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Updated Clostridioides difficile infection (CDI) guidelines published in 2018 recommend vancomycin as first-line treatment. Of 833 community-onset CDI cases in metropolitan Atlanta, Georgia in 2018, over half did not receive first-line treatment, although guideline adherence increased over the year. Second-line treatment was more common in patients treated in ambulatory settings.

Key Points Question What risk factors are associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seropositivity among health care personnel (HCP) inside and outside the workplace? Findings In this cross-sectional study of 24 749 HCP in 3 US states, contact with an individual with known coronavirus disease 2019 (COVID-19) exposure outside the workplace was the strongest risk factor associated with SARS-CoV-2 seropositivity, along with living in a zip code with higher COVID-19 incidence. None of the assessed workplace factors were associated with seropositivity. Meaning In this study, most risk factors associated with SARS-CoV-2 infection among HCP were outside the workplace, suggesting that current infection prevention strategies in health care are effective in preventing patient-to-HCP transmission in the workplace., This cross-sectional study evaluates the risk factors associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seropositivity among health care personnel., Importance Risks for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection among health care personnel (HCP) are unclear. Objective To evaluate the risk factors associated with SARS-CoV-2 seropositivity among HCP with the a priori hypothesis that community exposure but not health care exposure was associated with seropositivity. Design, Setting, and Participants This cross-sectional study was conducted among volunteer HCP at 4 large health care systems in 3 US states. Sites shared deidentified data sets, including previously collected serology results, questionnaire results on community and workplace exposures at the time of serology, and 3-digit residential zip code prefix of HCP. Site-specific responses were mapped to a common metadata set. Residential weekly coronavirus disease 2019 (COVID-19) cumulative incidence was calculated from state-based COVID-19 case and census data. Exposures Model variables included demographic (age, race, sex, ethnicity), community (known COVID-19 contact, COVID-19 cumulative incidence by 3-digit zip code prefix), and health care (workplace, job role, COVID-19 patient contact) factors. Main Outcome and Measures The main outcome was SARS-CoV-2 seropositivity. Risk factors for seropositivity were estimated using a mixed-effects logistic regression model with a random intercept to account for clustering by site. Results Among 24 749 HCP, most were younger than 50 years (17 233 [69.6%]), were women (19 361 [78.2%]), were White individuals (15 157 [61.2%]), and reported workplace contact with patients with COVID-19 (12 413 [50.2%]). Many HCP worked in the inpatient setting (8893 [35.9%]) and were nurses (7830 [31.6%]). Cumulative incidence of COVID-19 per 10 000 in the community up to 1 week prior to serology testing ranged from 8.2 to 275.6; 20 072 HCP (81.1%) reported no COVID-19 contact in the community. Seropositivity was 4.4% (95% CI, 4.1%-4.6%; 1080 HCP) overall. In multivariable analysis, community COVID-19 contact and community COVID-19 cumulative incidence were associated with seropositivity (community contact: adjusted odds ratio [aOR], 3.5; 95% CI, 2.9-4.1; community cumulative incidence: aOR, 1.8; 95% CI, 1.3-2.6). No assessed workplace factors were associated with seropositivity, including nurse job role (aOR, 1.1; 95% CI, 0.9-1.3), working in the emergency department (aOR, 1.0; 95% CI, 0.8-1.3), or workplace contact with patients with COVID-19 (aOR, 1.1; 95% CI, 0.9-1.3). Conclusions and Relevance In this cross-sectional study of US HCP in 3 states, community exposures were associated with seropositivity to SARS-CoV-2, but workplace factors, including workplace role, environment, or contact with patients with known COVID-19, were not. These findings provide reassurance that current infection prevention practices in diverse health care settings are effective in preventing transmission of SARS-CoV-2 from patients to HCP.

