The present study advances research on the negative consequences of precarious work experiences (PWE), which include perceptions of threats to one's job and financial security as well as a sense of powerlessness and inability to exercise rights in the workplace. Using the COVID-19 pandemic as a backdrop, we examine how PWE relate to sickness presenteeism and worry about work-related COVID-19 exposure. In a 12-week, four-wave study of workers working fully in-person, perceptions of powerlessness and job insecurity were associated with presenteeism (e.g., general presenteeism as well as attending work with known or possible COVID-19 infection) and concerns about disease exposure at work. Whereas powerlessness primarily operated at the between-person level of analysis, job insecurity's effects emerged at both levels of analysis. A sense of powerlessness at work also predicted sending children to school/daycare sick. In sum, the findings suggest that precarity related to being able to keep one's job and a sense of powerlessness at work contribute to concerns about the risk of COVID-19 exposure at work and, simultaneously, behaviors that may contribute to the health risks faced by others. This research provides added support to the argument that precarious work should be addressed in order to improve both worker well-being and public health. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

OBJECTIVES: Diabetes may delay milk letdown, and perceiving milk production as insufficient can lead to breastfeeding cessation. We evaluated whether prepregnancy or gestational diabetes is associated with cessation of breastfeeding by 1 week postpartum. METHODS: We analyzed 2016-2018 data from 42 sites in the Pregnancy Risk Assessment Monitoring System, a population-based survey of women with a recent live birth. Participants were surveyed 2-6 months after childbirth. We used logistic regression models to evaluate the relationship between prepregnancy or gestational diabetes only and breastfeeding <1 week postpartum among women who had initiated breastfeeding. RESULTS: Among 82 050 women who initiated breastfeeding, 4.5% reported breastfeeding <1 week postpartum. Overall, 11.7% of women reported any history of diabetes in the 3 months before becoming pregnant; 3.3% reported prepregnancy diabetes, and 8.4% reported gestational diabetes only. In both unadjusted and adjusted models, the prevalence of breastfeeding <1 week postpartum did not differ significantly among women with prepregnancy diabetes or gestational diabetes only compared with women without any history of diabetes. The prevalence of breastfeeding <1 week postpartum was 4.4% among women without any history of diabetes, 5.6% among women with prepregnancy diabetes (adjusted prevalence ratio [aPR] = 1.15; 95% CI, 0.91-1.46), and 4.5% among women with gestational diabetes only (aPR = 1.01; 95% CI, 0.84-1.20). CONCLUSIONS: We found no association between a history of diabetes prepregnancy or gestational diabetes only and breastfeeding <1 week postpartum in a large, population-based survey of postpartum women who initiated breastfeeding. Regardless of their diabetes status, women who want to breastfeed might benefit from interventions that support their ability to continue breastfeeding.

OBJECTIVE: There is a need to understand how and to what extent theory is used to inform OHP interventions. This study examines the utility of Michie and Prestwich's1 Theory Coding Scheme (TCS) to examine the theoretical base of OHP interventions. METHODS: We applied the TCS to a systematically derived sample of 27 papers that reported evaluation data for work-related interventions seeking to improve employee sleep quantity or quality. RESULTS: Results indicated that the original TCS was largely applicable to OHP sleep interventions. After several minor modifications to its evaluative criteria, the TCS successfully accommodates a range of OHP intervention designs. CONCLUSIONS: The revised TCS for OHP interventions allows for a more detailed understanding of the role and use of theory in OHP interventions and may prove to be a valuable tool for OHP researchers and practitioners.

