A distributed geospatial approach to describe community characteristics for multisite studies

doi:10.1017/cts.2021.7

A distributed geospatial approach to describe community characteristics for multisite studies

Research output: Contribution to journal › Article › peer-review

Abstract

Understanding place-based contributors to health requires geographically and culturally diverse study populations, but sharing location data is a significant challenge to multisite studies. Here, we describe a standardized and reproducible method to perform geospatial analyses for multisite studies. Using census tract-level information, we created software for geocoding and geospatial data linkage that was distributed to a consortium of birth cohorts located throughout the USA. Individual sites performed geospatial linkages and returned tract-level information for 8810 children to a central site for analyses. Our generalizable approach demonstrates the feasibility of geospatial analyses across study sites to promote collaborative translational research.

Original language	American English
Article number	e86
Journal	Journal of Clinical and Translational Science
Volume	5
Issue number	1
DOIs	https://doi.org/10.1017/cts.2021.7
State	Published - 1 Jan 2021
Externally published	Yes

Keywords

Determinants of health
GIS
Geospatial analysis
Multisite studies
Participant confidentiality

All Science Journal Classification (ASJC) codes

General Medicine

Access to Document

10.1017/cts.2021.7

Cite this

@article{1b1d0385c498429c9d4cb0e0364025ce,

title = "A distributed geospatial approach to describe community characteristics for multisite studies",

abstract = "Understanding place-based contributors to health requires geographically and culturally diverse study populations, but sharing location data is a significant challenge to multisite studies. Here, we describe a standardized and reproducible method to perform geospatial analyses for multisite studies. Using census tract-level information, we created software for geocoding and geospatial data linkage that was distributed to a consortium of birth cohorts located throughout the USA. Individual sites performed geospatial linkages and returned tract-level information for 8810 children to a central site for analyses. Our generalizable approach demonstrates the feasibility of geospatial analyses across study sites to promote collaborative translational research.",

keywords = "Determinants of health, GIS, Geospatial analysis, Multisite studies, Participant confidentiality",

author = "Ryan, {Patrick H.} and Cole Brokamp and Jeff Blossom and Nathan Lothrop and Miller, {Rachel L.} and Beamer, {Paloma I.} and Visness, {Cynthia M.} and Antonella Zanobetti and Howard Andrews and Bacharier, {Leonard B.} and Hartert, {Tina V.} and Johnson, {Christine C.} and Dennis Ownby and Lemanske, {Robert F.} and Heike Gibson and Weeberb Requia and Brent Coull and Zoratti, {Edward M.} and Wright, {Anne L.} and Martinez, {Fernando D.} and Seroogy, {Christine M.} and Gern, {James E.} and Gold, {Diane R.} and Julie Herbstman and Lori Hoepner and Frederica Perera and Matthew Perzanowski and Xinhua Liu and Judyth Ramirez and Janelle Rivera and Deliang Tang and Kylie Riley and Jacqueline Jezioro and A. Sitarik and S. Havstad and K. Woodcroft and A. Levin and G. Wegienka and B. Davidson and S. Finazzo and K. Bobbitt and E. Mann and S. Bellemore and S. Zhang and A. Wahlman and K. Jones and N. Lukacs and Susan Lynch and H. Boushey and Itai Kloog",

note = "Publisher Copyright: {\textcopyright} The Author(s), 2021. Published by Cambridge University Press on behalf of The Association for Clinical and Translational Science. This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.",

year = "2021",

month = jan,

day = "1",

doi = "10.1017/cts.2021.7",

language = "American English",

volume = "5",

journal = "Journal of Clinical and Translational Science",

issn = "2059-8661",

publisher = "Cambridge University Press",

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T1 - A distributed geospatial approach to describe community characteristics for multisite studies

AU - Ryan, Patrick H.

AU - Brokamp, Cole

AU - Blossom, Jeff

AU - Lothrop, Nathan

AU - Miller, Rachel L.

AU - Beamer, Paloma I.

AU - Visness, Cynthia M.

AU - Zanobetti, Antonella

AU - Andrews, Howard

AU - Bacharier, Leonard B.

AU - Hartert, Tina V.

AU - Johnson, Christine C.

AU - Ownby, Dennis

AU - Lemanske, Robert F.

AU - Gibson, Heike

AU - Requia, Weeberb

AU - Coull, Brent

AU - Zoratti, Edward M.

AU - Wright, Anne L.

AU - Martinez, Fernando D.

AU - Seroogy, Christine M.

AU - Gern, James E.

AU - Gold, Diane R.

AU - Herbstman, Julie

AU - Hoepner, Lori

AU - Perera, Frederica

AU - Perzanowski, Matthew

AU - Liu, Xinhua

AU - Ramirez, Judyth

AU - Rivera, Janelle

AU - Tang, Deliang

AU - Riley, Kylie

AU - Jezioro, Jacqueline

AU - Sitarik, A.

AU - Havstad, S.

AU - Woodcroft, K.

AU - Levin, A.

AU - Wegienka, G.

AU - Davidson, B.

AU - Finazzo, S.

AU - Bobbitt, K.

AU - Mann, E.

AU - Bellemore, S.

AU - Zhang, S.

AU - Wahlman, A.

AU - Jones, K.

AU - Lukacs, N.

AU - Lynch, Susan

AU - Boushey, H.

AU - Kloog, Itai

N1 - Publisher Copyright: © The Author(s), 2021. Published by Cambridge University Press on behalf of The Association for Clinical and Translational Science. This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.

PY - 2021/1/1

Y1 - 2021/1/1

N2 - Understanding place-based contributors to health requires geographically and culturally diverse study populations, but sharing location data is a significant challenge to multisite studies. Here, we describe a standardized and reproducible method to perform geospatial analyses for multisite studies. Using census tract-level information, we created software for geocoding and geospatial data linkage that was distributed to a consortium of birth cohorts located throughout the USA. Individual sites performed geospatial linkages and returned tract-level information for 8810 children to a central site for analyses. Our generalizable approach demonstrates the feasibility of geospatial analyses across study sites to promote collaborative translational research.

AB - Understanding place-based contributors to health requires geographically and culturally diverse study populations, but sharing location data is a significant challenge to multisite studies. Here, we describe a standardized and reproducible method to perform geospatial analyses for multisite studies. Using census tract-level information, we created software for geocoding and geospatial data linkage that was distributed to a consortium of birth cohorts located throughout the USA. Individual sites performed geospatial linkages and returned tract-level information for 8810 children to a central site for analyses. Our generalizable approach demonstrates the feasibility of geospatial analyses across study sites to promote collaborative translational research.

KW - Determinants of health

KW - GIS

KW - Geospatial analysis

KW - Multisite studies

KW - Participant confidentiality

UR - http://www.scopus.com/inward/record.url?scp=85105544035&partnerID=8YFLogxK

U2 - 10.1017/cts.2021.7

DO - 10.1017/cts.2021.7

M3 - Article

SN - 2059-8661

VL - 5

JO - Journal of Clinical and Translational Science

JF - Journal of Clinical and Translational Science

IS - 1

M1 - e86

ER -

A distributed geospatial approach to describe community characteristics for multisite studies

Abstract

Keywords

All Science Journal Classification (ASJC) codes

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