1. Bangkok School of Management, Thailand; wanputham@ac.th

*             Correspondence: wanputham@bsm.ac.th

Keywords: mental health, mental disorders, economic factors, social factors

1. INTRODUCTION

The number of persons experiencing mental health issues has been steadily increasing in recent years, making it a significant issue in many nations. Globally, 264 million individuals had depressive disorders in 2018, 45 million had bipolar illness, and 50 million had dementia in 2018 [1]. Mental illnesses such as depression, schizophrenia, other personality diseases, anxiety disorders, and developmental disabilities are examples of psychological illnesses, which are psychological situations linked to inappropriate feelings and behaviors, according to the WHO. In Thailand, these illnesses have escalated into a major issue. The Department of Mental Health [2] reports that from 1,510,279 in 2015 to 2,669,821 in 2017, there was a 76.77 percent growth in the number of people with mental disorders. This included a rise of 30.69 percent, from 16,186 in 2019 to 21,115 in 2022, in the number of patients with related symptoms mostly brought on by mental diseases. In the same time frame, the daily average of patients seeking treatment for mental disorders in hospitals increased from 7,971 to 8,458. Over the course of three years, the number of instances of psychosis increased from 5,325 to 6,209, those of depression from 3,119 to 3,683, those of suicidal thoughts from 58 to 284 cases, and those of other mental diseases from 2,087 to 2,804 cases. In Thailand, the number of instances with anxiety disorder decreased from 5,598 to 5,175 between 2019 and 2022, however this decrease is viewed as modest given the rise in other mental disorders.

