MSSA and MRSA: Both are dangerous!

Jessica Ericson and co-workers recently published a remarkable investigation of invasive Staphylococcus aureus infection in hospitalized infants. In it they describe a retrospective multicenter study of 348 NICUs in which a group of 3888 infants suffered invasive S. aureus infection between 1997 and 2012. They compare the demographics and mortality of infants with invasive MRSA and MSSA; determine the relative annual proportions of MSSA and MRSA; and calculate the risk of death after an invasive MSSA and MRSA infection. It's a fascinating study and I recommend reading it.

Among other results, they find that infant mortality following invasive MRSA and MSSA infection is essentially the same. Moreover, in their cohort of patients, MSSA was responsible for a larger burden of disease and death in infants than MRSA. Based on their findings, and consistent with previous studies, the authors recommend that

Measures to prevent S. aureus infection should include MSSA in addition to MRSA.

This is an important point. The goal of infection prevention is to protect patients; both MRSA and MSSA are deadly, so we should be mindful of each. Commonly, patients are screened for MRSA carriage only, and isolated and decolonized if found to be positive. Previous research suggests that it may be possible to reduce the incidence of both MRSA and MSSA infection by screening for S. aureus universally, and this latest study shows why this is critically important.

After drafting this post I realized that Mike Edmond, on the Controversies in Hospital Infection Prevention blog, had already written a great piece in connection with this paper. You should read the post. In it, he captures the issue powerfully in a single line: 

I often joke that I've never had a patient tell me that they don't want a MRSA infection, but they'll take an MSSA infection. 

Indeed, both pathogens deserve attention and respect, as has long been known. 

(image source: Wikipedia)

Rift Valley fever and the problem of forecasting

The notion that prediction is difficult, especially about the future, is absolutely true in the domain of infectious disease. Despite the difficulty we must try, and I think there's reason for hope, given recent studies utilizing powerful machine learning techniques and diverse data. There's much innovation being brought to the issue.

Recently, a topic I've written about in the past has appeared in the news: Forecasting of Rift Valley fever (RVF) in East Central Africa. Bernard Bett has written a thoughtful piece on the PLoS Translational Global Health blog, which I recommend reading, examining the gamut of public health tools and responses needed in order to combat RVF. He begins by framing the issue succinctly, 

Recent climate predictions suggest East Africa may be in line for an epidemic of Rift Valley fever -- an infectious disease which can hit people, their livestock and livelihoods, and national economies hard. Data from the Climate Prediction Centre and the International Research Institute for Climate and Society suggest there is a 99.9% chance there will be an El Niño occurrence this year, with a 90% chance it will last until March/April 2016. At least two of the most recent Rift Valley fever epidemics in East Africa -- those in 1997/98 and 2006/2007 -- were associated with El Niño weather patterns, with Kenya suffering losses amounting to US$32 million in the most recent. Given the strong predictions of an El Niño occurrence, and the established association between El Niño and Rift Valley fever risk, countries in the Horn of Africa need to start laying out measures to manage the developing risk. . . 

The health, economic, and social costs of this disease are well known and there is a wealth of research establishing both RVF epidemiology and its strong ties to climate (including El Niño) and the environment. Nonetheless, there was little early response undertaken given remotely sensed (i.e., satellite-based) RVF forecasting in 2006-07. Peter Roeder described the situation in a 2007 ProMED post (archive 20070112.0164),

It is interesting, if rather disheartening, to watch another RVF epizootic emerge and evolve in eastern Africa and to note that it is such a close recapitulation of events that occurred in 1997/8 and decades before. It is a recapitulation not only with respect to disease evolution but also in terms of national and international preparedness—or lack of it. Those who followed ProMED in those days will be aware that the epizootic attracted intense international attention and was closely reported in postings, which contain much useful information. Despite seminal work on developing early warning systems based on remote sensing . . . it seems that the capacity to respond has not improved greatly in the high-risk countries in Africa. 

We are presently seeing the emergence of a very powerful El Niño, possibly one of the strongest in the historical record, and this was forecast in mid-August of this year. While such a climate forecast, especially when combined with other data, could reasonably be interpreted as a 2-3 month warning of the potential for RVF in parts of Africa, it's important to appreciate the complexity of acting on such information. As I wrote in 2012,

A recent, comprehensive set of case studies of the 2006–2007 outbreak in East Central Africa was published in the American Journal of Tropical Medicine and Hygiene (August 2010), and many of the nuances are described there. For example, current preparations of the Smithburn vaccine have a shelf life of approximately 4 years. Outbreaks in the Horn of Africa region occur aperiodically, with a mean of near 10 years between outbreaks. Veterinary health authorities cannot spend scarce resources on continually replenishing a stock of RVF vaccine when other needs are present continuously. Nor can manufactures maintain large stocks that are likely to expire before sale. Thus, vaccine may not be available at any given time. Nonetheless, waiting until there is a need to manufacture vaccine is problematic.

In other words, although vaccination is a powerful strategy for protecting against RVF virus transmission, maintaining vaccine stocks isn't straightforward. Moreover, simply having vaccine available isn't enough: Effective and safe administration triggered by any early warning, such as the one described in the impressive study of Anyamba et al in 2009, is complicated. In the case of the 2006-07 outbreak, for example, by the time a warning was issued, early outbreak areas were already inundated by rains, making travel and delivery of supplies difficult. In fact, in some scenarios it may take up to 150 days from a RVF vaccine order until the successful acquisition of vaccine-associated herd immunity -- much greater than the few weeks of advanced warning the state of the art can current supply. (Note: There's a distinction between a statistical forecast for a specific disease and simply noting that the strongest El Nino in decades is going to mess with everything.)

