Emergency Department Dataset Research Articles

Health impact, and economic value, of meeting housing quality standards: a retrospective longitudinal data linkage study

BackgroundPoor-quality housing adversely affects residents’ health but there is a paucity of high-quality evidence to support this.ObjectiveThis research investigated the health impact of bringing housing to a national quality standard.DesignA natural experiment of improvements to housing quality analysed using repeated measures of health-care utilisation and economic outcomes at an individual person level.SettingCarmarthenshire, UK.ParticipantsA total of 32,009 residents registered for a minimum of 60 days at 8558 social homes that received housing improvements between January 2005 and March 2015.InterventionsMultiple internal and external housing improvements, including wall and loft insulation, windows and doors, heating system upgrades, new kitchens and bathrooms, garden path safety improvements and electrical system upgrades (adding power sockets, and extractor fans in kitchens and bathrooms).Main outcome measuresEmergency hospital admissions for cardiorespiratory conditions and injuries. Primary health-care utilisation for respiratory and common mental health disorders, emergency department injury attendances and health-care utilisation costs.Data sourcesCarmarthenshire County Council home address and intervention records were anonymously linked within the Secure Anonymous Information Linkage databank to demographic information from the Welsh Demographic Service data set; hospital admission data from the Patient Episode Dataset for Wales; primary care contacts and prescribed medications from general practice data; emergency department attendances from the Emergency Department Data Set; and deaths from the Office for National Statistics mortality register.MethodsThe study used a longitudinal panel design to examine changes in standard of eight housing cointervention from intervention records, and linked to individuals registered at intervention homes. Health outcomes were obtained retrospectively for each individual in a dynamic cohort and were captured for up to 123 consecutive months. An additional local authority region could not be utilised as a comparator owing to different reporting pressures resulting in the recording of a different intervention. The exposure group for each cointervention was compared with an internal reference group of people living in homes that did not receive the cointervention during their tenancy. A multilevel modelling approach was used to account for repeated observations for individuals living in intervention homes. Counts of health outcomes were analysed using negative binomial regression models to determine the effect of each cointervention that reached housing quality standards during an individual’s period of tenancy, compared with those living in properties that did not. We adjusted for potential confounding factors and for background trends in the regional general population. A cost–consequences analysis was conducted as part of the health economic evaluation.ResultsResidents aged ≥ 60 years living in homes in which electrical systems were upgraded were associated with 39% fewer admissions than those living in homes in which they were not [incidence rate ratio (IRR) 0.61, 95% confidence interval (CI) 0.53 to 0.72;p < 0.01]. Reduced admissions were also associated with windows and doors (IRR 0.71, 95% CI 0.63 to 0.81;p < 0.01), wall insulation (IRR 0.75, 95% CI 0.67 to 0.84;p < 0.01) and gardens and estates (IRR 0.73, 95% CI 0.64 to 0.83;p < 0.01) for those living in homes in which these cointervention were upgraded. There were no associations of change in emergency admissions with upgrading heating (IRR 0.91, 95% CI 0.82 to 1.01;p = 0.072), loft insulation (IRR 0.98, 95% CI 0.86 to 1.11;p = 0.695), kitchens (IRR 0.98, 95% CI 0.83 to 1.17;p = 0.843) or bathrooms (IRR 0.93, 95% CI 0.81 to 1.06;p = 0.287).LimitationsThere was no randomisation, there were incomplete data on the scale of the intervention for individual households and there were no estimates for the impact of the whole programme.ConclusionsThis complex interdisciplinary study found that hospital admissions could be avoided through improving housing quality standards.Future workAt their initiation, future non-health projects should have a built-in evaluation to allow intervention exposures to be randomly allocated to residents, with the simultaneous analysis of multiple health outcomes in one statistical model.FundingThe National Institute for Health Research Public Health Research programme.

