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Statistical Approaches for Investigating Periods of Susceptibility in Children’s Environmental Health Research

  • Jessie P. BuckleyEmail author
  • Ghassan B. Hamra
  • Joseph M. Braun
Methods in Environmental Epidemiology (AZ Pollack and NJ Perkins, Section Editors)
  • 38 Downloads
Part of the following topical collections:
  1. Topical Collection on Methods in Environmental Epidemiology

Abstract

Purpose of Review

Children’s environmental health researchers are increasingly interested in identifying time intervals during which individuals are most susceptible to adverse impacts of environmental exposures. We review recent advances in methods for assessing susceptible periods.

Recent Findings

We identified three general classes of modeling approaches aimed at identifying susceptible periods in children’s environmental health research: multiple informant models, distributed lag models, and Bayesian approaches. Benefits over traditional regression modeling include the ability to formally test period effect differences, to incorporate highly time-resolved exposure data, or to address correlation among exposure periods or exposure mixtures.

Summary

Several statistical approaches exist for investigating periods of susceptibility. Assessment of susceptible periods would be advanced by additional basic biological research, further development of statistical methods to assess susceptibility to complex exposure mixtures, validation studies evaluating model assumptions, replication studies in different populations, and consideration of susceptible periods from before conception to disease onset.

Keywords

Critical windows Susceptibility Vulnerability Children’s health Environmental epidemiology Statistical methods 

Notes

Funding

JPB and GBH: 5U24OD023382

JMB: R01 ES025214, R01 ES024381, R01 ES027408, and UG3 OD023313

Compliance with Ethical Standards

Conflict of Interest

Joseph M. Braun was financially compensated for serving as an expert witness for plaintiffs in litigation related to tobacco smoke exposures. Jessie P. Buckley and Ghassan B. Hamra report grants from NIH during the conduct of the study.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.

