Welcome to McMaster’s Epi and Biostats Blog!

Our research blog is an opportunity for you to write a post on a current epidemiology and/or biostatistics research topic corresponding to a subject area presented on our blog (ie. health economics, health policy analysis, health services research, and health technology assessment). You are encouraged to comment on your colleagues posts and contribute any novel thoughts, ideas, or criticisms that you may have to further advance the ideas of your peers. Submissions to our blog can be individual or collaborative pieces. All submissions must be directed to mcmastercseb@gmail.com with all contributing author names and blog post titles in the subject line of the email. Regular authors who are active on our blog will be given the opportunity to join as a blog contributor! If you would like to join the editing team, please send a piece that is indicative of your writing ability, along with your previous postings, to the email listed above with a subject line containing your name, student number, and research blog editor application. We look forward to hearing your creative ideas!

“Do not fear to be eccentric in opinion, for every opinion now accepted was once eccentric.” – Bertrand Russell

Health Research Skills Workshop Leaders: Jason Busse, Associate Professor

Jason Busse, PhD [2]
Clinical studies are of the utmost importance for evidence-based medicine. Jason Busse has many research interests within this field. As an associate professor in the McMaster University Department of Health Research Methods, Evidence, and Impact, Dr. Busse is interested in insurance medicine, orthopedic trauma, chronic pain, and complex disability [1]. Furthermore, Dr. Busse is a principal investigator of the TRUST trial, which is a randomized controlled trial pertaining to the treatment of tibial fractures taking place at multiple locations. Dr. Busse also has an interest in optimizing the methodology of health research. His work has been impressive enough to garner national recognition as he was honoured with the Canadian Institute of Health Research New Investigator Award for 2009-2013. On Sunday, March 26, Dr. Busse will be on hand at the CSEB Health Research Skills Workshop in CIBC hall to present an introduction to the research process, including the hierarchy of evidence in epidemiology and biostatistics and how to conduct systematic review, clinical trials, and data analysis. 

Muza Memon, 3rd Year Medical School Student

Muza is currently finishing up his final year of Medical school at McMaster. He is hoping to follow his dream this summer and become an orthopaedic surgeon. Muza Memon’s research interests are in the epidemiological field of orthopaedics. Orthopaedic epidemiological research is extremely diverse, challenging, but also fascinating.

When examining orthopaedic epidemiology, researchers are using front line evidence to find various methods to improve orthopaedic procedural outcomes. Often, studies must gather patient important outcomes, quality improved life years, various imaging results, and more in order to have a holistic understanding of the outcomes for certain procedures to adequately examine the effectiveness of various orthopaedic methods. Limitations in orthopaedic epidemiology include an narrow sample of the population in a study. Often times, orthopaedic research samples consists of athletes or of the elderly population, therefore it is hard to draw conclusions that may speak to the entire population in general.

It is important to understand the various ways that orthopaedic research can be conducted. Often times, it is through retrospective chart reviews, where a researcher may examine the past records of a patient to understand their procedure and outcomes from that procedure as well as various other demographic factors. Another method is through prospective sampling, where researchers gather information on a cohort of people as time goes on in select intervals. There are also randomized control trials where the cohort of individuals might be randomly selected to certain treatments or placebo to gather causal data of risk factors or treatments.

Anna (Aihua) Li, PhD; Post Doctoral Fellow; Department of Pathology and Molecular Medicine

Anna graduated from Huanan University in 1992 with an undergraduate degree in medicine. She then went on to complete dual MSc degrees at Capital University of Medical Sciences in Beijing, China, as well as at McMaster University in Clinical Epidemiology (1). She is currently working as a post-doctoral fellow in the department of Pathology and Molecular Medicine at McMaster. Her PhD degree focused on “Genetic Epidemiology on Obesity”. She is currently featured in 23 publications.

           As exemplified through her various research projects, genetic associations of obesity is observed through genotype-phenotype studies. However, through Anna’s studies, there are many fallacies in seemingly well-done studies that have drawn statistically significant conclusions on these associations. First of all, studies may be lacking in power, or more commonly known as sample size. Sample size is crucial for epidemiological studies to draw important conclusions, and with an inadequate sample size then there may be a chance of risking a type 1 error. A type 1 error is when a study concludes an association between 2 variables when there is none. Through these extensive studies, Anna has been able to discuss the weaknesses in many epidemiological studies as well as conduct her own studies investigating the associations between obesity and genotypes.

CSEB Graduate Student Profiles: Sara DiGregorio, MSc. Candidate, eHealth

Sara graduated from McMaster’s Life Sciences program in 2013 and is currently working on her MSc. in eHealth. According to Journal of Medical Internet Research (JMIR)  when eHealth first emerged it was defined to be

an emerging field in the intersection of medical informatics, public health and business, referring to health services and information delivered or enhanced through the Internet and related technologies. In a broader sense, the term characterizes not only a technical development, but also a state-of-mind, a way of thinking, an attitude, and a commitment for networked, global thinking, to improve health care locally, regionally, and worldwide by using information and communication technology (1).”

