scholarly journals National Healthcare Safety Network Standardized Antimicrobial Administration Ratios (SAARs): A Progress Report and Risk Modeling Update Using 2017 Data

2020 ◽  
Vol 71 (10) ◽  
pp. e702-e709 ◽  
Author(s):  
Erin N O’Leary ◽  
Jonathan R Edwards ◽  
Arjun Srinivasan ◽  
Melinda M Neuhauser ◽  
Amy K Webb ◽  
...  
Keyword(s):  
Patient Care ◽  
Negative Binomial ◽  
Eligible Patient ◽  
Negative Binomial Regression ◽  
Risk Modeling ◽  
National Healthcare ◽  
Clostridioides Difficile ◽  
National Healthcare Safety Network ◽  
Using Data ◽  
Antimicrobial Administration

Abstract Background The Standardized Antimicrobial Administration Ratio (SAAR) is a risk-adjusted metric of antimicrobial use (AU) developed by the Centers for Disease Control and Prevention (CDC) in 2015 as a tool for hospital antimicrobial stewardship programs (ASPs) to track and compare AU with a national benchmark. In 2018, CDC updated the SAAR by expanding the locations and antimicrobial categories for which SAARs can be calculated and by modeling adult and pediatric locations separately. Methods We identified eligible patient-care locations and defined SAAR antimicrobial categories. Predictive models were developed for eligible adult and pediatric patient-care locations using negative binomial regression applied to nationally aggregated AU data from locations reporting ≥9 months of 2017 data to the National Healthcare Safety Network (NHSN). Results 2017 Baseline SAAR models were developed for 7 adult and 8 pediatric SAAR antimicrobial categories using data reported from 2156 adult and 170 pediatric locations across 457 hospitals. The inclusion of step-down units and general hematology-oncology units in adult 2017 baseline SAAR models and the addition of SAARs for narrow-spectrum B-lactam agents, antifungals predominantly used for invasive candidiasis, antibacterial agents posing the highest risk for Clostridioides difficile infection, and azithromycin (pediatrics only) expand the role SAARs can play in ASP efforts. Final risk-adjusted models are used to calculate predicted antimicrobial days, the denominator of the SAAR, for 40 SAAR types displayed in NHSN. Conclusions SAARs can be used as a metric to prompt investigation into potential overuse or underuse of antimicrobials and to evaluate the effectiveness of ASP interventions.

scholarly journals Identifying Temporal Aggregation Effect on Crash-Frequency Modeling

2021 ◽  
Vol 13 (11) ◽  
pp. 6214
Author(s):  
Bumjoon Bae ◽  
Changju Lee ◽  
Tae-Young Pak ◽  
Sunghoon Lee
Keyword(s):  
Traffic Safety ◽  
Negative Binomial ◽  
Random Effect ◽  
Temporal Correlation ◽  
Negative Binomial Regression ◽  
Spatiotemporal Analysis ◽  
Parameter Estimates ◽  
Crash Frequency ◽  
Crash Frequency Modeling ◽  
Using Data

Aggregation of spatiotemporal data can encounter potential information loss or distort attributes via individual observation, which would influence modeling results and lead to an erroneous inference, named the ecological fallacy. Therefore, deciding spatial and temporal resolution is a fundamental consideration in a spatiotemporal analysis. The modifiable temporal unit problem (MTUP) occurs when using data that is temporally aggregated. While consideration of the spatial dimension has been increasingly studied, the counterpart, a temporal unit, is rarely considered, particularly in the traffic safety modeling field. The purpose of this research is to identify the MTUP effect in crash-frequency modeling using data with various temporal scales. A sensitivity analysis framework is adopted with four negative binomial regression models and four random effect negative binomial models having yearly, quarterly, monthly, and weekly temporal units. As the different temporal unit was applied, the result of the model estimation also changed in terms of the mean and significance of the parameter estimates. Increasing temporal correlation due to using the small temporal unit can be handled with the random effect models.

scholarly journals Changes in the Number of Intensive Care Unit Beds in U.S. Hospitals During the Early Months of the COVID-19 Pandemic, as reported to the National Healthcare Safety Network’s COVID-19 Module

2021 ◽  
pp. 1-12
Author(s):  
Lindsey M. Weiner-Lastinger ◽  
Margaret A. Dudeck ◽  
Katherine Allen-Bridson ◽  
Raymund Dantes ◽  
Cindy Gross ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Hospital Type ◽  
National Healthcare ◽  
National Healthcare Safety Network ◽  
Bed Capacity ◽  
Using Data

Abstract Using data from the National Healthcare Safety Network (NHSN), we assessed changes to intensive care unit (ICU) bed capacity during the early months of the COVID-19 pandemic. Changes in capacity varied by hospital type and size. ICU beds increased by 36%, highlighting the pressure placed on hospitals during the pandemic.