OBJECTIVES: To identify a pediatric ventilator-associated condition definition for use in neonates and children by exploring whether potential ventilator-associated condition definitions identify patients with worse outcomes. DESIGN: Retrospective cohort study and a matched cohort analysis. SETTING: Pediatric, cardiac, and neonatal ICUs in five U.S. hospitals. PATIENTS: Children 18 years old or younger ventilated for at least 1 day. INTERVENTIONS, MEASUREMENTS AND MAIN RESULTS: We evaluated the evidence of worsening oxygenation via a range of thresholds for increases in daily minimum fraction of inspired oxygen (by 0.20, 0.25, and 0.30) and daily minimum mean airway pressure (by 4, 5, 6, and 7 cm H2O). We required worsening oxygenation be sustained for at least 2 days after at least 2 days of stability. We matched patients with a ventilator-associated condition to those without and used Cox proportional hazard models with frailties to examine associations with hospital mortality, hospital and ICU length of stay, and duration of ventilation. The cohort included 8,862 children with 10,209 hospitalizations and 77,751 ventilator days. For the fraction of inspired oxygen 0.25/mean airway pressure 4 definition (i.e., increase in minimum daily fraction of inspired oxygen by 0.25 or mean airway pressure by 4), rates ranged from 2.9 to 3.2 per 1,000 ventilator days depending on ICU type; the fraction of inspired oxygen 0.30/mean airway pressure 7 definition yielded ventilator-associated condition rates of 1.1-1.3 per 1,000 ventilator days. All definitions were significantly associated with greater risk of hospital death, with hazard ratios ranging from 1.6 (95% CI, 0.7-3.4) to 6.8 (2.9-16.0), depending on thresholds and ICU type. Each definition was associated with prolonged hospitalization, time in ICU, and duration of ventilation, among survivors. The advisory board of the study proposed using the fraction of inspired oxygen 0.25/mean airway pressure 4 thresholds to identify pediatric ventilator-associated conditions in ICUs. CONCLUSIONS: Pediatric patients with ventilator-associated conditions are at substantially higher risk for mortality and morbidity across ICUs, regardless of thresholds used. Next steps include identification of risk factors, etiologies, and preventative measures for pediatric ventilator-associated conditions.

We challenge the notion that influenza B virus infection is milder than influenza A virus infection by finding similar clinical characteristics and outcomes between adults hospitalized with these two types of influenza. Among patients treated with oseltamivir, length of stay and mortality did not differ by type of virus infection.

In September 2011, the CDC convened a Ventilator-Associated Pneumonia (VAP) Surveillance Definition Working Group to organize a formal process for leaders and experts of key stakeholder organizations to discuss the challenges of VAP surveillance definitions and to propose new approaches to VAP surveillance in adult patients (Table 1). The charges to the Working Group were to: | Critically review a draft, streamlined VAP surveillance definition developed for use in adult patients; Suggest modifications to enhance the reliability and credibility of the surveillance definition within the critical care and infection prevention communities; Propose a final adult surveillance definition algorithm, to be implemented in the CDC’s National Healthcare Safety Network (NHSN), taking into consideration the potential future use of the definition algorithm in public reporting, interfacility comparisons, and pay-for-reporting and pay-for-performance programs.

This is the second case study published in a series in AJIC since the Centers for Disease Control and Prevention/National Healthcare Safety Network (NHSN) surveillance definition update of 2013. These cases reflect some of the complex patient scenarios Infection Preventionists (IP) have encountered in their daily surveillance of health care-associated infections (HAI) using NHSN definitions. This is the first case utilizing the new NHSN Ventilator-associated Events (VAE) module and criteria.

In September 2011, the Centers for Disease Control and Prevention (CDC) convened a Ventilator-Associated Pneumonia (VAP) Surveillance Definition Working Group to organize a formal process for leaders and experts of key stakeholder organizations to discuss the challenges of VAP surveillance definitions and to propose new approaches to VAP surveillance in adult patients (Table 1). The charges to the Working Group were to (1) critically review a draft, streamlined VAP surveillance definition developed for use in adult patients; (2) suggest modifications to enhance the reliability and credibility of the surveillance definition within the critical care and infection prevention communities; and (3) propose a final adult surveillance definition algorithm to be implemented in the CDC's National Healthcare Safety Network (NHSN), taking into consideration the potential future use of the definition algorithm in public reporting, interfacility comparisons, and pay-for-reporting and pay-for-performance programs.

This article is an executive summary of a report from the Centers for Disease Control and Prevention Ventilator-Associated Pneumonia Surveillance Definition Working Group, entitled "Developing a New, National Approach to Surveillance for Ventilator-Associatied Events," published in Critical Care Medicine, by Magill SS, Klompas M, Balk R, Burns SM, Deutschman CS, et al. 2013;41(11):2467-2475. The full report provides a comprehensive description of the Working Group's process and outcome.

This is the first in a series of case studies that will be published in American Journal of Infection Control following the Centers for Disease Control and Prevention/National Healthcare Safety Network (NHSN) surveillance definition update of 2013. These cases reflect some of the complex patient scenarios infection professionals encounter during daily surveillance of health care-associated infections using NHSN definitions. Answers to the questions posed and immediate feedback in the form of answers and explanations are available at: http://www.surveymonkey.com/s/AJIC-NHSN-LbId2013. All individual participant answers will remain confidential, although it is the authors' hope to share a summary of the findings at a later date. Cases, answers, and explanations have been reviewed and approved by NHSN staff. Active participation is encouraged and recommended. Review/reference Chapter 12-Multidrug-resistant organism &C difficile infection module protocol, of the NHSN Patient Safety Component Manual (http://www.cdc.gov/nhsn/PDFs/pscManual/12pscMDRO_CDADcurrent.pdf), for information you may need to answer the case study questions.

