******************** POSTING RULES & NOTES ******************** #1 YOU MUST clip all extraneous text when replying to a message. #2 This mail-list, like most, is publicly & permanently archived. #3 Subscribe and post under an alias if #2 is a concern. *****************************************************************
On Thu, Dec 7, 2017 at 8:24 AM, DW via Marxism <[email protected]> wrote: > ******************** POSTING RULES & NOTES ******************** > #1 YOU MUST clip all extraneous text when replying to a message. > #2 This mail-list, like most, is publicly & permanently archived. > #3 Subscribe and post under an alias if #2 is a concern. > ***************************************************************** > ...We absolutely KNOW that cigarette smoking causes (or greatly increases > the > risk of) lung cancer, heart disease, and many many other lethal and/or > serious medical disease. We KNOW that exposure to asbestos causes > mesothelioma. We KNOW that high exposure to lead paint chips by children > will cause lasting poisoning of their nerves and brains. > > What do these known and established facts have in common? (1) The effect > observed is HUGE, to is easy to see and study. As opposed to a case of > claiming that a given environmental exposure causes a few extra cases of a > rare disease, which can be near impossible to prove: Too big a study is > needed, too long a study is needed, and it becomes near impossible to rule > out the effects of random variation. (2) Proper methods were employed. We > not only identified statistical associations... we were able to highly > control for other issues that might be causing the diseases. In the > absolutely spectacularly brilliant original study of cigarette smoking, a > population of British physicians served as the study group... a group that > was very homogeneous in all other respects OTHER THAN the division into > those who smoked cigarettes and those who did not. > Speaking as a quantitative researcher who's worked on a couple of epidemiological projects, and whose work now is in the area of social science most concerned with causation, social program evaluation, I'd say that David's isn't quite correct on the relevant evidentiary and statistical issues. Regarding tobacco and smoking, the tobacco corporations, in their long struggle to discredit the evidence, were formally correct in pointing to the limitations of observational results such as from the classic British Doctors Study that David is alluding to. When looking at the causal relationships between an exposure and an outcome, an observational study is limited crucially because unlike a randomised trial of a treatment, the "assignment" to the exposure isn't random but caused by various things, *which might also affect the outcome*. Taking out possibly confounding social factors by looking at just one occupation might help (at the expense of losing any evidence of interaction between social factors and exposure), but doesn't really help with this crucial issue. So the tobacco corporations were able to be formally correct is arguing that, who knows, more stressed out doctors might be more likely to smoke because they take some relief with a soothing cigarette, but unfortunately for them stress causes lung cancer. The reason we can be pretty sure that this is spurious because David's first point is the most relevant: the massive effect size in repeated studies (of all sorts of social backgrounds), and the big proportions of smokers who get lung cancer. Formally what you really want to do, as ethics committees might frown on randomly assigning people to smoke or work with asbestos, is a "quasi-experiment", what we do in quantitative evaluation when random assignment to a program is impractical or unethical or invalid (pretty much all the time for social programs despite a current mania in this area for RCTs, IMO, but that's another issue): you separately model "assignment to exposure" if you do happen to have data that affects such assignment, e.g. stress levels of doctors before any of them started smoking, and use some weighting or matching techniques to synthesise randomisation to make the two exposure groups actually equivalent apart from exposure. I don't know if anyone's done that with regard to smoking but the massive, repeatedly-found associations make it probably a bit redundant (as opposed to say the tricky issue of whether the slightly higher incidence of serious mental health issues among cannabis smokers, about 2% c.f. 1% for the general population, means the weed is causing the problems of the problems are causing some self-medication). Now this all seems relevant here because there does seem to some clear and repeatedly found, if small, associations between cancers and living near a nuclear plant, but at least in the case of thyroid cancer, no credible confounding factor. David does also allude to the fact that general observational studies are limited with regard to rare disease, as we're looking at tiny proportions of the population and hence of a random sample of the population in a general observational study, so random variation looms large. The way epidemiology deals with that is through a case-control study, which is sort of a backwards quasi-experiment: you get data on all the cases, then you get a random sample of controls not having the disease, and work out which factors most differentiate them. A quick Google scholar revealed there were some at least (not a lot it seemed) which looked at cancers and distance from nuclear plants, which found the later a factor, e.g: Risk of Thyroid Cancer in the Bryansk Oblast of the Russian Federation after the Chernobyl Power Station Accidenthttp:// www.rrjournal.org/doi/abs/10.1667/RR3233?code=rrs-site http://www.rrjournal.org/doi/abs/10.1667/RR3233?code=rrs-site This population-based case–control study investigated whether exposure to radiation from the Chernobyl Power Station accident is associated with an increased risk of thyroid cancer in children and adolescents aged 0–19 years at the time of the accident who were residing in the more highly contaminated areas of the Bryansk Oblast. Cases were diagnosed with thyroid cancer before October 1, 1997 (n = 26); two controls per case were identified from the Russian State Medical Dosimetrical Registry and were matched by gender, birth year, and raion of residence and type of settlement (urban, town, rural) on April 26, 1986 (n = 52). Individual radiation doses to the thyroid were estimated using a semi-empirical model and data were collected in interviews, primarily of the participants' mothers. Based on a loglinear dose–response model treating estimated dose as a continuous variable, the trend of increasing risk with increasing dose was statistically significant (one-sided P = 0.009). These data suggest that exposure to radiation from Chernobyl is associated with an increased risk of thyroid cancer, and that the relationship is dependent on dose. These findings are consistent with descriptive reports from contaminated areas of Ukraine and Belarus, and the quantitative estimate of thyroid cancer risk is generally consistent with estimates from other radiation-exposed populations. Leukaemia in young children living in the vicinity of German nuclear power plants http://onlinelibrary.wiley.com/doi/10.1002/ijc.23330/full A case control study was conducted where cases were children younger than 5 years (diseased between 1980 and 2003) registered at the german childhood cancer registry (GCCR). Population-based matched controls (1:3) were selected from the corresponding registrar's office. Residential proximity to the nearest nuclear power plant was determined for each subject individually (with a precision of about 25 m). The report is focused on leukaemia and mainly on cases in the inner 5-km zone around the plants. The study includes 593 leukaemia cases and 1,766 matched controls. All leukaemia combined show a statistically significant trend for 1/distance with a positive regression coefficient of 1.75 [lower 95%-confidence limit (CL): 0.65]; for acute lymphoid leukaemia 1.63 (lower 95%-CL: 0.39), for acute nonlymphocytic leukaemia 1.99 (lower 95%-CL: −0.41). This indicates a negative trend for distance. Cases live closer to nuclear power plants than the randomly selected controls. A categorical analysis shows a statistically significant odds ratio of 2.19 (lower 95%-CL: 1.51) for residential proximity within 5 km compared to residence outside this area. _________________________________________________________ Full posting guidelines at: http://www.marxmail.org/sub.htm Set your options at: http://lists.csbs.utah.edu/options/marxism/archive%40mail-archive.com
