Zarni Lynn Kyaw

[Zarni Lynn Kyaw]

Farkhan SEIR Model (I’m just helping to make sure it is display correctly in the WordPress.)

[Zarni Lynn Kyaw]

Dear Arjan,

I’m available for both online or onsite discussion.

Your comment is very helpful. Here is my thought taking account of your suggestion, let me know my understanding is correct.

If the coverage of the needle safety intervention is 50% and the efficacy is 90%, then the updated transmission rate would be:

beta = beta_0 * (1 – 0.5 * 0.9) = 0.45 * beta_0

I can adjust the values of x and e to represent different levels of coverage and efficacy. I can also explore the effects of different combinations of interventions.

At the same time a few more question pop up in my mind

How does the intervention affect the transmission rate?
A needle safety intervention might reduce the transmission rate by reducing the number of people who share needles. A universal precautions intervention might reduce the transmission rate by reducing the risk of exposure to contaminated blood.

How does the intervention affect the recovery rate?
A treatment intervention might increase the recovery rate by helping people to recover from the infection more quickly.

Are my thought along the same line of your suggestions?

[Zarni Lynn Kyaw]

Dear Arjan,

Thank you for your insightful comment. Here are my initial thoughts.

Whether to include multiple stages of infection: The model is being used to simulate the long-term dynamics of HCV transmission and the impact of different interventions, then it may be necessary to include multiple stages of infection, such as acute infection, chronic infection, and liver disease.

Varying the beta: The beta parameter represents the transmission rate of HCV. It is important to consider how beta may vary in different populations and settings. For example, beta may be higher in populations where there is a high prevalence of HCV and where there is high-risk behavior, such as sharing needles. It may also be higher in healthcare settings where there is a risk of accidental exposure to HCV-infected blood.

Modeling the impact of control measures: There are a number of different control measures that can be used to reduce the transmission of HCV, such as needle-sharing prevention programs, harm reduction programs, and treatment as prevention. The impact of these control measures can be modeled by multiplying the beta parameter by a reduction factor. This reduction factor would depend on the coverage and efficacy of the control measure.

Please let me know your thoughts.

[Zarni Lynn Kyaw]

Dear Arjan,

Thanks for the suggestion, I’m reading up on the literature to get a better understanding as well. Although I have a simple research question “What are the key factors that contribute to the transmission of hepatitis C in Myanmar?” it can be updated if the existing literature point me in a different direction or I found there is a research gap.

I will also keep in mind to make the model simple but I think SEIR model is suitable in this case. I’m also happy to discuss it with you as well.

Thanks,
Zarni

[Zarni Lynn Kyaw]

Building up from Week 2 Discussion, to answer the first question,

What intervention(s) you are considering in your modeling and how it will be added to the model structure?

Safe sex practices: This can be represented in the model by reducing the transmission rate, beta.

Needle safety: This can be represented in the model by reducing the rate of progression from the exposed to the infected compartment, delta.

Universal precautions: This can be represented in the model by increasing the recovery rate, gamma.

I can add these interventions to the model structure by modifying the equations for beta, delta, and gamma. I could reduce beta by a factor of 0.5 to represent the effect of safe sex practices, same could be done for needle safety and universal precautions. By playing around with the model and if I incorporate cost effectiveness analysis into the model, I could calculate ICER and provide best-buy to the policy makers.

What are the characteristics of the intervention(s)?
The characteristics of the interventions for hepatitis prevention and control vary depending on the specific intervention.

Coverage: This is the proportion of the population that is reached by the intervention. In my case, it could be safe-sex practices like condom distribution coverage.

Efficacy: This is the effectiveness of the intervention in treating the disease. In SEIR model, effectiveness of drugs for hepatitis can influence Recovered population.

Cost-effectiveness: This is the cost of the intervention compared to the benefits it provides. For example, Mass Media Campaign cost-effectiveness can be a deciding factor when comparing with programs like needle safety programs.

Acceptability: This is the willingness of people to use the intervention. This factor depend on the culture and context of the region.

If I were to design the intervention, I would make sure to consider cost-effectiveness and acceptability in Myanmar as I’ve seen some well-designed program with high efficacy and coverage fail because it’s either too expensive or not being accepted by the community.

So, I would focus on needle safety and universal precautions for a hepatitis program in Myanmar. Plus HIV program already did a good job of safe sex practices in Myanmar.

[Zarni Lynn Kyaw]

In Week 1, I stated that I’m interested in Hepatitis Model, so to answer the first question

What model structure would the disease you are interested be and please start adjusting the R code to that structure.

I first started looking at the literatures,

I found for Hepatitis SEIR model is used before. My reasoning can be found here. SEIR model simulation for Hepatitis Hepatitis C Virus Dynamic Transmission Models

The SEIR model is considered to be an appropriate transmission dynamic model for a pathogen with a period of latency between time of infection and time that an infectious individual becomes infectious to others (according to Infectious Disease Epidemiology (Oxford Specialist Handbooks).

So, if I were to develop a model for Hepatitis, I would use SEIR model. After that I will update the R code.

beta: For hepatitis, the transmission rate is estimated to be between 0.5 and 2.0 1/person-time.
gamma: The recovery rate is estimated to be between 1/200 and 1/100 days.
delta: The rate of progression is estimated to be between 1/100 and 1/50 days.
I also increase SIR curves for time 0 to 1000 days

Full R Code:
library(deSolve)
SEIR.dyn <- function(t, var, par) {
S <- var[1]
E <- var[2]
I <- var[3]
R <- var[4]
N <- S + E + I + R
beta <- par[1]
gamma <- par[2]
delta <- par[3]
dS <- -beta * S * I / N
dE <- beta * S * I / N – gamma * E
dI <- gamma * E – delta * I
dR <- delta * I
list(c(dS, dE, dI, dR))
}
beta <- 2
gamma <- 1 / 200
delta <- 1 / 100
SEIR.par <- c(beta, gamma, delta)
SEIR.init <- c(99, 1, 0, 0)
SEIR.t <- seq(0, 1000, by = 0.1)
SEIR.sol <- lsoda(SEIR.init, SEIR.t, SEIR.dyn, SEIR.par)
TIME <- SEIR.sol[, 1]
S <- SEIR.sol[, 2]
E <- SEIR.sol[, 3]
I <- SEIR.sol[, 4]
R <- SEIR.sol[, 5]
N <- S + E + I + R
plot(TIME, S, type = ‘l’, col = ‘blue’, main = ‘Hepatitis SEIR model’, xlab = ‘t’, ylab = ‘Number of individuals’)
lines(TIME, E, type = ‘l’, col = ‘red’)
lines(TIME, I, type = ‘l’, col = ‘green’)
lines(TIME, R, type = ‘l’, col = ‘purple’)
legend(‘topleft’, c(‘Susceptible’, ‘Exposed’, ‘Infected’, ‘Recovered’), col = c(‘blue’, ‘red’, ‘green’, ‘purple’), lty = 1)

Please state the key characteristics of the disease you are focusing on e.g. transmission, pathogenicity, symptoms, control measures etc.