OBJECTIVE: To assess challenges to implementation of a new National Healthcare Safety Network (NHSN) surveillance definition, mucosal barrier injury laboratory-confirmed bloodstream infection (MBI-LCBI). DESIGN: Multicenter field test. SETTING: Selected locations of acute care hospitals participating in NHSN central line-associated bloodstream infection (CLABSI) surveillance. METHODS: Hospital staff augmented their CLABSI surveillance for 2 months to incorporate MBI-LCBI: a primary bloodstream infection due to a selected group of organisms in patients with either neutropenia or an allogeneic hematopoietic stem cell transplant with gastrointestinal graft-versus-host disease or diarrhea. Centers for Disease Control and Prevention (CDC) staff reviewed submitted data to verify whether CLABSIs met MBI-LCBI criteria and summarized the descriptive epidemiology of cases reported. RESULTS: Eight cancer, 2 pediatric, and 28 general acute care hospitals including 193 inpatient units (49% oncology/bone marrow transplant [BMT], 21% adult ward, 20% adult critical care, 6% pediatric, 4% step-down) conducted field testing. Among 906 positive blood cultures reviewed, 282 CLABSIs were identified. Of the 103 CLABSIs that also met MBI-LCBI criteria, 100 (97%) were reported from oncology/BMT locations. Agreement between hospital staff and CDC classification of reported CLABSIs as meeting the MBI-LCBI definition was high (90%; [Formula: see text]). Most MBI-LCBIs (91%) occurred in patients meeting neutropenia criteria. Some hospitals indicated that their laboratories' methods of reporting cell counts prevented application of neutropenia criteria; revised neutropenia criteria were created using data from field testing. CONCLUSIONS: Hospital staff applied the MBI-LCBI definition accurately. Field testing informed modifications for the January 2013 implementation of MBI-LCBI in the NHSN.

This report is a summary of Device-associated (DA) Module data collected by hospitals participating in the National Healthcare Safety Network (NHSN) for events occurring from January through December 2011 and reported to the Centers for Disease Control and Prevention (CDC) by August 1, 2012. This report updates previously published DA Module data from NHSN and provides contemporary comparative rates.1 This report complements other NHSN reports, including national and state-specific reports of standardized infection ratios (SIRs) for select healthcare-associated infections (HAIs).2,3,4 | NHSN data collection, reporting, and analysis are organized into three components: Patient Safety, Healthcare Personnel Safety, and Biovigilance, and use standardized methods and definitions in accordance with specific module protocols.5,6,7 Institutions may use modules singly or simultaneously, but once selected, they must be used for a minimum of one calendar month for the data to be included in CDC analyses. All infections are categorized using standard CDC definitions that include laboratory and clinical criteria.7 The DA Module may be used by facilities other than hospitals, including outpatient dialysis centers. A report of data from this module for outpatient dialysis centers was published separately.8 NHSN facilities contributing HAI surveillance data to this report did so voluntarily, in response to state mandatory reporting requirements or in compliance with the Centers for Medicare and Medicaid Services’ (CMS) Hospital Inpatient Quality Reporting (IQR) Program. CDC aggregated these data into a single national database for 2011, consistent with the stated purposes of NHSN, which were to: | Collect data from a sample of healthcare facilities in the United States to permit valid estimation of the magnitude of adverse events among patients and healthcare personnel. | Collect data from a sample of healthcare facilities in the United States to permit valid estimation of the adherence to practices known to be associated with prevention of these adverse events. | Analyze and report collected data to permit recognition of trends. | Provide facilities with risk-adjusted metrics that can be used for inter-facility comparisons and local quality improvement activities. | Assist facilities in developing surveillance and analysis methods that permit timely recognition of patient and healthcare worker safety problems and prompt intervention with appropriate measures. | Conduct collaborative research studies with NHSN member facilities (e.g., describe the epidemiology of emerging healthcare-associated infection [HAI] and pathogens, assess the importance of potential risk factors, further characterize HAI pathogens and their mechanisms of resistance, and evaluate alternative surveillance and prevention strategies). | Comply with legal requirements – including but not limited to state or federal laws, regulations, or other requirements – for mandatory reporting of healthcare facility-specific adverse event, prevention practice adherence, and other public health data. | Enable healthcare facilities to report HAI and prevention practice adherence data via NHSN to the U.S. Centers for Medicare and Medicaid Services (CMS) in fulfillment of CMS’s quality measurement reporting requirements for those data. | Provide state departments of health with information that identifies the healthcare facilities in their state that participate in NHSN. | Provide to state agencies, at their request, facility-specific, NHSN patient safety component and healthcare personnel safety component adverse event and prevention practice adherence data for surveillance, prevention, or mandatory public reporting. | Patient- and facility-specific data reported to CDC are kept confidential in accordance with sections 304, 306, and 308(d) of the Public Health Service Act (42 USC 242b, 242k, and 242m(d)).

