<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/NCT04474444</url>
  </required_header>
  <id_info>
    <org_study_id>ASAP01</org_study_id>
    <nct_id>NCT04474444</nct_id>
  </id_info>
  <brief_title>Ambulance Calls for Substance Use and Alcohol in a Pandemic (ASAP)</brief_title>
  <acronym>ASAP</acronym>
  <official_title>Ambulance Calls for Substance Use and Alcohol in a Pandemic (ASAP): Exploring Attendance at Incidents Involving Substance and/or Alcohol Use During COVID-19 Pandemic</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>University of Lincoln</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>University of Lincoln</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 Primary objective is to explore ambulance service attendance at incidents involving&#xD;
      alcohol and/or substance use over the period of the pandemic lockdown, and the following&#xD;
      months. This will be to determine prevalence and explore factors such as patient gender, age,&#xD;
      ethnicity or location. Analysis will examine the calls over the course of the year prior to&#xD;
      the lockdown, and then compare this to the period of lockdown and following months.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      The Primary objective is to explore ambulance service attendance at incidents involving&#xD;
      alcohol and/or substance use over the period of the pandemic lockdown, and the following&#xD;
      months. This will be to determine prevalence and explore factors such as patient gender, age,&#xD;
      ethnicity or location. Analysis will examine the calls over the course of the year prior to&#xD;
      the lockdown, and then compare this to the period of lockdown and following months.&#xD;
&#xD;
      A time series analysis will be conducted to examine the calls over the course of the year&#xD;
      prior to the lockdown, and then compare this to the period of lockdown and following months.&#xD;
      This will use the 'Interrupted Time Series' (ITS) approach. To explore this regression models&#xD;
      will be built that examine the causal models for attendance prior to the pandemic and&#xD;
      compared to the lockdown time frame.&#xD;
&#xD;
      A multivariable regression model will be built. Initially a Directed Acyclic Graph (DAG) will&#xD;
      allow the identification of confounders and exposures relevant to the model. A logistic&#xD;
      regression model will be used to calculate the relative risk of call during lockdown compared&#xD;
      to the data prior to lockdown. The model will be fit using p&lt;0.05 as the definition of&#xD;
      statistical significance.&#xD;
&#xD;
      Descriptive statistics, trend analysis and predictive analysis will be conducted on the data&#xD;
      set to determine trends across time, factors that predict patients requiring ambulance&#xD;
      attendance, and factors that predict treatment outcomes. Missing data will be examined for&#xD;
      systematic bias, and where found to be missing at random will be excluded from analysis.&#xD;
      Where not missing at random, sensitivity analysis will be conducted.&#xD;
&#xD;
      Analysis will examine covariates. Age will be defined as single year continuous variable and&#xD;
      examined in categories such as 5-year age groups. Ethnicity will be categorised as groups,&#xD;
      such as black, Asian, other minority and mixed ethnic groups will be explored. Census data&#xD;
      such as the deprivation, rurality, income, employment, disability and education will look at&#xD;
      the decile as defined.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Unknown status</overall_status>
  <last_known_status>Recruiting</last_known_status>
  <start_date type="Actual">March 23, 2019</start_date>
  <completion_date type="Anticipated">March 23, 2021</completion_date>
  <primary_completion_date type="Anticipated">March 23, 2021</primary_completion_date>
  <study_type>Observational</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <observational_model>Case-Only</observational_model>
    <time_perspective>Retrospective</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>Attendances for alcohol and drug use</measure>
    <time_frame>Full data set 23/03/2019 compared to 22/03/2021 to look at interruption (lockdown) in the time series.</time_frame>
    <description>Counts of attendances for alcohol and drug use by the East Midland's Ambulance Service over the time period. This will be a number per day of people attended.</description>
  </primary_outcome>
  <number_of_groups>2</number_of_groups>
  <enrollment type="Anticipated">55000</enrollment>
  <condition>Alcohol Use, Unspecified</condition>
  <condition>Substance Use</condition>
  <condition>Intoxication Alcohol</condition>
  <condition>Emergencies</condition>
  <condition>COVID-19 Pandemic</condition>
  <condition>Treatment</condition>
  <arm_group>
    <arm_group_label>Hear and treat</arm_group_label>
    <description>phone call, treatment, not attended</description>
  </arm_group>
  <arm_group>
    <arm_group_label>See and treat</arm_group_label>
    <description>Ambulance crew attended</description>
  </arm_group>
  <intervention>
    <intervention_type>Other</intervention_type>
    <intervention_name>attendance by ambulance crew</intervention_name>
    <description>hear, attendance, convey</description>
    <arm_group_label>See and treat</arm_group_label>
  </intervention>
  <eligibility>
    <study_pop>
      <textblock>
        All people who ring 999 or 111 in an emergency.&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  Patient of any age&#xD;
&#xD;
          -  Patient requested ambulance attendance between 23rd March 2019 and 22nd March 2021&#xD;
&#xD;
          -  The patient record is held by East Midlands Ambulance Service&#xD;
&#xD;
          -  Patient records that have recorded a clinical impression related to alcohol and&#xD;
             substance use will be included in the data set alongside a word search in the free&#xD;
             text response box for the following words/phrases: narcotic, spice, mamba, alcohol,&#xD;
             substance use, drug use, illicit drug, overdose, intoxication, intoxicated, drunk,&#xD;
             high.&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  The patient record was outside of the indicated date range.&#xD;
&#xD;
          -  The patient record is not accessible via EMAS&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>N/A</minimum_age>
    <maximum_age>N/A</maximum_age>
    <healthy_volunteers>Accepts Healthy Volunteers</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Graham Law, PhD</last_name>
    <role>Principal Investigator</role>
    <affiliation>University of Lincoln</affiliation>
  </overall_official>
  <overall_contact>
    <last_name>Graham Law, PhD</last_name>
    <phone>07905008828</phone>
    <email>glaw@lincoln.ac.uk</email>
  </overall_contact>
  <overall_contact_backup>
    <last_name>Sam Lewis</last_name>
    <email>samlewis@lincoln.ac.uk</email>
  </overall_contact_backup>
  <location>
    <facility>
      <name>University of Lincoln</name>
      <address>
        <city>Lincoln</city>
        <zip>LN64PB</zip>
        <country>United Kingdom</country>
      </address>
    </facility>
    <status>Recruiting</status>
    <contact>
      <last_name>Graham Law, PhD</last_name>
      <phone>07905008828</phone>
      <email>glaw@lincoln.ac.uk</email>
    </contact>
  </location>
  <location_countries>
    <country>United Kingdom</country>
  </location_countries>
  <verification_date>July 2020</verification_date>
  <study_first_submitted>July 12, 2020</study_first_submitted>
  <study_first_submitted_qc>July 15, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">July 16, 2020</study_first_posted>
  <last_update_submitted>July 15, 2020</last_update_submitted>
  <last_update_submitted_qc>July 15, 2020</last_update_submitted_qc>
  <last_update_posted type="Actual">July 16, 2020</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </responsible_party>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>COVID-19</mesh_term>
    <mesh_term>Emergencies</mesh_term>
    <mesh_term>Substance-Related Disorders</mesh_term>
    <mesh_term>Alcoholic Intoxication</mesh_term>
    <mesh_term>Alcohol Drinking</mesh_term>
  </condition_browse>
  <patient_data>
    <sharing_ipd>No</sharing_ipd>
    <ipd_description>There is no plan to share the IPD</ipd_description>
  </patient_data>
  <!-- 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/NCT04474444
org study id: ASAP01
nct id: NCT04474444
lead sponsor: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
The Primary objective is to explore ambulance service attendance at incidents involving
      alcohol and/or substance use over the period of the pandemic lockdown, and the following
      months. This will be to determine prevalence and explore factors such as patient gender, age,
      ethnicity or location. Analysis will examine the calls over the course of the year prior to
      the lockdown, and then compare this to the period of lockdown and following months.
The Primary objective is to explore ambulance service attendance at incidents involving
      alcohol and/or substance use over the period of the pandemic lockdown, and the following
      months. This will be to determine prevalence and explore factors such as patient gender, age,
      ethnicity or location. Analysis will examine the calls over the course of the year prior to
      the lockdown, and then compare this to the period of lockdown and following months.

