To establish the source and contamination routes resulting in positive clinical and surveillance microbiological cultures with carbapenem-resistant, GIM-1 metallo-β-lactamase–positive Acinetobacter pitii and Acinetobacter radioresistens from 21 patients in 8 departments

Retrospective, descriptive study.

A 1,300-bed tertiary care academic medical facility consisting of 90 buildings linked by a pneumatic transport system (PTS).

Microbiological workup of the cluster strains included matrix-assisted laser desorption/ionization time-of-flight species identification, phenotypic carbapenemase tests, polymerase chain reaction–based genotyping of carbapenemase, and pulsed-field gel electrophoresis. Outbreak management procedures were employed according to institutional regulations.

The rarity of GIM-1 Acinetobacter species in the hospital and region, the lack of epidemiological links between patients, and the fact that in some patients the apparent colonization was clearly nonnosocomial prompted the suspicion of a pseudo-outbreak. Numerous environmental cultures were positive for GIM-1-positive Acinetobacter (including archived sample requisition forms, PTS capsules, cultures from line-diverter and dispenser stations, and sterilized transport capsules following PTS delivery). Moreover, it was observed that condensation fluid from subterranean PTS tubing resulted in water entry in PTS capsules, possibly conferring specimen contamination. After extensive system disinfection, environmental surveys of the PTS were negative, and no further positive patient specimens were encountered.

This is the first report of a PTS-associated pseudo-outbreak. The large number of falsely positive patient-related specimens in conjunction with the potential hazard of airborne and contact spread of multidrug-resistant microorganisms (in this case, GIM-1 carbapenem-resistant Acinetobacter species) underscores the need for implementation of infection control–based monitoring and operating procedures in a hospital PTS.

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.

Infect Control Hosp Epidemiol 2018;39:170–176

To determine whether patients using the Centers for Medicare and Medicaid Services (CMS) Hospital Compare website (http://medicare.gov/hospitalcompare) can use nationally reported healthcare-associated infection (HAI) data to differentiate hospitals.

Secondary analysis of publicly available HAI data for calendar year 2013.

We assessed the availability of HAI data for geographically proximate hospitals (ie, hospitals within the same referral region) and then analyzed these data to determine whether they are useful to differentiate hospitals. We assessed data for the 6 HAIs reported by hospitals to the Centers for Disease Control and Prevention (CDC).

Data were analyzed for 4,561 hospitals representing 88% of registered community and federal government hospitals in the United States. Healthcare-associated infection data are only useful for comparing hospitals if they are available for multiple hospitals within a geographic region. We found that data availability differed by HAI. Clostridium difficile infections (CDI) data were most available, with 82% of geographic regions (ie, hospital referral regions) having >50% of hospitals reporting them. In contrast, 4% of geographic regions had >50% of member hospitals reporting surgical site infections (SSI) for hysterectomies, which had the lowest availability. The ability of HAI data to differentiate hospitals differed by HAI: 72% of hospital referral regions had at least 1 pair of hospitals with statistically different risk-adjusted CDI rates (SIRs), compared to 9% for SSI (hysterectomy).

HAI data generally are reported by enough hospitals to meet minimal criteria for useful comparisons in many geographic locations, though this varies by type of HAI. CDI and catheter-associated urinary tract infection (CAUTI) are more likely to differentiate hospitals than the other publicly reported HAIs.

Infect Control Hosp Epidemiol 2017;38:1167–1171

To evaluate the effect of the FAST (Find cases Actively, Separate safely, Treat effectively) strategy on time to tuberculosis diagnosis and treatment for patients at a general hospital in a tuberculosis-endemic setting.

Prospective cohort study with historical controls.

Patients diagnosed with pulmonary tuberculosis during hospitalization at Hospital Nacional Hipolito Unanue in Lima, Peru.

The FAST strategy was implemented from July 24, 2016, to December 31, 2019. We compared the proportion of patients with drug susceptibility testing and tuberculosis treatment during FAST to the 6-month period prior to FAST. Times to diagnosis and tuberculosis treatment were also compared using Kaplan-Meier plots and Cox regressions.

We analyzed 75 patients diagnosed with pulmonary tuberculosis through FAST. The historical cohort comprised 76 patients. More FAST patients underwent drug susceptibility testing (98.7% vs 57.8%; OR, 53.8; P < .001), which led to the diagnosis of drug-resistant tuberculosis in 18 (24.3%) of 74 of the prospective cohort and 4 (9%) of 44 of the historical cohort (OR, 3.2; P = .03). Overall, 55 FAST patients (73.3%) started tuberculosis treatment during hospitalization compared to 39 (51.3%) controls (OR, 2.44; P = .012). FAST reduced the time from hospital admission to the start of TB treatment (HR, 2.11; 95% CI, 1.39–3.21; P < .001).

Using the FAST strategy improved the diagnosis of drug-resistant tuberculosis and the likelihood and speed of starting treatment among patients with pulmonary tuberculosis at a general hospital in a tuberculosis-endemic setting. In these settings, the FAST strategy should be considered to reduce tuberculosis transmission while simultaneously improving the quality of care.