Article | Published:

A systematic review and meta-analysis of effects of early life non-cognitive skills on academic, psychosocial, cognitive and health outcomes

Nature Human Behaviourvolume 2pages867880 (2018) | Download Citation


Success in school and the labour market relies on more than high intelligence. Associations between ‘non-cognitive’ skills in childhood, such as attention, self-regulation and perseverance, and later outcomes have been widely investigated. In a systematic review of this literature, we screened 9,553 publications, reviewed 554 eligible publications and interpreted results from 222 better-quality publications. Better-quality publications comprised randomized experimental and quasi-experimental intervention studies (EQIs) and observational studies that made reasonable attempts to control confounding. For academic achievement outcomes, there were 26 EQI publications but only 14 were available for meta-analysis, with effects ranging from 0.16 to 0.37 s.d. However, within subdomains, effects were heterogeneous. The 95% prediction interval for literacy was consistent with negative, null and positive effects (−0.13 to 0.79). Similarly, heterogeneous findings were observed for psychosocial, cognitive and language, and health outcomes. Funnel plots of EQIs and observational studies showed asymmetric distributions and potential for small study bias. There is some evidence that non-cognitive skills associate with improved outcomes. However, there is potential for small study and publication bias that may overestimate true effects, and the heterogeneity of effect estimates spanned negative, null and positive effects. The quality of evidence from EQIs underpinning this field is lower than optimal and more than one-third of observational studies made little or no attempt to control confounding. Interventions designed to develop children’s non-cognitive skills could potentially improve outcomes. The interdisciplinary researchers interested in these skills should take a more strategic and rigorous approach to determine which interventions are most effective.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Data availability

The data used to undertake this systematic review and meta-analysis are freely available from our BetterStart website (

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


  1. 1.

    Bowles, S. & Gintis, H. Schooling in Capitalist America: Educational Reform and the Contradictions of Economic Life (Basic Books, New York, 1976).

  2. 2.

    Deming, D. J. The growing importance of social skills in the labor market. Q. J. Econ. 132, 1593–1640 (2017).

  3. 3.

    Skills for Social Progress: The Power of Social and Emotional Skills (OECD, Paris, 2015).

  4. 4.

    Institute of Education The Impact of Non-cognitive Skills for Young People (UK Cabinet Office, 2013).

  5. 5.

    Allen, G. Early Intervention: the Next Steps. An Independent Report to Her Majesty’s Government (UK Cabinet Office, 2011).

  6. 6.

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

  7. 7.

    Heckman, J. J. & Kautz, T. Hard evidence on soft skills. Labour Econ. 19, 451–464 (2012).

  8. 8.

    Lindqvist, E. & Vestman, R. The labor market returns to cognitive and noncognitive ability: evidence from the Swedish enlistment. Am. Econ. J. Appl. Econ. 3, 101–128 (2011).

  9. 9.

    Cunha, F., Heckman, J. J. & Schennach, S. M. Estimating the technology of cognitive and non-cognitive skill formation. Econometrica 78, 883–931 (2010).

  10. 10.

    Heckman, J. J., Stixrud, J. & Urzua, S. The effects of cognitive and noncognitive abilities on labor market outcomes and social behavior. J. Labor Econ. 24, 411–482 (2006).

  11. 11.

    Duncan, G. J. et al. School readiness and later achievement. Dev. Psychol. 43, 1428–1446 (2007).

  12. 12.

    Hendry, A., Jones, E. J. H. & Charman, T. Executive function in the first three years of life: precursors, predictors and patterns. Dev. Rev. 42, 1–33 (2016).

  13. 13.

    Diamond, A., Barnett, W. S., Thomas, J. & Munro, S. Preschool program improves cognitive control. Science 318, 1387–1388 (2007).

  14. 14.

    Borghans, L., Duckworth, A. L., Heckman, J. J. & Ter Weel, B. The economics and psychology of personality traits. J. Hum. Resour. 43, 972–1059 (2008).

  15. 15.

    Heckman, J. J. & Kautz, T. Fostering and Measuring Skills: Interventions that Improve Character and Cognition (National Bureau of Economic Research, 2013).

  16. 16.

    Diamond, A. & Lee, K. Interventions shown to aid executive function development in children 4 to 12 years old. Science 333, 959–964 (2011).

  17. 17.

    Pearce, A. et al. Do early life cognitive ability and self-regulation skills explain socio-economic inequalities in academic achievement? An effect decomposition analysis in UK and Australian cohorts. Soc. Sci. Med. 165, 108–118 (2016).

  18. 18.

    Eisenberg, N. et al. Relations among maternal socialization, effortful control, and maladjustment in early childhood. Dev. Psychopathol. 22, 507–525 (2010).

  19. 19.

