cultivating & crashing

an organic collection of notes, observations, and thoughts

Tag: epidemiology


Last week I attended the Canadian Society for Epidemiology and Biostatistics Student Conference, the first conference I traveled to present at. Sandro Galea gave a great keynote speech.

  • population health vs. personalized health
  • mismatch of funds spent and health of populations
  • article in Fortune, “Can genes really predict your health?”
    • our health has deteriorated, but our genes haven’t changed, so why all the fuss (and funding) about them?
  • population estimates do NOT allow us to predict individual event inference (a point about personalized medicine)
  • no lone ranger, no silver bullet. obesity is associated with a HUGE causal web, not one isolated factor
  • book written with Katherine Keyes

slides I was interested in

  • health determinants vs. health expenditures
  • social causes of death vs. others (poverty causes same no. as injury)
  • what percentage of your intelligence depends on genes?
    • it depends on your environment
  • need with grapple with health equity vs. efficiency trade-off
    • people of high SES benefit most from changes
  • embrace intellectual and moral challenges of our time: how to change the socio-economic context that influences health?


  • goalie is medicine. for every one goalie, ten players that are not goalies are needed to move the ball up the field. OTHERWISE WE LOSE.
  • goldfish in a bowl: can exercise, have safe sex, and eat not too much food, but if the water isn’t changed, it will still die.

I liked this talk because it confirmed everything I believe about the social nature of how society functions. our health is mostly determined by our social environment, and it’s those social factors that are at the crux of making sure populations are healthy. poverty stunts, sickens, and kills people. we must create societies that take care of the least privileged, the most vulnerable. otherwise, rich people will enjoy the advances of health while their neighbours rot in misery.

We had a session in which we spoke to a group of people who work in different epidemiology/public health positions.

National Collaboration Centre (NCC)

  • different centres in different provinces
  • internships
  • funded by Public Health Agency of Canada

data management and data cleaning are 90% of the work that must be done in data analysis. get experience working with horrible data sets.

Public Health Ontario epidemiologists – mailing list, great resources

make yourself stand out in an interview, educate yourself well about the place/department. also, want to be flexible, be a generalist with enough knowledge about a lot of things, not in-depth for one topic (advice is diametrically opposed to that for someone who wants to continue in academia).

working in government

  • BUREAUCRACY. the right thing to do takes 10 years, but it happens! sometimes even in just 2 years.
  • important impact on populations

Kue Young

  • FYI: WHO makes up data where none exists, read the fine print and footnotes of everything you use
  • Global Health Observatory data is cool, check it out
  • beware the reification of stats due to pretty charts and maps (ie they become true because they are visualized)
  • data don’t exist in a social vacuum

notes on epi after school

CRO contract research officer
SAS + R for govt or industry

Public Health Agency of Canada
Health Canada
Stats Can – health data analysis
Nat’l Defense (!)

Ministry of Health
Ministry of Social Services
Public Health
Specific agencies

Health authorities
Public health units
Hospitals (2-3 analysts)

Federal Student Work Experience Program
Field Epidemiology Program (infectious disease, very competitive)

work ethic
working on team
good writing
problem solving

CIHI, EBRI, OHRI, research institutes

data analyst, researcher, think tank

consulting/consultancies (eg IMS Health)
many in mental health (pharma, tech)
– check for internships
– refs from employers, not so much academia
job notifications lists on CIHI, OPHE bulletin, CSEB membership list

Edit (29/01/2016):

The epidemiology of drug overdose

From the BBC.

Would be interesting to model the data. I am certain that socioeconomic status would explain some of the variation, but wonder if it would be more so than population density, for instance.

Anything in the name of science

“Medical scientists are nice people, but you should not let them treat you.”

August Bier (1861—1949)


Today is an important day because it’s the day I first came across my dream job: field epidemiologist. And it’s the day that I found out that I can work for MSF without becoming a doctor.

I’m happy; I can go to sleep now. Or maybe start learning Arabic.

Notes from Bad Science

Make kedgeree

Read about this intervention


noun, Computer Slang.
a software or hardware configuration that, while inelegant, inefficient, clumsy, or patched together, succeeds in solving a specific problem or performing a particular task.

Nullis in verba — On the word of no one
Royal Society’s motto

How old is epidemiology?