Using Discharge Diagnoses for Early Notification of Reportable Diseases in Georgia

Objective: To describe how the Georgia Department of Public Health (DPH) uses ICD-9 and ICD-10-based discharge diagnoses (DDx) codes assigned to Emergency Department (ED) patients to support the early detection and investigation of outbreaks, clusters, and individual cases of reportable diseases.Introduction: The Georgia DPH has used its State Electronic Notifiable Disease Surveillance System (SendSS) Syndromic Surveillance (SS) module to collect, analyze and display analyses of ED patient visits, including DDx data from hospitals throughout Georgia for early detection and investigation of cases of reportable diseases before laboratory test results are available. Evidence on the value of syndromic surveillance approaches for outbreak or event detection is limited (1, 2). Use of the DDx field within datasets, specifically as it might be used for investigation of outbreaks, clusters, and / or individual cases of reportable diseases, has not been widely discussed.Methods: The DDx field of the ED data set sent to DPH by 120 facilities was queried for diseases that are immediately-reportable, as well as those reportable within 7 days of diagnosis. The query was performed twice a day using a combination of SAS 9.4 and the internal query capabilities of SendSS. District Epidemiologists (DE) were notified by email, with an Excel file attached that includes the details of the patient’s visit. DEs contacted Infection Control Practitioners (ICPs) of the facilities where the patients had received a discharge diagnosis of a reportable disease. True or false positives were determined after the outcome of the follow-up with the ICP had been known and after the patient had been entered as a case of reportable disease in SendSS by the DE. Hence, if the patient was a confirmed or probable case of a reportable disease, it was recorded as a True Positive, and True Negative otherwise. This led to the calculation of Predictive Value Positive (PVP) by reportable disease.Results: Table 1 shows the number of notifications sent to DEs that were later demonstrated to be True Positives and False Positives. It also shows the PVP by diseases being reported, for the period spanning from 05/01/2016 to 08/31/2017. Use of these notifications has allowed early investigation and identification of 158 cases of notifiable diseases by DEs. This includes 25 epi-linked cases (varicella=12, pertussis=4, cryptosporidiosis=3, shigellosis=2, malaria=2, and viral meningitis=2), as well as two clusters of varicella, one cluster of pertussis, and one outbreak of varicella in an elementary school that had not been reported to the local health department. A notable limitation of this study is that no systematic and exhaustive tracking is done of all notifications, as DEs have latitude to decide whether to follow up on a specific notification, depending on other local data that could be related to the event. Therefore, the PPVs may be biased due to over- / under-estimation of unknown size and direction. One exception to this is varicella notifications, whose outcomes have been exhaustively tracked by the DPH surveillance coordinator of this disease.Conclusions: The use of ED discharge diagnoses to examine potential cases of reportable diseases can help improve detection and early response by local health departments to outbreaks, clusters, and individual cases of reportable diseases. Exhaustive tracking of all the notifications by specific diseases may improve the estimation of the PPVs of the notifications sent to DEs.

PP19 Opioid Poisoning Deaths: A National Picture

Introduction:The factors associated with opioid poisoning death are poorly understood. We performed a retrospective autopsy study of decedents (a term used for people who are deceased) of opioid poisoning in Wales in 2015. Using anonymized linked data, we describe demographic characteristics, patterns of emergency service utilization, and clinical presentation prior to death.Methods:Decedents of opioid poisoning in Wales in 2015 were identified from the Office of National Statistics (ONS) mortality dataset. Records were linked with the Emergency Department Dataset (EDDS) by the National Welsh Informatics Service (NWIS); and held in the Secure Anonymized Information Linkage (SAIL) databank. The data were accessed and analyzed in the SAIL gateway.Results:Age at death ranged from eighteen to seventy-eight years, with a mean age of forty-two years. Average male age was forty-one years and average female age was forty-four and a half years. Seventy-three percent of decedents were men (n = 228/312). Eight-seven percent of decedents (n = 281/312) attended the emergency department in the three years prior to death. In total 2081 attendances were made, forty-one percent of which involved conveyance by ambulance. Attendances per individual ranged from one to 114, with over half of decedents attending more than three times. Diagnostic codes were mostly missing or non-specific, with only seven and a half percent of attendances representing eighty-two decedents, coded as drug related. Treatment codes were also mostly missing or non-specific, with sixteen percent of attendances representing 148 attendees attributed a treatment code. Thirty-nine percent of attendances (n = 822) ended in treatment and discharge, whilst twenty-seven percent (n = 562) led to hospital admission.Conclusions:Matching previously published data, we found that fatal opioid poisoning is preceded by a period of high emergency health service utilization. On average decedents were in their fifth decade and more likely to be male than female. Attendances varied widely, with men less likely to attend than women.