OBJECTIVE: To describe rates and pathogen distribution of device-associated infections (DAIs) in neonatal intensive care unit (NICU) patients and compare differences in infection rates by hospital type (children's vs general hospitals). PATIENTS AND SETTING: Neonates in NICUs participating in the National Healthcare Safety Network from 2006 through 2008. METHODS: We analyzed central line-associated bloodstream infections (CLABSIs), umbilical catheter-associated bloodstream infections (UCABs), and ventilator-associated pneumonia (VAP) among 304 NICUs. Differences in pooled mean incidence rates were examined using Poisson regression; nonparametric tests for comparing medians and rate distributions were used. RESULTS: Pooled mean incidence rates by birth weight category (750 g or less, 751-1,000 g, 1,001-1,500 g, 1,501-2,500 g, and more than 2,500 g, respectively) were 3.94, 3.09, 2.25, 1.90, and 1.60 for CLABSI; 4.52, 2.77, 1.70, 0.91, and 0.92 for UCAB; and 2.36, 2.08, 1.28, 0.86, and 0.72 for VAP. When rates of infection between hospital types were compared, only pooled mean VAP rates were significantly lower in children's hospitals than in general hospitals among neonates weighing 1,000 g or less; no significant differences in medians or rate distributions were noted. Pathogen frequencies were coagulase-negative staphylococci (28%), Staphylococcus aureus (19%), and Candida species (13%) for bloodstream infections and Pseudomonas species (16%), S. aureus (15%), and Klebsiella species (14%) for VAP. Of 673 S. aureus isolates with susceptibility results, 33% were methicillin resistant. CONCLUSIONS: Neonates weighing 750 g or less had the highest DAI incidence. With the exception of VAP, pooled mean NICU incidence rates did not differ between children's and general hospitals. Pathogens associated with these infections can pose treatment challenges; continued efforts at prevention need to be applied to all NICU settings.

BACKGROUND: In October 2008, the Centers for Medicare and Medicaid Services (CMS) discontinued additional payments for certain hospital-acquired conditions that were deemed preventable. The effect of this policy on rates of health care-associated infections is unknown. METHODS: Using a quasi-experimental design with interrupted time series with comparison series, we examined changes in trends of two health care-associated infections that were targeted by the CMS policy (central catheter-associated bloodstream infections and catheter-associated urinary tract infections) as compared with an outcome that was not targeted by the policy (ventilator-associated pneumonia). Hospitals participating in the National Healthcare Safety Network and reporting data on at least one health care-associated infection before the onset of the policy were eligible to participate. Data from January 2006 through March 2011 were included. We used regression models to measure the effect of the policy on changes in infection rates, adjusting for baseline trends. RESULTS: A total of 398 hospitals or health systems contributed 14,817 to 28,339 hospital unit-months, depending on the type of infection. We observed decreasing secular trends for both targeted and nontargeted infections long before the policy was implemented. There were no significant changes in quarterly rates of central catheter-associated bloodstream infections (incidence-rate ratio in the postimplementation vs. preimplementation period, 1.00; P=0.97), catheter-associated urinary tract infections (incidence-rate ratio, 1.03; P=0.08), or ventilator-associated pneumonia (incidence-rate ratio, 0.99; P=0.52) after the policy implementation. Our findings did not differ for hospitals in states without mandatory reporting, nor did it differ according to the quartile of percentage of Medicare admissions or hospital size, type of ownership, or teaching status. CONCLUSIONS: We found no evidence that the 2008 CMS policy to reduce payments for central catheter-associated bloodstream infections and catheter-associated urinary tract infections had any measurable effect on infection rates in U.S. hospitals. (Funded by the Agency for Healthcare Research and Quality.).

