Writing the Introduction to an Epidemiology Paper

This is some brief guidance from my advisor on how to write the introduction section of an epidemiology scientific paper. When addressing previous papers in the introduction, do so only briefly. Generally, save the thorough literature review for the discussion.


Paragraph 1

What is the public health or clinical importance of the topic? What is the primary problem that will be addressed? How many people will be affected? What level of impact does this problem have? Statistics from the World Health Organization are often cited here.


Paragraph 2

What is currently known about the problem?

For example, what has been published on health related quality of life (HRQOL) in type 2 diabetes mellitus (T2DM) patients?

Briefly describe a variety of primary literature papers on the topic. State the lacking knowledge that will be addressed by the rest of the paper.

There is much known about HRQOL in T2DM in populations of White Americans, but there have been no studies to date describing HRQOL in Pacific Islanders diagnosed with T2DM.

Address challenges unique to this study.

Are there variations in HRQOL perceptions among different cultures?


Paragraph 3

Clearly and concisely state the primary aim of this study.

For example, in the current analysis we will study the impact of T2DM on HRQOL in a population of Pacific Islanders living in Oahu, Hawaii.

Say something specific about the population being studied.

The Pacific Islander Cohort of Hawaiians is a longitudinal, population-based cohort that has been ongoing since 1999, with followup every 4 years.

Explain why this study is novel. Tell what you are going to show.

 Hemoglobin A1c (HbA1c) is a validated clinical measure of T2DM severity (citation here), and the SF-36 is a validated health questionnaire measuring HRQOL (citation here). To the extent of our knowledge, this is the first study to study a potential quantitative association between HbA1c and the SF-36 in a population-based cohort of Pacific Islanders.


Mendeley Reference Management

Mendeley is a convenient, free research resource that allows you to manage primary literature references. Mendeley’s Citation Plugin allows easy citations in Microsoft Word while drafting scientific papers from your library.

Online Population-Based Cohort Study

An Internet Survey in a Population-Based Cohort Study

Consumption of ultra-processed foods and cancer risk: results from NutriNet-Santé prospective cohort is a web-based survey looking at the association of cancer risk and consuming ultra-processed foods in people in France who responded to a survey. Population-based cohort studies were previously done by calling people’s landlines, asking them to fill out surveys, and requesting that they drive to the clinic for a health examination.

Perhaps further epidemiological studies will be done primarily using online surveys, as the authors did in this paper. It would make epidemiological studies much less expensive and more readily available. But the validity of the results have not yet been verified.

Using the internet selects for younger people responding to the survey. This may not be representative of the larger population. But as these generations age, using the internet for data collection may be a useful tool.

The internet is an anonymous place, and it is difficult to understand the population that is being studied when using the World Wide Web as the only data collection vehicle. This may be a worth-while sacrifice for the convenience of bypassing what has historically been the most arduous part of studying the public’s health.


Source

NCBI: Consumption of ultra-processed foods and cancer risk: results from NutriNet-Santé prospective cohort

The 7 Deadly Sins of Data Analysis

In her final lecture, my statistics professor described the “7 deadly sins” of statistics in cartoon form. Enjoy


1. Correlation ≠ Causation

Correlation

xkcd: Correlation

CausCorr2_Optimized.jpg

Dilbert: Correlation


2. Displaying Data Badly

Convincing

xkcd: Convincing

Further reading on displaying data badly

The American Statistician: How to Display Data Badly by Howard Wainer

Johns Hopkins Bloomberg School of Public Health: How to Display Data Badly by Karl Broman


3. Failing to Assess Model Assumptions

FailingModelAssumptions.png

DavidMLane.com: Statistics Cartoons by Ben Shabad


4. Over-Reliance on Hypothesis Testing

Null Hypothesis

xkcd: Null Hypothesis

While we’re on the topic of hypothesis testing, don’t forget…

We can fail to reject the null hypothesis.

But we never accept the null hypothesis.


5. Drawing Inference from Biased Samples

DilbertInferences.gif

Dilbert: Inferences


6. Data Dredging

If you try hard enough, eventually you can build a model that fits your data set.

DataDredging_Optimized.jpg

Steve Moore: Got one

The key is to test the model on a new set of data, called a validation set. This can be done by splitting your data before building the model. Build the model using 80% of your original data, called a training set. Validate the model on the last 20% that you set aside at the beginning. Compare how the model performs on each of the two sets.

For example, let’s say you built a regression model on your training set (80% of the original data). Maybe it produces an R-squared value of 0.50, suggesting that your model predicts 50% of the variation observed in the training set. In other words, the R-squared value is a way to assess how “good” the model is at describing the data, and at 50% it’s not that great.

