Emergency department crowding in The Netherlands: managers’ experiences

__Abstract__ __Background__ In The Netherlands, the state of emergency department (ED) crowding is unknown. Anecdotal evidence suggests that current ED patients experience a longer length of stay (LOS) compared to some years ago, which is indicative of ED crowding. However, no multicenter studies have been performed to quantify LOS and assess crowding at Dutch EDs. We performed this study to describe the current state of emergency departments in The Netherlands regarding patients’ length of stay and ED nurse managers’ experiences of crowding. __Methods__ A survey was sent to all 94 ED nurse managers in The Netherlands with questions regarding the type of facility, annual ED census, and patients’ LOS. Additional questions included whether crowding was ever a problem at the particular ED, how often it occurred, which time periods had the worst episodes of crowding, and what measures the particular ED had undertaken to improve patient flow. __Results__ Surveys were collected from 63 EDs (67%). Mean annual ED visits were 24,936 (SD ± 9,840); mean LOS for discharged patients was 119 (SD ± 40) min and mean LOS for admitted patients 146 (SD ± 49) min. Consultation delays, laboratory and radiology delays, and hospital bed shortages for patients needing admission were the most cited reasons for crowding. Admitted patients had a longer LOS because of delays in obtaining inpatient beds. Thirty-nine of 57 respondents (68%) reported that crowding occurred several times a week or even daily, mostly between 12:00 and 20:00. Measures taken by hospitals to manage crowding included placing patients in hallways and using a fasttrack with treatment of patients by trained nurse practitioners. __Conclusions__ Despite a relatively short LOS, frequent crowding appears to be a nationwide problem according to Dutch ED nurse managers, with 68% of them reporting that crowding occurred several times a week or even daily. Consultations delays, laboratory and radiology delays, and hospital bed shortage for patients needing admission were believed to be the most important factors contributing to ED crowding

An overview of geospatial methods used in unintentional injury epidemiology

BACKGROUND: Injuries are a leading cause of death and disability around the world. Injury incidence is often associated with socio-economic and physical environmental factors. The application of geospatial methods has been recognised as important to gain greater understanding of the complex nature of injury and the associated diverse range of geographically-diverse risk factors. Therefore, the aim of this paper is to provide an overview of geospatial methods applied in unintentional injury epidemiological studies. METHODS: Nine electronic databases were searched for papers published in 2000-2015, inclusive. Included were papers reporting unintentional injuries using geospatial methods for one or more categories of spatial epidemiological methods (mapping; clustering/cluster detection; and ecological analysis). Results describe the included injury cause categories, types of data and details relating to the applied geospatial methods. RESULTS: From over 6,000 articles, 67 studies met all inclusion criteria. The major categories of injury data reported with geospatial methods were road traffic (n = 36), falls (n = 11), burns (n = 9), drowning (n = 4), and others (n = 7). Grouped by categories, mapping was the most frequently used method, with 62 (93%) studies applying this approach independently or in conjunction with other geospatial methods. Clustering/cluster detection methods were less common, applied in 27 (40%) studies. Three studies (4%) applied spatial regression methods (one study using a conditional autoregressive model and two studies using geographically weighted regression) to examine the relationship between injury incidence (drowning, road deaths) with aggregated data in relation to explanatory factors (socio-economic and environmental). CONCLUSION: The number of studies using geospatial methods to investigate unintentional injuries has increased over recent years. While the majority of studies have focused on road traffic injuries, other injury cause categories, particularly falls and burns, have also demonstrated the application of these methods. Geospatial investigations of injury have largely been limited to mapping of data to visualise spatial structures. Use of more sophisticated approaches will help to understand a broader range of spatial risk factors, which remain under-explored when using traditional epidemiological approaches

A comparison of self-reported risk and protective factors and the death implicit association test in the prediction of future suicide attempts in adolescent emergency department patients

BackgroundConcerns have been raised about the utility of self-report assessments in predicting future suicide attempts. Clinicians in pediatric emergency departments (EDs) often are required to assess suicidal risk. The Death Implicit Association Test (IAT) is an alternative to self-report assessment of suicidal risk that may have utility in ED settings.MethodsA total of 1679 adolescents recruited from 13 pediatric emergency rooms in the Pediatric Emergency Care Applied Research Network were assessed using a self-report survey of risk and protective factors for a suicide attempt, and the IAT, and then followed up 3 months later to determine if an attempt had occurred. The accuracy of prediction was compared between self-reports and the IAT using the area under the curve (AUC) with respect to receiver operator characteristics.ResultsA few self-report variables, namely, current and past suicide ideation, past suicidal behavior, total negative life events, and school or social connectedness, predicted an attempt at 3 months with an AUC of 0.87 [95% confidence interval (CI), 0.84-0.90] in the entire sample, and AUC = 0.91, (95% CI 0.85-0.95) for those who presented without reported suicidal ideation. The IAT did not add significantly to the predictive power of selected self-report variables. The IAT alone was modestly predictive of 3-month attempts in the overall sample ((AUC = 0.59, 95% CI 0.52-0.65) and was a better predictor in patients who were non-suicidal at baseline (AUC = 0.67, 95% CI 0.55-0.79).ConclusionsIn pediatric EDs, a small set of self-reported items predicted suicide attempts within 3 months more accurately than did the IAT