2. LITERATURE REVIEW

It is thought that a number of variables, such as prenatal susceptibility to external dangers, individual inheritance, chemical deficiencies in the cerebral cortex, variations in sexual hormones, nutritional issues, and physical wellness problems, contribute to the emergence of disorders of the mind. Additionally, in addition to health-related issues, the social and economic background of the patients themselves may contribute to mental problems. Numerous research have extensively examined the causes of mental health issues. According to WHO [4], political and economic circumstances, together with living conditions, are possible explanations of mental diseases. According to the WHO, treatment levels for mental diseases are often lower in low- and middle-income nations than in high-income ones. Kurtzleben [5] demonstrates that poverty and unemployment are significant risk factors for mental illness, while Cifuentes et al. [6] discovered that economic inequality was a factor contributing to depression. According to Aziz and Steffens [7], social issues including stress, bereavement, and loss, as well as other difficult circumstances that leave a person feeling insecure, are additional causes of depression. Economic issues can contribute to mental problems, according to a number of studies. Economic risk, such as employment insecurity, financial unhappiness, income volatility, and expenditure instability, was found by Rohde et al. [8] to have a detrimental impact on the mental health of a group of Australian people.  Additionally, Cifuentes et al. [6] found a correlation between the Gini index’s measure of income disparity and the frequency of major depressive episodes, with the effect being stronger in more developed nations. According to Hong et al. [9], depression and income disparity are positively correlated in Korea. Economic and governmental crises made this association stronger. Additionally, it was discovered that income inequality was linked to various mental health issues, including suicidal thoughts and attempts. Additionally, Messias et al. [10] and Patel et al. [11] showed how wealth inequality may raise the risk of mental illness in the US. Unemployment is a significant factor influencing mental problems, according to numerous research. In Bosnia and Herzegovina, Mujanovic et al. [12] found that unemployment had a detrimental impact on mental health, particularly in those who are working age. While some research have shown that economic situations and unemployment can contribute to an increase in certain types of mental disorders, several studies have revealed that unemployment is the primary risk factor for mental health [13, 14, 15, 16]. However, Martnez-Jiménez and Vall Castelló [20] discovered that a decline in labor market conditions may result in a decrease in mental illness, as seen by the decline in the use of anxiolytic medications. Perhaps this was due to people spending more time on their health when they were unemployed. In their investigation of how economic conditions affect students’ mental health in the UK, Roberts and Golding [21] discovered that increased personal debt and lengthy workweeks may increase the prevalence of mental disorders.  According to Molarius et al. [22], educational characteristics had little bearing on mental problems in Sweden, whereas employment position and economic conditions did. Additionally, Silva et al. [23] shown how changes in the severity of mental disease could be caused by the economy, access to mental health care, and other factors. According to Kurtzleben [5], being unemployed lowers household income, which in turn makes families more stressed. Depression and other mental illnesses are hence more likely to develop. According to Linn et al. [24], mental health issues are more prevalent in Miami and Florida with higher unemployment rates. Additionally, it was discovered that this impact was more common in women than in males, and in people with lower self-esteem than in people who had more social support from family and friends. Drummond et al. [25] discovered that only gender had a significant impact on the risk of mental disorders in Brazil, whereas the World Health Organization [26] revealed that social and economic factors, including family issues, education, and unemployment, poverty, and income inequality, could all contribute to mental illness. Joshi [27] discovered that, on the other side, falling housing costs decreased people’s wealth and led to an increase in the number of self-reported mental health concerns in the US. Regarding studies of Thailand, the majority of papers have used survey data for their research, concentrating exclusively on particular population groups. Additionally, social variables have received the majority of attention. Jiranukool et al. [28] investigated how gender, relationship status, physical health, and friendship and family ties affected the incidence of mental diseases among Mahasarakham University students who visited the psychiatric clinic. They discovered that the primary contributors to depression and anxiety disorders among students were their gender and their marital status. According to Phunpho [29], factors affecting the mental health of non-commissioned police officers in Thailand include work-related stress, age, geographic location, and connections with family members. At the King Chulalongkorn Memorial Hospital’s Infertility Clinic, Srimuang and Roomruangwong [30] examined the connection between depression and marital satisfaction and found that those who were dissatisfied with their marriages had a higher risk of developing depression than those whose marriages were successful. Additionally, Pandii [31] discovered that among young Thais aged between 18 and 24 studying at Sisaket Technical College, family ties, including as the state of the parents’ marriage, the interaction between children and parents, friendships, and relationship issues, were factors contributing to depression. The impact of physical health factors on mental problems in Thailand has been studied in a number of articles. According to Churuchiraporn and Sasanus [32], schizophrenia and post-traumatic organic psychosis are two characteristics that contribute to postpartum depression. In their study of depressive disorders among patients at Srinagarind Hospital in the Thai province of Khon Kaen, Wattanapan et al. [33] discovered no association between the patients’ levels of depression and their age, gender, occupation, or income. However, a medical ailment known as spinal cord lesion was discovered to be the primary contributor of depression. According to Bunloet [34], chronic illnesses might cause depressive disorders in senior Thai people. There aren’t many studies of Thailand that look at the social and economic elements that can lead to mental illnesses. In the Lumlukka District of Pathumthanee Province, Sengkrewkarm [35] researched the variables influencing depression in the older population. She discovered that factors including money and physical fitness had a noticeable favorable impact on depressive symptoms. In Northeastern Thailand, prenatal clinics are frequented by pregnant teenagers. Kaewjanta et al. [36] studied the risk of depression among these teenagers and discovered that unemployment, economic position, and social support were significant influences on depression. Overall, there is still a gap in prior research on Thailand because they exclusively analyze survey data from certain populations and mostly concentrate on the impact of social or physical health factors on mental disorders. Furthermore, they generally concentrate on just one element of mental diseases, such as suicide rates or depression. Thus, using province data from throughout the entire nation, this study will close this gap by investigating the economic and social factors that affect mental diseases. The paper examines a number of mental disorders, including schizophrenia, depression, anxiety, and attempted suicide, among others. Additionally, we examine the consistency of our findings throughout the nation’s various regions. Our study’s findings will add to the understanding of this problem in Thailand and have consequences for policy.

