Please enable JavaScript.
Coggle requires JavaScript to display documents.
2023/9/5 5 epidemiology - Coggle Diagram
2023/9/5 5 epidemiology
-
disease causation
RR 1, Cl, Cl width - powerful?
OR
The chance of people who have food poisoning from eating pork is XXX times higher than those who have food poisoning.
-
causality & association (no association, no causality) (with association, not always causality) association: must discuss 1 bias 2 confounding 3 chance
-
-
quality of study
bias, confounding (residual confounding, only RCT - all known and unknown confounding factors)
-
RCT vs cohort vs cross-sectional vs ecological study (no population, quicker but ecological fallacy, proxy of the exposure, only useful for getting a hypothesis and do the next study)
-
-
diagnosis & screening
primary & secondary
screening (already have the disease, but no symptoms yet) Wilson and Jungner criteria: usually compare between diff screening, not just listing criteria (mass screening: inborn metabolism error)
-
sensitivity & specificity (overlap, cutoff pt, no both 100%)
given disease prevalence, and sensitivity & specificity
-
-
-
bigger area under curve, the better the test (A: perfect test, C: useless test, 50% have the disease) (every test between A and C)
implication, consider the disease (higher sensitivity: must treat, life-threatening) (high specificity: not life-threatening, lots of worry)
bias
-
2 lead time bias
never use survival time, use disease-specific mortality rates
3 length time bias
solved by RCT, even out slow and fast growing cases
-
-
-
-
-