Welcome to the eighth publication of a joint effort between the American Journal of Infection Control and the Centers for Disease Control and Prevention's National Healthcare Safety Network (NHSN). This collaboration is a series of case studies representing surveillance scenarios faced everyday by infection preventionists (IPs) using NHSN definitions. | With each case, a link to an online survey will be provided, where you may answer the questions posed and receive immediate feedback in the form of answers and explanations. All individual participant answers will remain confidential, although it is the authors' hope to share a summary of the findings at a later date. The content development was a partnership between the listed authors, and all cases, answers, and explanations have been reviewed and approved by NHSN.

Welcome to the seventh publication of a joint effort between the American Journal of Infection Control and the Centers for Disease Control and Prevention's National Healthcare Safety Network (NHSN). This collaboration is a series of case studies representing surveillance scenarios faced everyday by infection preventionists (IPs) using NHSN definitions. | With each case, a link to an online survey will be provided, where you may answer the questions posed and receive immediate feedback in the form of answers and explanations. All individual participant answers will remain confidential, although it is the authors' hope to share a summary of the findings at a later date. The content development was a partnership between the listed authors, and all cases, answers, and explanations have been reviewed and approved by NHSN.

The rationale for the case study series is presented, along with results of the first 5 American Journal of Infection Control-National Healthcare Safety Network case studies. Although the respondents were correct in their assessments more often than not, opportunities for improvement remain. Ten new case studies with questions are provided. Participants are provided with instructions on how to submit responses for continuing education credit through the Centers for Disease Control and Prevention. Answers with referenced explanations will be provided immediately to those who seek continuing education credit and at a later date via the online journal for those who do not.

BACKGROUND: In 2008, the Centers for Medicare and Medicaid Services (CMS) ceased additional payment for hospitalizations resulting in complications deemed preventable, including several health care-associated infections. We sought to understand the impact of the CMS payment policy on infection prevention efforts. METHODS: A national survey of infection preventionists from a random sample of US hospitals was conducted in December 2010. RESULTS: Eighty-one percent reported increased attention to HAIs targeted by the CMS policy, whereas one-third reported spending less time on nontargeted HAIs. Only 15% reported increased funding for infection control as a result of the CMS policy, whereas most reported stable (77%) funding. Respondents reported faster removal of urinary (71%) and central venous (50%) catheters as a result of the CMS policy, whereas routine urine and blood cultures on admission occurred infrequently (27% and 13%, respectively). Resource shifting (ie, less time spent on nontargeted HAIs) occurred more commonly in large hospitals (odds ratio, 2.3; 95% confidence interval: 1.0-5.1; P = .038) but less often in hospitals where front-line staff were receptive to changes in clinical processes (odds ratio, 0.5; 95% confidence interval: 0.3-0.8; P = .005). CONCLUSION: Infection preventionists reported greater hospital attention to preventing targeted HAIs as a result of the CMS nonpayment policy. Whether the increased focus and greater engagement in HAI prevention practices has led to better patient outcomes is unclear.

OBJECTIVE: The objective was to develop a new National Healthcare Safety Network (NHSN) risk model for sternal, deep incisional, and organ/space (complex) surgical site infections (SSIs) following coronary artery bypass graft (CABG) procedures, detected on admission and readmission, consistent with public reporting requirements. PATIENTS AND SETTING: A total of 133,503 CABG procedures with 4,008 associated complex SSIs reported by 293 NHSN hospitals in the United States. METHODS: CABG procedures performed from January 1, 2006, through December 31, 2008, were analyzed. Potential SSI risk factors were identified by univariate analysis. Multivariate analysis with forward stepwise logistic regression modeling was used to develop the new model. The c-index was used to compare the predictive power of the new and NHSN risk index models. RESULTS: Multivariate analysis independent risk factors included ASA score, procedure duration, female gender, age, and medical school affiliation. The new risk model has significantly improved predictive performance over the NHSN risk index (c-index, 0.62 and 0.56, respectively). CONCLUSIONS: Traditionally, the NHSN surveillance system has used a risk index to provide procedure-specific risk-stratified SSI rates to hospitals. A new CABG sternal, complex SSI risk model developed by multivariate analysis has improved predictive performance over the traditional NHSN risk index and is being considered for endorsement as a measure for public reporting.

