causation Flashcards
recall steps for ebvm
1) formula an answerable question
2) search for evidence
3) critically appraise the evidence: study design, epidemiological measures, bias and confounding, causality
4) apply answer to patient
5) audit outcome
focusing on causality
the child and the light switch
child concludes flicking switch causes light; tendency for simple one factor explanation of causation
in reality single cause outcomes tend to be exception; complex interplay of factors
cause
event, condition or characteristic which plays an essential role in producing an outcome (ex occurrence of disease)
causation
describes a combo of events that in correct sequence an timing inevitably result in an outcome
disease control does not require dependency on exact knowledge of agent or pathogenesis; give an example
cholera epidemic in london 1854 john snow removed handle of water pump, prevented disease without knowing infectious agent
inductive reasoning
- based on repeated observations, patterns, formulation of hypothesis to explore and finally general conclusions
- generalize from observations and develop scientific laws
example: edward jenner; milkmaid who develop cowpox don’t get smallpox; vaccine invented
deductive reasoning
- review what is already known, formulate hypothesis, collect data to test NULL hypothesis, lead to confirmation or not of original theory
- use general theory to predict cases
3 causal models
1) host- agent- environment triangle
2) component cause model
3) caudal diagrams
host agent environment triangle
- factors that affect risk of disease put into either host, egnt or environment factors
limitations of host agent environment triangle
- does not account for timing of sequence of events
- does not show how factors may be interlinked
- overemphasis on agent factors (not appropriate for toxins/ non infectious agents)
component cause model
- how many pieces needed to have full pie; when pie is full disease occurs
- Component causes; each piece of the pie, equal partners in producing effect
- Sufficient causes; represents the whole pie; set of components that in combo is producing the disease (particular disease might be produced by different sufficient causes- different pies)
- Necessary causes; the most important piece of the pie; factors that must be present for disease to occur
- Often not necessary to identify components (all pie pieces)
Some disease have no necessary cause (no most important piece of pie) - Necessary cause if often not sufficient cause (most important piece of pie does not equal the whole pie, other components are needed)
- Not all components must act at the same time (can interact years apart)
in component cause model what are component causes
each piece of the pie, equal partners in producing effect
in component cause model what is sufficient causes
represents the whole pie; set of components that in combo is producing the disease (particular disease might be produced by different sufficient causes- different pies)
in component causal model what is necessary causes
the most important piece of the pie; factors that must be present for disease to occur
causal diagrams
- path digram
- complex web of interactions
give 2 ways causal diagrams can be used
- to plan disease control activities
- to perform some types of statistical analyses
causal direct; direct and indirect causes/ exposures
direct; no known intervening variable between exposure and disease
indirect- effect of exposure if mediated through on or more intervening variables
3 parts to causal diagram
- outcome/ effect (disease)
- causes/ exposures (direct or indirect) can be both direct and indirect
- direction of associated (linear ie pos, neg, or non linear)
linear and non linear causes
- linear; pos or negative
- non linear; curve ex lambs with low and high birth weight increased risk of death
advantages of causal diagram
- possible to specify the sequence of effects
- possible to specify interrelationships between factors
- effects of any gents are more likely to be seen within context w other factors
3 causal guidelines
- koch’s postulates
- hill’s criteria
- evan’s concept of causation
koch’s postulates
conditions required for an agent to be considered as cause of a disease
1) agent must be present in every case of disease
2) agent has to be isolated and grown in pure culture
3) agent must cause specific disease
4) once isolated agent must reproduce disease and must be recovered from experimentally infected animals
what was first disease to meet koch’s postulates
anthrax
problems with koch’s postulates
ignored environmental influence, host factors, mixed infections and non infectious diseases
hill’s criteria
9 criteria that contribute a different amount of strength to likelihood a relationship between potential risk factor and disease is causal
1) strength
2) consistency
3) specificity
4) temporality
5) biological gradient
6) plausibility
7) coherence
8) experiment
9) analogy
hill’s criteria
1) strength of association
- strong associations more likely to be causal
- indicated by risk ratio greater than 2
- weak association does NOT rule out causal relationship
hill’s criteria
2) consistency
- consistent findings in studies using different methods and diff populations
- lack of consistency does NOT rule out a causal relationship
hill’s criteria
3) specificity
- when single cause produces a specific disease
- many exceptions
- when present, specificity does provide evidence of causality but its absence doe not rule out causation
hill’s criteria
4) temporality
- cause must be before effect
- if A comes after B then A did not cause B
- can be difficult to confirm
hill’s criteria
5) biological gradient
- dose-response relationship
- greater exposure should result in greater effect
- non linear effects may be present
- absence of dose-response does NOT rule out causal relationship
hill’s criteria
6) plausibility
- association makes biological sense; agrees w currently accepted understanding of processes
- not objective
hill’s criteria
7) coherence
- relates to absence of conflicting info
- fine distinction from plausibility
hill’s criteria
8) experiment
- investigator initiated interventions that modify exposure through prevention, treatment or removal should result in less disease
- randomized control trials best
hill’s criteria
9) analogy
has a similar relationship been observed with another exposure and or disease
evan’s concept
10 rules
- set out criteria for judging whether or not exposures cause disease
1) the proportion of individuals with disease should be higher in those exposed than those not
2) exposure to cause should be more common in those with disease than without
3) number of new cases should be higher in those exposed than those not
4) disease should follow supposed cause
5) a spectrum of host responses from mild to severe should follow exposure
6) host response should appear following exposure in those exposed but not in those not exposed
7) experimental reproduction should occur more often int hose exposed than those not exposed
8) elimination of cause should decrease frequency of occurrence
9) prevention/ modification of the host response should decrease the disease
10) all relationships should be biologically and epidemiologically credible
