1. Epidemiological darkness Birger Svihus, professor of nutrition

3. The experiment - the gold standard of science Randomisation –Distributes error/other contributing factors evenly among control groups and intervention groups Intervention –Gives the scientist control over the factor studied Blinding –Reduces bias in data collection

5. Bertrand Russell (1872-1970) ”The word ’cause’ is so inextricably bound up with misleading associations as to make its complete extrusion from the scientific vocabulary desirable.” ( Ward, Medical Health Care and Philosophy 12, 333, 2009)

6. John Snow, English physician (1813- 1858) Established a link between water and cholera by epidemiological studies (Freedman, Statistical Science 3, 243, 1999)

7. Major observational methods used in epidemiology Correlation studies –A large number of data are screened to search for associations between a response (e.g. obesity) and a factor (e.g. amount of a food) Cohort studies –Healthy persons are grouped according to factors of interest, and the incidence of a response is registered over time Case-control studies –Persons with a health problem (cases) are studied in regards to potential risk factors, and the odds ratio is compared with a control group without the health problem

8. Major problems in observational epidemiological studies Confounding –Other, unknown factors, can be underlying causes for both the factor and the response, e.g. the correlation between icecream consumption and drowning incidence Reverse causation –A factor that is correlated to a response may not be the cause for the response, but rather vice versa, e.g. the correlation between number of firefighters and the gravity of a fire Bias in data collection

9. Sir Ronald A. Fisher on epidemiology (Breslow, Journal of the American Statistical Association 91, 433, 1996) ”Statistics has gained a place of modest usefulness in medical research. It can deserve and retain this only by complete impartiality, which is not unattainable by rational minds … I do not relish the prospect of this science being now discredited by a catastrophic and conspicious howler. For it will be as clear in retrospect, as it is now in logic, that the data so far do not warrant the conclusions based on them.” (1957, on smoking and lung cancer)

10. Schünemann et al., Journal of Epidemiology, Community and Health 65, 392, 2011)

11. Probable These criteria are for evidence strong enough to support a judgement of a probable causal relationship. Evidence from at least two independent cohort studies, or at least five case-control studies. No substantial unexplained heterogeneity between or within study types in the presence or absence of an association, or direction of effect. Good quality studies to exclude with confidence the possibility that the observed association results from random or systematic error, including confounding, measurement error, and selection bias. Evidence for biological plausibility. Convincing These criteria are for evidence strong enough to support a judgement of a convincing causal relationship. Evidence from more than one study type. Evidence from at least two independent cohort studies. No substantial unexplained heterogeneity within or between study types or in different populations. Good quality studies to exclude with confidence the possibility of random or systematic error. Presence of a plausible biological gradient in the association. Strong and plausible experimental evidence, either from human studies or relevant animal models. World Cancer Fund criteria for causation from epidemiological data (http://www.dietandcancerreport.org/)

12. “What is required is much more than the application of a list of criteria. Instead, one must apply thorough criticism, with the goal of obtaining a quantified evaluation of the total error that afflicts the study. This type of assessment is not one that can be done easily by someone who lacks the skills and training of a scientist familiar with the subject matter and the scientific methods that were employed. Neither can it be applied readily by judges in court, nor by scientists who either lack the requisite knowledge or who do not take the time to penetrate the work.” Review paper by Rothman and Greenland (American Journal of Public Health s1, s144, 2011)

14. A hierarcical list of criteria to use for dietary recommendations. The food should: 1. provide enough nutrients 2. not provide too much energy and thus cause obesity 3. have a balanced content and quality of carbohydrates and fat to hinder diabetes 2 and/or atherosclerosis 4. not contain too much of ingredients thought to be carcinogenic, or too little of ingredients thought to protect against cancer

15. Review paper by Boffetta (Critical Reviews in Food Science and Nutrition, 50:13–16, 2010) “In conclusion, cancer epidemiology is, to a large extent, the determination of small effects and weak associations, and poses major challenges that are easier to overcome in certain areas (e.g., genetic epidemiology) than in others (e.g., environmental or nutritional epidemiology). Identifying the causal nature of a weak association is not impossible, but requires large, well-planned, and well-conducted studies and supporting evidence from molecular and experimental studies.”

16. New dietary recommendations from the government Eat less red meat

17. The example of red meat Rich in essential nutrients

18. The example of red meat Rich in essential nutrients Low in energy which protects against obesity

19. The example of red meat Rich in essential nutrients Low in energy which protects against obesity Low cho and fat which protects against diabetes/ atherosclerosis

20. The example of red meat Rich in essential nutrients Low in energy which protects against obesity Low cho and fat which protects against diabetes/ atherosclerosis Associated with colon cancer

21. The risk for colorectal cancer due to red meat (Cross et al., PloS Medicine 4, e325, 2007) The risk of developing cancer was 24 % higher for persons eating 170 gram red meat per day compared with those eating 30 gram per day In Norway, the incidence of colorectal cancer is around 80 per 100 000. Thus, if the association is causal, cancer incidence would increase to 100 per 100 000 if meat consumption in Norway was 30 gram and increased to 170 gram (it is currently around 80 gram per day)

22. Review paper on diet and cancer by Key et al. (The Lancet 360, 861, 2002) “Despite extensive research during the last 30 years, few specific dietary determinants of cancer risk have been established, even for cancers such as colorectal cancer for which most researchers agree that diet probably has important effects.”