Mosharop Hossian

Mosharop Hossian
University of Queensland | UQ · School of Health and Rehabilitation Sciences

Master of Public Health

About

25
Publications
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Introduction
Mosharop Hossian is a young public health professional with several years of experience in quantitative research and project management. His research interests are chronic disease, lifestyle modification, and occupational health. Currently, Mosharop is doing his Ph.D. in health and rehabilitation sciences at the University of Queensland.

Publications

Publications (25)
Article
Full-text available
Objectives: Pregnant women are more vulnerable to develop respiratory infections such as Coronavirus disease 2019 (COVID-19). However, there is a dearth of literature related to pregnancy and its outcome with COVID-19.This group comparison cross-sectional study aimed to assess the birth outcomes related to COVID-19 among Bangladeshi pregnant women....
Article
Full-text available
The August 2024 floods in Bangladesh have precipitated a major public health crisis, significantly elevating the risk of waterborne and vector-borne diseases and exacerbating existing health vulnerabilities. This disaster has impacted over 5 million people, causing widespread environmental disruption, population displacement, and strained healthcar...
Article
Background COVID-19 affects the Quality of Life (QoL) in a reverse way after recovery, which might be multiplied by the comorbid non-communicable diseases. This study explored the relationship between comorbidities and QoL of COVID-19 recovered people in Bangladesh. Methods The cross-sectional study was conducted among 3244 participants between Jun...
Conference Paper
Full-text available
Background: Most electronic cigarette users are university-going young adults, and it is necessary to make an in-depth inquiry about their beliefs and intention that might help formulate a better strategic approach to prevent its use. Therefore, we conducted a qualitative study to understand the beliefs and future use intentions of university stude...
Preprint
Introduction On the verge of vaccination, the most pressing issue seems to be vaccine hesitancy. In this era of communication, Bangladeshi people may have pre-determined concerns about receiving the vaccines. Accordingly, our study attempted to understand belief, attitude, and intention to take the COVID-19 vaccine among the country’s adult populat...
Article
Full-text available
Background There is a paucity of resources focusing on the climate change experience of readymade garment (RMG) workers in developing countries such as Bangladesh. Therefore, this mixed method approach aims to understand the distinctive types of climate change experiences from a health and occupational perspective, along with the consequences of th...
Article
Full-text available
Background: The COVID-19 pandemic posed a danger to global public health because of the unprecedented physical, mental, social, and environmental impact affecting quality of life (QoL). The study aimed to find the changes in QoL among COVID-19 recovered individuals and explore the determinants of change more than 1 year after recovery in low-resour...
Article
Full-text available
Background Low back pain (LBP) is a common condition contributing to impaired quality of life among professional vehicle drivers. Our study aimed to assess LBP prevalence and associated factors among professional bus drivers in Bangladesh. Methods A cross-sectional study was conducted among 368 professional bus drivers using a semi-structured ques...
Article
Full-text available
Background: Occupational health hazards and injuries are an alarming concern among traffic police. Occupational injuries affect the physical, social, and mental well-being of police personnel, which has various public health implications. The evaluation of occupational health and safety policies and regulations for the traffic police relies on thei...
Article
Full-text available
Objective: The recent increasing incidence of human monkeypox cases highlights the necessity of early detection, prompt response and preventive management to stop it in its tracks, and healthcare workers play the most crucial role here. This study aims at assessing the preparedness of Bangladeshi medical doctors by assessing their knowledge and att...
Preprint
BACKGROUND Occupational health hazards and injuries are an alarming concern among the traffic police. Occupational injuries affect the physical, social and psychosocial well-being of police officers, which has various public health implications. The evaluation of occupational health and safety policies and regulations for the traffic police relies...
