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Dear Jintana,
Your CRF design is quite compact and clean. However, there are several points that I would like to suggest. For the year of birth, you should specify the type year such as B.E.(Buddist) or CE(Chrost). The age section can be designed as an open-ended question for more precise information. It would be better if you add more sections on physical examination to provide more detail. The section for lab test reports should also be involved as it is the major finding of this study. You should also remove the type of vaccination section since this study is single-blind. It is to be noted that some of the questionnaires need to be clarified as single or multiple-choice. Overall, this CRF is quite easy to understand but some of the questionnaires that contribute to the finding of the study are still missing. Following the provided suggestion will surely improve the quality of your CRF. -
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In my opinion, the place holder format of blood pressure should be in the box format as the other variable in the same field. This should be fix to avoid the doubt of speciality that field among the other in filling the information. Even this point is kind of small, the improvement of this could potentially reduce the confusion and time to complete the CRF.
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one example of the benefits of having data standards for clinical research is improvement in the compatibility of the dataset among different studies. Data standards provide a guideline and framework for collecting, formatting, and storing data ensuring that all of the published data is in the same direction. From this, it will be easier to connect
the data between studies and allowing for merging the dataset into a larger sample group which benefits the reliability of the same study and the statistical power for later study. -
I would like to share my experience as data manager on landslide data hub project. The purpose of this project is to create an online data storage system for keeping the data related to landslide disaster such as rainfall, soil type, and geography. The web-based record form was construct according to the list of variable and requirement provided by project manager. Database system was established based on MySQL software to keep all of those data. The data management webpage was also implemented but secretly and can be only used by me. I would say that in this project there was neither perform audit trial nor time stamp sine I never deal with physical document. The authentication and access control level in this project was performed by setting up the new database account with specific database accessibility. More over, the login page was created for security. The edit and logical check were implemented in conditional statement and embedded in the webpage, so some input form will show or hide upon the corresponding choice. The backup plan was based on Simple Recovery Model to reduce size of backup file and the backup was performed every week.
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Based on my experience in data collection/management, I have done many projects related to data analysis and database management that cover most of the steps in the lecture. However, each work did not include all of the processes due to the different specifications of the task. The steps which I never do is Data Quality Control and SAE Reconciliation since I mostly work with secondary data and never work with clinical data.
Talking about the step I have learned that could improve my project, I will raise an example of feedback data gathering described in the previous section. The project contains several steps in data management workflow including, protocol preparation, Data design, Data acquisition, CRF development, DMP, Data Entry, Data analysis, and Study Report. As the amount of feedback was on a small scale, the database-related process is not required. However, there were some problems that occurred after the information-gathering process where some information was not clear and unusable. I would say that the Quality Control step would be a great step to include in the work to cross-check the integrity of the information gathered during the interview and reduce potential errors.
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I would like to share my experience in data collection during the “Human User Interface” study which I enrolled in during my bachelor’s degree. The subject is related to the design of graphic user interfaces (GUI). The data collection was performed during the project development which me and my team need to develop a GUI in mobile application.
Purpose of Data Collection: For the purpose of the data collection, I and my team need to let the user use the application and gather feedback, comments, and suggestion in order to use those feedback to improve the GUI.
Type of Data Collection: The information gathered during this study was considered primary data as the questionnaire of feedback was pre-defined and gathered directly by me and my team.
Method used for data collection: The data is collected through the paper-based approach administrated by the interviewer. The interviewer will introduce the application to the user, let the user test the application, and read out the list of questionnaires for the user. Lastly, the interviewer will fill in the feedback from the user.
Problem occurred during the data collection : During the data collection there were a few problems occurred such as:
– The questionnaire was quite open that lead to diverse types of the answer which is hard for data management
– The interview took some time which lead to the difficulties in appointment
– Many of the users are afraid of getting scammed and refuse to take the survey -
From my topic of interest on Tomato flu, as the limitation of information available on this disease, I instead do the research for effective intervention on HFMD, the origin of Tomato flu. There are two candidate interventions in concern which are vaccination and Educational communication.
