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Epidemeology - Coggle Diagram
Epidemeology
L5 - Cohort Studies
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Definition: Longitudinal study where N are selected and undergo follow up observation over a specified time period
Observe outcomes of interest, disease frequency, etc. Conditions are seperated through N who undergo certain exposure factors and those who do not. (ethical concerns are lesser for this reason, e.g. smoking as a risk factor - you cannot ask people to start smoking for an experiment)
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Can look like an experimantal design - picking from the GP a sample population, Regular follow up observations of each condition form the SP over time, observed outcomes are analysed
Key difference: SP cohort are not randomly allocated into conditions, exposure to the IV is recorded naturally through longitudinal observation
Measuring events and Associations - Find cumulative incidence and incidence frequence earlier in this module
Porta (2008) - "a quantity that measures the effect of a factor on the frequency or risk of a health outcome"
These allow us to "express the strength or degree of association between variables
Main Methods:
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Differences between rates/risks: how much greater is the frequency of disease in one group vs another? (see later in the module
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Study Design Impications
Not all measures of outcome, exposure or association are obtainable from each study design
Effective design allows for the calculation of cumulative incidence (risk), incidence rates and relative risk/rate
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Variability: so far, these are point estimates of occurance and effect measures
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Bias: in EPI - systematic error in the design, conduct or analysis in a study that leads to deviation from the truth
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Systematic Error
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Many types of bias exist, most falling into two groups
Selection bias- Systematic difference in characteristic between participants and the population from which they are selected or between groups compared in the study
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Can be introduced by the researcher/observer - responeder (social desirabilty or error in recall) - instrument (e.g. measuring device used)
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Information (measurement) bias - systematic differences in the classificationof exposure or outcome of study participants
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L1 - Intro to Epi
Definition
"The sudy of the distributions and Determinants of Health-related states of events in specified populations, and the application of this study to the control of health problems"
Distributions: person, place, time, etc
Determinants: biological, physical, social, behavioural
Health-Related States: diseases, behaviours (e.g. smoking), effectsof preventative programs and use of health services
Specified Populations: characterised by geography, demographics or common characteristics
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L8 - Causation, Bias, Confounding and Effect Modification
Confounding: (in EPI: a variable that is associated or has a relationship with both the exposure and the outcomebut it NOT the causal pathway)
To observe a confounding factor, calculate the odds ratio of the target variable and condition.
Whether or not the association is significant, a possible confounding factor may be identified.
Calculate the odds ratio between the confounding facotr and the target IV - this may show that the original IV was not a causal factor but a confounding factor that relates to the exposure AND outcome
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Summary Odds Ratio
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Reporting final summary statistics: the odds of [outcome] are x times higher in individuals with high [causal exposure] compared to those with low [causal factor], adjusting for the confounding effects of [confound]
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Residual Confounds - Accounting for all confounds is almsot impossible - residual confounding occurs when not all confounds are accounted for
Effect Modification: The assocation between an exposure and outcome is different depending on exposure to another factor
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Reporting: "[EM factor] has a modifying effect on the relationship between [exposure] and [outcome]."
Do not use a summary OR: this is innapropriate for EM identification as the EM is not meant to be eliminated only identified - just preport the stratified OR
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L9 Bias and Mediatoin
Effect Modification
Cause Definition (Rothman, 1986): An event, condition or characteristic (or a combination thereof) which plays an essential role in producing an occurance of disease
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Based on these, smoking is neither sufficient or necessary for lung cancer
Component Causes: Even with a necessary exposure, compenent factors will contribute to the likelihood of developping the disease in addition - a combination of factors = a sufficient cause
When a necessary exposure occurs, multiple single components have causal effects in addition, these can be different characteristics which interact with the necessary cause
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Removing even just one component cause can decrease the chances of developping the disease by compromising the causal mechanism
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Prevention
Prevention can be effective in reducing disease rates by removing the removable component causes to stop sufficient causal mechanisms
This compnent does not have to be the necessary cause as that is often impossible to prevent (e.g. HPV)
Bias and Error in Causal Associations: As shown above, certain factors in testing can prevent valid deptictions of causal mechanisms/components
Criteria for Causality in an Observed Association: Nine Viewpoints (Bradford Hill, 1897-1991) - Not all need to be present as they do not reflect every disease
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Reporting strength/consistency: Compare odds ratio findings over time between the target cause and rates of disease following exposure
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