Conference Paper
Background COVID-19 negatively affects the Quality of Life (QoL) of affected persons after recovery. Presence of co-morbid non-communicable diseases (NCDs) might further downgrade their QoL . This study explored the impact of comorbidities on the QoL of COVID-19 recovered people in Bangladesh. Methods The cross-sectional study was conducted betwee...
Article
Full-text available
Background Health care workers have been facing difficulties in coping with the COVID-19 infection from the beginning. The study aimed to compare Quality of Life (QOL) among health care workers (HCWs) with and without prior COVID-19 disease. Methods This study was conducted from July 2020 to January 2021 among 444 HCWs. We randomly interviewed 324...
Article
Full-text available
Background: Evaluating potential vaccine side effects is often a prerequisite to combat the coronavirus disease 2019 (COVID-19) pandemic more effectively in a low-resource setting where herd immunity could be the most feasible option. Case report: Here, we present, an 80-year-old man with multiple comorbidities was admitted into the coronary care u...
Conference Paper
Full-text available
The COVID-19 pandemic, caused by the SARS-CoV-2 virus, significantly affected healthcare workers (HCWs), with over 180,000 fatalities globally and many continuing to face intense physical and psychological pressures. This cross-sectional study evaluated the quality of life (QoL) of 322 HCWs in Bangladesh who recovered from COVID-19, utilizing the B...
Article
Full-text available
Background The Coronavirus Disease 2019 (COVID-19) caused by the SARS-CoV-2 virus has taken the lives of more than 100,000 healthcare workers (HCWs) so far. Those who survived continuously work under immense physical and psychological pressure, and their quality of life (QoL) is impacted. The study aimed to assess the QoL among HCWs in Bangladesh w...
Article
Full-text available
Background : The restricted movement period related to COVID-19 has presumably contributed to the deterioration of the Internet addiction crisis. Therefore, this study aimed to determine the prevalence of Internet addiction and identify the factors associated with the increase in severity of Internet addiction among the general population of Bangla...
Article
Full-text available
Background Widespread vaccination coverage is essential for reducing the COVID-19 havoc and regarded as a crucial tool in restoring normal life on university campuses. Therefore, our research aimed to understand the intention to be vaccinated for COVID-19 among Pakistani university students. Methods This cross-sectional study was conducted in five...
Article
Full-text available
Objectives: Low back pain (LBP) is a common chronic condition among sedentary workers that causes long-term productivity loss. This study aimed to identify the relationships of individual and occupational factors with LBP among Bangladeshi online professionals. Method: We conducted a cross-sectional study involving 468 full-time online professio...
Article
Full-text available
Coronavirus Disease-2019 (COVID-19) quickly surged the whole world and affected people’s physical, mental, and social health thereby upsetting their quality of life. Therefore, we aimed to investigate the quality of life (QoL) of COVID-19 positive patients after recovery in Bangladesh. This was a study of adult (aged ≥18 years) COVID-19 individuals...
Article
Full-text available
A 77-year-old man with a past medical history of type 2 diabetes mellitus, peripheral neuropathy, and chronic obstructive pulmonary disease was admitted to the intensive care unit of Bangladesh Medical College Hospital with acute encephalopathy and non-ST segment elevation myocardial infarction (NSTEMI). The patient was on antidiabetic medicine alo...
Article
Full-text available
Objectives: With COVID-19 vaccination underway, this study aimed to understand belief, attitude and intention of the people in the South Asia region towards the vaccine. Methods: We conducted a cross-sectional study using semi-structured questionnaires among 18201 in�dividuals in four South Asian countries; Bangladesh, India, Pakistan, and Nepal be...
Article
Full-text available
Aim: Our study aimed to understand the acceptance level of the COVID-19 vaccine and its determinants among the adult Bangladeshi population. Methodology: This cross-sectional study was conducted in all eight divisions of Bangladesh. Data from 7,357 adult respondents were collected between January 17 and February 2, 2021, using a self-administere...

