scholarly journals 837. Prior Hospitalizations Among Cases of Community-Associated Clostridioides difficile Infection—10 US States, 2014–2015

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S9-S10 ◽  
Author(s):  
Kelly M Hatfield ◽  
James Baggs ◽  
Lisa Gail Winston ◽  
Erin Parker ◽  
Helen Johnston ◽  
...  
Keyword(s):  
Medicare Beneficiary ◽  
Significant Risk ◽  
Significant Risk Factor ◽  
Healthcare Facility ◽  
Median Number ◽  
Fee For Service ◽  
Emerging Infections ◽  
Odds Ratios ◽  
Clostridioides Difficile ◽  
Clostridioides Difficile Infection

Abstract 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.

scholarly journals 752. Costs Attributable to Clostridioides difficile Infection in the Presence of Differential Mortality

2021 ◽  
Vol 8 (Supplement_1) ◽  
pp. S473-S474
Author(s):  
John Sahrmann ◽  
Dustin Stwalley ◽  
Margaret A Olsen ◽  
Holly Yu ◽  
Erik R Dubberke
Keyword(s):  
Healthcare Facility ◽  
Differential Mortality ◽  
Fee For Service ◽  
Research Support ◽  
Cost Estimates ◽  
Clostridioides Difficile ◽  
Clostridioides Difficile Infection ◽  
Parametric Survival Model ◽  
Healthcare Associated ◽  
Community Onset

Abstract Background CDI imposes a major burden on the U.S. healthcare system. Obtaining accurate estimates of economic costs is critical to determining the cost-effectiveness of preventive measures. This task is complicated by differences in epidemiology, mortality, and baseline health status of infected and uninfected individuals, and by the statistical properties of costs data (e.g., right-skewed, excess of zeros costs). Methods Incident CDI cases were identified from Medicare 5% fee-for-service data between 2011 and 2017 and classified into standard surveillance definitions: hospital-onset (HO); other healthcare facility-onset (OHFO); community-onset, healthcare-associated (CO-HCFA); or community-associated (CA). Cases were frequency matched 1:4 to uninfected controls based on age, sex, and year of CDI. Controls were assigned to surveillance definitions based on location at index dates. Medicare allowed costs were summed in 30-day intervals up to 3 years following index. One- and 3-year cumulative costs attributable to CDI were computed using a 3-part estimator consisting of a parametric survival model and a pair of 2-part models predicting costs separately in intervals where death did and did not occur, adjusting for underlying acute and chronic conditions. Results 60,492 CDI cases (Figure 1) were matched to 241,968 controls. Three-year mortality was higher among CDI cases compared to matched controls for HO (45% vs 26%) and OHFO (42% vs 36%), whereas mortality was slightly lower for CDI cases compared to controls for those with community onset (CO-HCFA: 28% vs 32%; CA: 10% vs 11%). One- and 3-year attributable costs due to CDI are shown in Figure 2. Adjusted 1-year attributable costs amounted to &26,954 (95% CI: &26,154–&27,939) for HO; &10,539 (&9,564–&11,518) for OHFO; &6,525 (&5,012–&8,171) for CO-HCFA; and &3,171 (&1,841–&4,200) for CA. Adjusted 3-year attributable costs were &44,736 (&43,063–&46,483) for HO; &13,994 (&12,529–&15,975) for OHFO; &7,349 (&4,738–&10,246) for CO-HCFA; and &2,377 (&166–&4,722) for CA. Figure 1. Proportion of Cases by CDI Surveillance Definitions Abbreviations: HO: hospital-onset; OHFO: other healthcare facility-onset; CO-HCFA: community-onset, healthcare-associated; CA: community-associated. Figure 2. Estimates of Costs Attributable to CDI by CDI Surveillance Definitions at One and Three Years after Onset Top panels: One-year cost estimates. Bottom panels: Three-year cost estimates. Abbreviations: HO: hospital-onset; OHFO: other healthcare facility-onset; CO-HCFA:community-onset, healthcare-associated; CA:community-associated. Conclusion CDI was associated with increased healthcare costs across surveillance definitions in Medicare fee-for-service patients after adjusting for survival and underlying conditions. Disclosures Dustin Stwalley, MA, AbbVie Inc (Shareholder)Bristol-Myers Squibb (Shareholder) Margaret A. Olsen, PhD, MPH, Pfizer (Consultant, Research Grant or Support) Holly Yu, MSPH, Pfizer (Employee) Erik R. Dubberke, MD, MSPH, Ferring (Grant/Research Support)Merck (Consultant)Pfizer (Consultant, Grant/Research Support)Seres (Consultant)Summit (Consultant)

scholarly journals 780. How Much Does Prior Hospitalization Contribute to Readmission with Community-onset Clostridioides difficile Infection?