If a disease forecast is to have impact, many factors must come into alignment, including the forecast supplying sufficient lead time, decisionmakers having enough confidence in the forecast to act, and the existence of a public health infrastructure capable of supporting an effective (and potentially complex) response. These are important issues to keep in mind when thinking about surveillance and early warning, regardless of the disease and setting.

(image source: Wikipedia)

Measles, vaccines, and the herd

The first confirmed measles death in the US since 2003 was recorded in Washington State recently, where a woman died from measles-associated pneumonia. According to a health department news release, she had an underlying condition and was taking medications that suppressed her immune system. People undergoing immunosuppressive therapy are at high risk of contracting infections and, if they develop infection, often do not exhibit the signs immunocompetent persons show. This woman is thought to have become infected at a medical clinic during a local outbreak; the etiology of her pneumonia wasn't recognized as measles until autopsy.

Measles is highly contagious (R₀, the basic reproduction ratio of the pathogen, can be as high as 18) and there are hundreds of thousands, if not millions, of immunocompromised persons in the US who depend upon the immunity of those around them for protection. When a large fraction the community possesses immunity to a pathogen, circulation of the pathogen becomes less intense. When the prevalence of immunity becomes high enough, it ceases to circulate. In this simple picture, if the fraction 1–1/R₀ of a population can be made immune, and that fraction is maintained over time over time, then a pathogen can be eradicated.

In reality, herd immunity is more complex than this. Many complications arise from imperfect vaccine immunity, population heterogeneity (including network effects), uneven vaccination, and those who opt not to receive vaccines. These complexities make it challenging, from a public health practice perspective, to protect populations with vaccines. Nonetheless, this woman's death illustrates how important it is to immunize as many people as possible: Doing so heightens protection of those vulnerable to vaccine preventable infections.

This case has been reported within the context of anti-vaccination notions, or as I prefer to think of it, vaccine skepticism. Regardless of the terminology, there is one simple truth to the incident: She developed what proved to be a fatal infection because someone in her community was not immune to the measles virus. That seems needless when a safe and effective vaccine that conveys long-lived immunity is available. Hopefully laws like those enacted in California and Vermont recently will spread to other states and help to increase the prevalence of vaccine-associated immunity in communities throughout the US.

(image source: Wikipedia)

MERS as (another) messenger of prevention

It's hard for me to know how to interpret the MERS situation in South Korea. At a high level, a recently recognized viral respiratory pathogen has traveled halfway around the world and is causing morbidity and mortality in a small section of an immunologically naive population. It appears to be associated with hospitals. What do we take away from this? Lessons will be learned when the event subsides and people study what happened, but to me, MERS reminds us that outbreaks of pathogens for which there are no vaccines or drug therapies underscore the importance of prevention.

When possible, preventing pathogens from physically reaching or entering a host by respiratory, percutaneous, alimentary, blood et al pathways is preferable to relying on pharmaceutics. Drugs tend to be complex and costly to develop, can take a long time to enter the marketplace, and -- especially in the case of antibiotics and antivirals -- they can become obsolete over time. Moreover, drugs are often toxic to the patient. Prevention is applicable in situations when appropriate drugs don't exist (e.g., for newly emerged pathogens), when it isn't possible to administer drugs in a timely manner, or when patients cannot tolerate them. 

Consider two anecdotes related to the spread of MERS virus in South Korean hospitals. As described by Choe Sang-Hun, it appears that the index patient in the South Korean event had "coughed and wheezed his way through four hospitals before officials figured out, nine days later, that he had something far more serious and contagious." Furthermore, ED wait times in Korea can be extraordinarily long by US standards. Another patient, who waited two-and-a-half days in the emergency department before a hospital bed became available, infected 55 additional individuals during their wait. Apparently, 2.5 days isn't an unusually long waiting time in some Seoul hospitals. 

Applying effective prevention measures to patients suspected of infection is the only way of stopping the chain of transmission in such environments. Unfortunately, it is unclear how to achieve good infection control for MERS and a range of other pathogens. Eli Perencevich described the issue clearly, as usual, in the Controversies in Hospital Infection Prevention blog recently: 

. . . we don't actually know how to achieve good infection control for MERS and the other diseases he [Tom Frieden] mentioned [measles, DR-TB, SARS, Ebola]. If only we invested in studies to understand how to best implement PPE in these [hospital] settings. One could imagine improved PPE technology, refined PPE donning and doffing algorithms and enhanced environmental cleaning as potential targets for future studies examining optimal protection from MERS. Not coincidentally, many of these are the same targets that Mike, Dan and I mentioned in our Ebola+PPE editorial several months ago. If we invest in infection prevention technology and implementation research, our health care system will be safer regardless of the pathogen du jour.

And that's the point that MERS makes me think about. Yes we need antimicrobials and vaccines that work against specific pathogens, of course we do, but developing such drugs is a major effort. Biochemical pathways must be understood, pathogen life histories and survival strategies must be elucidated, and the host response must be characterized among many, many other things. Doesn't it make sense that research on pathogen-agnostic approaches to prevention, which don't require such specific and complex information, might be simpler and broadly applicable? 

