1 / 15

Evaluating Whether Interventions on the Use of Antibiotics Work to Decrease Resistance

Evaluating Whether Interventions on the Use of Antibiotics Work to Decrease Resistance. Chris Ford Regina Joice 1/18/08. Study Designs. Quasi-Experimental Prospective, Randomized Trial Randomized Controlled Intervention Trial Mathematical Models. Quasi-Experimental. Characteristics:

mizell
Download Presentation

Evaluating Whether Interventions on the Use of Antibiotics Work to Decrease Resistance

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Evaluating Whether Interventions on the Use of Antibiotics Work to Decrease Resistance Chris Ford Regina Joice 1/18/08

  2. Study Designs • Quasi-Experimental • Prospective, Randomized Trial • Randomized Controlled Intervention Trial • Mathematical Models

  3. Quasi-Experimental • Characteristics: • Lack randomization • Three main types: • Controlled (control and experimental groups) • Descriptive (no control group) • Interrupted Time-Series Design: single group measured pre and post intervention • Example: • Descriptive study: Seppala et al study looking at the effect of changes in consumption of macrolide antibiotics on erythromycin resistance in streptococci. • Experiment setup: 2 time points, pre and post intervention • Data: odds ratio (assumes independent events) • Conclusions: Significant decline in frequency of resistance

  4. Prospective, Randomized Trial • Trial initiated before intervention, monitors patients both pre & post-treatment • Patients randomly assigned to treatment groups. • Helps avoid selection bias through subject randomization • Can be performed at the individual (single group) or group level (multiple groups). • Individual studies are prone to sampling bias and therefore should not be taken as evidence in isolation.

  5. Effect of Short-Course, High-Dose Amoxicillin Therapy on Resistant Pneumococcal Carriage- Schrag, S. et al. JAMA 2001- 286, 49-56 800 patients, randomized into two groups • Short-course, high-dose therapy has been suggested as an anti-resistance intervention • Investigation in a single clinic in the DR • Problems- • Time post treatment? • Applicability to other locals? Short-course High-dose Standard course/dose 77% NC 3% S. 20% NS 73% NC 7% S 21% NS Day 5 64%NC 13% S 23%NS 74% NC 6% S 20% NS Day10 49% NC 27% S 24% NS 46% NC 22% S 32%NS Day 28

  6. Effect of Short-Course, High-Dose Amoxicillin Therapy on Resistant Pneumococcal Carriage- Schrag, S. et al. JAMA 2001- 286, 49-56 800 patients, randomized into two groups Conclusions: • In the context of the current study: • The RR of being a carrier of PNSP post short course vs. standard therapy = 0.78 (given carrier status) 28 days post treatment initation • The RR of being a TMS-NS carrier post short course vs. standard course= 0.77 Short-course High-dose Standard course/dose 77% NC 3% S. 20% NS 73% NC 7% S 21% NS Day 5 64%NC 13% S 23%NS 74% NC 6% S 20% NS Day10 49% NC 27% S 24% NS 46% NC 22% S 32%NS Day 28

  7. Randomized Intervention Trial • Patients or hospitals are assigned to treatments randomly (individual or group randomized trials) • “No single epidemiologic study should be considereddefinitive”- Barry Farr* • Meta-analyses report that result variability was comparable for randomized and non-randomized studies targeted at the same question • Many types of selection bias, not just the selection of participants per group, i.e. non-blinded studies can cause a bias *Farr, B. Infection Control and Hospital Epidemiology. 2006 27(10):1096-1106.

  8. Blind Trials • Single-blind trial: patients blind to intervention • Double-blind trial: researcher and patient blind to intervention • Triple-blind trial: intervention administer (e.g. pharmacist), researcher and patient are blind to intervention *For treatment regimens of different lengths, placebos would be used to make equivalent *For cycling of drugs, no one would know which drug it was (dangerous because cannot predict drug interactions!)

  9. Mathematical Models • Limited reliable, quantitative measures of how well interventions work to decrease resistance. Models offer predictive power in the absence of conclusive data. • Duration of antibiotic therapy can increase the risk of becoming colonized by a resistant strain, because patients are less protected by their own flora while on treatment. • This increases the number of potentially dangerous contacts (the person is susceptible to colonization after contact with a health worker) • Models can predict the effect of interventions on the length of duration of antibiotic treatment and how this effects the rate of resistance. *Lipsitch, et al. Healthcare Epidemiology, CID 2001 33:1739-46.

  10. Issues • Central issue: Overwhelming majority of studies (24/25 according to meta-analysis) assume that events are independent. However, the fact that one person is infected by a resistant strain increases the chance that another becomes infected by a resistant strain. Therefore, these events are NOT independent. • There is a need for studies that do not assume independence of events of resistance. * Cooper et al. BMJ 2004; 329(7465):533

  11. By Chance? • With any communicable disease, an outbreak could cause a large spike in the data. If an intervention is done on the down slope of this epidemic, authors may suggest the intervention worked, and rates are dropping. • By only taking into consideration the possibility of independent events happening by chance, they misjudge the normal fluctuations in the rate of infected subjects • The commonly used Poisson distribution does not account for transmission dynamics

  12. Markov Models • Each event depends on the current state of the system, the past leading up to that has no effect • Hidden Markov Model: underlying unobserved state of the system that gets factored into the changes that occur in the system • Conventional models only account for the transmission of people known to be infected • Asymptomatic carriers also transmit the infection, yet their infections may not be detected • This model accounts for the observed and hidden infections when it makes predictions on the expected resistance rate in the population.

  13. Evaluating Model Fit This paper uses a hidden Markov model to study epidemic data using mechanistic transmission model for the underlying Markov chain • MRSA- Unstructured Hidden Markov (AIC= 132.03) • VRE- Unstructured Hidden Markov (AIC= 210.59) • R-GNR- Poisson (AIC=119.73), Unstructured Hidden Markov (AIC= 122.40)* *R-GNR: patient to patient transmission dynamics are ambiguous, majority of infections may be due to endogenous flora. • The hidden Markov model showed improvements to fits of data from MRSA and VRE. Though for R-GNR, Poisson distribution performed the best.

  14. Hypothesis testing • Do the collected data fit anSIS HMM model of no transmission (=0)? • VRE - reject the null (=0) (p<0.0001) • MRSA- reject the null (=0) (p< 0.001) • R-GNR- fail to reject (p=0.25) • The models reject the hypothesis that transmission does not impact incidence in VRE & MRSA.

  15. Summary • The occurrence of resistance can not be treated as an independent phenomenon. The statistical analysis assuming independence is not appropriate the investigation of the transmission of resistance. • Hidden Markov models allow for the investigation of resistance given that events cannot be treated as independent.

More Related