<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/NCT04472078</url>
  </required_header>
  <id_info>
    <org_study_id>1831</org_study_id>
    <nct_id>NCT04472078</nct_id>
  </id_info>
  <brief_title>Seroprevalence of Coronavirus Disease 2019 (COVID-19) Antibodies in a Vulnerable Neighbourhood, Buenos Aries Argentina</brief_title>
  <official_title>Seroprevalence of Antibodies to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) in a Vulnerable Neighbourhood, Buenos Aries Argentina</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Hospital Italiano de Buenos Aires</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>Hospital de Niños R. Gutierrez de Buenos Aires</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>Salud Comunitaria Ministerio de Salud GCBA</agency>
      <agency_class>Other</agency_class>
    </collaborator>
  </sponsors>
  <source>Hospital Italiano de Buenos Aires</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>
      Background The study aimed to establish the seroprevalence of SARS-COV-2 in an Argentinian&#xD;
      slum three months after the first case was reported.&#xD;
&#xD;
      Methods Between June 10th and July 1st, a cross-sectional design was carried out on people&#xD;
      over 14 years old, selected from a probabilistic sample of households. Finger prick puncture&#xD;
      ELISA test&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      A community-level seroprevalence survey was conducted in a cross-sectional design during a&#xD;
      Participatory Action Research (PAR).&#xD;
&#xD;
      Starter PAR team: researcher who developed the Elisa Test, virologist that perform the&#xD;
      analysis of the test and physician that served as volunteer field epidemiologist at the&#xD;
      surveillance system (DetectAr Barrio 31) offered technical cooperation to test people&#xD;
      experiencing homelessness.&#xD;
&#xD;
      PAR-First step: A sample for convenience of homeless people was tested during 3 days in a&#xD;
      popular dining room managed by a non-governmental organization. Samples were collected by a&#xD;
      DetectAr nurse and a volunteer epidemiologist.&#xD;
&#xD;
      PAR-Second step: feasibility was determined by the DetectAr coordinators of the Community&#xD;
      Health Division of the Ministry of Health and the 12 health community workers (HCWs) when two&#xD;
      volunteer epidemiologists from a University Institute explained the field organization&#xD;
      (sample technique, data collection and database entry) for obtaining a probabilistic sample&#xD;
      of residents. The PAR process was a cornerstone for reaching the sample size.&#xD;
&#xD;
      PAR-Third step: A cross-sectional study for seroprevalence survey was carried out.&#xD;
&#xD;
      Sample size and sample method Sample size was calculated for a seroprevalence of 5% according&#xD;
      to evidence. A two-stage random sampling method was applied. First level: sector of the slum,&#xD;
      Second level: geographical areas determined by the Department of Statistic and Census. Thirty&#xD;
      houses were selected at this level. People over 14 years old were tested at the front door of&#xD;
      their houses.&#xD;
&#xD;
      Serological test An enzyme linked immunosorbent assay [ELISA] developed in Argentina, by a&#xD;
      laboratory in Buenos Aires, Argentina, was used. Performance characteristics are a high&#xD;
      specificity (&gt;95%) and a high sensibility (&gt;95%) for SARS-COV-2 IgG. The test detects&#xD;
      antibodies against two viral antigens, trimeric spike and the receptor binding domain (RBD)&#xD;
      of the spike. Viral proteins were expressed in human cells. This kit has obtained regulatory&#xD;
      approval by Argentina's national drug regulatory agency (ANMAT, National Administration for&#xD;
      Drugs, Food and Medical Devices)[. Blood samples were collected in a capillary tube from a&#xD;
      finger prick, taken at the front door of each house. All HCWs were trained and&#xD;
      epidemiological data was entered in a database. Samples were processed and analyzed at the&#xD;
      virology laboratory in a pediatric hospital of Buenos Aires.&#xD;
&#xD;
      Statistical analysis To obtain the weighted prevalence, sample dataset was expanded to that&#xD;
      of the last census by 3 factors: at neighborhood level, at household level and at individual&#xD;
