<clinical_study>
  <!-- This xml conforms to an XML Schema at:
    https://clinicaltrials.gov/ct2/html/images/info/public.xsd -->
  <required_header>
    <download_date>ClinicalTrials.gov processed this data on September 20, 2023</download_date>
    <link_text>Link to the current ClinicalTrials.gov record.</link_text>
    <url>https://clinicaltrials.gov/ct2/show/NCT03254524</url>
  </required_header>
  <id_info>
    <org_study_id>14-01408</org_study_id>
    <nct_id>NCT03254524</nct_id>
  </id_info>
  <brief_title>Geography of ED Use and Population Health</brief_title>
  <official_title>Statewide Analysis of Emergency Department Use for Determining Geographic Patterns of Emergency Care Use, Hospital Choice and Measures of Population Health.</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>NYU Langone Health</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>NYU Langone Health</source>
  <oversight_info>
    <has_dmc>No</has_dmc>
    <is_fda_regulated_drug>No</is_fda_regulated_drug>
    <is_fda_regulated_device>No</is_fda_regulated_device>
  </oversight_info>
  <brief_summary>
    <textblock>
      The goal of this study is to create predictive models of emergency care and metrics for&#xD;
      population health that can be used to analyze how events like hospital closures or disasters&#xD;
      like Hurricane Sandy affect health care utilization by patients in specific populations or&#xD;
      geographic regions. Additionally, it will allow the development of metrics for population&#xD;
      health that can act as surveillance mechanisms to measure disease prevalence and identify&#xD;
      patterns in emergency department use that can be used to identify specific geographic regions&#xD;
      where health care is either optimized to promote health or needs to be improve so that&#xD;
      population health can be improved.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      The purpose of this study is to analyze the geographic patterns of emergency department&#xD;
      utilization. This study will look at the relationship that geographic proximity and local&#xD;
      population factors have on patient use of emergency departments. Geographic proximity of&#xD;
      alternative hospitals and elicit other patient and hospital specific factors, such as&#xD;
      demographic, insurance type, diagnosis, and socioeconomic factors that lead patients to&#xD;
      choose specific hospitals for emergency care or generally lead to patients accessing&#xD;
      emergency care will be compared.&#xD;
&#xD;
      Patterns of emergency department utilization by patients will be identified in specific&#xD;
      geographies such as Census tracts to determine clusters of high and low emergency department&#xD;
      use. We also analyze the patterns of emergency care use based on specific disease&#xD;
      conditions.Investigators will analyze the rate of emergency department use for patients with&#xD;
      diabetes to determine population prevalence of diseases using emergency department data.&#xD;
      Studying the pattern of use by specific geographies or disease conditions will also allow us&#xD;
      to understand how emergency department use varies among populations by geographies and the&#xD;
      socioeconomic and health care factors local to those regions.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Completed</overall_status>
  <start_date type="Actual">August 20, 2014</start_date>
  <completion_date type="Actual">December 31, 2020</completion_date>
  <primary_completion_date type="Actual">December 31, 2020</primary_completion_date>
  <study_type>Observational</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <observational_model>Ecologic or Community</observational_model>
    <time_perspective>Retrospective</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>Emergency Deparrtment (ED) visit by a patient</measure>
    <time_frame>1 Day</time_frame>
    <description>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</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>Surveillance of Disease Prevalence</measure>
    <time_frame>1 Day</time_frame>
    <description>Proportion of individuals who recieve a given diagnosis like diabetes during any emergency department visit including emergency inpatient admissions.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Extension to Other Disease Conditions and Geographies</measure>
    <time_frame>1 Day</time_frame>
    <description>Analysis of how disease prevalence can be measured at the level of the entire state. Populations will be stratified based on geography.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Hospital Selection</measure>
    <time_frame>1 Day</time_frame>
    <description>Identification of the selection of a given hospital by patients based on facility codes contained within the SPARCS database.</description>
  </secondary_outcome>
  <number_of_groups>1</number_of_groups>
  <enrollment type="Actual">100</enrollment>
  <condition>Emergencies</condition>
  <condition>Diabetes Mellitus</condition>
  <arm_group>
    <arm_group_label>Patients visiting an emergency department in New York State</arm_group_label>
  </arm_group>
  <intervention>
    <intervention_type>Other</intervention_type>
    <intervention_name>Statewide Planning and Research Cooperative System Database</intervention_name>
    <description>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.</description>
    <arm_group_label>Patients visiting an emergency department in New York State</arm_group_label>
    <other_name>SPARCS</other_name>
  </intervention>
  <eligibility>
    <study_pop>
      <textblock>
        Includes all patients visiting an emergency department in New York State&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  Patients visiting an emergency department in New York State&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  Depending on the population analyzed, may exclude some subpopulations such as children&#xD;
             (in order study adults), prisoners or patients transferred from other healthcare&#xD;
             facilities (in order to study non-institutionalized individuals).&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>N/A</minimum_age>
    <maximum_age>120 Years</maximum_age>
    <healthy_volunteers>Accepts Healthy Volunteers</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>David Lee, MD</last_name>
    <role>Principal Investigator</role>
    <affiliation>NYU Langone Health</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>New York University School of Medicine</name>
      <address>
        <city>New York</city>
        <state>New York</state>
        <zip>10016</zip>
        <country>United States</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>United States</country>
  </location_countries>
  <verification_date>February 2021</verification_date>
  <study_first_submitted>August 16, 2017</study_first_submitted>
  <study_first_submitted_qc>August 16, 2017</study_first_submitted_qc>
  <study_first_posted type="Actual">August 18, 2017</study_first_posted>
  <last_update_submitted>February 4, 2021</last_update_submitted>
  <last_update_submitted_qc>February 4, 2021</last_update_submitted_qc>
  <last_update_posted type="Actual">February 8, 2021</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </responsible_party>
  <keyword>Epidemiology Research</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Emergencies</mesh_term>
  </condition_browse>
  <!-- Results have not yet been posted for this study                                          -->
</clinical_study>


download date: ClinicalTrials.gov processed this data on September 20, 2023
link text: Link to the current ClinicalTrials.gov record.
url: https://clinicaltrials.gov/ct2/show/NCT03254524
org study id: 14-01408
nct id: NCT03254524
lead sponsor: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
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.
observational model: Ecologic or Community
time perspective: Retrospective
measure: Emergency Deparrtment (ED) visit by a patient
time frame: 1 Day
description: 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
measure: Surveillance of Disease Prevalence
time frame: 1 Day
description: Proportion of individuals who recieve a given diagnosis like diabetes during any emergency department visit including emergency inpatient admissions.
measure: Extension to Other Disease Conditions and Geographies
time frame: 1 Day
description: Analysis of how disease prevalence can be measured at the level of the entire state. Populations will be stratified based on geography.
measure: Hospital Selection
time frame: 1 Day
description: Identification of the selection of a given hospital by patients based on facility codes contained within the SPARCS database.
arm group label: Patients visiting an emergency department in New York State
intervention type: Other
intervention name: Statewide Planning and Research Cooperative System Database
description: 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.
arm group label: Patients visiting an emergency department in New York State
other name: SPARCS
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: N/A
maximum age: 120 Years
healthy volunteers: Accepts Healthy Volunteers
last name: David Lee, MD
role: Principal Investigator
affiliation: NYU Langone Health
facility: 
country: United States
responsible party type: Sponsor
mesh term: Emergencies