Epidemiology is the study of the distribution and determinants of health-related states or events in specified populations, and the application of this study to control of health problems (Last 2001). Epidemiology is the scientific study of factors affecting the health and illness of individuals and populations, and, in this capacity, it serves as the foundation and logic of interventions made in the interest of the public’s health. The acting epidemiologist works on issue from the practical, outbreak investigation, environmental exposure, and health promotion, to the theoretical including the development of statistical, mathematical, philosophical, and biological theory. To this end, epidemiologists employ a range of study designs from the observational to experimental with the purpose of revealing the unbiased relationships between exposures such as nutrition, HIV, stress, or chemicals to outcomes such as disease, wellness, and health indicators.

Epidemiological studies are generally categorized as descriptive, analytic (aiming to examine associations, commonly hypothesized causal relationships), and experimental (a term often equated with clinical or community trials of treatments and other interventions).

Epidemiologists work in a variety of settings. Some epidemiologists work "in the field", i.e., in the community, commonly in a public health service, and are at the forefront of investigating and combatting disease outbreaks.

Etymology

The etymology of "epidemiology" (Greek epi = upon, among; demos = people, district; logos = word, discourse) suggests that it applies only to human populations. But the term is widely used in studies of animal populations ("veterinary epidemiology"), although the term "epizoology" is available, and it has also been applied to studies of plant populations ("botanical epidemiology"); see Nutter 1999.

Epidemiology as 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. This conceptualization of epidemiology is difficult to grasp because our internal notions of causation are often poorly developed, frequently being predicated on the notion of a one-to-one relationship. For example, almost everyone would agree that gravity causes a dropped ball to fall towards the ground, but would most agree drinking one glass of milk per day will cause weight loss. Even very heavy smokers know that their vice causes lung cancer, but only 10% of life-long smokers will get lung cancer. How can this be?

The answer is complex and delves into the philosophical notions of causality, induction, deduction, logic and other dense topics. It is nearly impossible to say with perfect accuracy how even the most simple physical systems will behave, much less the complex field of epidemiology that draws on biology, sociology, mathematics, statistics, anthropology, psychology, and policy. However, for the epidemiologist the key is in the term inference. As epidemiologists we use gather data and generate theory that we use to make educated, informed assertions about what relationships are causal and exactly how they are causal.

Measures

1. Measures of occurrence
   1. Incidence measures
      1. Incidence rate
      2. Incidence density (Szklo & Nieto, 2000)
      3. Hazard rate
      4. Cumulative incidence
   2. Prevalence measures
      1. Point prevalence
      2. Period prevalence
2. Measures of association
   1. Relative measures
      1. Risk ratio
      2. Rate ratio
      3. Odds ratio
      4. Hazard ratio
   2. Absolute measures
      1. Risk/rate/incidence difference
      2. Attributable risk
         1. Attributable risk in exposed
         2. Percent attributable risk
         3. Levin’s attributable risk

History of epidemiology

Dr. John Snow is famous for the suppression of an 1854 outbreak of cholera in London's Soho district. He identified the cause of the outbreak as a public water pump in Broad Street, and had the handle removed, thus ending the outbreak.

This was a major event in the history of public health, and can be regarded as the founding event of the science of epidemiology.

In the early 20th century mathematical methods were introduced into epidemiology by Ronald Ross, Anderson Gray McKendrick and others.

Another breakthrough was the 1956 publication of results of the British doctors study, which lent statistical support to the suspicion that tobacco smoking was linked to lung cancer.

References

• Last JM (2001). "A dictionary of epidemiology", 4th edn, Oxford: Oxford University Press.
• Nutter FW Jr (1999) "Understanding the Interrelationships Between Botanical, Human, and Veterinary Epidemiology: The Ys and Rs of It All. Ecosystem Health 5 (3): 131-140".
• Szklo MM & Nieto FJ (2002). "Epidemiology: beyond the basics", Aspen Publishers, Inc.

External links

• Epidemiology (http://www.ncbi.nlm.nih.gov/books/bv.fcgi?rid=mmed.chapter.631). Chapter 9 from Medical Microbiology 4th ed textbook at US National Center for Biotechnology Information. Plain format at University of Texas Medical Branch at Galveston (http://gsbs.utmb.edu/microbook/)
• Epimonitor (http://www.epimonitor.net/index.htm) has a comprehensive list of links to associations, agencies, bulletins, etc.
• Epidemiology for the Uninitiated (http://bmj.bmjjournals.com/epidem/epid.html) On line text, with easy explanations.
• North Carolina Center for Public Health Preparedness Training (http://www.sph.unc.edu/nccphp/training/) On line training classes for epidemiology and related topics.
• Radiation Epidemiology (http://www.sckcen.be/sckcen_en/activities/research/radiationprotection/epidemi/index.shtml) Belgian Nuclear Research Centre, Mol, Belgium