      level. The calculation of expansion factors at the household level is the inverse of the&#xD;
      joint probability of selecting the last sampling unit (a household). The expansion factors at&#xD;
      the household level imply three types of adjustments. The first one is related with&#xD;
      non-response (given that some households did not want to answer the survey); the second one&#xD;
      corresponds to the projection of sample to the entire population, and the third one to&#xD;
      calibration techniques with a final adjustment by groups of age and gender, using external&#xD;
      information from population census. Therefore, calibration variables were people 14 year-old&#xD;
      or more, grouped by sex and by intervals of age: 14-30; 31-45; 46-59; 60 and more.&#xD;
&#xD;
      Prevalence of IgG antibodies were adjusted using sampling weights and post-stratification to&#xD;
      allow for differences in non-response rates based on age group, sex, and census-tract income.&#xD;
&#xD;
      As household members share exposure to COVID-19, thus the outcome (prevalence of COVID-19&#xD;
      IgG) should show some correlation within the household. To test clustering effect, a random&#xD;
      effects logistic regression model (multilevel model) was applied as it includes the variation&#xD;
      between clusters explicitly in the likelihood and therefore takes account of intracluster&#xD;
      correlation.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Completed</overall_status>
  <start_date type="Actual">June 10, 2020</start_date>
  <completion_date type="Actual">December 11, 2020</completion_date>
  <primary_completion_date type="Actual">June 26, 2020</primary_completion_date>
  <study_type>Observational [Patient Registry]</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <observational_model>Cohort</observational_model>
    <time_perspective>Cross-Sectional</time_perspective>
  </study_design_info>
  <target_duration>1 Day</target_duration>
  <primary_outcome>
    <measure>to establish the seroprevalence of SARS-CoV-2</measure>
    <time_frame>up to 20 weeks</time_frame>
    <description>Prevalence of positive IgG for SARS-CoV-2</description>
  </primary_outcome>
  <enrollment type="Actual">873</enrollment>
  <condition>SARS-CoV-2</condition>
  <intervention>
    <intervention_type>Diagnostic Test</intervention_type>
    <intervention_name>Serological Assay or IgG for SARS-CoV-2</intervention_name>
    <description>Serological Assay for detection of IgG for SARS-CoV-2</description>
  </intervention>
  <eligibility>
    <study_pop>
      <textblock>
        Barrio Mugica, Buenos Aires city, Argentina, This Barrio isone of the most overcrowded&#xD;
        slums with more than 50.000 inhabitants, and almost 1500 people experiencing homelessness&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  All subjects of 14 years or more who accept to be test&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  none&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>14 Years</minimum_age>
    <maximum_age>N/A</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Alicia Mistchenko, MD PHD</last_name>
    <role>Principal Investigator</role>
    <affiliation>Hospital de Niños Ricardo Gutierrez</affiliation>
  </overall_official>
  <overall_official>
    <last_name>Vanina L Pagotto, MD MG</last_name>
    <role>Study Chair</role>
    <affiliation>Hospital Italiano de Buenos Aires</affiliation>
  </overall_official>
  <overall_official>
    <last_name>Silvana Figar, MD MG</last_name>
    <role>Study Chair</role>
    <affiliation>Hospital Italiano de Buenos Aires</affiliation>
  </overall_official>
  <overall_official>
    <last_name>Andrea Gamarnik, PHD</last_name>
    <role>Study Chair</role>
    <affiliation>National Council of Scientific and Technical Research, Argentina</affiliation>
  </overall_official>
  <overall_official>
    <last_name>Ana M Gomez Saldaño, MD MG</last_name>
    <role>Study Chair</role>
    <affiliation>Salud Comunitaria Ministerio de Salud GCBA</affiliation>
  </overall_official>
  <overall_official>
    <last_name>Fernan Quiroz, MD MG</last_name>
    <role>Study Director</role>
    <affiliation>Ministerio de Salud GCBA</affiliation>
  </overall_official>
  <overall_official>
    <last_name>Lorena Luna, MG</last_name>
