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Association of lead-exposure risk and family income with childhood brain outcomes

Abstract

Socioeconomic factors influence brain development and structure, but most studies have overlooked neurotoxic insults that impair development, such as lead exposure. Childhood lead exposure affects cognitive development at the lowest measurable concentrations, but little is known about its impact on brain development during childhood. We examined cross-sectional associations among brain structure, cognition, geocoded measures of the risk of lead exposure and sociodemographic characteristics in 9,712 9- and 10-year-old children. Here we show stronger negative associations of living in high-lead-risk census tracts in children from lower- versus higher-income families. With increasing risk of exposure, children from lower-income families exhibited lower cognitive test scores, smaller cortical volume and smaller cortical surface area. Reducing environmental insults associated with lead-exposure risk might confer greater benefit to children experiencing more environmental adversity, and further understanding of the factors associated with high lead-exposure risk will be critical for improving such outcomes in children.

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Fig. 1: Lead-exposure risk scores predict elevated-blood-lead-level rates at the census tract level.
Fig. 2: Risk of lead exposure and cognition.
Fig. 3: Negative associations of increased risk of lead exposure are greater for children from lower-income families.
Fig. 4: Cortical volume–cognition associations are strongest in the most-at-risk children.

Data availability

ABCD data are publicly available through the National Institute of Mental Health Data Archive (https://data-archive.nimh.nih.gov/abcd). The blood-lead-level data were not collected as part of the ABCD Study and were made available by the corresponding agencies, entities or individuals identified in the Supplementary information (Supplementary Table 2); these data are, however, available from the authors upon reasonable request and with permission of each of the agencies, entities or individuals.

References

  1. 1.

    Reuben, A. et al. Association of childhood blood lead levels with cognitive function and socioeonomic status at age 38 years and with IQ change and socioeconomic mobility between childhood and adulthood. JAMA 317, 1244–1251 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Bellinger, D. C. Very low lead exposures and children’s neurodevelopment. Curr. Opin. Pediatr. 20, 172–177 (2008).

    Google Scholar 

  3. 3.

    Canfield, R. L. et al. Intellectual impairments in children with blood lead concentrations below 10 μg per deciliter. N. Engl. J. Med. 348, 1517–1526 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Lanphear, B. P., Dietrich, K., Auinger, P. & Cox, C. Cognitive deficits associated with blood lead concentrations <10 μg/dL in US children and adolescents. Public Health Rep. 115, 521–529 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Needleman, H. L. et al. Deficits in psychologic and classroom performance of children with elevated dentine lead levels. N. Engl. J. Med. 300, 689–695 (1979).

  6. 6.

    Wasserman, G. A. et al. The relationship between blood lead, bone lead and child intelligence. Child Neuropsychol. 9, 22–34 (2003).

    Google Scholar 

  7. 7.

    Dietrich, K. N., Ris, M. D., Succop, P. A., Berger, O. G. & Bornschein, R. L. Early exposure to lead and juvenile delinquency. Neurotoxicol. Teratol. 23, 511–518 (2001).

    CAS  Google Scholar 

  8. 8.

    Wright, J. P. et al. Association of prenatal and childhood blood lead concentrations with criminal arrests in early adulthood. PLoS Med. 5, e101 (2008).

    PubMed  PubMed Central  Google Scholar 

  9. 9.

    Braun, J. M., Kahn, R. S., Froehlich, T., Auinger, P. & Lanphear, B. P. Exposures to environmental toxicants and attention deficit hyperactivity disorder in U.S. children. Environ. Health Perspect. 114, 1904–1909 (2006).

    PubMed  PubMed Central  Google Scholar 

  10. 10.

    Guilarte, T. R., Opler, M. & Pletnikov, M. Is lead exposure in early life an environmental risk factor for schizophrenia? Neurobiological connections and testable hypotheses. NeuroToxicology 33, 560–574 (2012).

    CAS  Google Scholar 

  11. 11.

    Mazumdar, M. et al. Prenatal lead levels, plasma amyloid β levels, and gene expression in young adulthood. Environ. Health Perspect. 120, 702–707 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Wu, J. et al. Alzheimer’s disease (AD)-like pathology in aged monkeys after infantile exposure to environmental metal lead (Pb): evidence for a developmental origin and environmental link for AD. J. Neurosci. 28, 3–9 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    NTP Monograph on Health Effects of Low-Level Lead (National Toxicology Program (NTP), 2012); http://ntp.niehs.nih.gov/go/36443

  14. 14.

