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Because the suicide rate is generally rising in Thailand, the author would like to find the economic and social factors which relate to the suicide rate in different regions.
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Females as head of family was one factor related to suicide rate. A culture where males dominate led to the suffering of women from abusive husbands and depression. In this situation, the happiness among women decreased and led to suicide. In Thailand the culture of male domination might not happen in gender equality areas; however, this might still happen in some societies. Therefore, investigating areas with different numbers of females as the head of family, could reveal negative association toward the suicide rate. Areas which have a greater number of female family leaders might have less suicides.
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Suicide is not a contagious disease, and it seems to be an individual mental status problem. However, there are possible external factors which could alter mental status and push people into suicide such as economic status, social and culture. Living in certain areas, culture and status where people are immersed with factors contributing to potential suicide, might lead to higher suicide rate.
The proper modelling analysis is the way to alter data to reliable evidence which will benefit toward the environment investigation. From the data previous, the statistical model would help to find the relationship between factors and suicide rate, and also quantify the relationship to predict the suicide rate. After realizing the factors from the model, prioritizing areas could be performed to tackle the suicide problem from originating causes in different areas. -
Availability of location data might be the main burden of implementing location in analysis. Healthcare unit locations would be available from the reimbursement system. However, individual historical location might not have been recorded which would not be enough in some investigations. For example, finding location of work related to lung cancer. The work location would not be simple to find in the health records and it also consumes resources when conducting the interviews. Even though, location of work is linked with social security systems, data accessibility and preciseness would have to be considered.
Secondly, data privacy would reduce the ability to use the specific location. Location might be able to specify group of people which possibly reveal their personal information. Therefore, location might have to be taken off, especially if the specifying causes the embarrassment and traumatization.Location related health needs more than healthcare knowledge to identify common happening in that location, which possibly leads to the interesting situation. Accumulation of knowledge from interdisciplinary is necessary such as Economic to differentiate the rich and poor areas, Chemical science to explain the chemical waste from the factory which might effect to neighboring health and Zoology to identify the animal health and behavior which is plausible to disease pathogens.
Locations related to regular exposure are beneficially recorded to link with plausible cause of diseases. People are having most of their regular routine at home and work places. Therefore, environment and other people who they regularly contact with might be the factors activating diseases both contagious and chronic. For example, people who have locations of work near to electrical pylons for long periods, are more likely to develop cancer than the ones who aren’t. Living under the same house with scabies infested people might increase the chance of receiving the mite.
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Availability of location data might be the main burden of implementing location in analysis. Healthcare unit locations would be available from the reimbursement system. However, individual historical location might not have been recorded which would not be enough in some investigations. For example, finding location of work related to lung cancer. The work location would not be simple to find in the health records and it also consumes resources when conducting the interviews. Even though, location of work is linked with social security systems, data accessibility and preciseness would have to be considered.
Secondly, data privacy would reduce the ability to use the specific location. Location might be able to specify group of people which possibly reveal their personal information. Therefore, location might have to be taken off, especially if the specifying causes the embarrassment and traumatization.Location related health needs more than healthcare knowledge to identify common happening in that location, which possibly leads to the interesting situation. Accumulation of knowledge from interdisciplinary is necessary such as Economic to differentiate the rich and poor areas, Chemical science to explain the chemical waste from the factory which might effect to neighboring health and Zoology to identify the animal health and behavior which is plausible to disease pathogens.
Locations related to regular exposure are beneficially recorded to link with plausible cause of diseases. People are having most of their regular routine at home and work places. Therefore, environment and other people who they regularly contact with might be the factors activating diseases both contagious and chronic. For example, people who have locations of work near to electrical pylons for long periods, are more likely to develop cancer than the ones who aren’t. Living under the same house with scabies infested people might increase the chance of receiving the mite.
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This smartphone HR and RR rate measurement will be useful for personal health monitoring in general. I am working around tourist spots and have seen tourists seeking medical devices during travelling such as bp measuring machine. Smartphone vital sign measurement would suit travelers so that they don’t have to buy one for the short term use.
Apart from human, I am thinking of using this kind of technology in animal and pet health. Because the pet couldn’t talk, some of the owner would probably love to know and take care of their pet health status. Moreover, without intrusive methods, it could be used to measure the observation of animals in national parks as well. -
I am thinking about selection bias discussion 2, especially the characteristics of people included in this research which might not represent people who were not experienced on using technology when they started.
To extend the applicability of this kind of research toward developing countries, where general people are probably new to telemedicine, and reduce the selection toward the willing and experienced users. This research could be improved by collecting other concern characteristics such as past experiences with technology, by possibly collecting number and type of gadgets owned or years of using technology.
