Evidence map›Paper›PMID 38336080›Full record

ArticleThe Science of the total environment2024

GPS-derived environmental exposures during pregnancy and early postpartum - Evidence from the madres cohort.

Li Yi, Yan Xu, Sydney O'Connor, Jane Cabison, Marisela Rosales, Daniel Chu, Thomas A Chavez, Mark Johnson, Tyler B Mason, Sandrah P Eckel and 5 more

Abstract read
In one paragraph

Article in The Science of the total environment, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. Observational
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Li YiSpatial Sciences Institute, University of Southern California, United States of America. Electronic address: li_yi@hsph.harvard.edu.
Yan XuSpatial Sciences Institute, University of Southern California, United States of America.
Sydney O'ConnorDepartment of Population and Public Health Sciences, University of Southern California, United States of America.
Jane CabisonDepartment of Population and Public Health Sciences, University of Southern California, United States of America.
Marisela RosalesDepartment of Population and Public Health Sciences, University of Southern California, United States of America.
Daniel ChuDepartment of Population and Public Health Sciences, University of Southern California, United States of America.
Thomas A ChavezDepartment of Population and Public Health Sciences, University of Southern California, United States of America.
Mark JohnsonDepartment of Population and Public Health Sciences, University of Southern California, United States of America.
Tyler B MasonDepartment of Population and Public Health Sciences, University of Southern California, United States of America.
Sandrah P EckelDepartment of Population and Public Health Sciences, University of Southern California, United States of America.
Theresa M BastainDepartment of Population and Public Health Sciences, University of Southern California, United States of America.
Carrie V BretonDepartment of Population and Public Health Sciences, University of Southern California, United States of America.
John P WilsonSpatial Sciences Institute, University of Southern California, United States of America; Department of Population and Public Health Sciences, University of Southern California, United States of America; Departments of Civil & Environmental Engineering, Computer Science, and Sociology, University of Southern California, United States of America.
Genevieve F DuntonDepartment of Population and Public Health Sciences, University of Southern California, United States of America; Department of Psychology, University of Southern California, United States of America.
Rima HabreSpatial Sciences Institute, University of Southern California, United States of America; Department of Population and Public Health Sciences, University of Southern California, United States of America.

Funding

Southern California Clinical and Translational Science InstituteUL1TR001855 · NCATS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI Thomas A Buchanan, Michele D. Kipke · 2016 to 2026
$81.3M
Translational Research Support CoreP30ES007048 · NIEHS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI ROB S MCCONNELL · 1996 to 2026
$46.4M
Project 3: Maternal Exposure to Environmental Hazards and Psychosocial Stressors and Postpartum Allostatic LoadP50MD015705 · NIMHD · UNIVERSITY OF SOUTHERN CALIFORNIA · PI BASTAIN, THERESA M · 2020 to 2024
$8.1M
Project 2: Environmental Exposures, Stress, and Maternal Pregnancy-Related Weight OutcomesP50ES026086 · NIEHS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI BASTAIN, THERESA M · 2015 to 2019
$3.7M
Influence of prenatal psychosocial stressors on maternal and fetal circulating miRNAsR01MD011698 · NIMHD · UNIVERSITY OF SOUTHERN CALIFORNIA · PI BRETON, CARRIE VAN DOREN, MARSIT, CARMEN JOSEPH · 2017 to 2021
$3.3M
Prenatal air pollution, fetal development and early childhood obesity riskR01ES027409 · NIEHS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI BRETON, CARRIE VAN DOREN · 2018 to 2022
$3.3M
NCATS NIH HHS UL1 TR001855NIEHS NIH HHS P30 ES007048NIEHS NIH HHS P50 ES026086NIEHS NIH HHS R01 ES027409NIMHD NIH HHS P50 MD015705NIMHD NIH HHS R01 MD011698
6 · The paper itself

Abstract

The built and natural environment factors (e.g., greenspace, walkability) are associated with maternal and infant health during and after pregnancy. Most pregnancy studies assess exposures to environmental factors via static methods (i.e., residential location at a single point in time, usually 3rd trimester). These do not capture dynamic exposures encountered in activity spaces (e.g., locations one visits and paths one travels) and their changes over time. In this study, we aimed to compare daily environmental exposure estimates using residential and global positioning systems (GPS)-measured activity space approaches and evaluated potential for exposure measurement error in the former. To do this, we collected four days of continuous geolocation monitoring during the 1st and 3rd trimesters of pregnancy and at 4-6 months postpartum in sixty-two pregnant Hispanic women enrolled in the MADRES cohort. We applied residential and GPS-based methods to assess daily exposures to greenspace, access to parks and transit, and walkability, respectively. We assessed potential for exposure measurement error in residential vs GPS-based estimates using Pearson correlations for each measure overall and by study period. We found residential and GPS-based estimates of daily exposure to total areas of parks and open spaces were weakly positively correlated (r = 0.31, P < .001) across pregnancy and postpartum periods. Residential estimates of %greenspace (r = 0.52, P < .001) and tree cover (r = 0.55, P < .001) along walkable roads were moderately correlated with GPS-based estimates. Residential and GPS-based estimates of public transit proximity, pedestrian-oriented intersection density, and walkability index score were all highly positively correlated (r > 0.70, P < .001). We also found associations between residential and GPS-based estimates decreased among participants with greater daily mobility. Our findings suggest the popular approach that assessing the built and natural environment exposures using residential methods at one time point may introduce exposure measurement error in pregnancy studies. GPS-based methods, to the extent feasible, are recommended for future studies.

Indexed as

Environmental ExposureGeographic Information SystemsEnvironmentFemaleHumansInfantPostpartum PeriodPregnancyTravelActivity spaceBuilt environmentDaily mobilityExposure measurement errorGPSUrban green space

Identifiers

PMID38336080
PMCPMC10935580

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.