OBJECTIVE: To determine healthcare-associated infection (HAI) prevalence in 9 hospitals in Jacksonville, Florida; to evaluate the performance of proxy indicators for HAIs; and to refine methodology in preparation for a multistate survey. DSESIGN: Point prevalence survey. PATIENTS: Acute care inpatients of any age. METHODS: HAIs were defined using National Healthcare Safety Network criteria. In each facility a trained primary team (PT) of infection prevention (IP) staff performed the survey on 1 day, reviewing records and collecting data on a random sample of inpatients. PTs assessed patients with one or more proxy indicators (abnormal white blood cell count, abnormal temperature, or antimicrobial therapy) for the presence of HAIs. An external IP expert team collected data from a subset of patient records reviewed by PTs to assess proxy indicator performance and PT data collection. RESULTS: Of 851 patients surveyed by PTs, 51 had one or more HAIs (6.0%; 95% confidence interval, 4.5%-7.7%). Surgical site infections ([Formula: see text]), urinary tract infections ([Formula: see text]), pneumonia ([Formula: see text]), and bloodstream infections ([Formula: see text]) accounted for 75.8% of 58 HAIs detected by PTs. Staphylococcus aureus was the most common pathogen, causing 9 HAIs (15.5%). Antimicrobial therapy was the most sensitive proxy indicator, identifying 95.5% of patients with HAIs. CONCLUSIONS: HAI prevalence in this pilot was similar to that reported in the 1970s by the Centers for Disease Control and Prevention's Study on the Efficacy of Nosocomial Infection Control. Antimicrobial therapy was a sensitive screening variable with which to identify those patients at higher risk for infection and reduce data collection burden. Additional work is needed on validation and feasibility to extend this methodology to a national scale.

We applaud the successful reduction of healthcare-associated infection (HAI) rates achieved by hospitals | that participated for 5 years or more in the Duke Infection | Control Outreach Network (DICON), as described by Anderson and colleagues in their recent report.1 | DICON provides a successful example of HAI reduction, using validated, | risk-adjusted local data to drive prevention activities, and it | adds to existing evidence supporting this HAI prevention | strategy.2 | " | 4 | To help ensure an accurate understanding of the | current landscape of HAI-reporting infrastructures that can | contribute to such reductions, we would like to clarify that | the National Healthcare Safety Network (NHSN) can, and in | some states does, also function much as DICON does to | provide complete, validated, risk-adjusted data for local action, with the distinction that funding for NHSN and, particularly, validation requires public support, whereas DICON | is funded directly by the facilities it serves. We disagree with | the authors' statement that NHSN data are "obtained from | convenience samples (ie, are not complete), are not validated, | and are not fed back to individual hospitals in a timely fashion," for the following reasons. While it is true that not all | US hospitals report HAIs to NHSN and participation in | NHSN is still voluntary in some states, increasingly it is being | used as a platform for state mandatory reporting of HAIs (23 | states and the District of Columbia as of July 2011). Also, | beginning in January 2011, NHSN became the reporting tool | for central line-associated bloodstream infections in hospitals | with intensive care units that participate in the Hospital Inpatient Quality Reporting Program of the Centers for Medicare and Medicaid Services (CMS).5 | The result is that now | more than 4,200 of the 5,800 hospitals in the United States | belong to NHSN and are reporting data continuously on a | variety of HAIs, with central line-associated bloodstream infections and surgical site infections being the most common | focus of surveillance and reporting. Therefore, we believe that | as currently constituted, NHSN has become representative of | US hospitals for certain HAI types, and future expansion is | likely

BACKGROUND: After identifying a student with triple-reassortant swine influenza virus (SIV) infection and pig exposure at a livestock event, we investigated whether others were infected and if human-to-human transmission occurred. METHODS: We conducted a cohort study and serosurvey among persons exposed to (1) event pigs, (2) other pigs, (3) the index case, and (4) persons without pig or index case exposure. Confirmed cases had respiratory specimens positive for SIV within 2 weeks of the index case's illness. Probable and suspected cases had illness and (1) exposure to any pig or (2) contact with a confirmed case preceding illness. Probable cases were seropositive. Suspected cases did not give serum samples. RESULTS: Of 99 event pig-exposed students, 72 (73%) participated in the investigation, and 42 (42%) provided serum samples, of whom 17 (40%) were seropositive and 5 (12%) met case criteria. Of 9 students exposed to other pigs, 2 (22%) were seropositive. Of 8 index case-exposed persons and 10 without exposures, none were seropositive. Pig-exposed persons were more likely to be seropositive than persons without pig exposure (37% vs 0%, P < .01). CONCLUSIONS: We identified an outbreak of human SIV infection likely associated with a livestock event; there was no evidence of human-to-human transmission.

