Hospital-acquired bloodstream infections (HABSIs) cause increased morbidity, mortality, and hospital costs that are partially preventable. HABSI seasonality has been described for gram-negative bacteria but has not been stratified per infection origin.

To assess seasonality among all types of HABSIs and their associations with climate.

Hospitals performing surveillance for at least 1 full calendar year between 2000 and 2014 were included. Mixed-effects negative binomial regression analysis calculated the peak-to-low monthly ratio as an adjusted HABSI incidence rate ratio (IRR) with 95% confidence intervals (CIs). Another regression model examined associations between HABSI rates and climate variables. These analyses were stratified by microorganism and infectious origin.

The study population included 104 hospitals comprising 44,111 HABSIs. Regression analysis identified an incidence rate ratio (IRR) peak in August for gram-negative HABSIs (IRR, 1.59; 95% CI, 1.49–1.71), CLABSIs (IRR, 1.49; 95% CI, 1.30–1.70), and urinary tract HABSI (IRR, 1.52; 95% CI, 1.34–1.74). The gram-negative incidence increased by 13.1% (95% CI, 9.9%–16.4%) for every 5°C increase in temperature. Seasonality was most present among E. coli, K. pneumoniae, E. cloacae, and the nonfermenters. Gram-positive and pulmonary HABSIs did not demonstrate seasonal variation.

Seasonality with summer spikes occurred among gram-negative bacteria, CLABSIs, and urinary tract HABSIs. Higher ambient temperature was associated with gram-negative HABSI rates. The preventable causative factors for seasonality, such as the nurse-to-patient ratio, indoor room temperature or device-utilization, need to be examined to assess areas for improving patient safety.

Surveillance of central-line–associated bloodstream infections requires the labor-intensive counting of central-line days (CLDs). This workload could be reduced by sampling. Our objective was to evaluate the accuracy of various sampling strategies in the estimation of CLDs in intensive care units (ICUs) and to establish a set of rules to identify optimal sampling strategies depending on ICU characteristics.

Analyses of existing data collected according to the European protocol for patient-based surveillance of ICU-acquired infections in Belgium between 2004 and 2012.

CLD data were reported by 56 ICUs in 39 hospitals during 364 trimesters.

We compared estimated CLD data obtained from weekly and monthly sampling schemes with the observed exhaustive CLD data over the trimester by assessing the CLD percentage error (ie, observed CLDs – estimated CLDs/observed CLDs). We identified predictors of improved accuracy using linear mixed models.

When sampling once per week or 3 times per month, 80% of ICU trimesters had a CLD percentage error within 10%. When sampling twice per week, this was >90% of ICU trimesters. Sampling on Tuesdays provided the best estimations. In the linear mixed model, the observed CLD count was the best predictor for a smaller percentage error. The following sampling strategies provided an estimate within 10% of the actual CLD for 97% of the ICU trimesters with 90% confidence: 3 times per month in an ICU with >650 CLDs per trimester or each Tuesday in an ICU with >480 CLDs per trimester.

Sampling of CLDs provides an acceptable alternative to daily collection of CLD data.

Infect Control Hosp Epidemiol 2016;37:549–554

More than 10% of patients admitted to intensive care units (ICUs) experience a severe, healthcare-associated infection, such as ventilator-associated pneumonia (VAP) or bloodstream infection (BSI). What could be a public health target for prevention is hotly debated, because properly adjusting for intrinsic risk factors in the patient population is difficult. We aimed to estimate the proportion of ICU-acquired VAP and BSI cases that are amenable to prevention in routine conditions.

We analyzed routine data collected prospectively according to the European standard protocol for patient-based surveillance of healthcare-acquired infections in ICUs. We computed the number of infections to be expected if, after adjustment for case mix, the infection incidence in ICUs with higher infection rates could be reduced to that of the top-tenth-percentile-ranked ICU. Computations came from model-based simulation of individual patient profiles over time in the ICU. The preventable proportion was computed as the number of observed cases minus the number of expected cases divided by the number of observed cases.

Data for 78,222 patients admitted for more than 2 days to 525 ICUs in 6 European countries from 2005 to 2008 were available for analysis. We calculated that 52% of VAP and 69% of BSI was preventable.

Our pragmatic, if highly conservative, estimates quantify the potential for prevention of VAP and BSI in routine conditions, assuming that variation in infection incidence between ICUs can be eliminated with improved quality of care, apart from variation attributable to differential case mix.