Objective:To determine the effect of an electronic medical record (EMR) nudge at reducing total and inappropriate orders testing for hospital-onset Clostridioides difficile infection (HO-CDI).Design:An interrupted time series analysis of HO-CDI orders 2 years before and 2 years after the implementation of an EMR intervention designed to reduce inappropriate HO-CDI testing. Orders for C. difficile testing were considered inappropriate if the patient had received a laxative or stool softener in the previous 24 hours.Setting:Four hospitals in an academic healthcare network.Patients:All patients with a C. difficile order after hospital day 3.Intervention:Orders for C. difficile testing in patients administered a laxative or stool softener in Results:Of the 17,694 HO-CDI orders, 7% were inappropriate (8% prentervention vs 6% postintervention; P < .001). Monthly HO-CDI orders decreased by 21% postintervention (level-change rate ratio [RR], 0.79; 95% confidence interval [CI], 0.73–0.86), and the rate continued to decrease (postintervention trend change RR, 0.99; 95% CI, 0.98–1.00). The intervention was not associated with a level change in inappropriate HO-CDI orders (RR, 0.80; 95% CI, 0.61–1.05), but the postintervention inappropriate order rate decreased over time (RR, 0.95; 95% CI, 0.93–0.97).Conclusion:An EMR nudge to minimize inappropriate ordering for C. difficile was effective at reducing HO-CDI orders, and likely contributed to decreasing the inappropriate HO-CDI order rate after the intervention.

Background Interventions to reduce community-onset (CO) Clostridioides difficile Infection (CDI) are not usually hospital-based due to the perception that they are often acquired outside the hospital. We determined the proportion of admitted CO CDI that might be associated with previous hospitalization. Methods The CDC’s Emerging Infections Program conducts population-based CDI surveillance in 10 US sites. We defined an incident case as a C. difficile-positive stool collected in 2017 from a person aged ≥ 1 year admitted to a hospital with no positive tests in the prior 8 weeks. Cases were defined as CO if stool was collected within 3 days of hospitalization. CO cases were classified into four categories: long-term care facility (LTCF)-onset if patient was admitted from an LTCF; long-term acute care hospital (LTACH)-onset if patient was admitted from an LTACH; CO-healthcare-facility associated (CO-HCFA) if patient was admitted from a private residence but had a prior healthcare-facility admission in the past 12 weeks; or community-associated (CA) if there was no admission to a healthcare facility in the prior 12 weeks. We excluded hospitals with < 10 cases among admitted catchment-area residents. Results Of 4724 cases in 86 hospitals, 2984 (63.2%) were CO (median per hospital: 65.8%; interquartile range [IQR]: 58.3%-70.7%). Among the CO cases, 1424 (47.7%) were CA (median per hospital: 48.1%; IQR: 40.3%-57.7%), 1201 (40.3%) were CO-HCFA (median per hospital: 41.0%; IQR: 32.9%-47.8%), 350 (11.7%) were LTCF-onset (median per hospital: 10.0%; IQR: 0.6%-14.4%), and 9 (0.3%) were LTACH-onset. Of 1201 CO-HCFA cases, 1174 (97.8%) had a prior hospitalization; among these, 978 (83.3%) (median per hospital: 83.3%; IQR: 69.2%-90.6%), which consists of 32.8% of all hospitalized CO cases, had been discharged from the same hospital (Figure), and 84.4% of the 978 cases (median per hospital: 88.2%: IQR: 76.5%-100.0%) had received antibiotics sometime in the prior 12 weeks. Figure. Frequency of Cases Discharged in the 12 Weeks Prior to Readmission with Clostridioides difficile Infection (N=1138*) Conclusion A third of hospitalized CO CDI had been recently discharged from the same hospital, and most had received antibiotics during or soon after the last admission. Hospital-based and post-discharge antibiotic stewardship interventions could help reduce subsequent CDI hospitalizations. Disclosures Ghinwa Dumyati, MD, Roche Diagnostics (Consultant)

In 2016, Zika virus disease developed in a man (patient A) who had no known risk factors beyond caring for a relative who died of this disease (index patient). We investigated the source of infection for patient A by surveying other family contacts, healthcare personnel, and community members, and testing samples for Zika virus. We identified 19 family contacts who had similar exposures to the index patient; 86 healthcare personnel had contact with the index patient, including 57 (66%) who had contact with body fluids. Of 218 community members interviewed, 28 (13%) reported signs/symptoms and 132 (61%) provided a sample. Except for patient A, no other persons tested had laboratory evidence of recent Zika virus infection. Of 5,875 mosquitoes collected, none were known vectors of Zika virus and all were negative for Zika virus. The mechanism of transmission to patient A remains unknown but was likely person-to-person contact with the index patient.

