Epidemiology II University of Cologne Germany P. Morfeld Institute for Occupational Epidemiology and Risk Assessment (IERA)

Epidemiology II: e-learning Epidemiology II OBJECTIVES EPI 1 : You should be able to explain... Concepts/Definitions conception of epidemiology notion of stochastics Descriptive Approach study group (source/target population) responses covariables (exposure) statistic Analytical Approach regression techniques Bias (Ecological Fallacy, Confounding) notion of biased comparisons solutions with multiple regression Epidemiology II: e-learning P. Morfeld, IERA, e-mail: peter.morfeld@evonik.com / University of Cologne

Epidemiology II In Epidemiology I, we followed the famous analysis of John Snow and identified the Broad Street pump as the cause of the cholera mortality in Soho, September 1854. See the next slide to convince yourself again. P. Morfeld, IERA, e-mail: peter.morfeld@evonik.com / University of Cologne

Epidemiology II The Broad Street pump is obviously the „cause“ – but Snow, Sept, 1854 (John Snow, 1813 – 1858) The Broad Street pump is obviously the „cause“ – but not the Brewer Street pump ! C.F. Cheffins Map of Soho (London), modified by Snow (1850 – 1854) BROAD STREET pump • death from cholera  public water pump X BREWER STREET pump P. Morfeld, IERA, e-mail: peter.morfeld@evonik.com / University of Cologne

Epidemiology II Another obvious finding: The Brewer Street pump is not related to cholera mortality. Can we derive this finding with regression analysis as applied to the Broad street pump data in Epidemiology I? Let us do a parallel analysis of both pumps. P. Morfeld, IERA, e-mail: peter.morfeld@evonik.com / University of Cologne

Epidemiology II Cholera Epidemic in London, Sept. 1854 50 100 150 200 50 100 150 200 250 300 350 400 20 40 60 80 120 140 160 Cholera Epidemic in London, Sept. 1854 Data acc. to J. Snow, 1855 number of deaths from cholera per grid square distance from pump in yards BREWER STREET P. Morfeld, IERA, e-mail: peter.morfeld@evonik.com / University of Cologne

Epidemiology II Cholera Epidemic in London, Sept. 1854 50 100 150 200 50 100 150 200 250 300 350 400 20 40 60 80 120 140 160 distance from pump in yards number of deaths from cholera per grid square Cholera Epidemic in London, Sept. 1854 Data acc. to J. Snow, 1855 BROAD STREET BREWER STREET P. Morfeld, IERA, e-mail: peter.morfeld@evonik.com / University of Cologne

Epidemiology II x: distance to pump „BREWER Street“ y: number of deaths from cholera : residual model: y = bO + b1x +  estimation of parameters: p = 0.11 decrease, but not significant: < < P. Morfeld, IERA, e-mail: peter.morfeld@evonik.com / University of Cologne

Epidemiology II Cholera Epidemic in London, Sept. 1854 50 100 150 200 50 100 150 200 250 300 350 400 -20 20 40 60 80 120 140 160 Cholera Epidemic in London, Sept. 1854 Data acc. to J. Snow, 1855 number of deaths from cholera per grid square BREWER STREET distance from pump in yards P. Morfeld, IERA, e-mail: peter.morfeld@evonik.com / University of Cologne

Epidemiology II Cholera Epidemic in London, Sept. 1854 50 100 150 200 50 100 150 200 250 300 350 400 -50 BROAD STREET number of deaths from cholera per grid square Cholera Epidemic in London, Sept. 1854 Data acc. to J. Snow, 1855 distance from pump in yards BREWER STREET P. Morfeld, IERA, e-mail: peter.morfeld@evonik.com / University of Cologne

Epidemiology II Although the effect estimate is more pronounced for the Broad Street pump we observe a downward trend with the Brewer Street pump also. Thus, this regression analysis indicates that the Brewer street pump may also be related to cholera mortality. P. Morfeld, IERA, e-mail: peter.morfeld@evonik.com / University of Cologne