Rates and Risk Factors for Opioid Dependence and Overdose after Urological Surgery.

Effective pain management is a critical component of the perioperative process with opioids representing a mainstay of therapy. The opioid epidemic is a growing concern in the United States. The goal of this study was to quantify the risk of opioid dependence or overdose among patients undergoing urological surgery and to identify risk factors of opioid dependence or overdose. We retrospectively reviewed data on urological surgery from 2007 to 2011. Data sources included the HCUP (Healthcare Cost and Utilization Project) inpatient, ambulatory surgery and emergency department data sets. Outcomes of postoperative opioid dependence and overdose were identified by previously validated ICD-9 codes. Multivariable logistic regression adjusted for surgical procedure was performed to identify predictors of opioid dependence or overdose following urological surgery. Overall 675,527 patients underwent urological surgery, of whom 0.09% were diagnosed with opioid dependence or overdose. Patients in whom opioid dependence or overdose developed were younger (median age 51 vs 62 years), carried nonprivate insurance (69.6% vs 66%), underwent an inpatient procedure (81.0% vs 42.4%) and had a longer length of stay (median 3 vs 0 days) and a history of depression (14.4% vs 3.4%) or chronic obstructive pulmonary disease (20.3% vs 8.9%, all p <0.001). On adjusted multivariable analysis these factors remained independent risk factors for opioid dependence or overdose. Postoperative opioid dependence or overdose affects 1 of 1,111 urological surgery patients. Risk factors for opioid dependence or overdose included younger age, inpatient surgery and increasing hospitalization duration, baseline depression, tobacco use and chronic obstructive pulmonary disease as well as insurance provider, including Medicaid, Medicare (age less than 65 years) and noninsured status.

The use of multi-sourced linked datasets in evaluating the Emergency Medical Retrieval and Transfer Service (EMRTS) CYMRU

ABSTRACT&#x0D; ObjectivesEMRTS launched in April 2015 which saw consultants join critical care practitioners on air ambulance missions and emergency response vehicles. EMRTS delivers pre hospital, Emergency Department equivalent critical care for people in Wales with life or limb threatening injuries or illness from scene to definitive care. The evaluation will compare patients treated by EMRTS to cases both pre-EMRTs and EMRTS-offline (at night or bad weather) in improving survival and functional and quality of life outcomes.&#x0D; ApproachThe evaluation will require the use of multiple datasets to follow patients from time of incident, throughout hospital care and to calculate post care health status. Electronic reporting forms will be used to measure incident details, patient vitals and all treatments/procedures pre hospital, creating the EMRTS dataset and cohort. Patients’ data will be linked to the Emergency Department Dataset (EDDS), Patient Episode Database for Wales (PEDW), Critical Care dataset, Trauma Audit and Research Network (TARN) and Intensive Care National Audit and Research Centre (ICNARC). TARN and ICNARC will provide data on patients transferred to English specialist care facilities.&#x0D; ONS (Office of National Statistics) and WDS (Welsh Demographic Service) data will be used for demographic and survival analysis.&#x0D; GIS techniques will be used on WAST (Welsh Ambulance Service Trust) data for time saved analysis in transferring patients to definitive care.&#x0D; Following discharge from hospital patients will be interviewed six months and one-year post incident for longer-term functional and quality of life outcomes.&#x0D; ResultsDatasets are currently being assembled for the service evaluation. Data will be encrypted and anonymized through NWIS (NHS Wales Informatics Service) to enable research through the Secure Anonymised Information Linkage (SAIL) facility. Results will include rate of survival, effect on length of hospital stay, time to definitive care, the use and appropriateness of EMRTs specific interventions and functional and quality of life outcomes.&#x0D; ConclusionsTo allow for data collection the service evaluation is still in the early stages and will be compared with local and international data.

