OBJECTIVE: This article presents the National Healthcare Safety Network (NHSN)'s approach to automation for public health surveillance using digital quality measures (dQMs) via an open-source tool (NHSNLink) and piloting of this approach using real-world data in a newly established collaborative program (NHSNCoLab). The approach leverages Health Level Seven Fast Healthcare Interoperability Resources (FHIR) application programming interfaces to improve data collection and reporting for public health and patient safety beginning with common, clinically significant, and preventable patient harms, such as medication-related hypoglycemia, healthcare facility-onset Clostridioides difficile infection, and healthcare-associated venous thromboembolism. CONCLUSIONS: The NHSN's FHIR dQMs hold the promise of minimizing the burden of reporting, improving accuracy, quality, and validity of data collected by NHSN, and increasing speed and efficiency of public health surveillance.

As the third edition of the Compendium of Strategies to Prevent Healthcare-Associated Infections in Acute Care Hospitals is released with the latest recommendations for the prevention and management of healthcare-associated infections (HAIs), a new approach to reporting HAIs is just beginning to unfold. This next generation of HAI reporting will be fully electronic and based largely on existing data in electronic health record (EHR) systems and other electronic data sources. It will be a significant change in how hospitals report HAIs and how the Centers for Disease Control and Prevention (CDC) and other agencies receive this information. This paper outlines what that future electronic reporting system will look like and how it will impact HAI reporting.

Persons receiving maintenance dialysis are at increased risk for SARS-CoV-2 infection and its severe outcomes, including death. However, rates of SARS-CoV-2 infection and COVID-19-related deaths in this population are not well described. Since November 2020, CDC's National Healthcare Safety Network (NHSN) has collected weekly data monitoring incidence of SARS-CoV-2 infections (defined as a positive SARS-CoV-2 test result) and COVID-19-related deaths (defined as the death of a patient who had not fully recovered from a SARS-CoV-2 infection) among maintenance dialysis patients. This analysis used NHSN dialysis facility COVID-19 data reported during June 30, 2021-September 27, 2022, to describe rates of SARS-CoV-2 infection and COVID-19-related death among maintenance dialysis patients. The overall infection rate was 30.47 per 10,000 patient-weeks (39.64 among unvaccinated patients and 27.24 among patients who had completed a primary COVID-19 vaccination series). The overall death rate was 1.74 per 10,000 patient-weeks. Implementing recommended infection control measures in dialysis facilities and ensuring patients and staff members are up to date with recommended COVID-19 vaccination is critical to limiting COVID-19-associated morbidity and mortality.

Monitoring COVID-19 vaccination coverage among nursing home (NH) residents and staff is important to ensure high coverage and guide patient-safety policies. With the termination of the federal Pharmacy Partnership for Long-Term Care Program, another source of facility-based vaccination data is needed. We compared numbers of COVID-19 vaccinations administered to NH residents and staff reported by pharmacies participating in the temporary federal Pharmacy Partnership for Long-Term Care Program with those reported by NHs participating in new COVID-19 vaccination modules of CDC’s National Healthcare Safety Network (NHSN). Pearson correlation coefficients comparing the number vaccinated between the two approaches were 0.89, 0.96, and 0.97 for residents and 0.74, 0.90, and 0.90 for staff, in the weeks ending January 3, 10, and 17, respectively. Based on subsequent NHSN reporting, vaccination coverage with ≥1 vaccine dose reached 77% for residents and 50% for staff the week ending January 31 and plateaued through April 2021.Three-question summary boxWhat is the current understanding of the subject?Because of high risk of disease, nursing home residents and staff were prioritized for COVID-19 vaccination when doses were limited.What does this report add to the literature?National monitoring of nursing home residents and staff vaccination coverage through the CDC National Healthcare Safety Network (NHSN) correlated with vaccination administration reports from the federal Pharmacy Partnership for Long-Term Care Program in January 2021. NHSN-reported vaccination coverage rates plateaued from February through April 2021.What are the implications for public health practice?NHSN can track COVID-19 vaccination in nursing homes and help guide efforts to increase vaccine uptake in residents and staff.Competing Interest StatementThe authors have declared no competing interest.Funding StatementThe authors received no financial support for the research, authorship, and/or publication of this article.Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:This activity was reviewed by CDC and was conducted consistent with applicable federal law and CDC policy (See e.g., 45 C.F.R. part 46, 21 C.F.R. part 56; 42 U.S.C. section 241(d); 5 U.S.C. section 552a; 44 U.S.C. section 3501 et seq.).All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesData supporting the findings of the study are found in the manuscript and/or supplementary files. Any other data can be furnished upon request.

