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Gravity
Epi 101 Friis
Terms in this set (37)
Causal association
A relationship between an exposure factor and disease in the host. Example: Is there a causal relationship between smoking and ling cancer
Noncausal association
No relationship between an exposure factor and disease in host
Variable
Any quantity that varies. Any attribute, phenomenon, or event that can have different values
Epidemic Curve
A graphic plotting of the distribution of cases by time of onset." Unimodal curve aids in identifying the cause of a disease outbreak
Contingency Table
Another method for demonstrating associations. Type of table that tabulates data according to two dimensions (exposure and health outcome)
Point Estimate
A point estimate is a single value (sample-based) chosen to represent the population parameter. The value for the population is referred to as a parameter and the corresponding value for the sample is a statistic.
Association
Refers to a linkage between or among variables; variables that associated with one another can be positively or negatively related.
Positive Association
Association means that if the value of one variable increases, the value of the other variable increases as well
Negative Association
Association means that if the value of one variable increases, the value of the other variable decreases
Dose-Response relationship
The linear trend in the association between exposure and disease. A type of correlative association between an exposure and an effect
Threshold
Refers to the lowest dose at which a particular response occurs
Mode
The category in a frequency distribution that has the highest frequency of cases
Continuos Variable
Is a type of variable that can have an infinite number of values within a specified range. (i.e. height weight)
Method of difference
All of the factors in two or more domains are the same except for a single factor, which is hypothesized to the "cause" of a disease
Example: difference in coronary heart disease rates between sedentary and non-sedentary workers
Method of concomitant variation
A type of association in which the frequency of an outcome increases with the frequency of exposure to a factor, the hypothesized cause of the outcome
Example: Dose-response relationship between number of cigarettes smoked and mortality from lung cancer
Latency
Refers to the time period between initial exposure and a measureable response
Multifactorial
Many types of causal relationships that are involved with the etiology of diseases involve more than one causal factor. Ex. models of multifactorial causality are the web of causation and the epidemiologic triangle. Ex. multiple causal factors in the etiology of many chronic diseases include: Specific exposures (i.e., smoking), Family history, Lifestyle characteristics, Environmental influences
Operationalization
Refers to the process of defining measurement procedures for the variables used in a study. Ex. In a study of the association between tobacco use and lung disease, the variables might be the number of cigarettes smoked and the occurrence of asthma. many.
Interference
The process of passing from observations and axioms (self-evidently true) to generalizations. (draw conclusions about a parent population from sample based data)
Confidence Interval estimate
A range of values that with a certain degree of probability contain the population parameter. (alt. to point estimate)
Power
In statistics, power is "...ability of a study to demonstrate an association in one exists."Related to sample size and effect size. Effect size is related to the strength of the association that has been observed
Scatterplot
A scatter diagram plots two variables, one on the Z axis (horizontal) and one on the Y axis (vertical)
Biological Gradient
Also known as dose-response curve; shows a linear trend in the association between exposure and disease
Consistency
An association has been observed repeatedly
Specificity
Association in constrained to a particular disease-exposure relationship
Strength
Strong associations give support to a causal relationship between factor and disease
Temporality
The cause must be observed before the effect
Statistical Significance
The level of statistical significance is determined by the probability that this has not, in fact, happened. P-value is an estimate of the probability that the result has occurred by statistical accident. Therefore a large value of P represents a small level of statistical significance and vice versa.
Describe what is meant by a causal association?
A relationship between an exposure factor and disease in the host. Example: Is there a causal relationship between smoking and lung cancer
D. epi research strategies in regards to hypothesis, methods of difference and concomitant variation, and operationalization.
a. Epidemiologic Research Strategies
i. Hypothesis: Defined as "Any conjecture cast in a form that will allow it to be tested and refuted"
1. One of the most common types is the null hypothesis. An example would be to hypothesize that there is no difference between smokers and nonsmokers in the occurrence of lung cancer
2. Where do Hypotheses come from? → 1) Method of difference 2) Method of concomitant variation
ii. Method of difference: All of the factors in two or more domains are the same except for a single factor, which is hypothesized to be the "cause" of a disease
iii. Method of concomitant variation: A type of association in which the frequency of an outcome increases with the frequency of exposure to factor, the hypothesized case of the outcome
iv. Operationalization: Refers to the process of defining measurement procedures for the variables used in a study
What is the web of causation used for?
It is one of two models of Multifactorial causality, Used to view all the causal factors involved with the etiology of diseases
Ho do you account for chance in observed associations?
Epidemiologist employ statistical procedures to assess the degree to which chance may have accounted for observed associations (statistical significance).
Ex. continuous variables
height and weight
Know how a scatter plot can be used to display association between variables
a. A scatter diagram plots two variables, one on the X axis (horizontal) and one on the Y axis (vertical)
b. The measurements for each case are plotted as a single data point
c. The close the points lie with respect to the straight line of best fit through them (called the regression line), the stronger the association between variable X and variable Y
Categories included in the cycle of epi research.
a. Epidemiologists ask whether a particular exposure is causally associated with a given outcome
b. Investigators:
i. Examine existing facts and hypotheses
ii. Formulate a new or more specific hypothesis
iii. Obtain additional facts to test the acceptability of the new hypothesis
c. Epidemiologic Research Strategies
i. Hypothesis: Defined as "Any conjecture cast in a form that will allow it to be tested and refuted"
1. One of the most common types is the null hypothesis. An example would be to hypothesize that there is no difference between smokers and nonsmokers in the occurrence of lung cancer
2. Where do Hypotheses come from? → 1) Method of difference 2) Method of concomitant variation
ii. Method of difference: All of the factors in two or more domains are the same except for a single factor, which is hypothesized to be the "cause" of a disease
iii. Method of concomitant variation: A type of association in which the frequency of an outcome increases with the frequency of exposure to factor, the hypothesized case of the outcome
iv. Operationalization: Refers to the process of defining measurement procedures for the variables used in a study
Hill's criteria of causality:
a. Strength: Strong associations give support to a causal relationship between factor and disease
b. Consistency: An association has been observed repeatedly
c. Specificity: Association is constrained to a particular disease-exposure relationship
d. Temporality: The cause must be observed before the effect
e. Biological gradient: Also known as dose-response curve; shows a linear trend in the association between exposure and disease
f. Plausibility: The association must e biologically plausible from the standpoint of contemporary biological knowledge
g. Coherence: "...the cause-and-effect interpretation of our data should not seriously conflict with the generally known facts of the natural history and biology of the disease..."
h. Analogy: Relates to the correspondence between known associations and one that is being evaluated for causality (e.g., thalidomide and rubella)
2. Describe the relationship between positive and negative association among variable
a. Positive association: Association means that if the value of one variable increases, the value of the other variable increases as well
b. Negative association: Association means that if the value of one variable increases, the value of the other variable decreases
c. Positive association between dietary sugar and diabetes
i. Non-causal: A third factor may be related to both preference for dietary sugar and occurrence of diabetes
ii. Causal: High dietary sugar intake "causes" diabetes
d. Negative association between dietary sugar consumption and diabetes
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