    Noble, K. G., Houston, S. M., Kan, E. & Sowell, E. R. Neural correlates of socioeconomic status in the developing human brain. Dev. Sci. 15, 516–527 (2012).

    PubMed  PubMed Central  Google Scholar 

  15. 15.

    Luders, E., Narr, K. L., Thompson, P. M. & Toga, A. W. Neuroanatomical correlates of intelligence. Intelligence 37, 156–163 (2009).

    PubMed  PubMed Central  Google Scholar 

  16. 16.

    Hanson, J. L. et al. Family poverty affects the rate of human infant brain growth. PLoS ONE 8, e80954 (2013).

    PubMed  PubMed Central  Google Scholar 

  17. 17.

    Noble, K. G. et al. Family income, parental education and brain structure in children and adolescents. Nat. Neurosci. 18, 773–778 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Hair, N. L., Hanson, J. L., Wolfe, B. L. & Pollak, S. D. Association of child poverty, brain development, and academic achievement. JAMA Pediatr. 169, 822–829 (2015).

    PubMed  PubMed Central  Google Scholar 

  19. 19.

    Pirkle, J. L. et al. Exposure of the U.S. population to lead, 1991–1994. Environ. Health Perspect. 106, 745–790 (1998).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Guilarte, T. R., Toscano, C. D., McGlothan, J. L. & Weaver, S. A. Environmental enrichment reverses cognitive and molecular deficits induced by developmental lead exposure. Ann. Neurol. 53, 50–56 (2003).

    CAS  Google Scholar 

  21. 21.

    Schneider, J. S., Lee, M. H., Anderson, D. W. & Lidsky, T. I. Enriched environment during development is protective against lead-induced neurotoxicity. Brain Res. 896, 48–55 (2001).

    CAS  Google Scholar 

  22. 22.

    Dietrich, K. N. et al. Low-level fetal lead exposure effect on neurobehavioral development in early infancy. Pediatrics 80, 721–730 (1987).

    CAS  Google Scholar 

  23. 23.

    Dietrich, K. N., Succop, P. A., Berger, O. G., Hammond, P. B. & Bornschein, R. L. Lead exposure and the cognitive development of urban preschool children: the Cincinnati Lead Study cohort at age 4 years. Neurotoxicol. Teratol. 13, 203–211 (1991).

    CAS  Google Scholar 

  24. 24.

    Brubaker, C. J., Dietrich, K. N., Lanphear, B. P. & Cecil, K. M. The influence of age of lead exposure on adult gray matter volume. NeuroToxicology 31, 259–266 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Cecil, K. M. et al. Decreased brain volume in adults with childhood lead exposure. PLoS Med. 5, e112 (2008).

    PubMed  PubMed Central  Google Scholar 

  26. 26.

    Brubaker, C. J. et al. Altered myelination and axonal integrity in adults with childhood lead exposure: a diffusion tensor imaging study. NeuroToxicology 30, 867–875 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Frostenson, S. & Kliff, S. The risk of lead poisoning isn’t just in Flint. So we mapped the risk in every neighborhood in America. Vox https://www.vox.com/a/lead-exposure-risk-map (2016).

  28. 28.

    Washington Tracking Network, Washington State Department of Health. Childhood lead risk map. https://fortress.wa.gov/doh/wtn/WTNPortal/ (2017).

  29. 29.

    Jernigan, T. L., Brown, S. A. & Dowling, G. J. The adolescent brain cognitive development study. J. Res. Adolesc. 28, 154–156 (2018).

    Google Scholar 

  30. 30.

    The ABCD Consortium. Dataset: Release 2.0 and Fix Release 2.0.1 (2019); https://doi.org/10.15154/1503209

  31. 31.

    Jacobs, D. E. et al. The prevalence of lead-based paint hazards in U.S. housing. Environ. Health Perspect. 110, A599–A606 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Lanphear, B. P., Byrd, R. S., Auinger, P. & Schaffer, S. J. Community characteristics associated with elevated blood lead levels in children. Pediatrics 101, 264–271 (1998).

    CAS  Google Scholar 

  33. 33.

    Wheeler, D. C., Jones, R. M., Schootman, M. & Nelson, E. J. Explaining variation in elevated blood lead levels among children in Minnesota using neighborhood socioeconomic variables. Sci. Total Environ. 650, 970–977 (2019).

    CAS  Google Scholar 

  34. 34.

    Luciana, M. et al. Adolescent neurocognitive development and impacts of substance use: overview of the adolescent brain cognitive development (ABCD) baseline neurocognition battery. Dev. Cogn. Neurosci. 32, 67–79 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Hagler, D. J. et al. Image processing and analysis methods for the Adolescent Brain Cognitive Development Study. NeuroImage 202, 116091 (2019).

