Dr Wilber Sabiiti (He/Him/His)

• Calculate and interpret appropriate effect estimates, and describe and interpret confidence intervals

• Describe what is meant by confounding and bias

• Describe standardisation and explain why it is needed

• Differentiate association and causality, and describe the nine criteria for inferring causality

specificity (a casual link is more likely when disease is associated with one factor)

temporality (a causal link is more likely if exposure to putative cause has been shown to precede the outcome)

Biological gradient (a causal link is more likely different levels of exposure to the putative factor lead to different risk of acquiring the outcome e.g. greater the exposure leads to greater incident of event )

Plausibility (a casual link is more likely if a biological plausible mechanism is likely or demonstrated)

coherence (a casual link is more likely if the observed association conforms with current knowledge, e.g. epidemiological and labratory findings)

experiment ( a causal link is very likely if removal or prevention of th eputative factor leads to a reduced or non-existent risk of acquiring the outcome)

anaology (a causal link is more likely if an allergy exists with other diseases species or settings)

consistency (reproducibility, a causal link is more likely if the association is observed in different studies and different sub-groups)