Forum Replies Created
-
Posts
-
-
The interventions I am considering putting in the model are the use of LLIN and IRS. Indonesian government has a national program to distribute LLINs as a malaria control. The coverage of LLIN and IRS in Rejang Lebong area will be based on the report from the National Malaria Control Program (NMCP).
From this meta-analysis study, the efficacy of LLIN is 54% compared to not using any LLINs at all (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5877091/). LLINs are more effective in areas where the transmission occurred from the indoor biting mosquito species at night or early morning before people get up.
IRS has 62% efficacy as suggested by this study by Kim, et al (https://www.ajtmh.org/view/journals/tpmd/87/1/article-p117.xml), although the efficacy number may vary depending on the environment, initial malaria prevalence and the parasite that is dominant in the location. IRS also is most effective if the mosquito has the habit of indoor biting.
The interventions will be added as a new parameter so it will reduce the rate of transmission.
-
For modelling the risk of malaria for travelers to plantation area inside the forest in Rejang Lebong district, I will use the same compartments described in the following paper (https://malariajournal.biomedcentral.com/articles/10.1186/1475-2875-8-296#MOESM1). The only difference I made is I do not incorporate Latent Mosquito compartment, therefore the compartments I will use are:
With probe represent a cohort of travelers. The model’s parameters are:
The value of model parameters will be searched from several literatures that are relevant to malaria in Indonesia situation.
-
-
1. Thailand had an increasing suicide rate as any other country. However, Thailand had a different economic and social-cultural context from the countries that are mentioned in the paper which mostly has industrial economy. In addition, there is only one study that has been done to address the problem.
2. Interesting finding in this study is that the suicide rate increase as the income increase. Moreover, households with economic hardship and financial problems tend to have lower suicide rates. As mentioned in the paper, people who had a better economy were living in the cities and were relatively more ‘closed’ and individual compared to people who lives in urban areas which had a stronger community. This implies that in Thailand, social culture has a significant impact on people’s lives.
3. Statistical modelling is very helpful in investigating the epidemiology and spatial aspects of the Thai suicide problem since it can be used to determine whether there is some spatial relation to suicide. It can also be used to investigate if there is any clustering of suicide cases between the neighbouring provinces.
-
1. Spatial epidemiology can be considered an interdisciplinary science since to use the method well, we need to have knowledge of the epidemiology of the disease, geography to understand the technical terms that are used, and statistics for doing the analysis. The reason why it is not utilized well might be:
– Limited knowledge of the use of spatial information in the data since it is considered a complicated analysis. However, with the advance in technology, some of the complications can be reduced and it is now easier to use.
– Location data is private and sensitive data for patients and there is also an issue of location data quality. A precise coordinate location can expose where patients live or work, so recording a precise location needs to proceed with caution. The data quality also needs to be looked at carefully. For example, here in Indonesia, even the naming of a village can vary a lot depending on who records the data which makes us need to do cleaning and standardise the name for the analysis.2. Environment plays a big role in disease transmission. For example, vector-borne disease like malaria and dengue spreading depends a lot on the mosquito as the vector and mosquito habitats are really dependent on the environmental condition. Different species of mosquito transmit different diseases, so knowing the location and the environment of the location will help to decide which intervention is suitable. Different place of residential also has different access to healthcare, especially when we look at remote area.
-
Hi all,
Kindly see my Power BI dashboard here. I choose to use a black background with some light text colour to make it contrast with the background. The one-page dashboard includes all the basic info about the COVID-19 situation such as confirmed, recovery, and death. The number on the tables is daily for people who are interested in seeing the daily number. The highlighted visualisation in the dashboard is the map which can show us the distribution and do a quick comparison of different sizes of COVID-19 cases between the countries.
I hope you find the dashboard helpful. I’m glad to receive any comments on the dashboard.
-
Hi all,
kindly see my updated Power BI dashboard for COVID-19 cases visualization. The first page of the dashboard gives us summary of total cases and total reporting country by using “Cards” tool. The rest of the pages show the different approach of visualisation, for example using Line Chart to visualise the growth of daily COVID-19 cases and deaths. There is also a Map visualisation to show the distribution of COVID-19 cases accross the world.