    <role>Study Chair</role>
    <affiliation>Salud Comunitaria Ministerio de Salud GCBA</affiliation>
  </overall_official>
  <overall_official>
    <last_name>Magdalena Wagner Manslau</last_name>
    <role>Study Chair</role>
    <affiliation>Salud Comunitaria Ministerio de Salud GCBA</affiliation>
  </overall_official>
  <overall_official>
    <last_name>Julieta Salto</last_name>
    <role>Study Chair</role>
    <affiliation>Salud Comunitaria Ministerio de Salud GCBA</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>Hospital de Niños Ricardo Gutierrez</name>
      <address>
        <city>Buenos Aires</city>
        <state>Caba</state>
        <zip>1425</zip>
        <country>Argentina</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>Argentina</country>
  </location_countries>
  <link>
    <url>https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/community-level-seroprevalence-surveys.html</url>
    <description>Community-level Seroprevalence Surveys</description>
  </link>
  <link>
    <url>http://wadmin.uca.edu.ar/public/ckeditor/2017-Observatorio-Informes_Defensoria-CABA-24-10-VF.pdf</url>
    <description>Studies on the processes of social and urban integration in three Buenos Aires slums</description>
  </link>
  <link>
    <url>https://www.equinetafrica.org/sites/default/files/uploads/documents/PAR%20Methods%20Reader2014%20for%20web.pdf</url>
    <description>Participatory action research in health systems</description>
  </link>
  <link>
    <url>https://tallereduca.files.wordpress.com/2014/06/fuks.pdf</url>
    <description>Systemic Facilitation of Collective Processes: Supporting Creativity and Participative Processes in Groups, Communities and Networks</description>
  </link>
  <link>
    <url>https://www.argentina.gob.ar/noticias/reactivos-covid-19</url>
    <description>COVID-19 reagents</description>
  </link>
  <link>
    <url>https://www.estadisticaciudad.gob.ar/eyc/?p=39240</url>
    <description>Household and population census: villas 31 and 31 bis, City of Buenos Aires 2009</description>
  </link>
  <link>
    <url>https://www.apmresearchlab.org/covid/deaths-by-race</url>
    <description>The Color of Coronavirus: COVID-19 Deaths by Race and Ethnicity in the U.S.</description>
  </link>
  <link>
    <url>https://doi.org/10.1093/cid/cir007</url>
    <description>&quot;Herd Immunity&quot;: A Rough Guide</description>
  </link>
  <reference>
    <citation>Sood N, Simon P, Ebner P, Eichner D, Reynolds J, Bendavid E, Bhattacharya J. Seroprevalence of SARS-CoV-2-Specific Antibodies Among Adults in Los Angeles County, California, on April 10-11, 2020. JAMA. 2020 Jun 16;323(23):2425-2427. doi: 10.1001/jama.2020.8279.</citation>
    <PMID>32421144</PMID>
  </reference>
  <reference>
    <citation>Pereira RJ, Nascimento GNLD, Gratao LHA, Pimenta RS. The risk of COVID-19 transmission in favelas and slums in Brazil. Public Health. 2020 Jun;183:42-43. doi: 10.1016/j.puhe.2020.04.042. Epub 2020 May 8. No abstract available.</citation>
    <PMID>32422438</PMID>
  </reference>
  <reference>
    <citation>Corburn J, Vlahov D, Mberu B, Riley L, Caiaffa WT, Rashid SF, Ko A, Patel S, Jukur S, Martinez-Herrera E, Jayasinghe S, Agarwal S, Nguendo-Yongsi B, Weru J, Ouma S, Edmundo K, Oni T, Ayad H. Slum Health: Arresting COVID-19 and Improving Well-Being in Urban Informal Settlements. J Urban Health. 2020 Jun;97(3):348-357. doi: 10.1007/s11524-020-00438-6. Erratum In: J Urban Health. 2021 Apr;98(2):309-310.</citation>
    <PMID>32333243</PMID>
  </reference>
  <reference>
    <citation>Moen EL, Fricano-Kugler CJ, Luikart BW, O'Malley AJ. Analyzing Clustered Data: Why and How to Account for Multiple Observations Nested within a Study Participant? PLoS One. 2016 Jan 14;11(1):e0146721. doi: 10.1371/journal.pone.0146721. eCollection 2016.</citation>
    <PMID>26766425</PMID>
  </reference>
  <reference>
    <citation>Galbraith S, Daniel JA, Vissel B. A study of clustered data and approaches to its analysis. J Neurosci. 2010 Aug 11;30(32):10601-8. doi: 10.1523/JNEUROSCI.0362-10.2010.</citation>
    <PMID>20702692</PMID>
  </reference>
  <reference>
    <citation>Aarts E, Verhage M, Veenvliet JV, Dolan CV, van der Sluis S. A solution to dependency: using multilevel analysis to accommodate nested data. Nat Neurosci. 2014 Apr;17(4):491-6. doi: 10.1038/nn.3648. Epub 2014 Mar 26.</citation>
