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Thread: Some basic concept of epidemiology

  1. #1
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    Thumbs up Some basic concept of epidemiology

    In infectious diseases, the term incidence is used to describe the number of new cases of an infectious disease that occur within a defined population over an established period of time.

    Likewise, the term prevalence indicates the number of active cases at any given time.

    The degrees of epidemic can be divided into three situations, where endemic refers to spread and dissemination in a particular geographic region if the incidence and prevalence are expected and relatively stable, epidemic describes an abrupt and unexpected increase in the incidence of disease over endemic rates, and pandemic refers to the spread of disease beyond continental boundaries.

    The four basic elements to characterize a spread disease can be called the Four W principle, that includes "who, what, where, and when".
    Last edited by admin; Tue 9th September '14 at 3:07pm.
    B.S. Pharm, West China School of Pharmacy, Class of 2007, Health System Pharmacist, RPh. Hematology, Infectious Disease. Chengdu, Sichuan, China.

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  2. #2

    Angry Basic Principles of Clinical Epidemiology

    Principle 1

    Observations should address questions facing patients and clinicians.

    Principle 2

    Results should include patient-centerer health outcomes, including Death, Disease, Discomfort, Disability, and Dissatisfaction.

    Death: A bad outcome if untimely
    Disease: A set of symptoms, physical signs, and laboratory abnormalities
    Discomfort: Symptoms such as pain, nausea, dyspnea, itching, and tinnitus
    Disability: Impaired ability to go about usual activities at home, work, or recreation
    Dissatisfaction: Emotional reaction to disease and its care, such as sadness or anger
    Clinical Pharmacy Specialist - Infectious Diseases

  3. #3

    Default Terminology

    1.Absolute risk: Absolute risk is the probability of an event in a population under study. Its value is the same as that for incidence, and the terms are often used interchangeably.

    2.Attributeable risk/Risk difference: One might ask, "What is the additional risk (incidence) of disease following exposure, over and above that experienced by people who are not exposed?" The answer is expressed as attributable risk, the absolute risk (or incidence) of disease in exposed persons minus the absolute risk in non-exposed persons.

    3.Relative risk/Risk ratio/Odds ratio: One might ask, "How many times more likely are exposed persons to get the disease relative to non-exposed persons?" Relative risk answer this question. Relative risk is the ratio of incidence in exposed persons to incidence in non-exposed persons.

    4.Population-attributable risk: Population-attributable risk is the product of the attributable risk and the prevalence of exposure to the risk factor in a population. It measures the excess incidence of disease in a community that is associated with a risk factor.

    5.Population-attributable fraction: Population-attributable fraction is obtained by dividing the population-attributable risk by the total incidence of disease in the population.

    Examples of common calculation of risk factors
    Some basic concept of epidemiology-screen-shot-2015-08-19-at-10-44-40-pm-png

    6.Extraneous variables/Covariates: Extraneous variables is a general term for variables that are part of the system being studied but are not (i.e., are "extraneous" to) the exposure and disease of primary interest. For example, in a study of exercise and sudden death, the other variables that are relevant to that study include age, body mass index, coexisting diseases, all of the cardiovascular risk factors, and everything having to do with the ability to exercise.

    However, extraneous variables are not at all "extraneous" because they can have important effects of the exposure-disease relationship. Also, covariates may or may not "covary" (change in relation to each other, exposure, or disease).

    7.Confounding variable: A confounding variable is one that is:
    • Associated with exposure
    • Associated with disease
    • Not part of the causal chain from exposure to disease


    A confounding variable cannot be in the causal chain between exposure and disease; although variables that are in the chain are necessarily related to both exposure and disease, they are not initiating events. (Such variables are sometimes referred to as intermediate outcomes.) If their effects were removed, this would also remove any association that might exist between exposure and disease. For example, in a study of diet and cardiovascular disease, serum cholesterol is a consequence of diet; if the effect of cholesterol were removed, it would incorrectly diminish the association between diet and cardiovascular disease.

    In practice, while confounding variables (logically called confounders) may be examined one at a time, usually many variables can confound the exposure-disease relationship and all are examined and controlled for concurrently.
    Last edited by admin; Thu 20th August '15 at 11:58pm.
    Clinical Pharmacy Specialist - Hematology

  4. #4

    Default



    Odds ratio also = [EER / (1 - EER)] / [CER / (1-CER)]
    Last edited by admin; Sun 28th February '16 at 3:51pm.
    Clinical Pharmacy Specialist - Hematology

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