Do small effects matter more in vulnerable populations? an investigation using Environmental influences on Child Health Outcomes (ECHO) cohorts.
Child health outcome
Environmental exposure
Health disparities
Pregnancy outcomes
Social determinants of health
Journal
BMC public health
ISSN: 1471-2458
Titre abrégé: BMC Public Health
Pays: England
ID NLM: 100968562
Informations de publication
Date de publication:
28 Sep 2024
28 Sep 2024
Historique:
received:
12
03
2024
accepted:
13
09
2024
medline:
29
9
2024
pubmed:
29
9
2024
entrez:
28
9
2024
Statut:
epublish
Résumé
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. 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. 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. 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.
Sections du résumé
BACKGROUND
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
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
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
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.
Identifiants
pubmed: 39342237
doi: 10.1186/s12889-024-20075-x
pii: 10.1186/s12889-024-20075-x
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
2655Investigateurs
P B Smith
(PB)
L K Newby
(LK)
L P Jacobson
(LP)
D J Catellier
(DJ)
R Gershon
(R)
D Cella
(D)
J Cordero
(J)
J Meeker
(J)
L Gatzke-Kopp
(L)
M Swingler
(M)
J M Mansbach
(JM)
J M Spergel
(JM)
M E Samuels-Kalow
(ME)
M D Stevenson
(MD)
C S Bauer
(CS)
D Koinis Mitchell
(D)
S Deoni
(S)
V D 'Sa
(V)
C S Duarte
(CS)
C Monk
(C)
J Posner
(J)
G Canino
(G)
A J Elliott
(AJ)
J Gern
(J)
R Miller
(R)
E Zoratti
(E)
C Seroogy
(C)
D Jackson
(D)
L Bacharier
(L)
M Kattan
(M)
R Wood
(R)
K Rivera-Spoljaric
(K)
G Hershey
(G)
T Hartert
(T)
C Johnson
(C)
D Ownby
(D)
A Singh
(A)
T Bastain
(T)
S Farzan
(S)
R Habre
(R)
F Tylavsky
(F)
A Mason
(A)
Q Zhao
(Q)
N Bush
(N)
K Z LeWinn
(KZ)
B Carter
(B)
S Pastyrnak
(S)
C Neal
(C)
L Smith
(L)
J Helderman
(J)
L Leve
(L)
J Neiderhiser
(J)
S T Weiss
(ST)
G O Connor
(G)
R Zeiger
(R)
R Tepper
(R)
R Landa
(R)
S Ozonoff
(S)
S Dager
(S)
R Schultz
(R)
J Piven
(J)
H Simhan
(H)
C Buss
(C)
P Wadhwa
(P)
K Huff
(K)
R K Miller
(RK)
E Oken
(E)
J M Kerver
(JM)
C Barone
(C)
C Fussman
(C)
M Elliott
(M)
D Ruden
(D)
J Herbstman
(J)
S Schantz
(S)
J Stanford
(J)
C Porucznik
(C)
A Giardino
(A)
R J Wright
(RJ)
M Bosquet-Enlow
(M)
K Huddleston
(K)
R Nguyen
(R)
E Barrett
(E)
S Swan
(S)
F Perera
(F)
Informations de copyright
© 2024. The Author(s).
Références
Cohen J. Statistical power analysis for the behavioral sciences. 2nd ed. Hillsdale, N.J.: L. Erlbaum Associates; 1988.
Greenland S, Senn SJ, Rothman KJ, et al. Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations. Eur J Epidemiol. 2016;31(4):337–50. https://doi.org/10.1007/s10654-016-0149-3 .
Organisation WH. Low birth weight. https://www.who.int/data/nutrition/nlis/info/low-birth-weight . Accessed 21 Sept 2024.