    Fergusson, D. M., Boden, J. M. & Horwood, L. Childhood self-control and adult outcomes: results from a 30-year longitudinal study. J. Am. Acad. Child Adolesc. Psychiatry 52, 709–717.e1 (2013).

  20. 20.

    Evans, G. W., Fuller-Rowell, T. E. & Doan, S. N. Childhood cumulative risk and obesity: the mediating role of self-regulatory ability. Pediatrics 129, e68–e73 (2012).

  21. 21.

    Blair, C. & Razza, R. P. Relating effortful control, executive function, and false belief understanding to emerging math and literacy ability in kindergarten. Child Dev. 78, 647–663 (2007).

  22. 22.

    Mischel, W., Shoda, Y. & Peake, P. K. The nature of adolescent competencies predicted by preschool delay of gratification. J. Pers. Soc. Psychol. 54, 687–696 (1988).

  23. 23.

    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).

  24. 24.

    Kern, M. L. & Friedman, H. S. Do conscientious individuals live longer? A quantitative review. Health Psychol. 27, 505–512 (2008).

  25. 25.

    Raver, C. C. et al. CSRP’s Impact on low-income preschoolers’ preacademic skills: self-regulation as a mediating mechanism. Child Dev. 82, 362–378 (2011).

  26. 26.

    Deary, I. J., Whiteman, M. C., Starr, J. M., Whalley, L. J. & Fox, H. C. The impact of childhood intelligence on later life: following up the Scottish mental surveys of 1932 and 1947. J. Pers. Soc. Psychol. 86, 130–147 (2004).

  27. 27.

    Fergusson, D. M., John Horwood, L. & Ridder, E. M. Show me the child at seven II: childhood intelligence and later outcomes in adolescence and young adulthood. J. Child Psychol. Psychiatry 46, 850–858 (2005).

  28. 28.

    Kuh, D., Richards, M., Hardy, R., Butterworth, S. & Wadsworth, M. E. Childhood cognitive ability and deaths up until middle age: a post-war birth cohort study. Int. J. Epidemiol. 33, 408–413 (2004).

  29. 29.

    Whalley, L. J. & Deary, I. J. Longitudinal cohort study of childhood IQ and survival up to age 76. BMJ 322, 819–822 (2001).

  30. 30.

    Schweinhart, L. J. et al. Lifetime Effects: The High/Scope Perry Preschool Study through Age 40 (High/Scope Press, Ypsilanti, 2005).

  31. 31.

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

  32. 32.

    Weikert, D. P. Comparative Study of Three Preschool Curricula Report No. F244 (Bureau of Elementary and Secondary Education, 1969).

  33. 33.

    Schweinhart, L. J., Weikart D. P. & Barnes, H. V. Significant Benefits: The High/Scope Perry Preschool Study Through Age 27 (Monographs of the High/Scope Educational Research Foundation) (High/Scope Press, Ypsilanti, 1993).

  34. 34.

    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).

  35. 35.

    Campbell, F. & Ramey, C. Effects of early intervention on intellectual and academic achievement: a follow-up study of children from low-income families program title: Carolina Abecedarian Project. Child Dev. 65, 684–698 (1994).

  36. 36.

    Liberati, A. et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration. BMJ 339, b2700 (2009).

  37. 37.

    Webster‐Stratton, C., Jamila Reid, M. & Stoolmiller, M. Preventing conduct problems and improving school readiness: evaluation of the incredible years teacher and child training programs in high‐risk schools. J. Child Psychol. Psychiatry 49, 471–488 (2008).

  38. 38.

    Conduct Problems Prevention Research Group Initial impact of the Fast Track prevention trial for conduct problems: I. The high-risk sample. J. Consult. Clin. Psychol. 67, 631–647 (1999).

  39. 39.

    Dawson-McClure, S. et al. A population-level approach to promoting healthy child development and school success in low-income, urban neighborhoods: impact on parenting and child conduct problems. Prev. Sci. 16, 279–290 (2015).

  40. 40.

    Nix, R. L., Bierman, K. L., Domitrovich, C. E. & Gill, S. Promoting children’s social-emotional skills in preschool can enhance academic and behavioral functioning in kindergarten: findings from Head Start REDI. Early Educ. Dev. 24, 1000–1019 (2013).

  41. 41.

    Bierman, K. L. et al. Promoting academic and social‐emotional school readiness: the Head Start REDI program. Child Dev. 79, 1802–1817 (2008).

  42. 42.

    Bierman, K. L. et al. Effects of Head Start REDI on children’s outcomes 1 year later in different kindergarten contexts. Child Dev. 85, 140–159 (2014).

  43. 43.

    Egger, M. & Smith, G. D. Misleading meta-analysis. BMJ 310, 752–754 (1995).