3. RESEARCH METHODOLOGY

We collected secondary information from a number of resources to analyze the impact of socioeconomic variables on mental disorders in Thailand. The Department of Mental Health, Thailand, provided data on the number of instances of mental disorders in all 77 of its provinces from 2019 to 2022 (the most recent data available). The information on economic factors, such as economic health (as measured by Gross Provincial Product), employment, household debt, and population of each province, was acquired from the National Statistical Office of Thailand. The National Statistical Office of Thailand and the Department of Provincial Administration were also contacted for information on social factors, such as internet access, the availability of health services (measured by the number of doctors per population), the number of divorce cases, and the number of deaths. All level data were split by the population density of each province in order to account for variations in each province’s size. We produced the All Mental Disorder Rate variable by adding up all the different sorts of mental disorders under consideration. The statistics of the total sample used in our regression analysis are described in Table 1 in more detail. We initially determined the statistical significance of the primary economic and social components shown in Table 1 in order to better comprehend the dataset and any potential relationships between the rate of mental disorders and the factors to be examined. Table 2’s descriptive statistics in all panels revealed a relationship between social and economic factors and the prevalence of mental disorders. Table 2 demonstrates that, at the 1% level, higher levels of mental disorder are strongly linked with lower GPP per capita, more family debt, greater internet access, and lower availability of health services. To statistically examine any causal relationship, however, a straightforward t-statistical comparison between extremes in economic and social factors is inappropriate.

Therefore, the following step was to use an appropriate empirical model. The model used in this model is following.

where αi is the province I, t is the time period year t, and Mental is the proportion of patients with various types of mental disorders to the total population in province i in year t. GPPit, Emit, and Debtit are the actual GPP per capita in province i in year t, as well as the employment rate and household debt in that same province in that same year. The social factors include Divorceit, which measures the number of divorce cases to the total population in province i in year t, Technoit, which measures the percentage of people in province i with internet connection in year t, and others. Healthit, which measures the accessibility of healthcare in the region by comparing the number of doctors working in the provincial hospital to the total population in province i in year t, and Deathit, which measures the accessibility of mortality data. An adequate econometric model should take into account any potential unique characteristics in each province since the dataset consists of provincial data over a three-year period. We anticipated that rising rates of household debt, divorce, and death would result in rising incidence of mental disorders. A lower rate of mental disorders would be the outcome of more economic growth, as indicated by GPP per capita and a higher employment rate, better technology advancement, indicated by internet access, and greater accessibility to health services, indicated by the number of doctors.

4. RESULTS AND ANALYSIS

Table 3 contains the results of our estimation of Eq. (1) using different categories of diseases and average rates of psychological disorders. According to specification (1), Table 3 demonstrates that lower household debt and a greater GPP per capita are the two key economic elements that contribute to a nation’s overall lower rate of mental illness diagnoses. This is to be expected given that improving economic conditions increase people’s employment prospects, well-being, and income, all of which should reduce stress and, in turn, the prevalence of mental disorders. On the other hand, larger levels of household debt will result in increased stress, which might cause an increase in occurrences of mental disorders [6, 37]. We discovered that the technology access factor considerably increased the prevalence of mental disorders. This may be because people who spend too much time online, use it excessively for work, or become addicted to social media may receive inaccurate information, experience informational bullying, or become dependent on social media for their daily needs. As a result, this may raise their stress levels and increase the risk of mental disorders. Regarding healthcare accessibility, we discovered that reduced incidence of mental disorders will result from greater access to health services. This is in keeping with our anticipation and past research [23], which is that more healthcare assessments will give patients more opportunities to take preventative action and lessen the prevalence of mental disorders. But not every sign of a mental illness is the same. Individual economic and societal circumstances can have a variety of distinct effects on some. As a result, we now evaluate each symptom of a mental condition separately; the results are displayed in Table 3’s columns 2 to 6. With a few exceptions, the results qualitatively mirror the study of all mental disorders in column 1. In the equation involving the rate of suicide attempts, the employment rate becomes important. The justification is comparable to that previously provided citing improved economic conditions. When more people are employed, household income levels are greater generally, which reduces stress and decreases the number of suicide attempts. In terms of social factors, the divorce rate has a considerable impact on two different types of mental illness symptoms: it has a favorable impact on the rate of attempted suicide but a negative impact on the rate of schizophrenia. It is not unexpected that divorce has a positive link with the attempted suicide rate because divorce generally puts a pressure on one’s stress levels. The discovery of a negative correlation between the rates of divorce and schizophrenia, however, is extremely intriguing. It’s possible that when some relationships deteriorate, the divorce threshold is not actually crossed. Couples that remain together may experience increased levels of tension, stress, and paranoia, which may result in an increase in the occurrence of specific mental disorder symptoms, such as schizophrenia. Therefore, a negative correlation between the divorce rate and the schizophrenia rate can be observed because when people eventually decide to split, the tension, stress, and paranoia that were linked with the relationship vanish. With the exception of the anxiety rate in column 4, access to technology, as measured by internet access, exhibits a significant positive influence in the same way as the overall mental disorder rate in column 1. More people having access to the internet may make it easier for those with this subset of mental problems to find solace online. To help them feel less anxious, they might be able to talk about their worries, write a blog, or read about other situations that are comparable to their own. The provinces’ high mortality rate only has a positive correlation with anxiety levels, as would be expected given the associated grief and loss. But it has a detrimental impact on the suicide rate. Weir [38] states that “Pinpointing the reasons that suicide rates rise or fall is challenging because the causes of suicide are complex.” This is difficult to explain. In conclusion, we discover that all sorts of mental problems in Thailand are significantly impacted by access to technology. In general, lower rates of mental disorders are regularly linked to better economic circumstances, as indicated by high GPP, high employment rates, and reduced household debt, but health accessibility may also be a factor. However, there are conflicting correlations between the prevalence of mental disorders and other socioeconomic factors, such as death and divorce rates. We further separated the samples into four regions to see if the findings were consistent across all of Thailand. Northeast, Central, and South.

