Update from ECCMID: Infectious disease and medical overuse

This is a guest post by Dan Morgan, MD MS. He's an Associate Professor of Epidemiology and Medicine at the University of Maryland, Baltimore.


At the European Congress of Clinical Microbiology and Infectious Diseases (ECCMID) meeting last week in Vienna, Austria, a session was dedicated to Medical Overuse and Infectious Disease. To me, it was great to hear smart people thinking about my job, infectious diseases, in the context/language of my hobby, medical overuse.  Medical overuse has been defined as the provision of care in which harms outweigh benefits or the benefits are so small that informed patients would not want care. Although it was the last session of the meeting, it was surprisingly well attended. A few high points follow:

Céire Costelloe from the UK presented work showing 80% of antibiotics are given as outpatients and promote risk of resistance greatest in the first week but antibiotics have a persistent effect six months after use. Targeting overuse of antibiotics in outpatient settings is likely key to address antibiotic resistance.

Alexander Friedrich from the Netherlands, presented a balanced view of how tests can drive overuse—such as blood cultures or rapid malaria testing, but also that testing may have a role in reducing overuse in the use of procalcitonin to stop antibiotics. If clinicians default to antibiotics, then testing may help them step back. However, if clinicians aren’t prone to empiric treatment, testing may increase treatment.

Stephan Harbarth from Switzerland then gave a great overview of how implanted devices like urinary, central-venous catheters and endotracheal tubes are risks for HAIs, MDRO colonization and are often overused. His message was that reducing use of devices is the primary way of reducing infections. (Although note, CDC and other agencies use rates per catheter day, which discourages removal of low risk catheters, so we may need a better metric—like “days of utilization”)

Finally I spoke, closing the session and the conference. My talk focused on what do we know about Overuse more generally and what can we do to prevent Overuse? To me, the basis of Overuse is a skewed view of testing and treatment. I reviewed the evidence of physician over-enthusiasm for benefits of testing and treatment and tendency to underestimate harms of both. I presented a general model for how to address overuse, the Choosing Wisely campaign and how CW could be used by those encouraging stewardship. On the theme of stewardship, there have been interesting studies showing the benefits of diagnostic stewardship, identifying what seems to be a growing trend to modify testing to improve antibiotic use.

There was, of course, much more from ECCMID that is publicly available. Take advantage of their site that has recordings of talks!

Join The Race Against Resistance

This is a guest post from Judy Guzman-Cottrill. Dr. Guzman-Cottrill is a Professor of Pediatrics at Oregon Health & Science University and also an infection prevention and healthcare epidemiology consultant for the Oregon Health Authority’s HAI Program, where she serves as the Medical Director for Ebola and Emerging Pathogen Preparedness. 

Last week Judy and I attended a SHEA Foundation Board Meeting where, during strategic planning discussions, this important fundraising event was discussed. I hope you can run, ride or contribute! - EP

It’s time to dust off those running shoes and road bikes! Last year, the SHEA Education & Research Foundation (ERF) organized its first Race Against Resistance Fundraiser, and it was a great success. Runners raised over $23,500 for new SHEA ERF Scholarships, and these scholarships are currently open for applications, with a May 5th deadline.

Given the great success in establishing these new scholarships, we are now planning the 2017 Race Against Resistance! We would like to expand the volunteers to both runners and cyclists. Participants choose a local non-charity race between SHEA Spring 2017 and IDWeek 2017.  Funds raised will continue to support SHEA ERF-sponsored education opportunities in the field of antibiotic stewardship. The top fundraisers (and affiliated hospital) will be recognized at the SHEA Business Meeting as a part of IDWeek 2017. The race must be pre-approved by SHEA organizers, and all participants must finalize their race selection by March 30, 2017 to be listed on a competition webpage for fundraising purposes. Please contact Kristy Weinshel at kweinshel@shea-online.org to become a 2017 fundraiser, or if you’d like more details.

Are you wondering who raised the $23,500 last year? Here are last year’s racers and how much they were able to fundraise for SHEA ERF scholarships!