References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. 1.
    Barr M Jr, DeSesso JM, Lau CS, Osmond C, Ozanne SE, Sadler TW, et al. Workshop to identify critical windows of exposure for children’s health: cardiovascular and endocrine work group summary. Environ Health Perspect. 2000;108(Suppl 3):569–71.CrossRefGoogle Scholar
  2. 2.
    Selevan SG, Kimmel CA, Mendola P. Identifying critical windows of exposure for children’s health. Environ Health Perspect. 2000;108(Suppl 3):451–5.CrossRefGoogle Scholar
  3. 3.
    NIEHS (2012) Advancing science, improving health: a plan for environmental health research.Google Scholar
  4. 4.
    Braun JM, Gray K. Challenges to studying the health effects of early life environmental chemical exposures on children’s health. PLoS Biol. 2017;15(12):e2002800.CrossRefGoogle Scholar
  5. 5.
    Hamra GB, Buckley JP. Environmental exposure mixtures: questions and methods to address them. Curr Epidemiol Rep. 2018;5(2):160–5.CrossRefGoogle Scholar
  6. 6.
    Stafoggia M, Breitner S, Hampel R, Basagana X. Statistical approaches to address multi-pollutant mixtures and multiple exposures: the state of the science. Curr Environ Health Rep. 2017;4(4):481–90.CrossRefGoogle Scholar
  7. 7.
    Larsen WJ. Human embryology. Philadelphia: Churchill Livingstone; 2001.Google Scholar
  8. 8.
    Kim JH, Scialli AR. Thalidomide: the tragedy of birth defects and the effective treatment of disease. Toxicol Sci. 2011;122(1):1–6.CrossRefGoogle Scholar
  9. 9.
    Goderis J, De Leenheer E, Smets K, Van Hoecke H, Keymeulen A, Dhooge I. Hearing loss and congenital CMV infection: a systematic review. Pediatrics. 2014;134(5):972–82.CrossRefGoogle Scholar
  10. 10.
    Rawlinson WD, Boppana SB, Fowler KB, Kimberlin DW, Lazzarotto T, Alain S, et al. Congenital cytomegalovirus infection in pregnancy and the neonate: consensus recommendations for prevention, diagnosis, and therapy. Lancet Infect Dis. 2017;17(6):e177–e88.CrossRefGoogle Scholar
  11. 11.
    Diderichsen F, Hallqvist J, Whitehead M. Differential vulnerability and susceptibility: how to make use of recent development in our understanding of mediation and interaction to tackle health inequalities. Int J Epidemiol 2018.Google Scholar
  12. 12.
    Raz R, Roberts AL, Lyall K, Hart JE, Just AC, Laden F, et al. Autism spectrum disorder and particulate matter air pollution before, during, and after pregnancy: a nested case-control analysis within the Nurses’ Health Study II Cohort. Environ Health Perspect. 2015;123(3):264–70.CrossRefGoogle Scholar
  13. 13.
    Kalkbrenner AE, Windham GC, Serre ML, Akita Y, Wang X, Hoffman K, et al. Particulate matter exposure, prenatal and postnatal windows of susceptibility, and autism spectrum disorders. Epidemiology (Cambridge, Mass). 2015;26(1):30–42.CrossRefGoogle Scholar
  14. 14.
    •• Chen YH, Ferguson KK, Meeker JD, McElrath TF, Mukherjee B. Statistical methods for modeling repeated measures of maternal environmental exposure biomarkers during pregnancy in association with preterm birth. Environ Health. 2015;14:9 Compares several methods for examining susceptible periods with repeated biomarker measures in relation to a time-fixed binary outcome.CrossRefGoogle Scholar
  15. 15.
    Sanchez BN, Hu H, Litman HJ, Tellez-Rojo MM. Statistical methods to study timing of vulnerability with sparsely sampled data on environmental toxicants. Environ Health Perspect. 2011;119(3):409–15.CrossRefGoogle Scholar
  16. 16.
    • Wilson A, Chiu YM, Hsu HL, Wright RO, Wright RJ, Coull BA. Potential for Bias when estimating critical windows for air pollution in children’s health. Am J Epidemiol. 2017;186(11):1281–9 Allows for the timing of susceptible periods to depend on a modifier.CrossRefGoogle Scholar
  17. 17.
    Gasparrini A. Modeling exposure-lag-response associations with distributed lag non-linear models. Stat Med. 2014;33(5):881–99.CrossRefGoogle Scholar
  18. 18.
