Question

Epidemiology & relating health sciences.

If you reviewed or read from any mainstream press, what biases might be present? Do these corporations have their own agenda that could be effecting their tone or information presented?

Answer 1

1) Epidemiology is defined as, in a specific population the study of the distribution and determinants of the diseases or health related states or events and application of this knowledge to control the disease or the health problems. It is a cornerstone of public health, and shapes policy decisions and evidence based practice by identifying risk factors for disease and targets for preventive healthcare. Epidemiologists help with study design, collection, and statistical analysis of data, amend interpretation and dissemination of results (including peer review and occasional systematic reviw). Epidemiology has helped develop methodology used in clinical research, public health studies, and, to a lesser extent, basic research in the biological sciences. Major areas of epidemiological study include disease causation, transmission, outbreak investigation, disease surveillance, environmental epidemiology, forensic epidemiology, occupational epidemiology, screening, biomonitoring, and comparisons of treatment effects such as in clinical trials.Epidemiologists rely on other scientific disciplines like biology to better understand disease processes, statistics to make efficient use of the data and draw appropriate conclusions, social sciences to better understand proximate and distal causes, and engineering for exposure assessment.The distinction between "epidemic" and "endemic" was first drawn by Hippocrates, to distinguish between diseases that are visited upon a population (epidemic) from those that reside within a population (endemic). The term epidemiology appears to have first been used to describe the study of epidemics in 1802 by the Spanish physician Villalba in Epidemiologia Espanola. Epidemiologists also study the interaction of diseases in a population, a condition known as a syndemic.The term epidemiology is now widely applied to cover the description and causation of not only epidemic disease, but of disease in general, and even many non-disease, health related conditions, such as high blood pressure, depression and obesity.Therefore, this epidemiology is based upon how the pattern of the disease causes change in the function of human beings.

3) Different fields in epidemiology have different levels of validity. One way to assess the validity of findings is the ratio of false positives (claimed effects that are not correct) to false negatives (studies which fail to support a true effect). To take the field of genetic epidemiology, candidate-gene studies produced over 100 false positive findings for each false-negative. By contrast genome-wide association appear close to the reverse, with only one false positive for every 100 or more false negatives. This ratio has improved over time in genetic epidemiology as the field has adopted stringent criteria. By contrast, other epidemiological fields have not required such rigorous reporting and are much less reliable as a result.
Random error   Random error :- is the result of fluctuations around a true value because of sampling variability. Random error is just that: random. It can occur during data collection, coding, transfer, or analysis. Examples of random error include: poorly worded questions, a misunderstanding in interpreting an individual answer from a particular respondent, or a typographical error during coding. Random error affects measurement in a transient, inconsistent manner and it is impossible to correct for random error.
There is random error in all sampling procedures. This is called sampling error.

Precision in epidemiological variables is a measure of random error. Precision is also inversely related to random error, so that to reduce random error is to increase precision. Confidence intervals are computed to demonstrate the precision of relative risk estimates. The narrower the confidence interval, the more precise the relative risk estimate.

There are two basic ways to reduce random error in an epidemiological study. The first is to increase the sample size of the study. In other words, add more subjects to your study. The second is to reduce the variability in measurement in the study. This might be accomplished by using a more precise measuring device or by increasing the number of measurements. If sample size or number of measurements are increased, or a more precise measuring tool is purchased, the costs of the study are usually increased. There is usually an uneasy balance between the need for adequate precision and the practical issue of study cost.

Systematic error  

A systematic error or bias occurs when there is a difference between the true value (in the population) and the observed value (in the study) from any cause other than sampling variability. An example of systematic error is if, unknown to you, the pulse oximeter you are using is set incorrectly and adds two points to the true value each time a measurement is taken. The measuring device could be precise but not accurate. Because the error happens in every instance, it is systematic. Conclusions you draw based on that data will still be incorrect. But the error can be reproduced in the future (e.g., by using the same mis-set instrument).
A mistake in coding that affects all responses for that particular question is another example of a systematic error.The validity of a study is dependent on the degree of systematic error. Validity is usually separated into two components: Internal validity is dependent on the amount of error in measurements, including exposure, disease, and the associations between these variables. Good internal validity implies a lack of error in measurement and suggests that inferences may be drawn at least as they pertain to the subjects under study. External validity pertains to the process of generalizing the findings of the study to the population from which the sample was drawn (or even beyond that population to a more universal statement). This requires an understanding of which conditions are relevant (or irrelevant) to the generalization. Internal validity is clearly a prerequisite for external validity.

Selection bias :- Selection bias occurs when study subjects are selected or become part of the study as a result of a third, unmeasured variable which is associated with both the exposure and outcome of interest. For instance, it has repeatedly been noted that cigarette smokers and non smokers tend to differ in their study participation rates. (Sackett D cites the example of Seltzer et al., in which 85% of non smokers and 67% of smokers returned mailed questionnaires.) It is important to note that such a difference in response will not lead to bias if it is not also associated with a systematic difference in outcome between the two response groups.
Information bias :- Information bias is bias arising from systematic error in the assessment of a variable. An example of this is recall bias. A typical example is again provided by Sackett in his discussion of a study examining the effect of specific exposures on fetal health: "in questioning mothers whose recent pregnancies had ended in fetal death or malformation (cases) and a matched group of mothers whose pregnancies ended normally (controls) it was found that 28% of the former, but only 20% of the latter, reported exposure to drugs which could not be substantiated either in earlier prospective interviews or in other health records". In this example, recall bias probably occurred as a result of women who had had miscarriages having an apparent tendency to better recall and therefore report previous exposures.