Assignment 2.1

[Chawarat Rotejanaprasert]

For assignment 2.1, please discuss the following questions:

1. Why was the author interested in investigating the suicide problem in Thailand during the time?
2. Each of students picks one potential risk factor mentioned in the paper and explains how the variable can contribute to the suicide rate?
3. How statistical modeling can contribute to investigate the epidemiology and spatial aspects of Thai suicide problem?

[Palinee]

• 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.

[Ching To Chung]

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.

[Nichcha Subdee]

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.

[Soe Htike]

The author’s interest in investigating the suicide problem in Thailand during the specified time stems from the increasing recognition of suicide as a significant public health issue. Thailand, like many other countries, faces complex social and economic challenges that may influence mental health and suicide rates. By examining the determinants of suicide rates across different provinces, the author aimed to identify specific risk factors and their spatial distribution, thereby providing a deeper understanding of the underlying causes. This research is crucial for informing targeted public health interventions and policies to mitigate suicide risks and improve overall mental health outcomes in Thailand.

One of the potential risk factors mentioned in the paper is household income. Interestingly, the study found that higher household income is associated with increased suicide rates in Thailand. This counterintuitive finding suggests that economic affluence might contribute to social isolation, a factor that can significantly impact mental health. Higher-income individuals may experience greater pressure to maintain their economic status, leading to stress and anxiety. Additionally, wealthier individuals might have weaker community ties compared to those in lower-income brackets, where social cohesion is often stronger due to shared economic struggles. This lack of strong social support networks can leave individuals more vulnerable to mental health issues and suicidal behaviors. Therefore, understanding the role of income as a risk factor highlights the need for mental health support services that are accessible to all economic groups and the importance of fostering community connections regardless of economic status.

Statistical modeling plays a vital role in investigating the epidemiology and spatial aspects of the suicide problem in Thailand. By applying advanced statistical techniques, researchers can analyze complex data sets to identify significant risk factors and their interactions. Models allow for the control of confounding variables and the assessment of potential effect modifiers, providing a clearer picture of the relationships between different determinants and suicide rates. Spatial epidemiology, in particular, enables the examination of geographical patterns and clustering of suicides, highlighting areas with higher risks and facilitating targeted interventions. Bayesian models and spatio-temporal analyses can incorporate prior knowledge and account for spatial dependencies, improving the accuracy and reliability of the findings.

[Pyae Thu Tun]

During the last decade in Thailand, there has been an increasing trend in suicide cases which has a remarkable range between 3600 and 4000. It has been continuously rising since 2011 and this was around 6 suicides per 100,000 people. This drives the author to understand the factors contributing to the rising suicide rates. The lack of comprehensive research in this area and the need to address these issues for the well-being of the population also contribute to the author’s attention.

The divorce rate can contribute to the suicide rate in several ways. People can get emotional distress from getting divorced. They can face the feelings of loneliness, hopelessness. And it can also contribute to depression which is a known risk factor for suicide. There can be a significant loss of social support for a divorced individual which can worsen their vulnerability to suicidal thoughts. Furthermore, divorce can be associated with financial strain causing more stress and mental health challenges. These can elevate the risk of suicide. It’s also important to consider the cultural and social context of Thailand like our country, Myanmar which is more or less the same. There can be social stigma regarding a divorce together with isolation from society. This can be more prominent in women. This emotional burden can impact individuals’ mental well-being and increase their susceptibility to suicidal tendency.

In the investigation of epidemiology and spatial aspects of suicide in Thailand, statistical modelling can enhance significantly. It can identify the trends, risk factors and geographic patterns. The relationship between suicide rates and other variables like age, gender, socioeconomic status and alcohol drinking can be explained with the help of Time-series analysis and multivariate regression. Spatial regression models can help in mapping and identifying the high-risk area and hence it enables targeted interventions. This can support the development of public health strategies to be more effective. Finally, it can mitigate the risk of suicidal tendency and improve the mental health outcomes.

[Myat Htoo Linn]

1. I think the author is interested in investigating the suicide problem in Thailand due to reasons of unique socio-economic landscape with limited research at the micro level which differs from developed nations. A substantial portion of the Thailand population is engaged in agriculture, which is why agrarian employment is present in the research model to fill the research gap. There is also a high suicide rate annually, ranging from 3,600 to 4,000 between 2005 and 2014 and it has generally been on the rise since 2011. The author would interested in investigating this problem to provide insights into the impact of cultural, social, and economic differences among regions.