Background Healthcare personnel (HCP) may be at increased risk for COVID-19, but differences in risk by work activities are poorly defined. Centers for Disease Control and Prevention recommends cohorting hospitalized patients with COVID-19 to reduce in-hospital transmission of SARS-CoV-2, but it is unknown if occupational and non-occupational behaviors differ based on exposure to COVID-19 units. Methods We analyzed a subset of HCP from an ongoing CDC-funded SARS-CoV-2 serosurveillance study. HCP were recruited from four Atlanta hospitals of different sizes and patient populations. All HCP completed a baseline REDCap survey. We used logistic regression to compare occupational activities and infection prevention practices among HCP stratified by exposure to COVID-19 units: low (0% of shifts), medium (1–49% of shifts) or high (≥50% of shifts). Results Of 211 HCP enrolled (36% emergency department [ED] providers, 35% inpatient RNs, 17% inpatient MDs/APPs, 7% radiology technicians and 6% respiratory therapists [RTs]), the majority (79%) were female and the median age was 35 years. Nearly half of the inpatient MD/APPs (46%) and RNs (47%) and over two-thirds of the RTs (67%) worked primarily in the ICU. Aerosol generating procedures were common among RNs, MD/APPs, and RTs (26–58% performed ≥1), but rare among ED providers (0–13% performed ≥1). Compared to HCP with low exposure to COVID-19 units, those with medium or high exposure spent a similar proportion of shifts directly at the bedside and were about as likely to practice universal masking. Being able to consistently social distance from co-workers was rare (33%); HCP with high exposure to COVID-19 units were less likely to report social distancing in the workplace compared to those with low exposure; however, this was not significantly different (OR 0.6; 95% CI: 0.3, 1.1). Concerns about personal protective equipment in COVID-19 units were similar across levels of exposure (Table 1). Table 1: Occupational activities and infection prevention behaviors of healthcare personnel stratified by level of exposure to COVID-19 units Conclusion The proportion of time spent in dedicated COVID-19 units did not appear to influence time HCP spend directly at the bedside or infection prevention practices (social distancing and universal masking) in the workplace. Risk for SARS-CoV-2 infection in HCP may depend more on factors acting at the individual level rather than those related to location of work. Disclosures Jessica Howard-Anderson, MD, Antibacterial Resistance Leadership Group (ARLG) (Other Financial or Material Support, The ARLG fellowship provides salary support for ID fellowship and mentored research training) Ben Lopman, PhD, MSc, Takeda Pharmaceuticals (Advisor or Review Panel member, Research Grant or Support, Other Financial or Material Support, Personal fees)World Health Organization (Advisor or Review Panel member, Other Financial or Material Support, Personal fees for technical advice and analysis)

Author(s): Jones, Barbara E; Haroldsen, Candace; Madaras-Kelly, Karl; Goetz, Matthew B; Ying, Jian; Sauer, Brian; Jones, Makoto M; Leecaster, Molly; Greene, Tom; Fridkin, Scott K; Neuhauser, Melinda M; Samore, Matthew H | Abstract: BackgroundElectronic health records provide the opportunity to assess system-wide quality measures. Veterans Affairs Pharmacy Benefits Management Center for Medication Safety uses medication use evaluation (MUE) through manual review of the electronic health records.ObjectiveTo compare an electronic MUE approach versus human/manual review for extraction of antibiotic use (choice and duration) and severity metrics.Research designRetrospective.SubjectsHospitalizations for uncomplicated pneumonia occurring during 2013 at 30 Veterans Affairs facilities.MeasuresWe compared summary statistics, individual hospitalization-level agreement, facility-level consistency, and patterns of variation between electronic and manual MUE for initial severity, antibiotic choice, daily clinical stability, and antibiotic duration.ResultsAmong 2004 hospitalizations, electronic and manual abstraction methods showed high individual hospitalization-level agreement for initial severity measures (agreement=86%-98%, κ=0.5-0.82), antibiotic choice (agreement=89%-100%, κ=0.70-0.94), and facility-level consistency for empiric antibiotic choice (anti-MRSA r=0.97, Pl0.001; antipseudomonal r=0.95, Pl0.001) and therapy duration (r=0.77, Pl0.001) but lower facility-level consistency for days to clinical stability (r=0.52, P=0.006) or excessive duration of therapy (r=0.55, P=0.005). Both methods identified widespread facility-level variation in antibiotic choice, but we found additional variation in manual estimation of excessive antibiotic duration and initial illness severity.ConclusionsElectronic and manual MUE agreed well for illness severity, antibiotic choice, and duration of therapy in pneumonia at both the individual and facility levels. Manual MUE showed additional reviewer-level variation in estimation of initial illness severity and excessive antibiotic use. Electronic MUE allows for reliable, scalable tracking of national patterns of antimicrobial use, enabling the examination of system-wide interventions to improve quality.

