Tippa Wongstitwilairoong

[Tippa Wongstitwilairoong]

Dear Khun Boonyarat,

Thanks to your guidance, the code is now operating flawlessly. I spent two days searching the internet for a solution to no avail, and prior to this, I had no prior experience with R, and I encountered numerous difficulties while using it in this class when original code isn’t working. Once again, I want to extend my heartfelt thanks. If you hadn’t shared the code in the picture, I would have been unable to resolve these issues. You’ve made a positive difference in my life. 🙂 🙂

Dear Aj,

During the class presentation in web, I noticed that your code remained identical to the original version, yet it did not produce any errors and ran smoothly. Could you please explain why this is the case?

[Tippa Wongstitwilairoong]

Dear Aj, and Boonyarat,

When I run R code, I found problen in exceedance probability part:

> #####################################################
> # Exceedance probability
> #####################################################
> exc1 <- sapply(mod.eco.reg$marginals.fitted.values,FUN = function(marg){1 – inla.pmarginal(q = 1, marginal = marg)})
> summary(exc1)
Length Class Mode
0 list list
> exc1.cutoff <- c(0, 0.8, 0.9, 0.95, 1)
> cat.exc1<- cut(unlist(exc1),breaks=exc1.cutoff,include.lowest=TRUE)
Error in cut.default(unlist(exc1), breaks = exc1.cutoff, include.lowest = TRUE) :
‘x’ must be numeric
> maps.cat.exc1 <- data.frame(ID=data.suicides$ID, cat.exc1=cat.exc1)
Error in data.frame(ID = data.suicides$ID, cat.exc1 = cat.exc1) :
object ‘cat.exc1’ not found
> data.boroughs <- attr(london.gen, “data”)
> attr(london.gen, “data”) <- merge(data.boroughs, maps.cat.exc1, by=”ID”)
Error in h(simpleError(msg, call)) :
error in evaluating the argument ‘y’ in selecting a method for function ‘merge’: object ‘maps.cat.exc1’ not found
> #spplot(obj=london.gen, zcol= “cat.exc1″, main=list(label=”Exceedance”,cex=1), col.regions=gray(seq(0.9,0.1,length=4)))

Then I run continue but in last lain after combine plots: it show “Error in [.data.frame(obj@data, zcol) : undefined columns selected”

So, It isn’t showed plots. How to solve this problem?

[Tippa Wongstitwilairoong]

Question 1: Why was the author interested in investigating the suicide problem in Thailand during the time?
The author’s interest in investigating the suicide problem in Thailand was driven by the aim to identify and comprehend the determinants of suicide rates in the country. The research delves into several potential determinants, such as socio-economic factors, mental health indicators, cultural influences, and other variables, to gain a comprehensive understanding of the factors contributing to suicide prevalence in Thailand. The insights from this study can be invaluable for the government in formulating and implementing effective strategies to tackle the issue of suicide rates and enhance overall mental well-being in the nation.

Question2: Each of students picks one potential risk factor mentioned in the paper and explains how the variable can contribute to the suicide rate?
I want to emphasize a significant risk factor strongly associated with suicide rates, which is divorce. The linear regression analysis revealed estimated coefficients of 0.508 and 0.670 in model 1 and 2, respectively, indicating that as the percentage of divorce increases, there is a corresponding rise in suicide rates. The emotional and psychological stress that accompanies divorce can contribute to feelings of hopelessness and despair, leading to a higher likelihood of suicidal ideation and attempts. If individuals and family members facing divorce are left untreated or their mental health disorders are poorly managed, such as depression, anxiety, or bipolar disorder, they may experience overwhelming feelings of hopelessness and despair, which further increase the risk of suicide.

