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I am a Public Health Technical officer at the Division of Vector Borne Diseases (DVD). I take responsibility for the vector surveillance and vector control program in Thailand, especially in Dengue and other Aedes-borne diseases. Mostly, my job is usually about surveillance data so data cleaning, data preparation, and applied statistics have been used frequctly.
One of the most stat I have used is Probit analysis in SPSS software. I used this to calculate the quality of Public health sprayers by sizing their droplet size.
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Case-Fatality rate
Definition
Generally, the case fatality ratio (CFR) is the proportion of individuals diagnosed with a disease who die from that disease and is, therefore, a measure of severity among the detected cases.How to calculate?
This metric is calculated by dividing the total number of deaths from the disease by the total number of infected individuals; hence, in contrast to the CFR, the IFR incorporates asymptomatic and undiagnosed infections as well as reported cases.Main usefulness.
CFR can be used to assess the deadliness of an outbreak and evaluate any implemented public health measures are generally obtained at the end of an outbreak after all cases have been resolved. -
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. -
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1. The main reason that location in epidemiology research has not been incorporated as much is the lack of knowledge about how to conduct GIS-related epidemiology research and how to used GIS software. Researchers need to know the concept of GIS-related epidemiology, the GIS software, and the well-design methodology to collect, analyze, and visualized the spatial data appropriately.
Spatial epidemiology is widely considered an interdisciplinary science because it has been used to incorporates other aspects such as socio-economy, biology, and public health, etc. to find the solution to the problem.
2. The crucial factor that influences the outbreak of infectious diseases is geographical data. Some diseases occur in specific places. If the geographical data haven’t been used, the diseases control measures will be less effective. For example, if the movement of dengue patients in communities is tracked, the control measures will be operating at the place that has a risk of exposure to infected mosquitoes.
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According to http://www.qgis.org, the standalone version of QGIS for Windows OS is 3.18 so just make sure that you download the 64bit version if you ram over 4GB. The program interface has a bit different but still works perfectly for our practice.
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This week, I have learned about research integrity and the case study was interesting. I learned about self-plagiarism which is new for me to know that and it hard to distinguished. Moreover, I got an idea for double publication. However, it is dependent on your circumstances and the role of the journal.
On the other hand, Authorship is so important and it would be clarified at the beginning of the research by conducting the discussion with the team. -
I earned a lot about personal information and ethical issue. From the webinar, we agree that digital literacy is important for enforcing PDPA.
Moreover, I have known the definition of Personally identifiable information (PII) in terms of privacy objective and security risk. Although PDPA has been postponed, the government should prepare and follow the instructions of PDPA by using Record of Processing Activity.
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First, the meeting was fruitful. I have learned a lot about telemedicine in terms of ethical issues.
For the benefit of telemedicine, we agree that telemedicine is reduced cost in terms of the indirect budget such as travel cost and reduced waiting line in the hospital. However, telemedicine has several risks to concern about such as patient data breaches, reimbursement issues which is a new knowledge for me, and misdiagnosis.
In this scenario, the patient will take a risk to make telemedicine in terms of misdiagnosis and not receiving physical examination. Also, the doctor should understand the system and comfortable to use it. Because this system uses the only conversation between doctor and patient, so a doctor should be careful and make the right decision based on the patient information and legislation.
From my point of view, telemedicine should implement only in non-emergency cases or non-severe symptoms only under PDPA and related Act.
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Firstly, the webinar which was about AI and ethics in health was very interesting. The case study was a useful example of AI in healthcare. Moreover, the participants have overwhelmingly ideas so they made the topic discussion smoothly.
However, this AI may not be justified by some participants for particular reasons such as privacy, unemployed situation, and bias of the dataset. I have learned a lot and I think most of the participants are concerned about the privacy, accuracy, and unemployed situation of radiologists in case of replacement of AI. One interesting idea is the equality or accessibility of the AI system. If there are limitations of accessibility, the system would be not standardize nationwide.
To sum up, we also need double-checking by an expert or radiologist to prevent mistakes or errors of the AI.
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First, you are done a good job with your CRF design. It is a good structure and design, clear text, easy to read, and covers all of the variables.
