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Childhood forecasting of a small segment of the population with large economic burden

Nature Human Behaviour volume 1, Article number: 0005 (2017) Cite this article

Abstract

Policymakers are interested in early-years interventions to ameliorate childhood risks. They hope for improved adult outcomes in the long run that bring a return on investment. The size of the return that can be expected partly depends on how strongly childhood risks forecast adult outcomes, but there is disagreement about whether childhood determines adulthood. We integrated multiple nationwide administrative databases and electronic medical records with the four-decade-long Dunedin birth cohort study to test child-to-adult prediction in a different way, using a population-segmentation approach. A segment comprising 22% of the cohort accounted for 36% of the cohort’s injury insurance claims; 40% of excess obese kilograms; 54% of cigarettes smoked; 57% of hospital nights; 66% of welfare benefits; 77% of fatherless child-rearing; 78% of prescription fills; and 81% of criminal convictions. Childhood risks, including poor brain health at three years of age, predicted this segment with large effect sizes. Early-years interventions that are effective for this population segment could yield very large returns on investment.

Around the world, the population is ageing and total fertility rates are declining. As a result, nations increasingly view children and young people as valuable resources for the economic and social well-being of whole societies. This view is accompanied by public-policy interest in early interventions to help as many children as possible achieve their full potential. A key question concerns the potential size of the impact that might be brought about by interventions in the early years of children’s lives1,2. Research teams that have followed up on small samples of children who were enrolled in intervention experiments carried out decades ago point to reductions in school leaving, unemployment, crime, obesity and even blood pressure3–6. Some argue that today’s better-designed interventions might achieve greater reductions in adult problems than previous efforts7 (see also www.nuffieldfoundation.org, www.blueprintsprograms.com, http://ies.ed.gov/ncee/wwc and http://incredibleyears.com). Others assert that interventions for the youngest children will bring an even greater return on investment compared with interventions that begin when children are older8. However, a skeptic could point out that return on investment for society will depend not only on an intervention’s capacity to ameliorate childhood risks, but also on how relevant those risks are for downstream adult functioning in the general population. Thus, to a large extent, the question of how much early-years intervention can lift health and social well-being and reduce costs depends on how strongly early-years risk factors are tied to adult outcomes in the population. Our own research and that of others suggests that while childhood risk factors do predict adult outcomes with statistical significance, the effect sizes are typically modest9–11. The interpretation of these modest child-to-adult effect sizes is polarizing, and has sown confusion among scientists, policy makers and the public12–14. On the one hand, claims are made that the ‘child is father of the man’, because continuity from childhood risks to adult outcomes is stronger than expected, given the long duration of follow-up. On the other hand, on the basis of the same data, warnings are issued about the myth of early-childhood determinism and about unwarranted overemphasis on childhood.

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Figure 1: Measuring the concentration of economic-burden outcomes in a birth cohort.
Figure 2: The aggregation of adult economic-burden outcomes.
Figure 3: Predicting the probability of economic-burden outcomes.
Figure 4: The big footprint of multiple-high-cost users.

References

  1. Human Capital Development Before Age Five (eds Almond, D. & Currie, J.) (National Bureau of Economic Research, 2010); http://www.nber.org/papers/w15827

  2. Duncan, G. J. & Magnuson, K. Investing in preschool programs. J. Econ. Perspect. 27, 109–131 (2013).

    Article  Google Scholar 

  3. Campbell, F. et al. Early childhood investments substantially boost adult health. Science 343, 1478–1485 (2014).

    Article  CAS  Google Scholar 

  4. Gertler, P. et al. Labor market returns to an early childhood stimulation intervention in Jamaica. Science 344, 998–1001 (2014).

    Article  CAS  Google Scholar 

  5. Heckman, J., Pinto, R. & Savelyev, P. Understanding the mechanisms through which an influential early childhood program boosted adult outcomes. Am. Econ. Rev. 103, 2052–2086 (2013).

    Article  Google Scholar 

  6. Reynolds, A. J., Temple, J. A., Ou, S. R., Arteaga, I. A. & White, B. A. B. School-based early childhood education and age-28 well-being: effects by timing, dosage, and subgroups. Science 333, 360–364 (2011).

    Article  CAS  Google Scholar 

  7. Karoly, L. A., Kilburn, M. R. & Cannon, J. S. Proven Benefits of Early Childhood Interventions (Rand Corporation, 2005); http://www.rand.org/pubs/research_briefs/RB9145.html

  8. Heckman, J. J. Skill formation and the economics of investing in disadvantaged children. Science 312, 1900–1902 (2006).

    Article  CAS  Google Scholar 

  9. Moffitt, T. E. et al. A gradient of childhood self-control predicts health, wealth, and public safety. Proc. Natl Acad. Sci. USA 108, 2693–2698 (2011).

    Article  CAS  Google Scholar 

  10. Roberts, B. W., Kuncel, N. R., Shiner, R., Caspi, A. & Goldberg, L. R. The power of personality: the comparative validity of personality traits, socioeconomic status, and cognitive ability for predicting important life outcomes. Perspect. Psychol. Sci. 2, 313–345 (2007).

