Using National Healthcare Safety Network data, an interrupted time series of intravenous antimicrobial starts (IVAS) among hemodialysis patients was performed. Annual adjusted rates decreased by 6.64% (January 2012–March 2020) and then further decreased by 8.91% until December 2021. IVAS incidence trends have decreased since 2012, including during the early COVID-19 pandemic.

Background:Clostridioides difficile infection (CDI) is one of the most common laboratory-identified (LabID) healthcare-associated events reported to the National Healthcare Safety Network (NHSN). CDI prevention remains a national priority, and efforts to reduce infection burden and improve antibiotic stewardship continue to expand across the healthcare spectrum. Beginning in 2013, the Centers for Medicare and Medicaid Services (CMS) required acute-care hospitals participating in CMS’ Inpatient Quality Reporting program to report CDI LabID data to NHSN and, in 2015, extended this reporting requirement to emergency departments (ED) and 24-hour observation units. To assess national progress, we evaluated changes in hospital onset CDI (HO-CDI) incidence during 2010–2018. Methods: Cases of HO-CDI were reported to NHSN by hospitals using the NHSN’s LabID criteria. Generalized linear mixed-effects modeling was used to assess trends of HO-CDI by treating the hospital as a random intercept to account for the correlation of the repeated responses over time. The data were summarized at the quarterly level, the main effect was time, and the covariates of interest were the following: CDI test type, inpatient community-onset (CO) infection rate, hospital type, average length of stay, medical school affiliation, number of beds, number of ICU beds, number of infection control professionals, presence of an ED or observation unit , and an indicator for 2015 to account for CDI protocol changes that required hospitals to conduct surveillance in both inpatient and ED or observation unit setting. Results: During 2010–2013, the number of hospitals reporting CDI increased and then stabilized after 2013 (Table 1). Crude HO-CDI rates decreased over time, except for an increase in 2015 and steeper reduction thereafter. (Table 2). During 2010–2014, the adjusted quarterly rate of change was −0.45% (95% CI, −0.57% to −0.33%; P < .0001). The rate of reduction was smaller in 2010–2014 compared to those of 2015–2018 (−2.82%; 95% CI, −3.10% to −2.54%; P < .0001). Compared to 2014, the adjusted rate in 2015 increased by 79.14% (95% CI, 72.42%–86.11%; P < .0001). Conclusions: The number of hospitals reporting CDI LabID data grew substantially in 2013 as a result of the CMS requirement for reporting. Adjusted HO-CDI rates decreased over time, with a rate hike in the year of 2015 and a rapid decrease thereafter. The increase in 2015 may be explained by changes in the NHSN CDI surveillance protocol and better test type classification in later years. Overall decreases in HO-CDI rates may be influenced by prevention strategies.