BACKGROUND: The National Healthcare Safety Network (NHSN) has provided simple risk adjustment of surgical site infection (SSI) rates to participating hospitals to facilitate quality improvement activities; improved risk models were developed and evaluated. METHODS: Data reported to the NHSN for all operative procedures performed from January 1, 2006, through December 31, 2008, were analyzed. Only SSIs related to the primary incision site were included. A common set of patient- and hospital-specific variables were evaluated as potential SSI risk factors by univariate analysis. Some ific variables were available for inclusion. Stepwise logistic regression was used to develop the specific risk models by procedure category. Bootstrap resampling was used to validate the models, and the c-index was used to compare the predictive power of new procedure-specific risk models with that of the models with the NHSN risk index as the only variable (NHSN risk index model). RESULTS: From January 1, 2006, through December 31, 2008, 847 hospitals in 43 states reported a total of 849,659 procedures and 16,147 primary incisional SSIs (risk, 1.90%) among 39 operative procedure categories. Overall, the median c-index of the new procedure-specific risk was greater (0.67 [range, 0.59-0.85]) than the median c-index of the NHSN risk index models (0.60 [range, 0.51-0.77]); for 33 of 39 procedures, the new procedure-specific models yielded a higher c-index than did the NHSN risk index models. CONCLUSIONS: A set of new risk models developed using existing data elements collected through the NHSN improves predictive performance, compared with the traditional NHSN risk index stratification.

This report is a summary of Device-Associated (DA) module data collected by hospitals participating in the National Healthcare Safety Network (NHSN) for events occurring between January and December 2009 and reported to the Centers for Disease Control and Prevention (CDC) by October 18, 2010. This report updates previously published DA module data from the NHSN and provides contemporary comparative rates.1 Procedure-Associated module data will be reported separately. Surgical site infection data will be reported as standardized infection ratios using new logistic regression models, and postprocedure pneumonia rates for 2009 are available on the NHSN's public Web site. This report complements other NHSN reports, including national and state-specific standardized infection ratios for selected health care–associated infections (HAIs).2, 3, 4 | The NHSN was established in 2005 to integrate and supersede 3 legacy surveillance systems at the CDC: the National Nosocomial Infections Surveillance system, the Dialysis Surveillance Network, and the National Surveillance System for Healthcare Workers. NHSN data collection, reporting, and analysis are organized into 3 components—Patient Safety, Healthcare Personnel Safety, and Biovigilance—and use standardized methods and definitions in accordance with specific module protocols.5, 6, 7 The modules may be used singly or simultaneously, but once selected, they must be used for a minimum of 1 calendar month. All infections are categorized using standard CDC definitions that include laboratory and clinical criteria.7 The DA module may be used by facilities other than hospitals, including long-term care facilities and outpatient dialysis centers. A report of data from this module for outpatient dialysis centers has been published separately.8 For this report, only data from the Patient Safety component are presented. NHSN facilities report their HAI surveillance data voluntarily or in response to state mandatory reporting requirements. The CDC aggregates these data into a single national database for the stated purposes in place in 2009, as follows: | • | Collect data from a sample of US health care facilities to permit valid estimation of the magnitude of adverse events among patients and health care personnel. | • | Collect data from a sample of US health care facilities to permit valid estimation of the adherence to practices known to be associated with prevention of these adverse events. | • | Analyze and report collected data to permit recognition of trends. | • | Provide facilities with risk-adjusted metrics that can be used for interfacility comparisons and local quality improvement activities. | • | Assist facilities in developing surveillance and analysis methods that permit timely recognition of patient and health care worker safety problems and prompt intervention with appropriate measures. | • | Conduct collaborative research studies with NHSN member facilities (eg, describe the epidemiology of emerging HAIs and pathogens, assess the importance of potential risk factors, further characterize HAI pathogens and their mechanisms of resistance, and evaluate alternative surveillance and prevention strategies). | | The identity of each NHSN facility is kept confidential by the CDC in accordance with Sections 304, 306, and 308(d) of the Public Health Service Act [42 USC 242b, 242K, and 242m(d)].

Welcome to the fourth publication of a joint effort between the American Journal of Infection Control and the Centers for Disease Control and Prevention’s National Healthcare Safety Network (NHSN). This collaboration is a series of case studies representing surveillance scenarios faced everyday by infection preventionists (IPs) using NHSN definitions. | With each case, a link to an online survey will be provided, where you may answer the questions posed and receive immediate feedback in the form of answers and explanations. All individual participant answers will remain confidential, although it is the authors’ hope to share a summary of the findings at a later date. The content development was a partnership between the listed authors, and all cases, answers, and explanations have been reviewed and approved by the NHSN.

