Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 31

The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.

Racial and geographic variation in effects of maternal education and neighborhood-level measures of socioeconomic status on gestational age at birth: Findings from the ECHO cohorts

Dunlop, Anne L., Essalmi, Alicynne Glazier, Alvalos, Lyndsay, Breton, Carrie, Camargo, Carlos A., Cowell, Whitney J., Dabelea, Dana, Dager, Stephen R., Duarte, Cristiane, Elliott, Amy, Fichorova, Raina, Gern, James, Hedderson, Monique M., Thepaksorn, Elizabeth Hom, Huddleston, Kathi, Karagas, Margaret R., Kleinman, Ken, Leve, Leslie, Li, Ximin, Li, Yijun, Litonjua, Augusto, Ludena-Rodriguez, Yunin, Madan, Juliette C., Nino, Julio Mateus, McEvoy, Cynthia, O’Connor, Thomas G., Padula, Amy M., Paneth, Nigel, Perera, Frederica, Sathyanarayana, Sheela, Schmidt, Rebecca J., Schultz, Robert T., Snowden, Jessica, Stanford, Joseph B., Trasande, Leonardo, Volk, Heather E., Wheaton, William, Wright, Rosalind J., McGrath, Monica, on behalf of program collaborators for Environmental Influences on Child Health Outcomes·2021·PLOS ONE

Abstract

Preterm birth occurs at excessively high and disparate rates in the United States. In 2016, the National Institutes of Health (NIH) launched the Environmental influences on Child Health Outcomes (ECHO) program to investigate the influence of early life exposures on child health. Extant data from the ECHO cohorts provides the opportunity to examine racial and geographic variation in effects of individual- and neighborhood-level markers of socioeconomic status (SES) on gestational age at birth. The objective of this study was to examine the association between individual-level (maternal education) and neighborhood-level markers of SES and gestational age at birth, stratifying by maternal race/ethnicity, and whether any such associations are modified by US geographic region. Twenty-six ECHO cohorts representing 25,526 mother-infant pairs contributed to this disseminated meta-analysis that investigated the effect of maternal prenatal level of education (high school diploma, GED, or less; some college, associate’s degree, vocational or technical training [reference category]; bachelor’s degree, graduate school, or professional degree) and neighborhood-level markers of SES (census tract [CT] urbanicity, percentage of black population in CT, percentage of population below the federal poverty level in CT) on gestational age at birth (categorized as preterm, early term, full term [the reference category], late, and post term) according to maternal race/ethnicity and US region. Multinomial logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (CIs). Cohort-specific results were meta-analyzed using a random effects model. For women overall, a bachelor’s degree or above, compared with some college, was associated with a significantly decreased odds of preterm birth (aOR 0.72; 95% CI: 0.61–0.86), whereas a high school education or less was associated with an increased odds of early term birth (aOR 1.10, 95% CI: 1.00–1.21). When stratifying by maternal race/ethnicity, there were no significant associations between maternal education and gestational age at birth among women of racial/ethnic groups other than non-Hispanic white. Among non-Hispanic white women, a bachelor’s degree or above was likewise associated with a significantly decreased odds of preterm birth (aOR 0.74 (95% CI: 0.58, 0.94) as well as a decreased odds of early term birth (aOR 0.84 (95% CI: 0.74, 0.95). The association between maternal education and gestational age at birth varied according to US region, with higher levels of maternal education associated with a significantly decreased odds of preterm birth in the Midwest and South but not in the Northeast and West. Non-Hispanic white women residing in rural compared to urban CTs had an increased odds of preterm birth; the ability to detect associations between neighborhood-level measures of SES and gestational age for other race/ethnic groups was limited due to small sample sizes within select strata. Interventions that promote higher educational attainment among women of reproductive age could contribute to a reduction in preterm birth, particularly in the US South and Midwest. Further individual-level analyses engaging a diverse set of cohorts are needed to disentangle the complex interrelationships among maternal education, neighborhood-level factors, exposures across the life course, and gestational age at birth outcomes by maternal race/ethnicity and US geography.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1371/journal.pone.0245064

