To save content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about saving content to .
To save content items to your Kindle, first ensure firstname.lastname@example.org
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about saving to your Kindle.
Note you can select to save to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
Antibiotic prescribing practices across the Veterans’ Health Administration (VA) experienced significant shifts during the coronavirus disease 2019 (COVID-19) pandemic. From 2015 to 2019, antibiotic use between January and May decreased from 638 to 602 days of therapy (DOT) per 1,000 days present (DP), while the corresponding months in 2020 saw antibiotic utilization rise to 628 DOT per 1,000 DP.
To determine whether the Society for Healthcare Epidemiology of America (SHEA) and the Infectious Diseases Society of America (IDSA) Clostridioides difficile infection (CDI) severity criteria adequately predicts poor outcomes.
Retrospective validation study.
Setting and participants:
Patients with CDI in the Veterans’ Affairs Health System from January 1, 2006, to December 31, 2016.
For the 2010 criteria, patients with leukocytosis or a serum creatinine (SCr) value ≥1.5 times the baseline were classified as severe. For the 2018 criteria, patients with leukocytosis or a SCr value ≥1.5 mg/dL were classified as severe. Poor outcomes were defined as hospital or intensive care admission within 7 days of diagnosis, colectomy within 14 days, or 30-day all-cause mortality; they were modeled as a function of the 2010 and 2018 criteria separately using logistic regression.
We analyzed data from 86,112 episodes of CDI. Severity was unclassifiable in a large proportion of episodes diagnosed in subacute care (2010, 58.8%; 2018, 49.2%). Sensitivity ranged from 0.48 for subacute care using 2010 criteria to 0.73 for acute care using 2018 criteria. Areas under the curve were poor and similar (0.60 for subacute care and 0.57 for acute care) for both versions, but negative predictive values were >0.80.
Model performances across care settings and criteria versions were generally poor but had reasonably high negative predictive value. Many patients in the subacute-care setting, an increasing fraction of CDI cases, could not be classified. More work is needed to develop criteria to identify patients at risk of poor outcomes.
The purpose of this study was to quantify the effect of multidrug-resistant (MDR) gram-negative bacteria and methicillin-resistant Staphylococcus aureus (MRSA) healthcare-associated infections (HAIs) on mortality following infection, regardless of patient location.
We conducted a retrospective cohort study of patients with an inpatient admission in the US Department of Veterans Affairs (VA) system between October 1, 2007, and November 30, 2010. We constructed multivariate log-binomial regressions to assess the impact of a positive culture on mortality in the 30- and 90-day periods following the first positive culture, using a propensity-score–matched subsample.
Patients identified with positive cultures due to MDR Acinetobacter (n=218), MDR Pseudomonas aeruginosa (n=1,026), and MDR Enterobacteriaceae (n=3,498) were propensity-score matched to 14,591 patients without positive cultures due to these organisms. In addition, 3,471 patients with positive cultures due to MRSA were propensity-score matched to 12,499 patients without positive MRSA cultures. Multidrug-resistant gram-negative bacteria were associated with a significantly elevated risk of mortality both for invasive (RR, 2.32; 95% CI, 1.85–2.92) and noninvasive cultures (RR, 1.33; 95% CI, 1.22–1.44) during the 30-day period. Similarly, patients with MRSA HAIs (RR, 2.77; 95% CI, 2.39–3.21) and colonizations (RR, 1.32; 95% CI, 1.22–1.50) had an increased risk of death at 30 days.
We found that HAIs due to gram-negative bacteria and MRSA conferred significantly elevated 30- and 90-day risks of mortality. This finding held true both for invasive cultures, which are likely to be true infections, and noninvasive infections, which are possibly colonizations.
Hospital-acquired infections (HAIs) develop rapidly after brief and transient exposures, and ecological exposures are central to their etiology. However, many studies of HAIs risk do not correctly account for the timing of outcomes relative to exposures, and they ignore ecological factors. We aimed to describe statistical practice in the most cited HAI literature as it relates to these issues, and to demonstrate how to implement models that can be used to account for them.
