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                    John Gay, DVM PhD DACVPM               AAHP                  FDIU             VCS

Clinical Epidemiology & Evidence-Based Medicine Glossary:

Terminology Specific to Epidemiology

Updated July 02, 2005

Section Contents:

Disease, Outcome and Factor Measures:

    1. Proportion: A dimensionless number between 0.0 an 1.0 (if a probability) or, equivalently, between 0% and 100% (if a percentage) consisting of one count as the numerator divided by another count as the denominator. Note that for consistent, unbiased interpretation, 1) all the individuals in the numerator must also be included in the denominator, 2) each individual in the denominator must be at risk of being in the numerator, and 3) all the individuals at risk of being in the numerator in a group must be in the denominator. Equivalently, the probability that an at-risk individual will acquire a condition. Point prevalence is a proportion. Proportions are often miss-identified as "rates" (e.g. case-fatality "rate", attack "rate", pregnancy "rate", relapse "rate"). (Note: Some introductory texts mislabel these proportions as "rates".)
    2. Rate: An instantaneous or "velocity" measure that can range from 0.0 to infinity, has the dimensions of number of individuals per group - unit of time (e.g., 2.5 cases per dog-month), and is the number of individuals in the at-risk group that experience the event during one time unit (per hour, day, week, month, year, ...). A rate is a ratio of the number of events in a group of individuals at risk for the event divided by the total time units contributed by the individuals at-risk of the event and is not a proportion. Proportions are often miss-identified as "rates".
    3. Ratio: A numerator divided by a denominator that usually does not include subjects of the numerator and is not restricted to values between 0.0 and 1.0 as are proportions.
    4. Incidence Rate: The rate of onset of a disease in a population per unit time calculated as the number of new cases in a population divided by the total time units each individual in the population was observed before either disease onset occurred in the individual or observation of the individual ceased. Theoretically, incidence is an instantaneous rate.
    5. Cumulative Incidence: The proportion of a fixed population that become diseased within the stated time period (e.g. month, year); not a rate but often referred to as such (e.g. the annual incidence "rate" is actually the cumulative annual incidence, a proportion).
      1. Attack "Rate": The proportion of susceptible individuals exposed to a specific risk factor in a disease outbreak that become cases. For an infectious risk factor, the attack rate is the number of secondary cases occurring within the accepted incubation period divided by the number of susceptible individuals in a closed group exposed to the primary (index) case.
      2. Case Fatality "Rate": Cumulative incidence of death in the group of individuals that develop the disease over a time period (often unstated); a proportion, not a rate.
      3. Mortality "Rate": The proportion of individuals in a population that die in a given period of time, usually a year and usually multiplied by a 10n population size so it is expressed as the number per 1,000, 10,000, 100,000, ... individuals per year. These proportions are often broken into cause-specific and age-specific proportions and are often standardized so different groups can be compared and the population at the middle of the time interval is often used as the denominator.
    6. Prevalence (Point) (Pr): In the clinical setting, prevalence is the clinician’s estimate of the probability that an individual has a given disease, based on what the clinician knows to that point (e.g., history, physical exam), before doing a diagnostic test. In the population sense, prevalence is the probability at a specific point in time that an individual randomly selected from a group will have the condition, which is equivalent to the proportion of animals in the group that have the disease. Group prevalence is calculated by dividing the number of individuals in a group that have this disease by the total number of individuals in the group that are at risk of the disease. Note that prevalence is a good measure of the amount of a chronic, low mortality disease in a population, but is not a good measure of the amount of short duration or high fatality disease. Prevalence is often established by cross-sectional surveys. However, note that the prevalence of test positives in a survey is equivalent to the actual disease prevalence only if the test used is a perfect test.