The Probability of Hospitalizations for Mild-to-Moderate Injuries by Trauma Center Ownership Type.

To corroborate anecdotal evidence with systematic evidence of a lower threshold for admission among for-profit hospitals. The study used Florida emergency department and hospital discharge datasets for 2012 to 2014. The treatment variable of interest was for-profit-designated trauma center status. The dependent variable indicated whether a patient with mild-to-moderate injuries was admitted after presenting as a trauma alert and then discharged to home. A separate analysis was conducted of discharges that had a 1-day length of stay. Generalized estimation equations with logistic distribution models were used to control for the confounding influences and developed for four groups of patients: ICISS=1 (no probability of mortality), ICISS≥0.99, ICISS≥0.95, and ICISS≥0.85 (zero to 15 percent probability of mortality, which includes all mild and moderate injury patients). For the ICISS=1 and ICISS≥0.99 models, the centers' for-profit status was the most important predictor. In the ICISS≥0.95 and ICISS≥0.85 models, injury type played a more important role, but for-profit status remained important. For patients with a 1-day stay, for-profit status was associated with an even higher probability of hospitalization. Considerable differences exist between for-profit and not-for-profit trauma centers concerning hospitalization among the study population, which may be explained by supplier-induced demand.

Using a Data Quality Framework to Clean Data Extracted from the Electronic Health Record: A Case Study.

Objectives:We examine the following: (1) the appropriateness of using a data quality (DQ) framework developed for relational databases as a data-cleaning tool for a data set extracted from two EPIC databases, and (2) the differences in statistical parameter estimates on a data set cleaned with the DQ framework and data set not cleaned with the DQ framework.Background:The use of data contained within electronic health records (EHRs) has the potential to open doors for a new wave of innovative research. Without adequate preparation of such large data sets for analysis, the results might be erroneous, which might affect clinical decision-making or the results of Comparative Effectives Research studies.Methods:Two emergency department (ED) data sets extracted from EPIC databases (adult ED and children ED) were used as examples for examining the five concepts of DQ based on a DQ assessment framework designed for EHR databases. The first data set contained 70,061 visits; and the second data set contained 2,815,550 visits. SPSS Syntax examples as well as step-by-step instructions of how to apply the five key DQ concepts these EHR database extracts are provided.Conclusions:SPSS Syntax to address each of the DQ concepts proposed by Kahn et al. (2012)1 was developed. The data set cleaned using Kahn’s framework yielded more accurate results than the data set cleaned without this framework. Future plans involve creating functions in R language for cleaning data extracted from the EHR as well as an R package that combines DQ checks with missing data analysis functions.

Profile and costs of secondary conditions resulting in emergency department presentations and readmission to hospital following traumatic spinal cord injury

