scholarly journals Staffing levels and hospital mortality in England: a national panel study using routinely collected data

2021 ◽  
Author(s):  
Bruna Rubbo ◽  
Christina Saville ◽  
Chiara Dall’Ora ◽  
Lesley Turner ◽  
Jeremy Jones ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Random Effects ◽  
Negative Binomial ◽  
Panel Study ◽  
Hospital Staff ◽  
Mortality Rates ◽  
Random Effects Model ◽  
Staffing Levels ◽  
Professional Groups ◽  
Staff Group

AbstractBackgroundMost studies investigating the association between hospital staff levels and mortality have focused on single professional groups, in particular nursing. However, single staff group studies might overestimate effects or neglect important contributions to patient safety from other staff groups. We aimed to examine the association between multiple clinical staff levels and case-mix adjusted patient mortality in English hospitals.Methods and FindingsThis retrospective observational study used routinely available data from all 138 National Health Service hospital trusts that provided general acute adult services in England between 2015 and 2019. Standardised mortality rates were derived from the Summary Hospital level Mortality Indicator dataset. Estimates for the effect of clinical staffing from the single staff models were generally higher than estimates from models with multiple staff groups. Using a multilevel negative binomial random effects model, hospitals with higher levels of medical and allied healthcare professional (AHP) staff had significantly lower mortality rates (1.04, 95%CI 1.02 to 1.06, and 1.04, 95%CI 1.02 to 1.06, respectively), while those with higher support staff had higher mortality rates (0.85, 95%CI 0.79 to 0.91 for nurse support, and 1.00, 95%CI 0.99 to 1.00 for AHP support), after adjusting for multiple staff groups and hospital characteristics. Estimates of staffing levels on mortality were higher in magnitude between- than within-hospitals, which were not statistically significant in a within-between random effects model.ConclusionsWe showed the importance of considering multiple staff groups simultaneously when examining the association between hospital mortality and clinical staffing levels. Despite not being included in previous workforce studies, AHP and AHP support levels have a significant impact on hospital mortality. As the main variation was seen between-as opposed to within-hospitals, structural recruitment and retention difficulties coupled with financial constraints could contribute to the effect of staffing levels on hospital mortality.

scholarly journals Updates to the Regional Seismic Travel Time (RSTT) Model: 2. Path-dependent Travel-time Uncertainty

2021 ◽  
Vol 178 (2) ◽  
pp. 313-339
Author(s):  
Michael L. Begnaud ◽  
Dale N. Anderson ◽  
Stephen C. Myers ◽  
Brian Young ◽  
James R. Hipp ◽  
...  
Keyword(s):  
Travel Time ◽  
Random Effects ◽  
Mean Squared Error ◽  
Random Effects Model ◽  
Location Error ◽  
Crust And Upper Mantle ◽  
Time Uncertainty ◽  
Uncertainty Estimates ◽  
Event Location ◽  
Travel Time Uncertainty

AbstractThe regional seismic travel time (RSTT) model and software were developed to improve travel-time prediction accuracy by accounting for three-dimensional crust and upper mantle structure. Travel-time uncertainty estimates are used in the process of associating seismic phases to events and to accurately calculate location uncertainty bounds (i.e. event location error ellipses). We improve on the current distance-dependent uncertainty parameterization for RSTT using a random effects model to estimate slowness (inverse velocity) uncertainty as a mean squared error for each model parameter. The random effects model separates the error between observed slowness and model predicted slowness into bias and random components. The path-specific travel-time uncertainty is calculated by integrating these mean squared errors along a seismic-phase ray path. We demonstrate that event location error ellipses computed for a 90% coverage ellipse metric (used by the Comprehensive Nuclear-Test-Ban Treaty Organization International Data Centre (IDC)), and using the path-specific travel-time uncertainty approach, are more representative (median 82.5% ellipse percentage) of true location error than error ellipses computed using distance-dependent travel-time uncertainties (median 70.1%). We also demonstrate measurable improvement in location uncertainties using the RSTT method compared to the current station correction approach used at the IDC (median 74.3% coverage ellipse).

scholarly journals Statistical models for analyzing count data: predictors of length of stay among HIV patients in Portugal using a multilevel model

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ahmed Nabil Shaaban ◽  
Bárbara Peleteiro ◽  
Maria Rosario O. Martins
Keyword(s):  
Length Of Stay ◽  
Regression Model ◽  
Random Effects ◽  
Count Data ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Comprehensive Approach ◽  
Negative Binomial Regression Model ◽  
Hiv Patients ◽  
Binomial Regression