We conducted a literature search to identify 8 frequently cited articles having primary outcomes that were incident HAIs, were based on individual-level data, and used multivariate statistical methods. Next, using an inpatient cohort of incident Clostridium difficile infection (CDI), we compared 3 valid strategies for assessing risk factors for incident infection: a cohort study with time-fixed exposures, a cohort study with time-varying exposures, and a case-control study with time-varying exposures.
Of the 8 studies identified in the literature scan, 3 did not adjust for time-at-risk, 6 did not assess the timing of exposures in a time-window prior to outcome ascertainment, 6 did not include ecological covariates, and 6 did not account for the clustering of outcomes in time and space. Our 3 modeling strategies yielded similar risk-factor estimates for CDI risk.
Several common statistical methods can be used to augment standard regression methods to improve the identification of HAI risk factors.
Infect. Control Hosp. Epidemiol. 2016;37(4):411–419
Estimates of the excess length of stay (LOS) attributable to healthcare-associated infections (HAIs) in which total LOS of patients with and without HAIs are biased because of failure to account for the timing of infection. Alternate methods that appropriately treat HAI as a time-varying exposure are multistate models and cohort studies, which match regarding the time of infection. We examined the magnitude of this time-dependent bias in published studies that compared different methodological approaches.
We conducted a systematic review of the published literature to identify studies that report attributable LOS estimates using both total LOS (time-fixed) methods and either multistate models or matching patients with and without HAIs using the timing of infection.
Of the 7 studies that compared time-fixed methods to multistate models, conventional methods resulted in estimates of the LOS to HAIs that were, on average, 9.4 days longer or 238% greater than those generated using multistate models. Of the 5 studies that compared time-fixed methods to matching on timing of infection, conventional methods resulted in estimates of the LOS to HAIs that were, on average, 12.6 days longer or 139% greater than those generated by matching on timing of infection.
Our results suggest that estimates of the attributable LOS due to HAIs depend heavily on the methods used to generate those estimates. Overestimation of this effect can lead to incorrect assumptions of the likely cost savings from HAI prevention measures.
Infect. Control Hosp. Epidemiol. 2015;36(9):1089–1094
Standard estimates of the impact of Clostridium difficile infections (CDI) on inpatient lengths of stay (LOS) may overstate inpatient care costs attributable to CDI. In this study, we used multistate modeling (MSM) of CDI timing to reduce bias in estimates of excess LOS.
A retrospective cohort study of all hospitalizations at any of 120 acute care facilities within the US Department of Veterans Affairs (VA) between 2005 and 2012 was conducted. We estimated the excess LOS attributable to CDI using an MSM to address time-dependent bias. Bootstrapping was used to generate 95% confidence intervals (CI). These estimates were compared to unadjusted differences in mean LOS for hospitalizations with and without CDI.
During the study period, there were 3.96 million hospitalizations and 43,540 CDIs. A comparison of unadjusted means suggested an excess LOS of 14.0 days (19.4 vs 5.4 days). In contrast, the MSM estimated an attributable LOS of only 2.27 days (95% CI, 2.14–2.40). The excess LOS for mild-to-moderate CDI was 0.75 days (95% CI, 0.59–0.89), and for severe CDI, it was 4.11 days (95% CI, 3.90–4.32). Substantial variation across the Veteran Integrated Services Networks (VISN) was observed.
CDI significantly contributes to LOS, but the magnitude of its estimated impact is smaller when methods are used that account for the time-varying nature of infection. The greatest impact on LOS occurred among patients with severe CDI. Significant geographic variability was observed. MSM is a useful tool for obtaining more accurate estimates of the inpatient care costs of CDI.
Infect. Control Hosp. Epidemiol. 2015;36(9):1024–1030