This definition only seems more relevant as the world becomes more and more connected through the internet. eHealth is still an emerging field as only in 2005 did organizations like the WHO start to promote the use of electronic records and communication technologies in healthcare (2). Many hospital records are still kept on paper as staff and physicians can have a difficult time transitioning to electronic records. Some obstacles to converting to EMRs are time, cost and having to understand a new method of examining patient records (3). There are still pros and cons to switching to HER as well; privacy concerns, reduced storage, recovering lost data ,ease of access etc. (3) Sara works at OSCAR EMR, an electronic medical record system used by health professionals to access patient charts and a database of medication information.

CSEB Graduate Student Profiles: Michael Xu, MSc. Candidate, Health Technology Assessment

Michael Xu is a MSc. student at McMaster University studying Clinical Epidemiology and Biostatistics, with a specialization in Health Technology Assessment (HTA). HTA  is the evaluation of the economic, social and environmental consequences of technological advancements on healthcare (1). It is particularly focused on understanding the health side effects of different kinds of phenomena like offshore oil drilling, pesticides, automobile pollution, supersonic airplanes, weather modification, and the artificial heart on a whole population (the epidemiology part) (1).

He has plenty of research experience in the field of Biostatistics, particularly with regression related statistical methods. He is currently working with Professor Thabane and Dr. Fox-Robichaud to investigate rapid response system workflows and potential improvements to current system failures.  Michael has also been involved in many extracurricular organizations such as Bachelor of Health Sciences Society, McMaster Medicine and Health Society, McMaster Outdoor Club, and Canadian Society for Epidemiology and Biostatistics.

HTA can be used to help empower patient with regards to their decision making and being involved in their treatment process (2). Part of this process is educating and informing patients of their options. Michael’s research interests include economic empowerment, education, health, and science and technology.

CSEB Graduate Student Profiles: Salma Chaundry, MSc. Candidate, Global Health

Public health can be studied on many scales from numerous perspectives. Salma Chaundry uses her background in Sociolegal Studies at the University of Toronto to approach public health on a global scale as she completes her Master of Science in Global Health at McMaster. Salma specializes in health policy management and the global burden of disease. She also approaches public health on a regional scale internationally, as her work at the University of Toronto involves conducting research on maternal and child health in occupied Palestine. Salma’s research has the potential to improve the lives of many people.

As demonstrated by Salma’s research interests, students in the Global Health program make the world their classroom. This program offers various transcontinental learning, collaboration, and exchange opportunities to study everything from international law and macroeconomics to the spread of disease in relation to Global health. Students can also choose to specialize in Global Health Management like Salma, Global Health Sciences, or Global Health Management. Meet Salma at the CSEB speed mentoring event “Falling in Love with Research” on February 15 from 6-8 pm to learn more about her research and the Global Health program.

CSEB Graduate Student Profiles: Chloe Bedard, MSc. Candidate, Health Research Methodology

Are you interested in learning research skills that will allow you to improve clinical practice and strengthen health systems to ultimately improve public health? If so, you may also be interested in speaking with and learning from Chloe Bedard at the CSEB speed mentoring event “Falling in Love with Research” on February 15 from 6-8 pm. Chloe completed her undergraduate degree at McMaster in the Health Science program while earning a minor in psychology. Chloe is in the second year of a Master of Science in Health Research Methodology (HRM). Chloe applies her multidisciplinary background in health science and psychology to her research on young children both with and without Developmental Coordination disorder. This research involves the development of interventions to improve motor coordination and psychological skills. Chloe will share more about her research at the event.

McMaster’s HRM graduate program is much like Chloe’s research in that it is interdisciplinary in nature. Students in this program come from diverse academic backgrounds that include but are not limited to medicine, nursing, social sciences, and math and statistics. Graduates from the program will have undergone experiential learning to develop innovative new research strategies in their respective fields. Come meet Chloe on February 15 to learn more about HRM.

Evidence-Based Weight Loss

When we consider weight-loss as part of the general population, we think fad diets, juice cleanses, working out 3 hours a day, and more. Often, these trends and fads are not backed up by evidence, and if, by chance, the blogger, news outlet, or YouTube video does include evidence, there are major flaws in the study design. Therefore, it’s incredibly important to be able to comprehend research studies aimed to discuss diets, weight-loss, and provide suggestions on how to maximize health outcomes while shedding pounds. Our bodies are incredibly complex, and this requires us to value our body machinery and scrutinize studies.

