xml: <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/NCT06017843</url>
    </required_header>
    <id_info>
      <org_study_id>CGPH-TB2023-24</org_study_id>
      <nct_id>NCT06017843</nct_id>
    </id_info>
    <brief_title>Impact Evaluation of Use of MATCH AI Predictive Modelling for Identification of Hotspots for TB Active Case Finding</brief_title>
    <acronym>SPOT-TB</acronym>
    <official_title>Impact Evaluation of Use of MATCH AI Predictive Modelling for Identification of Hotspots for TB Active Case Finding in Pakistan: a Pragmatic Stepped Wedge Cluster Randomized Trial</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>Centre for Global Public Health Pakistan</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
      <collaborator>
        <agency>Mercy Corps Pakistan</agency>
        <agency_class>Other</agency_class>
      </collaborator>
    </sponsors>
    <source>Centre for Global Public Health Pakistan</source>
    <oversight_info>
      <has_dmc>Yes</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 aim of this pragmatic, stepped wedge cluster-randomized trial is to measure the&#xD;
        comparative yield (number of incident TB cases diagnosed during active case-finding camps)&#xD;
        using a site selection approach based on predictions generated via an artificial intelligence&#xD;
        software called MATCH-AI (intervention group) versus the conventional approach of camp site&#xD;
        selection using field-staff knowledge and experience (control group). The trial will help&#xD;
        inform whether a targeted approach towards screening for TB using artificial-intelligence can&#xD;
        improve yields of TB cases detected through community-based active case-finding.&#xD;
      </textblock>
    </brief_summary>
    <detailed_description>
      <textblock>
        Despite significant progress over the past decades, an estimated 10.6 million individuals&#xD;
        fell ill with tuberculosis (TB) in 2021 and the disease caused 1.6 million deaths globally.&#xD;
        Pakistan is ranked as the 5th highest TB burden country in the world and TB causes 42,000&#xD;
        deaths annually in the country. A key challenge in the Pakistan's response to TB is ensuring&#xD;
        diagnosis and treatment of all individuals with TB. In 2020, out of the 573,000 cases, a&#xD;
        total of 276,736 (48%) were notified. Bridging this case-detection gap is a critical&#xD;
        objective for the National TB Program (NTP). Active case-finding (ACF), is a potential&#xD;
        strategy to increase case-detection by systematic screening of communities for TB. Recent&#xD;
        evidence, indicates that ACF can also reduce population-level TB incidence and prevalence&#xD;
        through early detection. While ACF interventions have demonstrated effectiveness in&#xD;
        community-trials and are now being conducted at scale in Pakistan, concerns remain regarding&#xD;
        their yields and cost-effectiveness in programmatic settings.&#xD;
  &#xD;
        The primary aim of this study is to investigate whether a targeted approach towards&#xD;
        community-based screening using MATCH-AI, an artificial intelligence software that models&#xD;
        sub-district TB prevalence, can improve the yield of ACF interventions in Pakistan. In the&#xD;
        intervention arm, field-team will conduct community-based ACF activities (called chest camps)&#xD;
        primarily in locations predicted by MATCH-AI to have a higher prevalence of TB. In the&#xD;
        control arm, field-teams will continue to utilize existing approaches towards camp&#xD;
        site-selection. The trial will be conducted in 65 districts of Pakistan in collaboration with&#xD;
        implementation partners of the NTP.&#xD;
      </textblock>
    </detailed_description>
    <overall_status>Not yet recruiting</overall_status>
    <start_date type="Anticipated">September 2023</start_date>
    <completion_date type="Anticipated">December 2024</completion_date>
    <primary_completion_date type="Anticipated">August 2024</primary_completion_date>
    <phase>N/A</phase>
    <study_type>Interventional</study_type>
    <has_expanded_access>No</has_expanded_access>
    <study_design_info>
      <allocation>Randomized</allocation>
      <intervention_model>Crossover Assignment</intervention_model>
      <intervention_model_description>The MATCH-AI evaluation is designed as a stepped-wedge cluster randomized trial. In this study, clusters (mobile X-ray van teams) will successively switch over in groups of 3 to the intervention in a randomly assigned order until all clusters are eventually exposed to the intervention.&#xD;
  In the intervention arm, TB active case finding camps will be conducted primarily in locations guided by MATCH-AI a modelling software that predicts TB hotspots. In the control sites, field-teams will continue to utilize existing approaches such as local knowledge, historical data etc towards camp site-selection.</intervention_model_description>
      <primary_purpose>Health Services Research</primary_purpose>
      <masking>Single (Participant)</masking>
      <masking_description>Individuals visiting camps will be masked to the intervention status.</masking_description>
    </study_design_info>
    <primary_outcome>
      <measure>Camp positivity yield</measure>
      <time_frame>12 months</time_frame>
      <description>Counts of bacteriologically confirmed TB (B+) cases diagnosed in each camp</description>
