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Environmental Epidemiology
Published in Lorris G. Cockerham, Barbara S. Shane, Basic Environmental Toxicology, 2019
One of the problems encountered in comparing crude rates between populations is that these measures may not be directly comparable because of differences in risk factors that influence the occurrence of the disease. For example, many diseases resulting in morbidity and mortality are strongly related to age. As age increases, the probability of developing a disease such as cancer or heart disease also increases, and therefore age must be considered in the analysis of the study results. Age adjustment is a statistical procedure that is employed to remove the effects of aging when comparing rates for populations that may have differing age distributions. The procedure can also be used for other demographic characteristics such as sex or race if these factors influence the risk of a disease.
Computational modeling and analysis of thoracolumbar spine fractures in frontal crash reconstruction
Published in Traffic Injury Prevention, 2018
Xin Ye, James P. Gaewsky, Derek A. Jones, Logan E. Miller, Joel D. Stitzel, Ashley A. Weaver
is the measured axial compression force, and Mr is the resultant bending moment of Mx (lateral bending moment) and My (forward bending moment considering flexion and extension) in each vertebral level. The lumbar spine index considered the L1 to L5 vertebral levels as an approach to normalize all lumbar spine responses in a consistent manner. Critical values were defined as Fc = 1,305.6 N and Mc = 34.4 Nm, which corresponds to 90% of the average peak axial compression and resultant bending moment of vertebral levels L1 to L5, respectively, across the 11 baseline cases. Additionally, an age-adjusted version of the lumbar spine index was derived by scaling the lumbar spine index using an age adjustment factor. The age adjustment factor assumed a change of odds ratio (OR) as a function of age between 40 and 80 years old and was created based on previous literature, which reported an increased OR of 4% with each year of age (Figure A2, see online supplement; Kaufman et al. 2013).
Reconnoitering the linkage between cardiovascular disease mortality and long-term exposures to outdoor environmental factors in the USA using remotely-sensed data
Published in Journal of Environmental Science and Health, Part A, 2018
Ashraf Z. Al-Hamdan, Pooja P. Preetha, Mohammad Z. Al-Hamdan, William L. Crosson, Reem N. Albashaireh
In this study, we used county-level age-adjusted average total CVD mortality rate per 100,000 as the dependent variable of the statistical models described later. The total CVD mortality rate includes deaths due to coronary heart disease, acute myocardial infarction, cardiac dysrhythmia, heart failure, hypertension, ischemic stroke and hemorrhagic stroke. The average total CVD mortality rate value per 100,000 for every county was calculated over the period of 2005–2011 for every population subgroup. The county-level age-adjusted total CVD mortality rate data for 3,094 counties within the US between the years 2005 and 2011 (n > 3,780,000 CVD deaths) were obtained from the CDC's Interactive Atlas of Heart Disease and Stroke database.[51] The age-adjusted rates were used in the analysis of this study because CVD death rates change with age and are compared in different populations. Age adjustment (i.e., age standardization) is a technique used to allow populations to be compared when the age profiles of the populations are different. An age-adjusted rate is a weighted average of the age-specific rates, where the weights are the proportions of persons in the corresponding age groups of a standard million population.[52] The age-adjusted rates of CVD are calculated with age distribution ratios from the Year 2000 projected U.S. population, and the rates are shown per 100,000 population. Furthermore, these age-adjusted rates are determined by multiplying the age-specific rate for each age group by the corresponding weight from the specified standard population, then summing across all age groups, and then multiplying this result by 100,000.