This is the third in a series of clinical case studies published in AJIC to aid Infection Preventionists (IP) in applying the Centers for Disease Control and Prevention (CDC)/National Healthcare Safety Network (NHSN) surveillance definitions of health care–associated infections (HAIs). These cases reflect some of the complex patient scenarios that IPs encounter in their daily surveillance of HAIs using NHSN definitions. With each case, a link to an online survey is provided, where you may answer the questions posed and receive immediate feedback in the form of answers and explanations. All individual participants’ answers will remain confidential, although we hope to share a summary of our findings at a later date. All cases, answers, and explanations have been reviewed and approved by the NHSN.

Welcome to the second publication of a joint effort between the American Journal of Infection Control and the Centers for Disease Control and Prevention's National Healthcare Safety Network (NHSN). This collaboration is a series of case studies representing surveillance scenarios faced everyday by infection preventionists (IPs) using NHSN definitions. Please refer to the June 2010 issue for more information.1 | With each case, a link to an online survey will be provided, where you may answer the questions posed and receive immediate feedback in the form of answers and explanations. Each participant's answers will remain confidential, although we hope to share a summary of our findings at a later date. The content development was a partnership between the listed authors, and all cases, answers, and explanations have been reviewed and approved by the NHSN.

This report is a summary of Device-Associated (DA) and Procedure-Associated (PA) module data collected and reported by hospitals and ambulatory surgical centers participating in the National Healthcare Safety Network (NHSN) from January 2006 through December 2008 as reported to the Centers for Disease Control and Prevention (CDC) by July 6, 2009. This report updates previously published DA and PA module data from the NHSN.1 | The NHSN was established in 2005 to integrate and supersede 3 legacy surveillance systems at the CDC: the National Nosocomial Infections Surveillance (NNIS) system, the Dialysis Surveillance Network (DSN), and the National Surveillance System for Healthcare Workers (NaSH). Similar to the NNIS system, NHSN facilities voluntarily report their health care–associated infection (HAI) surveillance data for aggregation into a single national database for the following purposes: | • | Estimation of the magnitude of HAIs | • | Monitoring of HAI trends | • | Facilitation of interfacility and intrafacility comparisons with risk-adjusted data that can be used for local quality improvement activities | • | Assistance to facilities in developing surveillance and analysis methods that permit timely recognition of patient safety problems and prompt intervention with appropriate measures. | | In addition, many facilities use these same data to comply with state reporting mandates. Identity of all NHSN facilities is kept confidential by the CDC in accordance with Sections 304, 306, and 308(d) of the Public Health Service Act [42 USC 242b, 242k, and 242m(d)].

BACKGROUND: Methicillin-resistant Staphylococcus aureus (MRSA) is increasingly being reported to cause outbreaks in neonatal intensive care units (NICUs). We assessed the scope and magnitude of MRSA infections with disease onset after 3 days of age (late-onset MRSA infections) in NICUs. METHODS: We analyzed data reported by NICUs participating in the National Nosocomial Infections Surveillance system from 1995 through 2004. For each surveillance month, all healthcare-associated infections as defined by National Nosocomial Infections Surveillance criteria were reported, along with antimicrobial susceptibility patterns of the isolates. We pooled the data from all NICUs by birth weight category and calendar year. Poisson regression was used to assess changes in incidence of late-onset MRSA infections per 10,000 patient-days. RESULTS: Overall, 149 NICUs reported 4831 S. aureus infections and 5,878,139 patient-days. Methicillin testing data were available for 4302 S. aureus isolates, of which 975 (23%) were MRSA. Incidence of late-onset MRSA infection per 10,000 patient-days, combining all birthweight categories, increased 308% from 0.7 in 1995 to 3.1 in 2004 (P < 0.001). A significant increase in incidence of MRSA infections was observed among all 4 birthweight categories analyzed separately (<or=1000 g, 1001-1500 g, 1501-2500 g, and >2500 g). The distribution of MRSA infection by type of infection did not vary during the study period; 299 (31%) of MRSA infections were bloodstream infections, 174 (18%) were pneumonia, and 161 (17%) were conjunctivitis. CONCLUSION: The incidence of late-onset MRSA infections increased substantially between 1995 and 2004, indicating a need to reinforce infection control recommendations and to explore potential sources and routes of transmission.