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S434-S435
Author(s):  
Alice Guh ◽  
Lauren C Korhonen ◽  
Lisa Gail Winston ◽  
Brittany Martin ◽  
Helen Johnston ◽  
...  
Keyword(s):  
Care Facility ◽  
Healthcare Facility ◽  
Care Hospital ◽  
Emerging Infections ◽  
Post Discharge ◽  
Term Care ◽  
Clostridioides Difficile ◽  
Clostridioides Difficile Infection ◽  
Community Onset

Abstract 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)

scholarly journals Changes in Regional Hospital-Identified Clostridioides difficile Infection, 2015–2018

2020 ◽  
Vol 41 (S1) ◽  
pp. s158-s159
Author(s):  
Raymund Dantes ◽  
Jonathan Edwards ◽  
Qunna Li
Keyword(s):  
Emergency Department ◽  
Hospital Admissions ◽  
Healthcare Facility ◽  
Median Number ◽  
Clostridioides Difficile ◽  
Hospital Referral ◽  
Public Health Authorities ◽  
Clostridioides Difficile Infection ◽  
Region Data ◽  
Facility Level

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: NoneDisclosures: None

scholarly journals Analysis of National Healthcare Safety Network Clostridioides difficile Infection Standardized Infection Ratio by Test Type

2020 ◽  
Vol 41 (S1) ◽  
pp. s116-s118
Author(s):  
Qunna Li ◽  
Andrea Benin ◽  
Alice Guh ◽  
Margaret A. Dudeck ◽  
Katherine Allen-Bridson ◽  
...  
Keyword(s):  
Risk Adjustment ◽  
Healthcare Facility ◽  
Directional Pattern ◽  
Incidence Rates ◽  
Nucleic Acid Amplification ◽  
Test Type ◽  
Clostridioides Difficile ◽  
Clostridioides Difficile Infection ◽  
The Mean ◽  
The Difference

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: NoneFunding: None

Comorbidity and severity-of-illness risk adjustment for hospital-onset Clostridioides difficile infection using data from the electronic medical record

2020 ◽  
pp. 1-7
Author(s):  
Stephanie M. Cabral ◽  
Katherine E. Goodman ◽  
Natalia Blanco ◽  
Surbhi Leekha ◽  
Larry S. Magder ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Factor ◽  
Risk Adjustment ◽  
Community Hospital ◽  
Significant Risk ◽  
Significant Risk Factor ◽  
Bivariate Analysis ◽  
Patient Admissions ◽  
Clostridioides Difficile ◽  
Risk Adjustment Model

Abstract Objective: To determine whether electronically available comorbidities and laboratory values on admission are risk factors for hospital-onset Clostridioides difficile infection (HO-CDI) across multiple institutions and whether they could be used to improve risk adjustment. Patients: All patients at least 18 years of age admitted to 3 hospitals in Maryland between January 1, 2016, and January 1, 2018. Methods: Comorbid conditions were assigned using the Elixhauser comorbidity index. Multivariable log-binomial regression was conducted for each hospital using significant covariates (P < .10) in a bivariate analysis. Standardized infection ratios (SIRs) were computed using current Centers for Disease Control and Prevention (CDC) risk adjustment methodology and with the addition of Elixhauser score and individual comorbidities. Results: At hospital 1, 314 of 48,057 patient admissions (0.65%) had a HO-CDI; 41 of 8,791 patient admissions (0.47%) at community hospital 2 had a HO-CDI; and 75 of 29,211 patient admissions (0.26%) at community hospital 3 had a HO-CDI. In multivariable regression, Elixhauser score was a significant risk factor for HO-CDI at all hospitals when controlling for age, antibiotic use, and antacid use. Abnormal leukocyte level at hospital admission was a significant risk factor at hospital 1 and hospital 2. When Elixhauser score was included in the risk adjustment model, it was statistically significant (P < .01). Compared with the current CDC SIR methodology, the SIR of hospital 1 decreased by 2%, whereas the SIRs of hospitals 2 and 3 increased by 2% and 6%, respectively, but the rankings did not change. Conclusions: Electronically available patient comorbidities are important risk factors for HO-CDI and may improve risk-adjustment methodology.

scholarly journals The association between obesity and depression in adults: a meta-review

BJPsych Open ◽  
2021 ◽  
Vol 7 (S1) ◽  
pp. S267-S267
Author(s):  
Abdi Malik Musa ◽  
Samuele Cortese ◽  
Olivia Bloodworth
Keyword(s):  
Depressive Disorders ◽  
Data Extraction ◽  
Significant Risk ◽  
Significant Risk Factor ◽  
Structured Interview ◽  
Future Research ◽  
Odds Ratios ◽  
Medical File ◽  
Depressed Adolescent ◽  
Meta Review