Managing Inflation

2016 ◽  
Vol 63 (1) ◽  
pp. 77-87 ◽  
Author(s):  
William H. Fisher ◽  
Stephanie W. Hartwell ◽  
Xiaogang Deng
Keyword(s):  
Count Data ◽  
Regression Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Excess Zeros ◽  
Binomial Regression ◽  
Negative Binomial Models ◽  
Using Data ◽  
Over Dispersion ◽  
Binomial Models

Poisson and negative binomial regression procedures have proliferated, and now are available in virtually all statistical packages. Along with the regression procedures themselves are procedures for addressing issues related to the over-dispersion and excessive zeros commonly observed in count data. These approaches, zero-inflated Poisson and zero-inflated negative binomial models, use logit or probit models for the “excess” zeros and count regression models for the counted data. Although these models are often appropriate on statistical grounds, their interpretation may prove substantively difficult. This article explores this dilemma, using data from a study of individuals released from facilities maintained by the Massachusetts Department of Correction.

Safety Evaluation of Horizontal Alignment Design Consistency on Rural Two-Lane Highways

2019 ◽  
Vol 2673 (2) ◽  
pp. 628-636 ◽  
Author(s):  
Ian Hamilton ◽  
Scott Himes ◽  
R. J. Porter ◽  
Eric Donnell
Keyword(s):  
Negative Binomial ◽  
Safety Evaluation ◽  
Negative Binomial Regression ◽  
Expected Number ◽  
Horizontal Alignment ◽  
Highway Safety Manual ◽  
Horizontal Curves ◽  
Alternative Measures ◽  
Using Data ◽  
Design Consistency

Design consistency in the context of highway and street design refers to the conformance of highway geometry to driver expectancy. Existing design policies provide guidance related to horizontal alignment design consistency. While design consistency has safety implications and is intuitively linked to roadway departure crashes, the authors are only aware of a few studies that sought to link measures of design consistency to safety performance. This study explores relationships between alternative measures of horizontal alignment design consistency and the expected number of roadway departure crashes along horizontal curves on rural, two-lane, two-way roads. The authors analyzed 854 horizontal curves on rural two-lane highways in Indiana and Pennsylvania using data obtained from the SHRP 2 Roadway Information Database (RID) 2.0. Relationships between measures of design consistency and the expected number of roadway departure crashes were explored using a negative binomial regression modeling approach. The results indicate a relationship between the frequency of roadway departure crashes on a study curve and the radii of upstream and downstream curves. The ratio of the length of upstream and downstream tangents relative to a study curve radius was also statistically significant in Pennsylvania. Such findings are intuitive given the concept of design consistency and represent an advancement to existing predictive methods in the AASHTO Highway Safety Manual, which estimate the expected number of crashes on a segment as a function of the characteristics of only that segment.

scholarly journals The Relationship between Near-Repeat Street Robbery and the Environment: Evidence from Malmö, Sweden

2020 ◽  
Vol 9 (4) ◽  
pp. 188
Author(s):  
Markus Rasmusson ◽  
Marco Helbich
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Socioeconomic Indicators ◽  
Law Enforcement Agencies ◽  
Environmental Characteristics ◽  
Binomial Regression ◽  
Using Data ◽  
Near Repeat ◽  
The Relationship ◽  
Crime Event

Near-repeat crime refers to a pattern whereby one crime event is soon followed by a similar crime event at a nearby location. Existing research on near-repeat crime patterns is inconclusive about where near-repeat patterns emerge and which physical and social factors influence them. The present research addressed this gap by examining the relationship between initiator events (i.e., the first event in a near-repeat pattern) and environmental characteristics to estimate where near-repeat patterns are most likely to emerge. A two-step analysis was undertaken using data on street robberies reported in Malmö, Sweden, for the years 2006–15. After determining near-repeat patterns, we assessed the correlations between initiator events and criminogenic places and socioeconomic indicators using a negative binomial regression at a street segment level. Our results show that both criminogenic places and socioeconomic indicators have a significant influence on the spatial variation of initiator events, suggesting that environmental characteristics can be used to explain the emergence of near-repeat patterns. Law enforcement agencies can utilize the findings in efforts to prevent further street robberies from occurring.

scholarly journals Blood Levels of the Heavy Metal, Lead, and Caries in Children Aged 24-72 Months: NHANES III

2014 ◽  
Vol 49 (1) ◽  
pp. 26-33 ◽  
Author(s):  
R. Constance Wiener ◽  
D. Leann Long ◽  
Richard J. Jurevic
Keyword(s):  
Negative Binomial ◽  
Strong Association ◽  
Lead Level ◽  
Negative Binomial Regression ◽  
Blood Lead ◽  
Blood Lead Levels ◽  
Nhanes Iii ◽  
Blood Levels ◽  
Lead Levels ◽  
Using Data