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

Statistical modeling serves as a significant tool in investigating the epidemiology and spatial aspects of the Thai suicide problem through two main avenues:
1. Identifying Risk Factors: By analyzing extensive datasets, statistical models can effectively identify and quantify risk factors associated with suicide in Thailand. This paper explores various variables such as age 60+, FEMH, ALR 20, INCOME, etc., providing valuable insights into the key factors contributing to the prevalence of suicide.
2. Multivariate Analysis: Utilizing multivariate statistical methods, researchers can comprehensively assess the joint effects of multiple risk factors on suicide rates, as seen in this paper. This approach offers a better understanding of the intricate interplay between different variables and their combined influence on suicidal behavior.
In summary, statistical modeling offers a powerful and comprehensive approach to investigate the epidemiology and spatial aspects of the Thai suicide problem, shedding light on the underlying factors contributing to suicide rates.

[Tippa Wongstitwilairoong]

Question 1: There are several possible reasons why locations have not been incorporated as much as other components in epidemiological research:
1. Data availability and quality pose challenges: Collecting location-specific data is often difficult and expensive. In many countries or regions, comprehensive databases or surveillance systems may not exist, leading to a lack of reliable location-specific information for researchers to utilize.
2. Privacy and confidentiality concerns arise: Location-based data typically involves geospatial information, raising privacy concerns. Researchers must ensure that individuals’ identities are protected when using such data, a complex process requiring careful handling.
3. Resource constraints hinder incorporation: Research at a geographical level demands additional resources, including specialized expertise, geographic information systems (GIS) tools, and funding. Many studies may lack the necessary resources to include location-specific analyses.
4. Methodological complexities present challenges: Analyzing data at a geographic level introduces complexities in study design and statistical analysis. Researchers must consider spatial autocorrelation, clustering, and other geographic-specific factors, deterring some from incorporating location in their research.
5. Lack of awareness limits adoption: Some researchers may not fully understand the potential benefits of incorporating spatial aspects into epidemiological research. They may be more comfortable with traditional epidemiological methods, unaware of how spatial information can enhance their studies.
Now, let’s discuss how spatial epidemiology can be considered an interdisciplinary science because it integrates various fields to understand disease patterns better and improve public health outcomes:
1. It involves geography, studying diseases in relation to geographic locations, population distributions, and environmental factors.
2. Collaboration with public health facilitates the implementation of targeted interventions and more efficient resource allocation.
3. Environmental sciences contribute valuable insights into how environmental exposures may contribute to disease patterns.
4. Computer science and statistics play a vital role in developing robust models and improving data analysis techniques.
5. Urban planning aids in identifying areas with higher disease burdens and designing healthier living environments.
In conclusion, spatial epidemiology represents an interdisciplinary approach, leveraging expertise from multiple fields to comprehend disease spatial patterns, identify risk factors, and design effective interventions for enhancing public health outcomes.

Question 2:It is widely recognized that the place where an individual lives or works should be considered as a potential disease determinant due to several reasons:
1. Environmental Exposures: Different geographic locations have distinct environmental characteristics, such as air quality, water sources, and exposure to pollutants or toxins. These environmental factors can directly influence health outcomes. For example, living in an area with high air pollution can increase the risk of respiratory diseases.
2. Socioeconomic Factors: Place of residence often correlates with socioeconomic status, which, in turn, affects access to healthcare, education, and economic opportunities. Socioeconomic disparities can contribute to differences in disease prevalence and outcomes. For instance, individuals living in impoverished neighborhoods may face higher rates of chronic diseases due to limited access to healthcare and healthy food options.
3. Vector-Borne Diseases: The geographical distribution of certain diseases is closely linked to the presence of disease-carrying vectors, such as mosquitoes and ticks. Geographic location plays a crucial role in determining the risk of diseases like malaria, dengue fever, and Lyme disease.
4. Climate and Weather: Climate and weather patterns can impact disease transmission and prevalence. For example, warmer temperatures and increased humidity can lead to the proliferation of certain pathogens, affecting disease patterns.
5. Access to Healthcare Facilities: The availability and proximity of healthcare facilities can significantly influence an individual’s access to medical services. Living in a remote or underserved area might result in delayed diagnosis and treatment, impacting health outcomes.
6. Social Networks and Behaviors: Geographic location can influence an individual’s social networks and behaviors, which, in turn, affect health. For example, living in an area with a high prevalence of smoking may increase the likelihood of individuals engaging in smoking behavior.
Considering the place where an individual lives or works as a potential disease determinant allows epidemiologists to identify spatial patterns, explore underlying factors contributing to disease disparities, and develop targeted interventions to improve public health in specific regions or communities.