Investing in research on infection prevention approaches, and how to implement them sustainably in realistic clinical environments, would pay benefits far beyond helping to thwart the spread of exotic and newly emerged pathogens. We may learn how to better control and prevent the usual suspects of hospital associated infection, which, afterall, are responsible for a tremendous burden of disease day in and day out.

(image source: Wikipedia)

Vaccines, cancer, and science communication: Oh my!

Tara Haelle, writing for NPR recently, tells how a university press release, inaccurately entitled "Study explains how early childhood vaccination reduces leukemia risk", was covered widely in the press last month. The release attempted to explain newly published research carried out, in part, at UCSF. She describes how conversations with the senior author of the study failed to temper some questionable passages in the press release, and how follow-up with other researchers expert in vaccines helped to provide clarity. It's a good article and I recommend reading it. The bottom line, she suggests, is to always be skeptical of press releases.

That's certainly sage advice. I don't want to go into the details of this particular case; Haelle does that very competently, as do others, and it's easy enough to check the face validity of the claims of the release and author interview yourself via the SEER Website and FDA vaccine license time lines. Rather, I want to step back from the details and think about the episode more broadly.

This is a case of a group of scientists carrying out research that passed peer review and was published in a prestigious journal. When such a study is published, universities understandably want to make the presumed important information available to a broader audience. The titles and topics of many research articles probably won't draw the attention of casual readers, so universities have media relations teams that work with researchers to write press releases and help investigators interact with the press. One danger of this, and again it is understandable, is the potential for over simplifying and overselling research in order to make it accessible and relevant to the public.

It can also lead to a misunderstanding of the process of discovery. Medical science is a fluid, dynamic endeavor in which knowledge emerges iteratively, often triggered from conflicting results. Rarely does one study show anything definitively. In fact, many findings described in peer-reviewed studies are refuted later by the findings of other peer-reviewed studies. It has been argued that most published research findings are false, and recent evidence suggests that an alarming number of published results aren't reproducible. It reminds one of a quote attributed to physicist Wolfgang Pauli,

I don't mind your thinking slowly; I mind your publishing faster than you think.

Regardless of publication rates (and the pressures leading to those rates), knowledge emerges as conflicts are resolved, which can take years or even decades. It's unclear whether this is widely understood by those consuming medical and scientific information via the popular press. We need to think about how to communicate both new scientific findings and the process of science to the public more effectively. 

(image source: SEER)

Intensive care from afar: Caregiver versus patient watcher

A recent NPR story by Michael Tomsic recounted the remarkable story of how the Carolinas HealthCare System monitors ICU patients in 10 of its hospitals from a remote "command center"-like facility. Several critical care specialists staff the center; nurses are present around the clock and doctors work nights. Command center staff also spend time at the hospitals they monitor.

The system began doing this roughly two years ago and have since found that the quiet atmosphere of the command center ("none of the bells and whistles going off that most ICUs need to alert nurses and doctors down the hall that they're needed") allows medical staff in the center to maintain a constant focus on patients. The approach seems to be working for the system: They've observed a higher patient volume, lower mortality rate, and decreased length of stay since opening the center (though, as the article describes, such improvement likely isn't due solely to the remote monitoring program).

The issue of alarm fatigue is recognized as an important patient safety issue, so the idea of placing a group of specialists outside the immediate patient environment for monitoring purposes has a strong rationale. What I found most interesting about the article, however, was revealed in remarks from two nurses interviewed. One observed that "There are things that I'm able to view here [in the command center] — trends that I'm able to view here — that I'm not able to view at the bedside", while another noted that since the command center staff has easy access to patient data, handoffs are better and issues are less likely to be missed.

Assuming that these ICUs are not fundamentally different from ICUs in other facilities, the story highlights an issue that is endemic far beyond this particular set of hospitals: the frequent failure to bring data to the bedside in an effective way. This is ironic, as the big data and IT revolution brags -- incessantly, it sometimes seems -- about delivering data and analytics to the point where they can be most useful. That isn't consistent with the remarks from healthcare workers in this article.

Is caregiving versus patient monitoring an either-or proposition? I doubt it, as I've seen data-driven intensive care delivered reliably over long periods of time. Rather, I think the question is how to make data actionable through delivery to the right people without disrupting their workflow. It's a question for all clinical environments beyond the ICU. We need to make more effective use of routine clinical data.

(image source: Wikipedia)

Microbial ecology: Keeping one step ahead of the bad bugs

Two papers were published recently that apply notions of bacterial interference and competition rather elegantly. The first was a study by Dale Gerding et al on administering nontoxigenic Clostridium difficile spores to prevent recurrent C. diff infection. The study aimed to determine the safety, fecal colonization, recurrence rate, and optimal dosing schedule of nontoxigenic C. difficile, and the authors found that

Among patients with CDI who clinically recovered following treatment with metronidazole or vancomycin, oral administration of spores of NTCD-M3 was well tolerated and appeared to be safe. Nontoxigenic C. difficile strain M3 colonized the gastrointestinal tract and significantly reduced CDI recurrence. 

It's a fascinating study and I recommend reading it. In addition to contemplating this as a potential future treatment for recurrent CDI, it's intriguing to wonder if patients could have their GI tracts colonized by nontoxigenic C. diff prophylactically before receiving antibiotics associated with CDI.

