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The following targeted interventions are planned to integrate into the modelling of HIV transmission among key populations in Myanmar.
1. ART (Antiretroviral Therapy)
Taking ART treatment reduces the viral load, which makes the infected person less likely to transmit the virus. It will be the percentage of HIV-infected person on ART among the MSM, SW and OVP populations. When it has the higher coverage, there will be lower viral load within the person, and it will reduce the risk of transmission. In the model structure, the rate of individual entering the ART compartment with reduced transmission risk will be 96%-99%.
2. PrEP (Pre-exposure Prophylaxis)
If a person is taking PrEP properly, studies have shown it reduces the risk of HIV infection among the high-risk population. The coverage reflects the proportion of HIV-negative persons who are at risk of getting HIV but adhered to PrEP for prevention. The higher coverage of PrEP can reduce the key population infected with HIV. Although actual efficacy depends on adherence, PrEP efficacy levels will be above 90% in the model structure.
3. Condom Distribution
In reducing the risk of HIV transmission, proper use of condom is one of the effective ways. Condom distribution represents the proportion of key population who use the condom correctly and consistently at every sexual acts. According to studies, condoms reduce transmission will be considered approximately 80%. -
Key Characteristics of HIV
Transmission
The transmission of HIV is mainly through unprotected sexual contact, sharing of the contaminated needles, from mother to child during childbirth or breastfeeding. Regarding the key populations, it is mostly through sexual contact among MSM or SW with clients.
Pathogenicity
Getting infected with HIV targets the individual’s immune system which leads to a gradual decline in the functions of CD4 cells. The person becomes susceptible to opportunistic infections and certain cancers.
Symptoms
It is often asymptomatic or flu-like symptoms in the early stages of HIV infection. Without proper treatment, the immune system becomes compromised and leads to AIDS (acquired immunodeficiency syndrome).
Control Measures
To reduce the viral load and risk of transmission, the primary treatment is taking ART (antiretroviral therapy). In high-risk populations like MSM and SW, PrEP (pre-exposure prophylaxis) can be used as a preventive measure with condoms.Model Structure
S (susceptible): Individuals at risk of contracting HIV.
I (infectious): Individuals capable of transmitting the virus.
ART/PrEP: Separate compartments for individuals receiving ART or PrEP, which reduce their transmission risks.
R (removed): Individuals who are no longer part of the transmission cycle (e.g., due to death).R code for SIR model
SIR <- function(time, state, parameters) {
with(as.list(c(state, parameters)), {
dS <- -beta * S * I – tau_PrEP * S
# Susceptible population declines due to infection & PrEP initiation
dI <- beta * S * I – gamma * I – mu * I – tau_ART * I
# Infectious decreases with ART initiation, mortality, or recovery
dART <- tau_ART * I
# ART compartment grows from infectious individuals
dPrEP <- tau_PrEP * S
# PrEP compartment grows from susceptible individuals
dR <- gamma * I + mu * I
# Recovered population grows with recovery and mortalityreturn(list(c(dS, dI, dART, dPrEP, dR)))
})
}Source
https://www.unaids.org/en/resources/fact-sheet
https://www.who.int/data/gho/publications/world-health-statistics -
In Myanmar, I am interested in researching HIV (Human Immunodeficiency Virus) transmission among key populations. MSM, SW, and OVP significantly drive the HIV epidemic in Myanmar, with high prevalence rates making it challenging to control the disease. These individuals are at a higher risk of infection primarily due to behaviors, social, and economic-related reasons. Our study will focus on HIV transmission dynamics among key populations in Myanmar. We can now tailor interventions that leverage these drivers to control the transmission hazard among these groups better. The study will also assess the effect of public health antiretroviral therapy (ART) and PrEP in these high-risk population groups.
Some of the questions are:
How has (and will) the pattern of HIV transmission change among MSM, SW, and other most at-risk populations in Myanmar?
