Hospital-acquired Infection Data Research Articles

Impact of dry hydrogen peroxide on hospital-acquired infection at a pediatric oncology hospital

BackgroundThis study aims to describe the effect of Dry Hydrogen Peroxide (DHP), as an adjunct to environmental cleaning and disinfection, on the incidence of hospital-acquired infections (HAIs) at Unidad Nacional de Oncologia Pediatrica (UNOP) in Guatemala City, Guatemala. MethodsA retrospective study of all HAI data from the hospital's surveillance system, which follows Centers for Disease Control and Prevention (CDC) protocols, was conducted from January 2019 to November 2020. DHP was installed in all Pediatric Intensive Care Unit (PICU) rooms in January 2020, but nowhere else in the hospital, including the Intermediate Care Unit (IMCU). ResultsThere were 189 HAI cases during the study period, with 173 occurring in either the PICU or IMCU. A statistically significant decrease in HAI incidence rates occurred in the PICU in 2020 compared to 2019 (P = .028), including Clostridiodes-associated gastroenteritis (P = .048). Logistic multivariate regression yielded a significant association between DHP exposure and reduced odds of developing an HAI during the study (OR = 0.3857, P = .029). ConclusionThe use of DHP as an adjunct technology for environmental cleaning and disinfection contributed to the reduction in HAIs in the PICU. Our study highlights the value of such an approach as an addition to manual cleaning to decrease the risk of infection from environmental contamination.

Financial and Mortality Modeling as a Tool to Present Infection Prevention Data: What a SIR of 1.2 Means for the Hospital

Background: Presenting to hospital leadership is an annual requirement of many infection prevention (IP) programs. Most presentations include current statistical data of hospital-acquired infections (HAIs) and whether the hospital has met its goals according to the National Healthcare Safety Network (NHSN) criteria. We presented HAI data in a novel way, with financial and mortality modeling, to show the impact of IP interventions to leadership not attuned to NHSN metrics. Method: We looked at 4 HAIs, their trends, and their effect on our hospital, Santa Clara Valley Medical Center (SCVMC). To estimate the impact of specific HAIs, we used 2 metrics derived from a meta-analysis by the US Department of Health and Human Services (HHS): excess mortality and excess cost. Excess mortality is defined as the difference between the underlying population mortality and the affected population mortality expressed as deaths per 1,000 population. Excess cost is defined as the additional cost introduced per patient with a specific HAI versus a similarly admitted patient without that HAI. HHS data were multiplied by the number of HAI events at SCVMC to generate estimates. Result: In our presentation, we elucidated a previously unseen cost savings and decreased mortality with 2 HAIs, central-line–associated blood stream infections (CLABSIs) and catheter associated urinary tract infections (CAUTIs), which were below NHSN targets due to IP-led interventions. We then showed 2 other HAIs, Clostridium difficile infection (CDI) and surgical site infections (SSIs), which did not meet our expected NHSN and institutional goals and were estimated to increase costs and potential mortalities in the upcoming year. We argued that proactive monies directed toward expanding our IP program and HAI mitigation efforts would cost a fraction of the impending healthcare expenditures as predicted by the model. Conclusion: By applying financial and mortality modeling, we helped our leadership perceive the concrete effect of IP-led interventions versus presenting abstract NHSN metrics. We also emphasized that without proactive leadership investment, we would continue to overspend healthcare dollars while not meeting our goals. This format of presentation gave us critical leverage to advocate for and successfully expand our IP department. Further SHEA-led cost-analysis modeling and education are needed to help IP departments promote their efforts in an effective manner.Funding: NoneDisclosures: None

Estimating hospital quality based on performance measure and hospital acquired infection data: A Bayesian bivariate approach

Hospital quality data consists of dichotomous process measurements and count outcome measures. However, current statistical approaches to evaluating hospital quality often utilize one of these measurements. Moreover, when data are collected from multiple units within a hospital, the within-hospital correlation is typically ignored. To address this, we introduce a novel methodology for determining hospital quality that incorporates both outcomes within a Bayesian framework. We illustrate our approach using performance measures obtained from the Joint Commission and hospital acquired infection measures directly from hospitals. Using our approach, we differentiate high and low performing hospitals with a high degree of certainty.

Hospital-acquired infections documented by repeated annual prevalence surveys over 15 years

ObjectiveTo estimate the benefits of iterative prevalence surveys in detecting trends of hospital-acquired infections (HAIs). MethodsOn the basis of the French protocol for national prevalence studies, HAI data of 15 consecutive annual surveys performed at the same period by the same group of investigators was gathered in a single database to describe the trend of HAIs in a University Hospital over a 15-year period. ResultsA total of 20,401 patients were included. Overall, the prevalence of patients presenting with at least one HAI acquired in our University Hospital was 5.1% [95% CI, 4.8–5.4%]. The prevalence of HAIs and antimicrobial drug use significantly decreased over time (P<0.01). ConclusionDespite limitations, repeated prevalence surveys can be a useful tool for promoting control measures to better prevent HAIs.