Questions

Questions (21)
Question
I'm currently working on a project in the health sciences that involves handling missing data and am seeking insights on the following:
  1. Methodological Comparison: Is there any methodological paper or study that compares the random forest technique-based imputation with regression-based Multiple Imputation by Chained Equations (MICE)?
  2. Performance Evaluation: In what situations does random forest-based imputation outperform regression-based MICE, and vice versa? Are there specific contexts within health science where one method is recommended over the other?
  3. Health Science Applications: Based on your experience or literature, which imputation method generally performs better in health science research? Are there particular types of data or study designs where one method shows clear advantages?
Any references to relevant literature, personal experiences, or insights into these questions would be greatly appreciated!
Question
I'm currently working on a project involving group-based trajectory modelling and am seeking advice on handling multi-level factors within this context. Specifically, I'm interested in understanding the following:
  1. Multi-Level Factors in Trajectory Modelling: How can multi-level factors (e.g., individual-level and group-level variables) be effectively addressed in group-based trajectory modelling? Are there specific methods or best practices recommended for incorporating these factors?
  2. Flexmix Package: I’ve come across the Flexmix package in R, which supports flexible mixture modelling. How can this package be utilised to handle multi-level factors in trajectory modelling? Are there specific advantages or limitations of using Flexmix compared to other methods?
  3. Comparison with Other Approaches: In what scenarios would you recommend using Flexmix over other trajectory modelling approaches like LCMM, TRAJ, or GBTM? How do these methods compare in terms of handling multi-level data and providing accurate trajectory classifications?
  4. Adjusting for Covariates: When identifying initial trajectories (e.g., highly adherent, moderately adherent, low adherent), is it necessary to adjust for covariates such as age, sex, and socioeconomic status (SES)? Or is focusing on adherence levels at each time point sufficient for accurate trajectory identification? What are the best practices for incorporating these covariates into the modelling process?
Any insights, experiences, or references to relevant literature would be greatly appreciated!
Question
I'm currently working on a project involving group-based trajectory modelling and am seeking advice on handling multi-level factors within this context. Specifically, I'm interested in understanding the following:
  1. Multi-Level Factors in Trajectory Modelling: How can multi-level factors (e.g., individual-level and group-level variables) be effectively addressed in group-based trajectory modelling? Are there specific methods or best practices recommended for incorporating these factors?
  2. Flexmix Package: I’ve come across the Flexmix package in R, which supports flexible mixture modelling. How can this package be utilised to handle multi-level factors in trajectory modelling? Are there specific advantages or limitations of using Flexmix compared to other methods?
  3. Comparison with Other Approaches: In what scenarios would you recommend using Flexmix over other trajectory modelling approaches like LCMM, TRAJ, or GBTM? How do these methods compare in terms of handling multi-level data and providing accurate trajectory classifications?
  4. Adjusting for Covariates: When identifying initial trajectories (e.g., highly adherent, moderately adherent, low adherent), is it necessary to adjust for covariates such as age, sex, and socioeconomic status (SES)? Or is focusing on adherence levels at each time point sufficient for accurate trajectory identification? What are the best practices for incorporating these covariates into the modelling process?
Any insights, experiences, or references to relevant literature would be greatly appreciated!
Question
Let me first try to explain what is intraclass correlation coefficients (ICCs).
I see Intraclass Correlation Coefficient (ICC) as a statistical estimation used to measure how similar the observations within a group or cluster or class (intra-class means what then! within class, right?) in comparison to the total variation across all the observations of the dataset. Now another question! What makes total variation. In this case, total variation is between group variation and within group variation.
Other way speaking, ICC is basically the ratio of between-group variation and total variation. Why we are accounting "between-group variation" here, why not "within group variation"! To understand this, we have to know the purpose of ICC. ICC is formulated to get the measure how much of the total variation can be attributed to group differences. If it is not clear yet, it will be within no time!
The range of ICC is from 0 to 1. ICC = 0 indicates no similarity among the members of a group and ICC = 1 indicates perfect similarity within the groups.