Lead remains a significant pollutant. It has acute toxic and chronic effects on many tissues and accumulates in teeth and bones. The researchers for this study investigated the association of blood lead levels with the extent/severity of caries as measured by the number of decayed/filled teeth of children aged 24-72 months using data from NHANES III (the Third National Health and Nutrition Examination Survey), accounting for the excess zero caries in the analysis and using less than 2 µg/dl as the reference blood lead level (n = 3,127). Zero-inflated negative binomial regression models indicated unadjusted extent/severity mean ratios of 1.79, 1.88 and 1.94 for the number of decayed/filled teeth in children whose blood lead levels were 2-5, 5-10 and >10 µg/dl, respectively, compared with children having <2 µg/dl blood lead levels. The results did not attenuate when other variables were added to the model for the 5-10 and >10 µg/dl levels of exposure. The adjusted extent/severity mean ratios were 1.84, 2.14 and 1.91, respectively, for the categories. This study indicated a strong association of blood lead levels with increasing numbers of carious teeth in children aged 24-72 months. These findings support other studies in an innovative analysis handling cases of children with no caries. The findings may inform caries risk assessment. © 2014 S. Karger AG, Basel

scholarly journals Spatial distribution of snares in Ruma National Park, Kenya, with implications for management of the roan antelope Hippotragus equinuslangheldi and other wildlife

Oryx ◽  
2014 ◽  
Vol 49 (2) ◽  
pp. 295-302 ◽  
Author(s):  
Johnstone K. Kimanzi ◽  
Roy A. Sanderson ◽  
Stephen P. Rushton ◽  
Mware J. Mugo
Keyword(s):  
Spatial Distribution ◽  
National Park ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Geographical Information ◽  
Complete Spatial Randomness ◽  
Roan Antelope ◽  
Hippotragus Equinus ◽  
Using Data ◽  
Salt Licks

AbstractPoaching with snares has been identified as the main cause of decline of the endemic roan antelope Hippotragus equinuslangheldi in Ruma National Park, Kenya, from > 200 in 1979 to 37 in 2009. However, the spatial snaring patterns in the Park are not clearly understood. The focus of our study was to map the spatial distribution of snares in the Park and to identify the factors influencing this distribution, to develop effective methods of wildlife protection. Using data collected from 56 sample plots during 2006–2008, coupled with geographical information system techniques, we investigated the association between the occurrence of snares and the distribution of geographical features (slope, elevation), infrastructure (roads, fences), essential resources for wildlife (water, salt licks, forage), roan locations and wildlife density. Ripley's L function for assessing complete spatial randomness indicated that snares occurred in clumps (hotspots) up to 4 km apart. Negative binomial regression indicated that these hotspots occurred (1) near water resources, salt licks and the Park boundary, (2) far from roan locations and Park roads, (3) in areas with low gradients and low wildlife density, and (4) in areas with burned vegetation. We recommend concentrating routine security patrol efforts and resources on snare hotspots to reduce snaring and to protect the roan antelope and other threatened wildlife.

scholarly journals Impact of a pulsed xenon disinfection system on hospital onset Clostridioides difficile infections in 48 hospitals over a 5-year period

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sarah Simmons ◽  
Grady Wier ◽  
Antonio Pedraza ◽  
Mark Stibich
Keyword(s):  
Nearest Neighbor ◽  
Negative Binomial ◽  
Learning Algorithm ◽  
Compliance Rate ◽  
Negative Binomial Regression ◽  
Inverse Association ◽  
K Nearest Neighbor ◽  
Data Set ◽  
Clostridioides Difficile ◽  
The Impact

Abstract Background The role of the environment in hospital acquired infections is well established. We examined the impact on the infection rate for hospital onset Clostridioides difficile (HO-CDI) of an environmental hygiene intervention in 48 hospitals over a 5 year period using a pulsed xenon ultraviolet (PX-UV) disinfection system. Methods Utilization data was collected directly from the automated PX-UV system and uploaded in real time to a database. HO-CDI data was provided by each facility. Data was analyzed at the unit level to determine compliance to disinfection protocols. Final data set included 5 years of data aggregated to the facility level, resulting in a dataset of 48 hospitals and a date range of January 2015–December 2019. Negative binomial regression was used with an offset on patient days to convert infection count data and assess HO-CDI rates vs. intervention compliance rate, total successful disinfection cycles, and total rooms disinfected. The K-Nearest Neighbor (KNN) machine learning algorithm was used to compare intervention compliance and total intervention cycles to presence of infection. Results All regression models depict a statistically significant inverse association between the intervention and HO-CDI rates. The KNN model predicts the presence of infection (or whether an infection will be present or not) with greater than 98% accuracy when considering both intervention compliance and total intervention cycles. Conclusions The findings of this study indicate a strong inverse relationship between the utilization of the pulsed xenon intervention and HO-CDI rates.