Epidemiology II But we know that the Brewer street pump is not related to death from cholera (see slide 4). This is confusing! Why is the regression analysis misleading? Why does the regression analysis not return the conclusion that the Brewer Street pump is unrelated to cholera mortality? P. Morfeld, IERA, e-mail: peter.morfeld@evonik.com / University of Cologne

Epidemiology II Let us discuss this important phenomenon called “confounding” in a different setting: Coffee consumption and heart attacks P. Morfeld, IERA, e-mail: peter.morfeld@evonik.com / University of Cologne

Epidemiology II CONFOUNDING superficial coffee risk of consumption heart attack, risk of lung cancer Epidemiological studies showed a positive association of coffee consumption and risk of heart attack, as expected by some of the discussants based on mechanistic considerations - but the studies also showed an association with lung cancer risk. The lung cancer result was counterintuitive and shed doubt on the heart attack finding! P. Morfeld, IERA, e-mail: peter.morfeld@evonik.com / University of Cologne

Epidemiology II CONFOUNDING superficial coffee risk of consumption heart attack, risk of lung cancer more accurate coffee consumption tobacco risk of heart attack risk of lung cancer A more detailed analysis revealed a complicated role of tobacco consumption. P. Morfeld, IERA, e-mail: peter.morfeld@evonik.com / University of Cologne

Epidemiology II It was shown that coffee consumption was associated with tobacco consumption (“smokers drink more coffee than non-smokers”) smoking was linked with lung cancer (and cardiac diseases) The association between coffee consumption and heart attacks was illusory and due to uncontrolled smoking effects (“confounding”) P. Morfeld, IERA, e-mail: peter.morfeld@evonik.com / University of Cologne

Epidemiology II CONFOUNDING a covariable is a CONFOUNDER only if association covariable – response and association covariable – exposure  whether a covariable is a COUNFOUNDER or not depends on the concrete study e.g., if 1) is valid but 2) is not valid: despite 1) the covariable is no confounder! P. Morfeld, IERA, e-mail: peter.morfeld@evonik.com / University of Cologne

Epidemiology II A pronounced form of confounding is known as “Simpson’s paradox” On average mortality rates are lower in Alaska than in Florida but in each age group mortality rates are higher in Alaska than in Florida (see next slide) P. Morfeld, IERA, e-mail: peter.morfeld@evonik.com / University of Cologne

Epidemiology II SIMPSON´S PARADOX: change in direction of effect due to confounding [first described by K. Pearson 1899, published by E.H. Simpson 1951] Number of inhabitants, number of deaths and mortality rate (per 10,000 inhabitants and year) by age and US state, 1974. P. Morfeld, IERA, e-mail: peter.morfeld@evonik.com / University of Cologne

Epidemiology II Background of “Simpson’s paradox” People are older in Florida than in Alaska: Thus, age is associated with “exposure” (exposure = Alaska vs. Florida) And age is a strong risk factor for death. It follows that age is a potential confounder! P. Morfeld, IERA, e-mail: peter.morfeld@evonik.com / University of Cologne

Epidemiology II Geometrical explanation of confounding (see next slide): If tobacco consumption is associated with coffee consumption we will observe an inclining trend of lung cancer risk across coffee consumption (upper lines). This phenomenon will not occur if coffee and tobacco consumption are unrelated (lower lines). P. Morfeld, IERA, e-mail: peter.morfeld@evonik.com / University of Cologne

Epidemiology II lung cancer risk tabacco consumption coffee consumption tabacco consumption P. Morfeld, IERA, e-mail: peter.morfeld@evonik.com / University of Cologne

Epidemiology II The geometrical explanation provides a solution how to control for confounding: fit the full plane and not single straight lines. The problem is three-dimensional: single lines between two variables deal with the data as if the problem were of only two dimensions. The plane relies on all data simultaneously and shows both effects: an increase in lung cancer risk with smoking but no effect across coffee consumption. P. Morfeld, IERA, e-mail: peter.morfeld@evonik.com / University of Cologne

