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Depression and overweight are each associated with abnormal immune system activation. We sought to disentangle the extent to which depressive symptoms and overweight status contributed to increased inflammation and abnormal cortisol levels.
Participants were recruited through the Wellcome Trust NIMA Consortium. The sample of 216 participants consisted of 69 overweight patients with depression; 35 overweight controls; 55 normal-weight patients with depression and 57 normal-weight controls. Peripheral inflammation was measured as high-sensitivity C-Reactive Protein (hsCRP) in serum. Salivary cortisol was collected at multiple points throughout the day to measure cortisol awakening response and diurnal cortisol levels.
Overweight patients with depression had significantly higher hsCRP compared with overweight controls (p = 0.042), normal-weight depressed patients (p < 0.001) and normal-weight controls (p < 0.001), after controlling for age and gender. Multivariable logistic regression showed that comorbid depression and overweight significantly increased the risk of clinically elevated hsCRP levels ⩾3 mg/L (OR 2.44, 1.28–3.94). In a separate multivariable logistic regression model, overweight status contributed most to the risk of having hsCRP levels ⩾3 mg/L (OR 1.52, 0.7–2.41), while depression also contributed a significant risk (OR 1.09, 0.27–2). There were no significant differences between groups in cortisol awakening response and diurnal cortisol levels.
Comorbid depression and overweight status are associated with increased hsCRP, and the coexistence of these conditions amplified the risk of clinically elevated hsCRP levels. Overweight status contributed most to the risk of clinically elevated hsCRP levels, but depression also contributed to a significant risk. We observed no differences in cortisol levels between groups.
Data reported to the Centers for Disease Control and Prevention’s National Healthcare Safety Network (CDC NHSN) were analyzed to understand the potential impact of the COVID-19 pandemic on central-line–associated bloodstream infections (CLABSIs) in acute-care hospitals. Descriptive analysis of the standardized infection ratio (SIR) was conducted by location, location type, geographic area, and bed size.
The rapid spread of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) throughout key regions of the United States 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 coronavirus disease 2019 (COVID-19) data. We present time-series estimates of the critical hospital capacity indicators from April 1 to July 14, 2020.
From March 27 to July 14, 2020, the NHSN collected daily data on hospital bed occupancy, number of hospitalized patients with COVID-19, and the availability and/or 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.
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 from April to July, the proportion of occupied inpatient beds increased steadily. COVID-19 hospitalizations increased from mid-June in the South and Southwest regions after stay-at-home restrictions were eased. The proportion of inpatients with COVID-19 on ventilators decreased from April to July.
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 they declined from a peak in April. Patient outcomes appeared to improve from early April to mid-July.
This chapter addresses the challenges arising from the often unclear legal status of intra-state peace agreements in establishing their binding force, applicable law and relevant principles of interpretation, and considers how drafting techniques affect the implementation of such agreements. First, it maps out the means available to participants in a peace process to confer binding status on (the substance of) an agreement under either domestic or international law, including UN Security Council endorsement, domestic entrenchment or constitutional reform. Second, the chapter examines how (predominantly international) courts and tribunals have grappled with the task of determining the law applicable to peace agreements, as well as examining trends in the principles of interpretation applied by adjudicatory bodies. Finally, it turns to the effects of drafting techniques on the implementation of agreements from the perspectives of the constitutive and instrumental approaches, including weighing the merits of constructive ambiguity.
All patients and staff on the outbreak ward (case cluster), and randomly selected patients and staff on COVID-19 wards (positive control cluster) and a non-COVID-19 wards (negative control cluster) underwent reverse-transcriptase polymerase chain reaction (RT-PCR) testing. Hand hygiene and personal protective equipment (PPE) compliance, detection of environmental SARS-COV-2 RNA, patient behavior, and SARS-CoV-2 IgG antibody prevalence were assessed.
In total, 145 staff and 26 patients were exposed, resulting in 24 secondary cases. Also, 4 of 14 (29%) staff and 7 of 10 (70%) patients were asymptomatic or presymptomatic. There was no difference in mean cycle threshold between asymptomatic or presymptomatic versus symptomatic individuals. None of 32 randomly selected staff from the control wards tested positive. Environmental RNA detection levels were higher on the COVID-19 ward than on the negative control ward (OR, 19.98; 95% CI, 2.63–906.38; P < .001). RNA levels on the COVID-19 ward (where there were no outbreaks) and the outbreak ward were similar (OR, 2.38; P = .18). Mean monthly hand hygiene compliance, based on 20,146 observations (over preceding year), was lower on the outbreak ward (P < .006). Compared to both control wards, the proportion of staff with detectable antibodies was higher on the outbreak ward (OR, 3.78; 95% CI, 1.01–14.25; P = .008).
