Preparing & presenting epidemiological information: II

Preparing & presenting epidemiological information: II (Session 08)

Learning Objectives At the end of this session, you will be able to • Explain why epidemiologists and other scientists find causation complex, but key • interpret and use concepts of types of contribution to causal explanation • criticise faulty claims of causal linkage • appreciate the importance of controlled comparative experimentation • Recognise how observational data contributes to scientific understanding of causation

Introduction to causation: 1 A big component of epidemiology is the study of disease (or other event) causation (“aetiology” = disease causation); Of course ideas of cause and effect are also important in many other areas! Truly proving causation is very difficult in many cases. First an easy example: CAUSE ~ he was accidentally shot through the heart with a high-calibre bullet; EFFECT ~ he died. Next a more difficult example:-

Introduction to causation: 2 CAUSE? ~ he was knocked over by a cyclist; EFFECT ~ he died. This seems less inevitable! Maybe the cycle accident was an immediate part of the causation, but the victim was susceptible at the time because he was 85 years old, had weak bones, and a history of heart problems. “The cause” is in several parts; one was immediate; others were “pre-existing” “pre-disposing”; factors can also be “enabling”, “precipitating” or “reinforcing”.

Introduction to causation: 3 CAUSE? ~ he had been HIV positive for five years; EFFECT ~ he died of tuberculosis. Tuberculosis was perhaps immediate cause of death. Untreated HIV predisposed him, in the sense of lowering immune response, but was not sufficient to cause disease or death ~ he had to be infected, nor was it necessary ~ people without HIV contract TB, and can die if not effectively treated. Causation is often a complicated web.

Introduction to causation: 4 Causal description is often extended to (1) Surrogate causes ~ the age of the cycle accident victim is not a direct cause, but it is a easy substitute description instead of detailing age-related muscular wastage, bone fragility etc (2) More distant related factors ~ the TB victim had untreated HIV because of social taboos preventing acknowledgment of the illness.

Introduction to causation: 5 (2) More distant related factors can often be explained in differing terms. Maybe (rather than taboo) the victim was too poor to pay doctors’ fees to get prescriptions, or to pay for transport to the clinic. The complex web of causation often spreads indefinitely far back from the immediate effect, but agreeing on, & seeking evidence about, more distant determinants is often impossible … e.g. murder mystery if that shooting was perhaps not an accident.

Critiquing causal argument In ordinary life, and in non-expert interpretat-ion of quantitative evidence, the most common mistake is jumping to conclusions ~ logical gaps in reasoning: (i) claiming to know a cause when evidence does not prove cause/effect linkage beyond reasonable doubt; (ii) failure to see, and weigh up, other possible explanations; (iii) interpreting evidence as telling you about things it does not actually state or cover. Have you ever been wrongly accused..?

Experiments & direct causation If you can carry out a controlled experiment, say on the efficacy of a drug treatment, you may build up consistent, repeated, long-term evidence that one treatment works. “Control” here implies that all other factors you can think of have been accounted for so they could not interfere in the overall interpretation of the results.

Experiments – clinical trial design In a controlled, comparative clinical trial (CCT) you might administer standard drugs and treatment regime to a “control” group of disease patients, while administering a new experimental drug and the same quality of care to an “experimental” group with matched characteristics (average disease duration and severity, ages, sex mixture, other concurrent illnesses and general medical history)

Experiments – clinical trial data Given your two parallel groups you look at evidence of clinical outcomes e.g. more of experimental group survive, they get well sooner, they have fewer adverse side effects and tolerate treatment with fewer dropouts. If all above favour the new treatment, is there statistically significant evidence i.e. more difference than might arise by chance (see Higher modules) … and can it be afforded?

Cause-effect inference from CCTs If after careful independent professional review, CCT results are promising, it is usual for repeat trials to be carried out in several different settings e.g. different countries, different disease severities, age groups etc This is to demonstrate that the promising results are consistently reproducible, and to define the range of cases for which the treatment can be recommended.

