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What do case-control studies tell us about risk factors for antibiotic resistance?

What do case-control studies tell us about risk factors for antibiotic resistance?. Christie Y. Jeon EPI502 Jan 22 nd , 2008. Outline. Motivation Randomized Controlled Trial Understanding the outcomes OR S , OR C Variation of effect Confounding Multiplicative vs. Absolute. Motivation.

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What do case-control studies tell us about risk factors for antibiotic resistance?

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  1. What do case-control studies tell us about risk factors for antibiotic resistance? Christie Y. Jeon EPI502 Jan 22nd, 2008

  2. Outline • Motivation • Randomized Controlled Trial • Understanding the outcomes • ORS, ORC • Variation of effect • Confounding • Multiplicative vs. Absolute

  3. Motivation • To identify the risk factors for infection with resistant pathogen • Antibiotic use for a given infection • A  AR , B  AR • Who, where, what ? • Why? • Interventions • Restrictions

  4. Randomized Controlled Trial PRS = P[R]TX > 1 P[R]NoTx PRC = P[R I C]TX > 1 P[R I C]NoTx PRS < PRC

  5. Case-control studies

  6. Example – Harris et al. CID 2002 Prediction : Given that an individual has recently been treated with a particular AB, the increased likelihood that the pathogen is impR compared to not having been treated

  7. Example – Harris et al. CID 2002 Community effect : indirect effect of AB on the population as a whole by reducing the overall exposure to susceptible P. aeruginosa

  8. Appropriate case-control design • Q : “What are the risk factors for emergence of AB-resistance in pathogen X among patients previously infected with AB-susceptible pathogen X” • A : • Cases : individuals with resistant organism who previously had the susceptible form of the organism • Controls : individuals with the susceptible pathogen and no subsequent resistant organism

  9. Example – Harris et al. CID 2002

  10. B  AR PRcB= P[R I C]TX > 1 P[R I C]NoTx PRc B < PRc A

  11. Tx clears some R-pathogen PRS= P[R]TX < 1 P[R]NoTx PRC = P[R I C]TX >1 P[R I C]NoTx PRS < PRC

  12. Outbreak PRS = P[R]TX > 1 P[R]NoTx PRC = P[R I C]TX >> 1 P[R I C]NoTx PRS < PRC

  13. Confounding • Time at risk of resistant pathogen • Duration at risk of resistant pathogen • Comorbid illness • Location of recruitment

  14. Multiplicative vs. Absolute • Odds, Rate, Risk Ratio = multiplicative measure • Prediction • Causal association • Rate Difference = absolute measure • Burden of disease • Population effect

  15. Schwaber et al. 2004 D E C A B

  16. Recovering rates from case-control studies RateTX = # casesTX controlsTX x 3 RateNoTx = # casesNo Tx controlsNo Tx x 3

  17. Summary • Strength and meaning of the association of antibiotic use and resistance varies by • Definition of the controls • Effect on the susceptible strains • The nature of the antibiotic • Background incidence of resistance strains • Absolute measures are better measure of burden of resistance in the population

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