Then, lets say you try the model on the validation set (20% of the original data), and it produces an R-squared value of 0.25, suggesting your model predicts 25% of the variation observed in the validation set. The predictive ability of the model seems to depend on which data set is used; on the training set (R-squared 50%) it is better than on the validation set (R-squared 25%). This is called overfitting of the model to the training set. It gives off the impression that the model is more accurate than it really is. The true ability of the model can only be assessed once it has been validated on new data.


7. Extrapolating Beyond Range of Data

Extrapolating

xkcd: Extrapolating


Similar Ideas Elsewhere

Columbia: “Lies, damned lies, and statistics”: the seven deadly sins

Child Neuropsychology: Statistical practices: the seven deadly sins

Annals of Plastic Surgery: The seven deadly sins of statistical analysis

Statistics done wrong


Sources

xkcd: Correlation

Dilbert: Correlation

xkcd: Convincing

The American Statistician: How to Display Data Badly by Howard Wainer

Johns Hopkins Bloomberg School of Public Health: How to Display Data Badly by Karl Broman

DavidMLane.com: Statistics Cartoons by Ben Shabad

xkcd: Null Hypothesis

Dilbert: Inferences

Steve Moore: Got one

Wiki: Overfitting

xkcd: Extrapolating

Columbia: “Lies, damned lies, and statistics”: the seven deadly sins

Child Neuropsychology: Statistical practices: the seven deadly sins

Annals of Plastic Surgery: The seven deadly sins of statistical analysis

Statistics done wrong

Fixed Effects vs Random Effects Models

What is a fixed effects model? What is a random effects model? What is the difference between them? Many people around me have been using these terms over and over in the past few weeks. I finally compiled several 5-10 min videos of people answering these questions well online.

IndianJDermatol_2014_59_2_134_127671_f3_Vertical

If I had to answer the question of what fixed and random effects models are in one image, I would choose this one from the Indian Journal of Dermatology. Watch the videos and come back to this image for a quick reminder of these concepts.


Motivating Example: Meta-Analysis of Bieber Fever

This silly example is a simplistic demonstration of when fixed and random effects models should be used in designing a meta-analysis. This video is for the medical student and clinician.


Summary of Fixed and Random Effects Models

This summary video is a bit more technical and is aimed at a student of epidemiology or biostatistics.


What is Heterogeneity?

The concept of heterogeneity kept coming up in these videos. How is it different from random chance? This is a clear explanation of the difference that defines concepts alluded to in the previous videos.


Sources

Indian Journal of Dermatology: Understanding and evaluating systematic reviews and meta-analyses

Brian Cohn: Fixed and Random Effects Models and Bieber Fever

Terry Shaneyfelt: Fixed Effects and Random Effects Models

Terry Shaneyfelt: What is Heterogeneity?

Inigo Montoya & Openintro Statistics

I do not think it means what you think it means.

After reading Statistics Done Wrong, there were a couple of resources mentioned in the end of the book. One was a journal article written by a sassy pediatric orthopedist who quotes Inigo Montoya, challenging people to understand p values and to apply and interpret them correctly. The other was an free, open source introductory textbook on statistics, thus allowing people to learn about p values and other statistical concepts.


Sources

Statistics Done Wrong

NCBI

OpenIntro Statistics

Healthy Death

I was surprised when Dr. A brought up what the 50-year-old patient would want if he were to be placed on a ventilator. He was scheduled for a colonoscopy to screen for colon cancer, and I didn’t think there was much risk for complications in this routine procedure. The guy appeared generally healthy apart from a history of high blood pressure and a little anxiety. By the look on the man’s face, both of us were surprised. “Even he should have an advanced directive,” the doc continued as he gestured in my direction. The man’s face relaxed a little as he and I realized that this was a routine discussion to be had with all patients, even those in their twenties and thirties.

It was not the first-time end-of-life care had been brought to my attention in medical school. During one of the first-year electives, a doctor recommended we all write an advanced directive and give it to our loved ones. The idea seemed excessive, but I could understand why this person thought it was so important. She had probably seen numerous accidents involving young and old patients alike who had been placed on ventilators or undergone aggressive resuscitation efforts. She had watched while the family struggled with both the pain of their tragedy, and the uncertainty of what to do next. A written declaration of the patient’s desires would have avoided half their strife in the unlikely event they were incapable of making decisions. Sure, she was making a lot of sense in the crowded lecture hall, but at 24, I wasn’t too keen on facing my own mortality. I’m still not.

Sometimes the doctor visit is the intervention.

As I continued with my family medicine clerkship, we saw several other patients where Dr. A again brought up end-of-life discussion. One man was 80 with aches and pains from arthritis and new onset depression that he was facing at the prospects of his death. Dr. A had discussed advanced directives with him the previous month, and he had brought him a copy during this visit. But the lingering thoughts of his demise were weighing on him. I thought this was normal, and I was surprised when Dr. A probed further into his symptoms. The patient was not interested in taking any antidepressants, denied any suicidal ideations and left with a feeble reassurance and a three-week follow-up appointment. In our discussion after the patient left, Dr. A explained that men over 50 have the highest risk of successful suicide attempts. Although the man lived with his husband and had no history of depression, both good protective factors, he still had a real risk of suicide if his depression remained untreated. I asked why he scheduled a follow appointment so soon for an otherwise healthy patient. “Sometimes the doctor visit is the intervention.”

