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?

[Aung Thura Htoo]

1. The author stated that there has been an increase in the number of suicides in Thailand, higher than 6 suicides per 100,000 inhabitants. Additionally, Thailand is different from other countries in terms of economic and social factors. Besides, there is a lack of study using macro-level data on this topic.

2. One potential risk factor is the prevalence of drinking. In the study, the rate of suicide increases with the increase in the prevalence of drinking. The author stated that alcohol reduces self-control and encourages people with severe mental health problems like depression to commit suicide. I agree with his discussion. Alcohol reduces one’s ability to think clearly and, most of the time, even motivates one to perform harmful actions.

3. Statistical modeling quantifies the relationship between social, economic, and other relevant factors and the rate of suicide using provincial data. Without the use of statistical method, one would assume that the economic hardship and lower income would lead to higher suicide rate. However, the result of the study using statistical model shows that it is not true by providing the estimate of coefficients and its direction.
Additionally, using regression, the author clearly demonstrated the spatial aspects of Thai suicide problem. For example, provinces with higher rate of divorce can tend to increase the suicide rate if other variables are held constant. It can assist in allocating necessary resources and policies according to the predicted rate of each province.

[Wannisa Wongkamchan]

Thank you for these insights. I agree that alcohol significantly increases suicide risk because it impairs judgment and decision-making. I’m surprised that economic hardship doesn’t increase suicide rates as expected. This shows how important statistical analysis is for understanding complex social issues properly.

[Wannisa Wongkamchan]

1. The author is interested in studying the suicide problem in Thailand due to the increasing trend of suicide, the high suicide rate of more than 6 people per 100,000 population, and the lack of quantitative research at the macro-level.

2. Population over 60 years of age (AGE60) is one of the risk factors that significantly increases the suicide rate (p < 0.01 for both models). Many older adults in Thailand lack effective financial and social support. Many live without family support, without regular income or adequate living allowances, which may lead to depression and suicidal ideation.

3. Statistical modeling helps in studying the epidemiological and spatial studies of suicide problems in Thailand in the following ways:

– Identifying risk factors that influence suicide. For example, using multiple regression models allows for the identification of the level of the relationship between factors and the ability to distinguish which factors have a positive or negative relationship with suicide rates. This helps to decide which factor should be controlled first if we want to reduce suicide rates.

– Interpreting spatial data. For example, a model using data by province can compare differences between regions and show which regions have higher suicide rates. It can also help find local causes, such as being an agricultural or industrial area, which may relate to income and cost of living.

– Statistical models help control confounders and check if the model fits the data. This makes the results more reliable.

[Aung Thura Htoo]

Dear Wannisa, thank you for sharing your discussion. I agree with you that statistical analysis such as regression models are powerful in their ability to identify the direction of the relationship between predictors and outcome variables. This in turn can be beneficial in deciding intervention measures.

[Than Soe Oo]

Thank you for your insights, Wannisa. You raised a very important point. Overall, Thailand confronts a serious mental health and suicide crisis exacerbated by social, economic, and demographic challenges. The use of statistical and spatial epidemiological methods is essential for developing effective policies and interventions tailored to the most vulnerable groups and regions.

[Cing]

Q1. Why was the author interested in investigating the suicide problem in Thailand during that time?

The fact that the number of suicides has consistently ranged between 3,600 and 4,000 annually would draw noticeable attention to normal people. The author, including scholars, was interested in investigating the factors influencing the suicide problem in Thailand during that time since macro-level data on the topic for Thailand is limited and it has different economic and social characteristics than industrialized societies.

Q2. Picks one potential risk factor mentioned in the paper and explains how the variable can contribute to the suicide rate.

In the paper, a theoretical framework, the Utility Maximization Model, suggests that a person’s age and income influence their probability of suicide.

The question is, how is the divorce rate associated with the suicide rate, while the framework mentions only age and income?