    Article  Google Scholar 

  11. Felitti, V. J. et al. Relationship of childhood abuse and household dysfunction to many of the leading causes of death in adults. The Adverse Childhood Experiences (ACE) Study. Am. J. Prev. Med. 14, 245–258 (1998).

    Article  CAS  Google Scholar 

  12. Rutter, M. Nature, nurture, and development: from evangelism through science toward policy and practice. Child Dev. 73, 1–21 (2002).

    Article  Google Scholar 

  13. Sampson, R. The characterological imperative. J. Econ. Lit. 54, 493–513 (2016).

    Article  Google Scholar 

  14. Steuerle, E. & Jackson, L. M. Advancing the Power of Economic Evidence to Inform Investments in Children, Youth, and Families (National Academies, 2016).

    Book  Google Scholar 

  15. Shonkoff, J. P., Boyce, W. T. & McEwen, B. S. Neuroscience, molecular biology, and the childhood roots of health disparities: building a new framework for health promotion and disease prevention. J. Am. Med. Assoc. 301, 2252–2259 (2009).

    Article  CAS  Google Scholar 

  16. Diamond, A. Activities and programs that improve children’s executive functions. Curr. Dir. Psychol. Sci. 21, 335–341 (2012).

    Article  Google Scholar 

  17. Black, M. M. & Dewey, K. G. Promoting equity through integrated early child development and nutrition interventions. Ann. NY Acad. Sci. 1308, 1–10 (2014).

    Article  Google Scholar 

  18. National Prevention Council National Prevention Strategy (US Department of Health & Human Services, 2011); http://www.surgeongeneral.gov/priorities/prevention/strategy

  19. Power, C., Kuh, D. & Morton, S. From developmental origins of adult disease to life course research on adult disease and aging: insights from birth cohort studies. Annu. Rev. Public Health 34, 7–28 (2013).

    Article  Google Scholar 

  20. Bunkley, N. Jospeh Juran, 103, pioneer in quality control dies. The New York Times (3 March 2008); http://www.nytimes.com/2008/03/03/business/03juran.html?_r=0

  21. Rooney, P. Microsoft’s CEO: 80–20 rule applies to bugs, not just features. CRN Magazine (3 October 2002); http://www.crn.com/news/security/18821726/microsofts-ceo-80-20-rule-applies-to-bugs-not-just-features.htm

  22. Heckman, J., Moon, S. H., Pinto, R., Savelyev, P. & Yavitz, A. Analyzing social experiments as implemented: a reexamination of the evidence from the HighScope Perry Preschool Program. Quant. Econom. 1, 1–46 (2010).

    Article  Google Scholar 

  23. Pepe, M. S., Janes, H., Longton, G., Leisenring, W. & Newcomb, P. Limitations of the odds ratio in gauging the performance of a diagnostic, prognostic, or screening marker. Am. J. Epidemiol. 159, 882–890 (2004).

    Article  Google Scholar 

  24. Kraemer, H. C. et al. Coming to terms with the terms of risk. Arch. Gen. Psychiat. 54, 337–343 (1997).

    Article  CAS  Google Scholar 

  25. Rice, M. E. & Harris, G. T. Comparing effect sizes in follow-up studies: ROC Area, Cohen’s d, and r . Law Hum. Behav. 29, 615–620 (2005).

    Article  Google Scholar 

  26. Hosmer, D. & Lemeshow, S. Applied Logistic Regression (Wiley, 2005).

    Google Scholar 

  27. Caspi, A. et al. The p factor: one general psychopathology factor in the structure of psychiatric disorders? Clin. Psychol. Sci. 2, 119–137 (2014).

    Article  Google Scholar 

  28. Heckman, J. J., Moon, S. H., Pinto, R., Savelyev, P. A. & Yavitz, A. The rate of return to the High Scope Perry Preschool Program. J. Public Econ. 94, 114–128 (2010).

    Article  Google Scholar 

  29. Income distribution and poverty. OECD.Stathttps://stats.oecd.org/Index.aspx?DataSetCode=IDD (2012).

  30. Health Expenditure, Total (% of GDP) (The World Bank, 2016); http://data.worldbank.org/indicator/SH.XPD.TOTL.ZS/

  31. Every Child Matters (Department for Education, 2003); https://www.education.gov.uk/consultations/downloadableDocs/EveryChildMatters.pdf

  32. Thomas, D. C. Invited commentary: is it time to retire the “pack-years” variable? Maybe not! Am. J. Epidemiol. 179, 299–302 (2014).

    Article  Google Scholar 

  33. Rushton, J. P., Brainerd, C.J. & Pressley, M. Behavioral development and construct validity: the principle of aggregation. Psychol. Bull. 94, 18–38 (1983).