Thank you.
-
Hi all,
Kindly see my Power BI dashboard. This dashboard show Countries, GDP per capita and COVID-19 variables, such as daily cases, daily recovery, and daily death. The table is useful to make a comparison, for example between the GDP per capita and COVID-19 cases.
This is also my successful connection to the dataset
.
Thank you.
-
This is one example of a data visualization dashboard for COVID-19 cases https://www.worldometers.info/coronavirus/, developed by the collaboration of developer, researcher, and volunteer across the world. it is a simple and very informative dashboard that highlights the number of COVID-19 cases, deaths, and recovered cases. These numbers are also provided in a bar graph, which in my opinion, is very clear and minimalistic without crowding with too many numbers or labels. They instead provide the feature to hover over the graph to see the number of cases. The feature that I like the most from this dashboard is the table of reported COVID-19 cases and deaths by county. It can be sorted by column, so it is really convenient when we want to compare the number of COVID-19 cases between countries.
-
Hi Yanin,
The CRF layout is clear and conscience. The appropriate units are provided, and the date and time format are clearly stated. However, I would say it is better to change the time format to 24 hours instead of 12 hours (AM/PM) to reduce human error (for example do not tick the appropriate AM/PM). Another thing that could be improved is the separation of inclusion and exclusion checkbox criteria to help the investigator/interviewer see whether the subject is eligible for the study or not at a glance. One thing missing in the CRF is the Screening ID. Both Screening ID and Subject ID are needed in case we screened participant that is not eligible for enrolment (for example pregnant women).
-
Hi Fajar,
The CRF layout is clear and conscience. The date and time format are clearly stated, and appropriate units are provided. It is nice to put the “not measured” checkbox to clear the confusion about whether the data is not entered or not done. I also like the idea to put the method of temperature check and blood pressure measurement. However, there are several things that are missing and could be improved in the CRF:
• Screening ID and Subject ID are required and should be recorded since not all participants are eligible to enrol (for example if we screened pregnant women).
• There is no Subject ID field on every page of CRF. This is important so we can know which participant this CRF belong to.
• There is no checkbox that clearly stated whether the subject is eligible for enrolment.
• There is no demographic section or any field provided to record age, year of birth, sex, and race.
• There is no laboratory section or any field provided to record the urine pregnancy test (for screening visit), and antibody titer sample (for enrolment visit, before vaccination).
• In the vaccination section, we do not know which vaccine for which subject since it is an observer-blinded trial. We should put vaccination date and time, and vaccination arm in this section. -
-
-
I was involved in a clinical trial study that use OpenClinica as their database management. It has all the above-mentioned processes: audit trial/time stamp, user authentication and access control level, edit check and logical check, and data backup and recovery plan. My role in that study was only to be the user and did not involve in the database development. However, before the study started I was moved to another team so I was not able to try out all of the features.
-
-
Hi all,
kindly follow link for my warp up. Thank you for the fruitful discussion these past four weeks! -
In the research project I am currently working on, I am collecting data on mobile and migrant populations and malaria incidence in each district across Indonesia. It was a primary data collection, collected through an online survey platform and utilizing questionnaire instrument. It was a census since all the health officers in the districts are participating to fill in the questionnaire. The data collection is quite challenging since there are many questions asked in the questionnaire. There was also a technical difficulty because of the need for internet connection.