OBJECTIVES: To evaluate the incidence of a candidate definition of healthcare facility-onset, treated Clostridioides difficile (CD) infection (cHT-CDI) and to identify variables and best model fit of a risk-adjusted cHT-CDI metric using extractable electronic heath data. METHODS: We analyzed 9,134,276 admissions from 265 hospitals during 2015-2020. The cHT-CDI events were defined based on the first positive laboratory final identification of CD after day 3 of hospitalization, accompanied by use of a CD drug. The generalized linear model method via negative binomial regression was used to identify predictors. Standardized infection ratios (SIRs) were calculated based on 2 risk-adjusted models: a simple model using descriptive variables and a complex model using descriptive variables and CD testing practices. The performance of each model was compared against cHT-CDI unadjusted rates. RESULTS: The median rate of cHT-CDI events per 100 admissions was 0.134 (interquartile range, 0.023-0.243). Hospital variables associated with cHT-CDI included the following: higher community-onset CDI (CO-CDI) prevalence; highest-quartile length of stay; bed size; percentage of male patients; teaching hospitals; increased CD testing intensity; and CD testing prevalence. The complex model demonstrated better model performance and identified the most influential predictors: hospital-onset testing intensity and prevalence, CO-CDI rate, and community-onset testing intensity (negative correlation). Moreover, 78% of the hospitals ranked in the highest quartile based on raw rate shifted to lower percentiles when we applied the SIR from the complex model. CONCLUSIONS: Hospital descriptors, aggregate patient characteristics, CO-CDI burden, and clinical testing practices significantly influence incidence of cHT-CDI. Benchmarking a cHT-CDI metric is feasible and should include facility and clinical variables.

OBJECTIVE: To examine temporal changes in coverage with a complete primary series of coronavirus disease 2019 (COVID-19) vaccination and staffing shortages among healthcare personnel (HCP) working in nursing homes in the United States before, during, and after the implementation of jurisdiction-based COVID-19 vaccination mandates for HCP. SAMPLE AND SETTING: HCP in nursing homes from 15 US jurisdictions. DESIGN: We analyzed weekly COVID-19 vaccination data reported to the Centers for Disease Control and Prevention's National Healthcare Safety Network from June 7, 2021, through January 2, 2022. We assessed 3 periods (preintervention, intervention, and postintervention) based on the announcement of vaccination mandates for HCP in 15 jurisdictions. We used interrupted time-series models to estimate the weekly percentage change in vaccination with complete primary series and the odds of reporting a staffing shortage for each period. RESULTS: Complete primary series vaccination among HCP increased from 66.7% at baseline to 94.3% at the end of the study period and increased at the fastest rate during the intervention period for 12 of 15 jurisdictions. The odds of reporting a staffing shortage were lowest after the intervention. CONCLUSIONS: These findings demonstrate that COVID-19 vaccination mandates may be an effective strategy for improving HCP vaccination coverage in nursing homes without exacerbating staffing shortages. These data suggest that mandates can be considered to improve COVID-19 coverage among HCP in nursing homes to protect both HCP and vulnerable nursing home residents.