Transmission: Hepatitis is a blood-borne virus that can be transmitted through contact with infected blood or body fluids, such as semen, vaginal fluids, and saliva.

Pathogenicity: Hepatitis can cause acute or chronic infection. Acute hepatitis is a short-term infection that usually clears up on its own.Chronic hepatitis on the other hand can last for many years and can lead to serious liver damage, such as cirrhosis and liver cancer.

Symptoms: The symptoms of hepatitis can vary from person to person. Some people may not have any symptoms at all, while others may experience symptoms such as fatigue, fever, loss of appetite, nausea, vomiting, abdominal pain, dark urine, and jaundice (yellowing of the skin and eyes).

Control measures: There are a number of control measures that can be used to prevent the spread of hepatitis

1) Safe sex practices: Using condoms during sex can help to prevent the transmission of hepatitis through semen and vaginal fluids.

2) Needle safety: Needles and other sharp objects should be disposed of safely to prevent the transmission of hepatitis through blood.

3) Universal precautions: Healthcare workers should use universal precautions to prevent the transmission of blood-borne pathogens.

And again,
beta: For hepatitis, the transmission rate is estimated to be between 0.5 and 2.0 1/person-time.
gamma: The recovery rate is estimated to be between 1/200 and 1/100 days.
delta: The rate of progression is estimated to be between 1/100 and 1/50 days.

[Zarni Lynn Kyaw]

Disease topic: Hepatitis
Scope of research: South East Myanmar (Karen)

Research question: What are the key factors that contribute to the transmission of hepatitis C in Myanmar?

Some keywords that I could use to search for relevant literature:

1) Mathematical modeling of hepatitis in Myanmar
2) Transmission dynamics of hepatitis in Myanmar
3) Hepatitis C treatment in Myanmar
4) Economic impact of hepatitis in Myanmar

After a quick search using those keywords in PubMed, there are very little research done using mathematical modeling in Myanmar but there are more than 40 articles on Hepatitis treatment.

I found one specific paper which relate to my research question. Hepatitis C elimination in Myanmar: Modelling the impact, cost, cost-effectiveness and economic benefits

Having done a preliminary paper search I believe that there are 4 main domains in the knowledge gaps that I can help fill, if I were to conduct a mathematical modeling paper on hepatitis in south east Myanmar.

Assessing the impact of interventions:
I could use mathematical modeling to assess the impact of different interventions, such as vaccination programs, treatment programs, and public awareness campaigns. I could model the spread of hepatitis C in Myanmar under different intervention scenarios, to see how the number of new infections would be affected.

Estimating key parameters:
I could use mathematical modeling to estimate key parameters, such as the transmission rate of hepatitis or the effectiveness of various intervention. This information could be used to improve the design and implementation of interventions.

Better understanding the transmission:
I could use mathematical modeling to better understand the transmission dynamics of hepatitis. I could model how the virus is transmitted through different routes, such as blood, sexual contact, or mother-to-child transmission.

Impacts of external factors on transmission:
I could use mathematical modeling to study the impact of external factors on the transmission of hepatitis. I could model how the spread of hepatitis would be affected by changes in the population, such as the number of people who are infected with HIV.

[Zarni Lynn Kyaw]

1) Some of the developments that could happen if every current smartphone camera could accurately detect breathing and heart rates are

Personal health monitoring: People could use their smartphones to track their vital signs on a regular basis, which could help them to identify potential health problems early on. This could lead to earlier interventions and better health outcomes.

Remote patient monitoring: Healthcare providers could use smartphone cameras to remotely monitor the vital signs of patients who are at home or in other settings. This could help to improve the quality of care for patients who are unable to travel to a doctor’s office or hospital.

Sports performance: Athletes could use smartphone cameras to track their heart rate and breathing rate during training and competition. This information could help them to optimize their training regimens and improve their performance.

Public health surveillance: Governments and public health agencies could use smartphone cameras to track the spread of diseases. This could help to identify outbreaks early on and take steps to contain them.

Some smartphone gadgets that I would like to see are
A portable glucose monitor that can be connected to a smartphone. This would allow people with diabetes to easily track their blood sugar levels throughout the day, without having to carry around a bulky glucometer. This could help people with diabetes to better manage their condition and reduce the risk of complications.

A smartphone-based stethoscope. This would allow doctors and nurses to listen to heart and lung sounds without having to carry around a traditional stethoscope. This could make it easier for healthcare professionals to diagnose and treat patients, especially in remote or rural areas.

A smartphone-based microscope. This would allow people to magnify objects and see details that would be invisible to the naked eye. This could be used for a variety of purposes, such as scientific research, education, and even home repairs.

A smartphone-based translator. This would allow people to translate text and speech between different languages in real time. This could be a valuable tool for travelers, business people, and students.

[Zarni Lynn Kyaw]

1) I think the following are the factors contribute to the high incidence of drug-resistant Tuberculosis,

Previous history of TB treatment. People who have been treated for TB in the past are more likely to develop MDR-TB, because the bacteria may have become resistant to the drugs used in their previous treatment.

Direct contact with someone with MDR-TB. The most common way to get MDR-TB is through close contact with someone who has the disease. If you are exposed to MDR-TB bacteria, your risk of developing the disease is higher if you have a weakened immune system.

Living in a crowded or poorly ventilated area. MDR-TB bacteria can spread through the air, so people who live in crowded or poorly ventilated areas are at an increased risk of exposure.

HIV infection. People who are HIV-positive are more likely to develop MDR-TB than people who are HIV-negative. This is because HIV weakens the immune system, making it more difficult to fight off the TB bacteria.

Lack of access to quality healthcare. People who do not have access to quality healthcare are more likely to develop MDR-TB, because they may not be able to get the right treatment or they may not be able to complete their treatment course.

Poor infection control practices. Poor infection control practices, such as not wearing a mask when you are around someone who has TB, can also increase your risk of developing MDR-TB.

2) Some intervention strategies that could be used to reduce the risk of MDR-TB in a high-risk area are

Increase awareness of MDR-TB and its symptoms. Many people are not aware of the symptoms of MDR-TB, which can make it difficult to diagnose and treat. Increasing awareness of MDR-TB and its symptoms can help people to seek medical attention early, which can improve their chances of successful treatment.

Screen high-risk populations. Some populations are more at risk of developing MDR-TB than others. These include people who are HIV-positive, people who inject drugs, and people who are incarcerated. Screening these high-risk populations for MDR-TB can help to identify cases early and prevent the spread of the disease.

Improve access to quality healthcare. People who live in high-risk areas may not have access to quality healthcare. This can make it difficult to diagnose and treat MDR-TB. Improving access to quality healthcare in high-risk areas can help to ensure that people who are infected with MDR-TB receive the care they need.