What impact have ART and PrEP programs had on HIV transmission among MSM, SW, and OVP in Myanmar?
What kind of socio-economic and cultural determinantsᅳsuch as stigma, healthcare access, and legal barriersᅳdrive the transmission of HIV among key populations in Myanmar?
How much of an impact do targeted interventions (e.g., needle exchange programs for PWID or condom distribution for SW) have in reducing HIV transmission within Myanmar? -
Hello friends,
I also faced the same error by running the original code and it took me a long time to search for different options online.
Here comes the solution for me and you can try it too.## showing error after running the code
> cat.exc1<- cut(unlist(exc1),breaks=exc1.cutoff,include.lowest=TRUE)
Error in cut.default(unlist(exc1), breaks = exc1.cutoff, include.lowest = TRUE) :
‘x’ must be numeric## check the type and structure of exc1
> str(exc1_numeric)
Error: object ‘exc1_numeric’ not found
## it seems the variable was not successfully created## check the content of exc1 after the ‘sapply()’
> str(exc1)
list()
## exc1 is empty## check the data of previous object
> str(mod.eco.reg$marginals.fitted.values)
NULL
## it was not generated by the model## solution is to add options at the last part of the code
## instead of
mod.eco.reg <- inla(formula.eco.reg, family=”poisson”,
data=data.suicides, E=E,
control.compute=list(dic=TRUE))## use the following
mod.eco.reg <- inla(formula.eco.reg, family=”poisson”,
data = data.suicides, E = E,
control.compute = list(dic = TRUE, cpo = TRUE, waic = TRUE, return.marginals.predictor = TRUE))
## after editing, all the lines running smoothly for me and hope you can solve this too 🙂 -
During the last decade in Thailand, there has been an increasing trend in suicide cases which has a remarkable range between 3600 and 4000. It has been continuously rising since 2011 and this was around 6 suicides per 100,000 people. This drives the author to understand the factors contributing to the rising suicide rates. The lack of comprehensive research in this area and the need to address these issues for the well-being of the population also contribute to the author’s attention.
The divorce rate can contribute to the suicide rate in several ways. People can get emotional distress from getting divorced. They can face the feelings of loneliness, hopelessness. And it can also contribute to depression which is a known risk factor for suicide. There can be a significant loss of social support for a divorced individual which can worsen their vulnerability to suicidal thoughts. Furthermore, divorce can be associated with financial strain causing more stress and mental health challenges. These can elevate the risk of suicide. It’s also important to consider the cultural and social context of Thailand like our country, Myanmar which is more or less the same. There can be social stigma regarding a divorce together with isolation from society. This can be more prominent in women. This emotional burden can impact individuals’ mental well-being and increase their susceptibility to suicidal tendency.
In the investigation of epidemiology and spatial aspects of suicide in Thailand, statistical modelling can enhance significantly. It can identify the trends, risk factors and geographic patterns. The relationship between suicide rates and other variables like age, gender, socioeconomic status and alcohol drinking can be explained with the help of Time-series analysis and multivariate regression. Spatial regression models can help in mapping and identifying the high-risk area and hence it enables targeted interventions. This can support the development of public health strategies to be more effective. Finally, it can mitigate the risk of suicidal tendency and improve the mental health outcomes.
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The insights from the research article on electronic patient-reported outcomes (ePROs) among German rheumatologists can be applied in different healthcare settings for chronically ill patients to be beneficial. In mental health, the implementation of ePROs can provide insights into patient experiences and symptoms. This can lead to a more personalized and effective treatment plan. Also, in diabetes care, we can get a comprehensive understanding of the impact of disease on patients’ daily lives, which can lead to managing and engaging the patient effectively. Furthermore, in oncology, ePROs can help to assess the symptoms and side effects of the treatment regularly. This can contribute to improving communication between healthcare workers and patients and, hence, support research and quality improvement.