The other study, by Alice Deasy et al, demonstrates how nasal inoculation with the commensal Neisseria lactamica inhibits carriage of N. meningitidis in young adults. N. lactamica is a commensal occupying the same ecological niche (the nasopharynx) as the pathogenic organism N. meningitidis, which is associated with epidemic meningitis. They observed a significant inhibition of meningococcal carriage in carriers of N. lactamica, which was attributed to displacement of existing meningococci and to inhibition of new acquisition. Their findings suggest N. lactamica as a potential "novel bacterial medicine to suppress meningococcal outbreaks". Again, I recommend reading the complete study.

The notion of exploiting microbial ecology is appealing for many reasons, including that it doesn't require developing intrinsically new pharmacologic compounds and that it may have no significant side effects. At the same time, its important to remember previous trials employing bacterial interference, such as the deliberate colonization of newborn children with "low virulence" Staph. aureus, so that old missteps aren't repeated.

(image source: Wikipedia)

HAI and aliens: The Drake equation in epidemiology

In a 1961, Frank Drake introduced the following equation for the number N of civilizations in our galaxy with which radio-communication might be possible,

N = R x F_p x N_e x F_l x F_i x F_c x L

As described by the SETI institute,

• R is the average rate of star formation in the galaxy, 
• F_p is the fraction of those stars that have planets, 
• N_e is the average number of planets that can potentially support life per star that has planets, 
• F_l is the fraction of planets that could support life that actually develop life at some point, 
• F_i is the fraction of planets with life that actually go on to develop intelligent life (civilizations), 
• F_c is the fraction of civilizations that develop a technology that releases detectable signs of their existence into space, and 
• L is the length of time for which such civilizations release detectable signals into space. 

Drake's purpose in writing this equation was to facilitate discussion at a meeting. Its importance is not the numerical prediction of communicative civilizations in the galaxy (note there are 7 factors in the equation and errors in each term will combine to make any calculation wildly uncertain) but rather in the framing of issues related to the search for alien life. That said, the equation tells a story. Assuming that these are the relevant factors, then if any the terms are zero, N is zero and we are likely to be alone. If none of them are zero, then even if they are exceedingly small, there is a chance that there is life somewhere in the galaxy. Moreover, it's unlikely that any of these terms are zero, given the huge size of the galaxy. In epidemiological terms, then, the equation helps to frame our thinking about the potential prevalence of life in the Milky Way galaxy.

Given that NASA opined recently that we're likely to have strong indications of life beyond Earth within a decade, it made me wonder about Drake-like equations in medicine and epidemiology. As a toy example, suppose that we write the number of patients contracting hospital-acquired infections (HAIs) yearly in the US as the product of several factors, say

N = N_{hospital visits} x P_contact x P_{develop disease} x P_{disease reported}

where

• N_{hospital visits} is the number of patients visiting hospitals annually,
• P_contact is the probability that a patient comes into contact with infectious material (e.g., via environmental contamination or an infectious patient or HCW)
• P_{develop disease} is the probability of developing disease if infected, and 
• P_{disease reported} is the probability that an infection is recognized and reported.

According to the CDC, there are 35.1M hospital discharges annually in the US, so N_{hosp visits}~35M. Now suppose that P_contact and P_{develop disease} are both low, say 1% , and that we have excellent surveillance so that P_{disease reported} ~1. If that could be true, then we would expect to see 3,500 HAI per year. We should be so lucky. Being more realistic, however, might yield a 10% change of coming into contact to infection, P_contact~0.1, and a higher probability of contracting disease if infected, say 50%, so that P_{develop disease} ~ 0.5. In that case we get N=1.75M HAI annually, which is close to the CDC estimate of 1.7M.

How could this be decreased? The number of hospital visits, N, is unlikely to decrease drastically, so that's not really a control variable. Perhaps we could develop interventions to decrease P_contact and P_{develop disease}. Obviously there is tremendous focus on reducing P_contact through handwashing, alcohol based had rubs, contact precautions, better environmental cleaning, etc already. If P_contact could be reduced by a factor of 10, from 0.1 to 0.01 -- seemingly a tall order -- N could be dropped to 175K. That may not be possible, but suppose we could achieve a factor of 2 improvement so that P_contact ~ 0.05. If we could combine that with a similar decrease in P_{develop disease} by, say, better use of antimicrobials, then N could in turn fall from 1.7M to 438K. Thus, combination strategies could have great impact.

This is simply a back of the envelop calculation: the equation above is but an approximation and the estimates are completely arbitrary. Moreover, parameters will vary from facility to facility and even between patient populations (imagine how P_{develop disease} is likely to vary between transplant versus general surgery patients). That said, this toy model illustrates a simple point: Breaking a problem down into smaller pieces can be helpful in thinking about it.

While this way of thinking is not alien (pun intended) to biostatistics and epidemiology, and clearly has limitations, I think it's helpful for framing issues in one's mind. In addition to clearly laying out assumptions in whatever is being contemplated (in this case, HAI), toy model approaches can suggest what may be needed in order to get a better answer.

(image source: Wikipedia)

The information tsunami: Riding versus drowning

A few things have come across my Twitter feed in recent weeks that relate to cognition and the Internet. The first is an article by Thomas Erren et al on 10 elements of lifelong learning according to Richard Hamming. I'm a big fan of Hamming's ideas and research philosophy, and the authors do a nice job of updating some important points from his book. I recommend reading the paper; to wet your appetite, the first two rules they describe include

Rule 1. Cultivate Lifelong Learning as a “Style of Thinking” That Concentrates on Fundamental Principles Rather Than on Facts

Rule 2. Structure Your Learning to Ride the Information Tsunami Rather Than Drown in It

These strike a chord when thought about in the context of the recent study by Barr et al, which suggested that smartphones, as entrée to vast stores of information, can supplant critical thinking by making it easy for people to offload thinking to technology. Hamming, Erren et al, and Barr et al collectively remind us of the dangers of merely looking for facts on the Internet as opposed to concentrating on forming a coherent body of knowledge out of those facts.