Keeping Our Eyes on the Antimicrobial Stewardship Ball: Dr. Tom Price as the next secretary of HHS

This is a guest post from Judy Guzman-Cottrill. Dr. Guzman-Cottrill is a Professor of Pediatrics at Oregon Health & Science University and also an infection prevention and healthcare epidemiology consultant for the Oregon Health Authority’s HAI Program, where she serves as the Medical Director for Ebola and Emerging Pathogen Preparedness. 


Many healthcare providers ponder what the incoming 2017 administration will mean for their work, including myself. Dan and Eli have already written several blog posts about public health funding threats. As my description above says, I'm a hybrid of sorts: a part-time pediatric infectious disease clinical faculty member at Oregon Health and Science University, and a part-time consultant to the Oregon State Health Department’s HAI program.

First, we’ve all been thinking about our patients and their families. The next administration’s plan for the Affordable Care Act seems to change constantly. During President-Elect Trump’s campaign, he promised to completely repeal the ACA within his first hundred days in office. Post-election, he has suggested that the ACA will be repealed or amended. After meeting with President Obama, Mr. Trump has stated that he will maintain the continued coverage for preexisting conditions and young adult coverage on parents’ plans until 26 years of age. Most recently, however, Mr. Trump selected Dr. Tom Price to serve as secretary of Health and Human Services. What will his leadership mean for our patients, including those who rely on Medicaid and Medicare? Dr. Price has also supported changes which would not require insurers to cover pre-existing conditions. Almost more concerning to me is that Dr. Price is a member of the Association of American Physicians and Surgeons (AAPS), an organization that vehemently opposes antibiotic stewardship legislation, has publicly opposed the IDSA Lyme Disease guidelines on several occasions, and whose executive director has publicly supported a potential link between MMR vaccine and autism as recently as 2015. Important note: It is unclear to me which of these specific stances are also personally supported by Dr. Price. It would be good to know.

What about public health, MDRO prevention, and infection prevention? I already mentioned the AAPS opposition to antibiotic stewardship legislation. Will all of our hard work be left to the wayside? Over the past decade, I've been amazed by the accomplishments our field has made in improving judicious antibiotic use. Our surgical and critical care colleagues are finally starting to feel comfortable with shorter days of antibiotic therapy, and narrower spectrum. Hospitals are starting to fund physicians and pharmacists along with the informatics experts necessary to develop and maintain effective stewardship programs. During clinical rounds, even ID consultants are asking themselves, “Does this patient really need more antibiotics? Or am I prescribing them a personal anxiolytic?!” Stewardship progress is everywhere, including NICUs across the country, partnering with the CDC, to decrease antibiotic exposure in neonates. I worry that Dr. Price, an orthopedic surgeon, will tout stewardship as needless control over physicians who should prescribe antibiotics to whomever, whenever they please.

ID clinicians and public health colleagues, let’s all keep an eye on Dr. Price. We should be strong, vocal advocates for our at-risk patients and our public health programs, to ensure that our infection prevention and healthcare epidemiology work continues into the next decade.

Does CAUTI Exist?

A previous post with this title written by two guest bloggers was removed at the authors' request. The owners of this blog did not request the original post nor the post's removal. We take this issue very seriously and are considering guest-post policy changes going forward in order to avoid similar situations in the future.

Preventing Catheter-Associated Urinary Tract Infection in Acute Care

Dr. Mohamad Fakih

This guest post is by Mohamad Fakih, MD, MPH, Senior Medical Director of the Ascension Center of Excellence for Antimicrobial Stewardship and Infection Prevention; Sarah Krein, PhD, RN, Research Scientist and Research Professor at VA Ann Arbor Healthcare System and University of Michigan; and Sanjay Saint, MD, MPH, Chief of Medicine, VA Ann Arbor Healthcare System and University of Michigan Medical School

Catheter-associated urinary tract infection (CAUTI) – sometimes jokingly referred to as the “Rodney Dangerfield” of healthcare-associated infections – finally got respect in 2008 when the Centers for Medicare & Medicaid Services stopped reimbursing for these events if associated with a hospitalization. Since then, hospitals have attempted to decrease their CAUTI rates, with this work taking on more urgency now that CAUTI is publicly reported and associated with a reduction in compensation through value-based purchasing.