Cites

9 of 9 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 9 references

Research works printed but not linked: 60

  1. MH Bublitz, 2018, Preterm birth disparities between states in the United States: an opportunity for public health interventions, Journal of Psychosomatic Obstetrics & Gynecology, 1
  2. UM Reddy, 2011, Term pregnancy: a period of heterogeneous risk for infant mortality, Obstetrics & Gynecology, 117, 1279, 10.1097/AOG.0b013e3182179e28 · DOI
  3. SR Murray, 2017, Long term cognitive outcomes of early term (37–38 weeks) and late preterm (34–36 weeks) births: A systematic review, Wellcome open research, 2, 10.12688/wellcomeopenres.12783.1 · DOI
  4. M Galal, 2012, Postterm pregnancy, Facts, views & vision in ObGyn, 4, 175
  5. CY Spong, 2013, Defining “term” pregnancy: recommendations from the Defining “Term” Pregnancy Workgroup, Jama, 309, 2445, 10.1001/jama.2013.6235 · DOI
  6. JP King, 2014, Disparities in mortality rates among US infants born late preterm or early term, 2003–2005, Maternal and child health journal, 18, 233, 10.1007/s10995-013-1259-0 · DOI
  7. Behrman RE, Butler AS. Preterm birth: causes, consequences, and prevention: National academies press Washington, DC; 2007.
  8. PA Braveman, 2015, The role of socioeconomic factors in black–white disparities in preterm birth, American journal of public health, 105, 694, 10.2105/AJPH.2014.302008 · DOI
  9. L Rosenthal, 2011, Explaining racial disparities in adverse birth outcomes: Unique sources of stress for Black American women, Social Science & Medicine, 72, 977, 10.1016/j.socscimed.2011.01.013 · DOI
  10. DR Williams, 2010, Understanding racial-ethnic disparities in health: sociological contributions, Journal of health and social behavior, 51, S15, 10.1177/0022146510383838 · DOI · Paper page
  11. MR Kramer, 2011, Preconceptional stress and racial disparities in preterm birth: an overview, Acta obstetricia et gynecologica Scandinavica, 90, 1307, 10.1111/j.1600-0412.2011.01136.x · DOI
  12. DD Mendez, 2014, Institutional racism, neighborhood factors, stress, and preterm birth, Ethnicity & health, 19, 479, 10.1080/13557858.2013.846300 · DOI
  13. LC Messer, 2008, Socioeconomic domains and associations with preterm birth, Social science & medicine, 67, 1247, 10.1016/j.socscimed.2008.06.009 · DOI
  14. R Mehra, 2017, Racial residential segregation and adverse birth outcomes: a systematic review and meta-analysis, Social Science & Medicine, 191, 237, 10.1016/j.socscimed.2017.09.018 · DOI
  15. SL Carmichael, 2017, Social disadvantage and the black-white disparity in spontaneous preterm delivery among California births, PloS one, 12, e0182862, 10.1371/journal.pone.0182862 · DOI
  16. P O’Campo, 2007, Neighborhood deprivation and preterm birth among non-Hispanic Black and White women in eight geographic areas in the United States, American journal of epidemiology, 167, 155, 10.1093/aje/kwm277 · DOI
  17. M Huynh, 2014, Gentrification and preterm birth in New York City, 2008–2010, Journal of Urban Health, 91, 211, 10.1007/s11524-013-9823-x · DOI
  18. A Zeka, 2008, The effects of socioeconomic status and indices of physical environment on reduced birth weight and preterm births in Eastern Massachusetts, Environmental Health, 7, 60, 10.1186/1476-069X-7-60 · DOI
  19. GS Phillips, 2013, Neighborhood socioeconomic status in relation to preterm birth in a US cohort of black women, Journal of Urban Health, 90, 197, 10.1007/s11524-012-9739-x · DOI
  20. JF Culhane, 2005, Neighborhood context and reproductive health, American journal of obstetrics and gynecology, 192, S22, 10.1016/j.ajog.2005.01.071 · DOI
  21. MW Gillman, 2018, Environmental Influences on Child Health Outcomes, a research program of the NIH, Current opinion in pediatrics, 30, 260, 10.1097/MOP.0000000000000600 · DOI
  22. LP Jacobson, 2018, An ECHO Viewpoint of Data Analysis Centers for Collaborative Study Designs, Current opinion in pediatrics, 30, 269, 10.1097/MOP.0000000000000602 · DOI
  23. Obstetricians ACo Gynecologists, 2013, Definition of term pregnancy. Committee opinion No. 579, Obstet Gynecol, 122, 1139, 10.1097/01.AOG.0000437385.88715.4a · DOI
  24. MS Kramer, 2000, Socio-economic disparities in pregnancy outcome: why do the poor fare so poorly?, Paediatric and perinatal epidemiology, 14, 194, 10.1046/j.1365-3016.2000.00266.x · DOI
  25. JD Parker, 1994, Associations between measures of socioeconomic status and low birth weight, small for gestational age, and premature delivery in the United States, Annals of epidemiology, 4, 271, 10.1016/1047-2797(94)90082-5 · DOI
  26. C Brokamp, 2018, DeGAUSS: Decentralized Geomarker Assessment for Multi-Site Studies, J. Open Source Software, 3, 812, 10.21105/joss.00812 · DOI
  27. N Krieger, 2003, Race/ethnicity, gender, and monitoring socioeconomic gradients in health: a comparison of area-based socioeconomic measures—the public health disparities geocoding project, American journal of public health, 93, 1655, 10.2105/AJPH.93.10.1655 · DOI · Paper page
  28. N Krieger, 2005, Painting a truer picture of US socioeconomic and racial/ethnic health inequalities: the Public Health Disparities Geocoding Project, American journal of public health, 95, 312, 10.2105/AJPH.2003.032482 · DOI
  29. AS Butler, 2007, Preterm birth: causes, consequences, and prevention