OBJECTIVES: To evaluate the prevalence of hospital-onset bacteremia and fungemia (HOB), identify hospital-level predictors, and to evaluate the feasibility of an HOB metric. METHODS: We analyzed 9,202,650 admissions from 267 hospitals during 2015-2020. An HOB event was defined as the first positive blood-culture pathogen on day 3 of admission or later. We used the generalized linear model method via negative binomial regression to identify variables and risk markers for HOB. Standardized infection ratios (SIRs) were calculated based on 2 risk-adjusted models: a simple model using descriptive variables and a complex model using descriptive variables plus additional measures of blood-culture testing practices. Performance of each model was compared against the unadjusted rate of HOB. RESULTS: Overall median rate of HOB per 100 admissions was 0.124 (interquartile range, 0.00-0.22). Facility-level predictors included bed size, sex, ICU admissions, community-onset (CO) blood culture testing intensity, and hospital-onset (HO) testing intensity, and prevalence (all P < .001). In the complex model, CO bacteremia prevalence, HO testing intensity, and HO testing prevalence were the predictors most associated with HOB. The complex model demonstrated better model performance; 55% of hospitals that ranked in the highest quartile based on their raw rate shifted to a lower quartile when the SIR from the complex model was applied. CONCLUSIONS: Hospital descriptors, aggregate patient characteristics, community bacteremia and/or fungemia burden, and clinical blood-culture testing practices influence rates of HOB. Benchmarking an HOB metric is feasible and should endeavor to include both facility and clinical variables.

Monitoring COVID-19 vaccination coverage among nursing home residents and staff is important to ensure high coverage rates and guide patient-safety policies. With the termination of the federal Pharmacy Partnership for Long-Term Care Program, another source of facility-based vaccination data is needed. We compared numbers of COVID-19 vaccinations administered to nursing home residents and staff reported by pharmacies participating in the temporary federal Pharmacy Partnership for Long-Term Care Program with the numbers of COVID-19 vaccinations reported by nursing homes participating in new COVID-19 vaccination modules of the Centers for Disease Control and Prevention's National Healthcare Safety Network (NHSN). Pearson correlation coefficients comparing the number vaccinated between the 2 approaches were 0.89, 0.96, and 0.97 for residents and 0.74, 0.90, and 0.90 for staff, in the weeks ending January 3, 10, and 17, 2021, respectively. Based on subsequent NHSN reporting, vaccination coverage with ≥1 vaccine dose reached 73.7% for residents and 47.6% for staff the week ending January 31 and increased incrementally through July 2021. Continued monitoring of COVID-19 vaccination coverage is important as new nursing home residents are admitted, new staff are hired, and additional doses of vaccine are recommended.

OBJECTIVE: To evaluate hospital-level variation in using first-line antibiotics for Clostridioides difficile infection (CDI) based on the burden of laboratory-identified (LabID) CDI. METHODS: Using data on hospital-level LabID CDI events and antimicrobial use (AU) for CDI (oral/rectal vancomycin or fidaxomicin) submitted to the National Healthcare Safety Network in 2019, we assessed the association between hospital-level CDI prevalence (per 100 patient admissions) and rate of CDI AU (days of therapy per 1,000 days present) to generate a predicted value of AU based on CDI prevalence and CDI test type using negative binomial regression. The ratio of the observed to predicted AU was then used to identify hospitals with extreme discordance between CDI prevalence and CDI AU, defined as hospitals with a ratio outside of the intervigintile range. RESULTS: Among 963 acute-care hospitals, rate of CDI prevalence demonstrated a positive dose-response relationship with rate of CDI AU. Compared with hospitals without extreme discordance (n = 902), hospitals with lower-than-expected CDI AU (n = 31) had, on average, fewer beds (median, 106 vs 208), shorter length of stay (median, 3.8 vs 4.2 days), and higher proportion of undergraduate or nonteaching medical school affiliation (48% vs 39%). Hospitals with higher-than-expected CDI AU (n = 30) were similar overall to hospitals without extreme discordance. CONCLUSIONS: The prevalence rate of LabID CDI had a significant dose-response association with first-line antibiotics for treating CDI. We identified hospitals with extreme discordance between CDI prevalence and CDI AU, highlighting potential opportunities for data validation and improvements in diagnostic and treatment practices for CDI.

