1. Why was the author interested in investigating the suicide problem in Thailand during the time?
The author was interested in the investigation because the number of suicides in Thailand has seen a general increase over the past decade, ranging from 3600 to 4000 cases every year. While in other developed countries, scholars have conducted a huge number of empirical studies to assess whether economic factors lead to higher suicide rates, the issue has been relatively under-studied in Thailand. Thailand also differs significantly from other countries in both economic and social contexts, and it has limited macro-level data available. To the author’s knowledge, only one previous study has used time-series exploration to explain the suicide rates in Thailand. The author aims to use cross-sectional analysis, which would be able to better explain the impact of cultural, social, and economic differences among regions.
2. Each of students picks one potential risk factor mentioned in the paper and explains how the variable can contribute to the suicide rate?
An interesting variable is the percentage of woman that are counted as the head of the family. The author explained why this variable was chosen in the beginning based on the observation that females are often considered inferior to their male counterparts in Asian societies. The example of Japan was given, where the hardship on families and suffering the abuse of their husbands may lead to depression of women and thus depression. The source of this variable’s data in Thailand was from National Statistics Bureau of Thailand. Most provinces had percentage of female as head of family at around 31-45%. Angthong ranked first and Samutsongkran and Singburi ranked Second and Third. After performing the regression, the variable was found to demonstrate statistical significance at the 0.01 level in both models. The coefficients were -0.396 and -0.401. This means there is a negative association. When women are listed as the head of household, they are less overwhelmed by males, and less suppressed emotionally and financially in the family. Therefore, in provinces with higher ratio of female as the head of household, the suicide rates were significantly lower.
3. How statistical modeling can contribute to investigate the epidemiology and spatial aspects of Thai suicide problem?
The author constructed two models using multiple linear regression analysis. Both model included a variety of social and economic factors, only differing in the age range of the alcohol consumption variable. For each variable, data at the provincial level of precision is used. All data is from 2011 which served the purpose of cross-sectional investigation. The benefits of this approach is that it is able to capture both the general trend and the spatial distribution of certain risk factors. For example, it was known that provinces in the northern region has the highest suicide rates compared to other regions, while Narathiwas has the lowest suicide rate. By using provincial data, we would not only be able to know which variables are significant at the national level, but also those that are especially serious at the provincial level. This would help capture a better picture at a higher precision, give insight to the geographic patterns of various risk factors, and provide implications on policy suggestions. For example, Phayao is not only a northern province with higher suicide rate, it is also a province with the highest adult alcohol consumption rate. Therefore, when work has to be done to lower alcohol consumption, the government may think to start there first, where both the suicide problem and the risk factor is serious.