92.31	91.16	95.24	80.8	83.33
Public Hospital	2.03	1.88	-	4.35	1.75	6.25	4.17	-	2.21	-	2.56	-
Private Hospital	-	0.38	-	2.17	1.75	-	-	-	0.55	-	1.28	-
Specialized Mental Hospital	-	-	-	-	-	-	1.39	-	-	-	3.85	-
Chamber of Specialized Doctors of Mental Diseases	1.45	1.88	-	-	3.51	18.75	-	7.69	1.66	-	10.3	-
Chamber of Other Doctors	3.48	1.88	15.79	-	7.02	3.13	2.78	-	2.21	4.76	-	-
Homeopathic, and others	0.29	0.19	10.53	-	-	6.25	1.39	-	2.21	-	-	16.67
Others	1.16	0.56	5.26	2.17	1.75	-	1.39	-	-	-	1.28	-
Unknown	0.29	-	-	-	-	-	-	-	-	-	-	-
Total	100	100	100	100	100	100	100	100	100	100	100	100

Source: Authors' calculation from the National Mental Health Survey of Bangladesh 2019

Figure 2 presents the types of healthcare providers for providing healthcare services to mental health patients seeking treatment. More than half of the patients with mental disorders got health care services from the chambers of specialized (25%) and non-specialized (27%) doctors. At the same time, public hospitals provided treatment services for about one-fourth of mentally disordered patients. Homeopathic and other treatments offered mental health care services for about 17%.

Source: Authors' calculation from the National Mental Health Survey of Bangladesh 2019

Probit Results Of Health Care Seeking Behavior And Estimating Marginal Effects

In order to delineate the probability of taking mental health care services, we have subsumed gender identity, level of education, religious status, age, residential status, number of family members, marital status, and the existence of other mental patients in the family as independent variables. The results of the probit model show how these socioeconomic factors affect the probability of mental healthcare service seeking, although the magnitudes of the coefficients of probit model are not meaningful. The following Table 5 portrays the estimated results from the Probit regression models, and Table 6 depicts the marginal effects of the aforementioned socio-economic variables at their mean value. To show how changing gender identity affects the probability of seeking mental healthcare services, we have also estimated the regressions for male and female sample separately under Model-II and Model-III in Table 5 and Table 6, respectively. On the other hand, Model-I captures the entire sample of the study.

As the descriptive statistics show that only 10% of mentally ill persons have sought health care treatment, the estimated results from the Probit regression model depict a very similar picture. Model-I of Table 5 shows that only the coefficient of the existence of other mentally ill patients in the family is statistically significant. According to Table 5, already having the presence of a mentally ill person in the family positively influences the decision of the other mentally sick person to receive mental health treatment at a 1% level of significance. It implies that a family thatalready has a person with a mental health disorder will seek treatment for another mentally sick person. It happens because the victim's family may anticipate the consequence of the existence of a mental health disorder patients.. However, the remaining variables-gender identity, level of education, religious status, age, residential status, number of family members, and marital status are statistically insignificant. Although the male identity of gender, additional year of education, being Muslim, and being married positively influence the decision to seek mental health treatment, they are not statistically significant. Therefore, it implies that these socio-economic variables do not impact on mental healthcare-seeking behavior in Bangladesh. On the other hand, older people, people in urban residential areas, and more family members demotivate thos with mental health conditions to take mental healthcare services. However, they are also not statistically significant.

Table 5

Estimated Probit Regression Results (Dependent Variable: Probability of Mental Health Care Seeking Behavior)
Model and explanatory variables	Model-I (Total Sample)		Model-II (Women)		Model-III (Men)
Model and explanatory variables	Coefficient Estimate	95% CI	Coefficient Estimate	95% CI	Coefficient Estimate	95% CI
Gender identity	0.01 (0.10)	-0.19 to 0.22	-	-	-	-
Level of education	0.01 (0.01)	-0.01 to 0.04	0.03 (0.02)	-0.01 to 0.06	-0.01 (0.02)	-0.05 to 0.03
Religion	0.31 (0.20)	-0.08 to 0.70	0.14 (0.25)	-0.35 to 0.63	0.55 (0.34)	-0.12 to 1.22
Age	-0.00 (0.00)	-0.01 to 0.00	0.00 (0.01)	-0.01 to 0.01	-0.00 (0.01)	-0.01 to 0.01
Residential status	-0.01 (0.10)	-0.21 to 0.20	-0.07 (0.14)	-0.33 to 0.20	0.05 (0.16)	-0.26 to 0.37
Number of family members	-0.04 (0.03)	-0.09 to 0.02	0.01 (0.04)	-0.06 to 0.08	-0.10 (0.05)**	-0.19 to -0.01
Existence of other mentally disordered family members	0.35 (0.13)***	0.10 to 0.61	0.40 (0.17)**	0.07 to 0.74	0.31 (0.22)	-0.12 to 0.73
Marital status	0.15 (0.15)	-0.15 to 0.45	0.33 (0.21)*	-0.07 to 0.74	0.26 (0.27)	-0.79 to 0.27
Constant	-1.45 (0.32)***	-2.08 to -0.82	-1.91 (0.46)***	-2.82 to -1.00	-0.94 (0.52)	-1.95 to 0.07
Number of observations	1233	-	717		516	-
Chi-square	14.89	-	11.84		13.10	-
Prob > chi2	0.06	-	0.10		0.07	-
Pseudo R²	0.02		0.03		0.04	-
Note: *, , * indicate statistical significance at the 1%, 5%, and 10% level, respectively. Robust standard errors are shown in parentheses.

