Jarunee Siengsanan-Lamont

[Jarunee Siengsanan-Lamont]

Sawasdee ka,

Have you emailed the meeting link? or how can I join the meeting tomorrow?
thank you very much in advance, Jar

[Jarunee Siengsanan-Lamont]

Thank you very much for the reply. Could you please suggest material (book or youtube) that I could self-learning? I would like to apply them to my data.

[Jarunee Siengsanan-Lamont]

my apologies: exceedance prob is prob of observed/ expected.

[Jarunee Siengsanan-Lamont]

Hi,

From the website: https://rpubs.com/quarcs-lab/spatial-autocorrelation

“Getis-Ord approach
The Getis-Ord Gi Statistic looks at neighbours within a defined proximity to identify where either high or low values cluster spatially.”

which is to identify hotspots among neighbourhoods. For exceedance probability, it implies probability (of mean versus estimate) above the threshold (c).

“6.6 Exceedance probabilities
We can also calculate the probabilities of relative risk estimates being greater than a given threshold value. These probabilities are called exceedance probabilities and are useful to assess unusual elevation of disease risk.”
from https://www.paulamoraga.com/book-geospatial/sec-arealdataexamplespatial.html#exceedance-probabilities

hope this help

[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.

[Jarunee Siengsanan-Lamont]

1. Two main reasons that locations have not been incorporated in epidemiological research compared to other components are 1) availability of the health data associated with locations; 2) quality of the data, including accuracy and validity. However, as the computational and GIS technologies evolved rapidly. Data availability and quality have been gradually increased in the past decades. Spatial epidemiology incorporates spatial statistics, socioeconomy, anthropology, geography, biology and genetics to explain complex relationships between environmental and health. Thus, spatial epidemiology is an interdisciplinary science.

2. A place where individual lives or works represents relationships of the individual and environments which could be used as a potential disease determinant. For example, a high-income individual is likely to live and work in medium to high-socioeconomic areas and have different lifestyles compared to a low-income individual. Compared to the low socioeconomic areas, medium to high socioeconomic areas would have good locations closer to all amenities, safer and better sanitation. Thus, the potential health risks of these two individuals are likely to be different.

[Author]

Posts