Let's consider a simple example to understand the concept. Suppose, you are the secretary general of UNO, and you have three blocks of countries. Each block gates the same intervention to improve physically activity level of adolescents, and you are interested to know how similar the intervention effect within each block compared to the other blocks.
If the ICC value is near to 1, it means that countries within each block have shown very similar physical activity improvement, and most of the total variation is attributable to between block variation. Other way speaking, within block variation does not have much contribution to total variation. For example: Europe block might have shown very high improvement in physically activity level of adolescents, Africa block might have shown "so so" improvement in physical activity level of adolescents, and Arab block might have shown poor improvement in physical activity level of adolescents.
If the ICC value is near to 0, it means that countries within each block have shown very diverse physical activity improvement, and most of the total variation is attributable to within block variation. Other way speaking, between block variation does not have much contribution to total variation. It is because all the blocks similarly contain countries which have shown high, "so so", and poor improvement in physical activity level of adolescents.
If the ICC value is around 0.5, you may attribute half (or near about) of the variation to between group and other half (or near about) to the within group variation.
You may use two packages to extract ICC for multiple poisson regression in R - lme4 and performance.
install.packages("lme4")
install.packages("performance")
library(lme4)
library(performance)
Fit the multilevel poisson regression
pa_model <- glmer(pa_improvement ~ fixed_effects + (1 | country_block), data = physical_activity_data, family = poisson(link = "log"))
Now, you can extract ICC from the multilevel Poisson regression model
icc <- performance::icc(pa_model)
And print the result of icc
print(icc)
Note: An example to understand between and within block variation more precisely. Europe Block - Finland, France, Italy. Africa Block - Liberia, Kenya, Nigeria, Arab Block - Kuwait, Morocco, Oman. Within block variation means variation among Finland, France, and Italy. Between block variation means variation between Europe block and Africa Block.
Note: If you find any methodologically severe issue, please, give a chance to correct myself.
Question
Firth logistic regression is a special version of usual logistic regression which handles separation or quasi-separation issues. To understand the Firth logistic regression, we have to go one step back.
What is logistic regression?
Logistic regression is a statistical technique used to model the relationship between a categorical outcome/predicted variable, y(usually, binary - yes/no, 1/0) and one or more independent/predictor or x variables.
What is maximum likelihood estimation?
Maximum likelihood estimation is a statistical technique to find the best representative model that represents the relationship between the outcome and the independent/predictor variables of the underlying data (your dataset). The estimation process calculates the probability of different models to represent the dataset and then selects the model that maximizes this probability.
What is separation?
Separation means empty bucket for a side! Suppose, you are trying to predict meeting physical activity recommendations (outcome - 1/yes and 0/no) and you have three independent or predictor variables like gender (male/female), socio-economic condition (rich/poor), and incentive for physical activity (yes/no). Suppose, you have a combination, gender = male, socio-economic condition = rich, incentive for physical activity = no, which always predict not meeting physical activity recommendation (outcome - 0/no). This is an example of complete separation.
What is quasi-separation?
Reconsider the above example. We have 50 adolescents for the combination- gender = male, socio-economic condition = rich, incentive for physical activity = no. For 49/48 (not exactly 50, near about 50) of them, outcome is "not meeting physical activity recommendation" (outcome - 0/no). This is the instance of quasi-separation.
How separation or quasi-separation may impact your night sleep?
When separation or quasi-separation is present in your data, the traditional logistic regression will keep increasing the co-efficient of predictors/independent variables to infinite level (to be honest, not infinite, the wording should be without limit) to establish the bucket theory - one of the outcomes is completely or nearly empty. When the anomaly happens, it is actually suggesting that the traditional logistic regression model is outdated here.
There is a bookish name of the issue - convergence issue. But how to know convergence issues have occurred with the model?
- Very large co-efficient estimates. The estimates could be near infinite too!
- Along with large co-efficient estimates, you may see large standard errors too!
