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In cluster-randomized trials (CRT), groups rather than individuals are randomized to interventions. The aim of this study was to present critical design, implementation, and analysis issues to consider when planning a CRT in the healthcare setting and to synthesize characteristics of published CRT in the field of healthcare epidemiology.
A systematic review was conducted to identify CRT with infection control outcomes.
We identified the following 7 epidemiological principles: (1) identify design type and justify the use of CRT; (2) account for clustering when estimating sample size and report intraclass correlation coefficient (ICC)/coefficient of variation (CV); (3) obtain consent; (4) define level of inference; (5) consider matching and/or stratification; (6) minimize bias and/or contamination; and (7) account for clustering in the analysis. Among 44 included studies, the most common design was CRT with crossover (n = 15, 34%), followed by parallel CRT (n = 11, 25%) and stratified CRT (n = 7, 16%). Moreover, 22 studies (50%) offered justification for their use of CRT, and 20 studies (45%) demonstrated that they accounted for clustering at the design phase. Only 15 studies (34%) reported the ICC, CV, or design effect. Also, 15 studies (34%) obtained waivers of consent, and 7 (16%) sought consent at the cluster level. Only 17 studies (39%) matched or stratified at randomization, and 10 studies (23%) did not report efforts to mitigate bias and/or contamination. Finally, 29 studies (88%) accounted for clustering in their analyses.
We must continue to improve the design and reporting of CRT to better evaluate the effectiveness of infection control interventions in the healthcare setting.
To determine which healthcare worker (HCW) roles and patient care activities are associated with acquisition of vancomycin-resistant Enterococcus (VRE) on HCW gloves or gowns after patient care, as a surrogate for transmission to other patients.
Prospective cohort study.
Medical and surgical intensive care units at a tertiary-care academic institution.
VRE-colonized patients on Contact Precautions and their HCWs.
Overall, 94 VRE-colonized patients and 469 HCW–patient interactions were observed. Research staff recorded patient care activities and cultured HCW gloves and gowns for VRE before doffing and exiting patient room.
VRE were isolated from 71 of 469 HCWs’ gloves or gowns (15%) following patient care. Occupational/physical therapists, patient care technicians, nurses, and physicians were more likely than environmental services workers and other HCWs to have contaminated gloves or gowns. Compared to touching the environment alone, the odds ratio (OR) for VRE contamination associated with touching both the patient (or objects in the immediate vicinity of the patient) and environment was 2.78 (95% confidence interval [CI], 0.99–0.77) and the OR associated with touching only the patient (or objects in the immediate vicinity) was 3.65 (95% CI, 1.17–11.41). Independent risk factors for transmission of VRE to HCWs were touching the patient’s skin (OR, 2.18; 95% CI, 1.15–4.13) and transferring the patient into or out of bed (OR, 2.66; 95% CI, 1.15–6.43).
Patient contact is a major risk factor for HCW contamination and subsequent transmission. Interventions should prioritize contact precautions and hand hygiene for HCWs whose activities involve touching the patient.
A systematic review of quasi-experimental studies in the field of infectious diseases was published in 2005. The aim of this study was to assess improvements in the design and reporting of quasi-experiments 10 years after the initial review. We also aimed to report the statistical methods used to analyze quasi-experimental data.
Systematic review of articles published from January 1, 2013, to December 31, 2014, in 4 major infectious disease journals.
Quasi-experimental studies focused on infection control and antibiotic resistance were identified and classified based on 4 criteria: (1) type of quasi-experimental design used, (2) justification of the use of the design, (3) use of correct nomenclature to describe the design, and (4) statistical methods used.
Of 2,600 articles, 173 (7%) featured a quasi-experimental design, compared to 73 of 2,320 articles (3%) in the previous review (P<.01). Moreover, 21 articles (12%) utilized a study design with a control group; 6 (3.5%) justified the use of a quasi-experimental design; and 68 (39%) identified their design using the correct nomenclature. In addition, 2-group statistical tests were used in 75 studies (43%); 58 studies (34%) used standard regression analysis; 18 (10%) used segmented regression analysis; 7 (4%) used standard time-series analysis; 5 (3%) used segmented time-series analysis; and 10 (6%) did not utilize statistical methods for comparisons.
While some progress occurred over the decade, it is crucial to continue improving the design and reporting of quasi-experimental studies in the fields of infection control and antibiotic resistance to better evaluate the effectiveness of important interventions.
Risk adjustment is needed to fairly compare central-line–associated bloodstream infection (CLABSI) rates between hospitals. Until 2017, the Centers for Disease Control and Prevention (CDC) methodology adjusted CLABSI rates only by type of intensive care unit (ICU). The 2017 CDC models also adjust for hospital size and medical school affiliation. We hypothesized that risk adjustment would be improved by including patient demographics and comorbidities from electronically available hospital discharge codes.
Using a cohort design across 22 hospitals, we analyzed data from ICU patients admitted between January 2012 and December 2013. Demographics and International Classification of Diseases, Ninth Edition, Clinical Modification (ICD-9-CM) discharge codes were obtained for each patient, and CLABSIs were identified by trained infection preventionists. Models adjusting only for ICU type and for ICU type plus patient case mix were built and compared using discrimination and standardized infection ratio (SIR). Hospitals were ranked by SIR for each model to examine and compare the changes in rank.
Overall, 85,849 ICU patients were analyzed and 162 (0.2%) developed CLABSI. The significant variables added to the ICU model were coagulopathy, paralysis, renal failure, malnutrition, and age. The C statistics were 0.55 (95% CI, 0.51–0.59) for the ICU-type model and 0.64 (95% CI, 0.60–0.69) for the ICU-type plus patient case-mix model. When the hospitals were ranked by adjusted SIRs, 10 hospitals (45%) changed rank when comorbidity was added to the ICU-type model.
Our risk-adjustment model for CLABSI using electronically available comorbidities demonstrated better discrimination than did the CDC model. The CDC should strongly consider comorbidity-based risk adjustment to more accurately compare CLABSI rates across hospitals.
To develop an onsite syndromic surveillance system for the early detection of public health emergencies and outbreaks at large public events.
As the third largest public health jurisdiction in the United States, Maricopa County Department of Public Health has worked with academic and first-response partners to create an event-targeted syndromic surveillance (EVENTSS) system. This system complements long-standing traditional emergency department-based surveillance and provides public health agencies with rapid reporting of possible clusters of illness.
At 6 high profile events, 164 patient reports were collected. Gastrointestinal and neurological syndromes were most commonly reported, followed by multisyndromic reports. Neurological symptoms were significantly increased during hot weather events. The interview rate was 2 to 7 interviews per 50 000 people per hour, depending on the ambient temperature.
Study data allowed an estimation of baseline values of illness occurring at large public events. As more data are collected, prediction models can be built to determine threshold levels for public health response.
EVENTSS was conducted largely by volunteer public health graduate students, increasing the response capacity for the health department. Onsite epidemiology staff could make informed decisions and take actions quickly in the event of a public health emergency. (Disaster Med Public Health Preparedness. 2013;0:1–8)
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