Infection prevention: One size does not fit all

One size rarely fits all in life, and this appears to be true for infection control and prevention, too. In a 2011 review of hospital epidemiology and infection control in acute-care settings, Sydnor and Perl observed that:

Growing mandates and restrictions on payments have the potential to lead to increased unnecessary antimicrobial use in an effort to prevent infections, lack of time and resources to address other potentially preventable infections, and instances of individuals gaming surveillance systems (i.e., falsifying data) in order to lower reported infection rates. Broad mandates also impose a one-size-fits-all strategy, when in reality local epidemiology varies, and infection control programs need flexibility to address local problems.

That last point is key. Kirkland has described the issue with great clarity:

It seems intuitively obvious that not every intervention that has ever been shown to work must be implemented in every healthcare setting. However, too often, in an effort to identify “best practices,” guideline writers imply that there is indeed one “right size” that will fit all healthcare facilities. Although there probably are a handful of best practices (e.g., hand hygiene before patient care or the use of prophylactic antibiotics just prior to certain surgical procedures), there are many more interventions that could be considered “good practices,” useful in some settings, unnecessary in others. A better fit might be achieved if healthcare epidemiologists were to select from among these to customize their infection prevention programs. Which good practices to choose likely depends on local context.

Since infection prevention programs often involve a complex set of sociobehavioral interventions that depend on where, how, when, and why practices are implemented, guidance is needed on how to determine what interventions to adopt in specific situations. Lacking such a robust, validated decision support methodology, recent studies like that of Sadsad et al, which illustrate the importance of context by analyzing the impact of different interventions in various wards within a hospital, suggest that mathematical modeling can be an important tool. It is intriguing to imagine applying modeling tools to tailor interventions to specific situations. What is needed is a corps of people with the requisite, relevant skill sets (including inter alia epidemiology, surveillance, nursing, and ID) working together to consider the best evidence and tailor, apply, and validate models to inform the local decisionmaking process.

One size cannot fit all when it comes to infection control and prevention. We shouldn't necessarily expect that what works in one hospital or ward will in another. We need tools to help us determine what approach is likely to be best in different cases.

(image source: David Hartley)

Disease prediction versus forecasting: The importance of words

Knowledge is power. With knowledge, it is sometimes possible to affect outcomes. However, in order to affect an outcome, one must know early enough to undertake some action. The problem is timeliness; how to know early enough is a very important question.

In order to partially solve this problem, we often make estimates of what we think we know -- in colloquial English, we predict or forecast -- before we are certain. In this way, we hope to give ourselves time to act.

Prediction and forecast may seem like they mean the same thing, but a quick spin around the Internet suggests that there is little or no consensus on what it is. Some fields use the terms differently; the climatologists, for example, have a nice set of working definitions that state clearly what is meant by the two terms.

But, for the case of making prospective predictions about outbreaks of infectious disease, especially if the predictions are made with the use of mathematical models, I think we should consider using the term "forecasting" for a couple of reasons.

First, whereas "prediction" tends to specify the time, location, and magnitude of an event (e.g., "We predict that a large outbreak of disease X will occur in region Y around date Z"), "forecasting" connotes a statistical statement (e.g., "We forecast that there is a 70% chance that a significant outbreak of disease X will occur in region Y around date Z"). In other words, "prediction" is a categorical statement that is either proven right or wrong (like predictions from a crystal ball), but "forecast" implies a probabilistic statement. "Forecast" conveys a degree of confidence. On the other hand, if we predict disease risk (see, e.g., Hay et al 2013), which is thought of as the probability of disease occurrence, then I think we're on solid footing.

Second, in some fields of science (e.g., physics), natural "laws" are known to incredible degrees of confidence. Given initial conditions to a specified accuracy, predictions can be made that are very reasonably believed. Unfortunately, infectious disease epidemiology is not one of those fields. Moreover, as George Box observed, "Essentially all models are wrong, but some are useful". Models tell us things about problems and allow us to do analysis in a systematic and (hopefully) objective way, but models are only models. They are not laws.

As Neils Bohr and others have noted, "Prediction is very difficult, especially about the future". We need to convey the finite confidence, whether high or low, in models to those using them. 