    •• Wilson A, Chiu YM, Hsu HL, Wright RO, Wright RJ, Coull BA. Bayesian distributed lag interaction models to identify perinatal windows of vulnerability in children’s health. Biostatistics (Oxford, England). 2017;18(3):537–52 Demonstrates bias when using trimester-averaged exposure that is not present when using distributed lag models to identify susceptible periods.CrossRefGoogle Scholar
  19. 19.
    • Liu SH, Bobb JF, Lee KH, Gennings C, Claus Henn B, Bellinger D, et al. Lagged kernel machine regression for identifying time windows of susceptibility to exposures of complex mixtures. Biostatistics. 2018;19(3):325–41 Examines periods of susceptibility to exposure mixtures using a highly flexible extension of distributed lag models based on Bayesian Kernel Machine Regression.CrossRefGoogle Scholar
  20. 20.
    Warren J, Fuentes M, Herring A, Langlois P. Spatial-temporal modeling of the association between air pollution exposure and preterm birth: identifying critical windows of exposure. Biometrics. 2012;68(4):1157–67.CrossRefGoogle Scholar
  21. 21.
    Horton NJ, Laird NM, Zahner GEP. Use of multiple informant data as a predictor in psychiatric epidemiology. Int J Methods Psychiatr Res. 1999;8(1):6–18.CrossRefGoogle Scholar
  22. 22.
    Litman HJ, Horton NJ, Hernandez B, Laird NM. Incorporating missingness for estimation of marginal regression models with multiple source predictors. Stat Med. 2007;26(5):1055–68.CrossRefGoogle Scholar
  23. 23.
    Stacy SL, Papandonatos GD, Calafat AM, Chen A, Yolton K, Lanphear BP, et al. Early life bisphenol a exposure and neurobehavior at 8 years of age: identifying windows of heightened vulnerability. Environ Int. 2017;107:258–65.CrossRefGoogle Scholar
  24. 24.
    Vuong AM, Yolton K, Poston KL, Xie C, Webster GM, Sjodin A, et al. Prenatal and postnatal polybrominated diphenyl ether (PBDE) exposure and measures of inattention and impulsivity in children. Neurotoxicol Teratol. 2017;64:20–8.CrossRefGoogle Scholar
  25. 25.
    Vuong AM, Yolton K, Xie C, Webster GM, Sjodin A, Braun JM, et al. Childhood polybrominated diphenyl ether (PBDE) exposure and neurobehavior in children at 8 years. Environ Res. 2017;158:677–84.CrossRefGoogle Scholar
  26. 26.
    Braun JM, Chen A, Hoofnagle A, Papandonatos GD, Jackson-Browne M, Hauser R, et al. Associations of early life urinary triclosan concentrations with maternal, neonatal, and child thyroid hormone levels. Horm Behav. 2018;101:77–84.CrossRefGoogle Scholar
  27. 27.
    Vuong AM, Braun JM, Webster GM, Thomas Zoeller R, Hoofnagle AN, Sjodin A, et al. Polybrominated diphenyl ether (PBDE) exposures and thyroid hormones in children at age 3years. Environ Int. 2018;117:339–47.CrossRefGoogle Scholar
  28. 28.
    Vuong AM, Braun JM, Yolton K, Wang Z, Xie C, Webster GM, et al. Prenatal and childhood exposure to perfluoroalkyl substances (PFAS) and measures of attention, impulse control, and visual spatial abilities. Environ Int. 2018;119:413–20.CrossRefGoogle Scholar
  29. 29.
    Vuong AM, Yolton K, Poston KL, Xie C, Webster GM, Sjodin A, et al. Childhood polybrominated diphenyl ether (PBDE) exposure and executive function in children in the HOME Study. Int J Hyg Environ Health. 2018;221(1):87–94.CrossRefGoogle Scholar
  30. 30.
    Vuong AM, Yolton K, Wang Z, Xie C, Webster GM, Ye X, et al. Childhood perfluoroalkyl substance exposure and executive function in children at 8years. Environ Int. 2018;119:212–9.CrossRefGoogle Scholar
  31. 31.
    Zhang H, Yolton K, Webster GM, Ye X, Calafat AM, Dietrich KN, et al. Prenatal and childhood perfluoroalkyl substances exposures and children’s reading skills at ages 5 and 8years. Environ Int. 2018;111:224–31.CrossRefGoogle Scholar
  32. 32.
    Jackson-Browne MS, Papandonatos GD, Chen A, Calafat AM, Yolton K, Lanphear BP, et al. Identifying vulnerable periods of neurotoxicity to triclosan exposure in children. Environ Health Perspect. 2018;126(5):057001.CrossRefGoogle Scholar
  33. 33.
    Stacy SL, Papandonatos GD, Calafat AM, Chen A, Yolton K, Lanphear BP, et al. Early life bisphenol a exposure and neurobehavior at 8years of age: identifying windows of heightened vulnerability. Environ Int 2017.Google Scholar