I have some suggestions to improve your CRF. First, demographic data such as DoB, sex, rach will be moved to the visit1 screening section so your CRF will reduce redundancy. Moreover, to rid of more redundancy, you can merge physical examination in visit 1 enrollment to the screening section.
To increase accuracy in the screening section, the medical history will change with Yes/No questions because It is easy to fill up and not spending time reading the text. Also, the decimal should be added in the weight, especially in the body temperature data.
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Having a data standard gives many benefits include:
– Interoperability: the standard data can be shared and analyzed between organizations.
– Drive the strategy: without the same data standard, the appropriate strategy will not practical for disease control.
– Research purpose: the standard data will drive useful research in the country. -
I have experience with the data management process which is about conducting a national application named “TanRabad”.
1. Audit trial/Timestamp: TanRabad is software so audit trial and timestamp are always capturing when the data is changed.
2. User authentication and access control level: TanRabad has authentication and access control which is concerned by public health staff and who is going to use this application will be registered.
3. Edit check and logical check: at first TanRabad face a lot of errors in data entry in TanRabad-Survey [larva survey application] so the researcher team was conducted the edit check process to help potential users edit some values such as the name of the place, location, merging place, etc. Nowadays, TanRabad can rid of the garbage data which is entered inappropriately.
4. Data backup and recovery plan: Data is backup every month and we set a restore point in case the system down and some damage occurred to the database. -
Most projects that I have involved or conducted are surveillance or public health project which is not clinic or lab trial project so the certain step of data management work is skipped.
The step that I have done included:
1. Project Initiation
– Protocol Discussion: one of the most crucial steps that need to be done before conducting the project. In this step, the expert and participant were discussed according to the proposal and maybe this takes more than a one-time meeting until finalized.
– Data design: the variables were indicated.
– Data Acquisition: the tool was chosen. Most of the projects were paper-based.
– Database setup
2. During study conduct: I have done both of data entry process and QC. However, consistency and accuracy were the problems sometimes.
3. During study end:
– Data coding
– Data manipulation and analysis
– Data sharingThe step that I have not done included:
– DMP: I did not study in the clinical project so I didn’t write this.
– External data merging: the project is collecting the primary data so no external data were used.
– Database lock: when the project closes, sometimes we still back to edit the data.If I have a chance to go back to improve my project, I would like to:
– Spending more time in the protocol discussion because well-management and well-plan are the most important step to conduct the quality project.
– DPM: after learning this course, I will try to write DMP if I have a chance to conduct a clinical project.
– Data QC: I will enhance and strengthening this step.
– Set up database security and lock -
1. I am involved in the evaluation procedure called “3-3-1” which is Aedes strategy control in Thailand. This is one of the R2R in my division. I take responsibility for question-related to vector prevention and control.
2. This project collected primary data from the organization which is responsible for Aedes control in the area.
3. The paper-based questionnaires were used as the tool for the data collection process. It included both closed-end and open-end questions.
4. There are certain problems include:
– Methodology: In this case, due to the limited of interviewers and time, the result cannot representative of the whole country but may use in the same environmental areas.
– interviewer bias: some interviewers are not welled-train before the interview so they couldn’t understand some questions and it leads to missing or inaccurate data.
– Handwriting: due to the paper-based questionnaires, the unclear interviewer handwriting leads to misunderstanding the content.
– Data workload: time-consuming on the data entry and data cleaning process to analytic. -
In Thailand, we have both an expert in medical and an expert in IT but we still lack an informatician who is dealing with medical and IT information. The Thai government is now emphasis in digital transformation in the government sector focusing on the infrastructure of IT but lacking the building capacity of informatician and health informatics workforce.
In my experience, to build a health informatics workforce, the policy should include enhancing capacity for both human and IT’s infrastructure.
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If I am a charge of a data set at the national level, I will seek opportunities to share data with neighboring countries especially surveillance data. The surveillance data should be shared and appropriate management to combat the communicable diseases between the country. The aggregate data which hasn’t content the identity information can be shared.
However, the concern aspects are the data standard and infrastructure of IT in each country. if each country sharing the unstructured data, it will hard to consolidate the data. Moreover, the policy of data sharing and management should be a deal between the country.