-
Hi all, kindly follow this link for my wrap up. Thank you
https://drive.google.com/file/d/1VIa3_xYl0tGes_I0Qz42okwx2_XZP9Nu/view?usp=sharing
-
Kindly see this link for this week wrap up from me. Thank you very much for the fruitful discussion.
https://drive.google.com/file/d/1fYqWKpaZFmX5dpPpTBAUyb7uIzxBw_3x/view?usp=sharing
-
In the discussion about AI and Ethics in Health webinar, we discuss the risk and benefits of using AI and whether it is justified for doing so. In the example of using AI from the CT-Scan data to diagnose COVID-19 in China, it is justifiable to do so because in the era of the pandemic, the target is to diagnose as soon as possible to stop the spread of the disease. So, it is aligned with the “better safe than sorry” principle. There is also the benefit of reducing the workload of healthcare workers and being able to have a source of diagnosis validation. The role of healthcare workers is still important, the Ai can help to improve healthcare by being a decision support tool.
However, there are also concerns and risks of using AI. First, it is prone to bias. The AI’s decision is dependent on it training datasets. The dataset that generated in China, which is composed of China’s population, might not be suitable to use in another country since there will be several disparities such as race that can affect the AI’s decision. The AI can also skip the radiologist role. While it could be beneficial for the patients because it reduces the wait time, it could lead to disrupting their role in healthcare. Other concern that should be highlighted is data privacy and security. The patient might be worried about the leak of their private identity. The system developer and hospital management need to bring assurance to the patients. The patients have their right to raise protests and complaints to the developer and hospital management should there be any case of error in AI diagnosis, even though they have given consent to use their data for AI training.
The implementation of AI needs to be discussed according to the disease. Other diseases that might be benefited from AI are cancer, stroke, and heart disease. There is also a study on implementing AI for malaria diagnosis in Indonesia, where the AI counts the parasite density of the malaria patient.
-
I am much more familiar with frequentist methods because it is what has been thought in my undergrad study. I have tried to understand the Bayesian method but having a hard time because I am just too used to the frequentist method. The biggest difference between the frequentist and Bayesian methods is that for Bayesian you need a prior knowledge for the parameter. How do I know my prior knowledge is true? I also do not know anyone that is proficient in the Bayesian method, so it is hard to ask for advice. However, I agree that we do not have to lean into just one side only. We should select the method that is suitable for our data and can help us to answer our question.
-
Hi all, my name is Karina. I have a bachelor’s degree in Statistics and now I am working as a Statistician for EOCRU. We have a close collaboration with the National Malaria Control Program and one of our current projects aims to describe malaria in mobile and migrant populations. In my daily work, I mostly use descriptive statistics using tables, graphs, and maps to analyse the data.
I had experience in applying statistical analysis using logistic regression to describe the risk factors of scabies incidence in monasteries in Cambodia. It was quite challenging since the participants in each monastery is varied and we have to include the different monasteries as cluster. It eventually works out and we were able to describe the risk factors of scabies incidence.
-
-
One of the methods to help us understand the reluctance of using bednets is by doing in-depth interview. The participants of the study would be respondents that are not using bednets. The answer from in-depth interview will be helpful to know their opinions about bednets and the reason why they do not use it. This also can give suggestions for improvement so more people want to use the bednets.
-
-
Some of the possible external variables for new technology are:
– Age, it is relatively harder to teach older people about new technology
– Income status, wealthier people are exposed to new technology sooner
– Environment/peer influence, relates to whether people around them using and recommending this new technology -
1. Efficacy: the capacity to have the desired result of a given intervention under a controlled environment
2. Effectiveness: how well that given intervention works in normal condition
3. Efficiency: whether the given intervention is the most economical option (in terms of time, energy, money) to do
-
My workplace has enforced some strict practices to help control COVID-19 cases:
• Working from home is mandatory, unless the work needs to be carried out specifically at laboratories
• Minimised face-to-face meeting and utilise the online platform to meet virtually
• Mask is required, N95 or doubled mask
• Eating together is prohibited
• PCR negative is required for coming to office if you have just come back from other cities -
Yes, because data sharing has many benefits for the community in the long run. Most notably, data sharing provides benefits in public health research. The data that has been collected by one organisation can be used by others. This is especially advantageous if the data collection cannot be replicated. It also can help to increase transparency and thus increase the trust of research findings. This will also push the quality of data collection to be better.