Thank you, your presentation was great and interesting. -
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I do agree with K.Zarni on including the healthcare capacity as another parameter because it would be flexible to the situation when the country could have to weigh up between the ability to control the case and traveling. For topic 2, If I was the policy maker I would love to implement the risk assessment model to support policy design. It would be trustable and transparent if the decision will be evidence based and able to be traced back if needed. Moreover, Using models could reduce personal bias which possibly will create better, overall applicable policy.
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I was late for the class and missed the first part of seminar.🙏 The selected paper was interesting and gave the idea of factor investigation.
For topic 1, I think some human behavior and general routine could potentially increase the risk of exposure to mosquitos and differentiate the infection rate among each factor. For example, male might have occupations which involve travel outdoor with more risk to be bitten compared to women. Population in Lakeshores area undertook fishing as their main activity which expanded the time spent near to the water and caused higher infection rate.
Moreover, economic status would allow the ability to protect themself from the mosquito. Therefore, the area with poor quality of housing and lower economic status(Lakeshores area was mentioned in research paper) might have less ability of self protection resulting in higher infection rate.
The same behavior and economic status which occurred with people under the same characteristics and demographics potentially differentiated the infection rate.For topic 2, I looked at figure 1 comparing the prevalence in Dry season 2019 and 2020. Even though the seasons matched, the infection rates weren’t similar. Therefore, apart from the climate and level of natural water, there might be other factors related to the high infected rate in dry season 2019. Environment and behavior might be the cause of high rate such as people gathering more in dry season, the consistency of self protection methods which possibly weren’t strengthened enough and personal reserve ponds during dry season.
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I am new to GIS topic and also looking forward to the next class. Learning about QGIS is exciting for me.
I also think it took time to find the actual icons in the software compared to the pdf tutorial but there are no problems for me. I do actually like the pdf tutorial as a future record like what Aj.Chawarat has mentioned. -
Thank you for sharing, in my opinion your CRF is well managed and designed for users. Most questions are in closed-ended format and open-ended questions are convenient to fill out with unit preference. I do like the idea of pre-vaccination and follow up check box in the laboratory test section because we can reuse this form again.
As an amateur on CRF design, I have a few comments on the section, which took some time for me to understand the type of necessary answer and I felt unsure about the purpose of the question.
In the post vaccination reaction section, it could be that there are a few reactions occurring with different severity. I would suggest to have checkboxes for general, post vaccination reaction such as swelling, pain and redness. This would help to guide the data collector, reduce hand writing errors and provide enough space to record details for any reactions which occurred.
As same as Stage in chronic illness status, checkboxes for the status such as current and resolved can be prepared.
Lastly, there are a few questions mentioning illness status which probably create redundancy and confusion on the preference answer. Chronic illness in the screening form, Medical conditions and Chronic medical condition in the medical history and physical examination have some repeated questions which possibly can be merged to reduce the time spent on CRF. My idea is to have one chronic medical condition section, and the medical condition (current and not chronic) could be notified in the physical examination part.
Thank you. -
I don’t have experience with clinical research involving data management processes. From the lectures, there are many problems which could affect data quality and regulation compliance from the process of data acquisition to the process of data entry. That is, the regulation or problem including entry errors, system authentication, data acquisition plan etc. Controlling the data quality might be difficult or impossible by using only human management, capable software is needed with large amounts of data and complex data management. Moreover, using software which had been designed to comply with the information system regulators such as US-FDA, would assist the data managers and also guarantee the data which is produced by trusted systems. I have researched the commercial program on the internet. There are plenty of companies offering the data management software including IBM, TrialKit etc. Functionality, compatibility and price are varied. However, I am not sure on the situation in real practice and looking forward to other friends’ comments about their experience. 🙂
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Increase in productivity of work processes is the benefit after the standards are implemented. By applying the standards, data will have clear format with unique code of variables. These would save the time on data transferring, because researchers don’t have to reassess a new clinical record format each trial, and data might be automatically transferred via the code assigned. Moreover, this would reduce error on interpretation of the record which can sometimes have ambiguous collections.
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Comment above did mentioned many good points to improve this CRF. I would suggest amending the open ended question at Physical examination on Abnormal specifying. The blank spaces provided might not be enough for people who have many conditions and the hand writing might be hard to read if the condition is complicated.
Therefore, I suggest the checkboxes differentiate physical body parts such as HEENT, Cardiovascular and Abdomen.
Moreover, if there are other variables that need adding in the screening form, the checkboxes, for eligibility of the enrollment, could be added to conclude the screening results before entering to vaccination record. -
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From the discussion previous, I didn’t really have a plan on data management in my medicinal formulation research. Even though it didn’t involve personal data or interviews, a proper plan would have been better. If I could turn back the time, there would be a few steps which I could add to my data management to enhance work quality.
Firstly, better planning on data design and variables collected, part of the experiment was composed with different variables. Without a plan for variables, I sometimes figured the variables out in the morning before the experiment started which made the process slow and some variables were forgotten.
