Evidence map›Paper›PMID 35641714›Full record

ArticleJournal of urban health : bulletin of the New York Academy of Medicine2022

The Prenatal Neighborhood Environment and Geographic Hotspots of Infants with At-risk Birthweights in New York City.

Carol Duh-Leong, H Shonna Yin, Rachel S Gross, Brian Elbel, Lorna E Thorpe, Leonardo Trasande, Michelle J White, Eliana M Perrin, Arthur H Fierman, David C Lee

Abstract read
In one paragraph

Article in Journal of urban health : bulletin of the New York Academy of Medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

10 authors.

Carol Duh-LeongDepartment of Pediatrics, Division of General Pediatrics, NYU Grossman School of Medicine, 462 First Avenue, New York, NY, 10016, USA. carol.duh-leong@nyulangone.org.ORCID 0000-0001-5648-580X
H Shonna YinDepartment of Pediatrics, Division of General Pediatrics, NYU Grossman School of Medicine, 462 First Avenue, New York, NY, 10016, USA.
Rachel S GrossDepartment of Pediatrics, Division of General Pediatrics, NYU Grossman School of Medicine, 462 First Avenue, New York, NY, 10016, USA.
Brian ElbelDepartment of Population Health, NYU Grossman School of Medicine, New York, NY, USA.
Lorna E ThorpeDepartment of Population Health, NYU Grossman School of Medicine, New York, NY, USA.
Leonardo TrasandeDepartment of Population Health, NYU Grossman School of Medicine, New York, NY, USA.
Michelle J WhiteDuke Center for Childhood Obesity Research, Duke University Medical Center, Durham, NC, USA.
Eliana M PerrinDepartment of Pediatrics, Johns Hopkins School of Medicine and School of Nursing, Baltimore, MD, USA.
Arthur H FiermanDepartment of Pediatrics, Division of General Pediatrics, NYU Grossman School of Medicine, 462 First Avenue, New York, NY, 10016, USA.
David C LeeDepartment of Population Health, NYU Grossman School of Medicine, New York, NY, USA.

Funding

Project-005UL1TR001445 · NCATS · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI BREDELLA, MIRIAM ANTOINETTE, HOCHMAN, JUDITH S · 2015 to 2025
$103.5M
Institutional Career DevelopmentKL2TR001446 · NCATS · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI PILLINGER, MICHAEL · 2015 to 2025
$6.7M
Impact of the Built Environment on Child Body Mass IndexR01DK108682 · NIDDK · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI ELBEL, BRIAN, SCHWARTZ, AMY ELLEN · 2016 to 2019
$2.8M
Impact of the Food Environment on Child Body Mass Index - Resubmission - 1R01DK097347 · NIDDK · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI ELBEL, BRIAN · 2013 to 2016
$2.7M
NCATS NIH HHS KL2 TR001446NCATS NIH HHS UL1 TR001445NIDDK NIH HHS R01 DK097347NIDDK NIH HHS R01 DK108682
6 · The paper itself

Abstract

Infants born with low or high ("at-risk") birthweights are at greater risk of adverse health outcomes across the life course. Our objective was to examine whether geographic hotspots of low and high birthweight prevalence in New York City had different patterns of neighborhood risk factors. We performed census tract-level geospatial clustering analyses using (1) birthweight prevalence and maternal residential address from an all-payer claims database and (2) domains of neighborhood risk factors (socioeconomic and food environment) from national and local datasets. We then used logistic regression analysis to identify specific neighborhood risk factors associated with low and high birthweight hotspots. This study examined 2088 census tracts representing 419,025 infants. We found almost no overlap (1.5%) between low and high birthweight hotspots. The majority of low birthweight hotspots (87.2%) overlapped with a socioeconomic risk factor and 95.7% overlapped with a food environment risk factor. Half of high birthweight hotspots (50.0%) overlapped with a socioeconomic risk factor and 48.8% overlapped with a food environment risk factor. Low birthweight hotspots were associated with high prevalence of excessive housing cost, unemployment, and poor food environment. High birthweight hotspots were associated with high prevalence of uninsured persons and convenience stores. Programs and policies that aim to prevent disparities in infant birthweight should examine the broader context by which hotspots of at-risk birthweight overlap with neighborhood risk factors. Multi-level strategies that include the neighborhood context are needed to address prenatal pathways leading to low and high birthweight outcomes.

Indexed as

Infant, Low Birth WeightResidence CharacteristicsBirth WeightFemaleHumansInfantInfant, NewbornNew York CityPregnancySocioeconomic Factors

Identifiers

PMID35641714
PMCPMC9187826

What OpenQuestion holds

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Registered trials

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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.