Project description:BackgroundNursing home (NH) residents are frequent users of emergency departments (ED) and while prior research suggests that repeat visits are common, there is little data describing this phenomenon. Our objectives were to describe repeat ED visits over one year, identify risk factors for repeat use, and characterize "frequent" ED visitors.MethodsUsing provincial administrative data from Ontario, Canada, we identified all NH residents 65 years or older who visited an ED at least once between January 1 and March 31, 2010 and then followed them for one year to capture all additional ED visits. Frequent ED visitors were defined as those who had 3 or more repeat ED visits. We used logistic regression to estimate risk factors for any repeat ED visit and for being a frequent visitor and Andersen-Gill regression to estimate risk factors for the rate of repeat ED visits.ResultsIn a cohort of 25,653 residents (mean age 84.5 (SD?=?7.5) years, 68.2% female), 48.8% had at least one repeat ED visit. Residents who experienced a repeat ED visit were generally similar to others but they tended to be slightly younger, have a higher proportion male, and a higher proportion with minimal cognitive or physical impairment. Risk factors for a repeat ED visit included: being male (adjusted odds ratio 1.27, (95% confidence interval 1.19-1.36)), diagnoses such as diabetes (AOR 1.28 (1.19-1.37)) and congestive heart failure (1.26 (1.16-1.37)), while severe cognitive impairment (AOR 0.92 (0.84-0.99)) and 5 or more chronic conditions (AOR 0.82 (0.71-0.95)) appeared protective. Eleven percent of residents were identified as frequent ED visitors, and they were more often younger then 75 years, male, and less likely to have Alzheimer's disease or other dementias than non-frequent visitors.ConclusionsRepeat ED visits were common among NH residents but a relatively small group accounted for the largest number of visits. Although there were few clear defining characteristics, our findings suggest that medically complex residents and younger residents without cognitive impairments are at risk for such outcomes.

Project description:IntroductionThe American Hospital Association (AHA) has hospital-level data, while the Centers for Medicare & Medicaid Services (CMS) has patient-level data. Merging these with other distinct databases would permit analyses of hospital-based specialties, units, or departments, and patient outcomes. One distinct database is the National Emergency Department Inventory (NEDI), which contains information about all EDs in the United States. However, a challenge with merging these databases is that NEDI lists all US EDs individually, while the AHA and CMS group some EDs by hospital network. Consolidating data for this merge may be preferential to excluding grouped EDs. Our objectives were to consolidate ED data to enable linkage with administrative datasets and to determine the effect of excluding grouped EDs on ED-level summary results.MethodsUsing the 2014 NEDI-USA database, we surveyed all New England EDs. We individually matched NEDI EDs with corresponding EDs in the AHA and CMS. A "group match" was assigned when more than one NEDI ED was matched to a single AHA or CMS facility identification number. Within each group, we consolidated individual ED data to create a single observation based on sums or weighted averages of responses as appropriate.ResultsOf the 195 EDs in New England, 169 (87%) completed the NEDI survey. Among these, 130 (77%) EDs were individually listed in AHA and CMS, while 39 were part of groups consisting of 2-3 EDs but represented by one facility ID. Compared to the individually listed EDs, the 39 EDs included in a "group match" had a larger number of annual visits and beds, were more likely to be freestanding, and were less likely to be rural (all P<0.05). Two grouped EDs were excluded because the listed ED did not respond to the NEDI survey; the remaining 37 EDs were consolidated into 19 observations. Thus, the consolidated dataset contained 149 observations representing 171 EDs; this consolidated dataset yielded summary results that were similar to those of the 169 responding EDs.ConclusionExcluding grouped EDs would have resulted in a non-representative dataset. The original vs consolidated NEDI datasets yielded similar results and enabled linkage with large administrative datasets. This approach presents a novel opportunity to use characteristics of hospital-based specialties, units, and departments in studies of patient-level outcomes, to advance health services research.

Project description:Since its emergence in late 2019, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a pandemic with more than 55 million reported cases and 1.3 million estimated deaths worldwide. While epidemiological and clinical characteristics of COVID-19 have been reported, risk factors underlying the transition from mild to severe disease among patients remain poorly understood. In this retrospective study, we analysed data of 879 confirmed SARS-CoV-2 positive patients admitted to a two-site NHS Trust hospital in London, England, between January 1st and May 26th, 2020, with a majority of cases occurring in March and April. We extracted anonymised demographic data, physiological clinical variables and laboratory results from electronic healthcare records (EHR) and applied multivariate logistic regression, random forest and extreme gradient boosted trees. To evaluate the potential for early risk assessment, we used data available during patients' initial presentation at the emergency department (ED) to predict deterioration to one of three clinical endpoints in the remainder of the hospital stay: admission to intensive care, need for invasive mechanical ventilation and in-hospital mortality. Based on the trained models, we extracted the most informative clinical features in determining these patient trajectories. Considering our inclusion criteria, we have identified 129 of 879 (15%) patients that required intensive care, 62 of 878 (7%) patients needing mechanical ventilation, and 193 of 619 (31%) cases of in-hospital mortality. Our models learned successfully from early clinical data and predicted clinical endpoints with high accuracy, the best model achieving area under the receiver operating characteristic (AUC-ROC) scores of 0.76 to 0.87 (F1 scores of 0.42-0.60). Younger patient age was associated with an increased risk of receiving intensive care and ventilation, but lower risk of mortality. Clinical indicators of a patient's oxygen supply and selected laboratory results, such as blood lactate and creatinine levels, were most predictive of COVID-19 patient trajectories. Among COVID-19 patients machine learning can aid in the early identification of those with a poor prognosis, using EHR data collected during a patient's first presentation at ED. Patient age and measures of oxygenation status during ED stay are primary indicators of poor patient outcomes.