Attributable cost of a nosocomial infection in the intensive care unit: A prospective cohort study.

AIMTo study the impact of hospital-acquired infections (HAIs) on cost and outcome from intensive care units (ICU) in India.METHODSAdult patients (> 18 years) admitted over 1-year, to a 24-bed medical critical care unit in India, were enrolled prospectively. Treatment cost and outcome data were collected. This cost data was merged with HAI data collected prospectively by the Hospital Infection Control Committee. Only infections occurring during ICU stay were included. The impact of HAI on treatment cost and mortality was assessed.RESULTSThe mean (± SD) age of the cohort (n = 499) was 42.3 ± 16.5 years. Acute physiology and chronic health evaluation-II score was 13.9 (95%CI: 13.3-14.5); 86% were ventilated. ICU and hospital length of stay were 7.8 ± 5.5 and 13.9 ± 10 d respectively. Hospital mortality was 27.9%. During ICU stay, 76 (15.3%) patients developed an infection (ventilator-associated pneumonia 50; bloodstream infection 35; urinary tract infections 3), translating to 19.7 infections/1000 ICU days. When compared with those who did not develop an infection, an infection occurring during ICU stay was associated with significantly higher treatment cost [median (inter-quartile range, IQR) INR 92893 (USD 1523) (IQR 57168-140286) vs INR 180469 (USD 2958) (IQR 140030-237525); P < 0.001 and longer duration of ICU (6.7 ± 4.5 d vs 13.4 ± 7.0 d; P < 0.01) and hospital stay (12.4 ± 8.2 d vs 21.8 ± 13.9 d; P < 0.001)]. However ICU acquired infections did not impact hospital mortality (31.6% vs 27.2%; P = 0.49).CONCLUSIONAn infection acquired during ICU stay was associated with doubling of treatment cost and prolonged hospitalization but did not significantly increase mortality.

Lack of Patient Understanding of Hospital-Acquired Infection Data Published on the Centers for Medicare and Medicaid Services Hospital Compare Website.

Public reporting of hospital quality data is a key element of US healthcare reform. Data for hospital-acquired infections (HAIs) are especially complex. To assess interpretability of HAI data as presented on the Centers for Medicare and Medicaid Services Hospital Compare website among patients who might benefit from access to these data. We randomly selected inpatients at a large tertiary referral hospital from June to September 2014. Participants performed 4 distinct tasks comparing hypothetical HAI data for 2 hospitals, and the accuracy of their comparisons was assessed. Data were presented using the same tabular formats used by Centers for Medicare and Medicaid Services. Demographic characteristics and healthcare experience data were also collected. Participants (N=110) correctly identified the better of 2 hospitals when given written descriptions of the HAI measure in 72% of the responses (95% CI, 66%-79%). Adding the underlying numerical data (number of infections, patient-time, and standardized infection ratio) to the written descriptions reduced correct responses to 60% (55%-66%). When the written HAI measure description was not informative (identical for both hospitals), 50% answered correctly (42%-58%). When no written HAI measure description was provided and hospitals differed by denominator for infection rate, 38% answered correctly (31%-45%). Current public HAI data presentation methods may be inadequate. When presented with numeric HAI data, study participants incorrectly compared hospitals on the basis of HAI data in more than 40% of the responses. Research is needed to identify better ways to convey these data to the public.

Incentivizing "structures" over "outcomes" to bridge the knowing-doing gap.