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Risk:

    1. Risk: Risk is the likelihood, usually quantified as an incidence rate or cumulative incidence proportion, that an individual will develop a given disease in a given time period.
      1. Risk Factor (Condition Determinant, Predisposing Factor): An individual attribute or exposure that is positively or negatively associated with the occurrence of a disease.
        1. Attribute: Risk factor that is an intrinsic characteristic of the individual (e.g., genetic susceptibility, age, sex, breed, weight).
        2. Exposure: Risk factor that is in the environment external to the individual (e.g., nutrition, housing, husbandry practice, or toxic agent).
      2. Competing Risks: Other sets of risk factors than can cause the condition of interest which coexist with the set of factors of interest, that is; those things that cause "red herring" cases in outbreak investigations.
      3. Induction Period: Time between exposure to a specific risk factor and the initiation of the disease. Generally the longer the induction period, the more difficult is the assessment of the association between the risk factor(s) and the disease and thus the evaluation of causality.
      4. Latent Period: Time between biologic onset of disease and disease detection (clinical - appearance of clinical signs or subclinical - positive diagnostic tests).
      5. Risk (Key) Determinant: A term applied to risk factors that a veterinarian can modify or eliminate in a specific situation to prevent or correct the disease.
      6. Risk Marker: A non-causal factor associated sufficiently well with a risk factor that it can be used as a reliable marker, or indicator, of the risk factor’s presence.
    2. Risk Measures:
      1. Attributable Risk (AR): The risk in the group exposed to a risk factor minus the risk in the group not exposed to that risk factor. The underlying or background risk without that exposure is usually assumed to be the same in both groups.
      2. Etiologic Fraction: (Population attributable risk) The proportion of all cases of a disease that are attributable to an exposure or risk. This is the proportion of the disease in the population that would be eliminated if that exposure were eliminated or prevented.
      3. Relative Risk (RR): A ratio ranging from 0 to infinity that indicates the strength of the association between the risk factor and the disease outcome and is calculated by dividing the risk in the group exposed to a risk factor by the risk in the unexposed group. A RR value statistically significantly larger than 1 indicates the exposure is associated with increased risk of disease, a RR value not statistically significantly different from 1 indicates there is no association between the exposure and the risk of disease, and a RR value statistically significantly less than 1 indicates the exposure is associated with decreased risk of disease; that is, the exposure is protective.
      4. Exposure Odds Ratio (OR): An estimate of relative risk that is obtained from a case-control study and that is similar to the relative risk when the disease is relatively rare (a cumulative annual incidence of < 5% in the unexposed population). Otherwise, it over-estimates relative risk. The odds-ratio is interpreted in the same fashion as relative risk.
      5. Number Needed to Treat (NNT): NNT is the number of individuals a clinician would need to treat to prevent one adverse outcome in that group of similar individuals at risk of the problem. This measure establishes the benefit of an intervention compared to doing nothing against a disease in individuals at risk of that disease when adverse events would still be expected even with the intervention (e.g. daily aspirin to prevent myocardial infarction). NNT is the reciprocal of the attributable risk or the reciprocal of the difference between the proportions of treated and non-treated individuals experiencing events over some period of time.