Strengthen infection control measures. Infection control measures can help to prevent the spread of MDR-TB. These measures include things like isolating patients with MDR-TB, using personal protective equipment, and cleaning and disinfecting surfaces. Strengthening infection control measures in high-risk areas can help to reduce the risk of MDR-TB transmission.

[Zarni Lynn Kyaw]

For the 1st question,

I would definitely like to apply these results in my hospital. Sepsis is a very serious condition, and it is important to do everything we can to prevent deaths and admissions to the ICU.

Specifically, I would use these results to develop a risk stratification tool for patients with sepsis. This tool would allow us to identify patients who are at high risk of poor outcomes, so that we can provide them with more aggressive treatment and monitoring. I would also use these results to educate the staff about the risk factors for sepsis, so that they can be more vigilant in identifying and treating patients who are at risk.

I believe that applying these results would have a significant impact on the outcomes of patients with sepsis in my hospital. We would be able to identify patients who are at high risk of poor outcomes and provide them with the care they need to improve their chances of survival. This would ultimately lead to fewer deaths and admissions to the ICU.

The findings of this study could also be used to improve the quality of care for patients with sepsis in other hospitals.

For 2nd question,
I would consider the following

Random forests: This algorithm is relatively easy to interpret, which is important for clinical decision-making.

[Zarni Lynn Kyaw]

This is an interesting paper,

For the first question,
In my opinion, RWE outcomes should be considered supportive results to clinical trials, but they should not be taken as definitive evidence of the efficacy or safety of a medication. Clinical trials are still the gold standard for evaluating the effectiveness and safety of medications, but RWE can provide valuable insights into how medications perform in real-world settings.

Pros:

RWE can provide information about the effectiveness and safety of medications in real-world settings.
RWE can be used to identify potential gaps in care.
RWE can be used to inform treatment decisions for individual patients.
Cons:

RWE studies are often observational, which means that they cannot prove cause and effect.
RWE studies may be subject to biases.
RWE studies may not be generalizable to all patients.

For the 2nd questions.
Disease severity: The more severe the psoriasis, the more likely patients are to have difficulty managing their disease and adhering to treatment.

Adverse effects: The occurrence of adverse effects can also lead to poor adherence. Patients who experience significant adverse effects may be less likely to continue taking their medication or following other treatment recommendations.

Cost: The cost of treatment can be a barrier to adherence, especially for patients with limited financial resources.

Patient preferences: Patients’ preferences for treatment can also influence adherence. For example, patients who prefer topical treatments may be less likely to adhere to an injectable or oral medication.

Health literacy: Patients with low health literacy may have difficulty understanding their treatment plan and may be less likely to adhere to it.

Patient-provider communication: Good communication between patients and providers can help to improve adherence. Patients who feel that their providers understand their needs and concerns are more likely to be compliant with treatment.

[Zarni Lynn Kyaw]

For me there is no Print Compose function in my computer but I use the Print Layout function which is the same.

[Zarni Lynn Kyaw]

The YouTube link is not available.

For me the way I solve the issue is when saving the shp file I ticked the Save only Selected Feature box,

[Zarni Lynn Kyaw]

This is also an interesting paper, so for discussion I write mainly from the point of view of house office, which I myself have to go through,

1) In Myanmar,
Lack of recognition: This is likely because house officers in Myanmar are the most junior doctors in the hospital, and they tend to receive less attention and recognition. This can lead to feelings of isolation and devaluation, which can contribute to burnout.

Work overload: Myanmar has a rapidly growing population, and the healthcare system is struggling to keep up. This means that house officers are often overworked, with long hours and heavy workloads. This can lead to feelings of stress and fatigue, which can contribute to burnout.

Lack of control: House officers in Myanmar often have little control over their work. They may be assigned to tasks that they are not qualified to do. This can lead to feelings of frustration and helplessness, which can contribute to burnout.

Conflicting values: The healthcare system in Myanmar is still developing, and there is often a conflict between traditional and modern approaches to medicine. This can lead to feelings of uncertainty and ambiguity.

Difficult working conditions: The working conditions in Myanmar’s hospitals can be difficult. There is often a shortage of resources, and the environment can be noisy and chaotic. This can lead to feelings of stress and anxiety.

2) If those factors are addressed
Improving recognition: The government could implement policies that recognize the contributions of house officers. This could include providing them with more opportunities for professional development, or giving them more responsibility in the workplace.

Reducing work overload: The government could also implement policies that reduce the workload of house officers. This could include reducing the number of hours they work, or providing them with more support staff.

Giving house officers more control: The government could also give house officers more control over their work. This could include allowing them to have a say in their assignments, or giving them more flexibility in their work hours.

Resolving conflicting values: The government could also work to resolve the conflict between traditional and modern approaches to medicine. This could involve providing more training for house officers on traditional medicine, or creating a more supportive environment for both traditional and modern approaches.

Improving working conditions: The government could also improve the working conditions in Myanmar’s hospitals. This could include providing more resources, or creating a more peaceful and orderly environment.

[Zarni Lynn Kyaw]

This is a very timely paper for me, I read it and let me share my two cents,

1) In Myanmar

Lack of infrastructure: Myanmar has a limited telecommunications infrastructure, which can make it difficult to access telemedicine services.

Lack of government support: The government of Myanmar has not yet fully embraced telemedicine, which has made it difficult to develop and implement telemedicine programs.

Cultural factors: The culture of Myanmar may also be a barrier to the use of telemedicine. For example, some patients may be reluctant to consult with a doctor through a video call, as they may feel that this is not as personal or effective as an in-person consultation.

Language barriers: Myanmar is a multilingual country, and this can pose a challenge for telemedicine. For example, patients who speak a minority language may not be able to access telemedicine services if they are not available in their language.

Socioeconomic status/insurance coverage: Patients from lower socioeconomic groups were less likely to have access to telemedicine due to lack of insurance coverage or the inability to afford the cost of the service.

Digital literacy: Patients and providers with low digital literacy skills were less likely to use telemedicine due to the difficulty of using the technology.

Medicolegal liability concern: Providers were concerned about the medicolegal liability of providing care through telemedicine.

Quality of care: Patients and providers were concerned about the quality of care that could be provided through telemedicine.

2) Bias
The study did have some limitations, including the potential for selection bias. This can happen for a variety of reasons, such as if the participants were self-selected or if the groups were not randomly assigned.

There are a number of methods that can be used to prevent or reduce selection bias in quasi-experimental studies.

Random assignment: This is the best way to ensure that the two groups being compared are truly comparable but the use of random assignment is not always feasible in quasi-experimental studies.

Matching: This involves matching the participants in the two groups on key characteristics, such as age, sex, and socioeconomic status.

Propensity score matching: This is a more sophisticated method of matching that takes into account a wider range of factors that could affect selection bias.