Staff seroconversion was more likely during a short-term outbreak than from sustained duty on a COVID-19 ward. Environmental contamination and PPE use were similar on the outbreak and control wards. Patient noncompliance, decreased hand hygiene, and asymptomatic or presymptomatic transmission were more frequent on the outbreak ward.
Background: The CDC NHSN launched the Antimicrobial Use Option in 2011. The Antimicrobial Use Option allows users to implement risk-adjusted antimicrobial use benchmarking within- and between- facilities using the standardized antimicrobial administration ratio (SAAR) and to evaluate use over time. The SAAR can be used for public health surveillance and to guide an organization’s stewardship or quality improvement efforts. Methods: Antimicrobial Use Option enrollment grew through partner engagement, targeted education, and development of data benchmarking. We analyze enrollment over time and discuss key drivers of participation. Results: Initial 2011 Antimicrobial Use Option enrollment efforts awarded grant Funding: to 4 health departments. These health departments partnered with hospitals, which encouraged vendors to build infrastructure for electronic antimicrobial use reporting. CDC supported vendors through outreach and education. In 2012, with CDC support, Veterans’ Affairs (VA) Informatics, Decision-Enhancement, and Analytic Sciences Center and partners began implementation of Antimicrobial Use Option reporting and validation of submitted data. These early efforts led to enrollment of 64 facilities by 2014 (Fig. 1). As awareness of the antimicrobial use option grew, we focused on facility engagement and development of benchmark metrics. A second round of grant Funding: in 2015 supported submission to the Antimicrobial Use Option from additional facilities by Funding: a vendor, a healthcare system, and an antimicrobial stewardship network. In 2015, CMS recognized the Antimicrobial Use Option as a choice for public health registry reporting under Meaningful Use Stage 3, resulting in an increase in participating hospitals. Antimicrobial Use Option enrollment increased in 2015 (n = 120), coinciding with national prioritization of antimicrobial stewardship. In 2016, the SAAR, was released in NHSN. We leveraged the SAAR to encourage participation from additional facilities and began quarterly calls to encourage continued participation from existing users. In 2016, the Department of Defense began submitting data to the Antimicrobial Use Option, resulting in 207 facilities enrolled in 2016, which grew to 616 in 2017. As of November 2019, 12 vendors self-report submission capabilities and 1,470 facilities, of ~6,800 active NHSN participants, are enrolled in the Antimicrobial Use Option. Two states have passed requirements regulating Antimicrobial Use Option reporting with Tennessee’s requirement going into effect in 2021. Conclusions: The Antimicrobial Use Option offers evidence that collaboration with partners, and leveraging of benchmarking metrics available to a national surveillance system can lead to increased voluntary participation in surveillance of high-priority public health data. Moving forward, we will continue expanding analytic capabilities and partner engagement.
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.
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.
Background: The NHSN is the nation’s largest surveillance system for healthcare-associated infections. Since 2011, acute-care hospitals (ACHs) have been required to report intensive care unit (ICU) central-line–associated bloodstream infections (CLABSIs) to the NHSN pursuant to CMS requirements. In 2015, this requirement included general medical, surgical, and medical-surgical wards. Also in 2015, the NHSN implemented a repeat infection timeframe (RIT) that required repeat CLABSIs, in the same patient and admission, to be excluded if onset was within 14 days. This analysis is the first at the national level to describe repeat CLABSIs. Methods: Index CLABSIs reported in ACH ICUs and select wards during 2015–2108 were included, in addition to repeat CLABSIs occurring at any location during the same period. CLABSIs were stratified into 2 groups: single and repeat CLABSIs. The repeat CLABSI group included the index CLABSI and subsequent CLABSI(s) reported for the same patient. Up to 5 CLABSIs were included for a single patient. Pathogen analyses were limited to the first pathogen reported for each CLABSI, which is considered to be the most important cause of the event. Likelihood ratio χ2 tests were used to determine differences in proportions. Results: Of the 70,214 CLABSIs reported, 5,983 (8.5%) were repeat CLABSIs. Of 3,264 nonindex CLABSIs, 425 (13%) were identified in non-ICU or non-select ward locations. Staphylococcus aureus was the most common pathogen in both the single and repeat CLABSI groups (14.2% and 12%, respectively) (Fig. 1). Compared to all other pathogens, CLABSIs reported with Candida spp were less likely in a repeat CLABSI event than in a single CLABSI event (P < .0001). Insertion-related organisms were more likely to be associated with single CLABSIs than repeat CLABSIs (P < .0001) (Fig. 2). Alternatively, Enterococcus spp or Klebsiella pneumoniae and K. oxytoca were more likely to be associated with repeat CLABSIs than single CLABSIs (P < .0001). Conclusions: This analysis highlights differences in the aggregate pathogen distributions comparing single versus repeat CLABSIs. Assessing the pathogens associated with repeat CLABSIs may offer another way to assess the success of CLABSI prevention efforts (eg, clean insertion practices). Pathogens such as Enterococcus spp and Klebsiella spp demonstrate a greater association with repeat CLABSIs. Thus, instituting prevention efforts focused on these organisms may warrant greater attention and could impact the likelihood of repeat CLABSIs. Additional analysis of patient-specific pathogens identified in the repeat CLABSI group may yield further clarification.