The quote that opens the Institute ofMedicine’s 2007 report, TheLearningHealthcare System,1 succinctly captures the crux of the knowing-doing gap: the gap that exists between what weknowworksbasedon thebest available evidence andwhat weclinicallypractice. It is this gap that the evolving field of implementation science or knowledge translation seeks tomend. In this issueofJAMA InternalMedicine,Watersand colleagues2 evaluate the effectiveness of financial incentives directed toward clinical outcomes for closing theknowing-doinggap. Specifically, they examine the effect of the October 2008 Centers for Medicare and Medicaid Services (CMS) Nonpayment for HospitalAcquired Conditions Initiative on the prevention of 4 clinical “never events”: catheter-associated urinary tract infections (CAUTIs), central line–associated bloodstream infections (CLABSIs), falls, and pressure ulcers. Using multiple time points before and after implementationof theCMS initiative,Waters et al2 found that the slopedetermined by themonthly rate of CAUTIs andCLABSIs for the 9 monthsbefore theCMS initiativedroppedsignificantlyafter its implementation,whereas theslopedeterminedbythemonthly rate of falls and pressure ulcers was unaltered. These findings are different from those described in a seminal study published in 2012 by Lee and colleagues,3 which suggested no effect of the CMS financial penalties on hospital-acquired infection (HAI) rates. Two differences in the studies could have accounted for the inconsistent findings. First, the studybyLee et al3 examined fewer and different hospitals than the present study byWaters et al.2 Lee and colleagues evaluated 398 hospitals reportingHAI rates to theNationalHealthcareSafetyNetwork,whereasWatersandcolleaguesexaminedmore than600 hospitals reporting HAIs to the National Database of Nursing Quality Indicators. Second, because of differences in the reporting networks used in the studies (ie, National Healthcare Safety Network vs National Database of Nursing Quality Indicators), the study by Lee et al had 33months of HAI data availablebefore implementationof theCMS initiative,but thestudy byWaters et al had only 9months of preimplementation data. This limited amount of data may have been inadequate to define the true trend in HAI rates before the CMS initiative. The absence of an effect of the CMS initiative on the other 2 “never events” evaluated in theWaters et al study (falls and pressure ulcers) suggests thismayhave been the case. For both of these outcomes, the Waters et al study had 27 months of preimplementationdataavailable forexamination,similar to theamount available for the HAI outcomes in the Lee et al study. Despite the differences in preimplementation data available for the HAIs vs falls and pressure ulcers, Waters and colleagues2 suggest another reason for the differential effect of the CMS initiative on HAIs. They argue that because there was more robust evidence on how to prevent CLABSIs and CAUTIs at the time of the CMS initiative, hospitals weremore successful at reducing these never events when provided the Author Audio Interview at jamainternalmedicine.com

The Role of Plasma Granulocyte Colony Stimulating Factor and Bone Marrow Dysfunction after Severe Trauma

Bone marrow dysfunction is common in severely injured trauma patients, with release of hematopoietic progenitor cells (HPC) into the peripheral blood. Granulocyte colony stimulating factor (G-CSF) is a potent stimulator of HPC mobilization. We hypothesized that plasma G-CSF levels are elevated after trauma and correlate with postinjury anemia and infection. Blood from 83 severely injured patients was collected at several time points for determination of G-CSF levels and HPC mobilization and compared with that from healthy volunteers. Data were categorized by age, sex, Injury Severity Score (ISS), and whether the patient was in shock. Hemoglobin and transfusion requirements and hospital-acquired infection data were recorded. Data are expressed as mean ± SEM. After trauma, there is a 50-fold increase in plasma levels of G-CSF in trauma patients compared with controls (1,640.4 ± 304.3 pg/mL vs 33.0 ± 6.8 pg/mL, p < 0.001). Patients who presented in shock had 5-times higher G-CSF levels than nonshock trauma patients and a 75-fold increase compared with controls (2,528.7 ± 536.4 pg/mL vs 728.0 ± 191.0 pg/mL vs 33.0 ± 6.8 pg/mL, p < 0.001). Age, sex, and ISS had no effect on G-CSF levels. Mobilization of HPC was sustained for up to 10 days after injury and involved multiple cells types. Higher G-CSF levels were also associated with lower hemoglobin levels and greater transfusion requirements 3 weeks after injury and a higher incidence of hospital-acquired pneumonia and bacteremia. Plasma G-CSF is markedly elevated after injury and is greater in patients who present in shock. The rise in G-CSF was also associated with prolonged mobilization of HPC. Elevation of G-CSF in humans after severe trauma may play a significant role in the development of post-traumatic bone marrow dysfunction, anemia, and infection.

Hospital Acquired Infection in Obafemi Awolowo University Teaching Hospital, Ile-Ife, Southwest, Nigeria: A Ten Year Review (2000-2009)

Surveillance and proper hygiene have been identified as key components in the fight against HAIs and antimicrobial resistance in hospital setting. This study assesses the pattern of hospital acquired infections (HAIs) and state of hygiene in a tertiary hospital in southwest, Nigeria. Data collected routinely between January 2000 and December 2009 by the infection control committee on HAI and primary data generated on hygiene in the wards were analysed using appropriate statistical techniques. A total of 37,957 patients were admitted during the period under review and 1129 cases (3.0%) of HAI were reported. The highest prevalence of 9.0% was reported in 2006. The Intensive Care Unit (ICU) had the highest period prevalence of 14.7% followed by Orthopaedics ward (7.7%). Surgical ward contributed the highest number of cases with 433. Gram negative organisms were the most implicated (78%) of which Klebsiella species was 38% while Staphylococcus aureus was the only Gram positive organism identified (28%). Hand washing was practised universally by health workers but facilities for proper hand washing were inadequate. The pattern of HAI has not changed significantly in the past 10 years and Klebsiella was the most implicated organism in HAIs and ICU. Facilities for proper hand washing are suboptimal. We recommend the introduction of hand washing policy for the hospital and the provision of an environment conducive for its implementation by the hospital management as well as adequate support for the infection control committee in the discharge of her duties. Keywords: Hand washing, Hospital-acquired, Hygiene, Infection control

