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The goal of this study is to create predictive models of emergency care and metrics for population health that can be used to analyze how events like hospital closures or disasters like Hurricane Sandy affect health care utilization by patients in specific populations or geographic regions. Additionally, it will allow the development of metrics for population health that can act as surveillance mechanisms to measure disease prevalence and identify patterns in emergency department use that can be used to identify specific geographic regions where health care is either optimized to promote health or needs to be improve so that population health can be improved.
The purpose of this study is to analyze the geographic patterns of emergency department utilization. This study will look at the relationship that geographic proximity and local population factors have on patient use of emergency departments. Geographic proximity of alternative hospitals and elicit other patient and hospital specific factors, such as demographic, insurance type, diagnosis, and socioeconomic factors that lead patients to choose specific hospitals for emergency care or generally lead to patients accessing emergency care will be compared.
Patterns of emergency department utilization by patients will be identified in specific geographies such as Census tracts to determine clusters of high and low emergency department use. We also analyze the patterns of emergency care use based on specific disease conditions.Investigators will analyze the rate of emergency department use for patients with diabetes to determine population prevalence of diseases using emergency department data. Studying the pattern of use by specific geographies or disease conditions will also allow us to understand how emergency department use varies among populations by geographies and the socioeconomic and health care factors local to those regions.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Patients visiting an emergency department in New York State |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Statewide Planning and Research Cooperative System Database | Other | The Statewide Planning and Research Cooperative System (SPARCS) is a comprehensive data reporting system created to collect information on discharges from hospitals. SPARCS currently collects patient level detail on patient characteristics, diagnoses and treatments, services, and charges for every hospital discharge, ambulatory surgery patient, and emergency department admission in New York State. |
| Measure | Description | Time Frame |
|---|---|---|
| Emergency Deparrtment (ED) visit by a patient | In order to identify repeat ED visits by the same individual, unique identifiers within SPARCS match visits by the same individual throughout the study period | 1 Day |
| Measure | Description | Time Frame |
|---|---|---|
| Surveillance of Disease Prevalence | Proportion of individuals who recieve a given diagnosis like diabetes during any emergency department visit including emergency inpatient admissions. | 1 Day |
| Extension to Other Disease Conditions and Geographies |
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Inclusion Criteria:
Exclusion Criteria:
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Includes all patients visiting an emergency department in New York State
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| Name | Affiliation | Role |
|---|---|---|
| David Lee, MD | NYU Langone Health | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| New York University School of Medicine | New York | New York | 10016 | United States |
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| ID | Term |
|---|---|
| D004630 | Emergencies |
| D003920 | Diabetes Mellitus |
| ID | Term |
|---|---|
| D020969 | Disease Attributes |
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |
| D044882 | Glucose Metabolism Disorders |
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Analysis of how disease prevalence can be measured at the level of the entire state. Populations will be stratified based on geography.
| 1 Day |
| Hospital Selection | Identification of the selection of a given hospital by patients based on facility codes contained within the SPARCS database. | 1 Day |
| D008659 | Metabolic Diseases |
| D009750 | Nutritional and Metabolic Diseases |
| D004700 | Endocrine System Diseases |