I think that sharing surveillance data should be the IT policies in the country and regional countries. -
The cost of treatment in the US is higher than in certain developing countries due to medical overcharge. Universal Coverage may not be a solution for the US because it is not fit for all countries. In my opinion, the main factor that keeps medical overcharge in the US. is the capitalist health system which may lead to a disadvantage in the healthcare system. Moreover, the proper policy is crucial for improving the healthcare system.
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I agree that Net Pracharat would bring equality to using the internet nationwide. It helps people in a remote area to access Information easier and make their life more convenient. However, the wi-fi hotspot maybe not enough or a very weak connection so people can use the internet limited.
I also agreed that the article mentioned: “government officials always promote only the pros of their policies and innovation without ever mentioning the cons”. The user would be concerned about this point because the data can track if they haven’t strong data security, the user data will be breached and attacked by the hackers.
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I am not familiar with EMR in the hospital because I live at the national level organization. However, I have experience with digital transformation in my organization.
pros:
1. Track the history: by using a digital record, the activity can be tracked and observed.
2. Provide a quicker service: digital records can assist the process of service quicker than paper-based.
3. Provide more benefit: digital-based records can be used in research and other benefits.
4. Accessing anywhere: the user can access the data anywhere which is useful for analysis in an emergency.cons:
1. Privacy and data security: inappropriately managed the system may lead to data breach and cybercrime.
2. High cost of installing and training
3. People: user needs to well-understand the system before use.
4. Workload: certain systems increased work-load data entry. -
Big data analytics face certain challenges which lead to inappropriate disease management and could not defeat the burden of diseases. This article suggests some solution which is useful to overcome the challenge of big data management. However, I would like to add some suggestions and solutions as below:
1. Missing data: the common error in many systems is missing data. This error can indeed resolve with statistical methods but if the missing data over 10% of the total data, it cannot use statistical methods to solve the problem. To deal with the missing data issue, data standards and clear definitions will be involved in the data collection process.
2. Selection bias: a high volume of data set may lead to bias observations sometimes so to reduce a selection bias, hypotheses will be confirmed with RCT.
3. Data analysis and training: due to the large datasets, a single dataset analysis will be out and not powerful enough, thus the algorithms with multiple testing will be the solution for big data analytics. However, training in informatics, coding, data analysis, or other increasingly relevant skills to users will optimize the use of large volumes datasets.
4. Interpretation and Translational Applicability of Results: designing and defining the dataset will increase the possibility to integrate the between the different systems.
5. Privacy and ethical issue: maintaining security, data encryption, and access control will help protect the people’s identity in the server.
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Most paper-based projects will keep the data in Excel or SPSS format. Physical security is usually used to lock the data for example lock files or computer to keep its securely. This step is acceptable? because of the limitation of IT staff in the organization and they cannot handle many projects at the same times.
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As I mentioned about the limited interviewer and time, we train the interviewer in specific questions but we don’t train the technique for an interview to gather the information. Moreover, next time, we are trying to use google forms in the data collection which is design to collect the data appropriately and easy to manipulate. Peer review and expert reviewing proceed were add to data collection to monitor an error before data entering.
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Definitely, we usually send data which is not included identity or sensitive information, usually aggregate data. Sending the data via email is a kind of data sharing aspect but only a specific time or factor. People who need the information have to send a formal letter to request the data and we send it through an email because we don’t have the open data platform to people who need to use it. Thank you for responding krub.
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I strongly agree that confidentiality is crucial for data sharing. Nevertheless, an appropriate application or data sharing platform should be developed based on standards, confidentiality, and privacy. The wording ” please send me the data via email”. Is this a kind of data sharing? from your IT expertise, what do your opinion about this view?
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Thank you for your good example, as a national level, I had a chance to meeting with other countries who are trying to promote and develop their standard surveillance systems and i found that some of their applications don’t match our perspective and our circumstances so I think to develop a regional surveillance system, we need collaboration and co-shared in terms of resources management and quality control.
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The use of the internet in the nation should be the fundamental equity that Thai people should receive. The government should invest in this matter to push up the opportunity to access the internet by seeking collaboration with the private sector such as True AIS Dtac etc. and expand the cellular network to remote areas.
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