BMC Public Health (Sep 2024)

Do small effects matter more in vulnerable populations? an investigation using Environmental influences on Child Health Outcomes (ECHO) cohorts

  • Janet L. Peacock,
  • Susana Diaz Coto,
  • Judy R. Rees,
  • Odile Sauzet,
  • Elizabeth T. Jensen,
  • Raina Fichorova,
  • Anne L. Dunlop,
  • Nigel Paneth,
  • Amy Padula,
  • Tracey Woodruff,
  • Rachel Morello-Frosch,
  • Jessica Trowbridge,
  • Dana Goin,
  • Luis E. Maldonado,
  • Zhongzheng Niu,
  • Akhgar Ghassabian,
  • Leonardo Transande,
  • Assiamira Ferrara,
  • Lisa A. Croen,
  • Stacey Alexeeff,
  • Carrie Breton,
  • Augusto Litonjua,
  • Thomas G. O’Connor,
  • Kristen Lyall,
  • Heather Volk,
  • Akram Alshawabkeh,
  • Justin Manjourides,
  • Carlos A. Camargo,
  • Dana Dabelea,
  • Christine W. Hockett,
  • Casper G. Bendixsen,
  • Irva Hertz-Picciotto,
  • Rebecca J. Schmidt,
  • Alison E. Hipwell,
  • Kate Keenan,
  • Catherine Karr,
  • Kaja Z. LeWinn,
  • Barry Lester,
  • Marie Camerota,
  • Jody Ganiban,
  • Cynthia McEvoy,
  • Michael R. Elliott,
  • Sheela Sathyanarayana,
  • Nan Ji,
  • Joseph M. Braun,
  • Margaret R. Karagas,
  • on behalf of Program Collaborators for Environmental influences on Child Health Outcomes

DOI
https://doi.org/10.1186/s12889-024-20075-x
Journal volume & issue
Vol. 24, no. 1
pp. 1 – 13

Abstract

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Abstract Background A major challenge in epidemiology is knowing when an exposure effect is large enough to be clinically important, in particular how to interpret a difference in mean outcome in unexposed/exposed groups. Where it can be calculated, the proportion/percentage beyond a suitable cut-point is useful in defining individuals at high risk to give a more meaningful outcome. In this simulation study we compute differences in outcome means and proportions that arise from hypothetical small effects in vulnerable sub-populations. Methods Data from over 28,000 mother/child pairs belonging to the Environmental influences on Child Health Outcomes Program were used to examine the impact of hypothetical environmental exposures on mean birthweight, and low birthweight (LBW) (birthweight < 2500g). We computed mean birthweight in unexposed/exposed groups by sociodemographic categories (maternal education, health insurance, race, ethnicity) using a range of hypothetical exposure effect sizes. We compared the difference in mean birthweight and the percentage LBW, calculated using a distributional approach. Results When the hypothetical mean exposure effect was fixed (at 50, 125, 167 or 250g), the absolute difference in % LBW (risk difference) was not constant but varied by socioeconomic categories. The risk differences were greater in sub-populations with the highest baseline percentages LBW: ranging from 3.1–5.3 percentage points for exposure effect of 125g. Similar patterns were seen for other mean exposure sizes simulated. Conclusions Vulnerable sub-populations with greater baseline percentages at high risk fare worse when exposed to a small insult compared to the general population. This illustrates another facet of health disparity in vulnerable individuals.

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