The magnitude of the safety potential in Finland was calculated using the population risk differences (Fig. 5) and actual yearly fatality numbers in Finland.
This finding emphasizes an important difference between individual and population risk prediction; differences in individuals or subgroups may be important if the algorithm were to be applied to an individual, but these differences may not be as critical if applied for aggregate population estimates.
These differences across studies might be related with different population risk factors and/or with differences in genetic backgrounds.
Considerable evidence for a genetic predisposition to this disease comes from significant familial clustering (15, 16), population-specific risk differences (17), and increased risk with an affected first-degree relative (18).
The population cancer risk at the DMIRET varies by nearly two orders of magnitude across airports (factor of 99 difference between minimum and maximum population risk for benzene, factor of 71 difference for 1,3-butadiene, and factor of 93 difference for BaP).
In trials that did not report raw event rates, investigators reported the percentage or proportion of patients having the event under consideration, which we rounded off to whole numbers by using the sample size of the population; we then calculated the risk difference by using the formula 1/(1+%OR), where OR=odds ratio.
Additionally, this study used normalized data for the prevalence of neuropsychologic conditions from a US population; normative data from each of the countries that enrolled subjects in the study were not available, and the local differences in population risk may have an impact on the impairment assessed.
However, when population strata have been selected for sampling based on risk considerations, extrapolation of results to the general population is not possible unless the risk difference between high-risk and low-risk strata is known.
We showed that it is easy to calculate intervals for the exposure impact number (EIN), the case impact number (CIN), and the exposed cases impact number (ECIN) by making use of existing interval estimation methods for the risk difference (RD), the population attributable risk (PAR), and the attributable fraction among the exposed (AFe).
To address the challenge of absolute risk estimation in case control studies, we present a novel regression approach to quantify risk difference associations with population-based case control data using linear-expit (lexpit) regression.