Background Despite overall progress in preventing Clostridioides difficile Infection (CDI), community-associated (CA) infections have been steadily increasing. Although the incubation period of CDI is thought to be relatively short, gastrointestinal microbial disruption from remote healthcare exposures (e.g., inpatient antibiotic use) may be associated with CA-CDI. To assess this potential association, we linked CA-CDI infections identified through CDC’s Emerging Infections Program (EIP) to Medicare claims data to describe prior healthcare utilization. Methods We defined an EIP CA-CDI case as a positive C. difficile test collected in 2014–2015 from an outpatient or inpatient within 3 days of hospital admission, provided there was no positive test in the prior 8 weeks and no admission to a healthcare facility in the prior 12 weeks. We linked EIP CA-CDI cases aged ≥65 years to a Medicare beneficiary using unique combinations of birthdate, sex, and zip code. Cases were included if they maintained continuous fee-for-service coverage for 1 year prior to the event date. To calculate exposure odds ratios for previous hospitalizations, each case was matched to 5 control beneficiaries on age, sex, and county of residence. We used logistic regression to calculate adjusted matched odds ratios (amOR) that controlled for chronic conditions. Results We successfully linked 2,287/3,367 (68%) EIP CA-CDI cases. Of these, 1,236 cases met inclusion criteria; the median age was 77 years and 63% were female. We identified 69 (5.6%) cases with misclassification of prior healthcare exposures, most of whom (48, 70%) were hospitalized in the 12 weeks prior to their event. Among the 1,167 true CA-CDI cases, 33% were hospitalized in the prior 12 weeks to 1 year. The median number of weeks from prior hospitalization to CDI was 27 (IQR 18–38, Figure 1). Cases had a higher risk of hospitalization than matched controls in the prior 3–6 months (amOR: 2.33, 95% CI: 1.87, 2.90) and 6–12 months (amOR: 1.43 95% CI: 1.18, 1.74). Conclusion Remote hospitalization in the previous year was a significant risk factor for CA-CDI, especially in the 3–6 months prior to CA-CDI. Long-lasting prevention strategies implemented at hospital discharge and enhanced inpatient antibiotic stewardship may prevent CA-CDI among older adults. Disclosures All Authors: No reported Disclosures.

