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Policy Impacts of Risk Assessment
Published in Barry L. Johnson, Maureen Y. Lichtveld, Environmental Policy and Public Health, 2017
Barry L. Johnson, Maureen Y. Lichtveld
In a second study of this kind of comparative risk assessment, Danaei et al. estimated the mortality effects of 12 modifiable dietary, lifestyle, and metabolic risk factors in the U.S. [40]. Investigators used data on risk factor exposures in the U.S. population from nationally representative health surveys and disease-specific mortality statistics from the U.S. National Center for Health Statistics. Investigators obtained the etiological effects of risk factors on disease-specific mortality, by age, from systematic reviews and meta-analyses of epidemiological studies that had adjusted (1) for major potential confounders and (2) where possible for regression dilution bias. Findings revealed that in 2005, tobacco smoking and high blood pressure were responsible for an estimated 467,000 and 395,000 deaths, respectively, accounting for about one in five or six deaths, respectively, in U.S. adults. Overweight–obesity (216,000 deaths) and physical inactivity (191,000) were each responsible for nearly 1 in 10 deaths. High dietary salt (102,000), low dietary omega-3 fatty acids (84,000), and high dietary trans-fatty acids (82,000) were the dietary risks with the largest mortality effects. These kinds of health-focused comparative risk assessments provide valuable data for the design of public health interventions and for allocation of resources to address priority health problems.
Reproducibility of objectively measured physical activity: Reconsideration needed
Published in Journal of Sports Sciences, 2020
Eivind Aadland, Ada Kristine Ofrim Nilsen, Einar Ylvisåker, Kjersti Johannessen, Sigmund Alfred Anderssen
As noise in exposure (x) variables will lead to attenuation of regression coefficients (regression dilution bias), and noise in outcome (y) variables will increase standard errors (Hutcheon et al., 2010), unreliable measures weaken researchers ability to make valid conclusions in epidemiology. We argue that, in most cases, researchers are interested in the long-term “true” habitual PA level, rather than activity during the most recent days. Although some health characteristics, as for example, insulin resistance, lipid metabolism and blood pressure, might change with acute increases or decreases in PA (Thompson et al., 2001), a child’s level of fatness, aerobic fitness or motor skills takes months or years to develop. For such stable traits, association analyses (using PA as an exposure variable) will inherently suffer from regression dilution bias if relying on an insufficient snapshot of children’s habitual activity level. For studies evaluating intervention effects (using PA as the outcome variable), low reliability will decrease power. Thus, in both situations, low reliability increases the likelihood of type II errors (Hutcheon et al., 2010).
The relation of blood lead and QRS-T angle in American adults
Published in Archives of Environmental & Occupational Health, 2019
Jin Jing, Susan Thapa, Leanna Delhey, Saly Abouelenein, Wesam Morad, Robert Delongchamp, Mohammed F. Faramawi
The results of this study should be interpreted cautiously because it has several limitations. First, we conducted a cross-sectional analysis; therefore, there could be temporal ambiguity between blood lead and spatial QRS-T angle. Second, the measurement of blood lead levels may only reflect recent lead exposure and absorption. Therefore, we could not explore the accumulative effects of lead exposure. Third, we used a single blood lead measure to evaluate the lead effect on QRS-T angle; yet, blood lead can have temporal trends.5 Therefore, the single measurement of blood lead may result in measurement error with substantial underestimation of the magnitude of the association. The temporal trends in lead levels, as well as the within-person variability in blood lead levels might increase regression-dilution bias.5
The influence of reliability and variability of objectively measured physical activity on associations with lower body muscle strength in young children
Published in Journal of Sports Sciences, 2023
Eivind Aadland, Ada Kristine Ofrim Nilsen
As noise in exposure (x) variables leads to attenuation of regression coefficients (regression dilution bias), and noise in outcome (y) variables increases standard errors (Hutcheon et al., 2010), measurement error may lead to low, and potentially non-significant, effect sizes (i.e., type 2 errors). Variability of measurements may also lead to chance findings (i.e., type 1 errors). The low reliability/high variability of measurements inherently means that data are not reproducible (De Vet et al., 2011), which in turn challenges the replicability of study findings. Our study is well designed to examine the influence of reliability and variability of PA on findings regarding associations between PA and an outcome and shows substantial variability in associations across the measurement periods for both the cross-sectional analysis (1 day of measurement resulting in R2 of 5.9–11.1%; 3 days of measurement resulting in R2 of 6.8–14.4%; 1 week of measurement resulting in R2 of 10.4–13.8%; 2 weeks of measurement resulting in R2 of 11.9–17.8%) and the longitudinal analysis (associations were significant for monitoring period number 2 but not for monitoring periods 1 and 3). These differences can clearly lead to different study conclusions (i.e., some associations being statistically significant and others not), also depending on data reduction algorithms for accelerometry data, sample size, etc. We are not aware of similar studies allowing for a direct comparison with our findings. Yet, our findings suggest that the variability in associations between PA and various outcomes in children reported in the literature (Poitras et al., 2016; Veldman et al., 2021; Wiersma et al., 2020) partly results from measurement errors in PA estimates.