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?

[Auswin Rojanasumapong]

Why was the author interested in investigating the suicide problem in Thailand during the time?
– The author is interested in investigating because the suicide rate has been rising. While there are many studies about factors associated with suicide rates, most of them conducted in industrialized countries, and research about determinants of suicide using macro-level data in Thailand is lacking

Each of the students picks one potential risk factor mentioned in the paper and explains how the variable can contribute to the suicide rate?
– Alcohol consumption is one of the potential risk factors contributing to the suicide rate because the alcohol itself prevents self-control and brings the courage to commit suicide especially in patients with psychiatric problems. From the previous studies, suicide is 120 times more prevalent among adult alcoholics

How statistical modeling can contribute to investigating the epidemiology and spatial aspects of the Thai suicide problem?
– It helps to explore the associated factors related to the suicide rate from macro-level data and predict the suicide rates in any area with sufficient information.

[Napisa Freya Sawamiphak]

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

The author found that the suicide rate in Thailand has been increasing since 2011 compared with the lower and consistent rate previously. Therefore, the author wondered about factors (such as economic factors – income, unemployment rate, and social factors) that might cause an increase in the suicide rate. Also, the research and macro-level data on this topic are limited in Thailand. There are some researches studied about the factors related to suicide worldwide however, it was not yet adjusted and aligned with Thailand context. Therefore, the author would like to explore and understand the factors leading to the suicide rate in Thailand using local databases.

• Each of students picks one potential risk factor mentioned in the paper and explains how the variable can contribute to the suicide rate?

The Elderly (over 60 years old) had higher suicide rates. The potential reason is some elderly people have a lower capacity to look after themselves without family support, compared with the younger people. There are also no effective support programs to help both financially and socially in older people. Therefore, it might increase the suicide rate in this group.

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

There are multiple factors leading to the suicide rate. Using statistical modeling such as multiple regression analysis can analyze the impacts of multiple variables on suicide outcome. Successful modeling of complex data led to trustful results which support decisions making to solve Thai suicide problem. Additional, using the local data can reflect suicide problem in Thailand locally and it also show the differences of suicide rate based on geographic region and demographics, when examined across different locations. Knowing the factors leading to higher suicide rates can address the root causes and provide potential solutions to solve Thai suicide problem.

[Pongsakorn Sadakorn]

1. Why was the author interested in investigating the suicide problem in Thailand during the time?
– Over the decade (from 2005 to 2011), the number of suicide in Thailand has been rising higher than 6 suicides per 100,000.
– More available data sources have been used to analyzed the suicide problem in Thailand.
– More impact against social and economic of Thailand.

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?
– Drinking and addiction to alcohol is linked to health problems and other consequences, especially social, low level of household income, violence, and work problem and this may lead to the increased risk of suicide.

3. How statistical modeling can contribute to investigate the epidemiology and spatial aspects of Thai suicide problem?
– In this study, multiple regression analysis was used to analyze the factor associated suicide problem in Thailand. Moreover, statistical modeling can predict the factor that relates to the suicide problem
– The significant result may drive the law or policy which helps to decrease the suicide problem in Thailand.

[Wachirawit Supasa]

1. Why was the author interested in investigating the suicide problem in Thailand during the time?
I think the author would like to investigate becuase suicide rate has been increased.

2. Each of students picks one potential risk factor mentioned in the paper and explains how the variable can contribute to the suicide rate?
Debt can be count as a risk factor which contributes to suicide. Higher debt ratio to income can cause stress and economical difficulties and potentially led to depression and suicide.

3. How statistical modeling can contribute to investigate the epidemiology and spatial aspects of Thai suicide problem?
This model uses multiple factors including health, socio-economic, and demographic that can be study using spatial analysis.

[Kridsada Sirichaisit]

1. The suicidal rate was increasing in that period, but suicidal rate in men was about 3 times compare to women. The author want to know what is the factors that involve to suicidal rate.