CAUTI, however, presents not only a financial risk to hospitals, but a substantial safety risk to patients, especially with prolonged – and often unnecessary -- use of the catheter. The “On the CUSP Stop CAUTI” initiative attempted to implement best practices to reduce CAUTI in hospitals across the United States. The program was modeled after a successful statewide initiative in Michigan that found a 25% reduction in catheter use and CAUTI, and included a qualitative assessment to better understand the socio-adaptive aspects of CAUTI prevention. The national program focused on sharing best practices to reduce urinary catheter risk, and helping healthcare workers change their behavior to adopt measures that reduce CAUTI. This effort, sponsored by the Agency for Healthcare Research and Quality (AHRQ), included a collaboration of professional societies, academic researchers, government agencies -- including the CDC -- and state hospital associations. The program was led by the Health Research and Educational Trust with the support of faculty from the University of Michigan, St. John Hospital and Medical Center, the MHA Keystone Center, and Johns Hopkins Medicine Armstrong Institute for Patient Safety and Quality.

The newly published NEJM article presents the results of the first 4 (of 9) cohorts that participated in the initiative, and encompasses 926 units in 603 hospitals across 32 states, the District of Columbia, and Puerto Rico.  These results showed a 32% reduction in CAUTI in the non-intensive care units (ICUs), but no change in ICUs. In addition, a 7% reduction in catheter use was seen in non-ICUs. Early in the work, we evaluated the indications for catheter use and found marked differences between the ICU and non-ICU. Catheter use was reported to be indicated ~ 94% of the time in the ICUs compared to ~65% for the non-ICUs. In addition, the primary indication for using the catheter in the ICU was for fluid monitoring in the critically ill. The newly released Ann Arbor criteria for urinary catheter use may help better clarify the appropriate indications in critically ill patients.

The main elements of the initiative that helped to produce these results were to: 
1) integrate evaluation for catheter use as part of healthcare worker’s daily routine, particularly nurses
2) avoid catheter use by considering alternative urine collection methods
3) ensure that aseptic practices are used when inserting and maintaining the catheter such as hand hygiene.

Additional elements included regular feedback on performance related to catheter use and CAUTI events. Identified gaps were addressed and mitigated. A key component was ensuring adoption of best practices and buy-in from different stakeholders. This was achieved by addressing  catheter harm for specific stakeholders, ensuring leadership support of the essential disciplines, underscoring the collaborative nature of CAUTI prevention, and identifying champions within the organization to lead the effort.

The “On the CUSP Stop CAUTI” initiative demonstrates that scaling up complex interventions --with technical and socio-adaptive components -- to a national level is achievable at least in non-ICUs. CAUTI is certainly not a “sexy” healthcare-associated condition but how a hospital addresses CAUTI likely says much about how such a facility attacks other endemic and mundane harms such as falls, delirium and pressure sores. We are currently extending our CAUTI prevention program to long-term care and to those hospital units – both ICU and non-ICU – that have persistently elevated CAUTI rates. We hope to share those results soon.

The importance of considering time when evaluating risks of base jumping (and maybe even antibiotics)

This is a guest post by L. Silvia Munoz-Price, MD, PhD. Associate Professor of Medicine at the Medical College of Wisconsin. Enterprise Epidemiologist at Froedtert Health. Milwaukee.

Over a month ago, Eli asked me to write this piece to discuss my recent CID paper on handling time dependent variables. I knew this had to be done with an analogy but after several weeks of mulling over this, I was still uncertain on how to colloquially explain this concept to you. So, as I was almost ready to forget about this post while on a plane to Miami, I had sudden inspiration as I was about to nap! I really hope this helps everyone understand this statistical concept. If not, then I'm not sure reading the CID paper will help you much either…stick to 2x2 tables (sorry!!).