We can consider several key features and improvements to enhance the ePRO system to be more effective and user-friendly for both patients and healthcare providers. One of the features is to include multilingual support in the system, especially in multicultural societies. This is important for ensuring the accessibility and engagement of diverse patient populations. By offering interfaces and questionnaires in multiple languages, it will be effective in meeting the needs of non-native language speakers, which can lead to equitable participation and data collection across various linguistic backgrounds. Another one is the customizable reporting functions in the system to empower healthcare users. This can tailor the analysis and visualization according to the needs of the specific users and patient preferences. Including this flexibility can enable the report to be generated in a personalized way. That can facilitate more targeted and actionable insights for an informed decision-making process.
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1. If we can integrate the epidemiological data with environmental data, the accuracy and timeliness of predicting the malaria outbreak can be significantly improved. By integrating this, it allows us to identify the environmental precursors that can serve as early indicators of an emerging malaria epidemic. Thus, it can provide the opportunity for proactive intervention. In identifying the emerging malaria epidemics, the challenges include obtaining recent epidemiological surveillance data, aligning this information with data on environmental precursors in a timely manner.
2. In developing and implementing a system like EPIDEMIA, several benefits can be offered with the continuous involvement of public stakeholders. This can facilitate the ongoing development of the system including its application and testing. The stakeholders can also give input and recommendations to adjust the system design and operation phases. Hence, the system can be more responsive to the evolving needs and challenges faced by public health officials. The feedback also plays a crucial role in shaping the effectiveness of the system and ensuring its robustness to continual changes in the software.
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Thank you, Ko Soe Htike
I also got stuck at that error and it took me some time to solve the issue.
The following are the versions for which I was okay with the code you provided.install.packages(“INLA”, repos = c(getOption(“repos”), INLA = “https://inla.r-inla-download.org/R/stable”), dep = TRUE)
It was also learned that in posting the above code, the webpage changed some content, and the display is not exactly as we posted.
Instead of the phrase
R/stable”
you will see like this
R/stable & # 8221 ;
So please take care of this when copying the code.R : 4.4.1
R Studio : 2024.04.02
R INLA : 24.06.27
R tools : 4.4
Maptools : 1.1-8.tar.gz -
1. What are possible reasons locations in epidemiological research have not been incorporated as much as other components in epidemiological research? How can spatial epidemiology be considered as an interdisciplinary science?
In epidemiological research, there is limited incorporation of location data which may be due to several factors. The use of spatial information in data analysis may often be perceived as a complex process. But today’s technology improvements might reduce those complexities and make it more accessible to utilize spatial information.
Another challenge is that patient data comes with a private and sensitive nature including location data. Recording the precise location of an individual may potentially compromise their privacy of them and it is important to be cautious when utilizing such information. Furthermore, certain regions vary in data recording practices which need serious attention to ensure the quality of the location data.
We can consider spatial epidemiology an interdisciplinary science because it relies on knowledge from multiple fields. It needs to have a comprehensive understanding of epidemiology, geography, and statistics to utilize spatial epidemiology effectively. So it needs to comprehend the epidemiology of diseases, understand geographical concepts, and employ statistical methods for analysis.2. Explain why it is widely recognized that the place where an individual lives or works should be considered a potential disease determinant and give some examples?
Recognizing an individual’s living or working environment as a potentially disease-determining factor is important. Environmental factors play an important role in disease transmission as in the case of vector-borne diseases such as malaria and dengue. Environmental conditions have a significant influence on the habitats of disease vectors such as mosquitoes. Understanding location and environmental factors is important in implementing suitable interventions as different mosquito species transmit distinct diseases.
Furthermore, different locations or areas have different access to healthcare services. It is more prominent in rural or remote areas. We can adjust or adapt healthcare interventions to be effective by understanding the environmental and geographical aspects of these areas. For example, remote areas may have more challenges accessing healthcare services. It is highlighted that we need to consider the place of residence or work as a determinant of disease outcomes. -
The significant challenge to sustaining the health information system is the lack of financial resources. The system needs to invest huge amounts of funding in the startup, updating the software and maintaining the hardware require certain financial investments. Training for the staff who will take care of the system is also one of the financial needs. Addressing this challenge includes advocating for increased government funding and exploring public-private partnerships. And instead of using external resources, capacity building for locals to manage and maintain this system can contribute to long-term system sustainability.