Seminar by Alfredo Morabia

1. 17th century move toward centralized states, organization of Europe after 30 year war

2. Crisis of Western ideas
Francis Bacon
– biases
– tabulating data
– determinants of longevity including heredity, height and weight, age, diet, behaviour, exercise, housing, medical treatment

– only trust “evidence”
– study one determinant at a time
– ignore interactions

Jan Baptista Van Helmont
– “Dawn or the New Rise of Medicine”
– iatrochemistry
– anti Galenist medicine: believed in diseases, separate from the individual, with external causes
– did not believe in blood letting (Van Helmont contest) “how many funerals each of us will have funerals” idea of fraction that will get better will be greater than your fraction that will get better was opposed to all mine will be cured and none of yours will

this is Renaissance thinking; will be implemented in the Enlightenment

3. Data!
Bills of Mortality (death certificates) in England

Bubonic plague – disruption of society and absolutist states
idea: plague has astrological cause or is transmitted by miasms

person would diagnose and count plague deaths, then publish them weekly
idea was to have a retreat outside of London when there was an outbreak
solution to the crisis every time there was an outbreak, based on weekly death counts, then add other causes of death, then births

4. Emergence of political arithmetic and capitalism
birth of epi is related to birth of capitalism

only merchants could deal with numbers, or intellectuals

William Petty “political arithmetic”

John Graunt: Natural and Politcal Observations made on Bills of Mortality (1662)

births and deaths = gains and losses. the same skills for tracking small margins of business was applied to deaths.

5. Royal Society of London (1661)
– centralized intellectual activity and skills
– “Nvllivs in Verba” no words or no speculation, only observation

Graunt is the convergence of these things
“The Table of Casualties” shows there is regularity of health phenomena
Was able to say that plague came from outside London, which was not the same for stable diseases
as a result, more quarantine enforced, and the plague stops affecting London

The Table of Casualties

Huygen’s adaptation of Graunt’s lifetable – median survival age
why asked this? how much should ask from people buying life insurance!

So how old is epidemiology? 1662 Graunt’s observation, our Big Bang. So 353 years in 2015!


Marz. read the book, asks how to educate everyone in the world, as Morabia says we should. Need to promote “epidemiological literacy”. No one understands epi spontaneously; it requires learning to understand interpretation. That’s why epi should be taught in high school, and become part of general culture.
asks 16 year olds : what do you know about health?
marijuana does x. Can get STIs. HOW DO YOU KNOW?
Ah! That’s the question.

Things epidemiology taught me

Everything is about confounding.
Never leave home without a calculator.
Odds ratios are backwards and non-intuitive and misunderstood, but quite clever if used properly.

In other news, I plan on spending some time reading about the natural logarithm because I want to truly understand what the hell is going on in logistic regression, and how it can give risks/probabilities even when the odds ratio doesn’t approximate the risk ratio (?!).

In other other news, after months of research and asking around, I finally bought a little Opinel to fill the void left by the Victorinox of my childhood. Unassuming and efficient, it’s hard not to fall in love.

Causation in epidemiology

Yesterday we discussed with our prof what additive and multiplicative interaction actually mean in terms of the mechanism of the interacting factors. I wanted to know how multiplicative interaction is different from additive interaction in how they actually interact, possibly biologically, or whatever, as opposed to just how they differ by definition. What I took away from the discussion was that epidemiology doesn’t really deal with that, that epi only describes but does not explain. Today I found the following, which confirms this hunch. Epi describes rules in possibility of causality, but it never proves it. This kind of research can describe the association between two things, but it cannot rule in causality in that relationship.

“Epidemiology is concerned with the incidence of disease in populations and does not address the question of the cause of an individual’s disease. This question, sometimes referred to as specific causation, is beyond the domain of the science of epidemiology. Epidemiology has its limits at the point where an inference is made that the relationship between an agent and a disease is causal (general causation) and where the magnitude of excess risk attributed to the agent has been determined; that is, epidemiology addresses whether an agent can cause a disease, not whether an agent did cause a specific plaintiff’s disease.”

Green, Freedman, Gordis. Reference Guide on Epidemiology. In: Reference Manual on Scientific Evidence. 2011.

Health in populations vs. health in individuals

Yesterday in my population health class we discussed how health is different (and dealt with differently) in populations than it is in individuals. These are two distinctions I found particularly interesting.

Diet and genes on total cholesterolThe above table shows how genetic makeup may be more important in determining a health outcome for an individual (an individual’s options are seen by reading the table horizontally), but that societal trends are more important in determining the same outcome in a population (a population’s outcomes are seen by reading the table vertically).

Another interesting distinction is the best way to deal with a given outcome. An individual works to prevent developing disease outcomes that s/he is at high risk for, whereas a population focuses on reducing the risk for people at medium risk, as this is where the bulk of the incident cases of the disease will occur.

Semenza JC. Strategies to intervene on social determinants of infectious diseases. Euro Surveill. 2010;15(27):pii=19611.

A strategy that targets individuals at high risk of developing a disease will see far less benefit overall than if a total population-based approach is implemented (compare change in means in graph D versus graph B). It’s the prevention paradox at work: more improvement overall is achieved by creating smaller benefits in more people than in huge benefits in the people who need it the most/who would experience the greatest relative benefit.