Secondly, I should have created a proper recording format. Overall, it was hard to accumulate fractional data and transfer this to the electronic system. The experiment would have been better if I had a standard format for the study.
Thirdly, Database Accessing – my lab partner and I didn’t have a database for sharing finished records. It was slow to wait for each other to edit and send data forward/backward. Moreover, if we wanted to investigate the result, data between researchers was fractional which made it hard to trace back the data. Therefore, a shared database should had been set and authorized for two researchers.
Lastly – data entry and quality control. During that time I didn’t complete checkins while doing the data entry between two researchers. It might have been more precise if we cross checked each others data entry to confirm the accuracy of recorded data.
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I don’t have experience of clinical or public health data collection. However, during my bachelor degree study, I did have to collect data for medical formulation ratio in drug formulation experiments which didn’t involve personal data, yet some processes related to the study of data management.
1. The medical formulation involved repeated experiments to find the best formulation to carry medicinal substances. Therefore; there were many names of substances and numbers, of which some numbers were decimals and therefore very small.
2. It was the primary data collection because I had to perform the experiment and record the results for this purpose of study.
3. Paper record was used as the data collection method.
4. I have re-considered my data processing, and there are three major issues where I could have applied better management.
Firstly, I didn’t have proper records of all the data, the number and name of substances were quickly recorded by hand during the experiment. This was a problem when I had to transfer data to the electronic file for the analysis. Hand written records were hard to be read and data was randomly recorded on pieces of paper without a clear format. Therefore, it took time to transfer the precise data. Moreover, if I had lost the paper, this would result in the need to repeat the experiment.
Secondly, I was the only person who recorded and transferred data so it could have had mistakes without checks from others.
Lastly, my lab partner and I didn’t set up a sharing database, we collected our own parts on our individual laptops. Therefore, there was a risk of misplaced data and we needed to keep sending data and waiting for each other to communicate. -
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Below is the link to my wrap up assignment.
Link to my infographicThank you
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From the journal, there are many interpretations of results where I realized I have missed the concepts of the statistical tests. Below are some interpretations which I think are significant to keep in mind and use further.
Many times, previously, I have heard or interpreted P ≤ 0.05 as the chance of error in rejecting the test hypothesis is 5%. However, from no. 10 clarification page 342, there is up to 5% probability of how often the researcher might reject it, not the false positive chance. Therefore, the result shouldn’t be claimed as only 5% chances of error.
From no. 13 page 342, it’s important to remind the belonging of statistical significance toward the test, not the effect or population being studied. Statistical significance is only the description of the P value in particular tests and shouldn’t be used to claim as evidence of found or not found effect in studied phenomena. This is crucial for understanding result conflicts between journals published which studied the same effect or population, whilst further studies could illustrate possible differences in P value.
Lastly, I have never put my attention to the interpretation of 95%cl until reading no. 19 page 343. The two number interval doesn’t represent a 95% chance of containing the true effect size, in contrast, finding the 95% probability of containing the true value can be completed by other computation including prior distribution. The term 95% cl can be acclaimed as the 95% chance of confidence interval computed from many studies containing the true size. This shows the reliability of the estimation procedure instead of the specific interval. Therefore, the 95% confident interval illustrated, from particular study, might or might not have the real population value.
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Efficacy is the effect outcome which is from the measurement under controlled or ideal conditions. The efficacy study, such as Randomized control trials, will be done under strict inclusion/exclusion criteria and intervention. The result from efficacy measurement might be limited to generalizations depending on the population included in the trial. However, bias in this type of research is controlled and the efficacy outcome could answer whether the intervention worked or not?
Effectiveness is the effect outcome which is from the measurement under real life conditions or in real clinical practice. Observational studies are able to measure effectiveness of the intervention.The effectiveness study will be done in more flexible environments compared to the efficacy study. Therefore, there is possibly bias, confounding and interfering with the results. However, heterogeneity of the population in the study can generate broader result application and answer the question “Does the intervention benefit the patient?”
Efficiency is the effect outcome which is from the measurement of the interventions’ benefit along with the cost consideration. Cost-benefit and cost effectiveness analysis are examples of studies where efficiency is measured. The cost consideration could be in terms of time, energy, or money. For instance, if there are two medicines with the same effectiveness, the one which is more costly could be considered less efficient in comparison with the one which is cheaper. Measuring efficiency could answer the question “Does the intervention work in the most economical way?”
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Dear, K.Tippa.
Yours ‘exc1’ has no data inside.
> summary(exc1)
Length Class Mode
0 list listIf it was the same issue which the ‘exc1’ contains no data from the NA of mod.eco.reg$marginals.fitted.values.
I did added control.compute=list(return.marginals.predictor=TRUE) and control.predictor=list(compute=TRUE) into inla() function.
(under Model with covariates section, what i did is in the photo below)
Because by default,mod.eco.reg$marginals.fitted.values wouldn’t be produced without the command.Hope it work for yours.
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