EPIDEMIOLOGY FOR THE UNINITIATED PDF

University of Western Australia. Epidemiology for the uninitiated, 5th edition Other links View this book online, via Ebook Library, both on- and off-campus at http: Not open to the public You also may like to try some of epiemiology bookshopswhich may or may not sell this item. In order to set up a list of libraries that you have access to, you must first login or sign up. Gold Coast Campus Library. Sir Charles Gairdner Nuinitiated Library. Login to add to list.

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The RR is a more powerful effect measure than the OR, as the OR is just an estimation of the RR, since true incidence cannot be calculated in a case control study where subjects are selected based on disease status. Temporality can be established in a prospective study, and confounders are more easily controlled for. However, they are more costly, and there is a greater chance of losing subjects to follow-up based on the long time period over which the cohort is followed. Main article: Causal inference Although epidemiology is sometimes viewed as a collection of statistical tools used to elucidate the associations of exposures to health outcomes, a deeper understanding of this science is that of discovering causal relationships.

For epidemiologists, the key is in the term inference. Correlation, or at least association between two variables, is a necessary but not sufficient criterion for inference that one variable causes the other. Epidemiologists use gathered data and a broad range of biomedical and psychosocial theories in an iterative way to generate or expand theory, to test hypotheses, and to make educated, informed assertions about which relationships are causal, and about exactly how they are causal.

Epidemiologists emphasize that the "one cause — one effect" understanding is a simplistic mis-belief. If a necessary condition can be identified and controlled e. Bradford Hill criteria[ edit ] Main article: Bradford Hill criteria In , Austin Bradford Hill proposed a series of considerations to help assess evidence of causation, [41] which have come to be commonly known as the " Bradford Hill criteria ".

The more specific an association between a factor and an effect is, the bigger the probability of a causal relationship. However, in some cases, the mere presence of the factor can trigger the effect. In other cases, an inverse proportion is observed: greater exposure leads to lower incidence. However, Hill noted that " This question, sometimes referred to as specific causation, is beyond the domain of the science of epidemiology.

Conversely, it can be and is in some circumstances taken by US courts, in an individual case, to justify an inference that a causal association does exist, based upon a balance of probability.

The subdiscipline of forensic epidemiology is directed at the investigation of specific causation of disease or injury in individuals or groups of individuals in instances in which causation is disputed or is unclear, for presentation in legal settings. Population-based health management[ edit ] Epidemiological practice and the results of epidemiological analysis make a significant contribution to emerging population-based health management frameworks.

Modern population-based health management is complex, requiring a multiple set of skills medical, political, technological, mathematical, etc. This task requires the forward-looking ability of modern risk management approaches that transform health risk factors, incidence, prevalence and mortality statistics derived from epidemiological analysis into management metrics that not only guide how a health system responds to current population health issues but also how a health system can be managed to better respond to future potential population health issues.

Applied field epidemiology[ edit ] Applied epidemiology is the practice of using epidemiological methods to protect or improve the health of a population. Applied field epidemiology can include investigating communicable and non-communicable disease outbreaks, mortality and morbidity rates, and nutritional status, among other indicators of health, with the purpose of communicating the results to those who can implement appropriate policies or disease control measures.

Humanitarian context[ edit ] As the surveillance and reporting of diseases and other health factors becomes increasingly difficult in humanitarian crisis situations, the methodologies used to report the data are compromised. One study found that less than half Among the mortality surveys, only 3.

As nutritional status and mortality rates help indicate the severity of a crisis, the tracking and reporting of these health factors is crucial. Vital registries are usually the most effective ways to collect data, but in humanitarian contexts these registries can be non-existent, unreliable, or inaccessible. As such, mortality is often inaccurately measured using either prospective demographic surveillance or retrospective mortality surveys.

Prospective demographic surveillance requires lots of manpower and is difficult to implement in a spread-out population. Retrospective mortality surveys are prone to selection and reporting biases. Other methods are being developed, but are not common practice yet.

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 false-positive findings for each false-negative.

By contrast genome-wide association appear close to the reverse, with only one false positive for every or more false-negatives. By contrast, other epidemiological fields have not required such rigorous reporting and are much less reliable as a result.

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.

Note, that 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[ edit ] 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. 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[ edit ] 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. Sackett D cites the example of Seltzer et al. Information bias[ edit ] Information bias is bias arising from systematic error in the assessment of a variable.

Confounding[ edit ] Confounding has traditionally been defined as bias arising from the co-occurrence or mixing of effects of extraneous factors, referred to as confounders, with the main effect s of interest. The counterfactual or unobserved risk RA0 corresponds to the risk which would have been observed if these same individuals had been unexposed i.

Some epidemiologists prefer to think of confounding separately from common categorizations of bias since, unlike selection and information bias, confounding stems from real causal effects. One notable undergraduate program exists at Johns Hopkins University , where students who major in public health can take graduate level courses, including epidemiology, their senior year at the Bloomberg School of Public Health. Many other graduate programs, e. Reflecting the strong historical tie between epidemiology and medicine, formal training programs may be set in either schools of public health and medical schools.

Epidemiologists can also work in for-profit organizations such as pharmaceutical and medical device companies in groups such as market research or clinical development.

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