Epidemiology II How to do this? What is a plane algebraically? The answer is simple: Just add a further x-variable to the usual straight line equation. And all standard statistical programs will do the analysis for you. P. Morfeld, IERA, e-mail: peter.morfeld@evonik.com / University of Cologne

  Epidemiology II < < x1: distance to pump „BROAD Street“ x2: distance to pump „BREWER Street“ y: number of deaths from cholera : residual model: y = b0 + b1x1 + b2x2 +  estimation of parameters: „BROAD Street“ „BREWER Street“  <  < P. Morfeld, IERA, e-mail: peter.morfeld@evonik.com / University of Cologne

Epidemiology II The Broad Street pump is linked to cholera mortality in this extended analysis. The result is very similar to the finding we got from the simple regression analysis in Epidemiology I. But the Brewer Street pump is not related to cholera mortality in this extended analysis, very different from the findings in the simple regression analysis. P. Morfeld, IERA, e-mail: peter.morfeld@evonik.com / University of Cologne

Epidemiology II This extended approach is called - multiple regression or - multivariable regression and can tackle the problem of confounding. P. Morfeld, IERA, e-mail: peter.morfeld@evonik.com / University of Cologne

Epidemiology II To produce confounding in the simple Brewer Street analysis we need a confounder, i.e., a variable that is linked with the Brewer Street data and is a risk factor for cholera mortality P. Morfeld, IERA, e-mail: peter.morfeld@evonik.com / University of Cologne

Epidemiology II Cholera Epidemic in London, Sept. 1854 Note: 50 100 150 200 250 300 350 400 p= 0.10 distance to pump "BREWER Street" in Yards Cholera Epidemic in London, Sept. 1854 Data acc. to J. Snow, 1855 distance to pump "BROAD Street" in Yards Note: even a non- significant association can produce a strong confounding! P. Morfeld, IERA, e-mail: peter.morfeld@evonik.com / University of Cologne

Epidemiology II The Broad Street pump is a risk factor and distances from the Broad Street pump are correlated with distances from the Brewer Street pump. Thus, the Broad Street pump is a confounder for the association of the Brewer Street pump and death from cholera. P. Morfeld, IERA, e-mail: peter.morfeld@evonik.com / University of Cologne

Epidemiology II CONFOUNDING another example: Armstrong et al 1994, Armstrong and Theriault 1996 Investigation of the effect of benzo(a)pyrene (BaP) on lung cancer risk in aluminium production plant workers in Canada BaP is an important polynuclear aromatic hydrocarbon that is produced and released during the production of aluminium (Soderberg process) BaP can produce lung cancer in experimental animals and has been shown to cause lung cancer in humans Aim of the study: estimate the effect of BaP on lung cancer quantitatively taking a possible confounding by smoking habits into account P. Morfeld, IERA, e-mail: peter.morfeld@evonik.com / University of Cologne

Epidemiology II smoking: STRONG risk factor! P. Morfeld, IERA, e-mail: peter.morfeld@evonik.com / University of Cologne

Epidemiology II smoking: NO confounder! P. Morfeld, IERA, e-mail: peter.morfeld@evonik.com / University of Cologne

Epidemiology II CONFOUNDING a covariable is a CONFOUNDER only if association covariable – response and association covariable – exposure  whether a covariable is a COUNFOUNDER or not depends on the concrete study e.g., if 1) is valid but 2) is not valid: despite 1) the covariable is no confounder! Examples: - Broad Street pump confounds the effect of the Brewer Street pump - Smoking does not confound the effect of BaP P. Morfeld, IERA, e-mail: peter.morfeld@evonik.com / University of Cologne

University of Cologne P. Morfeld, IERA, e-mail: peter.morfeld@evonik.com / University of Cologne