In designing EHIS, features like flexibility and interoperability are crucial to adapting to changing needs and technologies. It should be easy to integrate new functionalities and technologies in emerging technologies. Regarding data exchange and integration with other systems, it also needs to ensure that the system possesses open standards and interoperability protocols. Lastly, having user-centered design and collecting feedbacks from users to continuously improve the mechanism will facilitate the EHIS to evolve so that it meets the requirements of healthcare professionals and patients.
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I agree with what my colleagues suggest here, it is important to continue and improve the quality data collection process. Accurate and timely collection of syphilis data is crucial, and every healthcare professional should be aware of every step. And every successful implementation of the healthcare system needs the effective coordination and collaboration of different stakeholders who are involved in syphilis surveillance. Those can be different counterparts such as healthcare providers, researchers and public health agencies. They can facilitate information sharing, resource allocation to approach syphilis prevention and control comprehensively.
One of the strengths is that the surveillance system can detect unusual patterns of disease early which allows for quick response for public health interventions. But one of the challenges is it cannot represent generalizability since it cannot capture the information from all of the population. This can lead to potential biases in disease information.
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Since AI comes with an automated process, it can reduce the burden of manually analyzing data. A faster and more efficient detection of disease outbreaks can be achieved by analyzing large datasets. Some of the patterns that human analysts cannot easily notice can be detected by AI algorithms.
Using AI technology will also facilitate the collaboration of different counterparts that may need to be involved in epidemic surveillance. Those can be different healthcare providers, departments and agencies. By sharing outbreak alerts and relevant information in a timely manner, AI systems can improve the overall response to disease outbreaks.AI can reduce the reliance on human intervention in epidemic surveillance systems. Since the analysis of data can be done with automated processes and it is effective in pattern recognition, AI can overcome the challenges of weak health systems. And it can provide the information needed to detect the disease accurately and in a timely manner.
We can use AI technology to enhance surveillance methods by using huge data sources. The sources can be updated with new articles and systematic electronic health records. By using those data, the disease trends can be quickly understood. This comprehensive and real-time information enables us to take proactive measures accordingly.
The use of AI in public health surveillance raises ethical considerations such as privacy, consent, and potential discrimination. It is crucial to deploy AI technologies to maintain and promote equity for all individuals. -
In identifying the barriers to health information seeking among youth for HIV/STI and RH, we should consider stigma and discrimination which can deter young people from seeking information. Fear of judgement may prevent the young population from accessing the necesary resources. Another thing is the accessibility of the healthcare services including HIV and RH services can vary depeding on different locations. Compared to urban, rural areas may have limited access to health facilities and information. Next to consider is the language barriers by young people’s which hinder the ability to understand the health information. To imrpove the accessibility, it is important to proivde the health infromation in languages they can understand.
In receiving necessary health information, some peoply living with HIV may left behind due to stigma and discrimination. It can be difficult for them to seek support and get the necessary HIV care and treatment including reproductive health services. People working in neighboring countries can also face challenges in accesing those kind of health information due to their migration status. To reach these individuals, mobile technology can play an important role to disseminatie health informations. These can leverage the information to reach up to remote and marginalized populations. Developing health information materials that are culturally and linguistically appropriate will also reach the target populations accordingly. These can enable every individual to enhance their understanding with the help of plain language and visual aids.
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1. The integration of decision tree models into clinical practice can assist surgeons in preoperative planning and decision-making. These models can provide risk assessment by analyzing preoperative patient characteristics and predicting the likelihood of massive bleeding in pancreatic surgery. The operation team can use this kind of prediction for the intended client and plan to approach the required precautions and preparations accordingly. But the decision tree model should only be used as a tool to support the clinical decision since the expertise of the operation team is more valuable.