[Zarni Lynn Kyaw]

Thanks, I’m reading the paper now and it is very interesting.

[Zarni Lynn Kyaw]

I also agrees, QGIS is very powerful and it have a lot of functionality but thinking from my country context, in Myanmar, we don’t have good data, QGIS is a mapping application and it have some data functionality but I think it’s still garbage in and garbage out. If we can’t provide good and representative data, plotting everything on a map would be useless. So, me and my team are working on getting good quality data, at least from our project areas now, so when we use QGIS in the future, it will be a powerful tool.

[Zarni Lynn Kyaw]

This is also a very interesting paper and I made a quick summary of it.

Methods

The risk stratification and assessment framework for international travel considers four key factors, which are categorized into four pillars:

(i) incidence of cases,
(ii) reliability of case data,
(iii) vaccination, and
(iv) variant surveillance.

These measures are combined based on weights that reflect their corresponding importance in risk assessment within the context of the pandemic to calculate the risk score for each country.

For each pillar, specific parameters are considered.

In Pillar 1 (incidence of cases), the parameter is the number of cases per million population over a 7-day average.

Pillar 2 (reliability of case data) includes parameters such as the number of tests conducted per 100 population and the test positivity rate, both based on a 7-day average.

Pillar 3 (vaccination) focuses on the percentage of the population that has received at least one dose of a COVID-19 vaccine and the percentage that has completed the recommended vaccine series.

Pillar 4 (variant surveillance) takes into account the proportion of COVID-19 cases sequenced over the preceding 14 days, the number of days since the last sequencing data was uploaded, and the presence of very high priority variants of concern.

Weights are assigned to each parameter and pillar based on the local and global landscape of the pandemic at the time the model is applied. These weights reflect the relative order of importance. The weights are determined by considering factors such as guidelines from organizations like the WHO, best practices observed in travel policies of other countries, and expert opinions from epidemiologists and public health specialists.

The risk assessment process involves two steps: intra-pillar risk value calculation and inter-pillar risk score calculation. In the intra-pillar step, parameters within each pillar are combined to obtain a risk score for that pillar. In the inter-pillar step, the risk scores from each of the four pillars are combined to obtain an overall country risk score.

It is important to note that the specific weights assigned to parameters and pillars may vary based on the evolving nature of the pandemic and the available data.

Results

The results section presents the risk scores for each of the four pillars and the overall country risk scores for 224 countries and territories. The risk scores are generated using the risk stratification and assessment framework described in the method section.

The results show that the overall country risk scores range from 0.00 to 1.00, with higher scores indicating higher risk. The countries with the highest overall risk scores are those with high incidence of cases, low vaccination rates, and poor reliability of case data and variant surveillance. The countries with the lowest overall risk scores are those with low incidence of cases, high vaccination rates, and good reliability of case data and variant surveillance.

The results also show that the risk scores for each pillar vary across countries and territories. For example, some countries have high incidence of cases but good vaccination rates, while others have low incidence of cases but poor reliability of case data and variant surveillance.

The authors note that the risk scores presented in the results section are based on data available as of October 2021 and may change as the pandemic evolves and new data becomes available. The risk assessment framework and the results presented in the PDF can be used as a tool to inform travel policies and decisions, but should be used in conjunction with other factors such as local regulations and guidelines.

Discussion

The discussion section of the PDF provides an analysis and interpretation of the findings presented in the results section. It begins by highlighting the importance of risk stratification and assessment in informing travel policies and decisions during the COVID-19 pandemic.

The authors discuss the significance of the four pillars used in the risk assessment framework: incidence of cases, vaccination rates, reliability of case data, and variant surveillance. They emphasize that considering these pillars collectively provides a comprehensive understanding of the risk associated with international travel.

The discussion also addresses the limitations of the study. The authors acknowledge that the risk scores are based on data available as of October 2021 and may not reflect the current situation. They also note that the risk assessment framework relies on publicly available data, which may vary in quality and consistency across countries.

Furthermore, the authors discuss the potential implications of the study’s findings for travel policies and measures. They suggest that countries with high risk scores should consider implementing stricter travel restrictions and quarantine measures, while countries with low risk scores may adopt more relaxed measures.

The discussion section concludes by emphasizing the need for ongoing monitoring and updating of risk assessments as the pandemic evolves. The authors highlight the importance of collaboration between countries and international organizations in sharing data and information to improve the accuracy and effectiveness of risk assessments.

My questions are

1. The paper discusses the importance of risk stratification and assessment in informing travel policies during the COVID-19 pandemic. How can countries balance the need to protect public health while also facilitating safe travel?

2. The study proposes a risk assessment framework based on four pillars: incidence of cases, reliability of case data, vaccination rates, and variant surveillance. Do you think these pillars adequately capture the key drivers of risk in international travel?

My answer to the discussion points are:

1)
The risk assessment framework proposed in the paper is based on four pillars: incidence of cases, reliability of case data, vaccination rates, and variant surveillance. While these pillars provide a comprehensive understanding of the risk associated with international travel, there may be additional factors that could be included to increase the generalizability and predictive ability of the model.

For example, some possible parameters that could be considered include the capacity of the healthcare system in each country, the availability of medical resources and supplies, and the effectiveness of public health measures such as contact tracing and isolation protocols. Including these parameters could provide a more nuanced understanding of the risk associated with international travel and help countries develop more effective travel policies and measures.

2)
If I were a policy maker, I would consider applying this risk assessment model to support decision making for several reasons:

1. Data-driven approach: The model is based on data and provides a systematic framework for assessing the risk associated with international travel. By utilizing this model, policy makers can make decisions based on objective and evidence-based information rather than relying solely on subjective judgments.

2. Comprehensive risk assessment: The model takes into account multiple factors such as incidence of cases, vaccination rates, reliability of case data, and variant surveillance. This comprehensive approach provides a more holistic understanding of the risk and can help policy makers make informed decisions that consider various aspects of the pandemic situation.

3. Adaptability and flexibility: The model is designed to accommodate the dynamic nature of the pandemic and evolving national priorities. It allows for adjustments based on emerging variants of concern or changes in the epidemiological situation. This adaptability can be valuable for policy makers who need to respond to changing circumstances and make timely decisions.

[Zarni Lynn Kyaw]

This is a very interesting paper and I made a quick summary,

Risk associations of submicroscopic malaria infection in lakeshore, plateau and highland areas of Kisumu County in western Kenya

The main findings of the study on submicroscopic malaria infection in western Kenya are as follows:

1. The study found that blood smear microscopy, which is commonly used for malaria diagnosis, exclusively identified P. falciparum infections, while RT-PCR identified P. malariae and P. ovale mono-infections and co-infections with P. falciparum.
2. The routine microscopy method severely underestimates the burden of infections, as submicroscopic infections may act as a reservoir of infectious gametocytes and can develop into clinical infections.
3. The prevalence of submicroscopic malaria infections in the study area was 14.2%.
4. Topographic features of the local landscape and seasonality were identified as major correlates of submicroscopic malaria infection in the Lake Victoria area of western Kenya.
5. The study highlights the need for diagnostic tests that are more sensitive than blood smear microscopy to accurately detect submicroscopic infections and guide targeted interventions in areas with high transmission.

