Lecture 9- Variable Associations, Causation and the role of Change Flashcards
Koch’s Postulates
Demonstrated association between a microorganism and a disease
- Organism must be observed in every case of disease
- Must be isolated and grown in pure culture
- Pure culture must, when inoculated into a susceptible animal, reproduce the disease
- Organism must be observed in, and recovered from, the experimental animal
Richard Doll and Bradford Hill discovered what
Found strong associations between smoking and lung cancer deaths
Hills criteria for causation
- Strength
- Consistency
- Specificity
- Temporality
- Biological gradient
- Plausibility
- Coherence
- Experiment
- Analogy
Hill’s strength for causation
Strong associations give support to causal relationship between factor and disease
Hill’s consistency for causation
An association has been observed repeatedly
Hill’s specificity for causation
Association is constrained to a particular disease-exposure relationship, specific cause leads to specific effect
Hill’s temporality for causation
The cause/exposure must be observed before the effect
Hill’s biological gradient for causation
Also known as dose response; shows a linear trend in the association between exposure and disease
Hill’s plausibility for causation
The association must be biologically plausible form the standpoint of contemporary biological knowledge
Hill’s coherence for causation
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
Ex: histopathologic effects of smoking on bronchial epithelium
Hill’s experiment for causation
Preventative actions alter the frequency of the outcome
Hill’s analogy of causation
Should be similarities between known associations and one that is being evaluated for causality
Ex: persons exposed to secondhand smoke should also have increase in lung cancer risk
Multi factorial causality
Many types of causal relationships involve diseases with more than one causal factor
Ex: specific exposures, family history, lifestyle characteristics, environmental influences
What are the two models of multifactorial causality
Epidemiological triangle and web of causation
Epidemiological triangle
Includes 3 major factors- host, agent, environment
Affected by influences such as time, transmission type, and vectors/fomites
What are the 4 illustrations of association
Scatter plots, dose response curve, epidemic curve, contingency table
Scatter plot diagram
Closer to the points lie with respect to the straight line of best fit the stronger the association between variable X and Y
Dose response curve
Type of correlative association between an exposure and an effect
Threshold refers to the lowest dose at which a particular response occurs
Epidemic curve
Plotting of distribution of case by time of onset, aids in identifying the cause of disease outbreak
2x2 contingency table
True positive, false positive, true negative, false negative
What are 3 major challenges to validity of study designs
- Internal validity vs external validity
- Error
- Bias
Internal validity
Degree to which a study has used methodological sound procedures
External validity
Ones ability to generalize the results of the study
Error
Difference between the value obtained and true value for population
Two general categories: sampling error, non-sampling error
Sampling error
Variation that occurs because we are studying a sample rather than an entire population. There will always be natural variation between the different sample that are selected
What are 4 ways to express sampling error
Confidence intervals, standard error, margin of error, coefficient of variance
Non-sampling
Term for errors that are a result of factors other than using a sample, these result in bias
What are 4 common types of bias
Recall bias, selection bias, observer bias, confounding
Is bias more prevalent in descriptive or analytic studies
More prevalent in analytic studies
How to control recall bias
Aware of limitation when selected study method
How to control observer bias
Blind or double blind procedures, script, multiple observers
How to control selection bias
Randomization
How to control confounding bias
Design phase: think through potential confounders, analysis phase: some types of analysis can control for confounders