    CAS  Google Scholar 

  36. 36.

    Kind, A. J. H. et al. Neighborhood socioeconomic disadvantage and 30-day rehospitalization: a retrospective cohort study. Ann. Intern. Med. 161, 765–774 (2014).

    PubMed  PubMed Central  Google Scholar 

  37. 37.

    Bradley, R. H. & Corwyn, R. F. Socioeconomic status and child development. Annu. Rev. Psychol. 53, 371–399 (2002).

    Google Scholar 

  38. 38.

    Lanphear, B. P. et al. Low-level environmental lead exposure and children’s intellectual function: an international pooled analysis. Environ. Health Perspect. 113, 894–899 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Wolf, S., Magnuson, K. A. & Kimbro, R. T. Family poverty and neighborhood poverty: links with children’s school readiness before and after the Great Recession. Child. Youth Serv. Rev. 79, 368–384 (2017).

    PubMed  PubMed Central  Google Scholar 

  40. 40.

    Chetty, R., Hendren, N. & Katz, L. F. The effects of exposure to better neighborhoods on children: new evidence from the Moving to Opportunity Experiment. Am. Econ. Rev. 106, 855–902 (2016).

    Google Scholar 

  41. 41.

    Farah, M. J. et al. Environmental stimulation, parental nurturance and cognitive development in humans. Dev. Sci. 11, 793–801 (2008).

    Google Scholar 

  42. 42.

    Muller, C., Sampson, R. J. & Winter, A. S. Environmental inequality: the social causes and consequences of lead exposure. Annu. Rev. Sociol. 44, 20.21–20.20 (2018).

    Google Scholar 

  43. 43.

    Magzamen, S. et al. Quantile regression in environmental health: early life lead exposure and end-of-grade exams. Environ. Res. 137, 108–119 (2015).

    CAS  Google Scholar 

  44. 44.

    Lanphear, B. P. & Roghmann, K. J. Pathways of lead exposure in urban children. Environ. Res. 74, 67–73 (1997).

    CAS  Google Scholar 

  45. 45.

    Federal Action Plan to Reduce Childhood Lead Exposures and Associated Health Impacts (US Environmental Protection Agency, 2018); https://www.hud.gov/sites/dfiles/HH/documents/fedactionplan_lead_final.pdf

  46. 46.

    Children at Risk: Gaps in State Lead Screening Policies (Safer Chemicals Healthier Families, 2017); https://saferchemicals.org/get-the-facts/children-at-risk/

  47. 47.

    Sargent, J. D. et al. Childhood lead poisoning in Massachusetts communities: its association with sociodemographic and housing characteristics. Am. J. Public Health 85, 528–534 (1995).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Akkus, C. & Ozdenerol, E. Exploring childhood lead exposure through GIS: a review of the recent literature. Int. J. Environ. Res. Public Health 11, 6314–6334 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Krieger, N. et al. Choosing area based socioeconomic measures to monitor social inequalities in low birth weight and childhood lead poisoning: the Public Health Disparities Geocoding Project (US). J. Epidemiol. Community Health 57, 186–199 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Sadler, R. C. How ZIP codes nearly masked the lead problem in Flint. https://theconversation.com/how-zip-codes-nearly-masked-the-lead-problem-in-flint-65626 (2016).

  51. 51.

    Miranda, M. L., Dolinoy, D. C. & Overstreet, M. A. Mapping for prevention: GIS models for directing childhood lead poisoning prevention programs. Environ. Health Perspect. 110, 947–953 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Bressler, J. Blood Lead Testing Among Children Aged <72 Months—Alaska, 2013–2018. State of Alaska Epidemiology Bulletin http://www.epi.alaska.gov/bulletins/docs/b2019_2016.pdf (2019).

  53. 53.

    Roy, S. & Edwards, M. A. Preventing another lead (Pb) in drinking water crisis: lessons from the Washington D.C. and Flint MI contamination events. Curr. Opin. Environ. Sci. Health 7, 34–44 (2019).

    Google Scholar 

  54. 54.

    Elardo, R. & Bradley, R. H. The home observation for measurement of the environment (HOME) scale: a review of research. Dev. Rev. 1, 113–145 (1981).

    Google Scholar 

  55. 55.

    Chen, A., Dietrich, K. N., Ware, J. H., Radcliffe, J. & Rogan, W. J. IQ and blood lead from 2 to 7 years of age: are the effects in older children the residual of high blood lead concentration in 2-year-olds? Environ. Health Perspect. 113, 597–601 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. 56.

