A distributed geospatial approach to describe community characteristics for multisite studies

The CREW Consortium

doi:10.1017/cts.2021.7

A distributed geospatial approach to describe community characteristics for multisite studies

The CREW Consortium

Mailman School of Public Health

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

6 Citations (Scopus)

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	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
Publication status	Published - 2021

Bibliographical note

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.

Funding

CREW is funded by HHS/NIH grant 5UG3OD023282. Additional support was provided by individual cohorts' grants/contracts: Columbia University: P01 ES09600, R01 ES008977, P30 ES09089, R01 ES013163, R827027. Tucson Children's Respiratory Study (TCRS): R01HL132523, R01HL56177, K25HL103970. Infant Immune Study (IIS): R01AI42268, K25HL103970. Childhood Origins of Asthma Study (COAST): P01 HL070831, U10 HL064305, R01 HL061879. Urban Environment and Childhood Asthma Study (URECA): NO1-AI-25496, NO1-AI-25482, HHSN272200900052C, HHSN272201000052I, NCRR/NIH RR00052, M01RR00533, 1UL1RR025771, M01RR00071, 1UL1RR024156, UL1TR001079, 5UL1RR024992-02, NCATS/NIH UL1TR000040. Cincinnati Childhood Allergy and Air Pollution Study (CCAAPS): R01 ES11170, R01 ES019890. The Epidemiology of Home Allergens and Asthma Study (EHAAS): R01 AI035786. Wayne County Health, Environment, Allergy and Asthma Longitudinal Study (WHEALS): R01 AI050681, R56 AI050681, R01 AI061774, R21 AI059415, K01 AI070606, R21 AI069271, R01 HL113010, R21 ES022321, P01 AI089473, R21 AI080066, R01 AI110450, R01 HD082147 and the Fund for Henry Ford Health System. Childhood Allergy Study (CAS): R01 AI024156, R03 HL067427, R01 AI051598, Blue Cross Foundation Johnson, and the Fund for Henry Ford Hospital. Microbes, Allergy, Asthma and Pets (MAAP): P01 AI089473 and Fund for Henry Ford Hospital. Infant Susceptibility to Pulmonary Infections and Asthma following RSV Exposure (INSPIRE): NIH/NIAID U19 AI 095227, NIH/NCATS UL1 TR 002243, NIH/NIAID K24 AI 077930, NIH/NHLBI R21 HD 087864, NIH/NHLBI X01 HL 134583. Wisconsin Infant Study Cohort (WISC): U19 AI104317, NCATS UL1TR000427, the charitable donors to the Marshfield Clinic Health System Foundation, and the Upper Midwest Agricultural Safety and Health Center (UMASH) U54 OH010170. CREW is funded by HHS/NIH grant 5UG3OD023282. Additional support was provided by individual cohorts’ grants/contracts: Columbia University: P01 ES09600, R01 ES008977, P30 ES09089, R01 ES013163, R827027. Childhood Allergy Study (CAS): R01 AI024156, R03 HL067427, R01 AI051598, Blue Cross Foundation Johnson, and the Fund for Henry Ford Hospital.

Funders	Funder number
Blue Cross Foundation Johnson
Cincinnati Childhood Allergy and Air Pollution Study	R01 ES11170, R01 ES019890
EHAAS	R01 HD082147, R21 AI080066, R01 AI110450, K01 AI070606, R21 AI069271, R01 AI061774, R01 AI035786, R56 AI050681, R21 AI059415, R01 HL113010, R21 ES022321, P01 AI089473, R01 AI050681
Fund for Henry Ford Hospital
Henry Ford Hospital
Home Allergens and Asthma Study
Marshfield Clinic Health System Foundation
Upper Midwest Agricultural Safety and Health Center	U54 OH010170
National Institutes of Health	5UG3OD023282, R827027, P30 ES09089, R01 ES013163, P01 ES09600, R01 ES008977
U.S. Department of Health and Human Services
National Heart, Lung, and Blood Institute	R21 HD 087864, X01 HL 134583, U19 AI104317, UL1TR000427
National Institute of Allergy and Infectious Diseases	K24 AI 077930, UL1 TR 002243, U19 AI 095227
National Center for Advancing Translational Sciences	UL1TR000040
Henry Ford Health System	R03 HL067427, R01 AI024156, R01 AI051598

ASJC Scopus Subject Areas

General Medicine

Access to Document

10.1017/cts.2021.7

Cite this

@article{069188eb477f4c94be3ab5a9ae557640,

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.",

author = "{The CREW Consortium} and 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 Wayne Morgan",

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",

doi = "10.1017/cts.2021.7",

language = "English",

volume = "5",

journal = "Journal of Clinical and Translational Science",

issn = "2059-8661",

publisher = "Cambridge University Press",

number = "1",

}

TY - JOUR

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

AU - The CREW Consortium

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 - Morgan, Wayne

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

Y1 - 2021

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.

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

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

U2 - 10.1017/cts.2021.7

DO - 10.1017/cts.2021.7

M3 - Article

AN - SCOPUS:85105544035

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

Bibliographical note

Funding

ASJC Scopus Subject Areas

Access to Document

Other files and links

Fingerprint

Cite this