This study, published in the British Medical Journal in 2006, was a randomized control trial of four commercial weight-loss programs (1). The study indicates that all four programs (Atkins, Weight Watchers, Slim Fast, and Rosemary Conley) showed statistically significant weight-loss differences (p<0.001) compared to control group. The average weight-loss after 6 months in the intervention groups was around 5kg, compared to -0.6kg in the control group. The Atkins diet group had the greatest weight loss. From initial impressions, it seems that this study has proven the effectiveness of four commercial diet programs in weight loss, especially the Atkins diet, but there are detrimental flaws in this study that may have inflated their results. First of all, the study did not blind any of the participants to the interventions they were on. This may have resulted in participants in various programs to either try harder to lose weight compared to other groups. Also, all participants were chosen from volunteers, therefore there are inherent differences between the participants in the study compared to the general population. Second of all, the study did not make any efforts to control for co-interventions. Weight-loss can be greatly affected by how much exercise participants receive, therefore it is unknown if the weight-loss stems from theirdegital-scale diets or from other co-interventions such as exercising. Lastly, the study fails to adjust for differences in baseline BMI, family history of obesity, and other factors that may have significantly influenced results. After such adjustments, it may be found that the interventions may be very effective for specific groups while completely ineffective for others.

When we examine epidemiology studies studying weight-loss programs, it is easy to be blind-sided by large numbers, great effects, and words such as “RCT” which may cause us to automatically assume that it is of utmost quality of evidence. However, we must critically examine all aspects of the study in order to judge it! As such, take all weight-loss programs with a grain of salt, and make sure to speak carefully with your family physician to see which program is suitable for you!



Nutrition Epidemiology

Nutritional epidemiology is the study of the relationship between aspects of the diet and occurrence of human illness (1). In the past, advances in nutritional epidemiology have been pivotal in curing and preventing many diseases. For instance, the discovery that lemons and oranges can prevent sailors from having scurvy was incredibly important for voyages across the Atlantic or Pacific. Although past nutritional epidemiological studies have concentrated on nutritional deficiencies, recent literature has focused on nutritional indicators and risk factors for chronic diseases such as obesity, diabetes, and cardiovascular disease (1).  For instance, Funda et al. (2) explored how non-gluten diets may prevent diabetes in mice. We may appreciate how nutritional epidemiology has evolved to match our most pressing concerns for maintaining health and preventing chronic or acute illnesses.


There are many different types of nutritional studies published. Most commonly, studies are observational in nature, and allocation of persons to dietary exposure groups is not under the control of the investigator (1). Instead, the exposure group and comparison group are compared with each other, and allocation of participants to groups are not determined by the experimenter. There are many ethical issues surrounding conducting randomized control trials in nutritional studies, because allocating certain children to receive a diet in higher fat content than another child, or allocating pregnant mothers to a non-dairy diet compared to a diet with dairy content may not be healthy. Therefore, most nutritional epidemiological studies focus on observing people in their normal lives. One of the leading nutritional epidemiologists at McMaster is Dr. Russell de Souza! (3) His research interests involve advancing methodology for systematic reviews and meta-analysis and clinical trials in the field of nutrition (3).

Model Behaviour Part 2: Assumption and the SIR Model

Computer programs can model and extrapolate data about the world surprisingly accurately, some can even out-diagnose doctors [1] and function as digital birth control [2]. Despite these capabilities, models, much like humans, cannot account for every possible outcome. I can write a model that simulates rolling a six-sided die, but it won’t account for my friend coloring in the one spot so it has seven dots. This model assumes that the die will always give a readable result from one to six. In most cases this is a reasonable assumption, so it is taken to simplify the model, increasing its ease of use and its efficiency.

This same principle of making assumptions can be applied to any kind of model; the more a model assumes, the simpler it becomes and the less accurate. Deciding what to assume in a model is a question of optimizing simplicity, computational strain and accuracy. Look at the figures provided to show the difference assumptions can make:

Both make the following assumptions [3]:

  • Fixed population size (population does not change over time)
  • Constant recovery rate (the rate at which people recover from the infection is constant)
  • Well-mixed population (infected people will have sufficient contact with susceptible to spread the infection)
  • No Vital Dynamics (There are no births and no deaths, either from the infection or otherwise)

The first model (left) assumes that infection rate is constant, while the second one (right) models that infection rate changes as a function of the number of infected based on this equation:

New Infection Probability = 1 – (1 – Infection Probability)^ (Initial Number of Infected)

Infection probability increases during each generation, so there is a huge immediate increase in the infected population, leading to the sharp peak on the right. Varicella (AKA Chicken Pox) is a non-lethal disease that spreads from person to person. Since the likelihood of infection varies with how much contact you have with infected individuals, the second model will likely give a more accurate prediction of a Varicella outbreak [4].

While Varicella can be studied with the SIR model, other infections cannot be constrained by the same assumptions. Some pathogens are lethal, some have effective vaccines, some can affect how well-mixed the population is. Human factors like conferred immunity, herd immunity and incubation periods can also affect the spread of a disease.

When designing models, one of the most important things to do is to consider the assumptions you can make, and how they reflect the reality of the disease.


Here is a link to the SIR model I wrote for this piece and used to generate the figures:  https://github.com/DJSiddharthVader/CSEB/blob/master/SIR%20Model%20Siddharth%20Reed.py