    Menezes-Filho, J. A. et al. Environmental co-exposure to lead and manganese and intellectual deficit in school-aged children. Int. J. Environ. Res. Public Health 15, 2418 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. 57.

    Lucchini, R. G. et al. Inverse association of intellectual function with very low blood lead but not with manganese exposure in Italian adolescents. Environ. Res. 118, 65–71 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. 58.

    Hornung, R. W., Lanphear, B. P. & Dietrich, K. N. Age of greatest susceptibility to childhood lead exposure: a new statistical approach. Environ. Health Perspect. 117, 1309–1312 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Arora, M. et al. Determining prenatal, early childhood and cumulative long-term lead exposure using micro-spatial deciduous dentine levels. PLoS ONE 9, e97805 (2014).

    PubMed  PubMed Central  Google Scholar 

  60. 60.

    Garavan, H. et al. Recruiting the ABCD sample: design considerations and procedures. Dev. Cogn. Neurosci. 32, 16–22 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. 61.

    American Community Survey (US Census Bureau, 2010); https://www.census.gov/programs-surveys/acs/about.html

  62. 62.

    Casey, B. J. et al. The Adolescent Brain Cognitive Development (ABCD) study: imaging acquisition across 21 sites. Dev. Cogn. Neurosci. 32, 43–54 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. 63.

    Barch, D. M. et al. Demographic, physical and mental health assessments in the adolescent brain and cognitive development study: rationale and description. Dev. Cogn. Neurosci. 32, 55–66 (2018).

    Google Scholar 

  64. 64.

    Bauer, P. J. & Zelazo, P. D. I. X. NIH Toolbox Cognition Battery (CB): summary, conclusions, and implications for cognitive development. Monogr. Soc. Res. Child Dev. 78, 133–146 (2013).

    Google Scholar 

  65. 65.

    Weintraub, S. et al. Cognition assessment using the NIH Toolbox. Neurology 80, S54–S64 (2013).

    PubMed  PubMed Central  Google Scholar 

  66. 66.

    Akshoomoff, N. et al. TheNIH Toolbox Cognition Battery: results from a large normative developmental sample (PING). Neuropsychology 28, 1–10 (2014).

    Google Scholar 

  67. 67.

    Singh, G. K. Area deprivation and widening inequalities in US mortality, 1969-1998. Am. J. Public Health 93, 1137–1143 (2003).

    PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank the Adolescent Brain Cognitive Development (ABCD) participants and their families for their time and dedication to this project, and G. Dowling and C. Chieh Fan for their comments and feedback during the development of this manuscript. We also thank W. Thompson for statistical support, comments and feedback during the development of this manuscript. ABCD acknowledgement: data used in the preparation of this article were obtained from the ABCD Study (https://abcdstudy.org/), and are held in the NIMH Data Archive. This is a multisite, longitudinal study designed to recruit more than 10,000 children aged 9–10 and follow them over 10 yr into early adulthood. The ABCD Study is supported by the National Institutes of Health (NIH) and additional federal partners under award numbers U01DA041022, U01DA041028, U01DA041048, U01DA041089, U01DA041106, U01DA041117, U01DA041120, U01DA041134, U01DA041148, U01DA041156, U01DA041174, U24DA041123 and U24DA041147. A full list of supporters is available at https://abcdstudy.org/federal-partners/. A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/principal-investigators/. ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators. The ABCD data repository grows and changes over time. The ABCD data used in this report came from https://doi.org/10.15154/1503209.

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A.T.M., R.M., B.P.L. and E.R.S. conceived and designed the experiments/analysis. A.T.M., S.B. and E.C.K. collected the data. A.T.M. and B.P.L. contributed data or analysis tools. A.T.M., S.B. and E.C.K. analyzed the data. A.T.M. and E.R.S. wrote the paper.

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Correspondence to Elizabeth R. Sowell.

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Peer review information Jennifer Sargent was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 The distribution of random coefficients for each geographic region.

Each data point represents the random intercept (left) or slope (right) for each state/city (i.e., fixed effect coefficient + random effects deviation). The lines surrounding the data points represent the 95% confidence interval of the coefficient. Aside from Oregon and Colorado (for which the 95% confidence intervals included 0), there were significant increases in elevated-blood-lead-level rates with increasing lead-risk scores for each state/city (right). Analysis employed generalized linear-mixed effects models, which tested the statistical significance of coefficients against a t-distribution.

Extended Data Fig. 2 Risk of lead exposure and cognition.