[Tippa Wongstitwilairoong]

Dear Aj,

I try to search solution for install “INLA” package in R by use code as below:
install.packages(“INLA”,repos=c(getOption(“repos”),INLA=”https://inla.r-inla-download.org/R/stable&#8221;), dep=TRUE)
The installation process took some time, but I am pleased to inform you that I was able to successfully download the INLA packages. Now, I can run R code to display figure 6.5 of the assignment.

Best regards,
Tippa Wongstitwilairoong

[Tippa Wongstitwilairoong]

Question 1: There are several possible reasons why locations have not been incorporated as much as other components in epidemiological research:
1. Data availability and quality pose challenges: Collecting location-specific data is often difficult and expensive. In many countries or regions, comprehensive databases or surveillance systems may not exist, leading to a lack of reliable location-specific information for researchers to utilize.
2. Privacy and confidentiality concerns arise: Location-based data typically involves geospatial information, raising privacy concerns. Researchers must ensure that individuals’ identities are protected when using such data, a complex process requiring careful handling.
3. Resource constraints hinder incorporation: Research at a geographical level demands additional resources, including specialized expertise, geographic information systems (GIS) tools, and funding. Many studies may lack the necessary resources to include location-specific analyses.
4. Methodological complexities present challenges: Analyzing data at a geographic level introduces complexities in study design and statistical analysis. Researchers must consider spatial autocorrelation, clustering, and other geographic-specific factors, deterring some from incorporating location in their research.
5. Lack of awareness limits adoption: Some researchers may not fully understand the potential benefits of incorporating spatial aspects into epidemiological research. They may be more comfortable with traditional epidemiological methods, unaware of how spatial information can enhance their studies.
Now, let’s discuss how spatial epidemiology can be considered an interdisciplinary science because it integrates various fields to understand disease patterns better and improve public health outcomes:
1. It involves geography, studying diseases in relation to geographic locations, population distributions, and environmental factors.
2. Collaboration with public health facilitates the implementation of targeted interventions and more efficient resource allocation.
3. Environmental sciences contribute valuable insights into how environmental exposures may contribute to disease patterns.
4. Computer science and statistics play a vital role in developing robust models and improving data analysis techniques.
5. Urban planning aids in identifying areas with higher disease burdens and designing healthier living environments.
In conclusion, spatial epidemiology represents an interdisciplinary approach, leveraging expertise from multiple fields to comprehend disease spatial patterns, identify risk factors, and design effective interventions for enhancing public health outcomes.

