The researcher intended to summarize whether there is any association between socioeconomic factors that lead to suicide rates in Thailand. Models of these factors may have been made in previous studies conducted in developing countries, but the context is definitely distinct from Thailand where the vast majority of people work in the agricultural sector so the employment and income of Thai agrarians are included in the model. In addition, studies that collect the macro-level of data are rare. A previous similar study was conducted in Thailand, but it was outdated since this knowledge was generated, and it did not explore more specifically the factors of cultural, social, and economic differences among regions.

I would like to highlight one of the risk factors that is most associated with suicide rates, namely the percentage of elderly people (age above 60). The linear regression model produced a positive coefficient with a value close to 1 (almost perfect), which was interpreted that if the value of one variable increases, it is likely that the value of the other variables would also increase. In this case, provinces with a higher percentage of elders had higher suicide rates and it ascertains the previous descriptive analysis that explained the trend of increasing suicides among people age over 40 years.

Although the Thai suicide research did not explain causal relationships and did not predict trends of suicide in the future, statistical modeling with linear regression in epidemiological research explains the underlying mechanism of how predictors are associated with suicide problems as the outcome. With the multiple regression model, we can see the variation of the strength of independent variables to the outcome, as well as what is the association’s direction among the two variables. Back to the results about larger rates of suicide in provinces with a higher percentage of elderly people, this linear regression model helps spatial epidemiology to map the vulnerability of each province. The vulnerability map is useful for the government in prioritizing intervention program.

1.
Because the suicide rate is generally rising in Thailand, the author would like to find the economic and social factors which relate to the suicide rate in different regions.
2.
Females as head of family was one factor related to suicide rate. A culture where males dominate led to the suffering of women from abusive husbands and depression. In this situation, the happiness among women decreased and led to suicide. In Thailand the culture of male domination might not happen in gender equality areas; however, this might still happen in some societies. Therefore, investigating areas with different numbers of females as the head of family, could reveal negative association toward the suicide rate. Areas which have a greater number of female family leaders might have less suicides.
3.
Suicide is not a contagious disease, and it seems to be an individual mental status problem. However, there are possible external factors which could alter mental status and push people into suicide such as economic status, social and culture. Living in certain areas, culture and status where people are immersed with factors contributing to potential suicide, might lead to higher suicide rate.
The proper modelling analysis is the way to alter data to reliable evidence which will benefit toward the environment investigation. From the data previous, the statistical model would help to find the relationship between factors and suicide rate, and also quantify the relationship to predict the suicide rate. After realizing the factors from the model, prioritizing areas could be performed to tackle the suicide problem from originating causes in different areas.

Question 1: Why was the author interested in investigating the suicide problem in Thailand during the time?
The author’s interest in investigating the suicide problem in Thailand was driven by the aim to identify and comprehend the determinants of suicide rates in the country. The research delves into several potential determinants, such as socio-economic factors, mental health indicators, cultural influences, and other variables, to gain a comprehensive understanding of the factors contributing to suicide prevalence in Thailand. The insights from this study can be invaluable for the government in formulating and implementing effective strategies to tackle the issue of suicide rates and enhance overall mental well-being in the nation.

Question2: Each of students picks one potential risk factor mentioned in the paper and explains how the variable can contribute to the suicide rate?
I want to emphasize a significant risk factor strongly associated with suicide rates, which is divorce. The linear regression analysis revealed estimated coefficients of 0.508 and 0.670 in model 1 and 2, respectively, indicating that as the percentage of divorce increases, there is a corresponding rise in suicide rates. The emotional and psychological stress that accompanies divorce can contribute to feelings of hopelessness and despair, leading to a higher likelihood of suicidal ideation and attempts. If individuals and family members facing divorce are left untreated or their mental health disorders are poorly managed, such as depression, anxiety, or bipolar disorder, they may experience overwhelming feelings of hopelessness and despair, which further increase the risk of suicide.

Question 3: How statistical modeling can contribute to investigate the epidemiology and spatial aspects of Thai suicide problem?

Statistical modeling serves as a significant tool in investigating the epidemiology and spatial aspects of the Thai suicide problem through two main avenues:
1. Identifying Risk Factors: By analyzing extensive datasets, statistical models can effectively identify and quantify risk factors associated with suicide in Thailand. This paper explores various variables such as age 60+, FEMH, ALR 20, INCOME, etc., providing valuable insights into the key factors contributing to the prevalence of suicide.
2. Multivariate Analysis: Utilizing multivariate statistical methods, researchers can comprehensively assess the joint effects of multiple risk factors on suicide rates, as seen in this paper. This approach offers a better understanding of the intricate interplay between different variables and their combined influence on suicidal behavior.
In summary, statistical modeling offers a powerful and comprehensive approach to investigate the epidemiology and spatial aspects of the Thai suicide problem, shedding light on the underlying factors contributing to suicide rates.

1. The author is interested in this study because of the increasing rate of suicide in Thailand and there was no previous study on socioeconomic factors associated with suicide in Thailand, which differs from developed countries’ studies.

2. Alcohol consumption is the risk factor that I want to discuss. Drinking alcohol leads to an inability to self-control and encourages one to commit suicide, especially with depression and a high risk of suicide. Thus, alcohol consumption leads to a higher suicide rate.

3. Statistical modeling help to analyze factor associated with suicide using regression analysis. Furthermore, it may demonstrate the extent to which certain variables affect the suicide rate. Additionally, it may be used to pinpoint the areas with greater rates than others, necessitating effective and appropriate policies for that region.

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