Abstract Background This study offers a comprehensive approach to precisely analyze the complexly distributed length of stay among HIV admissions in Portugal. Objective To provide an illustration of statistical techniques for analysing count data using longitudinal predictors of length of stay among HIV hospitalizations in Portugal. Method Registered discharges in the Portuguese National Health Service (NHS) facilities Between January 2009 and December 2017, a total of 26,505 classified under Major Diagnostic Category (MDC) created for patients with HIV infection, with HIV/AIDS as a main or secondary cause of admission, were used to predict length of stay among HIV hospitalizations in Portugal. Several strategies were applied to select the best count fit model that includes the Poisson regression model, zero-inflated Poisson, the negative binomial regression model, and zero-inflated negative binomial regression model. A random hospital effects term has been incorporated into the negative binomial model to examine the dependence between observations within the same hospital. A multivariable analysis has been performed to assess the effect of covariates on length of stay. Results The median length of stay in our study was 11 days (interquartile range: 6–22). Statistical comparisons among the count models revealed that the random-effects negative binomial models provided the best fit with observed data. Admissions among males or admissions associated with TB infection, pneumocystis, cytomegalovirus, candidiasis, toxoplasmosis, or mycobacterium disease exhibit a highly significant increase in length of stay. Perfect trends were observed in which a higher number of diagnoses or procedures lead to significantly higher length of stay. The random-effects term included in our model and refers to unexplained factors specific to each hospital revealed obvious differences in quality among the hospitals included in our study. Conclusions This study provides a comprehensive approach to address unique problems associated with the prediction of length of stay among HIV patients in Portugal.

scholarly journals Long-Term Exposure to Fine and Coarse Particulate Matter and COVID-19 Incidence and Mortality Rate in Chile during 2020

2021 ◽  
Vol 18 (14) ◽  
pp. 7409
Author(s):  
Macarena Valdés Salgado ◽  
Pamela Smith ◽  
Mariel Opazo ◽  
Nicolás Huneeus
Keyword(s):  
Mortality Rate ◽  
Incidence Rate ◽  
Air Pollutants ◽  
Negative Binomial ◽  
Transport Model ◽  
Surface Concentration ◽  
Mortality Rates ◽  
Incidence And Mortality ◽  
Pm2.5 And Pm10

Background: Several countries have documented the relationship between long-term exposure to air pollutants and epidemiological indicators of the COVID-19 pandemic, such as incidence and mortality. This study aims to explore the association between air pollutants, such as PM2.5 and PM10, and the incidence and mortality rates of COVID-19 during 2020. Methods: The incidence and mortality rates were estimated using the COVID-19 cases and deaths from the Chilean Ministry of Science, and the population size was obtained from the Chilean Institute of Statistics. A chemistry transport model was used to estimate the annual mean surface concentration of PM2.5 and PM10 in a period before the current pandemic. Negative binomial regressions were used to associate the epidemiological information with pollutant concentrations while considering demographic and social confounders. Results: For each microgram per cubic meter, the incidence rate increased by 1.3% regarding PM2.5 and 0.9% regarding PM10. There was no statistically significant relationship between the COVID-19 mortality rate and PM2.5 or PM10. Conclusions: The adjusted regression models showed that the COVID-19 incidence rate was significantly associated with chronic exposure to PM2.5 and PM10, even after adjusting for other variables.

914 A Comparison of Mortality, Indicators of Treatment Success and Complications of Resuscitative Endovascular Balloon Occlusion of Aorta (REBOA): A Systematic Review and Meta-Analysis

2021 ◽  
Vol 108 (Supplement_2) ◽  
Author(s):  
H J Ko ◽  
H F Koo ◽  
S Froghi ◽  
N Al-Saadi
Keyword(s):  
Hospital Mortality ◽  
Meta Analysis ◽  
Treatment Success ◽  
Estimation Method ◽  
Kidney Injury ◽  
Balloon Occlusion ◽  
Random Effects Model ◽  
Resuscitative Thoracotomy ◽  
Significant Difference ◽  
Endovascular Balloon Occlusion

Abstract Introduction This study aims to provide an updated review on in-hospital mortality rates in patients who underwent Resuscitative Endovascular Balloon Occlusion of Aorta (REBOA) versus Resuscitative thoracotomy (RT) or standard care without REBOA, to identify potential indicators of REBOA use and complications. Method Cochrane and PRISMA guidelines were used to perform the study. A literature search was done from 01 January 2005 to 30 June 2020 using EMBASE, MEDLINE and COCHRANE databases. Meta-analysis was conducted using a random effects model and the DerSimonian and Laird estimation method. Results 25 studies were included in this study. The odds of in-hospital mortality of patients who underwent REBOA compared to RT was 0.18 (p < 0.01). The odds of in-hospital survival of patients who underwent REBOA compared to non-REBOA was 1.28 (p = 0.62). There was a significant difference found between survivors and non-survivors in terms of their pre-REBOA systolic blood pressure (SBP) (19.26 mmHg, p < 0.01), post-REBOA SBP (20.73 mmHg, p < 0.01), duration of aortic occlusion (-40.57 mins, p < 0.01) and ISS (-8.50, p < 0.01). Common complications of REBOA included acute kidney injury, multi-organ dysfunction and thrombosis. Conclusions Our study demonstrated lower in-hospital mortality of REBOA versus RT. Prospective multi-centre studies are needed for further evaluation of the indications, feasibility, and complications of REBOA.

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