Observational study CCT experimental approach is enormously expensive, partly as huge professional effort goes into ensuring the comparison is fair, cases representative, other causal factors excluded or balanced out. This is often impossible and we use other methods without the controls. Naturalistic observation implies that we look at what happens in real life, where various unknown factors may be at play.

Observation and association Observation is generally not enough to demonstrate causation. It is normal in scientific work to claim only an association. Variables are positively associated statistically when they occur together more frequently than would be expected by chance. Such association may or may not be causal; for example two variables may be affected by a common precursor, though not themselves interacting at all.

Example There was a theory (promoted by cigarette companies, but long discredited) that smoking did not cause lung cancer. It was claimed some indefinable physiological “Factor X” had two effects, i.e. (i) causing some people to get lung cancer, and (ii) causing some people to want to smoke. An experimental disproof of this would need to force some non-smokers to start smoking to see the outcome – ethically impossible!

The unethical experiment Sample:- take a sample of 20 – 23 yr old adults who smoke (S), and a similar who do not (D). • Treatment:- (i) split Ss into two equivalent groups forcing half to continue smoking (S-S), half to stop (S-D), and (ii) forcibly split Ds similarly as (D-D) and (D-S)... unethical! • Result .. after 30 years:- If X is responsible high prevalence amongst (S-S) and (S-D); if smoking is responsible, high prevalence amongst (S-S) and (D-S).

Observation ~ causation links Evans* offered a variety of challenges to the assumption that a plausibly argued mechanism is indeed a causal one. His postulates are phrased in terms of disease, but selections of the same broad principles can profitably be applied in many other contexts – not just epidemiological ones * Evans, A.S. (1976) Causation and disease. The Henle-Koch postulates revisited. Yale Journal of Biology and Medicine, 49, 175 – 195.

Infer causation? Evans postulates: 1 • The proportion of the individuals with the disease should be significantly higher in these exposed to the supposed cause than those who are not • Exposure to the supposed cause should be present more commonly in those with than those without the disease • The number of new cases of the disease should be significantly higher in those exposed to the supposed cause than in those not so exposed, as shown in prospective studies

Infer causation? Evans postulates: 2 • Temporally, the disease should follow exposure to the supposed cause with a distribution of incubation periods on a unimodal, possibly skewedcurve • A spectrum of host responses, from mild to severe, should follow exposure to the supposed cause along a logical biological gradient • A measurable host response (e.g. antibody, cancer cells) should appear regularly following exposure to the supposed cause in those lacking this response before exposure, or should increase in magnitude if present before exposure; this pattern should not occur in individuals not so exposed

Infer causation? Evans postulates: 3 • Experimental reproduction of the disease should occur with greater frequency in animals or man appropriately exposed to the supposed cause than in those not so exposed; this exposure may be deliberate in volunteers, experimentally induced in laboratory, or demonstrated in a controlled regulation of natural exposure • Elimination (e.g. removal of a specific infectious agent) or modification (e.g. alteration of a deficient diet) of the supposed cause should decrease the frequency of occurrence of the disease

Infer causation? Evans postulates: 4 • Prevention or modification of the host’s response (e.g. by immunisation or use of the specific lymphocyte transfer factor in cancer) should decrease or eliminate the disease that normally occurs on exposure to the supposed cause • MOST IMPORTANT OF ALL:- All relationships and associations should be biologically and epidemiologically credible, preferably more so than any other explanation, and consistent with the best modern scientific understanding

Further reading • As a practical supplement to the above, find and read Epidemiology Supercourse lecture 19091 “Measures of Association” by Dr. Thomas Songer. • To reinforce and deepen understanding of “Cause and effect: the epidemiological approach”, find and read lecture 18481 by Prof. Raj Bhopal. These are well-structured, not hard to follow, and very informative ~ enjoy!

Practical work follows to ensure learning objectives are achieved…

Preparing & presenting epidemiological information: II