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.
Background: To provide a standardized, risk-adjusted method for summarizing antimicrobial use (AU), the Centers for Disease Control and Prevention developed the standardized antimicrobial administration ratio, an observed-to-predicted use ratio in which predicted use is estimated from a statistical model accounting for patient locations and hospital characteristics. The infection burden, which could drive AU, was not available for assessment. To inform AU risk adjustment, we evaluated the relationship between the burden of drug-resistant gram-positive infections and the use of anti-MRSA agents. Methods: We analyzed data from acute-care hospitals that reported ≥10 months of hospital-wide AU and microbiologic data to the National Healthcare Safety Network (NHSN) from January 2018 through June 2019. Hospital infection burden was estimated using the prevalence of deduplicated positive cultures per 1,000 admissions. Eligible cultures included blood and lower respiratory specimens that yielded oxacillin/cefoxitin–resistant Staphylococcus aureus (SA) and ampicillin-nonsusceptible enterococci, and cerebrospinal fluid that yielded SA. The anti-MRSA use rate is the total antimicrobial days of ceftaroline, dalbavancin, daptomycin, linezolid, oritavancin, quinupristin/dalfopristin, tedizolid, telavancin, and intravenous vancomycin per 1,000 days patients were present. AU rates were modeled using negative binomial regression assessing its association with infection burden and hospital characteristics. Results: Among 182 hospitals, the median (interquartile range, IQR) of anti-MRSA use rate was 86.3 (59.9–105.0), and the median (IQR) prevalence of drug-resistant gram-positive infections was 3.4 (2.1–4.8). Higher prevalence of drug-resistant gram-positive infections was associated with higher use of anti-MRSA agents after adjusting for facility type and percentage of beds in intensive care units (Table 1). Number of hospital beds, average length of stay, and medical school affiliation were nonsignificant. Conclusions: Prevalence of drug-resistant gram-positive infections was independently associated with the use of anti-MRSA agents. Infection burden should be used for risk adjustment in predicting the use of anti-MRSA agents. To make this possible, we recommend that hospitals reporting to NHSN’s AU Option also report microbiologic culture results.
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.
Background:Staphylococcus aureus has long been an important cause of healthcare-associated infections (HAIs) and remains the second most common HAI pathogen in the United States. Often resistant to several antibiotics, S. aureus infections are difficult to treat and can leave patients at risk for serious complications such as pneumonia and sepsis. HAI pathogens and their antimicrobial susceptibility testing (AST) results have been reported to NHSN since its inception in 2005. Previous NHSN surveillance reports have presented national annual benchmarks for antimicrobial resistance phenotypes, such as methicillin-resistant S. aureus (MRSA). Whether there have been any significant changes over time in the prevalence of methicillin resistance among S. aureus infections reported to NHSN has not been previously assessed. Methods:S. aureus AST data from central-line–associated bloodstream infections, catheter-associated urinary tract infections, and inpatient surgical site infections reported from acute-care hospitals between 2009 and 2018 were analyzed. S. aureus was defined as MRSA if it was reported as resistant to oxacillin, cefoxitin, or methicillin. A national percentage resistant (%R) was calculated for each year as the number of resistant pathogens divided by the number of pathogens tested for susceptibility multiplied by 100. A generalized linear mixed model with logistic function was created to evaluate annual changes in the percentage resistant. Several patient-level and hospital-level characteristics were assessed as potential covariates. To account for differential baseline %R values between individual hospitals, specification of random intercept and slope were used during model creation. Differences in the trend of %R between HAI types were assessed using interaction terms. Data were analyzed using SAS v 9.3 software, and P < .05 was considered significant. Results: Overall, 3,317 hospitals reported at least 1 S. aureus pathogen tested for susceptibility between 2009 and 2018. The national unadjusted %R decreased from 49.2% (2009) to 41.2% (2018), with similar decreases seen in each HAI type (Table 1). After adjusting for significant covariates, a statistically significant annual 3% decrease in the prevalence of resistance was observed (Fig. 1). Significant differences between HAI types did not exist. Conclusions: The percentage of healthcare-associated S. aureus resistant to oxacillin, cefoxitin, or methicillin has declined consistently over the past 10 years. Continued efforts in infection prevention and antimicrobial stewardship are vital to sustaining this decline.