Eric Topol captures this perfectly in a recent tweet:

The future of medicine is not about looking things up on the Internet; it's being able to generate one's own real world data+super-analytics

I couldn't agree more. Data and "super-analytics" need to be aimed at generating and conveying a coherent picture of health so that consumers of those analytics -- whether researchers, physicians, or non-specialists -- are informed and educated.

As the sea of facts gets larger and more accessible, becoming a tsunami, we face the risk of drowning, as Erren et al suggest, or becoming cognitively lazy, as Barr et al suggest. How do humans synthesize knowledge from data, especially when the data may be messy, variable, or uncertain? In the case of public health, the issue is hugely important, because people will synthesize their own knowledge based on facts they find compelling (e.g., the University of Google, "My science is named Evan"). How do we leverage the information superhighway to produce insight and decisions grounded in the relevant facts?

(image source: Wikipedia)

The digital epidemiology of Staphylococcus aureus

Digital epidemiology encompasses an emerging set of analytic techniques and approaches to data collection. Data in these studies are almost always born digital -- they are not recorded or transcribed by hand -- and often the research involves online networks in one guise or another. While these methods are being utilized increasingly, studies combining both digital network data and microbiological data on the spread of hospital associated pathogens have, so far as I know, been missing. 

Obadia et al have published an exemplary study doing just this for the case of MRSA and MSSA in a long term care center. Many researchers have in the past adopted a very reasonable and plausible hypothesis regarding the spread of staph in hospitals: namely, that it depends to a large extent upon person to person contact. If that's true, then obviously the ways in which patients and healthcare workers (HCWs) interact with one another, i.e., the patient-patient and patient-HCW contact networks, must be important for understanding spread. To my knowledge, until this study, nobody has really documented this with clarity at the individual level.

Obadia et al have illustrated this relationship between staph infection and contact network structure quite clearly by utilizing wireless proximity sensing and spa typing. They demonstrate how to employ digital technology to measure who interacts with whom, how frequently, and for how long, over long periods of time, and how to combine that data with microbiological surveillance in order to observe how transmission depends on the web of contacts in a facility. The authors found that close proximity interaction (CPI) paths existed between those colonized with like staph strains, and that those path lengths were significantly shorter than paths between random pairs in the study population. This is in agreement with what is expected from the transmission hypothesis. Their study also highlighted the importance of HCWs as links in the chain of contacts between infected patients.

One important implication of this work is that it might be possible to prevent infections by managing and monitoring close contact paths between patients and patients and between HCWs and patients. The approach may also be useful for developing targeted surveillance strategies that can detect spread and break the contact pathways most likely to result in further spread. I recommend reading the paper, and also the excellent comments regarding it by Eli Perencevich at the Controversies in Hospital Infection Prevention blog.

Overall, I think this study is a great illustration of the power of digital epidemiology methods for gathering detailed data in order to understand how disease is spread in the real -- as opposed to the simplified, theoretical -- world. We need more like it to inform both our thinking about hospital associated infections and analytic models of such pathogens.

(image source: CDC)

The calculus of online credibility

It's been estimated that roughly one quarter of the global population will soon be using smartphones. Recently, Nathaniel Barr et al have studied how the instant and ubiquitous access to information from smartphone use is impacting our propensity for critical thinking. The paper's abstract captures the issue:

With the advent of Smartphone technology, access to the Internet and its associated knowledge base is at one’s fingertips. What consequences does this have for human cognition? We frame Smartphone use as an instantiation of the extended mind—the notion that our cognition goes beyond our brains—and in so doing, characterize a modern form of cognitive miserliness. Specifically, that people typically forego effortful analytic thinking in lieu of fast and easy intuition suggests that individuals may allow their Smartphones to do their thinking for them. Our account predicts that individuals who are relatively less willing and/or able to engage effortful reasoning processes may compensate by relying on the Internet through their Smartphones. . . . These findings demonstrate that people may offload thinking to technology, which in turn demands that psychological science understand the meshing of mind and media to adequately characterize human experience and cognition in the modern era.

Tania Lombrozo, writing for NPR, put it more succinctly:

We all know a little knowledge can be a dangerous thing. Research increasingly supports a related proposition — that easy knowledge can be a dangerous thing.

I don't want to go into the strengths and weaknesses of the study, or it's ability to assess causality. Researchers will no doubt address these important issues in time, and Lombrozo discusses them nicely in her essay. However, it's important to contemplate some of the implications of the study.

One implication is that, as the Internet and mobile technology have changed the way we acquire information, they have also changed the way in which we assess its credibility. We've all seen people read claims and discussions from different online media and rapidly accept them as true. Sometimes the information they glean is true -- online communities of people possessing similar interests and expertise are often rich sources of specialized information -- whereas other times online discourse is not so authoritative. Regardless, acceptance of what surfers read is often rapid. If people increasingly rely on digital information and forgo complex, analytic thinking -- the "cognitive miserliness" that Barr et al describe -- perhaps it's because they trust what they're reading.