Increased Mortality and Length of Stay Among Patients With Inflammatory Bowel Disease and Hospital-Acquired Infections

Hospitalized patients with inflammatory bowel disease (IBD) could be at increased risk for hospital-acquired infections (HAIs). By using HAI outcome data from Pennsylvania, we examined the influence of HAIs on in-patient mortality and length of stay (LOS) in the hospital among patients with IBD. Data were generated by linking the Clinical Research Databases from CareFusion (formerly MediQual), which includes all acute care hospitals in Pennsylvania, with publicly reported HAI data from Pennsylvania. The study population included all patients discharged in 2004 with International Classification of Diseases, 9th Clinical Modification codes of 555.x or 556.x (2324 IBD cases from 161 hospitals). Controls were selected using risk-score matching with a 5:1 ratio. Mortality and LOS end points were estimated and corroborated with regression methods. Among the IBD patients studied, there were 20 deaths and 22 reported cases of HAI. The mortality from HAI among patients with IBD was 13.6%, compared with 0.9% among controls (P = .0146, Fisher exact test). The odds ratio for mortality was 17.2 (95% confidence interval, 1.7-174.3). The median LOS for patients with IBD and HAI was 22 days, versus 6 days for controls (P < .001, Wilcoxon). Of the 22 cases with HAIs, 15 were urinary tract infections, 5 were blood stream infections, and 2 were from multiple sources. Results from a population-based data set indicate that mortality and LOS are increased among IBD patients who develop HAIs. A majority of the HAIs were from urinary sources. Although HAIs are low-frequency events, increased vigilance to avoid HAI among patients with IBD could improve outcomes.

The Impact of Hospital-Acquired Infection on Outcome in Acute Pancreatitis

Little is known regarding the impact of hospital-acquired infection (HAI) in acute pancreatitis (AP). We conducted a population-based assessment of the impact of HAI on outcome in AP. Patient data were obtained from the Cardinal Health Clinical Outcomes Research Database, a large population-based data set. Cases with principal diagnosis by International Classification of Diseases, ninth revision, clinical modification 577.0 (AP) between January 2004 and January 2005 were identified. These cases were linked with recently reported HAI data collected by the Pennsylvania Health Care Cost Containment Council. Identification of HAI was based on definitions set forth by the National Nosocomial Infection Surveillance System. We conducted a 5:1 multivariate propensity-matched cohort study to determine the independent contribution of HAI to in-hospital mortality, length of stay (LOS), and hospital charges. From 177 participating hospitals, there were 11,046 AP cases identified. Eighty-two (0.7%) patients developed an HAI. Mortality in the overall AP population was 1.2% vs 11.4% among 405 matched non-HAI controls vs 28.4% among patients who developed HAI (chi(2) test, P < .0001). Fifteen percent of all deaths was associated with an HAI. Both average LOS and hospital charges were significantly increased among patients with HAI compared with matched non-HAI controls. We determined that HAI had a major impact on mortality in AP. Patients who developed HAI also had significantly increased LOS and hospital charges. These differences were not explained by increased disease severity alone. Reducing HAI is an important step to improving outcome in AP.

House investigates hospital-acquired infections

About 99,000 patients die each year of hospital-acquired infections (HAIs), according to the Centers for Disease Control and Prevention (CDC). If that many people were dying of avian influenza, said Marc P. Volavka, executive director of the Pennsylvania Health Care Cost Containment Council (PHC4), “we’d be calling that an epidemic.” Volavka’s remarks were made at a recent House hearing examining the public reporting of HAI data. Some witnesses at the hearing argued in favor of mandating public reporting of infection rates, while others advocated for a voluntary approach. Requiring the collection and public reporting of HAI data, Volavka asserted, are “two steps in measuring the extent of the problem and identifying solutions.” “We cannot improve what we do not measure,” he declared. Beginning in 2004, Volavka noted, Pennsylvania hospitals were required to submit data to PHC4 on four types of HAIs: surgical site, urinary tract, bloodstream, and pneumonia. As of January, he added, hospitals in the state are now required to report on all HAIs.