  34. 34.
    Gasparrini A, Scheipl F, Armstrong B, Kenward MG. A penalized framework for distributed lag non-linear models. Biometrics. 2017.Google Scholar
  35. 35.
    Chiu YH, Hsu HH, Coull BA, Bellinger DC, Kloog I, Schwartz J, et al. Prenatal particulate air pollution and neurodevelopment in urban children: examining sensitive windows and sex-specific associations. Environ Int. 2016;87:56–65.CrossRefGoogle Scholar
  36. 36.
    Hsu HH, Chiu YH, Coull BA, Kloog I, Schwartz J, Lee A, et al. Prenatal particulate air pollution and asthma onset in urban children. Identifying sensitive windows and sex differences. Am J Respir Crit Care Med. 2015;192(9):1052–9.CrossRefGoogle Scholar
  37. 37.
    Martens DS, Cox B, Janssen BG, Clemente DBP, Gasparrini A, Vanpoucke C, et al. Prenatal air pollution and newborns’ predisposition to accelerated biological aging. JAMA Pediatr. 2017;171(12):1160–7.CrossRefGoogle Scholar
  38. 38.
    Raz R, Levine H, Pinto O, Broday DM, Yuval, Weisskopf MG. Traffic-related air pollution and autism spectrum disorder: a population-based nested case-control study in Israel. Am J Epidemiol. 2018;187(4):717–25.CrossRefGoogle Scholar
  39. 39.
    Vicedo-Cabrera AM, Olsson D, Forsberg B. Exposure to seasonal temperatures during the last month of gestation and the risk of preterm birth in Stockholm. Int J Environ Res Public Health. 2015;12(4):3962–78.CrossRefGoogle Scholar
  40. 40.
    Vicedo-Cabrera AM, Iniguez C, Barona C, Ballester F. Exposure to elevated temperatures and risk of preterm birth in Valencia, Spain. Environ Res. 2014;134:210–7.CrossRefGoogle Scholar
  41. 41.
    Benmarhnia T, Auger N, Stanislas V, Lo E, Kaufman JS. The relationship between apparent temperature and daily number of live births in Montreal. Matern Child Health J. 2015;19(12):2548–51.CrossRefGoogle Scholar
  42. 42.
    Cox B, Vicedo-Cabrera AM, Gasparrini A, Roels HA, Martens E, Vangronsveld J, et al. Ambient temperature as a trigger of preterm delivery in a temperate climate. J Epidemiol Community Health. 2016.Google Scholar
  43. 43.
    Claus Henn B, Austin C, Coull BA, Schnaas L, Gennings C, Horton MK, et al. Uncovering neurodevelopmental windows of susceptibility to manganese exposure using dentine microspatial analyses. Environ Res. 2018;161:588–98.CrossRefGoogle Scholar
  44. 44.
    • Bello GA, Arora M, Austin C, Horton MK, Wright RO, Gennings C. Extending the distributed lag model framework to handle chemical mixtures. Environ Res. 2017;156:253–64 Extends distributed lag models to address periods of susceptibility to exposure mixtures using weighted quantile sum or tree-based methods.CrossRefGoogle Scholar
  45. 45.
    Welty LJ, Peng RD, Zeger SL, Dominici F. Bayesian distributed lag models: estimating effects of particulate matter air pollution on daily mortality. Biometrics. 2009;65(1):282–91.CrossRefGoogle Scholar
  46. 46.
    Roberts EM, English PB. Bayesian modeling of time-dependent vulnerability to environmental hazards: an example using autism and pesticide data. Stat Med. 2013;32(13):2308–19.CrossRefGoogle Scholar
  47. 47.
    Lee A, Leon Hsu HH, Mathilda Chiu YH, Bose S, Rosa MJ, Kloog I, et al. Prenatal fine particulate exposure and early childhood asthma: effect of maternal stress and fetal sex. J Allergy Clin Immunol. 2018;141(5):1880–6.CrossRefGoogle Scholar
  48. 48.
    Chiu YM, Hsu HL, Wilson A, Coull BA, Pendo MP, Baccarelli A, et al. Prenatal particulate air pollution exposure and body composition in urban preschool children: examining sensitive windows and sex-specific associations. Environ Res. 2017;158:798–805.CrossRefGoogle Scholar
  49. 49.
    Bose S, Chiu YM, Hsu HL, Di Q, Rosa MJ, Lee A, et al. Prenatal nitrate exposure and childhood asthma. Influence of maternal prenatal stress and fetal sex. Am J Respir Crit Care Med. 2017;196(11):1396–403.CrossRefGoogle Scholar
  50. 50.
    Brunst KJ, Sanchez-Guerra M, Chiu YM, Wilson A, Coull BA, Kloog I, et al. Prenatal particulate matter exposure and mitochondrial dysfunction at the maternal-fetal interface: effect modification by maternal lifetime trauma and child sex. Environ Int. 2018;112:49–58.CrossRefGoogle Scholar