Model-II of Table 5 presents the estimated results for female sample, Model-III represents the results for the male sample. The obtained results from the female sample reveal that the estimated coefficient of marital status is positive and statistically significant in addition to the existence of another mentally disordered member in the family. It implies that a married woman is more likely to seek mental healthcare services than an unmarried woman. However, the other socioeconomic variables again remained statistically insignificant, like Model-I. On the other hand, all the socio-economic variables but family size are statistically insignificant in the Model-III. Only the family size variable significantly influences the men's mental healthcare-seeking behavior. However, a greater number of family members negatively govern the men's decision to seek mental health care services. Many family members may limit the financial relaxation to spend money on the men's mental health treatment.

On the other hand, Table 6 provides the marginal effects at their mean values. The marginal impact helps us comprehend the extent of change in the binary dependent variable due to changes in the explanatory variables. The following Table 6 summarizes the marginal effect of our considered independent variables. As the marginal probabilities have been derived from the above (Table 5) normal cumulative distribution function (CDF), the sign and level of significance of the marginal effects should be approximately identical to the Probit model (Table 5). Therefore, the existence of mentally disordered family members is also statistically significant in estimating its marginal effect on the dependent variable, mental health treatment-seeking behavior (Model-I of Table 6). Table 6 portrays that the probability of seeking mental healthcare services with another mentally ill person in the family is 6% higher than the family with no other mentally sick people. This implies that having previous experiences dealing with mentally ill patients may make wary the family members about the mental ailment and positively influence them to take mentalhealthcare services for mental disorders. However, the other variables under Model-I are not statistically significant in line with the Probit regression results.

Table 6

Estimating Marginal Effects on Mental Healthcare-Seeking Behavior
Model and Explanatory Variables	Model-I (Total Sample)		Model-II (Women)		Model-III (Women)
Model and Explanatory Variables	Mean	The marginal effect at Means (Delta-method Std. Err.)	Mean	The marginal effect at Mean (Delta-method Std. Err.)	Mean	The marginal effect at Mean (Delta-method Std. Err.)
Gender identity	0.42	0.00 (0.02)	-	-	-	-
Level of education	4.82	0.00 (0.00)	4.43	0.00 (0.00)	5.36	-0.00 (0.00)
Religion	0.91	0.05 (0.03)	0.91	0.02 (0.04)	0.90	0.09 (0.05)*
Age	44.52	-0.00 (0.00)	41.87	0.00 (0.00)	48.20	-0.00 (0.00)
Residential status	0.47	-0.00 (0.02)	0.47	-0.01 (0.02)	0.47	0.01 (0.03)
Number of family members	4.51	-0.01 (0.00)	4.36	0.00 (0.01)	4.71	-0.02 (0.01)**
Existence of mentally disordered family members	0.14	0.06 (0.02)***	0.14	0.07 (0.03)**	0.13	0.05 (0.03)
Marital status	0.86	0.03 (0.03)	0.82	0.06 (0.03)*	0.92	-0.04 (0.04)
Number of observations	1233		717		516
Note: *, , * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. Robust standard errors are shown in parentheses.

Model-II of Table 6 depicts the marginal probability of the healthcare-seeking behavior for women due to the changes in considered socio-economic variables. Similar to the findings of the Probit model, the estimation of the marginal effect shows that existence of a mentally sick person in the family and marital status have a significant impact on female treatment-seeking behavior. According to the Model-II, a woman has 7% higher probability of receiving mental healthcare services than a family without a mentally sick person. In addition, a married woman has a 6%greater probability of going for mental healthcare services compared unmarried woman. No other catalysts can cause a woman to seek health care services except the aforementioned two independent variables. On the other hand, having an additional member in the family reduces the chances of seeking healthcare services by a 2% probability for men (Model-III of Table 6). The remaining variables have remained statistically insignificant in influencing the men's mental healthcare-seeking behavior.

Post-estimation of all the regression models shows that all the models fit the data very well, as the value of the chi-squared statistic is highly statistically significant. The probability values of the chi-squared distribution under every specification of Table 5 have varied between 5–10% statistical level of significance, indicating that the models behaved well statistically.

Mental health problems have increasingly become a matter of concern in the healthcare sector in Bangladesh, as the data show that the prevalence of mental diseases is continuously increasing over the last three decades. The National Mental Health Survey 2019 shows thatabout 10% of mentally ill persons have sought mental health care services. The treatment gap for mental diseases is very high, and the treatment-seeking tendency is also very low. The study found that severity of mental health disorders plays a significant role in seeking treatment for mental health diseases. The patients with mental health disorders having less severe symptoms (such as, addictive, depressive, personality, anxiety, somatic symptoms disorders, and neurocognitive disorders) are very reluctant to get mental health treatment.

