Characterizing the neighborhood risk environment in multisite clinic-based cohort studies: A practical geocoding and data linkages protocol for protected health information
<h4>Background</h4> Maintaining patient privacy when geocoding and linking residential address information with neighborhood-level data can create challenges during research. Challenges may arise when study staff have limited training in geocoding and linking data, or when non-study staf...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9799318/?tool=EBI |
| _version_ | 1797973916580315136 |
|---|---|
| author | Ariann Nassel Marta G. Wilson-Barthes Chanelle J. Howe Sonia Napravnik Michael J. Mugavero Deana Agil Akilah J. Dulin |
| author_facet | Ariann Nassel Marta G. Wilson-Barthes Chanelle J. Howe Sonia Napravnik Michael J. Mugavero Deana Agil Akilah J. Dulin |
| author_sort | Ariann Nassel |
| collection | DOAJ |
| description | <h4>Background</h4> Maintaining patient privacy when geocoding and linking residential address information with neighborhood-level data can create challenges during research. Challenges may arise when study staff have limited training in geocoding and linking data, or when non-study staff with appropriate expertise have limited availability, are unfamiliar with a study’s population or objectives, or are not affordable for the study team. Opportunities for data breaches may also arise when working with non-study staff who are not on-site. We detail a free, user-friendly protocol for constructing indices of the neighborhood risk environment during multisite, clinic-based cohort studies that rely on participants’ protected health information. This protocol can be implemented by study staff who do not have prior training in Geographic Information Systems (GIS) and can help minimize the operational costs of integrating geographic data into public health projects. <h4>Methods</h4> This protocol demonstrates how to: (1) securely geocode patients’ residential addresses in a clinic setting and match geocoded addresses to census tracts using Geographic Information System software (Esri, Redlands, CA); (2) ascertain contextual variables of the risk environment from the American Community Survey and ArcGIS Business Analyst (Esri, Redlands, CA); (3) use geoidentifiers to link neighborhood risk data to census tracts containing geocoded addresses; and (4) assign randomly generated identifiers to census tracts and strip census tracts of their geoidentifiers to maintain patient confidentiality. <h4>Results</h4> Completion of this protocol generates three neighborhood risk indices (i.e., Neighborhood Disadvantage Index, Murder Rate Index, and Assault Rate Index) for patients’ coded census tract locations. <h4>Conclusions</h4> This protocol can be used by research personnel without prior GIS experience to easily create objective indices of the neighborhood risk environment while upholding patient confidentiality. Future studies can adapt this protocol to fit their specific patient populations and analytic objectives. |
| first_indexed | 2024-04-11T04:11:46Z |
| format | Article |
| id | doaj.art-568528e521244ed090f8c0a7bd2fd7de |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-11T04:11:46Z |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-568528e521244ed090f8c0a7bd2fd7de2023-01-01T05:32:20ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-011712Characterizing the neighborhood risk environment in multisite clinic-based cohort studies: A practical geocoding and data linkages protocol for protected health informationAriann NasselMarta G. Wilson-BarthesChanelle J. HoweSonia NapravnikMichael J. MugaveroDeana AgilAkilah J. Dulin<h4>Background</h4> Maintaining patient privacy when geocoding and linking residential address information with neighborhood-level data can create challenges during research. Challenges may arise when study staff have limited training in geocoding and linking data, or when non-study staff with appropriate expertise have limited availability, are unfamiliar with a study’s population or objectives, or are not affordable for the study team. Opportunities for data breaches may also arise when working with non-study staff who are not on-site. We detail a free, user-friendly protocol for constructing indices of the neighborhood risk environment during multisite, clinic-based cohort studies that rely on participants’ protected health information. This protocol can be implemented by study staff who do not have prior training in Geographic Information Systems (GIS) and can help minimize the operational costs of integrating geographic data into public health projects. <h4>Methods</h4> This protocol demonstrates how to: (1) securely geocode patients’ residential addresses in a clinic setting and match geocoded addresses to census tracts using Geographic Information System software (Esri, Redlands, CA); (2) ascertain contextual variables of the risk environment from the American Community Survey and ArcGIS Business Analyst (Esri, Redlands, CA); (3) use geoidentifiers to link neighborhood risk data to census tracts containing geocoded addresses; and (4) assign randomly generated identifiers to census tracts and strip census tracts of their geoidentifiers to maintain patient confidentiality. <h4>Results</h4> Completion of this protocol generates three neighborhood risk indices (i.e., Neighborhood Disadvantage Index, Murder Rate Index, and Assault Rate Index) for patients’ coded census tract locations. <h4>Conclusions</h4> This protocol can be used by research personnel without prior GIS experience to easily create objective indices of the neighborhood risk environment while upholding patient confidentiality. Future studies can adapt this protocol to fit their specific patient populations and analytic objectives.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9799318/?tool=EBI |
| spellingShingle | Ariann Nassel Marta G. Wilson-Barthes Chanelle J. Howe Sonia Napravnik Michael J. Mugavero Deana Agil Akilah J. Dulin Characterizing the neighborhood risk environment in multisite clinic-based cohort studies: A practical geocoding and data linkages protocol for protected health information PLoS ONE |
| title | Characterizing the neighborhood risk environment in multisite clinic-based cohort studies: A practical geocoding and data linkages protocol for protected health information |
| title_full | Characterizing the neighborhood risk environment in multisite clinic-based cohort studies: A practical geocoding and data linkages protocol for protected health information |
| title_fullStr | Characterizing the neighborhood risk environment in multisite clinic-based cohort studies: A practical geocoding and data linkages protocol for protected health information |
| title_full_unstemmed | Characterizing the neighborhood risk environment in multisite clinic-based cohort studies: A practical geocoding and data linkages protocol for protected health information |
| title_short | Characterizing the neighborhood risk environment in multisite clinic-based cohort studies: A practical geocoding and data linkages protocol for protected health information |
| title_sort | characterizing the neighborhood risk environment in multisite clinic based cohort studies a practical geocoding and data linkages protocol for protected health information |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9799318/?tool=EBI |
| work_keys_str_mv | AT ariannnassel characterizingtheneighborhoodriskenvironmentinmultisiteclinicbasedcohortstudiesapracticalgeocodinganddatalinkagesprotocolforprotectedhealthinformation AT martagwilsonbarthes characterizingtheneighborhoodriskenvironmentinmultisiteclinicbasedcohortstudiesapracticalgeocodinganddatalinkagesprotocolforprotectedhealthinformation AT chanellejhowe characterizingtheneighborhoodriskenvironmentinmultisiteclinicbasedcohortstudiesapracticalgeocodinganddatalinkagesprotocolforprotectedhealthinformation AT sonianapravnik characterizingtheneighborhoodriskenvironmentinmultisiteclinicbasedcohortstudiesapracticalgeocodinganddatalinkagesprotocolforprotectedhealthinformation AT michaeljmugavero characterizingtheneighborhoodriskenvironmentinmultisiteclinicbasedcohortstudiesapracticalgeocodinganddatalinkagesprotocolforprotectedhealthinformation AT deanaagil characterizingtheneighborhoodriskenvironmentinmultisiteclinicbasedcohortstudiesapracticalgeocodinganddatalinkagesprotocolforprotectedhealthinformation AT akilahjdulin characterizingtheneighborhoodriskenvironmentinmultisiteclinicbasedcohortstudiesapracticalgeocodinganddatalinkagesprotocolforprotectedhealthinformation |