Funding: None

Disclosures: None

Background: The Centers for Disease Control and Prevention’s National Healthcare Safety Network (NHSN) has included surveillance of laboratory-identified (LabID) methicillin-resistant Staphylococcus aureus (MRSA) bacteremia events since 2009. In 2013, the Centers for Medicare & Medicaid Services (CMS) began requiring acute-care hospitals (ACHs) that participate in the CMS Inpatient Quality Reporting program to report MRSA LabID events to the NHSN and, in 2015, ACHs were required to report MRSA LabID events from emergency departments (EDs) and/or 24-hour observation locations. Prior studies observed a decline in hospital-onset MRSA (HO-MRSA) rates in national studies over shorter periods or other surveillance systems. In this analysis, we review the national reporting trend for HO-MRSA bacteremia LabID events, 2010–2018. Method: This analysis was limited to MRSA bacteremia LabID event data reported by ACHs that follow NHSN surveillance protocols. The data were restricted to events reported for overall inpatient facility-wide and, if applicable, EDs and 24-hour observation locations. MRSA events were classified as HO (collected >3 days after admission) or inpatient or outpatient community onset (CO, collected ≤3 days after admission). An interrupted time series random-effects generalized linear model was used to examine the relationship between HO-MRSA incidence rates (per 1,000 patient days) and time (year) while controlling for potential risk factors as fixed effects. The following potential risk factors were evaluated: facility’s annual survey data (facility type, medical affiliation, length of facility stay, number of beds, and number of intensive care unit beds) and quarterly summary data (inpatient and outpatient CO prevalence rates). Result: The number of reporting ACHs increased during this period, from 473 in 2010 to 3,651 in 2018. The crude HO-MRSA incidence rates (per 1,000 patient days) have declined over time, from a high of 0.067 in 2011 to 0.052 in 2018 (Table 1). Compared to 2014, the adjusted annual incidence rate increased in 2015 by 16.38%, (95% confidence interval [CI], 10.26%–22.84%; P < .0001). After controlling for all significant risk factors, the estimated annual HO-MRSA incidence rates declined by 5.98% (95% CI, 5.17%–6.78%; P < .0001) (Table 2). Conclusions: HO-MRSA bacteremia incidence rates have decreased over the past 9 years, despite a slight increase in 2015. This national trend analysis reviewed a longer period while analyzing potential risk factors. The decline in HO-MRSA incidence rates has been gradual; however, given the current trend, it is not likely to meet the Healthy People 2020 objectives. This analysis suggests the need for hospitals to continue and/or enhance HO-MRSA infection prevention efforts to reduce rates further.

Funding: None

Disclosures: None

Background: Regional changes in United States C. difficile infection (CDI) are not well understood but important for targeting prevention strategies. Methods: Community-onset (CO) CDI was defined as positive C. difficile stool tests collected on or before hospital day 3 (where admission was day 1), reported by acute-care hospitals to the CDC NHSN over 3 years: year 1, July 1, 2015–June 30, 2016; year 2, July 1, 2016–June 30, 2017; year 3, July 1, 2017–June 30, 2018. Healthcare facility-onset CDI (HO-CDI) was similarly defined but with stool collection after hospital day 3. Hospital referral regions (HRRs) were defined by the Dartmouth Atlas of Health Care, and they represent 306 healthcare markets. Standardized infection ratios (SIRs) were calculated using separate multivariable models for (1) CO-CDI events in an emergency department/observation unit (ED/Obs), (2) CO-CDI events among inpatients, and (3) HO-CDI, accounting for facility-level factors, They resulted in ratios of observed to predicted infections, similar to established methods. SIRs were pooled within each facility to create a hospital-identified SIR by summing observed and predicted events for CO-CDI events in both testing locations and HO-CDI events, then pooled by HRR by summing all facility observed and predicted events within the region. Data from facilities not within an HRR were excluded. Results: Total CO-CDI (ED/Obs and inpatient) and HO-CDI events decreased, even as the number of reporting facilities slightly increased over the 3-year period (Fig. 1). Among 306 HRRs in year 3, the median number of hospitals was 10 (IQR, 6–17), with a median of 526 (IQR, 272–1,002) hospital-identified CDI events per HRR. Variables significantly associated with CDI incident rate and included in SIR models 1–3 included C. difficile test type, hospital type, teaching affiliation, hospital bed size, and presence of an ED/Obs unit. Intensive care unit capacity was included in models 2 and 3, and the ratio of hospital admissions to emergency department encounters in model 1. Pooled mean HRR hospital-identified C. difficile SIRs decreased each year (0.972, 0.914, and 0.838), and decreases also varied by HRR (Fig. 2). Conclusions: National decreases in a combined hospital-identified C. difficile SIR are widespread but may be more aggregated in particular regions. Although SIR adjustments were limited to facility-level factors, aggregation of CDI SIR by HRR may be useful for infection preventionists and public health authorities to further understand regional CDI patterns.

