Thursday, February 20, 2014

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)

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