    Article  Google Scholar 

  34. Evans, G. W., Li, D. & Whipple, S. S. Cumulative risk and child development. Psychol. Bull. 139, 1342–1396 (2013).

    Article  Google Scholar 

  35. Adverse childhood experiences (ACEs). Centers for Disease Control and Preventionhttp://www.cdc.gov/violenceprevention/acestudy (2016).

  36. Kulminski, A. M. et al. Do gender, disability, and morbidity affect aging rate in the LLFS? Application of indices of cumulative deficits. Mech. Ageing Dev. 132, 195–201 (2011).

    Article  Google Scholar 

  37. Gormley, W. T. Jr From science to policy in early childhood education. Science 333, 978–981 (2011).

    Article  CAS  Google Scholar 

  38. Heckman, J. J. The economics, technology, and neuroscience of human capability formation. Proc. Natl. Acad. Sci. USA 104, 13250–13255 (2007).

    Article  Google Scholar 

  39. Gabrieli, C., Ansel, D. & Krachman, S. B. Ready to be Counted: The Research Case for Education Policy Action on Non-cognitive Skills (Transforming Education, 2015); http://www.transformingeducation.org/resources/

  40. Hoedemaekers, R. & Dekkers, W. Justice and solidarity in priority setting in health care. Health Care Anal. 11, 325–343 (2003).

    Article  Google Scholar 

  41. Kohler, H. P. & Behrman, J. R. Benefits and Costs of the Population and Demography Targets for the Post-2015 Development Agenda. (Copenhagen Consensus Center, 2015); http://www.copenhagenconsensus.com/sites/default/files/population_assessment_-_kohler_behrman_0.pdf

  42. Poulton, R., Moffitt, T. E. & Silva, P. A. The Dunedin Multidisciplinary Health and Development Study: overview of the first 40 years, with an eye to the future. Soc. Psychiatry Psychiatr. Epidemiol. 50, 679–693 (2015).

    Article  Google Scholar 

  43. Poulton, R. et al. Association between children’s experience of socioeconomic disadvantage and adult health: a life-course study. Lancet 360, 1640–1645 (2002).

    Article  Google Scholar 

  44. Caspi, A. et al. Role of genotype in the cycle of violence in maltreated children. Science 297, 851–854 (2002).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank Dunedin Study members, their families and Dunedin Study founder Phil Silva. This research received support from US National Institute on Aging (NIA) grants AG032282, AG048895, AG049789, UK Medical Research Council (MRC) grant MR/K00381X and ESRC grant ES/M010309/1. The Dunedin Study was supported by the New Zealand Health Research Council and New Zealand Ministry of Business, Innovation and Employment (MBIE). Additional support was provided by the Jacobs Foundation and the Avielle Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We thank D. Reiss, J. Heckman and his seminar members, and the New Zealand agencies that offered guidance and assistance to the Dunedin Study. We also thank Z. van der Merwe (ACC), C. Lewis (Ministry of Health), M. Wilson and R. Ota (Ministry of Social Development), the Otago Police District Commander, P. Stevenson, J. Curren and the Dunedin Police. The Otago University Ethics Committee, Duke University and King’s College London provided ethical approval for the Dunedin Study. Participants gave written consent before data were collected.

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Authors and Affiliations

  1. Department of Psychology & Neuroscience, Duke University, Durham, North Carolina 27708, USA

    Avshalom Caspi, Renate M. Houts, Honalee Harrington & Terrie E. Moffitt

  2. Department of Psychiatry & Behavioural Sciences, Duke University School of Medicine, Durham, North Carolina 27708, USA

    Avshalom Caspi & Terrie E. Moffitt

  3. Center for Genomic and Computational Biology, Duke University, Durham, North Carolina 27708, USA

    Avshalom Caspi & Terrie E. Moffitt

  4. Social, Genetic, & Developmental Psychiatry Research Centre, Institute of Psychiatry, Psychology, & Neuroscience, King’s College London, London SE5 8AF, UK

    Avshalom Caspi & Terrie E. Moffitt

  5. Department of Medicine, Duke University School of Medicine, Durham, North Carolina 27708, USA

    Daniel W. Belsky

  6. Social Science Research Institute, Duke University, Durham, North Carolina 27708, USA

    Daniel W. Belsky

  7. Department of Psychology, University of Otago, Dunedin 9016, New Zealand

    Sean Hogan, Sandhya Ramrakha & Richie Poulton

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Contributions

A.C., R.P. and T.E.M. designed the research, and A.C., R.M.H. and T.E.M. wrote the manuscript. A.C., S.H., S.R., R.P. and T.E.M. collected the data, and it was analysed by A.C., R.M.H. and H.H. All authors reviewed drafts, provided critical feedback and approved the final manuscript.

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Supplementary Methods, Supplementary Data Analyses, Supplementary Tables 1–4, Supplementary Figure 1. (PDF 1029 kb)

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Caspi, A., Houts, R., Belsky, D. et al. Childhood forecasting of a small segment of the population with large economic burden. Nat Hum Behav 1, 0005 (2017). https://doi.org/10.1038/s41562-016-0005

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