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    1. Association: An association exists if two variables appear to be related by a mathematical relationship; that is, a change of one appears to be related to the change in the other. Association is necessary for a causal relationship to exist but association alone does not prove that a causal relationship exists. A correlation coefficient or the risk measures often quantify associations.
      1. Negative Association (Inverse Relationship): The magnitude of one variable appears to move in the opposite direction of the other associated variable. The correlation coefficient is negative and, if the relationship is causal, higher levels of the risk factor are protective against the outcome.
      2. Positive Association (Direct Relationship): The magnitudes of both variables appear to move together up or down. The correlation coefficient is positive and, if the relationship is causal, higher levels of the risk factor cause more of the outcome.
    2. Cause: The combination of necessary and sufficient factors (e.g., attributes and exposures) the presence of which, alone or in combination, at some time during an individual’s life, inevitably result in disease in that individual.
      1. Causal Pathway (Causal Web, Cause and Effect Relationships): The actions of risk factors acting individually, in sequence, or together that result in disease in an individual. These pathways are often different with different sets of risk factors for individuals in different situations. Understanding these pathways and their differences is necessary to devise effective preventive or corrective measures (interventions) for a specific situation. What is effective in one pathway may not be in another because of the differences in the component risk factors. (e.g., bronchopneumonia in a housed calf vs. in a feedlot calf).
      2. Etiology: The study of disease causes and their modes of operation.
      3. Necessary Cause: A risk factor that must be, or have been, present for the disease to occur (e.g., a specific infectious agent for a particular infectious disease). Although necessary, few infectious agents cause disease by themselves.
      4. Sufficient Cause: The minimal combination of risk factors acting on the individual, on the etiologic agent if one is involved, or in the environment whose occurrence in an individual’s life inevitably results in disease. A disease can often be caused by more than one set of sufficient causes and thus different causal pathways for individuals contracting the disease in different situations.
    3. Henle-Koch Postulates: (1877) A set of 4 criteria to be met before the relationship between a particular infectious agent and a particular disease is accepted as causal. These postulates enabled the germ theory of disease to achieve dominance in medicine over other theories, such as humors and miasma. They are insufficient for multi-causal and non-infectious diseases because the postulates presume that an infectious agent is both necessary and sufficient cause for a disease. Fulfilling the postulates experimentally can be surprisingly difficult, even when the infectious process is thought to be well understood. Now archaic and superseded by the Hill's-Evans Postulates.
    4. Hill-Evans Postulates: (1965) A set of 9 or 10 criteria (depending on interpretation of original papers) that each contribute a different amount of strength to the likelihood that a relationship between a potential risk factor and a disease is causal. The entire set constitutes very strong evidence of causality when fulfilled. As noted above, these supersede the Henle-Koch Postulates and are extensions of Mill’s Five Methods of Inductive Inference  for discovering causal relationships.
      1. Mill's Eliminative Methods of Induction (System of Logic, 1843):
        1. Method of Agreement: "If two or more instances of the phenomenon have only one circumstance in common, the circumstance in which alone all instances agree is the cause or effect of the given phenomenon."
        2. Method of Difference: "If an instance in which the phenomenon under investigation occurs, and an instance in which it does not occur, have every circumstance in common save one, that one occurring in the former, the circumstance in which alone the two instances differ, is the effect, or the cause, or an indispensable part of the cause, of the phenomenon."
        3. Method of Residues: "Subduct from any phenomenon such part as is known by previous inductions to be the effect of certain antecedents, and the residue of the phenomenon is the effect of the remaining antecedents."
        4. Method of Concomitant Variations: "Whatever phenomenon varies in any manner whenever another phenomenon varies in some particular manner, is either a cause or an effect of that phenomenon, or is connected with it through some fact of causation."
      2. Hill's Criteria of Causation (1965):
        1. Strength of Association: The larger the relative effect, the more likely the causal role of the factor.
        2. Dose-response: If the risk increases with increasing dose of the risk factor, the more likely the causal role of the factor.
        3. Consistency: If similar associations are found in different studies in different populations, the more likely the causal role of the factor.
        4. Temporality: Risk factor exposure must precede the outcome.
        5. Intervention: Reduction or removal of the risk factor must reduce the risk of the outcome.
        6. Biological Plausibility
        7. Coherence: Associations between the risk factor and the outcome must be consistent with existing knowledge.
      3. Evan's Postulates (1976):
        1. Prevalence of the disease should be significantly higher in those exposed to the risk factor than those not.
        2. Exposure to the risk factor should be more frequent among those with the disease than those without.
        3. In prospective studies, the incidence of the disease should be higher in those exposed to the risk factor than those not.
        4. The disease should follow exposure to the risk factor with a normal or log-normal distribution of incubation periods.
        5. A spectrum of host responses along a logical biological gradient from mild to severe should follow exposure to the risk factor.
        6. A measurable host response should follow exposure to the risk factor in those lacking this response before exposure or should increase in those with this response before exposure. This response should be infrequent in those not exposed to the risk factor.
        7. In experiments, the disease should occur more frequently in those exposed to the risk factor than in controls not exposed.
        8. Reduction or elimination of the risk factor should reduce the risk of the disease.
        9. Modifying or preventing the host response should decrease or eliminate the disease.
        10. All findings should make biological and epidemiological sense.

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