Monday, March 19, 2012

Optimal Epidemiological Methods for Infection Prevention Studies

I've spent the last 12+ years writing about epidemiological methods for conducting risk-factor, outcomes and intervention studies of hospital-acquired infections. Instead of always reviewing the latest and greatest studies, I thought it might be fun to look back at some of the epi-methods papers that many of my colleagues and I have published since 2000. I think we've made some important contributions to infection prevention research, so it's kinda fun to look back at these. Over the next month or so, I hope to review other epi-methods topics that I think are particularly relevant to the study of hospital-acquired infections.

A decade ago, Anthony Harris and Yehuda Carmeli (and other folks) outlined optimal control-group selection in risk-factor studies for antibiotic resistant infections. (see here, here and here) Prior to these important studies, authors would frequently use patients infected with the susceptible organism as the control group. For example, when looking at risks for MRSA they would select MSSA controls, which is incorrect.  Unfortunately, many authors still select the wrong control group and unknowingly publish conditional odds-ratios.  I will discuss this more in a later post.

My first ever publication, and in some ways still my favorite, was a letter to the editor of CID that I wrote in 2000 pointing out a common flaw in outcome studies of infectious diseases. In the letter, I discussed a paper that looked at the outcomes (death) associated with methicillin-resistance in patients with S. aureus bacteremia. In the analysis, the authors controlled for septic shock in their regression model. I pointed out that shock is in the causal pathway between infection and death and, therefore, should not be controlled for in regression in models. This would be like controlling for car accidents when looking at the association between cell phone use and death. In infectious diseases, if you remove shock from the causal pathway, it is hard to see how you might otherwise die.

The error of controlling for intermediates is frequently repeated in ID outcome studies when, for example, authors control for illness severity using the APACHE score. If the APACHE is measured after the infection manifests, this variable would be in the causal pathway and should not be controlled for in the regression model. The APACHE should be measured before the infection manifests, as we did here. Jessina McGregor and JJ Furuno (both now at Oregon State) published a nice systematic review on optimal methods for ID outcome studies in CID back in 2007. Wouter Rottier (with Marc Bonten) just published a meta-analysis looking at the impact of confounders and intermediates (factors in the causal pathway) on ESBL-bacteremia outcomes. (JAC, March 5, 2012) I highly recommend that you read these studies prior to undertaking an ID outcome study.

Anthony Harris and I have also written extensively on the appropriate use and analysis of quasi-experimental studies looking at interventions to prevent hospital-acquired infections. In a trilogy of CID review articles, we reviewed the optimal quasi-experimental designs (2004), the frequency of each design's use (2005) and appropriate statistical analysis of time-series data (2007). If you're planning on doing a non-randomized study of any infection prevention intervention, please look these papers over. Following the optimal methods outlined in these reviews will improve your studies and also increase the chances that your intervention study's results will make the grade and be included in future systematic reviews, such as Cochrane reviews.

Image Reference: DA Grimes, Lancet 2002;359:57-61


  1. Can you please explain the sense in which these methods are optimal? Looking at the referenced article didn't clarify this.

  2. I'm not sure which article you are referring to as I referenced eleven. In general, selecting the wrong control group or controlling for factors that are not confounders but rather intermediates will lead to incorrect conclusions concerning risk-factors or incorrect measurement of attributable outcomes, respectively.