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.