(image source: ABE Books)

How to avert an antibiotic apocalypse: We need more than new drugs alone

Forbes magazine ran a story recently entitled "How to avert an antibiotic apocalypse". It begins with a simple proposition:

Want to protect your kids from drug-resistant bacteria? Open your wallet. Governments and insurance companies need to commit to paying 10 or 50 times more than they already do if industry is going to put resources into fighting the threat of superbugs.

Who wouldn't agree that we need an invigorated pipeline of new, effective, and safe antimicrobial drugs to help us counter the specter of resistance? But it does make me wonder: Is it really a good idea to place new weapons in our arsenal when we have demonstrated few reasons to think that we will use them responsibly?

Of course new antimicrobial agents are desperately needed to treat infections resistant to currently available drugs, and understanding the reasons for the stalled pipeline is key to to achieving development goals. However, it seems to me that the question of whether, given our current practices and the state of research, we are doomed to repeat the past with a new set of effective drugs -- assuming they can be and are developed -- is fair game.

The problem of resistance is complex and has been reviewed several times (see, e.g., here, here, and here) but the ultimate solutions remain unclear. Almost certainly they include a combination of new antimicrobial drug and vaccine development, antibiotic stewardship, better hospital infection prevention, and management or elimination of environmental reservoirs of resistance such as those produced by large farms and wastewater treatment facilities, among others.

Hopefully, by comprehensively addressing these and related issues, we can avoid repeating the past when new drugs do appear. If we don't improve in all areas, it stands to reason that we run the risk of seeing resistance develop against new drugs, too.

(image source: Wikipedia)

Big data and infection: The need for theory

Richard Hamming (1915-1998) was an American mathematician who did influential and important work in computer science and telecommunications. He spent the bulk of his career at Bell Laboratory and then the Naval Postgraduate School. As part of the latter position, he taught a graduate course in engineering. I recently discovered that there's a book on these lectures, and that the lectures themselves can be found on YouTube. The book, The Art of Doing Science and Engineering: Learning to Learn, is a delightful read and is appropriate for many audiences, including those in the biomedical domain. It oozes wisdom; see, for example, the transcript of a talk he once gave, which ultimately became a chapter in the book.

I hope you acquire a copy of The Art of Doing Science and enjoy it. If you do read it, you will come across one of the pearls of wisdom that speaks to me:

The purpose of computing is insight, not numbers.

How true it is. And if we take "computing" as a synonym for "big data", I think this idea is very applicable to the current trends in bio-medicine. While the methods that are emerging to analyze and draw inferences from extremely large data sets are powerful and hold much promise when applied to relevant data, there are several things to be kept in mind. 

First, often big data sets are collected for one set of goals and purposes, but then used at a later time by researchers interested in completely different subjects. The problem with this is made clear by a chapter in Hamming's book entitled "You get what you measure". It begins (p. 202),

You may think the title means if you measure accurately you will get an accurate measurement, and if not then not; but it refers to a much more subtle thing—the way you choose to measure things controls to a large extent what happens. I repeat the story Eddington told about the fishermen who went fishing with a net. They examined the size of the fish they caught and concluded there was a minimum size to the fish in the sea. The instrument you use clearly affects what you see. 

And so it is with data sets. It is necessary to understand the details of large data sets before throwing machine methods at them in search of an answer to your particular question. How was the data collected? For what purpose was it collected? Are there biases lurking within the data? To what accuracy and precision where they collected? Et cetera.

Second, although machine methods often produce compelling visualizations, elaborate visualizations can be misleading. As Ezra Klein has noted, the trappings of data and charts can be used to make bad arguments sound persuasive. Great looking graphics of data that lack integrity or relevancy aren't necessarily helpful, and may even be harmful.

Third, by themselves, data alone aren't enough. While the value of exploratory data analysis for hypothesis building and related purposes has long been known, the importance of theoretical context seems to have been forgotten (or, worse, recanted) in the era of big data. Without theory, data may tell us facts -- assuming that the data are measured in a known way, are characterized to a known accuracy and precision, and don't have serious biases in them -- but facts lacking context are little more then random "just so" stories. On the other hand, there are very exciting notions being investigated and discussed in the area of what might be called machine-assisted discovery. Some of that conversation is quite spirited.

As will probably come up in future blogs, there are many other things to be borne in mind when dealing with big data, or any data for that matter. Understanding these may help us use the tools better.

(image source: ABE Books)