Funding: None

Disclosures: None

Background: An indwelling urinary catheter is used in ~12%–16% of adult hospital inpatients during their hospitalization, which poses risks for acquiring a catheter-associated urinary tract infection (CAUTI). CAUTI data have been reported to the NHSN since 2005, and national benchmarks are annually reported in NHSN progress reports. Trends analyses in the incidence of CAUTI reported to the NHSN over long time have not been previously assessed. Objective: We investigated the national trends of CAUTI incidence separately for intensive care units (ICUs) and wards in acute-care hospitals (ACHs) from 2009 through 2018. Methods: We analyzed CAUTI data from ACHs reported to NHSN in 2009–2018. To evaluate trends of CAUTI incidence (per 1,000 catheter days), we conducted interrupted time-series analysis using negative-binomial mixed-effects modeling, separately for ICUs (nonneonatal ICUs) and wards. Due to the reporting requirement for adult and pediatric ICUs in 2012, and medical, surgical, and medical-surgical wards in 2015 by the CMS and the institution of the NHSN CAUTI definitional changes in 2015, calendar years 2012 and 2015 were treated as interruptions to the outcome in ICU models, and year 2015 was treated as a single interruption in the ward models. Regression models were assessed and adjusted, as appropriate, for patient care location type and facility-level characteristics such as hospital type, teaching status, bed size, number (and percentage) of ICU beds, and average length of inpatient stay. Random intercept and slope models were evaluated with covariance tests and were included to account for differential baseline incidence and trends among reporting hospitals. Results: The volume of patient care locations and hospitals reporting to the NHSN increased over time. Among the ICUs, the CAUTI incidence rate did not change in 2009–2012 and increased at an average of 5.6% per year in 2012–2014 (Fig. 1). CAUTI incidence rate dropped nearly 40% in 2015; thereafter, it decreased at an average of 8.9% per year. Among the wards, CAUTI incidence rate decreased at an average of 4.3% per year beginning 2009 (Fig. 2). The CAUTI incidence rate dropped almost 28% in 2015 and then decreased at an average of 4.3% per year. Conclusions: CAUTI incidence decreased substantially in 2015 among both ICUs and wards, which was partially attributable to CAUTI definitional change (see also Fig. 7 at https://www.cdc.gov/hai/data/archive/data-summary-assessing-progress.html). The significant decline of CAUTI incidence in both location types since 2015 is encouraging, and continued efforts in prevention of CAUTI are vital to sustaining this decline in the future.

Funding: None

Disclosures: None

Background: The NHSN has used positive laboratory tests for surveillance of Clostridioides difficile infection (CDI) LabID events since 2009. Typically, CDIs are detected using enzyme immunoassays (EIAs), nucleic acid amplification tests (NAATs), or various test combinations. The NHSN uses a risk-adjusted, standardized infection ratio (SIR) to assess healthcare facility-onset (HO) CDI. Despite including test type in the risk adjustment, some hospital personnel and other stakeholders are concerned that NAAT use is associated with higher SIRs than are EIAs. To investigate this issue, we analyzed NHSN data from acute-care hospitals for July 1, 2017 through June 30, 2018. Methods: Calendar quarters for which CDI test type was reported as NAAT (includes NAAT, glutamate dehydrogenase (GDH)+NAAT and GDH+EIA followed by NAAT if discrepant) or EIA (includes EIA and GDH+EIA) were selected. HO CDI SIRs were calculated for facility-wide inpatient locations. We conducted the following analyses: (1) Among hospitals that did not switch their test type, we compared the distribution of HO incident rates and SIRs by those reporting NAAT vs EIA. (2) Among hospitals that switched their test type, we selected quarters with a stable switch pattern of 2 consecutive quarters of each of EIA and NAAT (categorized as pattern EIA-to-NAAT or NAAT-to-EIA). Pooled semiannual SIRs for EIA and NAAT were calculated, and a paired t test was used to evaluate the difference of SIRs by switch pattern. Results: Most hospitals did not switch test types (3,242, 89%), and 2,872 (89%) reported sufficient data to calculate SIRs, with 2,444 (85%) using NAAT. The crude pooled HO CDI incidence rates for hospitals using EIA clustered at the lower end of the histogram versus rates for NAAT (Fig. 1). The SIR distributions of both NAAT and EIA overlapped substantially and covered a similar range of SIR values (Fig. 1). Among hospitals with a switch pattern, hospitals were equally likely to have an increase or decrease in their SIR (Fig. 2). The mean SIR difference for the 42 hospitals switching from EIA to NAAT was 0.048 (95% CI, −0.189 to 0.284; P = .688). The mean SIR difference for the 26 hospitals switching from NAAT to EIA was 0.162 (95% CI, −0.048 to 0.371; P = .124). Conclusions: The pattern of SIR distributions of both NAAT and EIA substantiate the soundness of NHSN risk adjustment for CDI test types. Switching test type did not produce a consistent directional pattern in SIR that was statistically significant.