One of the five assumptions delivered by the theory (Lester and Yang, 1997) is that divorce leads to the reduction of household income; therefore, such circumstances are more likely to commit suicide.

In the paper, the relationship between divorce rates and suicide rates is found to be statistically significant, with a coefficient of 0.508 showing a positive impact on suicide rates; meaning as the divorce rate increases, the suicide rate also tends to increase.

Q3. How can statistical modeling contribute to investigating the epidemiology and spatial aspects of the Thai suicide problem?

First, the author was interested in investigating the factors influencing the suicide rate in Thailand. So, to mathematically make a valid conclusion, statistical modeling helps to identify and quantify the impact of various factors on suicide rates across different places. By regression analysis, the author explored which socio-economic variables are statistically significant determinants of suicide rates.

To my understanding, spatial epidemiology is the description and analysis of geographic variations in disease with respect to demographic, environmental, behavioral, socioeconomic, genetic, and infectious risk factors (Elliott & Wartenberg, 2004). The paper focuses on a multiple regression analysis to find correlations between these factors, rather than analyzing the geographical distribution or patterns of the disease.

However, in associating spatial aspects of the suicide problem, the study identifies that the northern region of Thailand had the highest suicide rates, while provinces in the northeastern, central, and Bangkok regions had lower rates. This helps to map out and identify specific areas that may require targeted interventions.

Reference
Prasertthai, S., & Panyathorn, J. (2019). Determinants of Suicide Rates in Thailand. *Journal of Community Development Research (Humanities and Social Sciences)*, *12*(1), 15-24. [https://doi.org/10.14456/jcdr.2019.2](https://www.google.com/search?q=https://doi.org/10.14456/jcdr.2019.2)

Elliott, P., & Wartenberg, D. (2004). Spatial epidemiology: Current approaches and future challenges. *Environmental Health Perspectives*, *112*(9), 998-1006. [https://doi.org/10.1289/ehp.6720](https://www.google.com/search?q=https://doi.org/10.1289/ehp.6720)

[Wannisa Wongkamchan]

Thank you for your discussion, Your point about the study’s limitation in spatial analysis is very insightful. While the regression model identifies risk factors well, it doesn’t fully explore geographical patterns. Adding GIS or spatial clustering methods in future research would help better understand why northern provinces have such high suicide rates compared to other regions.

[Cing]

Thank you for sharing your valuable insight about how GIS / spatial clustering methods would help, Wannisa.

[Than Soe Oo]

Thank you for your discussion. You bring up a very important point.The investigation into Thailand’s suicide problem is crucial due to the steady high number of suicides, the distinct economic and social context of Thailand compared to industrialized societies, and the significant influence of divorce rates on suicide that necessitates focused, data-driven public health responses.

[Cing]

Thank you for your comment, Than. Data-driven public health responses without ecological fallacy are an effective solution.

[Than Soe Oo]

• Why was the author interested in investigating the suicide problem in Thailand during the time?
The researcher aims to identify the significant factors influencing suicide rates in Thailand. This study is prompted by the rising number of suicide cases, which ranged from 3,600 to 4,000 annually between 2005 and 2014. The average suicide rate exceeded 6 cases per 100,000 population, especially after 2011. While numerous studies have been conducted in developed countries to address the increasing trend of suicide, this research focuses on exploring the factors affecting suicide rates within the Thai context. The study is designed as a cross-sectional analysis to examine how cultural, social, and economic factors influence the population across different regions of the country.
• Each of students picks one potential risk factor mentioned in the paper and explains how the variable can contribute to the suicide rate?
The researcher aims to identify the significant factors influencing suicide rates in Thailand, noting a concerning rise in the number of suicide cases, which ranged from 3,600 to 4,000 annually between 2005 and 2014. The average suicide rate exceeded 6 cases per 100,000 population, particularly following 2011. While numerous studies have been conducted in developed countries to address the increasing trend of suicide, this research seeks to explore the specific factors affecting suicide rates within the Thai context. It was conducted as a cross-sectional study examining how cultural, social, and economic influences vary across different regions of the country.
• How statistical modeling can contribute to investigate the epidemiology and spatial aspects of Thai suicide problem?
The primary contribution of statistical modeling in epidemiology is its ability to identify patterns and correlations between various risk factors and outcomes, such as suicide rates. By employing multiple regression analysis with two models, the study can investigate the impact of economic and social factors related to suicide rates in Thailand. Additionally, spatial analysis can reveal disparities across different geographical areas, providing insights into the suicide problem. This approach allows for the identification of high-risk areas and can assist policymakers in implementing targeted interventions and allocating resources effectively.