2. Using the decision tree model in a clinical setting has many benefits for risk identification, personalized treatment and the decision-making process. Since it can identify high-risk patients, specific interventions can be addressed to improve outcomes and patient satisfaction. But there are some limitations with the model which include data availability and quality, model complexity and interpretability. It also needs regular updates to be used effectively. Thus, it is important to validate and interpret the model’s predictions for every patient and clinical expertise.
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Electronic-based clinical decision support systems offer several advantages for managing NCD patients in remote areas. Those can improve decision making and patient outcomes by providing timely alerts and supporting healthcare workers in areas with limited resources. However, there are some drawbacks to consider, such as alert fatigue, lack of customization, technical challenges, limited access to technology, and the dependency on accurate data.
Nurse or community health worker facilitated NCD management tools, supported by clinical decision support systems, can effectively compensate for the shortage of healthcare workers in remote areas. These tools allow for task sharing, capacity development, and improved access to care. Additionally, alternative options like telemedicine, mHealth interventions, training and capacity building, and collaborative care models can also address the insufficient healthcare workforce and promote quality care for NCD patients in remote areas.
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In my opinion, we should develop a system that can provide us with explanations for the decisions and recommendations from the system. So that it can help us build trust and a more comprehensive understanding among healthcare workers and patients. Another thing is that having a mechanism to monitor and provide feedback from users for both healthcare workers and patients can help identify and address any issues or limitations.
The medical AI systems should have fairness, transparency and accountability which are important areas to adhere to ethical principles. It will make sure the technologies build trust and confidence in the community to use the system.
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1. Enhancing PIH Prediction Accuracy
When it comes to predicting pregnancy-induced hypertension (PIH), we can consider the follwing. First, by monitoring antihypertensive meds and vasopressors real-time dosages during surgery, we can capture crucial fluctuations. Second is the data related to fluid administration and blood loss during the operation. These variables play a role in blood pressure dynamics, so including them could be beneficial to our model.2. Future Research Directions
We should test our model on diverse patient groups—people with varying surgical risks and different health conditions since we want this thing to work well for everyone. Integrating our predictive model into clinical decision support systems will help doctors getting real-time alerts during anesthesia induction, helping them manage PIH proactively. And finally, running some randomized controlled trials can find out if our model can really make a difference in reducing PIH cases and improving patient outcomes. -
Thanks for sharing this interesting topic, Toby.
1. Stakeholders in Machine Learning for Symptom Prediction
Researchers and data scientists are important to develop machine learning models, while clinicians validate predictions and provide domain expertise. Patients and caregivers can contribute feedback, ensuring models align with individual needs. Regulatory bodies oversee ethical aspects, and industry provides infrastructure.2. Ethical Considerations and Challenges
Addressing bias and ensuring fairness is crucial, as machine learning models can inherit biases from training data. Privacy safeguards are essential when handling sensitive health data. Transparency, informed consent, and clinical integration are also key ethical considerations. -
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Mortality Rate
Mortality rate is a measure of the number of deaths, either in general or due to a specific cause, in a particular population, scaled to the size of that population, per unit of time. It is typically expressed in units of deaths per 1,000 individuals per year.Calculation – The mortality rate is calculated by dividing the number of deaths in a specified period by the total population and then multiplying by a constant (e.g., 1,000) to express it as a rate per 1,000 population.
Usefulness – The mortality rate provides a general overview of the health status of a population. It is crucial for public health planning, resource allocation, and evaluating the impact of healthcare interventions.
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Efficacy refers to the performance of an intervention under ideal and controlled conditions. It assesses whether an intervention produces the expected result under optimal circumstances.
Effectiveness measures the degree of beneficial effect of an intervention in real-world clinical settings. It evaluates whether an intervention works in terms that matter to the patient and is applicable to routine clinical practice.