Disclosures: None

Funding: None

Background: Hospitals have submitted surveillance data for surgical site infections (SSIs) following colon surgeries (COLO) to the Centers for Disease Control and Prevention’s National Healthcare Safety Network (NHSN) since 2005. COLO SSI data submissions to NHSN have increased substantially beginning in 2012 as result of a Centers for Medicare and Medicaid Services (CMS) mandatory reporting requirement that began that year. A trend analysis of COLO SSIs, using data submitted to NHSN, has not been previously reported. To estimate the national trend of COLO SSI rates, we analyzed data reported from acute-care hospitals during 2009–2018. Methods: We analyzed inpatient adult COLO procedures with primary closure and resulting deep incisional primary and organ-space SSIs detected during the same hospitalization or rehospitalization in the same hospital. SSIs reported as infection present at time of surgery (PATOS) were included in the analysis. A protocol change that reprioritized COLO above small bowel surgery (SB) in the multiprocedural abdominal operations selection list for SSI attribution beginning in 2013 was a potential interruption to COLO SSI outcome. An interrupted time series with mixed-effects logistic regression was used to estimate the annual change in the log odds of COLO SSI. The estimates were adjusted for the following variables: hospital bed size, gender, emergency, trauma, general anesthesia, scope, ASA score, wound classification, medical school affiliation type, procedure duration and age. We also assessed the slope and level change of log odds before and after 2013. Results: The number of hospitals and procedures increased and then stabilized after 2012 (Table 1). The annual crude SSI rates ranged from 2.40% to 3.10%. There was no statistically significant slope change in 2013 and after. Compared to 2009–2012, the log odds of COLO SSI increased in 2013–2018 (OR, 1.1975; P < .0001). Based on this model, we estimate a 0.58% annual decrease in the odds of having a COLO SSI during 2009–2012 and 2013–2018 after controlling for the aforementioned variables (Table 2). Conclusions: We observed a substantial increase in the volume of hospitals and procedures reported to the NHSN since 2012 and an increase in odds of having a COLO SSI in 2013–2018 associated with surveillance protocol changes. After adjusting for these changes, we found a slight annual decrease in the overall odds of COLO SSI. Greater prevention efforts are needed for COLO SSI.