Background. The National Action Plan for Combating Antibiotic Resistance Bacteria calls for standardized methods to monitor and benchmark inpatient antibiotic usage. Inter-facility comparisons will require risk adjustment to account for differences in patient mix and hospital characteristics. We examined facility level factors associated with inter-hospital variability in a large cohort of US hospitals. Methods. We used data from the Truven Health MarketScan Hospital Drug Database, which contains detailed administrative records, including inpatient drug utilization data based on billing records, for all patients discharged from a convenience sample of over 500 US hospitals. We retrospectively estimated days of therapy (DOT)/1000 patient days (PDs) by year, for hospitals reporting data between 2006 and 2012. We also created a multivariable model that adjusts for hospital-specific location of antibiotic use (ICU versus other) and hospital-specific annual summary measures for the following: average patient age, average patient co-morbidity score, number of hospital beds, teaching status, urban or rural location, proportion of discharges with a surgical diagnosis related code, case mix index, and proportion of patient days with an infectious disease primary ICD-9-CM discharge code. Results. The mean DOT for all hospitals across all years was 777/1000 PDs (SD = 189). DOT varied significantly between hospitals; the 10th to 90th percentile values for hospital DOT ranged from 546 to 998/1000 PDs. The variables included in our model accounted for 47%-53% of the inter-facility variability, depending on year, almost all of which (46%–51%) was explained by 2 predictors: proportion of PDs with an infectious disease diagnosis code and hospital location (ICU versus other). Conclusion. Inter-facility comparisons of antimicrobial use may require adjusting for the proportion of PDs with an infectious disease primary diagnosis code and the hospital location of antibiotic use. Because a large proportion of inter-hospital variability in antimicrobial use remains unexplained, we hypothesize that the residual variability may be the result of variations in prescribing behavior that could potentially be addressed through antibiotic stewardship programs. Disclosures. All authors: No reported disclosures.

BACKGROUNDCase mix index (CMI) has been used as a facility-level indicator of patient disease severity. We sought to evaluate the potential for CMI to be used for risk adjustment of National Healthcare Safety Network (NHSN) healthcare-associated infection (HAI) data.METHODSNHSN facility-wide laboratory-identified Clostridium difficile infection event data from 2012 were merged with the fiscal year 2012 Inpatient Prospective Payment System (IPPS) Impact file by CMS certification number (CCN) to obtain a CMI value for hospitals reporting to NHSN. Negative binomial regression was used to evaluate whether CMI was significantly associated with healthcare facility-onset (HO) CDI in univariate and multivariate analysis.RESULTSAmong 1,468 acute care hospitals reporting CDI data to NHSN in 2012, 1,429 matched by CCN to a CMI value in the Impact file. CMI (median, 1.49; interquartile range, 1.36–1.66) was a significant predictor of HO CDI in univariate analysis (PPCONCLUSIONSCMI was a significant predictor of NHSN HO CDI incidence. Additional work to explore the feasibility of using CMI for risk adjustment of NHSN data is necessary.Infect. Control Hosp. Epidemiol. 2015;37(1):19–25

DISCLOSUREThe findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention or the Agency for Toxic Substances and Diseases Registry.OBJECTIVEDescribe the impact of standardizing state-specific summary measures of antibiotic resistance that inform regional interventions to reduce transmission of resistant pathogens in healthcare settings.DESIGNAnalysis of public health surveillance data.METHODSCentral line–associated bloodstream infection (CLABSI) data from intensive care units (ICUs) of facilities reporting to the National Healthcare Safety Network in 2011 were analyzed. For CLABSI due to methicillin-resistant Staphylococcus aureus (MRSA), extended-spectrum cephalosporin (ESC)-nonsusceptible Klebsiella species, and carbapenem-nonsusceptible Klebsiella species, we computed 3 state-level summary measures of nonsusceptibility: crude percent nonsusceptible, model-based adjusted percent nonsusceptible, and crude infection incidence rate.RESULTSOverall, 1,791 facilities reported CLABSIs from ICU patients. Of 1,618 S. aureus CLABSIs with methicillin-susceptibility test results, 791 (48.9%) were due to MRSA. Of 756 Klebsiella CLABSIs with ESC-susceptibility test results, 209 (27.7%) were due to ESC-nonsusceptible Klebsiella, and among 661 Klebsiella CLABSI with carbapenem susceptibility test results, 70 (10.6%) were due to carbapenem-nonsusceptible Klebsiella. All 3 state-specific measures demonstrated variability in magnitude by state. Adjusted measures, with few exceptions, were not appreciably different from crude values for any phenotypes. When linking values of crude and adjusted percent nonsusceptible by state, a state’s absolute rank shifted slightly for MRSA in 5 instances and only once each for ESC-nonsusceptible and carbapenem-nonsusceptible Klebsiella species. Infection incidence measures correlated strongly with both percent nonsusceptibility measures.CONCLUSIONSCrude state-level summary measures, based on existing NHSN CLABSI data, may suffice to assess geographic variability in antibiotic resistance. As additional variables related to antibiotic resistance become available, risk-adjusted summary measures are preferable.Infect Control Hosp Epidemiol 2015;36(1): 54–64