The implications of the study’s results for malaria control and prevention efforts in sub-Saharan Africa

1. Improved diagnostic methods: The study highlights the need for more sensitive diagnostic tests to accurately detect submicroscopic malaria infections. Implementing such tests in sub-Saharan Africa could help identify and target these hidden infections, leading to more effective malaria control and prevention efforts.

2. Targeted interventions: The identification of topographic features and seasonality as correlates of submicroscopic malaria infection suggests that targeted interventions can be implemented in specific areas and during specific times to reduce transmission. This could involve focusing vector control measures, such as insecticide-treated bed nets and indoor residual spraying, in areas with higher risk.

3. Integrated vector management: The study mentioned the evaluation of larviciding with Bti and community education and mobilization as supplementary interventions for malaria control. This highlights the importance of integrated vector management approaches that combine multiple strategies to target different stages of the mosquito life cycle and engage communities in malaria prevention efforts.

4. Regional context: While the study focused on western Kenya, the findings may have broader implications for sub-Saharan Africa. Understanding the factors influencing submicroscopic malaria infections and their impact on transmission can help inform malaria control and prevention strategies in other regions with similar ecological and epidemiological characteristics.

Introduction

The introduction of the paper discusses the current malaria control interventions in Kenya and the challenges faced in reducing malaria morbidity and transmission. It highlights the high vector densities in western Kenya and the presence of submicroscopic Plasmodium infections that are not detectable by standard diagnostic methods like blood smear microscopy. The introduction emphasizes the need for more sensitive diagnostic tests to accurately detect submicroscopic infections and guide targeted interventions. The study aims to investigate the correlates of submicroscopic malaria infection in the Lake Victoria area of western Kenya, particularly focusing on topographic features and seasonality. The introduction sets the context for the study and highlights the importance of understanding the factors influencing submicroscopic malaria infections for effective malaria control and elimination efforts.

Methods

The methods section of the paper describes the design and implementation of the study conducted in the Lake Victoria basin in Kisumu County, Kenya. The study utilized a cross-sectional community survey approach to assess malaria infection in three eco-epidemiologically distinct zones in Nyakach sub-County.

The surveys were conducted during both wet and dry seasons in 2019 and 2020. A total of 1,777 healthy volunteers participated in the study. Finger prick blood smears and dry blood spots on filter paper were collected from the participants for microscopic inspection and real-time PCR (RT-PCR) diagnosis of Plasmodium infection.

Participants who tested positive for Plasmodium infection through RT-PCR but had negative blood smears were considered to have submicroscopic infections. The prevalence of submicroscopic infections was correlated with topographical, demographic, and behavioral risk factors.

The data collected were analyzed using IBM SPSS Software Version 21.0. The Chi-square test was used to assess the significance of the association between malaria infection prevalence and seasons and topography. The Kruskal-Wallis test followed by Dunn’s multiple comparison test was used for multiple comparisons between Plasmodium species and seasonality.

Additionally, the agreement between microscopy and RT-PCR results was measured using Cohen’s kappa statistic, sensitivity, specificity, positive predicted value, negative predicted value, and diagnostic accuracy. Univariate binary logistic regression and multivariate mixed effect binary logistic regression analyses were employed to identify the risk factors associated with submicroscopic malaria infection.

Results

The study conducted cross-sectional community surveys during the wet and dry seasons in 2019 and 2020. A total of 458 and 388 participants were included in the wet season surveys, while 456 and 475 participants were included in the dry season surveys.

The prevalence of submicroscopic malaria infection varied across different topographic regions, with the lakeshore region having the highest prevalence. Gender, age, bed net usage, wall type, occupation, education level, household population size, bed net type, symptoms, and seasonality were among the variables investigated as potential risk factors.

In the univariate analysis, all risk factors were tested, and those with a p-value less than 0.50 were included in the multivariate analysis. In the multivariate analysis, variables with a p-value less than 0.05 were considered significant risk factors.

The results showed that topography, age, bed net usage, wall type, and seasonality were significant risk factors associated with submicroscopic malaria infection. The lakeshore region had a higher risk compared to the hillside and highland plateau regions. Older age groups, inconsistent bed net usage, mud wall type, and the wet season were also associated with increased risk.

The results provide insights into the risk factors associated with submicroscopic malaria infection in the study area, highlighting the importance of topography, age, bed net usage, wall type, and seasonality. These findings can inform targeted interventions and strategies for malaria control and prevention in the Lake Victoria basin in Kenya.

Discussion

The study found that despite the overall decrease in malaria burden in Kenya due to vector control interventions, malaria transmission remained high in the western regions bordering Lake Victoria. The high prevalence of submicroscopic malaria infections in the lakeshore region suggests the presence of a reservoir of infections that may contribute to ongoing transmission.

The results showed that topography, specifically living in the lakeshore region, was a significant risk factor for submicroscopic malaria infection. This finding is consistent with previous studies that have identified lakeshore areas as hotspots for malaria transmission due to favorable breeding conditions for malaria vectors.

Age was also identified as a significant risk factor, with school-aged children having a higher risk of submicroscopic infection. This finding aligns with the understanding that children are more susceptible to malaria due to their developing immune systems and increased exposure to mosquito bites.

Inconsistent bed net usage was associated with an increased risk of submicroscopic malaria infection. This highlights the importance of consistent and proper use of bed nets as a preventive measure against malaria.

The discussion also highlights the limitations of the study, such as the cross-sectional design, which limits the ability to establish causality. Additionally, the study focused on a specific geographic area, and the findings may not be generalizable to other regions.

The study emphasizes the need for more sensitive diagnostic tools, such as RT-PCR, to detect submicroscopic infections accurately. This is crucial for targeted interventions and surveillance efforts to identify and treat individuals with submicroscopic infections, who may serve as a reservoir for malaria transmission.

Conclusion

The study found that topography, age, bed net usage, wall type, and seasonality were significant risk factors associated with submicroscopic malaria infection. The lakeshore region had the highest prevalence of submicroscopic infections, highlighting the importance of targeted interventions in this area.

The study also emphasizes the need for more sensitive diagnostic tools, such as RT-PCR, to detect submicroscopic infections accurately. This is crucial for identifying and treating individuals with submicroscopic infections, who may serve as a reservoir for malaria transmission.

The findings of the study have important implications for malaria control and prevention efforts in the Lake Victoria basin in Kenya. The study highlights the importance of targeted interventions that consider the specific risk factors associated with submicroscopic malaria infection in the region.