Funding: None

Disclosures: None

Background: Central-line–associated bloodstream infections (CLABSIs) are a major source of healthcare-associated infections (HAIs) in neonatal intensive care unit (NICU) patients, and they are associated with increased morbidity, mortality, and costs. CLABSI surveillance has been a critical component for hospitals participating in the Center for Disease Control and Prevention’s National Healthcare Safety Network (NHSN) for many years. CLABSI reporting grew substantially as a result of state reporting mandates first introduced in 2005 and federal reporting requirements for all intensive care units that began in 2011. However, no recent assessment of NHSN CLABSI incidence rate changes have been performed. The objective of this analysis was to estimate the overall trends in annual CLABSI incidence rates in NICUs from 2009 to 2018. Methods: We analyzed NHSN CLABSI data reported from NICUs during 2009–2018. CLABSIs further classified as mucosal barrier injury were included in this analysis. To evaluate the trends of CLABSI incidence (per 1,000 central-line days), and to account for the potential impact of definition changes introduced in 2015, we conducted an interrupted time-series analysis using mixed-effects negative binomial regression modeling. Birth weight category, patient care location type and hospital-level characteristics such as hospital type, medical affiliation, teaching status, bed size, and average length of inpatient stay) were assessed as potential covariates in regression analysis. Random intercept and slope models were evaluated with covariance tests and used to account for differential baseline incidence and trends among reporting NICUs. Results: The number of NICUs reporting to NHSN increased significantly following the federal mandate and has remained slightly >1,000 NICUs since 2013. The crude incidence of CLABSI dropped from 2.24 in 2009 to 0.98 infections per 1,000 central-line days in 2018, except for an increase in 2015 (Table 1). The CLABSI incidence, adjusted for birth weight category, decreased by an average of 11.6% per year from 2009 to 2018 except for a 35.8% increase in 2015 (Table 2). Conclusion: These findings suggest that hospitals have made significant strides in reducing the occurrence of CLABSIs in NICUs over the last 10 years. The increase in 2015 could be explained in part by the implementation and application of new definitional changes. Continued practices and policies that target, assess and prevent CLABSI in this setting may have been effective and remain vital to sustaining this decline nationally in subsequent years.

Funding: None

Disclosures: None

Background: Central-line–associated bloodstream infections (CLABSIs) are an important cause of healthcare-associated morbidity and mortality in the United States. CLABSI surveillance in the CDC NHSN began in 2005 and has been propelled by state CLABSI reporting requirements, first introduced in 2005, and subsequently by the CMS requirements for intensive care units (ICUs) in 2011 and select ward locations in 2015. Although trend analyses were previously reported, no recent assessment of the NHSN CLABSI incidence rate changes has been performed. In this analysis, we evaluated trends in CLABSI rates in nonneonatal ICUs and all wards reported from acute-care hospitals. Methods: CLABSI rates, including blood stream infections attributed to mucosal barrier injury reported to the NHSN from 2009 to 2018, were analyzed. To evaluate trends in CLABSI incidence and to account for the potential impact of definitional changes in catheter-associated urinary tract infections (CAUTIs) that indirectly impacted CLABSI rates, as well as the CMS mandate for select wards, we conducted an interrupted time-series analysis using negative binomial random-effects modeling with an interruption in 2015. ICUs and ward locations were analyzed separately. Models were adjusted for patient care location type and hospital-level characteristics: hospital type, medical affiliation, teaching status, bed size, number of ICU beds, and average length of inpatient stay. Random intercept and slope models were used to account for differential baseline incidence and trends among reporting hospitals. Results: The overall crude incidence of CLABSI per 1,000 central-line days decreased from 1.6 infections in 2009 to 0.9 infections in 2018, except for an increase in 2015. Similar trends were observed by location type. Among the ICUs, adjusted CLABSI incidence decreased by 10% annually in 2009–2014, increased nearly 29% in 2015, and thereafter decreased at an average of 6.8% per year. Among the wards, adjusted CLABSI incidence decreased at an average of 7.9% annually, except for a 29.3% increase in 2015. Conclusions: Substantial progress has been made in reducing CLABSIs in both ICUs and wards over the last 10 years. Indirect effects of CAUTI definitional changes may explain the immediate increase in ICUs, whereas the CMS mandate may explain the similar increase in wards in 2015. Despite this increase, these findings suggest that policies and practices aimed at prevention of CLABSI have likely been effective on a national level.