Question 2:It is widely recognized that the place where an individual lives or works should be considered as a potential disease determinant due to several reasons:
1. Environmental Exposures: Different geographic locations have distinct environmental characteristics, such as air quality, water sources, and exposure to pollutants or toxins. These environmental factors can directly influence health outcomes. For example, living in an area with high air pollution can increase the risk of respiratory diseases.
2. Socioeconomic Factors: Place of residence often correlates with socioeconomic status, which, in turn, affects access to healthcare, education, and economic opportunities. Socioeconomic disparities can contribute to differences in disease prevalence and outcomes. For instance, individuals living in impoverished neighborhoods may face higher rates of chronic diseases due to limited access to healthcare and healthy food options.
3. Vector-Borne Diseases: The geographical distribution of certain diseases is closely linked to the presence of disease-carrying vectors, such as mosquitoes and ticks. Geographic location plays a crucial role in determining the risk of diseases like malaria, dengue fever, and Lyme disease.
4. Climate and Weather: Climate and weather patterns can impact disease transmission and prevalence. For example, warmer temperatures and increased humidity can lead to the proliferation of certain pathogens, affecting disease patterns.
5. Access to Healthcare Facilities: The availability and proximity of healthcare facilities can significantly influence an individual’s access to medical services. Living in a remote or underserved area might result in delayed diagnosis and treatment, impacting health outcomes.
6. Social Networks and Behaviors: Geographic location can influence an individual’s social networks and behaviors, which, in turn, affect health. For example, living in an area with a high prevalence of smoking may increase the likelihood of individuals engaging in smoking behavior.
Considering the place where an individual lives or works as a potential disease determinant allows epidemiologists to identify spatial patterns, explore underlying factors contributing to disease disparities, and develop targeted interventions to improve public health in specific regions or communities.

[Tippa Wongstitwilairoong]

I concur with Aj’s viewpoint. I retrieve updated share summary data from the Ministry of Public Health and the National Statistics Office website. In order to analyze the spatial distribution of diseases, it is necessary to aggregate this data with various other sources using GIS.

[Tippa Wongstitwilairoong]

Same like me, no Print Compose in my version. I used print layout to work on practical# 3 and 4 to work on it.

[Tippa Wongstitwilairoong]

I also use the Malaria incidence when I saw morbidity. I remember in previous practical train us to calculate incidence not morbidity. Some pictures in practical pdf file are not match with text that need to update ka.

[Tippa Wongstitwilairoong]

I used intersection method. In intersection parameter at advanced parameter, we can save outcome of intersection to save shp file. I save for keep outcome of intersection results for future analysis.

[Tippa Wongstitwilairoong]

Dear Aj,

New version of QGIS that I downloaded and installed has slightly different toolbar and property options compared to what is mentioned in the practical guide. However, I have managed to work through the guide successfully by adapting to the changes.

Additionally, I adjusted the map scale to 5,500,000, which worked well for both maps. While reviewing page 21, I have a comment regarding the legend for Pf and Pv. I believe it would be beneficial to have different scales for each legend.

Regards,

Tippa Wongstitwilairoong

[Tippa Wongstitwilairoong]

Dear Petcharat,
I encountered a similar problem as you did. However, I managed to resolve it by sending a ZIP file containing only two specific files: “Tippa_week1_Assignment.doc” and “Westernmalaria_Tippa_practical_1.qgis” within the “Tippa_week1_projectfile” folder. I made sure to remove all other files such as the Raster folder, table folder, temple folder, and vector folder from the project file before submitting it. This approach seemed to work, and I believe the issue may have been related to the large size of the initial submission with all the additional folders. I suggest you try the same method, as it might work for you as well.

Tippa

[Tippa Wongstitwilairoong]

In order to resolve this issue, I made some adjustments to my submission. I removed the base folder, which included the Raster folder, table folder, temple folder, and vector folder in project folder. Instead, I submitted only the files “Tippa_week1_Assignment.doc” and “Westernmalaria_Tippa_practical_1.qgis.” This modification led to a smaller ZIP file size. I can confirm that the submission process has been successfully completed.

[Tippa Wongstitwilairoong]

In order to resolve this issue, I made some adjustments to my submission. I removed the base folder, which included the Raster folder, table folder, temple folder, and vector folder. Instead, I submitted only the files “Tippa_week1_Assignment.doc” and “Westernmalaria_Tippa_practical_1.qgis.” This modification led to a smaller ZIP file size. I can confirm that the submission process has been successfully completed.

[Tippa Wongstitwilairoong]

I have also submitted in 2.4 QGIS Practical 1. However, I noticed that the left-hand side menu for 2.4 QGIS Practical 1 does not display a green checkmark to indicate completion, unlike the other menus in 2.1, 2.2, 2.3, 2.5, and 2.6.

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