IntroductionPeople with traumatic spinal cord injury (SCI) face complex challenges in their care, recovery and life. Secondary conditions can develop to involve many body systems and can impact health, function, quality of life, and community participation. These secondary conditions can be costly, and many are preventable. The aim of this study was to describe the type and direct costs of secondary conditions requiring readmission to hospital, or visit to an emergency department (ED), within the first two years following traumatic spinal cord injury (SCI). MethodsA retrospective cohort study using population-level linked data from hospital ED and admission datasets was undertaken in Victoria, Australia. The incidence and direct treatment costs of readmission to hospital and ED visit within 2-years post-injury for secondary conditions related to SCI were measured for the 356 persons with traumatic SCI with a date of injury from 2008 to 2011. ResultsOf the 356 cases, 141 (40%) experienced 366 (median 2, range 1–11) readmissions to hospital for secondary conditions. 95 (27%) visited an ED at least once, within two years of injury for a secondary condition. The cost of hospital readmissions was AUD$5,553,004 and AUD$87,790 for ED visits. The mean±SD cost was AUD$15,172±$20,957 per readmission and AUD$670±$198 per ED visit. Urological conditions (e.g. urinary tract infection) were most common, followed by pressure areas/ulcers for readmissions, and fractures in the ED. ConclusionsHospitalisation for complications within two years of traumatic SCI was common and costly in Victoria, Australia. Improved bladder and pressure area management could result in substantial morbidity and cost savings following SCI.

Consideration of guidelines, recommendations and quality indicators for treatment of stroke in the dataset "Emergency Department" of DIVI

The dataset "Emergency Department" of the German Interdisciplinary Association of Critical Care and Emergency Medicine (DIVI) has been developed during several expert meetings. Its goal is an all-encompassing documentation of the early clinical treatment of patients in emergency departments. Using the example of the index disease acute ischemic stroke (stroke), the aim was to analyze how far this approach has been fulfilled. In this study German, European and US American guidelines were used to analyze the extent of coverage of the datasets on current emergency department guidelines and recommendations from professional societies. In addition, it was examined whether the dataset includes recommended quality indicators (QI) for quality management (QM) and in a third step it was examined to what extent national provisions for billing are included. In each case a differentiation was made whether the respective rationale was primary, i.e. directly apparent or whether it was merely secondarily depicted by expertise. In the evaluation an additional differentiation was made between the level of recommendations and further quality relevant criteria. The modular design of the emergency department dataset comprising 676 data fields is briefly described. A total of 401 individual fields, divided into basic documentation, monitoring and specific neurological documentation of the treatment of stroke patients were considered. For 247 data fields a rationale was found. Partially overlapping, 78.9 % of 214 medical recommendations in 3 guidelines and 85.8 % of the 106 identified quality indicators were primarily covered. Of the 67 requirements for billing of performance of services, 55.5 % are primarily part of the emergency department dataset. Through appropriate expertise and documentation by a board certified neurologist, the results can be improved to almost 100 %. The index disease stroke illustrates that the emergency department dataset of the DIVI covers medical guidelines, especially 100 % of the German guidelines with a grade of recommendation. All necessary information to document the specialized stroke treatment procedure in the German diagnosis-related groups (DRG) system is also covered. The dataset is also suitable as a documentation tool of quality management, for example, to participate in the registry of the German Stroke Society (ADSR). Best results are obtained if the dataset is applied by a physician specialized in the treatment of patients with stroke (e.g. board certified neurologist). Finally the results show that changes in medical guidelines and recommendations for quality management as well as billing-relevant content should be implemented in the development of datasets for documentation to avoid duplicate documentation.

Prehospital Naloxone Administration as a Public Health Surveillance Tool: A Retrospective Validation Study.

