Epidemiological studies variables

Costs to the patient and costs of individual products are variable but may be substantial. An epidemiological study of human immunodeficiency virus (HIV)-infected patients found that patients spent an average of 18 per month on herbs (range, 0-175) [46]. Unfortunately most U.S. prescription insurance companies do not cover the cost of dietary supplements. One exception is the American Western Life Insurance Company, San Mateo, California, which offers their subscribers a Prevention Plus option that covers herbal medicines [34]. In Germany, herbs that are prescribed by physicians are covered by insurance, whereas non-prescription herbs are not covered [34]. 

The situation becomes bleaker still when we recognize that, for a given substance, there may be many different conditions A under which its adverse effects and their dose-response characteristics have been investigated Results may be available from several different epidemiological studies, in different groups exposed under different circumstances, and with results that are not entirely consistent with each other. Some of the conditions may involve experimental data, similarly variable in outcome and in how they will be interpreted by different scientists. So, when we are faced with toxic hazard and dose-response data from studies involving conditions A1 through A12, which, if any, are most useful and relevant for extrapolation to condition B ... 

Using knowledge gained from animal studies or observations from human populations, a more formal human epidemiology study may be performed. Human studies have the obvious advantage of being done on the subject of most interest, but they are time consuming and expensive, and often have many variables that are difficult to control. 

Gu et al (2012) Selection of key ambient particulate variables for epidemiological studies — applying cluster and heatmap analyses as tools for data reduction. Sci Total Environ 435 136 541-550... 

We are not certain which comorbid risk factors cause mortality independent of sleep effects, and therefore, we cannot be certain whether we controlled too much or too little for comorbidities. For example, since short sleep or long sleep may cause a person to be sick at present or to get little exercise or to have heart disease (17), diabetes (18), etc., controlling for these possible mediating variables may have incorrectly minimized the hazards associated with sleep durations. This would be overcontrol. The hazard ratios for participants who were rather healthy at the time of the initial questionnaires were unlikely to be overcontrolled for initial illness. Since the 32-covariate models and the hazard ratios for initially healthy participants were similar, this similarity reduced concern that the 32-covariate models were overcontrolled. On the other hand, there may have been residual confounding processes that caused both short or long sleep and early death that we could not adequately control in the CPSII data set, either because available control variables did not adequately measure the confound or because the disease did not yet manifest itself. Depression, sleep apnea, and dysregulation of cytokines are plausible confounders that were not adequately controlled. It may be impossible to be confident that all conceivable confounds are adequately controlled in epidemiological studies of sleep. 

Breast cancer has a multifactorial etiology involving numerous genetic, metabolic, and cultural variables. Table I presents a summary of many epidemiological studies designed to identify risk factors for breast cancer (2-8). 

Kritchevsky and Kritchevsky (2000) provided a summary of the evidence linking dietary cholesterol to the risk of CHD in 10 cohorts from eight large, well-conducted prospective studies that were reported since 1980, which included the Nurses Health Study, the Health Professionals Followup Study and the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study. In eight of the cohorts there was no statistical association between cholesterol intake and the risk of CHD. In one of the positive studies the association was established by simple univariate analysis and was not adjusted for other dietary variables. The other study adjusted only for fat intake. There is no compelling evidence from these epidemiological studies that dietary cholesterol is associated with the risk of CHD. 

Table 9-2 also presents data from occupational and epidemiologic studies that indicate that the respiratory system is the primary target for sulfur dioxide. There was variability in the study findings that probably resulted from a lack of adequate analytical measurements (use of area sampling rather than personnel monitoring) the multiplicity of confounding, concurrent exposures to other chemicals and participates and the study indices investigated. However, some reasonable correlations between effects reported and exposure bounds can be determined. 

The analytical plan of epidemiological studies should use descrip tive and analytical techniques in describing the sample and results. Descriptive statistics, such as frequency distributions, cross-tabulations, measures of central tendency, and variation, can help explain underlying distributions of variables and direct the assessment of appropriateness of more advanced statistical techniques. Careful weighing of study findings with respect to the design and methods helps to ensure the validity of results. 

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