  51. 51.
    Warren J, Fuentes M, Herring A, Langlois P. Bayesian spatial-temporal model for cardiac congenital anomalies and ambient air pollution risk assessment. Environmetrics. 2012;23(8):673–84.CrossRefGoogle Scholar
  52. 52.
    Warren JL, Fuentes M, Herring AH, Langlois PH. Air pollution metric analysis while determining susceptible periods of pregnancy for low birth weight. ISRN Obstet Gynecol. 2013;2013:387452.CrossRefGoogle Scholar
  53. 53.
    Warren JL, Stingone JA, Herring AH, Luben TJ, Fuentes M, Aylsworth AS, et al. Bayesian multinomial probit modeling of daily windows of susceptibility for maternal PM2.5 exposure and congenital heart defects. Stat Med. 2016;35(16):2786–801.CrossRefGoogle Scholar
  54. 54.
    • Chang HH, Warren JL, Darrow LA, Reich BJ, Waller LA. Assessment of critical exposure and outcome windows in time-to-event analysis with application to air pollution and preterm birth study. Biostatistics. 2015;16(3):509–21 Addresses susceptible periods when the outcome is time-varying.CrossRefGoogle Scholar
  55. 55.
    Perrier F, Giorgis-Allemand L, Slama R, Philippat C. Within-subject pooling of biological samples to reduce exposure misclassification in biomarker-based studies. Epidemiology (Cambridge, Mass). 2016;27(3):378–88.CrossRefGoogle Scholar
  56. 56.
    Kuchenhoff H, Mwalili SM, Lesaffre E. A general method for dealing with misclassification in regression: the misclassification SIMEX. Biometrics. 2006;62(1):85–96.CrossRefGoogle Scholar
  57. 57.
    Cole SR, Chu H, Greenland S. Multiple-imputation for measurement-error correction. Int J Epidemiol. 2006;35(4):1074–81.CrossRefGoogle Scholar
  58. 58.
    Rosner B, Willett WC, Spiegelman D. Correction of logistic regression relative risk estimates and confidence intervals for systematic within-person measurement error. Stat Med. 1989;8(9):1051–69 discussion 71–3.CrossRefGoogle Scholar
  59. 59.
    Gore AC, Chappell VA, Fenton SE, Flaws JA, Nadal A, Prins GS, et al. EDC-2: the endocrine society’s second scientific statement on endocrine-disrupting chemicals. Endocr Rev. 2015;36(6):E1–E150.CrossRefGoogle Scholar
  60. 60.
    Carlin DJ, Rider CV, Woychik R, Birnbaum LS. Unraveling the health effects of environmental mixtures: an NIEHS priority. Environ Health Perspect. 2013;121(1):A6–8.CrossRefGoogle Scholar
  61. 61.
    Braun JM, Gennings C, Hauser R, Webster TF. What can epidemiological studies tell us about the impact of chemical mixtures on human health? Environ Health Perspect. 2016;124(1):A6–9.CrossRefGoogle Scholar
  62. 62.
    Warren JL, Son JY, Pereira G, Leaderer BP, Bell ML. Investigating the impact of maternal residential mobility on identifying critical windows of susceptibility to ambient air pollution during pregnancy. Am J Epidemiol. 2018;187(5):992–1000.CrossRefGoogle Scholar
  63. 63.
    Dionisio KL, Chang HH, Baxter LK. A simulation study to quantify the impacts of exposure measurement error on air pollution health risk estimates in copollutant time-series models. Environ Health. 2016;15(1):114.CrossRefGoogle Scholar
  64. 64.
    Keller JP, Drton M, Larson T, Kaufman JD, Sandler DP, Szpiro AA. Covariate-adaptive clustering of exposures for air pollution epidemiology cohorts. Ann Appl Stat. 2017;11(1):93–113.CrossRefGoogle Scholar
  65. 65.
    Weisskopf MG, Seals RM, Webster TF. Bias amplification in epidemiologic analysis of exposure to mixtures. Environ Health Perspect. 2018;126(4):047003.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Jessie P. Buckley
    • 1
    • 2
    Email author
  • Ghassan B. Hamra
    • 2
  • Joseph M. Braun
    • 3
  1. 1.Department of Environmental Health and EngineeringJohns Hopkins Bloomberg School of Public HealthBaltimoreUSA
  2. 2.Department of EpidemiologyJohns Hopkins Bloomberg School of Public HealthBaltimoreUSA
  3. 3.Department of EpidemiologyBrown University School of Public HealthProvidenceUSA

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