Another day a 70-year-old male and his wife came in after she realized his skin had tinged yellow and become jaundiced. They had already visited a gastroenterologist, who had scheduled an ERCP procedure for the next week. They had come to Dr. A because he had been their primary care provider for over a decade, and they felt it was important to update him. The patient was jovial, and didn’t seemed amused by his change in complexion. But his wife was a nurse practitioner. She was the one that first noticed the yellow tinge in his eyes. She was hyper-focused on the details of his lab results and the nuances of his care plan. Dr. A calmly addressed each of her issues while her husband interjected with light hearted jokes and validation of his wife’s statements. At the end of the interrogation, Dr. A asked him what he would want if he did not recover from the ERCP procedure. “No heroics” he said with a smile, oblivious to the scowl and furrowed brow that came across his spouse’s face behind him.

After they left, Dr. A debriefed me. “What’s the prognosis for new onset, painless jaundice in the elderly?” I admitted I didn’t know, and he explained it likely indicated biliary cancer that has a poor 5-year survival rate. The ERCP was a relatively low risk operation, but the real value in bringing up an advanced directive was that he would likely be needing one in the next few months. He further explained that although these discussions may be off-putting for the patient and their families now, it has the potential to prevent unneeded suffering down the line. And he knew how that looked first hand.

No heroics.

Dr. A spends three of his afternoons each week in the case management department of the hospital adjacent to his office. A list of patients is printed for each of the meeting’s attendees. The list includes patients who have Medicare insurance that have been in the hospital for longer than 5 days. The team talks about skilled nursing facilities that could take stable patients, and hospice care for those nearing their death. At first these meetings seemed like a calloused business strategy to preserve limited hospital resources; the government cuts funding for these patients after 5 days, forcing the hospital to pick up the rest of the tab. But I soon realized that many of the patients on the list did not have a medical reason to be in the hospital any longer.

During these meetings the case managers, nurses, and social workers present each patient on the list to Dr. A. Often the family is insisting the patient remain in the hospital to receive every treatment option possible; they cannot accept that it is the end of their loved-one’s time. The blame appeared to fall on the relatives. But after the first meeting Dr. A explained that this situation often occurred because no physician was stepping up to have the end-of-life discussion with the patient and their family. Sometimes this was because the internist shirked this unpleasant part of his or her responsibilities. Other times it was because the patient had never had a primary care physician before being admitted to the hospital; no one had prepared them for the end of their life, and it was too frightening for them and their families this late in the game.

The worst case I saw at these meetings was a 70-year-old man who lost consciousness while being treated for lung cancer at the hospital. He had shown up on the list during my first week; it was the sixth day of his hospital stay. The radiographs showed multiple metastasis to his brain. There was a brain surgery that could potentially bring him back to consciousness and increase his quality of life, but the odds of success were low. Normally this discussion would be had with the family, but this man was completely alone. The case was deferred to the medical ethics committee, a team of physicians, lawyers, and other hospital personnel who collectively decide on the most ethical course of action for patients in these types of situations. The committee had decided to move forward with the surgery.

They’re fighting for a healthy death.

By the end of my clerkship, the patient had been in the hospital over 20 days. Dr. A and I paid him a visit in his hospital room. The operation had technically brought him back to a low level of consciousness: he stirred when his name was shouted, but he soon closed his eyes again without making a sound. His mouth hung open wide, and his face was sunken in. The sides of his forehead were indented, and there was a large U-shaped surgical scar on one side. You could see his ribs beneath his gown. It was clear this man did not have much time left, and it seemed cruel to leave his feeding tube in any longer to extend his life. There were no family members to serve as his advocate, and he had undergone a risky surgery that likely prolonged his suffering. The patient’s chart read that all medical interventions had now been exhausted, and there was no further action to be done. The ethics committee was scheduled to revisit his case to decide whether to prepare him for hospice.

Dr. A works on both ends of the spectrum of death. He prepares his healthy patients for the end by discussing their wishes over multiple office visits. At the hospital, he salvages patients from end-of-life catastrophes that might have been avoided by a healthy relationship with an involved primary care physician. I naturally avoid thoughts and discussions about death. I’m more interested in health and living an optimum life, and these values led me to pursue medicine as a career. Before beginning the family medicine clerkship, I was expecting to help outpatients lead healthier lives and recover from asthma, headaches, and the occasional sprained ankle. I was surprised how much of family medicine is about living well and about dying well. Shadowing Dr. A taught me how great an impact a family medicine doctor can have by relieving people’s suffering at any age. Physicians aren’t fighting death. They’re fighting for a healthy death.

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