Overall cognitive test scores declined most steeply with increasing risk of environmental lead exposure in children of low-income parents. The data reflect individual participants. Solid lines represent means of the marginal fitted values of the model. Analysis employed linear mixed-effects models, which tested the statistical significance of coefficients against a t-distribution. Age, sex, parental education, and race/ethnicity were included as covariates in this analysis. The scale of the ordinate differs from that in Fig. 2.

Extended Data Fig. 3 Negative associations of increased risk of lead exposure are greater for children from lower-income families.

Whole-brain cortical surface area declined most steeply with increasing risk of environmental lead exposure in children of low-income parents. The data reflect individual participants. Solid lines represent means of the marginal fitted values of the model. Analysis employed linear mixed-effects models, which tested the statistical significance of coefficients against a t-distribution. Age, sex, parental education, and race/ethnicity were included as covariates in this analysis. The scale of the ordinate differs from that in Fig. 3a.

Extended Data Fig. 4 Negative associations of increased risk of lead exposure are greater for children from lower-income families.

Whole-brain cortical volume declined most steeply with increasing risk of environmental lead exposure in children of low-income parents. The data reflect individual participants. Solid lines represent means of the marginal fitted values of the model. Analysis employed linear mixed-effects models, which tested the statistical significance of coefficients against a t-distribution. Age, sex, parental education, and race/ethnicity were included as covariates in this analysis. The scale of the ordinate differs from that in Fig. 3b.

Extended Data Fig. 5 Associations between family income, lead-exposure risk, and area deprivation index.

Zero-order Spearman’s rank-order correlation matrix of parent-report household income, lead risk and its 2 subcomponents, and the area deprivation index (ADI) and its 17 subcomponents. Along the ordinate, from top to bottom, the variables refer to parent-report total annual household income (“Household Income”), the composite lead-risk score (“Lead Risk: Composite”), estimated percentage of homes at risk for lead exposure given lead-based paint (“Lead Risk: Housing”), percentage of individuals below −125 percent of the poverty level (“Lead Risk: Poverty”), the national ADI percentile (“ADI: Composite”), the percentage of the population at least 25 years old with less than 9 years of education (“ADI: % No HS”; HS = high school), the percentage of the population at least 25 years old with at least a high school diploma (“ADI: % HS Diploma”), the percentage of employed persons at least 16 years old in white-collar jobs (“ADI: % White Collar”), median family income (“ADI: Family Income”), income disparity as defined by Singh67 (“ADI: Income Disparity”), median home value (“ADI: Home Value”), median gross rent (“ADI: Gross Rent”), median monthly mortgage (“ADI: Mortgage”), percentage of owner-occupied housing units (“ADI: % Owned Homes”), percentage of occupied housing units with at least 1 person per room (“ADI: % Crowding”), percentage of civilian labor force at least 16 years old who are unemployed (“ADI: Unemployment”), percentage of families below the poverty level (“ADI: % Poverty”), percentage of the population below 138% of the poverty threshold (“ADI: % −138 Poverty”), percentage of single-parent homes with children who are less than 18 years old (“ADI: % Single-Parent Homes”), percentage of occupied housing units with a motor vehicle (“ADI: % No Vehicle”), percentage of occupied housing units without a telephone (“ADI: % No Phone”), and the percentage of occupied housing units with complete plumbing (“ADI: % Incomplete Plumbing”). With the exception of parent-report household income (which was specific to each family), the lead-risk and ADI data had census-tract-level resolution.

Extended Data Fig. 6 Associations between family income, lead-exposure risk, and area deprivation index.

Zero-order Spearman’s rank-order correlation matrix of parent-report household income, lead risk and its 2 subcomponents, and the area deprivation index (ADI) and its 17 subcomponents, as in Extended Data Fig. 5, except that all correlation coefficients were squared (i.e., pseudo-R2). See Extended Data Fig. 5 caption for variable names.

Extended Data Fig. 7 Lead exposure risk scores predict Maryland’s blood lead levels at the census-tract level.

Left: Distribution of the census-tract-level geometric means of blood lead levels in Maryland, collapsed across the years of 2010 to 2014. Right: Geometric mean blood lead levels as a function of the estimated risk of lead exposure. The smaller gray data points represent individual census tracts. Two measures of central tendency are provided: The larger darker data points represent the means at each risk level, while the larger open data points represent the medians at each risk level. Analysis employed a Spearman’s rank-order correlation.

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Marshall, A.T., Betts, S., Kan, E.C. et al. Association of lead-exposure risk and family income with childhood brain outcomes. Nat Med 26, 91–97 (2020). https://doi.org/10.1038/s41591-019-0713-y

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