    <PMID>24671065</PMID>
  </reference>
  <reference>
    <citation>Zhang CH, Schwartz GG. Spatial Disparities in Coronavirus Incidence and Mortality in the United States: An Ecological Analysis as of May 2020. J Rural Health. 2020 Jun;36(3):433-445. doi: 10.1111/jrh.12476. Epub 2020 Jun 16.</citation>
    <PMID>32543763</PMID>
  </reference>
  <reference>
    <citation>Theel ES, Slev P, Wheeler S, Couturier MR, Wong SJ, Kadkhoda K. The Role of Antibody Testing for SARS-CoV-2: Is There One? J Clin Microbiol. 2020 Jul 23;58(8):e00797-20. doi: 10.1128/JCM.00797-20. Print 2020 Jul 23.</citation>
    <PMID>32350047</PMID>
  </reference>
  <reference>
    <citation>Buckley RM. Targeting the World's Slums as Fat Tails in the Distribution of COVID-19 Cases. J Urban Health. 2020 Jun;97(3):358-364. doi: 10.1007/s11524-020-00450-w.</citation>
    <PMID>32488763</PMID>
  </reference>
  <verification_date>December 2020</verification_date>
  <study_first_submitted>July 12, 2020</study_first_submitted>
  <study_first_submitted_qc>July 13, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">July 15, 2020</study_first_posted>
  <last_update_submitted>December 13, 2020</last_update_submitted>
  <last_update_submitted_qc>December 13, 2020</last_update_submitted_qc>
  <last_update_posted type="Actual">December 17, 2020</last_update_posted>
  <responsible_party>
    <responsible_party_type>Principal Investigator</responsible_party_type>
    <investigator_affiliation>Hospital de Niños R. Gutierrez de Buenos Aires</investigator_affiliation>
    <investigator_full_name>ALICIA MISTCHENKO</investigator_full_name>
    <investigator_title>Principal Investigator</investigator_title>
  </responsible_party>
  <keyword>Slums</keyword>
  <keyword>Seroprevalence study</keyword>
  <keyword>Participatory Research</keyword>
  <keyword>Coronavirus</keyword>
  <keyword>Community health</keyword>
  <keyword>Clustering</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Coronavirus Infections</mesh_term>
  </condition_browse>
  <patient_data>
    <sharing_ipd>Undecided</sharing_ipd>
    <ipd_description>undecided</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/NCT04472078
org study id: 1831
nct id: NCT04472078
lead sponsor: 
collaborator: 
collaborator: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
Background The study aimed to establish the seroprevalence of SARS-COV-2 in an Argentinian
      slum three months after the first case was reported.

      Methods Between June 10th and July 1st, a cross-sectional design was carried out on people
      over 14 years old, selected from a probabilistic sample of households. Finger prick puncture
      ELISA test
A community-level seroprevalence survey was conducted in a cross-sectional design during a
      Participatory Action Research (PAR).

      Starter PAR team: researcher who developed the Elisa Test, virologist that perform the
      analysis of the test and physician that served as volunteer field epidemiologist at the
      surveillance system (DetectAr Barrio 31) offered technical cooperation to test people
      experiencing homelessness.

      PAR-First step: A sample for convenience of homeless people was tested during 3 days in a
      popular dining room managed by a non-governmental organization. Samples were collected by a
      DetectAr nurse and a volunteer epidemiologist.

      PAR-Second step: feasibility was determined by the DetectAr coordinators of the Community
      Health Division of the Ministry of Health and the 12 health community workers (HCWs) when two
      volunteer epidemiologists from a University Institute explained the field organization
      (sample technique, data collection and database entry) for obtaining a probabilistic sample
      of residents. The PAR process was a cornerstone for reaching the sample size.

      PAR-Third step: A cross-sectional study for seroprevalence survey was carried out.

      Sample size and sample method Sample size was calculated for a seroprevalence of 5% according
      to evidence. A two-stage random sampling method was applied. First level: sector of the slum,
      Second level: geographical areas determined by the Department of Statistic and Census. Thirty
      houses were selected at this level. People over 14 years old were tested at the front door of
      their houses.