Efficiency refers to the ability of an intervention to achieve desired outcomes with minimal resources, such as time, cost, or effort. It assesses the cost-effectiveness and resource utilization of an intervention.
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Hello, friends. Here’s for me:
https://snipboard.io/4mJkFv.jpg -
This is what I learned this week
https://snipboard.io/0J7Kk1.jpg
https://snipboard.io/3jcW29.jpg -
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I would like to choose point 6: Statistical significance indicates a scientifically or substantively important relation has been detected from page 341.
Statistical significance doesn’t mean that a finding is important or practically relevant. If the null hypothesis were true, the observed result is not happened by chance. The observed effect is unlikely to be a random.
Even in large studies, when it is not really matter in practice, the very small effects or minor errors in assumptions can show up as very statistically significant. A low p-value for the null hypothesis doesn’t necessarily mean the result is important in real life. It just means that the observed data are unlikely if the null hypothesis were true.
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Hi all, I am Pyae Thu Tun from Myanmar and I currently serve as a team member of the M&E team in an NGO focused on HIV prevention, care and treatment program. As part of my job, I have gained experience collecting and analyzing data using tools such as Excel and Power BI.
For the job nature, I only need to focus on the descriptive statistical analysis like summarizing the monthly headcounts, calculating average client visits per month and examining HIV positivity rates among different key populations and implementing sites. I have limited knowledge of inferential statistical analysis and I recognize the importance of expanding my knowledge in that area. I also hope we will have a chance to explore solutions together for any challenges while learning this course.
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Since it is essential to understand the reasons behind this behavior in analyzing the responses for this survey, we need to consider further exploration methods.
One approach is to conduct qualitative surveys and interviews to directly ask individuals about their reasons for not using bednets. This can provide valuable insights into the specific barriers or concerns that may be preventing bednet usage. Additionally, organizing focus group discussions can help gather collective opinions and experiences related to bednet usage, uncovering common themes and concerns among different groups of people.
Furthermore, by reviewing the existing research and surveys on bednet usage, we can get valuable insights into common reasons for non-usage. It can include factors such as cost, accessibility, awareness, and perceived effectiveness. These methods, as part of qualitative research, can provide deeper insights into real-world problems and generate hypotheses to understand the reasons for non-usage.
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The TAM explains why people use new technology. TAM says the usefulness of new technology compared to old technology matters a lot. People are more likely to use new things they find helpful and better than the old ones. Perceived usefulness is key to adoption.
When replacing old technology with something easier to use, we need to consider its benefits. If the new technology can provide extra features and improved function, it’s likely to be seen as more useful. If it lacks key features or offers no clear advantage, people might not adopt it.
Usefulness is subjective and varies among different user groups. Considering the user’s needs, preferences, and expectations is required in assessing the usefulness of new technology. User research, feedback, and involving them in development can help ensure the new technology meets their needs and is perceived as valuable.
According to TAM, new, easier-to-use technology should be at least as useful as the old it replaces to get people to use it. Thus, perceived usefulness is important for adopting the new technology.
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I agree with the variables my colleagues mentioned that can influence an individual’s perceived ease of use or perceived usefulness of a new technology. I would like to add the following external factors:
Social influence: The opinions of a family member and colleagues can influence how an individual perceives a new technology. It can impact an individual’s perception of it when those around them are positive towards technology.
Subjective Norms: Subjective norms can significantly impact how an individual feels about technology, including the social pressure to adopt a particular technology. If society feels the pressure to use technology, it can affect their attitude towards it.
Voluntariness: Regarding technology perception, if an individual feels that they have the freedom to choose to use or not to use the technology, it can have an impact. Feeling forced to use a technology can affect their attitude towards ease of use and usefulness.
Compatibility and Relevance: If the technology meets the individual’s needs, it can also influence their perception of ease of use and usefulness towards technology. When their current setup and needs are aligned by the use of technology, they are more likely to view it positively.