  30. RL Goldenberg, 2008, Epidemiology and causes of preterm birth, The lancet, 371, 75, 10.1016/S0140-6736(08)60074-4 · DOI · Paper page
  31. AS Bryant, 2010, Racial/ethnic disparities in obstetric outcomes and care: prevalence and determinants, American journal of obstetrics and gynecology, 202, 335, 10.1016/j.ajog.2009.10.864 · DOI
  32. JS Garrow, 1985, Quetelet’s index (W/H2) as a measure of fatness, International journal of obesity, 9, 147
  33. EA Stuart, 2009, Multiple imputation with large data sets: a case study of the Children’s Mental Health Initiative, American journal of epidemiology, 169, 1133, 10.1093/aje/kwp026 · DOI
  34. S van Buuren, 2011, mice: Multivariate Imputation by Chained Equations in R, Journal of Statistical Software, 45, 1
  35. RJA Little, 1988, Missing data adjustments in large surveys (with discussion), Journal of Business Economics and Statistics, 6, 287, 10.1080/07350015.1988.10509663 · DOI
  36. TP Morris, 2014, Tuning multiple imputation by predictive mean matching and local residual draws, BMC medical research methodology, 14, 75, 10.1186/1471-2288-14-75 · DOI · Paper page
  37. WN Venables, 2002, Modern Applied Statistics with S, 10.1007/978-0-387-21706-2 · DOI
  38. Brand JPL. Development, Implementation and Evaluation of Multiple Imputation Strategies for the Statistical Analysis of Incomplete Data Sets. Ph.D. Thesis Rotterdam: Erasmus University; 1999.
  39. IR White, 2010, Avoiding bias due to perfect prediction in multiple imputation of incomplete categorical variables, Computational statistics & data analysis, 54, 2267, 10.1016/j.csda.2010.04.005 · DOI
  40. CR Lesko, 2018, Collaborative, pooled and harmonized study designs for epidemiologic research: challenges and opportunities, Int J Epidemiol, 47, 654, 10.1093/ije/dyx283 · DOI
  41. Guido S. meta: An R package for meta-analysis,. R News; 2007:40–5.
  42. JS Kaufman, 1997, Socioeconomic status and health in blacks and whites: the problem of residual confounding and the resiliency of race, Epidemiology, 621
  43. TP Dominguez, 2011, Adverse birth outcomes in African American women: the social context of persistent reproductive disadvantage, Social work in public health, 26, 3, 10.1080/10911350902986880 · DOI
  44. M Oliver, 2013, Black wealth/white wealth: A new perspective on racial inequality, 10.4324/9780203707425 · DOI
  45. J Byrnes, 2015, Spatial and temporal patterns in preterm birth in the United States, Pediatric research, 77, 836, 10.1038/pr.2015.55 · DOI
  46. S Markowitz, 2017, Effects of state-level Earned Income Tax Credit laws in the US on maternal health behaviors and infant health outcomes, Social Science & Medicine, 194, 67, 10.1016/j.socscimed.2017.10.016 · DOI
  47. KM Rappazzo, 2015, The associations between environmental quality and preterm birth in the United States, 2000–2005: a cross-sectional analysis, Environmental Health, 14, 50, 10.1186/s12940-015-0038-3 · DOI
  48. M Bertin, 2015, Association between prenatal exposure to traffic-related air pollution and preterm birth in the PELAGIE mother–child cohort, Brittany, France. Does the urban–rural context matter?, Environmental research, 142, 17, 10.1016/j.envres.2015.06.005 · DOI
  49. ST Kent, 2013, Area-level risk factors for adverse birth outcomes: trends in urban and rural settings, BMC pregnancy and childbirth, 13, 129, 10.1186/1471-2393-13-129 · DOI
  50. GB Raglan, 2016, Racial and ethnic disparities in preterm birth among American Indian and Alaska Native women, Maternal and child health journal, 20, 16, 10.1007/s10995-015-1803-1 · DOI
  51. H Huang, 2018, Investigation of association between environmental and socioeconomic factors and preterm birth in California, Environment international, 121, 1066, 10.1016/j.envint.2018.07.027 · DOI
  52. AM Padula, 2014, Traffic-related air pollution and risk of preterm birth in the San Joaquin Valley of California, Annals of epidemiology, 24, 888, 10.1016/j.annepidem.2014.10.004 · DOI · Paper page
  53. SC Gray, 2014, Assessing the impact of race, social factors and air pollution on birth outcomes: a population-based study, Environmental Health, 13, 4, 10.1186/1476-069X-13-4 · DOI
  54. NE Adler, 2002, Socioeconomic disparities in health: pathways and policies, Health affairs, 21, 60, 10.1377/hlthaff.21.2.60 · DOI
  55. G Meng, 2013, Pathways of neighbourhood-level socio-economic determinants of adverse birth outcomes, International journal of health geographics, 12, 32, 10.1186/1476-072X-12-32 · DOI · Paper page
  56. M Delnord, 2019, Seminars in Fetal and Neonatal Medicine, 3
  57. M Pearl, 2018, Life-course neighbourhood opportunity and racial-ethnic disparities in risk of preterm birth, Paediatric and perinatal epidemiology, 32, 412, 10.1111/ppe.12482 · DOI
  58. GD Shapiro, 2017, Paternal education and adverse birth outcomes in Canada, Journal of Epidemiology and Community Health, 71, 67, 10.1136/jech-2015-206894 · DOI
  59. JC Slaughter-Acey, 2016, Racism in the form of micro aggressions and the risk of preterm birth among black women, Annals of epidemiology, 26, 7, 10.1016/j.annepidem.2015.10.005 · DOI
  60. P Braveman, 2017, Worry about racial discrimination: A missing piece of the puzzle of Black-White disparities in preterm birth?, PloS one, 12, 10.1371/journal.pone.0186151 · DOI