2. A potential risk factor mentioned in the paper is alcohol consumption. Both of the two models showed that suicide rates in Thailand were significantly driven by alcohol, expected increase rate of an average of 0.086 and 0.090, respectively. Alcohol consumption among the population can lead to an increase in the likelihood of suicide which impairs judgment, and loss of self-control, and sometimes makes individuals more prone to act on suicidal thoughts, especially for severely depressed people. A reflection of our surroundings is also apparent that alcohol addicts are more prone to attempt suicide and lose their lives based on many social, economic, and cultural influences. Moreover, there is a correlation between alcohol consumption among adults and underage with higher suicide rates can also suggest that alcohol use worsens mental health issues or life stressors, contributing to higher suicide rates.

3. The main contribution of statistical modeling in epidemiology would be the ability to identify the patterns and correlations between various risk factors and the outcome, suicide rates in this case. Using the multiple regression analysis with the two models, the study can explore the impact of economic and social factors that may have a relationship with the suicide rate in Thailand. The spatial analysis also contributed to spotting the disparities based on different geographical areas of provinces which also provided insights on understanding of suicide problems. These allowed for the identification of high-risk areas and can help policymakers to implement targeted interventions and resource allocations.

[Chawarat Rotejanaprasert]

Great discussions! The connection between spatial epidemiology and communicable diseases is well established. In this class, I’d like you to further explore the intersection between spatial epidemiology and non-communicable diseases. Mental health issues, for instance, have been on the rise, especially following the COVID-19 pandemic. For the class project, you will have the opportunity to analyze real data on suicide cases in London, UK. I hope this hands-on experience will deepen your understanding of how spatial analysis can be applied in epidemiology. 🙂

[Phyo]

My apologies for the belated attempt in this week’s discussion.

1. Why was the author interested in investigating the suicide problem in Thailand during the time?

The researcher would like to identify the significant factors influencing the suicide rates in Thailand. It was based on the fact that the number of suicide cases had been rising, with the figure ranging between 3,600 and 4,000 cases annually from 2005 to 2014. The average suicide rate was higher than 6 cases per 100,000 population, particularly after 2011. A considerable number of studies had been conducted in developed countries to address the increasing trend of suicide. Thus, to explore the factors influencing the suicide rate in the Thailand context, the research was conducted as a cross-sectional study on how cultural, social and economic impacts have an influence over the population in different areas of the country.

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.

I would rather choose alcohol as one of the highly significant factors that contribute to the increasing suicide rate. In the study, suicide rates were significantly related to alcohol consumption in Thailand.
It was also highlighted that alcohol consumption in both groups aged 20 and above and aged 15-19 were compared in two models to find out how alcohol abuse in both groups affected suicide.

Alcohol intake in teenagers and grown-up populations was lower in the high economic status group, and it is believed that those groups belonged to higher education levels and seemed to follow the regulations on alcohol. In the given two models, suicide cases were significantly related to alcohol drinking in adults over 20 years old group and adolescent groups between 15 and 19 years old, respectively. Model-1 has shown that one unit of increasing adult population alcohol intake could result in higher suicide rates by 0.086, while one unit of the rising population aged 15-19 drinking explained 0.01 unit of increasing suicide rate in model-2. R2 values were 0.6 in model-1 and 0.59 in model-2, representing that around 60% of the total variations in suicide rates were explained by independent variables in the models. There was not much difference in the values of the two models.

The relation between the growing number of suicides and alcohol was probably due to a loss of self-control under the alcohol, which provoked uncontrolled and aggressive behaviour, and those could be rooted in chronic depression that the person might not disclose to others.

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

Multiple linear regression statistic test was applied for both models in the paper to investigate the underlying risks of suicide. Provincial data were collated from different sectors to explore social and economic factors over dependent variables. Regression models scientifically demonstrated how social factors such as occupation, alcohol consumption, female as a head of household, divorce rate, and economic factors including income, debt and unemployment, were significantly related to the suicide rate in specific parts of the country and type of relation between. The prediction model estimated the suicide rate depending on changes in risk factors and it can alarm the public health authorities to address health issues timely. Appropriate intervention programs and regulation on negative factors can be established after prioritizing the significant factors from the model.

[Author]

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