A Practical Approach, Second Edition=Ronald D. Ho.pdf

HUMAN STUDIES 837with spina bifida and only 2.8 were expected from the country rate of the malformation. A Poissonmodel gives p = 0.008, which seems rather convincing. This is the probability that eight casesoccurred (with 2.8 expected) if this area had been selected for analysis for some specific reason,e.g., that an industry emitted something that was thought to cause spina bifida.In cluster analysis the situation is reversed; one observes an accumulation of cases and secondarilytries to fit that to an exposure situation. There are numerous areas where an aggregation ofspina bifida cases could have occurred, and there are many different types of malformations orother adverse reproductive outcomes that could have been clustered. Thus, it is not unlikely thatan aggregation of the magnitude described will occur in one of the areas for one of the malformations.The p value reached therefore does not have the common meaning of a p value, estimatingthe probability for the occurrence to be due to chance.We once conducted a study of the distribution per municipality of infants with Down syndromein Sweden. The distribution exactly followed what could be expected from a Poisson distribution,but nevertheless when specific municipalities and time periods were scrutinized, rather impressiveclusters were found. During the years 1978 to 1986 in one such municipality, 11 cases were foundwhen the expected number was 4.0 (p = 0.003); in another municipality, 7 infants with Downsyndrome were born in 1978 against the expected number of 1.5 (p = 0.0009). These strongaggregations can be due to chance, but obviously they can also have a specific cause, and the lattercluster resulted in an intense follow-up (without any reasonable cause being identified).XI. CONCLUDING WORDSEpidemiological studies of human reproduction are burdened with many problems and difficulties.It is rare that single epidemiological studies give a final answer on hazards associated with specificexposure. Each epidemiological study should be regarded as a piece in a jigsaw puzzle. When manypieces have been put together, perhaps together with information from other sources, a picture ofthe truth develops. It is not necessarily the truth, but hopefully resembles the truth as much aspossible. If too much importance is paid to the statistical significance levels in a single study, thereis a definite risk that the importance of the findings will be overestimated. One must be careful indrawing practical conclusions from single studies if they do not unequivocally show very high risksas well as biological plausibility. One phenomenon should be realized — mass significance alsoexists with respect to scientific studies. If 20 different researchers investigate the same problem(e.g., a possible role between exposure to electromagnetic fields and child leukemia), it is quiteprobable that in one of the studies a “statistically significant” effect will be seen, even if no causalrelationship exists between exposure and outcome. To this is added the “publication bias” problem.Studies that show an association usually have an advantage in competing for publication in scientificjournals. Thus, there is a tendency for the first reports appearing in the literature to overestimaterisks, sometimes even when the risk does not actually exist.To the above is added the information problem, namely, how to convey to society and to thepublic the digested interpretation of the available evidence. There are special problems in informingthe public, who often cannot accurately appreciate the concept of risk. Individual risks may besmall, but the attributable risk for the outcome under study may be large in the total population.Individual risks may be high (and may influence decisions about termination of pregnancy), butthe total number of damaged infants born may be negligible. Epidemiological studies may be“negative,” but they do not prove that the exposure under study is harmless. To transform thescientific information into recommendations for society and the public is a delicate matter, andthe scientists who conducted the studies may not be the best persons to interpret their findingsin an unbiased way. Indeed, probably one of the strongest biases in the field is to favor one’sown study!© 2006 by Taylor & Francis Group, LLC