Funding: None

Disclosures: None

Background: The National Healthcare Safety Network (NHSN) has used positive laboratory tests for surveillance of Clostridioides difficile infection (CDI) LabID events since 2009. Typically, CDIs are detected using enzyme immunoassays (EIAs), nucleic acid amplification tests (NAATs), or various test combinations. The NHSN uses a risk-adjusted, standardized infection ratio (SIR) to assess healthcare facility-onset (HO) CDI. Despite including test type in the risk adjustment, some hospital personnel and other stakeholders are concerned that NAAT use is associated with higher SIRs than EIA use. To investigate this issue, we analyzed NHSN data from acute-care hospitals for July 1, 2017, through June 30, 2018. Methods: Calendar quarters where CDI test type was reported as NAAT (includes NAAT, glutamate dehydrogenase (GDH)+NAAT and GDH+EIA followed by NAAT if discrepant) or EIA (includes EIA and GDH+EIA) were selected. HO-CDI SIRs were calculated for facility-wide inpatient locations. We conducted the following 2 analyses: (1) Among hospitals that did not switch their test type, we compared the distribution of HO incident rates and SIRs by those reporting NAAT versus EIA. (2) Among hospitals that switched their test type, we selected quarters with a stable switch pattern of 2 consecutive quarters of each of EIA and NAAT (categorized as EIA-to-NAAT or NAAT-to-EIA). Pooled semiannual SIRs for EIA and NAAT were calculated, and a paired t test was used to evaluate the difference in SIRs by switch pattern. Results: Most hospitals did not switch test types (3,242, 89%), and 2,872 (89%) reported sufficient data to calculate an SIR, with 2,444 (85%) using NAAT. The crude pooled HO CDI incidence rates for hospitals using EIAs clustered at the lower end of the histogram versus rates for NAATs (Fig. 1). The SIR distributions, both NAATs and EIAs, overlapped substantially and covered a similar range of SIR values (Fig. 1). Among hospitals with a switch pattern, hospitals were equally likely to have an increase or decrease in their SIRs (Fig. 2). The mean SIR difference for the 42 hospitals switching from EIA to NAAT was 0.048 (95% CI, −0.189 to 0.284; P = .688). The mean SIR difference for the 26 hospitals switching from NAAT to EIA was 0.162 (95% CI, −0.048 to 0.371; P = .124). Conclusions: The pattern of SIR distribution for both NAAT and EIA substantiate the soundness of the NHSN’s risk adjustment for CDI test types. Switching test type did not produce a consistent directional pattern in SIR that was statistically significant.

Funding: None

Disclosures: None

Background: Surveillance data for surgical site infections (SSIs) following abdominal hysterectomy (HYST) have been reported to the CDC NHSN since 2005. Beginning in 2012, HYST SSI surveillance coverage expanded substantially as a result of a CMS mandatory reporting requirement as part of the Hospital Inpatient Quality Reporting Program. A trend analysis of HYST SSI using data submitted to the NHSN has not been previously reported. To estimate the overall trend of HYST SSI incidence rates, we analyzed data reported from acute-care hospitals with surgery performed between January 1, 2009, and December 31, 2018. Methods: We analyzed inpatient adult HYST procedures with primary closure resulting deep incisional primary and organ-space SSIs detected during the same hospitalization or rehospitalization to the same hospital. SSIs reported as infection present at time of surgery (PATOS) were included in the analysis. Due to the surveillance definition changes for primary closure in 2013 and 2015, these were tested separately as interruptions to HYST SSI outcome using an interrupted time-series model with a mixed-effects logistic regression. Because the previously described changes were not significantly associated with changes in HYST SSI risk, mixed-effects logistic regression was used to estimate the annual change in the log odds of HYST SSI. The estimates were adjusted for the following covariates: hospital bed size, general anesthesia, scope, ASA score, wound classification, medical school affiliation type, procedure duration and age. Results: The number of hospitals and procedures reported to NHSN for HYST increased and then stabilized after 2012 (Table 1). The unadjusted annual SSI incidence rates ranged from 0.60% to 0.81%. Based on the model, we estimate a 2.58% decrease in the odds of having a HYST SSI annually after controlling for variables mentioned above (Table 2). Conclusions: The volume of hospitals and procedures for HYST reported to NHSN increased substantially because of the CMS reporting requirement implemented in 2012. The overall adjusted HYST SSI odds ratio decreased annually over 2009–2018, which indicates progress in preventing HYST SSIs.

Funding: None

Disclosures: None