1. Why was the author interested in investigating the suicide problem in Thailand during the time?
= The author conducted the study to investigate the rising suicide rate in Thailand by examining economic, non-economic, and social factors. This interest was driven by the limited research available on this topic using macro-level data. Additionally, existing research primarily used time-series analysis, which might not fully capture the relationship between suicide rates and these factors. In contrast, this study used cross-sectional analysis to provide a better understanding of the cultural, social, and economic differences between regions and their impact on the suicide rate.

2. Each of the students picks one potential risk factor mentioned in the paper and explains how the variable can contribute to the suicide rate?
= Age 60+
The study indicated that provinces with a higher percentage of elderly had higher suicide rates. In Thailand, many elderly people over 60 live in rural areas, while younger generations tend to live/move to urban and industrial areas. This often leaves the elderly without adequate family support. Additionally, as they age, they face more health problems, which can increase stress levels and contribute to suicidal behavior. There are also not enough financial and social support programs for the elderly in Thailand, making it difficult for them to care for themselves. Moreover, the regression results in Models 1 and 2 showed an increasing trend of suicide rates for individuals over 40, which correlates with the findings for those aged 60 and above.

3. How statistical modeling can contribute to investigate the epidemiology and spatial aspects of Thai suicide problem?
= Statistical modeling, specifically multiple regression analysis, significantly contributes to investigating the pattern of suicide rates in Thailand by identifying and quantifying the relationships between various risk factors and suicide rates across different regions. This approach also aids in predicting trends in suicide rates, allowing for the identification of high-risk locations (provinces or regions). By pinpointing these factors and areas, policymakers, healthcare providers, and other involved organizations can develop targeted strategies to reduce the high suicide rate in those particular areas.