[Aung Thura Htoo]

Dear Than Soe Oo, thank you for sharing your discussion. As you have mentioned, the limited number of study in the Thailand context is one of the main reason why this research was conducted. Additionally, I agree with you that spatial analysis can assist policy makers in identifying high risk areas and implementing targeted interventions as well as allocation of necessary resources in much needed provinces.

[Cing]

Thank you for sharing your discussion, Than. My curiosity was piqued about whether the finding would differ from other kinds of research designs, such as longitudinal analysis, cohort studies, and time-series analysis, when you mentioned it’s a cross-sectional analysis. Regarding spatial analysis, I am not sure whether adding location data as an independent variable of regression can be said to be spatial analysis. I think your response to the second question seems to be miscopied.

[Aye Thinzar Oo]

1. The author examined the suicide issue in Thailand due to an increasing trend and a rate exceeding 6 suicides per 100,000 inhabitants. Thailand’s situation is distinct from other countries because of its unique cultural, social, and economic contexts. Possible contributing factors include economic conditions (e.g., income levels, unemployment rate) and social influences.
2. Let me pick one potential risk factor that the author mentions in the paper is “Other Social factors that contributed to the higher suicide rates were divorce rate”.
One social factor the author identifies as contributing to higher suicide rates is the divorce rate. The study found that provinces such as Saraburi, Pathumthani, Samutprakan, Chonburi, Rayong, and Phuket had an average divorce rate exceeding 8 couples per 1,000 households. In Thailand, higher divorce rates are observed in more educated and economically prosperous societies. These areas also tend to have a higher proportion of female-headed households, ranging from 31% to 45%. The social and emotional strain of marital dissolution, combined with potential economic and familial instability, can increase suicide risk.
3. Statistical modeling used in studying the epidemiological and spatial aspects of the suicide rate in Thailand according to a data-driven approach. The data used in the province comparison of the divorce rate. The divorce rates showed that the findings of other social backgrounds statistically had an impact on the suicide rates.

[Cing]

As you discussed, I believe that divorce can bring serious mental complications in places where people consider marriage as a higher cultural value and societal expectation, as the paper mentioned. Statistical modeling, to be specific, two models of regression analysis are used for prediction and inference, by the relationship between the dependent variable (suicide rate) and the independent variables.

[Tanaphum Wichaita]

1.Suicide numbers in Thailand had been going up again since 2011, with more than 3,600 deaths each year. Similarly, in 2005, the suicide rate was 6.34 per 100,000 people and dropped to 6.08 in 2014. The author thought this information could help find what factors made it drop and what made it rise again

2. Community support : The study found that provinces with stronger social connections, like in rural areas, tended to have lower suicide rates. People with close ties to neighbors and community have more emotional support and are less likely to feel alone, which can help protect against suicide.

3. Statistical modeling shows which factors have the biggest impact on suicide rates and where these impacts are strongest. It can also help the government predict how changes such as improving basic support might reduce suicide rates.

[Cing]

I love the fact that you mentioned people in rural areas tend to have lower suicide rates due to their relationship (community support). Although such a strong social connection is not a risk factor, I agree that a lack of social connection would impose the risk.

[Author]

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