AimsObesity and depression are increasing in prevalence and have become key issues in the public health of the modern day. We performed a meta-review to summarise the association between obesity and depression in adults.MethodA systematic literature search was undertaken on MEDLINE, PsychINFO, EMBASE and Web of Science for systematic reviews (SRs) with or without meta-analyses (MA) on the association between obesity and depression in adults (>18 years) published before 18 September 2018. Any approach to define depressive disorders (e.g. via structured interview or code in medical file) was accepted. Likewise, any method to assess obesity was accepted. Screening, data extraction and quality assessment was completed by two reviewers independently, with a third reviewer to arbitrate any disagreement. AMSTAR 2 tool was used to assess the methodological quality and risk of bias of the pertinent SRs/MAs.ResultAfter duplicate removal, we identified 6007 potentially pertinent citations. Following, title, abstract and full-text screening, 10 studies were included in the review; nine SRs with MAs and one SR. A statistically significant association between obesity and depression was reported in all nine SRs with MAs, with odds ratios ranging from 1.18 (95% CI = 1.11-1.26) to 1.57 (95% CI = 1.53-2.01). Increased severity of obesity (body mass index over 40) was associated with a greater odds of becoming depressed. Odds of developing depression were greater for obese females, compared to obese males, but this difference was not statistically significant. Depression was shown to be a significant risk factor for future obesity in all four relevant MAs with odds ratios ranging from 1.18 (95% CI = 1.13-1.23) to 1.40 (95% CI = 1.14-1.71) . Depressed adolescent females had the highest odds of becoming obese, significantly more so than depressed adolescent males and depressed adults. The quality of the included studies were mixed with five scoring moderate quality, three low quality and two critically low quality.ConclusionThe findings suggest a reciprocal association between depression and obesity, which may be modulated by age and gender. Future research should assess the potential effect of obesity and depression severity more carefully while also exploring the underlying mechanisms. These results warrant the investigation of the effect of obesity or depression intervention on the outcomes of the other.FUNDINGThis research received no financial sponsorship.

Using diagnostic stewardship to reduce rates, healthcare expenditures and accurately identify cases of hospital-onset Clostridioides difficile infection

2020 ◽  
Vol 42 (1) ◽  
pp. 51-56
Author(s):  
Dipesh Solanky ◽  
Derek K. Juang ◽  
Scott T. Johns ◽  
Ian C. Drobish ◽  
Sanjay R. Mehta ◽  
...  
Keyword(s):  
Healthcare Facility ◽  
Nucleic Acid Amplification ◽  
Fiscal Year ◽  
Hospital Acquired Infection ◽  
Laxative Use ◽  
Clostridioides Difficile ◽  
Hospitalization Costs ◽  
Clostridioides Difficile Infection ◽  
Hospital Acquired ◽  
Hospital Days

AbstractObjective:Lack of judicious testing can result in the incorrect diagnosis of Clostridioides difficile infection (CDI), unnecessary CDI treatment, increased costs and falsely augmented hospital-acquired infection (HAI) rates. We evaluated facility-wide interventions used at the VA San Diego Healthcare System (VASDHS) to reduce healthcare-onset, healthcare-facility–associated CDI (HO-HCFA CDI), including the use of diagnostic stewardship with test ordering criteria.Design:We conducted a retrospective study to assess the effectiveness of measures implemented to reduce the rate of HO-HCFA CDI at the VASDHS from fiscal year (FY)2015 to FY2018.Interventions:Measures executed in a stepwise fashion included a hand hygiene initiative, prompt isolation of CDI patients, enhanced terminal room cleaning, reduction of fluoroquinolone and proton-pump inhibitor use, laboratory rejection of solid stool samples, and lastly diagnostic stewardship with C. difficile toxin B gene nucleic acid amplification testing (NAAT) criteria instituted in FY2018.Results:From FY2015 to FY2018, 127 cases of HO-HCFA CDI were identified. All rate-reducing initiatives resulted in decreased HO-HCFA cases (from 44 to 13; P ≤ .05). However, the number of HO-HCFA cases (34 to 13; P ≤ .05), potential false-positive testing associated with colonization and laxative use (from 11 to 4), hospital days (from 596 to 332), CDI-related hospitalization costs (from $2,780,681 to $1,534,190) and treatment cost (from $7,158 vs $1,476) decreased substantially following the introduction of diagnostic stewardship with test criteria from FY2017 to FY2018.Conclusions:Initiatives to decrease risk for CDI and diagnostic stewardship of C. difficile stool NAAT significantly reduced HO-HCFA CDI rates, detection of potential false-positives associated with laxative use, and lowered healthcare costs. Diagnostic stewardship itself had the most dramatic impact on outcomes observed and served as an effective tool in reducing HO-HCFA CDI rates.