Objective.Central line–associated bloodstream infection (BSI) rates are a key quality metric for comparing hospital quality and safety. Traditional BSI surveillance may be limited by interrater variability. We assessed whether a computer-automated method of central line–associated BSI detection can improve the validity of surveillance.Design.Retrospective cohort study.Setting.Eight medical and surgical intensive care units (ICUs) in 4 academic medical centers.Methods.Traditional surveillance (by hospital staff) and computer algorithm surveillance were each compared against a retrospective audit review using a random sample of blood culture episodes during the period 2004–2007 from which an organism was recovered. Episode-level agreement with audit review was measured with κ statistics, and differences were assessed using the test of equal κ coefficients. Linear regression was used to assess the relationship between surveillance performance (κ) and surveillance-reported BSI rates (BSIs per 1,000 central line–days).Results.We evaluated 664 blood culture episodes. Agreement with audit review was significantly lower for traditional surveillance (κ [95% confidence interval (CI)] = 0.44 [0.37–0.51]) than computer algorithm surveillance (κ [95% CI] [0.52–0.64]; P = .001). Agreement between traditional surveillance and audit review was heterogeneous across ICUs (P = .001); furthermore, traditional surveillance performed worse among ICUs reporting lower (better) BSI rates (P = .001). In contrast, computer algorithm performance was consistent across ICUs and across the range of computer-reported central line–associated BSI rates.Conclusions.Compared with traditional surveillance of bloodstream infections, computer automated surveillance improves accuracy and reliability, making interfacility performance comparisons more valid.Infect Control Hosp Epidemiol 2014;35(12):1483–1490

We describe the Centers for Disease Control and Prevention's National Nosocomial Infections Surveillance (NNIS) system. Elements of the system critical for successful reduction of nosocomial infection rates include voluntary participation and confidentiality; standard definitions and protocols; identification of populations at high risk; site-specific, risk-adjusted infection rates comparable across institutions; adequate numbers of trained infection control professionals; dissemination of data to health-care providers; and a link between monitored rates and prevention efforts.

To determine frequency of methicillin-resistant Staphylococcus aureus infections caused by strains typically associated with community-acquired infections (USA300) among persons with healthcare-related risk factors (HRFs), we evaluated surveillance data. Of patients with HRFs, 18%-28% had a "community-associated" strain, primarily USA300; of patients without HRFs, 26% had a "healthcare-associated" strain, typically USA100.

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.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.Design.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 (n = 18), urinary tract infections (n = 9), pneumonia (n = 9), and bloodstream infections (n = 8) 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.Infect Control Hosp Epidemiol 2012;33(3):283-291