OBJECTIVE: To evaluate if facility-level vaccination after an initial vaccination clinic was independently associated with COVID-19 incidence adjusted for other factors in January 2021 among nursing home residents. DESIGN: Ecological analysis of data from the CDC's National Healthcare Safety Network (NHSN) and from the CDC's Pharmacy Partnership for Long-Term Care Program. SETTING AND PARTICIPANTS: CMS-certified nursing homes participating in both NHSN and the Pharmacy Partnership for Long-Term Care Program. METHODS: A multivariable, random intercepts, negative binomial model was applied to contrast COVID-19 incidence rates among residents living in facilities with an initial vaccination clinic during the week ending January 3, 2021 (n = 2843), vs those living in facilities with no vaccination clinic reported up to and including the week ending January 10, 2021 (n = 3216). Model covariates included bed size, resident SARS-CoV-2 testing, staff with COVID-19, cumulative COVID-19 among residents, residents admitted with COVID-19, community county incidence, and county social vulnerability index (SVI). RESULTS: In December 2020 and January 2021, incidence of COVID-19 among nursing home residents declined to the lowest point since reporting began in May, diverged from the pattern in community cases, and began dropping before vaccination occurred. Comparing week 3 following an initial vaccination clinic vs week 2, the adjusted reduction in COVID-19 rate in vaccinated facilities was 27% greater than the reduction in facilities where vaccination clinics had not yet occurred (95% confidence interval: 14%-38%, P < .05). CONCLUSIONS AND IMPLICATIONS: Vaccination of residents contributed to the decline in COVID-19 incidence in nursing homes; however, other factors also contributed. The decline in COVID-19 was evident prior to widespread vaccination, highlighting the benefit of a multifaced approach to prevention including continued use of recommended screening, testing, and infection prevention practices as well as vaccination to keep residents in nursing homes safe.

BACKGROUND: The Centers for Disease Control and Prevention's (CDC's) National Healthcare Safety Network (NHSN) is the most widely used health care-associated infection (HAI) and antimicrobial use and resistance surveillance program in the United States. Over 37,000 health care facilities participate in the program and submit a large volume of surveillance data. These data are used by the facilities themselves, the CDC, and other agencies and organizations for a variety of purposes, including infection prevention, antimicrobial stewardship, and clinical quality measurement. Among the summary metrics made available by the NHSN are standardized infection ratios, which are used to identify HAI prevention needs and measure progress at the national, regional, state, and local levels. OBJECTIVE: To extend the use of geospatial methods and tools to NHSN data, and in turn to promote and inspire new uses of the rendered data for analysis and prevention purposes, we developed a web-enabled system that enables integrated visualization of HAI metrics and supporting data. METHODS: We leveraged geocoding and visualization technologies that are readily available and in current use to develop a web-enabled system designed to support visualization and interpretation of data submitted to the NHSN from geographically dispersed sites. The server-client model-based system enables users to access the application via a web browser. RESULTS: We integrated multiple data sets into a single-page dashboard designed to enable users to navigate across different HAI event types, choose specific health care facility or geographic locations for data displays, and scale across time units within identified periods. We launched the system for internal CDC use in January 2019. CONCLUSIONS: CDC NHSN statisticians, data analysts, and subject matter experts identified opportunities to extend the use of geospatial methods and tools to NHSN data and provided the impetus to develop NHSNViz. The development effort proceeded iteratively, with the developer adding or enhancing functionality and including additional data sets in a series of prototype versions, each of which incorporated user feedback. The initial production version of NHSNViz provides a new geospatial analytic resource built in accordance with CDC user requirements and extensible to additional users and uses in subsequent versions.

During March 27-July 14, 2020, the CDC's National Healthcare Safety Network extended its surveillance to hospital capacities responding to COVID-19 pandemic. The data showed wide variations across hospitals in case burden, bed occupancies, ventilator usage, and healthcare personnel and supply status. These data were used to inform emergency responses.

OBJECTIVE: The rapid spread of SARS-CoV-2 throughout key regions of the United States (U.S.) in early 2020 placed a premium on timely, national surveillance of hospital patient censuses. To meet that need, the Centers for Disease Control and Prevention's National Healthcare Safety Network (NHSN), the nation's largest hospital surveillance system, launched a module for collecting hospital COVID-19 data. This paper presents time series estimates of the critical hospital capacity indicators during April 1-July 14, 2020. DESIGN: From March 27-July 14, 2020, NHSN collected daily data on hospital bed occupancy, number of hospitalized patients with COVID-19, and availability/use of mechanical ventilators. Time series were constructed using multiple imputation and survey weighting to allow near real-time daily national and state estimates to be computed. RESULTS: During the pandemic's April peak in the United States, among an estimated 431,000 total inpatients, 84,000 (19%) had COVID-19. Although the number of inpatients with COVID-19 decreased during April to July, the proportion of occupied inpatient beds increased steadily. COVID-19 hospitalizations increased from mid-June in the South and Southwest after stay-at-home restrictions were eased. The proportion of inpatients with COVID-19 on ventilators decreased from April to July. CONCLUSIONS: The NHSN hospital capacity estimates served as important, near-real time indicators of the pandemic's magnitude, spread, and impact, providing quantitative guidance for the public health response. Use of the estimates detected the rise of hospitalizations in specific geographic regions in June after declining from a peak in April. Patient outcomes appeared to improve from early April to mid-July.