Abuse or unintended overdose (OD) of opiates and heroin may result in prehospital and emergency department (ED) care. Prehospital naloxone use has been suggested as a surrogate marker of community opiate ODs. The study objective was to verify externally whether prehospital naloxone use is a surrogate marker of community opiate ODs by comparing Emergency Medical Services (EMS) naloxone administration records to an independent database of ED visits for opiate and heroin ODs in the same community. A retrospective chart review of prehospital and ED data from July 2009 through June 2013 was conducted. Prehospital naloxone administration data obtained from the electronic medical records (EMRs) of a large private EMS provider serving a metropolitan area were considered a surrogate marker for suspected opiate OD. Comparison data were obtained from the regional trauma/psychiatric ED that receives the majority of the OD patients. The ED maintains a de-identified database of narcotic-related visits for surveillance of narcotic use in the metropolitan area. The ED database was queried for ODs associated with opiates or heroin. Cross-correlation analysis was used to test if prehospital naloxone administration was independent of ED visits for opiate/heroin ODs. Naloxone was administered during 1,812 prehospital patient encounters, and 1,294 ED visits for opiate/heroin ODs were identified. The distribution of patients in the prehospital and ED datasets did not differ by gender, but it did differ by race and age. The frequency of naloxone administration by prehospital providers varied directly with the frequency of ED visits for opiate/heroin ODs. A monthly increase of two ED visits for opiate-related ODs was associated with an increase in one prehospital naloxone administration (cross-correlation coefficient [CCF]=0.44; P=.0021). A monthly increase of 100 ED visits for heroin-related ODs was associated with an increase in 94 prehospital naloxone administrations (CCF=0.46; P=.0012). Frequency of naloxone administration by EMS providers in the prehospital setting varied directly with frequency of opiate/heroin OD-related ED visits. The data correlated both for short-term frequency and longer term trends of use. However, there was a marked difference in demographic data suggesting neither data source alone should be relied upon to determine which populations are at risk within the community.

Defining emergency department asthma visits for public health surveillance, North Carolina, 2008-2009.

IntroductionWhen using emergency department (ED) data sets for public health surveillance, a standard approach is needed to define visits attributable to asthma. Asthma can be the first (primary) or a subsequent (2nd through 11th) diagnosis. Our study objective was to develop a definition of ED visits attributable to asthma for public health surveillance. We evaluated the effect of including visits with an asthma diagnosis in primary-only versus subsequent positions.MethodsThe study was a cross-sectional analysis of population-level ED surveillance data. Of the 114 North Carolina EDs eligible to participate in a statewide surveillance system in 2008–2009, we used data from the 111 (97%) that participated during those years. Included were all ED visits with an ICD-9-CM diagnosis code for asthma in any diagnosis position (1 through 11). We formed 11 strata based on the diagnosis position of asthma and described common chief complaint and primary diagnosis categories for each. Prevalence ratios compared each category’s proportion of visits that received either asthma- or cardiac-related procedure codes.ResultsRespiratory diagnoses were most common in records of ED visits in which asthma was the first or second diagnosis, while primary diagnoses of injury and heart disease were more common when asthma appeared in positions 3–11. Asthma-related chief complaints and procedures were most common when asthma was the first or second diagnosis, whereas cardiac procedures were more common in records with asthma in positions 3–11.ConclusionED visits should be defined as asthma-related when asthma is in the first or second diagnosis position.

Guidelines for Navigating Human Subjects Review and Preparing Data Sets for Sharing with the ISDS Technical Conventions Committee

The purpose of this panel is to facilitate the dissemination of surveillance-related use cases by public health practitioners with accompanying benchmark datasets to method developers. The panel will present their experiences with preparing patient-level emergency department data sets to accompany a use case submitted to the ISDS Technical Conventions Committee. We will review methods for cultivating a data set that is appropriate for sharing; experiences with and strategies for navigating the IRB human subjects review process; and legal and ethical issues related to sharing syndromic surveillance data with third parties.

Concordance of Motor Vehicle Crash, Emergency Department, and Inpatient Hospitalization Data Sets in the Identification of Drugs in Injured Drivers

Objective: Prescription drug overdoses, abuse, and sales have increased dramatically in the United States in the last decade. The purpose of the present study was to link crash data with emergency department (ED) and inpatient hospitalization data to assess the concordance between the data sets in the identification of the presence of drugs among injured motor vehicle drivers (passenger cars, passenger trucks, light trucks, and semi-trucks) in Kentucky. Methods: Kentucky CRASH data were probabilistically linked to ED data sets for years 2008–2010 and to inpatient hospitalization data sets for years 2000–2010. Statistical analyses were performed. Results: Of the 72,529 linked crash/ED visits, there were 473 drivers with an associated nondependent abuse of drugs diagnosis in the ED, and 930 drivers had drug involvement recorded in the CRASH data (only 163 cases overlapped with drug involvement both recorded in CRASH data and coded as nondependent abuse of drugs in the ED); 64 drivers had multiple drug types present in their system. Of the 20,860 total linked crash/inpatient hospitalization cases, there were 973 drivers diagnosed with nondependent abuse of drugs in the inpatient hospitalization record and 499 drivers had drug involvement recorded in the CRASH data (only 207 overlapped); 250 drivers were diagnosed with multiple drugs in their system. Conclusions: Surveillance data from multiple public health data sets is necessary to identify the presence of drugs in injured drivers involved in motor vehicle crashes. The use of a single surveillance data set alone may significantly underreport the number of drugged drivers who were injured in a motor vehicle collision.

