• Why was the author interested in investigating the suicide problem in Thailand during the time?
The author was interested in investigating the suicide problem in Thailand primarily because the suicide rate was increasing over the years. The author aimed to examine whether there was an association between economic and social factors and the rising suicide rates. Additionally, the author wanted to explore the economic perspectives as a means to combat the issue of suicide.
• Each of students picks one potential risk factor mentioned in the paper and explains how the variable can contribute to the suicide rate?
The study found that the level of alcohol consumption was significantly associated with higher suicide rates in Thailand. Additionally, alcohol consumption among young people contributed to increased suicide rates, a pattern commonly observed in earlier research. Alcohol can impair one’s ability to control actions and judgment. The government could address this issue by implementing policies that target alcohol addiction, such as introducing community-based addiction treatment programs. Moreover, enforcing a ban on alcohol consumption among young adults, accompanied by appropriate fines, could also be effective.
• How statistical modeling can contribute to investigate the epidemiology and spatial aspects of Thai suicide problem?
Statistical modeling provides a scientific and logical approach to identifying factors related to suicide. In this case, the author employed multiple regression analysis to examine the relationship between social and economic factors and suicide rates. This method allows for the assessment of how various factors, such as household income, debt, expenditure, alcohol consumption, population density, divorce rates, and unemployment rates, are associated with suicide rates.
Spatially, the author utilized provincial-level data to explore these relationships. By analyzing regional variations in social and economic variables, the study aimed to determine which demographic areas are at higher risk for suicide. This spatial analysis helps in identifying regions with elevated suicide rates and allows for targeted policy interventions and resource allocation to address the specific needs of those areas.