2. Alcohol consumption is the potential factor for suicide. In this paper 20 year alcohol consumption has significant more than 15 year. Alcohol may be cause direct and indirect effect to suicide and men population have higher rate of alcohol consumption than female that can explain why male suicidal rate was higher than the other.

3. The north and northeast regions had highest prevalence of suicide that accordingly to some factor like a alcohol consumption and this is the relation between spatial data and potential factor to suicidal rate.

[Pacharapol Withayasakpunt]

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

– Within 2005 to 2014, the suicide rates in Thailand had been shockingly numbered at around 3,600 to 4,000 people every year. Also, the suicide rates had generally been rising since 2011, at more than 6 suicides per 100,000 inhabitants.

2. Pick one potential risk factor mentioned in the paper and explain how the variable can contribute to the suicide rate?

– FEMH (Female as the head of the household), or when look conversely, male as the head of the household is risk factor.
– After AGE60 (age > 60) and DIV (divorce), this can easily be the third biggest risk factor.
– When male counterpart is the head, the female are more prone to abuse by male; but not as much vice versa. This contributes to overall happiness.

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

– On the epidemiology itself, statistical modeling can identify risk factors, for example, with multiple linear regression.
– On the spatial aspect, this study in confined to a target population, in a specific time period. However, it didn’t tell much about clustering or hotspots.

[Jarunee Siengsanan-Lamont]

1. The author aimed to investigate known economic and social factors impacting the suicide problem in order to identify the risk factors of the suicide rate in Thailand.

2. The Age60+ factor showed a positive correlation with the suicide rate. The estimated coefficient of Age60+ in the model1 is 0.704. Thus, the suicide rate will increase 0.704 for a 1% increase of the Age60+ factor.

3. The statistic models estimate relationships of the dependent variable (suicide rate) and independent variables (economic and social factors). As the independent variable was classified by provinces, the models indicated that there were the suicide rates of these provinces are varied by these independent variables.

[Saravalee Suphakarn]

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

The study used the data during 2011 because it is only year that alcohol data consumption available and the suicide rate has generally been rising since this year.

2. Each of students picks one potential risk factor mentioned in the paper and explains how the variable can contribute to the suicide rate?

Divorce rate is one of the potential risk factor that had coefficient about 0.505 and 0.670 with significant at 0.01 level from the model 1 and the model 2, respectively. Suffering from the divorce cause from feel alone, depression, stress. In addition psychological problem, divorced person also face with financial problem and social blaming especially in traditional society like Thailand. As the author mention, suicide often occur when individuals feel they do not belong to a family, community, or society and thus lack social integration. Divorced person is high probability to feel like that and risk to suicide.

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

The statistical modeling prove the hypothesis in epidemiology and spatial, demonstrate the relation between various factors and the effect in term of quantitative data. The result of the study point out socioeconomic factors that should be concerns in Thailand and also be used to plan, improve, correct policy or any relative law to reduce suicide rate in Thailand.

[John Robert Medina]

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

Aside from the continued increase of suicide rates over the past decade, studying the influence of economic and social factors on suicide rates had caught the interest of scholars. In addition, there is paucity of local literatures that investigated suicide in Thailand. Given that Thailand has a different context from developed countries, where there are many published studies on suicides, this reason, along with the aforementioned reasons, prompted the author to investigate the suicide problem.

2. Each of students picks one potential risk factor mentioned in the paper and explains how the variable can contribute to the suicide rate?
Regression analysis showed that income is a significant regressor in the first model. In the first model, 60.9% of the variability of the regressand is attributable to the included independent variables. Controlling for the effects of other independent variables, suicide rate increases by 0.010 unit for every unit increase in income.

3. How statistical modeling can contribute to investigate the epidemiology and spatial aspects of Thai suicide problem?
Statistical modelling allows us to understand how social and economic factors drove the variation in our outcome of interest, which is Thai suicide problem. Hence, we obtain which of the many factors tends to affect the latter more within some degree of certainty.

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