-
-
Case-fatality rate
• Definition:
The proportion of people who die from disease among all individuals who also got the disease over a certain period.• Calculation:
CFR in % = Number of people die from the disease / Number of confirmed cases of the disease x 100• Usefulness:
CFR is used as a measure for disease severity. The number of CFR may change depending on the population, time/period, or treatment change. -
In terms of research and data collection, I think one of the ways to reduce the occurrence of missing data is to determine variables that are needed to help answer the research question. Another thing that needs to have thought about is the validation of each column. Oftentimes, the column is filled with wrong things that will be deleted in the data cleaning stages which lead to missing data. For example, putting “211” in the age section or having “Pregnant” in a “Male” patient.
In a questionnaire setting, we need to ask the questions clearly and avoid open-ended questions. Participants often skip the question if it is open-ended and/or if a long answer is needed.
-
I agree with the recommendations that the author gives in the paper. However, as other classmates said, the implementation of the recommended points is not as easy as it sounds. Even taking the first step of discussion about what is corruption might be hard. In my opinion, the hierarchy culture, as mentioned by other classmates, is one of the obstacles. People at the low level of the hierarchy are reluctant to speak because they feel powerless.
Other recommendation I can think of is transparency between the stakeholders and the public. I think clearly stating the plan/process, budget, and doing routine reporting can help prevent the act of corruption because the public will watch your action.
-
An example of health system improvement in Indonesia is the establishment of Universal Health Coverage in 2014. Although it is still a work in progress because improvement of the system is needed, but I appreciate that the government take action to provide health protection and service to the citizen.
One of the issues I can think of is the limited number of healthcare professionals. The ratio of doctors and the population is 4:10.000 which is the second-lowest in Southeast Asia. Moreover, there are also casualties from Covid-19 pandemic. If there are fewer healthcare professionals that can provide service to the public, the wait time for medication will also be longer and thus can result in decreasing public health and well-being.
-
When developing disaster recovery plan, the back-up and restoration data procedure should be included in the plan. My workplace has been dealing with clinical trials data, in the form of hardcopies (informed consent, case report form) and softcopies (data entered to database). For the hardcopies, the papers were stored in a fire-proof cabinet inside a safe room with lock and the database is already online using database platforms that has a regular back-up regulation. For now, the physical server that we had is only one in the building and with the growing number of people, we need to have a back-up server that replicates the data.
-
High Availability minimised the impact of IT disruption for the healthcare workflow in the hospital thus it is very beneficial to have HA system implemented. It enables a more timely and appropriate medication for patients because the clinicians can access the data easily and with minimal interruption. This leads to improved satisfaction from the patients of the hospital services.
-
I do not have such experience since I mostly work on secondary and aggregated data. Although for the project that I currently working on, I need to collect personal information of survey participants that include name, phone number, and email. These sensitive data will be protected by limiting access to the data. I have full access to the data since I am the one that will manage and analyse it. However, if there is a need for other team members to see the data, I am going to de-identified and give a unique ID before sharing it.
It would be interesting to hear other people experiences of handling and managing sensitive data.
-
In regard to healthcare sectors
Social: The social distancing protocols to minimised Covid-19 risk has change several habits mainly in seeking healthcare or consultation. Telemedicine is now used widely by the population.Technologies: Related to the social distancing, technologies really help this telemedicine to grow. It is really easy to use the health application that can be installed in your mobile phone. Technologies also enable easier communication through instant messaging and video call application.
Environment: Because of Covid-19, many people are now aware of the air quality. Many offices, and possibly also at home, installed an air purifier to clean the air in the room.
Economy: Agreeing with classmates, during the pandemic a lot of people cut off from their job or being demoted so people tend to save the money and not spending on anything that unnecessary. This could also affect the healthcare seeking behaviour, people won’t go to the hospital or seek treatment until the symptoms get worse.
Political: Government has been changing their rules during the pandemic and adjusting to the current situation. While this is a good thing, sometimes the changes timeline is not clear enough and make the public confused.
-
Hi Fajar, thank you for sharing the malaria surveillance system development. It is a clear and comprehensive presentation.