If true, what are the implications for public health? Insofar as health behavior is influenced through online information gathering, we must understand how people determine trust and credibility of online information. Professional achievement in terms of academic degrees, certifications, or job titles of those publishing information online may be important, but also important are a source's online profile in terms of site appearance and ease of navigation, number of followers, and number of page views. In fact, the latter may be more important than the former.

If it's true that we are increasingly putting our brains in our pockets and avoiding critical thinking, as Barr et al suggest, then it's important to understand the calculus of credibility in cyberspace. If people find Facebook, Reddit threads, and Fox News more credible than the CDC or their own doctor -- rightly or wrongly -- we had better understand why and engage it as a tool.

(image source: David Hartley)

Vaccines: What do we think?

CNN published a poll on Monday of this week that contains some interesting statistics. A story announcing the poll began

A new CNN/ORC poll shows nearly 8 of 10 Americans believe parents should be required to vaccinate their healthy children against preventable diseases such as measles, mumps, rubella and polio. If the children are not vaccinated, most agree the child should not be allowed to attend public school or day care . . . 

The basic methodology and results are described here. Overall, 78% of respondents believed parents should be required to vaccinate children against preventable diseases if they are healthy. The age stratified results depict an interesting trend: Older Americans are most supportive of required vaccinations (84% of those 50+ versus 72% of those under 50) and those at the younger end of the spectrum -- and in particular, those of common childbearing ages -- are much less supportive (only 67% of those 18-34 years of age).

Pondering these statistics might lead one to muse that it would have been useful if the poll, rather than asking if parents should be required to vaccinate, had instead asked simply if parents should vaccinate. On Tuesday another poll appeared, this time by Reuters/Ipsos, that asked just that. Information on that poll can be found here. A Reuters news story summarized this poll:

Seventy-eight percent of respondents in the online survey said all children should be vaccinated unless there is a direct health risk to them from vaccination. Only 13 percent opposed vaccinations. . . 

The story went on to note that the "numbers are absolutely overwhelming in favor of vaccinations with a consistent minority in opposition." That's good, but probably not good enough. Herd immunity likely needs to be over 90% in order to eliminate measles. If the poll was representative of the larger US population, then the 78% statistic suggests that we have some work to do.

Of course, polls are not compete studies, and it's hard to know what to make of such results. However, I don't think they're entirely reassuring.

(image source: CDC)

Elimination, not eradication

In discussions of past and present measles activity in the United States, one sometimes reads that measles was once eradicated here.

It wasn't, though in 2000 it was declared eliminated.

Measles elimination is defined as interruption of continuous (i.e., endemic) transmission lasting ≥12 months. Eradication, on the other hand, implies global elimination. Smallpox was declared eradicated in 1980 and we're trying hard to eradicate polio and others at present. Measles has not been eradicated.

Eradication of measles may be possible, though there significant challenges. Sadly, despite the availability of a safe and effective vaccine, the disease continues to maintain a strong foothold in many regions of the globe. This persistence poses a threat to non-immune persons in our mobile world, as we are currently seeing in the US.

If you hear someone confuse elimination for eradication, you might gently correct them. It's important that people understand the threats to their health and wellbeing. 

(image source: CDC)

Your belief does not trump his right to recover

One story connected to the California measles episode in particular speaks to me. It concerns a dad speaking out, in defense of his son's fragile health, against the decisions of many not to vaccinate their children. The man's son is recovering from leukemia and cannot yet be vaccinated against measles. He is justifiably concerned about unvaccinated classmates posing a potentially mortal infection risk to his son and has requested that such children be barred from school.

The question of why some don't vaccinate their children (or themselves) is complex and multifaceted, but it seems to have one thing in common with other major public health issues of recent times: the idea that "it's my right to". In addition to it's my right to not vaccinate my children, we often hear that it's my right to possess assault rifles and it's my right to have raw milk on the market.

Should these be individual rights? From a public health perspective I would argue no, and point out that there's another fundamental question to be answered: Do we want to live in a society where someone's "rights" endanger the health and wellbeing of others? We've answered that question before for other major public health issues: there are mandatory seat belt laws in many states; it's not legal to drive under the influence of alcohol; and it's not legal to smoke in public areas in many parts of the nation. Such laws attempt to limit the ability of an individual to place others at risk. The dad in California has the right -- in fact, the obligation -- to protect his son's health and wellbeing. Could enacting legislation mandating vaccination except in specific medical circumstances be a solution?

I resonated with the man's concern for his son partially because cancer has touched the lives of close friends of mine. Those at risk from infection due to therapy-related immunocompromise and chronic disease are thought to number in the millions in the US. They have rights and deserve to be protected. Legislation on this issue, if possible, won't happen quickly. Pragmatically, I think we need to understand why some people believe that vaccines are dangerous when there's no evidence to support that claim and much evidence demonstrating that measles -- and other vaccine-preventable preventable diseases -- are lethally dangerous. Why are the likes of Jenny McCarthy more credible to some than the US Institute of Medicine? Understanding such issues may provide a basis for a conversation and, ultimately, change.

(image source: CDC)

When it comes to measles, it is a small world after all

Dan Diamond wrote an essay recently in Forbes in which he notes the asymmetry of public reaction to Ebola versus measles. He describes how on the one hand, even though Ebola was unlikely to cause an epidemic in the US the public went nuts with Fearbola, while on the other hand measles represents a much more realistic threat of spread but people are somewhat apathetic about it. It seems a valid observation.