Project description:ObjectivesAdministrative claims data sets are often used for emergency care research and policy investigations of healthcare resource utilization, acute care practices, and evaluation of quality improvement interventions. Despite the high profile of emergency department (ED) visits in analyses using administrative claims, little work has evaluated the degree to which existing definitions based on claims data accurately captures conventionally defined hospital-based ED services. We sought to construct an operational definition for ED visitation using a comprehensive Medicare data set and to compare this definition to existing operational definitions used by researchers and policymakers.MethodsWe examined four operational definitions of an ED visit commonly used by researchers and policymakers using a 20% sample of the 2012 Medicare Chronic Condition Warehouse (CCW) data set. The CCW data set included all Part A (hospital) and Part B (hospital outpatient, physician) claims for a nationally representative sample of continuously enrolled Medicare fee-for-services beneficiaries. Three definitions were based on published research or existing quality metrics including: 1) provider claims-based definition, 2) facility claims-based definition, and 3) CMS Research Data Assistance Center (ResDAC) definition. In addition, we developed a fourth operational definition (Yale definition) that sought to incorporate additional coding rules for identifying ED visits. We report levels of agreement and disagreement among the four definitions.ResultsOf 10,717,786 beneficiaries included in the sample data set, 22% had evidence of ED use during the study year under any of the ED visit definitions. The definition using provider claims identified a total of 4,199,148 ED visits, the facility definition 4,795,057 visits, the ResDAC definition 5,278,980 ED visits, and the Yale definition 5,192,235 ED visits. The Yale definition identified a statistically different (p < 0.05) collection of ED visits than all other definitions including 17% more ED visits than the provider definition and 2% fewer visits than the ResDAC definition. Differences in ED visitation counts between each definition occurred for several reasons including the inclusion of critical care or observation services in the ED, discrepancies between facility and provider billing regulations, and operational decisions of each definition.ConclusionCurrent operational definitions of ED visitation using administrative claims produce different estimates of ED visitation based on the underlying assumptions applied to billing data and data set availability. Future analyses using administrative claims data should seek to validate specific definitions and inform the development of a consistent, consensus ED visitation definitions to standardize research reporting and the interpretation of policy interventions.

Project description:BackgroundAs the leading cause of emergency department visits in Canada, pain disproportionately affects socioeconomically disadvantaged populations. We examine the association between household food insecurity and individuals' pain-driven emergency department visits.MethodsWe designed a cross-sectional study linking the Canadian Community Health Survey 2005-2017 to the National Ambulatory Care Reporting System 2003-2017. Food insecurity was measured using a validated questionnaire. We excluded individuals with missing food insecurity status, individuals younger than 12 years and jurisdiction-years with partial emergency department records. We assessed emergency department visits driven by pain at different sites (migraine, other headaches, chest-throat pain, abdomen-pelvis pain, dorsalgia, joint pain, limb pain, other pain) and their characteristics (frequency, cause, acuity and time of emergency department visit) in Ontario and Alberta. We adjusted for sociodemographic characteristics, lifestyle and prior non-pain-driven emergency department visits in the models.ResultsThe sample contained 212 300 individuals aged 12 years and older. Compared with food-secure individuals, marginally, moderately and severely food-insecure people had 1.42 (95% confidence interval [CI] 1.20-1.68), 1.64 (95% CI 1.37-1.96) and 1.99 (95% CI 1.61-2.46) times higher adjusted incidence rates of pain-driven emergency department visits, respectively. The association was similar across sexes and significant among adults but not adolescents. Food insecurity was further associated with site-specific pain, with severely food-insecure individuals having significantly higher pain incidence than food-secure individuals. Severe food insecurity predicted more frequent, multicause, high-acuity and after-hours emergency department visits.InterpretationHousehold food insecurity status is significantly associated with pain-driven emergency department visits in the Canadian population. Policies targeting food insecurity may reduce pain and emergency department utilization.

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Emergency department performance assessment using administrative data: A managerial framework

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