      Serological test An enzyme linked immunosorbent assay [ELISA] developed in Argentina, by a
      laboratory in Buenos Aires, Argentina, was used. Performance characteristics are a high
      specificity (>95%) and a high sensibility (>95%) for SARS-COV-2 IgG. The test detects
      antibodies against two viral antigens, trimeric spike and the receptor binding domain (RBD)
      of the spike. Viral proteins were expressed in human cells. This kit has obtained regulatory
      approval by Argentina's national drug regulatory agency (ANMAT, National Administration for
      Drugs, Food and Medical Devices)[. Blood samples were collected in a capillary tube from a
      finger prick, taken at the front door of each house. All HCWs were trained and
      epidemiological data was entered in a database. Samples were processed and analyzed at the
      virology laboratory in a pediatric hospital of Buenos Aires.

      Statistical analysis To obtain the weighted prevalence, sample dataset was expanded to that
      of the last census by 3 factors: at neighborhood level, at household level and at individual
      level. The calculation of expansion factors at the household level is the inverse of the
      joint probability of selecting the last sampling unit (a household). The expansion factors at
      the household level imply three types of adjustments. The first one is related with
      non-response (given that some households did not want to answer the survey); the second one
      corresponds to the projection of sample to the entire population, and the third one to
      calibration techniques with a final adjustment by groups of age and gender, using external
      information from population census. Therefore, calibration variables were people 14 year-old
      or more, grouped by sex and by intervals of age: 14-30; 31-45; 46-59; 60 and more.

      Prevalence of IgG antibodies were adjusted using sampling weights and post-stratification to
      allow for differences in non-response rates based on age group, sex, and census-tract income.

      As household members share exposure to COVID-19, thus the outcome (prevalence of COVID-19
      IgG) should show some correlation within the household. To test clustering effect, a random
      effects logistic regression model (multilevel model) was applied as it includes the variation
      between clusters explicitly in the likelihood and therefore takes account of intracluster
      correlation.
observational model: Cohort
time perspective: Cross-Sectional
measure: to establish the seroprevalence of SARS-CoV-2
time frame: up to 20 weeks
description: Prevalence of positive IgG for SARS-CoV-2
intervention type: Diagnostic Test
intervention name: Serological Assay or IgG for SARS-CoV-2
description: Serological Assay for detection of IgG for SARS-CoV-2
study pop: 
sampling method: Probability Sample
criteria: 
gender: All
minimum age: 14 Years
maximum age: N/A
healthy volunteers: No
last name: Alicia Mistchenko, MD PHD
role: Principal Investigator
affiliation: Hospital de Niños Ricardo Gutierrez
last name: Vanina L Pagotto, MD MG
role: Study Chair
affiliation: Hospital Italiano de Buenos Aires
last name: Silvana Figar, MD MG
role: Study Chair
affiliation: Hospital Italiano de Buenos Aires
last name: Andrea Gamarnik, PHD
role: Study Chair
affiliation: National Council of Scientific and Technical Research, Argentina
last name: Ana M Gomez Saldaño, MD MG
role: Study Chair
affiliation: Salud Comunitaria Ministerio de Salud GCBA
last name: Fernan Quiroz, MD MG
role: Study Director
affiliation: Ministerio de Salud GCBA
last name: Lorena Luna, MG
role: Study Chair
affiliation: Salud Comunitaria Ministerio de Salud GCBA
last name: Magdalena Wagner Manslau
role: Study Chair
affiliation: Salud Comunitaria Ministerio de Salud GCBA
last name: Julieta Salto
role: Study Chair
affiliation: Salud Comunitaria Ministerio de Salud GCBA
facility: 
country: Argentina
url: https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/community-level-seroprevalence-surveys.html
description: Community-level Seroprevalence Surveys
url: http://wadmin.uca.edu.ar/public/ckeditor/2017-Observatorio-Informes_Defensoria-CABA-24-10-VF.pdf
description: Studies on the processes of social and urban integration in three Buenos Aires slums
url: https://www.equinetafrica.org/sites/default/files/uploads/documents/PAR%20Methods%20Reader2014%20for%20web.pdf
description: Participatory action research in health systems
url: https://tallereduca.files.wordpress.com/2014/06/fuks.pdf
description: Systemic Facilitation of Collective Processes: Supporting Creativity and Participative Processes in Groups, Communities and Networks
url: https://www.argentina.gob.ar/noticias/reactivos-covid-19
description: COVID-19 reagents
url: https://www.estadisticaciudad.gob.ar/eyc/?p=39240
description: Household and population census: villas 31 and 31 bis, City of Buenos Aires 2009
url: https://www.apmresearchlab.org/covid/deaths-by-race
description: The Color of Coronavirus: COVID-19 Deaths by Race and Ethnicity in the U.S.