The average values of the parameters are displayed in Table 4 with the standard deviations in parenthesis. Comparing the Northern region of Thailand to other regions, it is clear that the Northern region has the highest overall rate of mental disorders. The Northern and Northeastern regions have substantially greater rates of schizophrenia, depression, and anxiety compared to other regions when it comes to various sorts of mental disorders. Aside from the prevalence of attempted suicide, the central region appears to have lower rates of mental disorders. In comparison to the Northern and Northeastern regions, the Southern region has a lower prevalence of mental disorders. The Central and Southern parts of the country appear to have better economic conditions, as demonstrated by the comparatively greater Gross Provincial Product and employment rates, which might be used to explain this. Due to changes in the incidence of mental disorders in accordance with the economic and social factors displayed in Table 4. Work that depends on variables that people can’t control can make people feel more stressed. Regarding societal determinants, there is still a correlation between a lower rate of mental disorders and healthcare accessibility. As in the aggregate study, the divorce and mortality rates continue to exhibit some inconsistent results. Finally, aside from anxiousness, which is still a problem, access to technology generally leads to an increase in occurrences of mental disorders.

5. DISCUSSION AND CONCLUSION

People’s capacity to sustain good physical health is significantly influenced by their mental wellbeing. As a result, mental illnesses like sadness or anxiety can make it difficult to carry out daily tasks like working. Studies on mental health disorders have largely ignored the potential effects of economic issues in favor of social and health considerations. Additionally, prior research has mostly ignored other subtypes of mental health illnesses in favor of concentrating on just one element of mental disorders, particularly depression. By studying the impact of both economic and social factors on various forms of mental diseases, this paper fills a void in the literature. The findings, which are based on data from 77 Thai provinces from 2015 to 2017, demonstrate a strong impact of social and economic factors on all mental diseases in the nation. In general, we found that better economic conditions—measured by higher GPP per capita, higher employment rates, and lower household debt—help reduce mental disorder rates, while social factors like greater accessibility to health services and less accessibility to technology also contribute to this reduction. However, the impact of death and divorce rates on rates of mental disorders varies depending on the type of mental condition under consideration. With the exception of employment, we discovered that the results were qualitatively similar to the aggregate analysis when we examined whether they were consistent across various Thai areas. Increased employment levels in the Northeastern and Southern regions were linked to greater rates of mental illness, which may be connected to increased stress from working in climates with unpredictable weather. The study’s findings have some significant ramifications and questions for Thailand’s policymakers. We found that economic factors have a significant impact on various types of mental disorders in the nation. Since policymakers typically work to prevent economic instability and financial and economic risks, they should be ready for an increase in mental disorder cases during economic downturns. In order to lower the incidence of mental disorders, the government should provide funds to ease the financial burden. The Department of Mental Health should improve health accessibility to more people in order to prevent cases, since we discovered that societal factors like the divorce rate, access to technology and healthcare, and the death rate can contribute to increased mental disorder cases in the nation. It may also be necessary for the Department of Mental Health to issue guidelines on safe internet usage, encourage best practices when using social media, raise awareness of online bullying, and establish a special unit that could provide online support for those affected given that it has been observed that access to technology (internet access) can result in more mental disorders.

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