The Centers for Disease Control and Prevention (CDC) convened a conference on March 12–13, 2003, in Atlanta, Georgia, to discuss improving state-based surveillance of drug-resistant Streptococcus pneumoniae (DRSP) and methicillin-resistant Staphylococcus aureus (MRSA). The Council of State and Territorial Epidemiologists, the Association of Public Health Laboratories, and CDC co-sponsored the conference; 120 participants from 38 states attended. The conference was organized by the Divisions of Healthcare Quality Promotion and Bacterial and Mycotic Diseases, National Center for Infectious Diseases, CDC. Goals of the meeting included 1) reviewing the rationale for surveillance of DRSP and MRSA, 2) presenting scientific studies highlighting valid and meaningful methods of performing state-level surveillance, 3) sharing state-level surveillance experiences, and 4) identifying unmet needs of the state health departments in performing such surveillance. The primary theme of the conference general sessions was the public health impact of DRSP and MRSA and the need for accurate surveillance data to track and monitor resistance trends. S. pneumoniae is a major cause of respiratory infections in the United States. Since the early 1990s, the prevalence of resistance to single and multiple antibiotics has been increasing in pneumococci. Antimicrobial drug resistance in S. pneumoniae can vary among populations and is influenced by local prescribing practices and the prevalence of resistant clones. Conference presenters discussed the role of surveillance in raising awareness of the resistance problem and in monitoring the effectiveness of prevention and control programs. National- and state-level epidemiologists discussed the benefits of including state-level surveillance data with appropriate antibiotic use programs designed to address the antibiotic prescribing practices of clinicians. The potential for local surveillance to provide information on the impact of a new pneumococcal vaccine for children was also examined; the vaccine has been shown to reduce infections caused by resistance strains Since the early 1990s, S. aureus infections resistant to oxacillin (MRSA) have increased steadily. Several scientists reported two recent changes in the epidemiology of MRSA: its emergence in persons without established risk factors and the emergence of vancomycin-resistant S. aureus (VRSA). These new developments underscore the need for scientifically valid, yet financially feasible, state-based surveillance that will aid in understanding the changing epidemiology of MRSA disease. Such understanding will allow effective implementation of MRSA prevention and control programs. Prevention programs will differ on the basis of which populations are most affected (long-term care, community, and hospice). Current programs focus on reducing cross-transmission through improved hand hygiene and wound care. (More information is available from: URL: http://www.cdc.gov/ncidod/hip/Aresist/aresist.htm). Conference participants generally agreed that a lack of resources for surveillance has challenged state public health agencies committed to monitoring emerging antimicrobial drug resistance. A conclusion drawn from conference sessions was that statically sound methods of data collection that capture valid, meaningful, and useful data and meet the financial restrictions of state budgets are indicated. Active, population-based surveillance for collecting relevant isolates is considered the standard criterion. Unfortunately, this type of surveillance is labor-intensive and costly, making it an impractical choice for many states. The challenges of isolate collection, packaging and transport, data collection, and analysis may place an unacceptable workload on laboratory and epidemiology personnel. Epidemiologists from several state health departments that have elected to implement enhanced antimicrobial drug-resistance surveillance programs presented alternative surveillance methods currently implemented in their states. Several surveillance models and knowledge gained by state-based epidemiologists provided key insights into the challenges and benefits of implementing enhanced surveillance programs. Two methods frequently used by states are sentinel (i.e., survey of subset of laboratories) and antibiogram (i.e., cumulative susceptibility data) surveillance. Common difficulties were identified with implementing sentinel systems. Those difficulties included logistical obstacles with isolate or data processing and communication breakdowns between laboratory, epidemiology, and hospital infection control personnel. Care must be taken in selecting the numbers and types of laboratories to participate in the sentinel network States collecting antibiograms from hospitals and state laboratories also face challenges, including incompatible formatting of drug-testing panels, the inconsistent inclusion of duplicate or repeat isolates, and inconsistent reporting of denominator data. Solutions to these problems commonly involve improving communication between clinical microbiology laboratories and state health departments, including laboratory input in decision making and providing feedback of data from the system to participants. Guidance for aggregating cumulative susceptibility data (i.e., antibiograms) has been published and can serve as a guide for states and clinical microbiology laboratories in conducting surveillance. Also, having designated staff was essential for successfully implementing most programs. Presenters agreed that the benefits of collecting local data from these systems are substantial and will assist prevention programs. Another aspect of surveillance focuses on detecting rare events. Such reports may include new changes in susceptibility, new mechanisms of resistance, susceptibility of unusual pathogens, and unexpected sources of resistant organisms. Establishing good communication among personnel in health departments and clinical laboratories is important for improving reporting of such events. Allocating resources for improved surveillance is considered a practical and responsive step for states interested in tracking local resistant trends. Local data are important for raising public awareness, establishing resources and prevention activities, developing and informing treatment guidelines, monitoring trends, and motivating behavior change among clinicians. This meeting and ongoing efforts to study and validate surveillance methods will assist local health authorities in making decisions programs to monitor antimicrobial drug resistance. The 2-day conference provided an opportunity to initiate an exchange of current practices and knowledge gained among states and territories. This process marks the first phase in building networks that may potentially enhance training resources, provide guidance for program development, and identify further technical assistance needed from CDC.