scholarly journals Comparative effectiveness of dimethyl fumarate in Multiple Sclerosis

2021 ◽  
Author(s):  
Pauline Bosco-Levy ◽  
Marc Debouverie ◽  
Bruno Brochet ◽  
Céline Louapre ◽  
Elisabeth Maillard ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Negative Binomial ◽  
Real Life ◽  
Logistic Models ◽  
Negative Binomial Regression ◽  
Dimethyl Fumarate ◽  
Population Based ◽  
Disability Progression ◽  
Real Life Setting ◽  
Using Data

Abstract Objectives: To assess the effectiveness of dimethyl fumarate (DMF) on annual rate of relapse (ARR) and disability progression in multiple sclerosis (MS) compared to injectable immunomodulators (IMM), teriflunomide (TERI) and fingolimob (FTY), in real life setting. Methods: A population-based cohort study was conducted using data of the French nationwide claims database, SNDS. All patients initiating IMM, TERI, FTY or DMF between July 1, 2015 and December 12, 2017, with 4.5 years of database history and 1 to 3.5 years of follow-up were included in this study. DMF patients were 1:1 matched to IMM, TERI or FTY using a high dimensional Propensity Score. Negative binomial regression and a regression logistic models were used to estimate the relative risk (RR ± [95% CI]) of ARR and the Odds Ratio (OR ± [95% CI]) of disability progression, respectively. Results: Overall, 9 304 subjects were identified: 29.0% initiated DMF, 33.2% TERI, 5.6% FTY and 32.2% an IMM. The matched cohorts consisted of 1779 DMF- IMM, patients, 1679 DMF-TERI patients, and 376 DMF-FTY patients. DMF significantly reduced ARR compared to IMM (RR 0.72 [0.61 - 0.86]) and TERI (0.81 [0.68 - 0.96]). The risk of the progression of MS specific disability was not significantly different for any matched cohorts.Interpretation: DMF is associated with lower risk of relapse for patients with RRMS than other first-line RRMS agents (TERI and IIM).

scholarly journals 1153. National Trend of Urinary Catheter Device Utilization by Hospital Type, National Healthcare Safety Network (2015–2019)

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S411-S412
Author(s):  
Minn M Soe
Keyword(s):  
Acute Care ◽  
Negative Binomial ◽  
Urinary Catheter ◽  
Inpatient Rehabilitation ◽  
Acute Care Hospitals ◽  
Equation Modeling ◽  
Quality Reporting ◽  
Factors Associated ◽  
National Healthcare ◽  
National Healthcare Safety Network

Abstract Background Reducing unnecessary urinary catheter use and optimizing insertion techniques and catheter maintenance and care practices are the most important urinary tract infection (CAUTI) prevention strategies. To monitor device use (DU) as quality improvement activity, the Centers for Disease Control and Prevention’s National Healthcare Safety Network (NHSN) developed the risk adjusted, standardized urinary catheter device utilization ratio in 2015. This study aims to assess national trends of DU from the baseline year 2015 through 2019. Methods For our trend analysis, we analyzed DU data (catheter days per 100 inpatient-days) that acute care hospitals (ACHs), long-term acute care hospitals (LTACHs), inpatient rehabilitation facilities (IRFs), and critical access hospitals (CAHs) reported to NHSN from 2015Q1 through 2019Q1. The ward and intensive care unit patient care locations included in our analysis are those that ACHs, LTACHs, IRFs and CAHs are required to report to CMS to comply with CMS Inpatient Quality Reporting program requirements. We regressed DU by quarterly period using generalized estimating equation modeling with the negative-binomial distribution, after adjusting for factors associated with corresponding SUR models of 2015 baseline and accounting for autocorrelation of error terms within a location. For graphic display, we also computed quarterly DU using marginal predictive models. Results The DU decreased over time (P ≤ 0.05, average percent change per quarter (%change): −0.54 [95% CI: −0.54, −0.53]) among ACHs (Table 1, Figure 1), and −0.54 [95% CI: −0.58, −0.49] among LTACHs (Table 1, Figure 2). Among IRFs, quarterly DU in 2015Q2–2016Q3 were similar relative to 2015Q1, but decreased from 2016Q4 onward (P ≤ 0.05, % change: −0.51 [95% CI: −0.61, −0.40]) (Table 1, Figure 3). Among CAHs, quarterly DU in 2015Q2–2016Q4 were similar relative to 2015Q1, but decreased from 2017Q1 onward (P ≤ 0.05, % change: −0.22 [95% CI: −0.39, −0.04]) (Table 1, Figure 4). Conclusion There was a statistically significant decrease in National DU of urinary catheter during 2015–2019 across NHSN, although the magnitude of change per quarter was not large. Further research is needed to explore causal factors associated with such reduction. Disclosures All authors: No reported disclosures.

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