Abstract TP398: Stroke prevention opportunities being missed: the Australian Stroke Clinical Registry experience.

Introduction: Stroke utilizes a large portion of hospital resources. Little is known about the frequency of contacts with hospitals prior to first-ever stroke and potentially missed opportunities for stroke prevention. In addition, re-admissions may indicate failed secondary prevention. Hypothesis: Many patients have had a presentation or admission to hospital in the year prior to a first-ever stroke event. Methods: Data from the prospective, national Australian Stroke Clinical Registry (AuSCR) obtained between 15 June 2009 and 31 December 2010 from a large hospital in Melbourne, Victoria (Australia) were linked to the Victorian government emergency department (ED) and hospital discharge datasets for a 3 year ‘look-back’ period and any re-presentation up until February 2011 using stepwise deterministic linkage methods. Descriptive statistics are presented. Results: Matched linkage to ED (731/788) and hospital discharge (736/788) datasets was achieved in 93% of AuSCR registrants, of whom 513 were first-ever strokes (51% male, average age 74 [±16] years, 82% ischemic). Prior to the first-ever stroke, 221 (47%) registrants had ED presentations and 283 had a hospital admission on average 2.9 months before stroke. The mean number of ED presentations within 3 years of AuSCR registration for a first-ever event was 2.1 (SD 1.6); and 48/466 (10%) occurred in the month prior to stroke. Among first-ever stroke registrants, 200 were re-admitted on average within 5 months following discharge; 3.5% for recurrent stroke/TIA. Conclusion: Contact with hospitals was common (~50%) before first stroke, raising opportunities to screen and intervene in people at risk of stroke. As one in 5 hospital re-admissions is for recurrent stroke, a closer monitoring of secondary prevention measures in the early post-discharge period may be warranted.

Language affects length of stay in emergency departments in Queensland public hospitals.

A long length of stay (LOS) in the emergency department (ED) associated with overcrowding has been found to adversely affect the quality of ED care. The objective of this study is to determine whether patients who speak a language other than English at home have a longer LOS in EDs compared to those whose speak only English at home. A secondary data analysis of a Queensland state-wide hospital EDs dataset (Emergency Department Information System) was conducted for the period, 1 January 2008 to 31 December 2010. The interpreter requirement was the highest among Vietnamese speakers (23.1%) followed by Chinese (19.8%) and Arabic speakers (18.7%). There were significant differences in the distributions of the departure statuses among the language groups (Chi-squared=3236.88, P<0.001). Compared with English speakers, the Beta coefficient for the LOS in the EDs measured in minutes was among Vietnamese, 26.3 (95%CI: 22.1-30.5); Arabic, 10.3 (95%CI: 7.3-13.2); Spanish, 9.4 (95%CI: 7.1-11.7); Chinese, 8.6 (95%CI: 2.6-14.6); Hindi, 4.0 (95%CI: 2.2-5.7); Italian, 3.5 (95%CI: 1.6-5.4); and German, 2.7 (95%CI: 1.0-4.4). The final regression model explained 17% of the variability in LOS. There is a close relationship between the language spoken at home and the LOS at EDs, indicating that language could be an important predictor of prolonged LOS in EDs and improving language services might reduce LOS and ease overcrowding in EDs in Queensland's public hospitals.