My questions are

1. The study identified topography, specifically living in the lakeshore region, as a significant risk factor for submicroscopic malaria infection. What interventions or strategies could be implemented to specifically target and reduce malaria transmission in these high-risk areas?

2. The study found that inconsistent bed net usage was associated with an increased risk of submicroscopic malaria infection. What are some potential barriers or challenges to consistent bed net usage in other setting? How can these barriers be addressed to promote proper and consistent bed net usage as a preventive measure against malaria?

My answer to the discussion points are:
1)
1. Vector breeding sites: Mosquitoes that transmit malaria require suitable breeding sites, such as stagnant water bodies, for their larvae to develop. During the dry season, there may be fewer breeding sites available due to reduced rainfall and water scarcity. However, the remaining breeding sites could become more concentrated and productive, leading to higher mosquito populations and increased malaria transmission.

2. Human behavior and exposure: During the dry season, people may engage in activities that increase their exposure to mosquito bites. For example, water scarcity may lead to increased reliance on alternative water sources, such as open containers or uncovered wells, which can serve as breeding sites for mosquitoes. Additionally, agricultural activities or outdoor work may be more common during the dry season, increasing the chances of mosquito bites and subsequent malaria transmission.

3. Vector behavior and survival: Mosquito behavior and survival can be influenced by environmental conditions. In some cases, mosquitoes may adapt their behavior during the dry season to seek out alternative sources of water or blood meals, potentially increasing their contact with humans and the transmission of malaria.

2)
1. Longitudinal studies: The current study utilized a cross-sectional design, which limits the ability to establish causality or determine the temporal relationship between risk factors and submicroscopic malaria infection . Future research could utilize a longitudinal design to follow individuals over time and better understand the dynamics of malaria transmission and the impact of risk factors on infection outcomes.

2. Intervention studies: The current study focused on identifying risk factors for submicroscopic malaria infection, but did not evaluate the effectiveness of specific interventions to prevent or control malaria transmission. Future research could evaluate the impact of interventions such as insecticide-treated bed nets, indoor residual spraying, or larviciding on malaria transmission and submicroscopic infection outcomes.

3. Molecular epidemiology: The current study utilized PCR-based methods to detect submicroscopic malaria infection, but did not analyze the genetic diversity or distribution of malaria parasites in the study population. Molecular epidemiology approaches could provide insights into the transmission dynamics and genetic diversity of malaria parasites in the study area, which could inform targeted control strategies.

4. Health system factors: The current study focused on individual-level risk factors for submicroscopic malaria infection, but did not evaluate the impact of health system factors on malaria control and prevention. Future research could explore the role of health system factors such as access to diagnostic testing, treatment, and surveillance in reducing the burden of malaria in the study area.

[Zarni Lynn Kyaw]

I mean explanation are necessary and very helpful to understand the application but for me mixing step-by-step guide with all the explanation is very difficult to understand. I would prefer the explanation to be in a separate text box and maybe higher resolution screenshot, because now when I zoom into each screenshot it became blurry.

[Zarni Lynn Kyaw]

Dear Arjan,

I found the files from the Week 1 zip files so, I’m using them now.

I hope it is the same files and we can use it for Week 2 practice.

Thanks,
Zarni

[Zarni Lynn Kyaw]

Please kindly see my dashboard.

Zarni’s Dashboard

Sure, here is a detailed description of a Looker Studio dashboard for malaria cases in Thailand:

– Dropdown control to select year: This control allows users to select the year of data they want to view.
– Scorecard for total cases: This scorecard shows the total number of malaria cases in Thailand for the selected year.
– Number of districts: This metric shows the total number of districts in Thailand with malaria cases for the selected year.
– Number of provinces: This metric shows the total number of provinces in Thailand with malaria cases for the selected year.
– Download report button: This button allows users to download a report of the malaria data for the selected year.
– Age slider control: This control allows users to filter the data by age group. The default age range is 0-95 years old.
– Map of Thai provinces and districts: This map shows the distribution of malaria cases in Thailand for the selected year.
– Table of number of cases in each province: This table shows the number of malaria cases in each province for the selected year.
– Occupation control multiple choice selector: This selector allows users to filter the data by occupation.

The dashboard also includes a number of other features, such as:

– Filters: Users can filter the data by year, age group, occupation, and province.
– Sorting: Users can sort the data by any of the metrics.

The dashboard is designed to be user-friendly and easy to navigate. It provides a comprehensive overview of malaria cases in Thailand, and it can be used to track trends over time and identify areas where interventions are needed.

– The dashboard is built using Looker Studio.
– The data for the dashboard is sourced from the Thai Ministry of Public Health.

[Zarni Lynn Kyaw]

I also have to spend long hours to go through the PDF, I think I’m like you, I prefer flowchart and diagrams better as well.
I think the reason Arjan want to submit in one Zip file is that it will be easier for them to grade. On my laptop, I just select all the files I want to submit and click compress to make it into ZIP file, so I can submit easily.

There are several free tools like WinZip 7zip if your explorer doesn’t have compress function builtin.

Hope it’s help.

[Zarni Lynn Kyaw]

When I try to submit the ZIP file it went well. Just a friendly reminder, the site only accept a certain kind of file format for submission.

[Zarni Lynn Kyaw]

Thanks Arjan for your kind reply.

Can the PDF instruction be something similar to this

E.g. to save a file
Project –> Save –> Week1_QGIS

instead of long worded written instruction. I’m use to that kind of direction when I’m learning other application.

This is just a suggestion, if it is too much trouble, I’m also fine with current version as well.

[Zarni Lynn Kyaw]

Since the data structure is different from the dataset in the video, in order to show

1) Running sum and comparison, I calculate monthly daily column and running cumulative column beside them. For the sake of clarity I filtered only with Thailand as a country.

For comparison, I used cumulative of each country and percentage of each country

2) Running Delta, for the sake of clarity I filtered Thailand and sorted by date,

3) Drill Down and Date, 1st picture is top level drill down and 2nd picture is 2nd level drill down

4) Pivot Table , I compare Country vs Year and use heatmap in each column to easy see the highest number

5) Score Card,

6) Timeseries,

7) Bar chart,

[Zarni Lynn Kyaw]

I blended the data from COVID Surveillance sheet’s country field to Population by Country’s sheet country field using many to one relationship.

Using that blended data, I was able to display and sort highest death total per country with it’s population size.

And I was able to display the total death using a Map.

[Zarni Lynn Kyaw]

https://covid19.who.int/

The WHO COVID-19 Dashboard is a valuable resource for anyone who wants to stay up-to-date on the global spread of the pandemic. It provides a wealth of information, including data on cases, deaths, testing, and vaccination.

Pros:
The dashboard is updated regularly, so we can be sure that the information we’re seeing is accurate.

The data is presented in a clear and concise way, making it easy to understand.

The dashboard includes data from all over the world, so we can see how the pandemic is affecting different countries.

The dashboard is free to use, so anyone can access it.