If I may add, I think it is also necessary to measure the timeliness of the data reporting so if there is any outbreak happened the authorities can act properly and in a timely manner. -
Hi Ashara, thank you for the great presentation.
If the flooding is consistent in several regions, do you think it is possible or necessary to incorporate weather conditions in the system? For example, when the weather is above the normal condition (based on weather history), the system will alert the relevant stakeholder so they can be more prepared to handle the disease?
-
Hi Arwin, thank you for sharing the project development. I realise you talk a little too fast, but in general, it is still a clear and great presentation!
Thank you for the insight to do the adaptive types of surveillance. Since the situation of the disease of changing and our knowledge of the diseases is also evolving, the surveillance system needs to be adapted and be able to capture the changes.
I agree with your emphasis on protecting the patient’s data in the data flow diagram. The patients’ data privacy must be protected and the variables that we are collecting are only the ones that are relevant for our analysis. -
I would like to share a research project that I have been working on. It aims to identify the type, size, burden of malaria of mobile and migrant population (MMP) and their movement routes as well as exploring possibilities of interventions to the specific MMP such as miners, forest workers and indigenous population in low-endemic districts. This study will involve multiple quantitative and qualitative approaches including literature review, online data collection, secondary data exploration, and field works such as mass blood survey, focus group discussion and in-depth interviews.
The Gantt chart is attached below.
https://drive.google.com/file/d/1UreDM3vsOJn6qkNC_Te0Zc0CjhTAKTmZ/view?usp=sharing
-
TMHG528 Disease Surveillance and Public Health Investigation › Week 4 Topic discussion 1
Please watch VDOs of public communication regarding COVID-19 situation from the leaders of two countries: Singapore and the USA. Then try to observe whether the two leaders are good communicators or not. When watching the VDOs try to look for the six principles of CREC, recommended by the CDC:1. Be first:
Both Singaporean PM and President of USA promptly shares the information of current situation of COVID-19 in their country, but Singaporean PM is quicker telling the public compared to the President of US.2. Be right:
Both Singaporean PM and President of USA shares credible information of what is known. Although, in my opinion, Singaporean PM has given clearer and more detail information on what is unknown.3. Be credible:
Both Singaporean PM and President of USA given credible scientific evidence to encourage public trust their information.4. Express empathy:
Both Singaporean PM and President of USA expressed empathy. However, Singaporean PM approach in expressing the empathy makes me, as the audience, believe that he does care about the public concern and fear.5. Promote action:
Both Singaporean PM and President of USA promote action for public to help in stopping the spread of Covid-19.6. Show respect:
Singaporean PM showed more respectful speech to the audience compared to the President of USA. The president of USA often talked about the other country fault which, in my opinion, is not necessary. -
-
Thank you for the feedback, Ajarn Pan. I’m interested in doing similar things to this paper https://malariajournal.biomedcentral.com/articles/10.1186/1475-2875-8-296#Sec8 which trying estimate the risk of non-immune individual acquiring falciparum malaria when traveling to the Amazon region of Brazil. Indonesia has similar forestry areas where the workers come from low transmission areas so they are very much at risk of getting malaria when working in the forest. However, I’m not sure if the data on the immunity of the travelers is available here. I’ll try to look up in some of the previous studies.
-
-
-
I think most of the complaints of BPJS users in Indonesia is a long wait time for treatment. There is also a sentiment that you are not treated well if you use BPJS, while that is not true. The problem is the healthcare personnel and facilities are limited and not distributed equally across the country. Improvement of service by increasing the workforce and adding more facilities is definitely needed.
-
I am agreeing with Fajar’s answers here. I think the most challenging issue for health informatics in Indonesia is data integration and interoperability. There are many data collected for different databases but there is no way to integrate between the data. While there is a rising awareness about the importance of health informatics, the number of health informaticians in each of the health departments is still lacking. So, there is still a gap and different understanding between the stakeholders, healthcare professionals, and IT professionals.
-
-