Cited by

Metadata

Authors
Dunlop, A. L., Essalmi, A. G., Alvalos, L., Breton, C., Camargo, C. A., Cowell, W. J., Dabelea, D., Dager, S. R., Duarte, C., Elliott, A., Fichorova, R., Gern, J., Hedderson, M. M., Thepaksorn, E. H., Huddleston, K., Karagas, M. R., Kleinman, K., Leve, L., Li, X., Li, Y., Litonjua, A., Ludena-Rodriguez, Y., Madan, J. C., Nino, J. M., McEvoy, C., O’Connor, T. G., Padula, A. M., Paneth, N., Perera, F., Sathyanarayana, S., Schmidt, R. J., Schultz, R. T., Snowden, J., Stanford, J. B., Trasande, L., Volk, H. E., Wheaton, W., Wright, R. J., McGrath, M., & Outcomes, O. B. O. P. C. F. E. I. O. C. H.
Venue
PLOS ONE
Published
2021-01-08
Volume
16
Issue
1
Pages
e0245064
ISSN
1932-6203
Cited by
31
References
9
Language
en
Funders
NIH Office of the Director (UH3OD023318, UH3OD023251, UH3OD023253, UH3OD023248, UH3OD023328, UH3OD023279, UH3OD023289, UH3OD023282, UH3OD023287, UH3OD023365, UH3OD023244, UH3OD023275, UH3OD023271, UH3OD023389, UH3OD023344, UH3OD023268, UH3OD023288, UH3OD023342, UH3OD023349, UH3OD023286, UH3OD023285, UH3OD023290, UH3OD023272, UH3OD023249, UH3OD023337, U24OD023382)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.