Cons:
The dashboard can be overwhelming, especially if you’re not used to looking at data.

The data may not be complete, as some countries may not be reporting all of their cases and deaths.

The dashboard does not provide information on the severity of the cases, so we can’t tell how many people are experiencing mild, moderate, or severe symptoms.

https://ourworldindata.org/covid-sources-comparison

Our World in Data – This website provides a wide range of data visualizations on a variety of topics, including COVID-19. The data is sourced from a variety of organizations, including the WHO, the CDC, and the World Bank.

Pros
Our World in Data provides a wide range of data visualizations on a variety of topics, including COVID-19. This makes it easy to see how different factors are related to each other and to track changes over time.

The data on Our World in Data is sourced from reputable sources, such as the WHO, the CDC, and the World Bank. This means that the data is likely to be accurate and reliable.

Our World in Data is easy to use. The website is well-designed and the data visualizations are clear and easy to understand.

Cons:
The data on Our World in Data is updated regularly, but it is possible that some of the data may be outdated. This is because the data is collected from a variety of sources and it can take time for the data to be updated.

The data on Our World in Data can be misinterpreted by people who do not understand how to read and interpret data visualizations. This is why it is important to read the accompanying text and to consult with a data expert if we are unsure about how to interpret the data.

https://coronavirus.jhu.edu/map.html

This dashboard provides up-to-date information on the global spread of COVID-19, including confirmed cases, deaths, and recoveries. It also includes data on testing, vaccination, and other factors.

Pros
The Johns Hopkins Coronavirus Resource Center provides up-to-date information on the global spread of COVID-19. This information is updated daily, and it is based on data from a variety of sources, including the Centers for Disease Control and Prevention (CDC), and national governments.

The Johns Hopkins Coronavirus Resource Center provides a wide range of data on COVID-19, including confirmed cases, deaths, recoveries, testing, vaccination, and other factors. This data can be used to track the spread of the virus, to assess the effectiveness of public health measures, and to make informed decisions about personal health and safety.

The Johns Hopkins Coronavirus Resource Center is easy to use. The website is well-designed and the data is presented in a clear and concise way.

Cons
The data on the Johns Hopkins Coronavirus Resource Center may be inaccurate because some countries might not report the actual numbers.

The data on the Johns Hopkins Coronavirus Resource Center might not represent the on ground situation in LMIC countries.

[Zarni Lynn Kyaw]

Dear Tanyawat,

Your CRF is very detail and information rich and I like it but here are a few points you can use to improve upon it.

1) Eligible Criteria – I also was thinking about displaying full Eligible Criteria in my CRF but in order to reduce redundancy, some of the variable can be reduced. e.g. Aged between 18 and 60 years old on the day of screening could be calculated by the researcher from the DOB field after the fact. So I would advise to remove some of the Eligible Criteria which can be collected from other variables.

2) I got inputs from Arjans that we should use DD-MM-YYYY format for all the dates so Year of Birth could be DOB in that format

3) I like that you don’t include open ended variables that much, it would be helpful in the data analysis phase.

4) Arjans also guide me that For blinded study, vaccine type variable should not be included in the CRF. So let me provide the same comment here.

5) Maybe we think differently, but I put the vital sign variables in the Screen Form, which is inline with my first point, some of the data from the vital sign variables to answer the Eligible Criteria and reduce redundancy.

Overall, you’ve done a great work and please keep it up.

[Zarni Lynn Kyaw]

Dear Preut,

Thanks for your insightful reply. Great minds do think alike, some of your comments are same as Arjan to improve my CRF.

1) I was trying to choose between EU formant and The Good Clinical and Data Management Practice (GCDMP) and settled on MM-DD-YYYY but you are absolutely right. I will use DD-MM-YYYY from now on.
2) Yes, I will try to be more clear about endocrine domain from now on
3) Yes, it is the same comments from Arjan as well, I will do that next time.
4) Thanks

Sincerely,
Zarni

[Zarni Lynn Kyaw]

Arjan do we need to put up our CRF here on our own? I was waiting to review my assign CRF but it is sill not available.

[Zarni Lynn Kyaw]

I believe using
consistent formats for the dates (DD/MM/YYYY) is a good idea, as well as using
consistent units for the measurements (like making weight and hight to KG and Meter, so BMI could be easily calculated),
Age could be two [] [] boxes as the study is only between 18 to 60,
race could also be a multiple choice as well, and
replacing “given” with “administered” in the title to “Immunogenicity and Safety of a Quadrivalent Influenza Vaccine Administered Intramuscularly in Participants Aged 18 to 60 Years” would make it more professional

[Zarni Lynn Kyaw]

Data standardization enables the rapid design, build, analysis, and submission of clinical trials. This means new treatments, procedures and tools get to market more quickly, for less cost. Data standards also support semantic interoperability, which is the ability to fully understand the meaning of data across different systems and sources. This facilitates data sharing, reuse, and integration for clinical research and practice.

Although I can’t give exact example from Myanmar because Myanmar’s health systems is driven by vertical programs and most programs operate in a silo nature. e.g. HIV/TV team is developing their own systems and Malaria team have their own systems. Having said that since 6th AeHIN general meeting in Nay Pyi Taw, Myanmar suppose to use DHIS 2 as a national platform but that program itself is in silo now. Only a handful of government hospitals are using DHIS 2. There is very little interoperability between those systems. I’m only talking about the public systems, the private hospitals are using their own different systems as well.

Due to the learning from this course I understand the benefit of having data standards for clinical research, in Myanmar we still have a long way to go, to make use of data interoperability.

Having said that, I could give one example from the private sector who is doing well (but sometime controversial in the news) regarding data standards for clinical research. FAME Pharma is a well established traditional medicine producer in Myanmar who supposedly use clinical trials to produce their traditional medicine but Myanmar’s FDA is very weak and corrupted. Since FAME pharma is a private company, we rarely see their clinical research data and their FDA application is not public record. They claimed that they use several patients data from various regions of Myanmar to test their drug. So, they might be using some kind of data standards for clinical research.

[Zarni Lynn Kyaw]

I would like to showcase two projects that I have worked on in my experience.

1) The People’s Health in Southeastern Myanmar: Results from a Household Survey and the Way Forward where we collect data from 486 households
2) Ethnic Health Organizations in Myanmar Readiness to provide Essential Health Services and Plans of Action where we collected data from 5 health organizations

The outcomes of these projects are valuable in providing much needed data for the underserved ethnic population in Myanmar. However, I have to admit that we faced some challenges in ensuring data quality and integrity in the data management process, such as:

– Audit trail/Time stamp: All of the raw data were in paper-based format and in the case of the second project, we did not have a proper audit trail/time stamp, because we lacked the necessary knowledge and skills. We did not have a qualified data manager to standardize the data collection, recording and storage. We used Google Drive to store our data. Data collectors just uploaded everything into the same Google Drive and I, as the researcher at that time, had to piece all the data together. Although we could check who accessed the folder and when using Google Drive’s administrator features, we did not do so regularly.