url: https://doi.org/10.1093/cid/cir007
description: "Herd Immunity": A Rough Guide
citation: Sood N, Simon P, Ebner P, Eichner D, Reynolds J, Bendavid E, Bhattacharya J. Seroprevalence of SARS-CoV-2-Specific Antibodies Among Adults in Los Angeles County, California, on April 10-11, 2020. JAMA. 2020 Jun 16;323(23):2425-2427. doi: 10.1001/jama.2020.8279.
PMID: 32421144
citation: Pereira RJ, Nascimento GNLD, Gratao LHA, Pimenta RS. The risk of COVID-19 transmission in favelas and slums in Brazil. Public Health. 2020 Jun;183:42-43. doi: 10.1016/j.puhe.2020.04.042. Epub 2020 May 8. No abstract available.
PMID: 32422438
citation: Corburn J, Vlahov D, Mberu B, Riley L, Caiaffa WT, Rashid SF, Ko A, Patel S, Jukur S, Martinez-Herrera E, Jayasinghe S, Agarwal S, Nguendo-Yongsi B, Weru J, Ouma S, Edmundo K, Oni T, Ayad H. Slum Health: Arresting COVID-19 and Improving Well-Being in Urban Informal Settlements. J Urban Health. 2020 Jun;97(3):348-357. doi: 10.1007/s11524-020-00438-6. Erratum In: J Urban Health. 2021 Apr;98(2):309-310.
PMID: 32333243
citation: Moen EL, Fricano-Kugler CJ, Luikart BW, O'Malley AJ. Analyzing Clustered Data: Why and How to Account for Multiple Observations Nested within a Study Participant? PLoS One. 2016 Jan 14;11(1):e0146721. doi: 10.1371/journal.pone.0146721. eCollection 2016.
PMID: 26766425
citation: Galbraith S, Daniel JA, Vissel B. A study of clustered data and approaches to its analysis. J Neurosci. 2010 Aug 11;30(32):10601-8. doi: 10.1523/JNEUROSCI.0362-10.2010.
PMID: 20702692
citation: Aarts E, Verhage M, Veenvliet JV, Dolan CV, van der Sluis S. A solution to dependency: using multilevel analysis to accommodate nested data. Nat Neurosci. 2014 Apr;17(4):491-6. doi: 10.1038/nn.3648. Epub 2014 Mar 26.
PMID: 24671065
citation: Zhang CH, Schwartz GG. Spatial Disparities in Coronavirus Incidence and Mortality in the United States: An Ecological Analysis as of May 2020. J Rural Health. 2020 Jun;36(3):433-445. doi: 10.1111/jrh.12476. Epub 2020 Jun 16.
PMID: 32543763
citation: Theel ES, Slev P, Wheeler S, Couturier MR, Wong SJ, Kadkhoda K. The Role of Antibody Testing for SARS-CoV-2: Is There One? J Clin Microbiol. 2020 Jul 23;58(8):e00797-20. doi: 10.1128/JCM.00797-20. Print 2020 Jul 23.
PMID: 32350047
citation: Buckley RM. Targeting the World's Slums as Fat Tails in the Distribution of COVID-19 Cases. J Urban Health. 2020 Jun;97(3):358-364. doi: 10.1007/s11524-020-00450-w.
PMID: 32488763
responsible party type: Principal Investigator
investigator affiliation: Hospital de Niños R. Gutierrez de Buenos Aires
investigator full name: ALICIA MISTCHENKO
investigator title: Principal Investigator
mesh term: Coronavirus Infections
sharing ipd: Undecided
ipd description: undecided