The University of Alabama at Birmingham explains Epidemiology and Public Health. It states
“Public Health is a blend of sciences, skills and convictions that is focused on the preservation and improvement of the health of all people through preventive (rather than curative) measures.
Epidemiology is considered a basic science of public health. Epidemiology is: a) a quantitative discipline built on a working knowledge of probability, statistics, and sound research methods; b) a method of causal reasoning based on developing and testing hypotheses pertaining to occurrence and prevention of morbidity and mortality; and c) a tool for public health action to promote and protect the public’s health based on science, causal reasoning, and a dose of practical common sense (1).
The word epidemiology comes from the Greek words epi, meaning “on or upon,” demos, meaning “people,” and logos, meaning “the study of.” Many definitions have been proposed; here are two that capture the underlying principles and the public health spirit of epidemiology:”
Read more here : http://www.soph.uab.edu/epi/academics/studenthandbook/what
Link : http://www.bmj.com/about-bmj/resources-readers/publications/statistics-square-one/4-statements-probability-and-confiden
Source : https://onlinecourses.science.psu.edu/stat507/node/34
Source : http://ocw.jhsph.edu/courses/fundepiii/PDFs/Lecture18.pdf
Source : http://jech.bmj.com/content/58/8/635.full
- Correspondence to:
Professor M Delgado-Rodríguez
Division of Preventive Medicine and Public Health, Building B-3, University of Jaen, 23071-Jaén, Spain; email@example.com
- Accepted 15 November 2003
The concept of bias is the lack of internal validity or incorrect assessment of the association between an exposure and an effect in the target population in which the statistic estimated has an expectation that does not equal the true value. Biases can be classified by the research stage in which they occur or by the direction of change in a estimate. The most important biases are those produced in the definition and selection of the study population, data collection, and the association between different determinants of an effect in the population. A definition of the most common biases occurring in these stages is given.
Source : http://ije.oxfordjournals.org/content/18/1/269.short
Greenland S (Division of Epidemiology, UCLA School of Public Health, Los Angeles, California 90024, USA) and Morgenstern H. Ecological bias, confounding, and effect modification. International Journal of Epidemiology 1989, 18: 269–274.
Ecological bias is sometimes attributed to confounding by the group variable (ie the variable used to define the ecological groups), or to risk factors associated with the group variable. We show that the group variable need not be a confounder (in the strict epidemiological sense) for ecological bias to occur: effect modification can lead to profound ecological bias, whether or not the group variable or the effect modifier are independent risk factors. Furthermore, an extraneous risk factor need not be associated with the study variable at the individual level in order to produce ecological bias. Thus the conditions for the production of ecological bias by a covariate are much broader than the conditions for the production of individual-level confounding by a covariate. We also show that standardization or ecological control of variables responsible for ecological bias are generally insufficient to remove such bias.
On Mortality and Case-fatality
In epidemiology, a case fatality risk (CFR) — or case fatality rate, case fatality ratio or just fatality rate — is the proportion of deaths within a designated population of “cases” (people with a medical condition), over the course of the disease. A CFR is conventionally expressed as a percentage and represents a measure of risk. CFRs are most often used for diseases with discrete, limited time courses, such as outbreaks of acute infections.
For example: Assume 9 deaths among 100 people in a community all diagnosed with the same disease. This means that among the 100 people formally diagnosed with the disease, 9 died and 91 recovered. The CFR, therefore, would be 9%. If some of the cases have not yet resolved (either died or recovered) at the time of analysis, this could lead to bias in estimating the CFR.
A mortality rate — often confused with a CFR — is a measure of the number of deaths (in general, or due to a specific cause) in a population, scaled to the size of that population, per unit of time. (For example, a rate of 50 deaths per 10,000 population in a year resulting from diabetes. The mortality rate, therefore, would be 50:10,000 or 5:1,000.)
Technically, CFRs are actually risks (or “incidence proportions“) and take values between 0 and 1. They are not rates, incidence rates, or ratios (none of which are limited to the range 0-1). If one wants to be very precise, the term “case fatality rate” is incorrect, because the time from disease onset to death is not taken into account. Nevertheless, the term case fatality rate (and the abbreviation “CFR”) is often used in the scientific literature.
Source : http://en.wikipedia.org/wiki/Case_fatality_rate