– User authentication and access control level: Only people with my organization’s email could access that Google Drive, but it was still possible for someone to download the folder and share it with others. We tried to limit access to certain people, but there was still room for improvement.

– Edit check and logical check: For the first project, we applied edit checks and logical checks when we cleaned the data, but we did not perform double data entry, due to limited resources. For the second project, we used CommCare by Dimagi, which reduced the use of paper-based methods, but we could have implemented better edit checks.

– Data backup and recovery plan: Google Drive automatically backed up our data, but after the data cleaning process was completed, we saved a copy onto a USB drive. However, we did not have a qualified data manager or any health informaticians, so we were unaware of some best practices at that time. Now we have restructured our M&E department and integrated it with our health systems strengthening team. We hope to improve our data management process in the future.

What computer software did you/they use to store and manage data?
We used CommCare by Dimagi before, but due to budget constraints (it cost 10,000 USD per year as subscription fees), we are in the process of developing our own system using Javascript for the front end and Oracle Systems for the back end. It is currently in testing.

[Zarni Lynn Kyaw]

[Zarni Lynn Kyaw]

– Steps in the data management workflow:

A data management workflow for a research project consists of six steps: planning and design, collect, generate and store, clean, analyze and visualize, manage, store and preserve, share and publish, discover, reuse and cite. We have done some of these steps in our household survey, such as planning and design, collect, generate and store, manage, store and preserve, share and publish. However, we have not done some other steps, such as clean the data systematically, analyze using statistical methods and visualize using Google Studio and we didn’t publish in a peer-reviewed journal because we could not convinced the ethnic health leaders about the importance of publishing.

– Steps that we should have done to improve our project: To improve our project, we should have done the following steps:

– Clean our data systematically by checking for errors, inconsistencies, and missing values, and correcting or removing them as appropriate as well as work with staff who collected the data to make sure data consistency. This would improve the accuracy and reliability of our data.
– Analyze and visualize our data by using appropriate statistical methods and tools to explore patterns, trends, relationships, and outliers in our data. This would help us answer our research questions and test our hypotheses. We only wanted to do a baseline study at that time and we didn’t have enough resources to properly analyzed the data.
– Discover, reuse and cite other relevant data sources that could complement or support our own data. This would help us contextualize our findings and compare them with existing knowledge, in other words, publish in a peer review journal would help us turning this data into a scientific evidence.

[Zarni Lynn Kyaw]

I would like to share about a bi-annual study we did a few years back called : The People’s Health in Southeastern Myanmar: Results from a Household Survey and the Way Forward

– Purpose of data collection: The purpose of data collection was to monitor and evaluate a strategic purchasing project that aimed to improve health service delivery and cooperation with ethnic health organizations in conflict-affected areas in Southeastern Myanmar. The data collection also provided information on the health status and needs of ethnic minority populations living in those areas.

– Primary or secondary data collection: The data collection was primary, as it involved gathering data directly from the source, i.e., the households and clinics involved in the project.

– Methods used for data collection: The methods used for data collection were: Automated data collection functions built into the strategic purchasing project’s monitoring and evaluation system, which recorded the number of visits, blood pressure measurements, and types of services provided by the clinics.

– A household survey conducted in March 2019, which reached 486 households containing 2,167 individuals. The survey asked questions on various topics such as socioeconomic status, health-seeking behavior, reproductive health, and satisfaction with the clinic services. The survey also measured blood pressure for some respondents. The survey was conducted using android tablets and phones, and the questions were asked in Kayin language and recorded in Myanmar into computers which later translated to English for data analysis.

– Problems that occurred regarding data collection: Some of the problems that occurred regarding data collection were:
– Data quality issues, such as errors, inconsistencies, and missing values in the raw data. Some staff just didn’t fill some field correctly or skip it.
– Finding relevant data, as there were a wide range of systems and sources to navigate, and some data was not available or accessible due to security or logistical reasons.
– Deciding what data to collect, as there were trade-offs between the scope, depth, and feasibility of data collection.
– Dealing with big data, as there were challenges in storing, processing, and analyzing large amounts of data.

[Zarni Lynn Kyaw]

[Zarni Lynn Kyaw]

[Zarni Lynn Kyaw]

Dear Preut,

Thanks for your ideas. I like them. Regards to Health as a bridge for peace, I also think it is very possible, with a lot of Myanmar public eager for a federal systems, if there is cessation of conflicts soon, we should be able to start a dialogue for decentralization. I hoped the 2021 Military coup won’t have much permanent demages.

For HIS, I think one comment during the Zoom call suggest to focus on HR, but I think both HR and LMIS are important. According to the World Bank survey, Myanmar waste 10% to 20% of our health budget on inefficiencies, medical wastes is in the top 3 cause. So, investing in LMIS will be critical.

So, thanks for your comments.

[Zarni Lynn Kyaw]

Yes, I also use the Malaria incidence for submission but I’m wondering where is that Malaria Morbidity comes from.

[Zarni Lynn Kyaw]

Yes, PDF is very detailed and helpful but personally I’m more of a visual learner and I like diagrams and flowchart so that’s why I was exploring. But you are completely right a detail PDF could be useful for a program like QGIS.

[Zarni Lynn Kyaw]

Thanks Arjan for pointing it out. Well noted and I will not over emphasize on one sample’s results.

[Zarni Lynn Kyaw]

By the way, you can use https://snipboard.io/ to share the infographic directly here. https://prnt.sc/ doesn’t work here because it doesn’t give a link ending with .jpg, but snipboard do.

[Zarni Lynn Kyaw]

I also used Canva, this is a great infographic. Thanks for sharing it with us.

[Zarni Lynn Kyaw]

Your explanation is direct and easier to understand than what is written in the text book but let me put my thoughts here as well 🙂
We cannot say that an effect size outside the confidence interval is wrong or impossible, because the confidence interval depends on the assumptions we make and the level of certainty we choose. Even if the effect size is outside the confidence interval, it might still be possible under different assumptions or a different level of certainty.

[Zarni Lynn Kyaw]

Yes, it seem our cohort all seems to think this is a big revelation after reading the whole thing. We read so many papers that just accept P ≤ 0.05, and not share the exact P value.

I read this article An unhealthy obsession with p-values is ruining science in Vox and it was a well written piece.

[Zarni Lynn Kyaw]

Wow, you actually include multiple points and I liked that cheers.

[Zarni Lynn Kyaw]

Nicely written, I agree, the best way to compare groups or studies is to use a test statistic and a P value that directly measure the difference and the chance of seeing it by chance.

[Zarni Lynn Kyaw]

I agree with both you and Farkhan point, the only exception is when the P value is very small (e.g., under 0.001), because then it does not matter much and it might be hard to calculate accurately.

[Author]

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