DEVELOPMENT OF A RECORD LINKAGE SYSTEM TO SUPPORT INJURY SURVEILLANCE AND THE EVALUATION OF INTERVENTIONS

BackgroundObservational and interventional epidemiology have developed apart with separate data systems and skill sets. There is a need to bring these together to speed translation of knowledge to support developing...

CONCORDANCE OF IDENTIFIED DRUGS IN INJURED DRIVERS USING LINKED MOTOR VEHICLE CRASH, EMERGENCY DEPARTMENT, AND INPATIENT HOSPITALISATION DATASETS

BackgroundKentucky has the 6th highest drug overdose rate, 5th highest medical use rate of opioid pain relievers, and 11th highest opioid pain reliever sales rate in the USA (CDC, 2011).Aims/Objectives/PurposeThe...

Semantic-enhanced models to support timely admission prediction at emergency departments

With the rapid outstripping of limited health care resources by the demands on hospital care, it is of critical importance to find more effective and efficient methods of managing care. Our research addresses the problem of emergency department (ED) crowding by building classification models using various types of pre-admission information to help predict the hospital admission of individual patients. We have developed a framework of hospital admission prediction and proposed two novel approaches that capture semantic information in chief complaints to enhance prediction. Our experiments on an ED data set demonstrate that our proposed models outperformed several benchmark methods for admission prediction. These models can potentially be used as decision support tools at hospitals to improve ED throughput rate and enhance patient care.

Developing a Diagnosis‐based Severity Classification System for Use in Emergency Medical Services for Children

Lack of adequate risk adjustment methodologies has hindered the progress of emergency medicine health services research. The authors hypothesized that a consensus-derived, diagnosis-based severity classification system (SCS) would be significantly associated with actual measures of emergency department (ED) resource use and could ultimately be used to examine severity-adjusted outcomes across patient populations. A panel of subject matter experts used consensus methods to assign severity scores (1 = lowest severity to 5 = highest severity) to 3,041 ED International Classifications of Diseases (ICD), 9th revision, diagnosis codes. SCS scores were assigned to ED visits using the visit diagnosis code with the highest severity. We tested the association between the SCS scores and measures of ED resource use in three data sets: the Pediatric Emergency Care Applied Research Network Core Data Project (PCDP), the National Hospital Ambulatory Medical Care Survey (NHAMCS), and the Connecticut state ED data set. There was a significant association between the five-level SCS and all six measures of resource use: triage category, disposition, ED resource use, Current Procedural Terminology Evaluation and Management (CPT E&M) codes, ED length of stay, and ED charges within the three ED data sets. The SCS demonstrates validity in its strong association with actual ED resource use. The use of readily available ICD-9 diagnosis codes makes the SCS useful as a risk adjustment tool for health services research.

Emergency Department Eye Care in Florida

To describe trends in health insurance coverage for emergency department (ED) eye care in Florida from January 1, 2005, through December 31, 2009. The Florida Agency for Health Care Administration ED data sets for ED outpatient visits and ED admissions for eye care were analyzed for type of insurance coverage and stratified according to age younger than 18 years and 18 years or older. Negative binomial regression models were used to measure the percentage of change in payer distribution for each 1-year increase in calendar year. During the 5-year study period, commercial insurance was the most frequent payer of ED outpatient services (31.1%), followed by self-pay (26.2%) and Medicaid (22.0%). For persons younger than 18 years, Medicaid and self-payment made up 67.7% of principal payers. For outpatient ED visits, the percentage of change in Medicaid increased 5.9% for each calendar year (P < .001) and commercial coverage declined 4.5% (P < .001 ). The proportion of Florida residents receiving Medicaid during the study period was less than the national average. A substantial proportion of ED eye care in Florida is reimbursed through Medicaid or is paid for out of pocket. How the Patient Protection and Affordable Care Act of 2010 and the national economic recovery will affect safety-net institutions such as EDs and hospital staff is speculative, but the effect could be substantial.