It may be difficult to understand public perception of threat when it comes to infectious disease, but, epidemiologically speaking, there are some important differences between the two, as partially summarized in the table below.

	Measles	Ebola
R0	~7-18	~2
Serial interval	8-12 days	5-15 days
Incubation period	10-12 days	2-12 days
CFR	3%	25-90%
Infectious period	~ 4 days before rash to several days after onset of rash	At onset of symptoms
Vaccine preventable	Yes	No

Importantly, persons infected with measles virus are infectious before they begin to feel ill, so they are able to spread the virus in the course of their normal activities. Fearbola -- the epidemic of hyped and often unfounded messages surrounding the threat of Ebola to the US -- struck in part because of the high case fatality rate (CFR) and the lack of a vaccine conveying immunity to the Ebola virus. That contrasts strongly with measles. Even though the measles outbreak that started at Disneyland Resort Theme Parks in California is expanding, I doubt there will be a Fearmeasles epidemic, even though measles can be fatal and cause long term sequelae.

That said, this is a fascinating event due partially to people's attitudes regarding vaccines. Recently, the ramifications of such attitudes, in terms of implications for public health agencies, has been expressed very clearly by Lisa Aliferis (writing for NPR):

Local health officers in counties [in California] affected are busy tracing those who infected patients have been in contact with. Dr. Erica Pan, deputy health officer of Alameda County, says the county has shifted resources from Ebola preparedness to contact tracing for measles. Last year there were four cases of measles in Alameda County, she said, "but we had 400 contacts to investigate."

This is remarkable. On 23 January, the California Department of Health reported that in LA and Orange counties alone there were 31 confirmed cases. A simple back-of-the-envelope calculation suggests that if 4 cases required 400 contacts to be investigated (100 contacts per case on average), then 31 cases could require 3100 contacts to be investigated. No wonder health departments are refocusing resources away from Ebola and onto measles.

People who do not vaccinate their children, or catch up on missed vaccines as adults, do not only place themselves in danger of infection, they place the community in danger. Moreover, they cause scarce public health resources to be spent on controlling a vaccine preventable disease. It's ironic that the lyrics to It's a small world -- the theme song of a ride at Disneyland of the same name -- read

It's a world of laughter, a world of tears.
It's a world of hopes and a world of fears.
There's so much that we share,
That it's time we're aware
It's a small world after all. 

Indeed.

(image source: Wikipedia)

Recall bias, or how I learned to stop worrying about measles and love my vaccination beliefs

The multi-state outbreak of measles in the US, which originated at Disneyland theme parks in California, has received much attention in the press recently. Sadly, the event isn't terribly surprising: many people, including children, lack immunity. The reasons for this are varied, and include the MMR vaccine being recommended for children over 12 months of age; persons not completing the recommended vaccination sequence; waning immunity in older persons; and lack of vaccination.

This latter issue is related to the anti-vaccination movement and this dimension is receiving attention in the media. Some of the discourse is revealing. One example appeared in the New York Times recently, which quoted a Santa Monica pediatrician who has cautioned against the way vaccines are used in the past (but does administer the vaccine in his practice):

“I think whatever risk there is -- and I can’t prove a risk -- is, I think, caused by the timing,” he said, referring to when the shot is administered. “It’s given at a time when kids are more susceptible to environmental impact. Don’t get me wrong; I have no proof that this vaccine causes harm. I just have anecdotal reports from parents who are convinced that their children were harmed by the vaccine.”

Perhaps such comments could be rephrased more bluntly: I believe that the vaccine could be dangerous to kids because people who know less about medicine and epidemiology than I do tell me, with great conviction, that they think it's dangerous. Even blunter still might be: The idea that vaccines can be dangerous has a high truthiness, so count me in. Or even, After talking to some patients I've decided to stop worrying about measles and love my unfounded beliefs about vaccination.

I've written before about the importance of understanding those who subscribe to anti-vaccination notions. Statements like this from a physician illustrate how much work has to be done. Several years ago Delgado-Rodríguez and Llorca wrote a very nice continuing professional education paper on bias in epidemiology, which physicians with similar beliefs should read. The following passage is especially important:

Recall bias: if the presence of disease influences the perception of its causes (rumination bias) or the search for exposure to the putative cause (exposure suspicion bias), or in a trial if the patient knows what they receive may influence their answers (participant expectation bias). This bias is more common in case-control studies, in which participants know their diseases, although it can occur in cohort studies (for example, workers who known their exposure to hazardous substances may show a trend to report more the effects related to them), and trials without participants’ blinding.

If someone wants to find a cause for a medical event, they will. The plural of "anecdote" is not "data", no matter how convincing the anecdotes may seem.

(image source: Wikipedia)

New antibiotics: Prevention is important, too!

Losee Ling et al recently described a new antibiotic compound, called teixobactin, that kills pathogens without detectable resistance. The abstract of their study notes that

. . . We developed several methods to grow uncultured organisms by cultivation in situ or by using specific growth factors. Here we report a new antibiotic that we term teixobactin, discovered in a screen of uncultured bacteria. Teixobactin inhibits cell wall synthesis by binding to a highly conserved motif of lipid II (precursor of peptidoglycan) and lipid III (precursor of cell wall teichoic acid). We did not obtain any mutants of Staphylococcus aureus or Mycobacterium tuberculosis resistant to teixobactin. The properties of this compound suggest a path towards developing antibiotics that are likely to avoid development of resistance.