On the other hand, mentally disordered patients with more severe symptoms (such as disruptive and impulse, schizophrenia spectrum, sexual dysfunctions, and bipolar disorders) mostly try to seek treatment for the diseases. Disruptive and impulse, schizophrenia spectrum, sexual dysfunction, and bipolar disorders experienced relatively higher coverage rates, as these disorders are primarily driven by the severe symptoms and behavior disruptions caused by both these conditions. For Instance, the comparatively higher treatment-seeking of people with bipolar disorders can be explained by the more extreme disruption that happens during the manic episodes of bipolar disorder, which drives a greater need for help-seeking. Addictive, depressive, personality, anxiety, somatic symptoms disorders, and neurocognitive disorders experienced a higher treatment gap, perhaps due to the less severe symptoms and behavior disruptions caused by these diseases. Therefore, the urgency of treatment may significantly influence the mental healthcare-seeking behavior of the people in the country. However, more research and studies are needed to model the behavior of mental healthcare seeking.

Both the demand-side and supply-side barriers are influencing this very low level of service utilization. Superstitions, social stigmas, and lack of consciousness among people are generally the major demand-side barriers to seeking mental health care services.² Lack of financial strength among the people may also contribute to prioritizing the urgency of treatment. They may spend their scarce resources on the items that provide them greater marginal utility leading them to spend money based on urgency. Inadequate infrastructure and investment in mental healthcare sectors, inadequate service providers, lack of adequate advocacy in favor of seeking mental healthcare services, and lack of coordination between different departments of the health sector are the major supply-side constraints for the low mental health treatment-seeking behavior in Bangladesh.^2,11 Bangladesh had 250 psychiatrists (0.2 per 100,000 people) and 565 psychologists for 162 million population (0.3 per 100,000).¹² This low number of specialists is also contributing to supply-side barriers.

The estimated results from the Probit regression from all the disaggregate models show that most of the explanatory variables do not significantly influence healthcare-seeking behavior among the adult population in Bangladesh. Only the existence of other mentally ill patients, marital status, and family size (all specifications) significantly govern the decision to seek mental healthcare services. The other demographic and socio-economic indicators, such as gender, religion, and residential status, do not significantly impact the mental healthcare-seeking behavior of the adult population. Both the demand and supply-side factors jointly contribute to the negligence in seeking mental healthcare treatment in the country. The obtained econometric results from the Probit model and marginal effect analysis clearly indicate that most mental health-disordered patients in the country are reluctant to receive mental healthcare services. The negligence in seeking mental healthcare services from the demand side, along with the poor infrastructure and lack of adequate skilled professionals for providing mental healthcare services on the supply side, have contributed to the lower service-seeking tendency in Bangladesh. Therefore, this mechanism paves the way for the country's large treatment gap in mental healthcare services.

It was also found that the majority of the patients who desiredtreatments sought treatment from ' 'doctor's chambers while a lowest portion of the patients (around 4%) sought treatment from a specialized mental hospital. Perhaps treatment in doctors' chambers is easily accessible to the patients compared to the other sources of treatment. However, further research is required to identify the more and additional reasons.

In order to encourage people to receive mental health care services to minimize the treatment gap, priority should be given to implementing the National Mental Health Strategy 2020. In light of our study findings, we suggest the following recommendations. First, an adequate investment is needed to make mental health care services accessible to all, and mental health care should be integrated at the primary health care level with appropriate referral linkage. Second, mass awareness through providing health education at the primary and secondary stages of schools. Third, adequate monetary and non-monetary incentives with proper career paths should be ensured for the health professionals providing mental health care services. Finally, additional research are required to identify the major barrries in seeking mental health care, cost-efefctive interventions to reduce the treatment gap for people with mental disorders, and to integratemental health care in primary health care setting. Implementing these above recommendations may improve the country's mental health and bring a productive nation by reducing the days lost in the workplace.

Ethical approval: We have analysed secondary data of a nationally representative survey. Hence, ethical clearance was not required to carry out the analysis.

Comepting interest: None

Authors’ Contribution: Rumana Huque (RH) conceived the study. Abul Kalam Azad (AKA) carried out the analysis. Helal Uddin Ahmed (HUA) provided technical guidance in data analysis. RH and AKA prepared the first draft and the subsequent drafts of the manuscript. Khaleda Islam, Mohammad Robed Amid and HUA reviewed and edited the drafts and provided comments.

Funding: None

Availability of data and materials: available on request

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No competing interests reported.

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Mental Health Care Seeking Behavior in Bangladesh: Evidence from the National Mental Health Survey 2019

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Abstract

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Introduction

Data, Variables, And Method Estimation

Data

Variables

Method Estimation

Developing Wealth Index

Results

Descriptive Statistics

Mental Health Service Coverage And The Source Of Service Coverage In Bangladesh

Probit Results Of Health Care Seeking Behavior And Estimating Marginal Effects

Discussion

Declarations

References

Additional Declarations

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