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To characterize residential social vulnerability among healthcare personnel (HCP) and evaluate its association with severe acute respiratory coronavirus virus 2 (SARS-CoV-2) infection.
This study analyzed data collected in May–December 2020 through sentinel and population-based surveillance in healthcare facilities in Colorado, Minnesota, New Mexico, New York, and Oregon.
Data from 2,168 HCP (1,571 cases and 597 controls from the same facilities) were analyzed.
HCP residential addresses were linked to the social vulnerability index (SVI) at the census tract level, which represents a ranking of community vulnerability to emergencies based on 15 US Census variables. The primary outcome was SARS-CoV-2 infection, confirmed by positive antigen or real-time reverse-transcriptase– polymerase chain reaction (RT-PCR) test on nasopharyngeal swab. Significant differences by SVI in participant characteristics were assessed using the Fisher exact test. Adjusted odds ratios (aOR) with 95% confidence intervals (CIs) for associations between case status and SVI, controlling for HCP role and patient care activities, were estimated using logistic regression.
Significantly higher proportions of certified nursing assistants (48.0%) and medical assistants (44.1%) resided in high SVI census tracts, compared to registered nurses (15.9%) and physicians (11.6%). HCP cases were more likely than controls to live in high SVI census tracts (aOR, 1.76; 95% CI, 1.37–2.26).
These findings suggest that residing in more socially vulnerable census tracts may be associated with SARS-CoV-2 infection risk among HCP and that residential vulnerability differs by HCP role. Efforts to safeguard the US healthcare workforce and advance health equity should address the social determinants that drive racial, ethnic, and socioeconomic health disparities.
Healthcare personnel with severe acute respiratory coronavirus virus 2 (SARS-CoV-2) infection were interviewed to describe activities and practices in and outside the workplace. Among 2,625 healthcare personnel, workplace-related factors that may increase infection risk were more common among nursing-home personnel than hospital personnel, whereas selected factors outside the workplace were more common among hospital personnel.
We assessed the appropriateness of initiating antibiotics in 49 nursing home (NH) residents receiving antibiotics for urinary tract infection (UTI) using 3 published algorithms. Overall, 16 residents (32%) received prophylaxis, and among the 33 receiving treatment, the percentage of appropriate use ranged from 15% to 45%. Opportunities exist for improving UTI antibiotic prescribing in NH.
To facilitate surveillance and describe the burden of healthcare-associated infection (HAI) in nursing homes (NHs), we compared the quality of resident-level data collected by NH personnel and external staff.
A 1-day point-prevalence survey
SETTING AND PARTICIPANTS
Overall, 9 nursing homes among 4 Centers for Disease Control and Prevention (CDC) Emerging Infection Program (EIP) sites were included in this study.
NH personnel collected data on resident characteristics, clinical risk factors for HAIs, and the presence of 3 HAI screening criteria on the day of the survey. Trained EIP surveillance officers collected the same data elements via retrospective medical chart review for comparison; surveillance officers also collected available data to identify HAIs (using revised McGeer definitions). Overall agreement was calculated among residents identified by both teams with selected risk factors and HAI screening criteria. The impact of using NH personnel to collect screening criteria on HAI prevalence was assessed.
The overall prevalence of clinical risk factors among the 1,272 residents was similar between NH personnel and surveillance officers, but the level of positive agreement (residents with factors identified by both teams) varied between 39% and 87%. Surveillance officers identified 253 residents (20%) with ≥1 HAI screening criterion, resulting in 67 residents with an HAI (5.3 per 100 residents). The NH personnel identified 152 (12%) residents with ≥1 HAI screening criterion; 42 residents had an HAI (3.5 per 100 residents).
We identified discrepancies in resident-level data collection between surveillance officers and NH personnel, resulting in varied estimates of the HAI prevalence. These findings have important implications for the design and implementation of future HAI prevalence surveys.
The current central line–associated bloodstream infection (CLABSI) surveillance rate calculation does not account for multiple concurrent central venous catheters (CVCs). The presence of multiple CVCs creates more points of entry into the bloodstream, potentially increasing CLABSI risk. Multiple CVCs may be used in sicker patients, making it difficult to separate the relative contributions of multiple CVCs and comorbidities to CLABSI risk. We explored the relative impact of multiple CVCs, patient comorbidities, and disease severity on the risk of CLABSI.
A total of 197 case patients and 201 control subjects with a CVC inserted during hospitalization at a tertiary care academic medical center from January 1, 2008, to December 31, 2010.
Multiple CVCs was the exposure of interest; the primary outcome was CLABSI. Multivariable logistic regression was conducted to estimate odds ratios (ORs) and 95% confidence intervals (CIs) describing the association between CLABSI and multiple CVCs with and without controlling for Acute Physiology and Chronic Health Evaluation (APACHE) II and Charlson comorbidity index (CCI) scores as measures of disease severity and patient comorbidities, respectively.
Patients with multiple CVCs (n = 78) showed a 4.2 (95% CI, 2.2–8.4) times greater risk of CLABSI compared with patients with 1 CVC after adjusting for CLABSI risk factors. When including APACHE II and CCI scores, multiple CVCs remained an independent risk factor for CLABSI (OR, 3.4 [95% CI, 1.7–6.9]).
Multiple CVCs is an independent risk factor for CLABSI even after adjusting for severity of illness. Adjustment for this risk may be necessary to accurately compare rates between hospitals.
Infect Control Hosp Epidemiol 2014;35(9):1140-1146
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