      A time series analysis will be conducted to examine the calls over the course of the year
      prior to the lockdown, and then compare this to the period of lockdown and following months.
      This will use the 'Interrupted Time Series' (ITS) approach. To explore this regression models
      will be built that examine the causal models for attendance prior to the pandemic and
      compared to the lockdown time frame.

      A multivariable regression model will be built. Initially a Directed Acyclic Graph (DAG) will
      allow the identification of confounders and exposures relevant to the model. A logistic
      regression model will be used to calculate the relative risk of call during lockdown compared
      to the data prior to lockdown. The model will be fit using p<0.05 as the definition of
      statistical significance.

      Descriptive statistics, trend analysis and predictive analysis will be conducted on the data
      set to determine trends across time, factors that predict patients requiring ambulance
      attendance, and factors that predict treatment outcomes. Missing data will be examined for
      systematic bias, and where found to be missing at random will be excluded from analysis.
      Where not missing at random, sensitivity analysis will be conducted.

      Analysis will examine covariates. Age will be defined as single year continuous variable and
      examined in categories such as 5-year age groups. Ethnicity will be categorised as groups,
      such as black, Asian, other minority and mixed ethnic groups will be explored. Census data
      such as the deprivation, rurality, income, employment, disability and education will look at
      the decile as defined.
observational model: Case-Only
time perspective: Retrospective
measure: Attendances for alcohol and drug use
time frame: Full data set 23/03/2019 compared to 22/03/2021 to look at interruption (lockdown) in the time series.
description: Counts of attendances for alcohol and drug use by the East Midland's Ambulance Service over the time period. This will be a number per day of people attended.
arm group label: Hear and treat
description: phone call, treatment, not attended
arm group label: See and treat
description: Ambulance crew attended
intervention type: Other
intervention name: attendance by ambulance crew
description: hear, attendance, convey
arm group label: See and treat
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: N/A
maximum age: N/A
healthy volunteers: Accepts Healthy Volunteers
last name: Graham Law, PhD
role: Principal Investigator
affiliation: University of Lincoln
last name: Graham Law, PhD
phone: 07905008828
email: glaw@lincoln.ac.uk
last name: Sam Lewis
email: samlewis@lincoln.ac.uk
facility: 
status: Recruiting
contact: 
country: United Kingdom
responsible party type: Sponsor
mesh term: COVID-19
mesh term: Emergencies
mesh term: Substance-Related Disorders
mesh term: Alcoholic Intoxication
mesh term: Alcohol Drinking
sharing ipd: No
ipd description: There is no plan to share the IPD