    </primary_outcome>
    <secondary_outcome>
      <measure>Camp positivity rate</measure>
      <time_frame>12 months</time_frame>
      <description>Bacteriologically confirmed TB (B+) cases per population screened</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>Camp All-Forms yield</measure>
      <time_frame>12 months</time_frame>
      <description>Counts of All-Forms TB (AF-TB) cases diagnosed in each camp</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>Camp All-Forms TB rate</measure>
      <time_frame>12 months</time_frame>
      <description>All-Forms TB (AF-TB) cases per population screened</description>
    </secondary_outcome>
    <number_of_arms>2</number_of_arms>
    <enrollment type="Anticipated">180000</enrollment>
    <condition>Tuberculosis</condition>
    <arm_group>
      <arm_group_label>Intervention</arm_group_label>
      <arm_group_type>Experimental</arm_group_type>
      <description>Camps site selection for active case finding for TB using MATCH-AI</description>
    </arm_group>
    <arm_group>
      <arm_group_label>Control</arm_group_label>
      <arm_group_type>No Intervention</arm_group_type>
      <description>Camps site selection for active case finding for TB using existing approaches.</description>
    </arm_group>
    <intervention>
      <intervention_type>Other</intervention_type>
      <intervention_name>Camps site selection for active case finding for TB using MATCH-AI</intervention_name>
      <description>The primary intervention in this study is the roll-out of MATCH-AI, an artificial intelligence software that models sub-district TB prevalence, to guide site selection of ACF camps. The MATCH-AI tool uses a Bayesian modelling approach to predict TB prevalence to a resolution of 10,000 population that are mapped as polygons. The model integrates data from a range of sources including historical TB facility notification data, previous ACF data as well as contextual factors such as demographics, income, population density, health indicators such as vaccination coverage and climate related variables to predict localized TB prevalence. In the intervention arm, camps will be conducted primarily in locations guided by MATCH-AI.</description>
      <arm_group_label>Intervention</arm_group_label>
    </intervention>
    <eligibility>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            -  All individuals &gt;15 years of age presenting to camp sites&#xD;
  &#xD;
            -  Individuals with previous history of TB disease&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            -  Children and adolescents &lt;15 years of age&#xD;
  &#xD;
            -  Pregnant women&#xD;
        </textblock>
      </criteria>
      <gender>All</gender>
      <minimum_age>15 Years</minimum_age>
      <maximum_age>N/A</maximum_age>
      <healthy_volunteers>Accepts Healthy Volunteers</healthy_volunteers>
    </eligibility>
    <overall_official>
      <last_name>Faran Emmanuel</last_name>
      <role>Principal Investigator</role>
      <affiliation>Centre for Global Public Health Pakistan</affiliation>
    </overall_official>
    <overall_contact>
      <last_name>Amna Mahfooz, MS(PH)</last_name>
      <phone>+923438101441</phone>
      <email>amna.mahfooz@cgph.org.pk</email>
    </overall_contact>
    <overall_contact_backup>
      <last_name>Faheem Baig</last_name>
      <phone>+923345379004</phone>
      <email>faheem@cgph.org.pk</email>
    </overall_contact_backup>
    <verification_date>September 2023</verification_date>
    <study_first_submitted>August 21, 2023</study_first_submitted>
    <study_first_submitted_qc>August 28, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">August 30, 2023</study_first_posted>
    <last_update_submitted>September 4, 2023</last_update_submitted>
    <last_update_submitted_qc>September 4, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">September 6, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Sponsor</responsible_party_type>
    </responsible_party>
    <keyword>Active case finding</keyword>
    <keyword>Artificial Intelligence</keyword>
    <keyword>Hotspots</keyword>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Tuberculosis</mesh_term>
    </condition_browse>
    <patient_data>
      <sharing_ipd>No</sharing_ipd>
      <ipd_description>At the conclusion of the trial, aggregate, summary data used for the final analysis will be stored on a public repository for archiving.</ipd_description>
    </patient_data>
    <!-- Results have not yet been posted for this study                                          -->
  </clinical_study>
  
  
text: 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/NCT06017843
  org study id: CGPH-TB2023-24
  nct id: NCT06017843
  lead sponsor: 
  collaborator: 
  has dmc: Yes
  is fda regulated drug: No
  is fda regulated device: No
  The aim of this pragmatic, stepped wedge cluster-randomized trial is to measure the
        comparative yield (number of incident TB cases diagnosed during active case-finding camps)
        using a site selection approach based on predictions generated via an artificial intelligence
        software called MATCH-AI (intervention group) versus the conventional approach of camp site
        selection using field-staff knowledge and experience (control group). The trial will help
        inform whether a targeted approach towards screening for TB using artificial-intelligence can
        improve yields of TB cases detected through community-based active case-finding.