For vaccination, though the conservational vaccination for preventing HFMD, FI-EV-A71, is commonly used in several regions, There is a study report(https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4685682/) on the inefficiency of the vaccination on CA16, a major cause of Tomato flu. Besides, Some of the research had reported the development of alternative vaccines using β-propiolactone (BPL) to inactivate coxsackievirus
(https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4130287/), the vaccine is still under development and needs much more information to confirm its efficiency. Thus, this preventive measure is considered unusable due to the lack of evidence and information.As the disease reported is self-limited and no fatal case reported. Educational communication is the only choice left which not most effective but should change something on the outcome. There are several reports have reported that Educational communication plays an important role to prevent HFMD(http://www.jmatonline.com/index.php/jmat/article/view/10816, https://www.sciencedirect.com/science/article/pii/S2213398422000690 ) as it could increase awareness of the exposure group. However, to be a successful intervention, educational communication relies on several factors such as educators (public health staff), the audience(exposure-related group), and the use of that information. The coverage of the intervention also relies on the information that the messenger delivers to the audience.
From this, I will include the perception of the preventive acts on all populations in simulation such as the isolation after being infected. Accordingly, the compartmental model may be altered by adding the Exposed population to the model result which is resulted in the SEIR model.
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For my study on Tomato Flu, I plan to use the SIR model.
There is a change on scope of work to focus on only Kerala, a state in India, since it is a solely state where Tomato flu case reports are publicly available.
According to (https://www.bmj.com/content/378/bmj.o2101.long), Tomato flu, a self-limited disease, is caused by enterovirus(EV) Coxsackievirus A16(CA16). As it is confirmed as the variant of Hand, Foot, and Mouth Disease(HFMD), it has same mode of transmission spreading through faces and fluid in the spots. Moreover, clinician in Kerala had confirmed that it is not transmitted by mosquitoes.Differently from HFMD, Tomato Flu show sign of symptoms as presence of tomato-like red welts all over the body. The control measure for this disease is follow same procedure as in HFMD which isolated the patient for 2 weeks since the disease will resolve without treatment within 1-2 weeks after infections and there is no to record a single death on this disease.
These are the variables which I have discovered from my research on Tomato Flu.
*Beta is estimated based on the compartmental model along with the case report history of Tomato flu as reported of 82 cases after 82 days from first reported case. I have fitted all of the parameter to SIR model and rearrange the value of Beta until N(82) of Infectious match to the actual case report where the number of cases is beginning to rise. From this, I have calculated reproduction number from estimated Beta and averaged Gamma resulting in ~1.5 R0 which closely to reproductive number of HFMD in China between 2008 and 2009 (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6805791/).
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I agree with you that there is a lot of space for improvement in several sections of my CRF. It is a good suggestion to put the check box on post-vaccination reaction. For chronic illness, in my opinion, if the disease is already resolved, then it should not be assigned in the first place. For the redundancy suggestion on chronic illness and medical condition, since both are induced in different CRF which are used for different purposes. While the chronic illnesses section intends to gather only chronic disease tends to affect the result of screening, the medical information on enrollment contains much more information on all diseases, lesions, and disorders of that patient. I might redesign my medical condition section to gather information that is not related to what is already gathered.
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I suggest you should consider to keep the data in the google sheet as it is easy to use, user friendly, and provides several feature such as real-time team editing, migrate function, edit log, and several data management plugin. Moreover, there is also the javascript library that can save the data into google sheet.
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Thank you for your reply Aj.Wirichada,
According to this journal (https://journals.lww.com/pidj/Fulltext/9900/Kerala_Tomato_Flu___A_Manifestation_of_Hand_Foot.160.aspx), Tomato flu is caused by enteroviruses(EV) Coxsackie A16 (CA16) which is the same EV that cause Hand Foot and Mouth disease(HFMD). Although their sequence are not identical, Phylogenetic analysis show that both shared same ancestor as clade in China(https://cdn-links.lww.com/permalink/inf/e/inf_1_1_2022_08_03_tang_pidj-222-641_sdc1.pdf).
Therefore, a new virus which emerge in India is merely a variant of HFMD. If that’s the case, I may adopt idea, structure and dataset of HFMD modeling in other country to estimate the case and to find the risk factor in India.
Do you have any comment for this idea?
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