Peacock JL, Sauzet O, Ewings SM, Kerry SM. Dichotomising continuous data while retaining statistical power using a distributional approach. Stat Med. 2012;31(26):3089–103. https://doi.org/10.1002/sim.5354 .
doi: 10.1002/sim.5354
pubmed: 22865598
Sauzet O, Ofuya M, Peacock JL. Dichotomisation using a distributional approach when the outcome is skewed. BMC Med Res Methodol. 2015;15:40. https://doi.org/10.1186/s12874-015-0028-8 .
doi: 10.1186/s12874-015-0028-8
pubmed: 25902850
pmcid: 4422142
Sauzet O, Peacock JL. Estimating dichotomised outcomes in two groups with unequal variances: a distributional approach. Stat Med. 2014;33(26):4547–59. https://doi.org/10.1002/sim.6255 .
doi: 10.1002/sim.6255
pubmed: 24989698
Sauzet O, Breckenkamp J, Borde T, et al. A distributional approach to obtain adjusted comparisons of proportions of a population at risk. Emerg Themes Epidemiol. 2016;13:8. https://doi.org/10.1186/s12982-016-0050-2 .
doi: 10.1186/s12982-016-0050-2
pubmed: 27279891
pmcid: 4897957
Zivanovic S, Peacock J, Alcazar-Paris M, et al. Late outcomes of a randomized trial of high-frequency oscillation in neonates. N Engl J Med. 2014;370(12):1121–30. https://doi.org/10.1056/NEJMoa1309220 .
doi: 10.1056/NEJMoa1309220
pubmed: 24645944
pmcid: 4090580
Peacock JL, Lo J, Rees JR, Sauzet O. Minimal clinically important difference in means in vulnerable populations: challenges and solutions. BMJ Open. 2021;11(11): e052338. https://doi.org/10.1136/bmjopen-2021-052338 .
doi: 10.1136/bmjopen-2021-052338
pubmed: 34753761
pmcid: 8578978
Knapp EA, Kress AM, Parker CB, et al. The Environmental Influences on Child Health Outcomes (ECHO)-Wide Cohort. Am J Epidemiol. 2023;192(8):1249–63. https://doi.org/10.1093/aje/kwad071 .
doi: 10.1093/aje/kwad071
pubmed: 36963379
pmcid: 10403303
LeWinn KZ, Caretta E, Davis A, Anderson AL, Oken E, program collaborators for Environmental influences on Child Health O. SPR perspectives: Environmental influences on Child Health Outcomes (ECHO) Program: overcoming challenges to generate engaged, multidisciplinary science. Pediatr Res. 2022;92(5):1262–9. https://doi.org/10.1038/s41390-021-01598-0 .
Duncan AF, Montoya-Williams D. Recommendations for reporting research about racial disparities in medical and scientific journals. JAMA Pediatr. 2024. https://doi.org/10.1001/jamapediatrics.2023.5718 .
doi: 10.1001/jamapediatrics.2023.5718
pubmed: 39133513
Rose G. Sick individuals and sick populations. Int J Epidemiol. 1985;14(1):32–8. https://doi.org/10.1093/ije/14.1.32 .
doi: 10.1093/ije/14.1.32
pubmed: 3872850
Sauzet ORJ, Breiding JH. DistdichoR a R Package for the distributional dichotomisation of continuous outcomes. 2018. https://arxiv.org/abs/1809.03279 . Accessed 11 Sept 2018.
Team RC. R: A Language and environment for statistical computing_. https://www.R-project.org/ . Accessed 21 Sept 2024.
Yu KM, Moyeed RA. Bayesian quantile regression. Stat Probabil Lett. 2001;54(4):437–47. https://doi.org/10.1016/S0167-7152(01)00124-9 . (In English).
doi: 10.1016/S0167-7152(01)00124-9
Martenies SE, Zhang M, Corrigan AE, et al. Developing a National-Scale Exposure Index for Combined Environmental Hazards and Social Stressors and Applications to the Environmental Influences on Child Health Outcomes (ECHO) Cohort. Int J Environ Res Public Health 2023;20(14). https://doi.org/10.3390/ijerph20146339 .
Forrest CB, Blackwell CK, Camargo CA Jr. Advancing the science of children’s positive health in the national institutes of health Environmental Influences on Child Health Outcomes (ECHO) research program. J Pediatr. 2018;196:298–300. https://doi.org/10.1016/j.jpeds.2018.02.004 .
doi: 10.1016/j.jpeds.2018.02.004
pubmed: 29567045
pmcid: 5996976