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In controlling COVID-19, treating everyone with fairness and respect is essential. This means avoiding favoritism and treating all individuals equally. Since everyone’s opinions are important, we should value them to create a safe environment. This involves setting ground rules for respectful communication and being open to diverse viewpoints.
We should not be afraid to raise ethical concerns posed by COVID-19 with the right people, whether it’s a supervisor, colleague, or trusted one. Together, we can navigate challenges and protect ourselves. Speaking up is the key to finding ourselves in a sticky situation.Building a strong ethical culture in the context of COVID-19 starts with each of us. We need to emphasize integrity and honesty in everything we do. We should encourage others to lead and make ethical choices. When we spot unethical behavior related to COVID-19 management, we must seek guidance from superiors or trusted individuals. It is crucial to prevent problems and report concerns to maintain a healthy and ethical environment.
If we work with respect and openness together, it will create a strong and inclusive community for everyone.
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Myanmar has planned to achieve the universal health coverage by 2030. Though there were efforts to improve the coverage for health service, the progress is challenging together with political instability.
As per assessment Progress towards universal health coverage in Myanmar (Su Myat Han, 2018), previous government has recognized the needs in accessible health service provision and affordability. At both national and subnational levels, it included comprehensive assessment of health service coverage and financial risk protection. It also highlights the variations and inequalities and health service coverage and health payments. This identification helps to address the specific needs of vulnerable populations in targeted intervention and resource allocation.
Weakness includes the coverage is below the target of UHC in Myanmar which is 80% which indicates the need of significant improvements the accessible and available health service throughout the country. Large proportion of population are facing catastrophic payments for healthcare service highlighting the requirement of better financial risk protection system. Different regions also show inequalities in coverage for health service, and we need to tailor the interventions to meet the specific needs.
To be more effective the UHC in Myanmar, we can consider the following actions:
– Investing in infrastructure, capacity building of the health care professional, continuous supply of medical equipment and commodities to improve the quality healthcare services.
– Implementing the strong financial system for health insurance to reduce the financial burden.
– Focusing on reaching the disadvantage populations from remote areas to ensure the equitable access to healthcare.
– Promoting the awareness program for prevention, early detection and health seeking behavior to enhance the health literacy and health outcomes. -
In Myanmar, there are many challenges to having a strong workforce for health informatics. There are few clinicians or policymakers who are aware of the importance of information technology in healthcare. Most are unaware of how to improve the healthcare quality which can have a better health outcome in the long term.
One challenge is the lack of collaboration and coordination with the different stakeholders. Actually we do have some experts in the health informatics field who are working separately in different organizations. But the situation is not favored to collaborate and working closely with each other. Next challenge is the education training for health informaticians which my colleague, Ko Aung Thura has discussed. That’s why we are here to learn the necessary skills and knowledge. This kind of program plays a vital role in emerging the professionals to overcome the health problems. And lastly, we also need to harmonize the inequities in healthcare access and digital literacy to address the challenges.
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In my personal view, it would depend on the situation. As mentioned in the discussion, if it is a crisis period like COVID-19, that is a definite share with anonymizing the data. Sharing data has many benefits since it helps with analysis and research which could contribute to new ideas and solutions for public health problems. Such collaboration enhances solving challenges and improves the decision-making process faster.
But it is also crucial to follow the regulations for data protection as we have many experiences of cyber security threats faced by developed countries. We must respect everyone’s privacy and their data should be protected by laws and regulations.
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In our country setting, there will be many challenges to implementing EMR system:
The first thing we will face is an unstable electric supply throughout the country. Frequent power shortages together with low coverage of internet across the country hinder in accessing the client records which can affect the healthcare delivery.
Secondly, we will need to have ready infrastructure for technology and professionals from different areas for deploying the system in a resource-constraint country. As we discussed in the previous course, privacy concerns is another area to be aware of in the EMR system as many developed countries are also facing this challenge with the continuous growth of technology.