Setting: Let’s imagine Eli and his wife invited my hubby and me to go base jumping in New Zealand for a week (See figure…Eli take note!!). So, now let’s observe our jumping habits: of course, I would jump once and be done with it for lifetime. My hubby would probably not jump at all and just enjoy watching crazy people jump. Let’s say Eli decides to jump every day (two days he jumps twice!) and his wife jumps three consecutive days.

Study design: Ok. Not to be morbid, but the easiest outcome to evaluate is mortality (binary variable; 1: dead or 0:alive) by the end of the vacation. The exposure variable of interest is base jumping. 

Option 1: The easiest way to look at this association is to construct a 2x2 table: Did you jump? (yes/no) Did you die? (yes/no). See, the problem with this analysis is that it ignores the intensity of the exposure as Eli, his wife, and I would be considered as a “yes” and only my husband would be a “no”. But, is it reasonable to analyze the exposures for Eli, his wife and I the same way? Intuitively, we probably could say no.

Option 2: A tad more elaborate way to look at this would be to count the number of jumps per person and enter these numbers in the analysis. So, Eli would have 9, his wife would have 3, I would have 1, and my husband would have 0. What is the problem with this approach? Well, it completely disregards time of exposures, correct? It is like having all those jumps in only one day. We need to ask: when was “that” day that all those exposures got summed? Was it at the beginning of the week or towards the end? Did the outcome happen at the beginning of the week, in the middle or at the end? Is it reasonable to analyze all those jumps clustered in time within a single day? Intuitively, I would say no. A similar problem happens with number of days that jumps occurred, especially for me. When did my one jump happen (at the beginning of the trip or towards the end?).

Option 3: A more elaborate way to determine the association between jumping and mortality is to account for the richness of the exposures. Not just taking into account the specific days the jumps occurred, but how many jumps occurred each day and from which different altitudes these jumps took place. Then we can calculate the hazard of dying on a daily basis based on the previous 24 hours of jumps. Let’s go over this a bit further. The hazard on day 1 would be calculated using 3 people. Assuming we all survived, on day 2 the hazard would be calculated only among the people that jumped (2). On day 3, assuming we all survived, the hazard would be calculated again only among the people who jumped that day (2 jumps). On day 4, assuming we all survived the hazard would be calculated only among the people who jumped (1). If any of us were to arrive to the outcome during the observation, then that person would be removed from the analysis. This is the concept of time dependent exposures. You measure the outcome as the exposure occurs over time. This is in contrast to what we usually do in our hospital epi studies: exposure treated as binary variable (yes/no) or exposure treated as number of days exposed (9 or 3 or 1) or even as number of jumps performed. More concerning, the outcome on the latter examples is fixed towards the end of the observation rather than measured as time progresses.

Bringing it home: ANTIBIOTIC EXPOSURES. Antibiotics are such rich exposures. Think about it. They can be given during many different days throughout the hospital stay and there are many types of antibiotics, with various doses and routes. Outcome variables, such as acquiring a multidrug resistant organism or even developing an infection by this organism also vary in time during hospitalization. Is it reasonable to analyze all those antibiotic exposures clustered in time within a single day or even worse as binary variables? Is it optimal to fix the outcome variable as happening at the end of hospitalization? Intuitively, I would say no to both. There are a couple of examples in the ID literature that compare these analyses. One of them by my co-author Marc Bonten. However, specifically for antibiotics it is not fully clear to me if the associations found would justify the additional cost and time of obtaining all this rich information about exposures and outcomes (note: think about relooking at your cohort datasets using this method).

Let’s end this post here [so that I can take a quick nap before landing] and see the feedback I get with this example. If the feedback is good then I will explain the biases that can occur by not accounting for time in your analyses, and maybe go over delayed effect of antibiotics. In the meantime, I will be sipping a mojito with my hubby while enjoying Miami. Salud!