During the beginning of the coronavirus disease 2019 (COVID-19) pandemic, nursing homes were identified as congregate settings at high risk for outbreaks of COVID-19 (1,2). Their residents also are at higher risk than the general population for morbidity and mortality associated with infection with SARS-CoV-2, the virus that causes COVID-19, in light of the association of severe outcomes with older age and certain underlying medical conditions (1,3). CDC's National Healthcare Safety Network (NHSN) launched nationwide, facility-level COVID-19 nursing home surveillance on April 26, 2020. A federal mandate issued by the Centers for Medicare & Medicaid Services (CMS), required nursing homes to commence enrollment and routine reporting of COVID-19 cases among residents and staff members by May 25, 2020. This report uses the NHSN nursing home COVID-19 data reported during May 25-November 22, 2020, to describe COVID-19 rates among nursing home residents and staff members and compares these with rates in surrounding communities by corresponding U.S. Department of Health and Human Services (HHS) region.* COVID-19 cases among nursing home residents increased during June and July 2020, reaching 11.5 cases per 1,000 resident-weeks (calculated as the total number of occupied beds on the day that weekly data were reported) (week of July 26). By mid-September, rates had declined to 6.3 per 1,000 resident-weeks (week of September 13) before increasing again, reaching 23.2 cases per 1,000 resident-weeks by late November (week of November 22). COVID-19 cases among nursing home staff members also increased during June and July (week of July 26 = 10.9 cases per 1,000 resident-weeks) before declining during August-September (week of September 13 = 6.3 per 1,000 resident-weeks); rates increased by late November (week of November 22 = 21.3 cases per 1,000 resident-weeks). Rates of COVID-19 in the surrounding communities followed similar trends. Increases in community rates might be associated with increases in nursing home COVID-19 incidence, and nursing home mitigation strategies need to include a comprehensive plan to monitor local SARS-CoV-2 transmission and minimize high-risk exposures within facilities.

OBJECTIVE: To describe common pathogens and antimicrobial resistance patterns for healthcare-associated infections (HAIs) among pediatric patients that occurred in 2015-2017 and were reported to the Centers for Disease Control and Prevention's National Healthcare Safety Network (NHSN). METHODS: Antimicrobial resistance data were analyzed for pathogens implicated in central line-associated bloodstream infections (CLABSIs), catheter-associated urinary tract infections (CAUTIs), ventilator-associated pneumonias (VAPs), and surgical site infections (SSIs). This analysis was restricted to device-associated HAIs reported from pediatric patient care locations and SSIs among patients <18 years old. Percentages of pathogens with nonsusceptibility (%NS) to selected antimicrobials were calculated by HAI type, location type, and surgical category. RESULTS: Overall, 2,545 facilities performed surveillance of pediatric HAIs in the NHSN during this period. Staphylococcus aureus (15%), Escherichia coli (12%), and coagulase-negative staphylococci (12%) were the 3 most commonly reported pathogens associated with pediatric HAIs. Pathogens and the %NS varied by HAI type, location type, and/or surgical category. Among CLABSIs, the %NS was generally lowest in neonatal intensive care units and highest in pediatric oncology units. Staphylococcus spp were particularly common among orthopedic, neurosurgical, and cardiac SSIs; however, E. coli was more common in abdominal SSIs. Overall, antimicrobial nonsusceptibility was less prevalent in pediatric HAIs than in adult HAIs. CONCLUSION: This report provides an updated national summary of pathogen distributions and antimicrobial resistance patterns among pediatric HAIs. These data highlight the need for continued antimicrobial resistance tracking among pediatric patients and should encourage the pediatric healthcare community to use such data when establishing policies for infection prevention and antimicrobial stewardship.