- It may also happen that logistic regression tried several times (known as iterations) but failed to get the best model or in bookish language, failed to converge.
What to do if such convergence issues have occurred?
Forget all the hard works you have done so far! You have to start your new journey with an alternative logistic regression, which is known as Firth logistic regression. But what Firth logistic regression actually does? Without using much technical terms, Firth logistic regression actually leads to more reliable co-efficients, which helps to choose best representative model for your data ultimately.
How to conduct Firth logistic regression?
First install the package "logistf" and load it in your R-environment.
install.packages("logistf")
library(logistf)
Now, assume you have a dataset "physical_activity" with a binary outcome variable "meeting physical activity recommendation" and three predictor/independent variables: gender (male/female), socio-economic condition (rich/poor), and incentive for physical activity (yes/no).
pa_model <- logistf(meet_PA ~ gender + sec + incentive, data = physical_activity)
Now, display the result.
summary(pa_model)
You got log odds. Now, we have to convert it into odds.
odds_ratios_pa <- exp(coef(pa_model))
print(odds_ratios_pa)
Game over! Now, how to explain the result?
Don't worry! There is nothing special. The explanation of Firth logistic regression's result is same as traditional logistic regression model. However, if you are struggling with the explanation, let me know in the comment. I will try my best to reduce your stress!
Note: If you find any serious methodological issue here, my inbox is open!
Question
I am interested in understanding the most effective public health interventions that have successfully addressed health inequalities across diverse global settings. In your experience or from your research, which interventions have shown the greatest impact in reducing health disparities and improving overall population health? Additionally, what are the key factors that contribute to the success of these interventions, and how can they be adapted for use in different contexts? I am eager to hear your insights and engage in a fruitful discussion that can help inform future public health strategies.
Question
Dear Researchers,
Could you please suggest some public health journal considering the following criteria?
  • Q1 Journal
  • Fast processing
  • Affordable APC
Question
Could you please tell me-
1. How do we decide which interaction terms should be included in our model?
2. If there is any pre-checking involved, how can we do so using STATA?
TIA
Question
I would like to know:
1. How to decide either we should go for polynomial regression or not?
2. If we go for polynomial model, which one degree and multiple degree ivs should be included in model?
3. Is there any guideline to perform polynomial regression using STATA?
4. Could you please share some public health articles, in which the authors used polynomial linear regression analysis?
TIA
Question
To estimate prevalence/proportion
n = (z2*p*q)/d2
Points to be remembered: d, allowable error could be relative or absolute. If we know the absolute value, that is fine. But often, we go for relative allowable error. The maximum allowable is 20% of prevalence. For example: If prevalence is 56%, the maximum allowable precision will be: 56%*0.2 = 11.2%. In this case, our study will be able to detect the actual prevalence if the calculated prevalence will be 56%-(11.2/2)% = 50.4% or more.
To estimate mean
n = (z2* sigma2)/d2
Points to be remembered: Sigma is population standard deviation. If there is no mention of zalpha or zbeta, rather simply z, we consider it as zalpha (z score for entry 1-level of significance to the z-table).
To compare between two means
n = ((zalpha+zbeta)2 x (sigma1+sigma2)2)/(mean1-mean2)2
Points to be remembered:
1 represents group 1
2 represents group 2
zbeta (z score for entry power value to the z-table).
*if we would like to use the formula for clinical trial, we may consider 1 as experimental group, 2 as control group.
To compare between two proportions
n = ( (zalpha+zbeta)2 x ((p1(100-p1)) + (p2(100-p2))) )/(p1-p2)2
Points to be remembered:
1 represents group 1
2 represents group 2
To calculate sample size for diagnostic test
n = (z2*p*q)/d2
Point to be remembered: n is the sample size required when our expected sensitivity is p.
Final sample size n~ = n / prevalence of disease
To calculate sample size for correlation test
n = ((zalpha+zbeta)2 )/c2) + 3
c = (log ((1+r)/(1-r))) x 0.5
Point to be remembered: r is correlation-coefficient.
No harm to know:
1. Power is the probability of avoiding type ii error or false negative. But, what is type ii error? Type ii error is nothing but something like we certified someone as HIV free when he/she is actually HIV positive.
2. What is level of significance? You may regard it as type i error or false positive too. Suppose, we identified someone as an OCD patient when he/she is actually OCD free. This is called false positive event.
I have tried to accumulate the most-used sample size calculation formulas. Please, correct me if you find anything wrong.

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