scholarly journals Correlation of prevention practices with rates of health care-associated Clostridioides difficile infection

2019 ◽  
Vol 41 (1) ◽  
pp. 52-58
Author(s):  
Jackson S. Musuuza ◽  
Linda McKinley ◽  
Julie A. Keating ◽  
Chidi Obasi ◽  
Mary Jo Knobloch ◽  
...  
Keyword(s):  
Healthcare Facility ◽  
Cross Sectional Study ◽  
Cross Sectional ◽  
Electronic Survey ◽  
Contact Precautions ◽  
Clostridioides Difficile ◽  
Increased Risk ◽  
Clostridioides Difficile Infection ◽  
Bed Days ◽  
Acute Care Facilities

AbstractObjective:We examined Clostridioides difficile infection (CDI) prevention practices and their relationship with hospital-onset healthcare facility-associated CDI rates (CDI rates) in Veterans Affairs (VA) acute-care facilities.Design:Cross-sectional study.Methods:From January 2017 to February 2017, we conducted an electronic survey of CDI prevention practices and hospital characteristics in the VA. We linked survey data with CDI rate data for the period January 2015 to December 2016. We stratified facilities according to whether their overall CDI rate per 10,000 bed days of care was above or below the national VA mean CDI rate. We examined whether specific CDI prevention practices were associated with an increased risk of a CDI rate above the national VA mean CDI rate.Results:All 126 facilities responded (100% response rate). Since implementing CDI prevention practices in July 2012, 60 of 123 facilities (49%) reported a decrease in CDI rates; 22 of 123 facilities (18%) reported an increase, and 41 of 123 (33%) reported no change. Facilities reporting an increase in the CDI rate (vs those reporting a decrease) after implementing prevention practices were 2.54 times more likely to have CDI rates that were above the national mean CDI rate. Whether a facility’s CDI rates were above or below the national mean CDI rate was not associated with self-reported cleaning practices, duration of contact precautions, availability of private rooms, or certification of infection preventionists in infection prevention.Conclusions:We found considerable variation in CDI rates. We were unable to identify which particular CDI prevention practices (i.e., bundle components) were associated with lower CDI rates.

scholarly journals Association between cumulative serum urate and development of diabetes type II: the Kailuan Study

2019 ◽  
Vol 39 (2) ◽  
pp. 339-346
Author(s):  
Yixuan Han ◽  
Yanying Liu ◽  
Xuejun Liu ◽  
Wenhao Yang ◽  
Ping Yu ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Risk Factor ◽  
Significant Risk ◽  
Significant Risk Factor ◽  
Serum Urate ◽  
Diabetes Type ◽  
Cumulative Exposure ◽  
Type Ii ◽  
Odds Ratios ◽  
Diabetes Type Ii

Abstract Objective To explore whether cumulative serum urate (cumSU) is correlated with diabetes type II mellitus incidence. Methods In this study, we recruited individuals participating in all Kailuan health examinations from 2006 to 2013 without stroke, cancer, gestation, myocardial infarction, and diabetes type II diagnosis in the first three examinations. CumSU was calculated by multiplying the average serum urate concentration and the time between the two examinations (umol/L × year). CumSU levels were categorized into five groups: Q1–Q5. The effect of cumSU on diabetes type II incidence was estimated by logistic regression. Results A total of 36,277 individuals (27,077 men and 9200 women) participated in the final analysis. The multivariate logistic regression model showed the odds ratios (95% confidence intervals) of diabetes type II from Q1 to Q5 were 1.00 (reference), 1.25 (1.00 to 1.56), 1.43 (1.15 to 1.79), 1.49 (1.18 to 1.87), and 1.80 (1.40 to 2.32), respectively. Multivariable odds ratios per 1-standard deviation increase in cumSU were 1.26 (1.17 to 1.37) in all populations, 1.20 (1.10 to 1.32) for men, and 1.52 (1.27 to 1.81) for women, respectively. Conclusions CumSU is a significant risk factor for diabetes type II. Individuals with higher cumSU, especially women, are at a higher risk of diabetes type II independent of other known risk factors.Key Points• Cumulative exposure to serum urate is a significant risk factor for diabetes type II.• Individuals with higher cumSU, especially women, are at a higher risk of diabetes type II.

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