Lastly, people are the most important part of the transition and there may be people who are used to the traditional method of recording could resist changing the new workflows. Adopting the new technologies from paper based records will have an impact on the productivity.
If we can cope with and overcome those challenges, there are many benefits to implementing the EMR system and some of these are:
We can reduce the amount of paperwork in patient information recording and history taking. It will reduce the physical storage area and also save time for healthcare workers. Since the system comes with keeping the data in a relevant structure and organization, the service providers can retrieve client information easily. Since the clients’ data can be accessed quickly, it can improve the accessibility and availability and that in turn leads to the efficient analysis of the data and improve decision making process.
This can reduce the risk of transcription errors from misinterpreting the handwriting. It can also help to improve the safety of the patient and his health outcomes. Another benefit of the system is the seamless communication between healthcare providers. We can quickly share the client information, investigation results and other relevant data and that kind of collaboration can lead to efficient and effective care for the patient. -
Missing data
I believe missing data is the common challenge we will face in handling the big data as prioritized in the paper. Even with a small application or tool designed to record day to day data, there are chances of not completing all the fields especially if it is not mandatory. That can be due to the data entry focal not being aware of the linkage between the original dataset and further analysis and useful information. Sometimes, a client chooses not to answer questions about his or her rights and some privacy concerns. To cope with this, it is learned that depending on the portion of the missing data, we can choose two kinds of solutions. If the missing data is low, we can substitute a relevant guess as Ching To Chung mentioned. If the missing data is at random, it is suggested to remove the entire data point so that there is no more bias for that data.Selection bias
Sometimes, it is possible that the study dataset does not represent the population intended to be analyzed. This can lead to results that are not strong enough to validate. Choosing the sample randomly may be a way of reducing the biased sample and this in turn improves the result.Data Analysis and Training
Handling big data is the biggest challenge we are facing today since it is no longer efficient with previous analysis tools. Advanced technology like machine learning can identify patterns and its prediction can help the big data in changing the valuable insights and outcomes. Together with the advanced tools, there is a need to train the relevant focal to promote their ability to analyze and use the big health data effectively.Interpretation and Translational Applicability of Results
If we cannot interpret correctly the findings in real world setting, it’s a waste use of technology. We will surely need the help of service providers and professionals from health sectors to understand the practical use of findings and interpretations so that it is a more usable dataset.Privacy and Ethical Issues
A thorough ethical review and informed consent from clients are a must to prevent any ethical related issues. Ethical guidelines and regulations to follow should be in place for any data collection and usage. We should also minimize the risk of identifying personal information by making the data anonymizing procedure. -
I completely agree with the four recommended points for fighting corruption in health systems. Convening key stakeholders, prioritizing action, taking a holistic view, and establishing research on the causes of corruption are all crucial steps that public health professionals should take to effectively combat corruption.
It’s also important that these should be implemented alongside other strategies and tailored to the specific context of each health system. Ongoing evaluation and adaptation of anti-corruption efforts are also necessary to ensure their effectiveness in the long run.
Additionally, I believe that enhancing transparency and accountability in healthcare systems is a key aspect of fighting corruption. This means advocating for open access to information, promoting financial transparency, and conducting regular audits. By making information readily available to the public and holding individuals and institutions accountable for their actions, we can significantly reduce the risk of corruption.
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Hi, Ko Aung Thura, Thank you for sharing the importance of being cautious about interpreting conflicting results based only on differing p values. It reminds us that statistical significance alone is not always sufficient to draw conclusions and it is essential to consider the broader context. It also highlights that statistical analysis are complex and approaching it with a thorough understanding of the full context is important.
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Indeed, I agree with Ko Aung Thura since he reflects on the overall situation in Myanmar. The last point he discussed is that we need to collaborate with others to develop the country, and I am with him on this, not only in the health sector but also in areas such as education, economy and infrastructure.
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