An Introduction to Epidemiology for Health Professionals by Jørn Olsen, Kaare Christensen, Jeff Murray, Anders Ekbom

By Jørn Olsen, Kaare Christensen, Jeff Murray, Anders Ekbom

An advent to Epidemiology for wellbeing and fitness Professionals

Jorn Olsen, Kaare Christensen, Jeff Murray, and Anders Ekbom

Who will get unwell? What factors—genetic, environmental, social—contribute to their illness?

Easy sufficient to invite, however the solutions have gotten more and more advanced. this day, because the public concerns approximately rising illnesses and the be aware epidemic is a part of the final dialogue, epidemiology could be a simple part of scientific education, but frequently it really is undertaught or maybe overlooked. Concise and readable whereas additionally rigorous and thorough, An advent to Epidemiology for healthiness Professionals is going past average textbook content material to floor the reader in clinical tools such a lot correct to the present healthiness panorama and the evolution of evidence-based medicine—valuable keys to raised realizing of illness technique, powerful prevention, and detailed remedy. This volume:

  • Presents fabric accessibly for readers who can have now not studied epidemiology.
  • Focuses both in descriptive and analytic branches of epidemiology.
  • Demonstrates functions of descriptive and analytic tools in public future health, genetic epidemiology, and medical epidemiology.
  • Includes a "Sources of errors" part addressing difficulties in inference and decision-making, choice bias, and different universal pitfalls.

In addition to its usefulness for graduate scholars in public overall healthiness and clinical scholars in medical epidemiology, An advent to Epidemiology for overall healthiness Professionals is a well timed reference for practitioners desiring a refresher during this very important self-discipline.

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Example text

Very similar ideas on causation were independently developed by Kenneth Rothman and elegantly presented in his widely cited paper from 1976 (reprinted in 2004) [2]. The causal field model also explains the time lag between the onset of exposure and the disease (there is no time lag between the causal field and D, or between Hume’s strong causes and their effects). The time from onset of, say, E1 to D will be the time until the onset of E2 (induction time) and the time from completion of the causal field (the start of the biological process) and until D surfaces to clinical detection (latency time) [2].

Our criterion for usefulness is related to whether it fits observations and explains phenomena we observe or whether it inspires new studies. It explains, for example, that there are many different approaches in disease prevention. We may prevent D entirely if we eliminate one cause in each of the described causal fields (we need not eliminate all four). If causal field 2 accounts for 90% of the diseased, eliminating E3 (or E4 ) would reduce disease occurrence by 90% (E1 or E2 by 10%). There is no reason to assume that causes sum up to 100%, which follows from the fact that component causes have to operate together to produce an effect.

1 A cross-sectional study Mobile phone users Brain cancer No brain cancer All Yes No a c b d N1 N0 The prevalence ratio is PR = a/N1 c/N0 Such a survey needs to be very large to provide a reasonable number of prevalent cases of brain cancer, and this design is a more obvious choice when both the exposure and the disease are more frequent. If we have the data and PR shows an association (PR>1) our concerns will be as follows: 1. We do not know the causal direction. Perhaps brain cancer leads to mobile phone use because patients need to be in contact with their social network.

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