It's a beautiful study, and obviously everybody hopes these implications are realized, and soon; new drugs are very badly needed. Eli Perencevich, writing in the blog Controversies in Hospital Infection Prevention, summarizes some of the important results from the study and also offers an important perspective,

I agree with Dr. William Schaffner's comments in the NY Times as he called the study/method “ingenious” yet also cautioned that "it’s at the test-tube and the mouse level, and mice are not men or women, and so moving beyond that is a large step, and many compounds have failed.” I would add one additional caveat  -- teixobactin had little activity against most Gram-negative bacteria including E. coli, Klebsiella and Pseudomonas. . . . Since the real resistance crisis is in multi drug-resistant Gram-negatives (think CRE, NDM-1), we better get back to digging in the dirt.

Certainly, these and other Gram negatives are important. As I've mused before, it's critically important to research infection prevention approaches in addition to investing in new drug development. We must understand how to prevent infections from occurring and spreading in healthcare (and other) settings before new drugs are introduced. It is clear that we do not possess this understanding, at least on any significant scale or in any sustainable way, at present.

(image source: CDC)

Blogging in R

Previous posts have discussed how computers, IT, and open source software have made incredible strides in recent years and how those advances are helpful for infectious disease epidemiology. On that theme, I recently found something very cool, which is illustrated here: This entire post was written in the RStudio IDE using the markdown and knitr packages in R.

Markdown is a text-to-HTML conversion tool that allows conversion of prose into structurally valid XHTML or HTML. Details can be found at http://rmarkdown.rstudio.com and a short tutorial is available at https://support.rstudio.com/hc/en-us/articles/200552086-Using-R-Markdown. knitr is an engine for dynamic report generation with R. It is possible to produce sophisticated documents that include bibliographic referencing and mathematical typesetting using markdown and knitr. Moreover, one can generate documents with embedded chunks of R code in order to display the code itself and/or the output.

As a simple example, below is a word cloud made from searching the Twitter API for tweets containing the terms “vaccine” or “vaccinated” in a 24 hour period. The code is shown first (it could be written better, clearly), followed by the output:

rm(list=ls())

library(twitteR)
library(tm)
library(wordcloud)
library(RColorBrewer)
library(RJSONIO)

#====================================================

path_var <- "~/Dropbox/Docs/Blogs/"
auth_var <- "my_oauth.Rdata" 
load(paste(path_var, auth_var, sep=""))
registerTwitterOAuth(my_oauth)

the_search_term <- "-RT vaccine OR vaccinated"
today <- as.character(Sys.Date())
yesterday <- as.character(as.Date(today) - 1)

max_tweets <- 300 
the_filename <- paste(path_var, "twitter_", today, ".csv", sep="")

the_search <- searchTwitter(searchString=the_search_term, 
                            n=max_tweets, 
                            since = yesterday, 
                            until = today)
  
tweets_df <- twListToDF(the_search)
write.csv(tweets_df, file=the_filename)
search_text = sapply(the_search, function(x) x$getText())
search_corpus = Corpus(VectorSource(search_text))

tdm <- TermDocumentMatrix(
  search_corpus,
  control=list(
    removePunctuation=TRUE,
    stopwords=c("amp", "vaccine", "vaccinated", 
                  stopwords("english")), 
    removeNumbers=TRUE, 
    tolower=TRUE)
) # Note we do not display the search terms in the cloud 

m <- as.matrix(tdm)
word_freqs <- sort(rowSums(m), decreasing=TRUE)  
dm <- data.frame(word=names(word_freqs), freq=word_freqs)
layout(matrix(c(1, 2), nrow=2), heights=c(1, 4))
par(mar=rep(0, 4))
plot.new()
fh <- paste("Twitter search API, ", 
            length(tweets_df[,1]), " tweets returned, ", 
            today, sep="")
text(x=0.5, y=0.5, fh)
wordcloud(dm$word, dm$freq, min.freq=5, 
          random.order = FALSE, 
          random.color=FALSE, 
          max.words=Inf, colors = brewer.pal(8, "Dark2"))

The user can control whether the R code appears in the document or not.

This blog was “knitted” into HTML with a single click, and the resulting HTML code was pasted into the blogspot composition tool. A very small amount of editing of this code was needed to make it work on the blogspot.com platform (though the R code is supposed to be depicted in a gray box, so there is at least one thing to sort out as a purest). Voila.

Were it necessary to change the code for some reason, say to add a new API search term or update the word cloud for the same search in a week – or even to fix a bug – then the resulting HTML document can be regenerated and the update published, seamlessly. Thus, R has become a method for producing dynamic documents. Imagine the power of working collaboratively with others through an R Markdown document saved in a shared folder (e.g., in Dropbox).

Do I plan on preparing future blogs in R? No -- it’s not a document preparation environment per se, and the other tools at my disposal are more than sufficient. However, if I were writing a blog that updated figures daily, weekly, or monthly based on changing data, or wanted to share fragments of code, say, I probably would use it.

Getting back to epidemiology, the word cloud is interesting. It shows that people are tweeting about the high levels of flu activity (see, e.g., http://www.cdc.gov/flu/) at present, as well as other topics including Ebola, cancer, and fraud. By reviewing the text of the tweets themselves (which is possible by inspecting the object “tweets_df” in R), a better sense of the diversity of conversation surrounding vaccines and vaccination can be had.