  Despite significant progress over the past decades, an estimated 10.6 million individuals
        fell ill with tuberculosis (TB) in 2021 and the disease caused 1.6 million deaths globally.
        Pakistan is ranked as the 5th highest TB burden country in the world and TB causes 42,000
        deaths annually in the country. A key challenge in the Pakistan's response to TB is ensuring
        diagnosis and treatment of all individuals with TB. In 2020, out of the 573,000 cases, a
        total of 276,736 (48%) were notified. Bridging this case-detection gap is a critical
        objective for the National TB Program (NTP). Active case-finding (ACF), is a potential
        strategy to increase case-detection by systematic screening of communities for TB. Recent
        evidence, indicates that ACF can also reduce population-level TB incidence and prevalence
        through early detection. While ACF interventions have demonstrated effectiveness in
        community-trials and are now being conducted at scale in Pakistan, concerns remain regarding
        their yields and cost-effectiveness in programmatic settings.
  
        The primary aim of this study is to investigate whether a targeted approach towards
        community-based screening using MATCH-AI, an artificial intelligence software that models
        sub-district TB prevalence, can improve the yield of ACF interventions in Pakistan. In the
        intervention arm, field-team will conduct community-based ACF activities (called chest camps)
        primarily in locations predicted by MATCH-AI to have a higher prevalence of TB. In the
        control arm, field-teams will continue to utilize existing approaches towards camp
        site-selection. The trial will be conducted in 65 districts of Pakistan in collaboration with
        implementation partners of the NTP.
  allocation: Randomized
  intervention model: Crossover Assignment
  intervention model description: The MATCH-AI evaluation is designed as a stepped-wedge cluster randomized trial. In this study, clusters (mobile X-ray van teams) will successively switch over in groups of 3 to the intervention in a randomly assigned order until all clusters are eventually exposed to the intervention.
  In the intervention arm, TB active case finding camps will be conducted primarily in locations guided by MATCH-AI a modelling software that predicts TB hotspots. In the control sites, field-teams will continue to utilize existing approaches such as local knowledge, historical data etc towards camp site-selection.
  primary purpose: Health Services Research
  masking: Single (Participant)
  masking description: Individuals visiting camps will be masked to the intervention status.
  measure: Camp positivity yield
  time frame: 12 months
  description: Counts of bacteriologically confirmed TB (B+) cases diagnosed in each camp
  measure: Camp positivity rate
  time frame: 12 months
  description: Bacteriologically confirmed TB (B+) cases per population screened
  measure: Camp All-Forms yield
  time frame: 12 months
  description: Counts of All-Forms TB (AF-TB) cases diagnosed in each camp
  measure: Camp All-Forms TB rate
  time frame: 12 months
  description: All-Forms TB (AF-TB) cases per population screened
  arm group label: Intervention
  arm group type: Experimental
  description: Camps site selection for active case finding for TB using MATCH-AI
  arm group label: Control
  arm group type: No Intervention
  description: Camps site selection for active case finding for TB using existing approaches.
  intervention type: Other
  intervention name: Camps site selection for active case finding for TB using MATCH-AI
  description: The primary intervention in this study is the roll-out of MATCH-AI, an artificial intelligence software that models sub-district TB prevalence, to guide site selection of ACF camps. The MATCH-AI tool uses a Bayesian modelling approach to predict TB prevalence to a resolution of 10,000 population that are mapped as polygons. The model integrates data from a range of sources including historical TB facility notification data, previous ACF data as well as contextual factors such as demographics, income, population density, health indicators such as vaccination coverage and climate related variables to predict localized TB prevalence. In the intervention arm, camps will be conducted primarily in locations guided by MATCH-AI.
  arm group label: Intervention
  criteria: 
  gender: All
  minimum age: 15 Years
  maximum age: N/A
  healthy volunteers: Accepts Healthy Volunteers
  last name: Faran Emmanuel
  role: Principal Investigator
  affiliation: Centre for Global Public Health Pakistan
  last name: Amna Mahfooz, MS(PH)
  phone: +923438101441
  email: amna.mahfooz@cgph.org.pk
  last name: Faheem Baig
  phone: +923345379004
  email: faheem@cgph.org.pk
  responsible party type: Sponsor
  mesh term: Tuberculosis
  sharing ipd: No
  ipd description: At the conclusion of the trial, aggregate, summary data used for the final analysis will be stored on a public repository for archiving.