be obtained directly from the institution . However , the burden of data collection often limits research to smaller studies from single institutions . Single-site studies often lack sufficient sample size and have limited statistical power .”

They continue , “ Much of the reviewed research used crosssectional or cohort design , which limits the interpretation to association , not causality . For example , one multisite study had many strengths , including the fact that all hospitals used the CDC protocols to identify HAI and multiple methods were used to control for patient severity of illness and differences among settings . However , even though the researchers found significant associations between staffing and various HAIs , the interpretation was limited by the cross-sectional study design . It is possible that there were unmeasured organizational traits , besides staffing , which were responsible for the observed effect . Another group of researchers who used administrative data alone conducted a longitudinal study and analyzed variation over time within a setting using a fixed-effect statistical model . Analytically , this is a much stronger design than a cross-sectional study , because it controls for differences in the setting that are time invariant ( e . g ., patient population served ). Although these researchers found that nurse staffing had a diminishing marginal effect on reducing inpatient mortality , they did not find a similar significant effect on HAI . This may be attributable to the use of the administrative data to identify HAI .”

For many researchers , the bottom line is that more rigorous research is needed , and that staffing and infection variables should be operationalized with the use of reliable and valid measures . As Weinstein , et al . ( 2008 ) note , “ The strongest design would be a multi-site , randomized , controlled trial ; however , this may not be

It ’ s taken not only those 40 years , but a global pandemic for us to get to the place where we are finally able to prioritize the work of IPs . think the reason for that is a lot of what we do is invisible , in that it ’ s risk mitigation and avoidance of infection , and that ’ s a lot harder to see or to display to others ."

feasible or pragmatic . A study that uses a cluster randomization may be possible . At the very least , researchers should use longitudinal fixed effect and / or instrumental variable research designs ( both of which help control for underlying unmeasured differences in setting ).”

Crapanzano-Sigafoos concurs that there is still improvement needed for formulations dictating something as critical as staffing .

“ That initial staffing ratio from the SENIC study — the number of IPs to inpatient beds — has been quoted widely ,” she says . “ That a simple formula to calculate but it is not representative of what happens in a healthcare facility . We ’ ve spent a lot of time trying to identify what are those factors , what can we add to tell the story of how different one facility is from another , what increases the amount of time infection preventionists spend on various tasks and types of work , what increases the risk of having a patient population that may be more likely to being colonized with a certain disease and the impact that that has on an IP & C program , what are the type of services provided and the level of

The Infection Prevention Exchange

By MD-Medical Data Quality & Safety Advisors , LLC

The IPEX Bundle

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Patient & Staff Safety

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