  44. 44.

    Bailey, D., Duncan, G., Odgers, C. & Yu, W. Persistence and fadeout in the impacts of child and adolescent interventions. J. Res. Educ. Eff. 10, 7–39 (2017).

  45. 45.

    Fewell, Z., Davey Smith, G. & Sterne, J. A. The impact of residual and unmeasured confounding in epidemiologic studies: a simulation study. Am. J. Epidemiol. 166, 646–655 (2007).

  46. 46.

    Franco, A., Malhotra, N. & Simonovits, G. Publication bias in the social sciences: unlocking the file drawer. Science 345, 1502–1505 (2014).

  47. 47.

    Allan, N. P., Hume, L. E., Allan, D. M., Farrington, A. L. & Lonigan, C. J. Relations between inhibitory control and the development of academic skills in preschool and kindergarten: a meta-analysis. Dev. Psychol. 50, 2368–2379 (2014).

  48. 48.

    Brotman, L. M. et al. Cluster (school) RCT of parentcorps: impact on kindergarten academic achievement. Pediatrics 131, e1521–e1529 (2013).

  49. 49.

    Barnett, W. S. et al. Educational effects of the Tools of the Mind curriculum: a randomized trial. Early Child. Res. Q. 23, 299–313 (2008).

  50. 50.

    Ialongo, N. S. et al. Proximal impact of two first-grade preventive interventions on the early risk behaviors for later substance abuse, depression, and antisocial behavior. Am. J. Commun. Psychol. 27, 599–641 (1999).

  51. 51.

    Raver, C. C. et al. Targeting children’s behavior problems in preschool classrooms: a cluster-randomized controlled trial. J. Consult. Clin. Psychol. 77, 302–316 (2009).

  52. 52.

    Shelleby, E. C. et al. Behavioral control in at-risk toddlers: the influence of the family check-up. J. Clin. Child Adolesc. Psychol. 41, 288–301 (2012).

  53. 53.

    NICHD Early Child Care Research Network Do children’s attention processes mediate the link between family predictors and school readiness? Dev. Psychol. 39, 581–593 (2003).

  54. 54.

    Ramani, G. B., Brownell, C. A. & Campbell, S. B. Positive and negative peer interaction in 3- and 4-year-olds in relation to regulation and dysregulation. J. Genet. Psychol. 171, 218–250 (2010).

  55. 55.

    Runions, K. C. & Keating, D. P. Anger and inhibitory control as moderators of children’s hostile attributions and aggression. J. Appl. Dev. Psychol. 31, 370–378 (2010).

  56. 56.

    Mintz, T. M., Hamre, B. K. & Hatfield, B. E. The role of effortful control in mediating the association between maternal sensitivity and children’s social and relational competence and problems in first grade. Early Educ. Dev. 22, 360–387 (2011).

  57. 57.

    Booth-Laforce, C. & Oxford, M. L. Trajectories of social withdrawal from grades 1 to 6: prediction from early parenting, attachment, and temperament. Dev. Psychol. 44, 1298–1313 (2008).

  58. 58.

    Weiland, C. & Yoshikawa, H. Impacts of a pre kindergarten program on children’s mathematics, language, literacy, executive function, and emotional skills. Child Dev. 84, 2112–2130 (2013).

  59. 59.

    Bradley, R. T., Galvin, P., Atkinson, M. & Tomasino, D. Efficacy of an emotion self-regulation program for promoting development in preschool children. Glob. Adv. Health Med. 1, 36–50 (2012).

  60. 60.

    Ford, R. M., McDougall, S. J. & Evans, D. Parent-delivered compensatory education for children at risk of educational failure: improving the academic and self-regulatory skills of a Sure Start preschool sample. Br. J. Psychol. 100, 773–797 (2009).

  61. 61.

    Slavin, R. E. Best evidence synthesis: an intelligent alternative to meta-analysis. J. Clin. Epidemiol. 48, 9–18 (1995).

  62. 62.

    Egger, M., Juni, P., Bartlett, C., Holenstein, F. & Sterne, J. How important are comprehensive literature searches and the assessment of trial quality in systematic reviews? Empirical study. Health Technol. Asses. 7, 1–76 (2003).

  63. 63.

    Diamond, A. Executive functions. Annu. Rev. Psychol. 64, 135–168 (2013).

  64. 64.

    Chalmers, I. et al. How to increase value and reduce waste when research priorities are set. Lancet 383, 156–165 (2014).

  65. 65.

    Ioannidis, J. P. et al. Increasing value and reducing waste in research design, conduct, and analysis. Lancet 383, 166–175 (2014).

  66. 66.

    Open Science Collaboration Estimating the reproducibility of psychological science. Science 349, aac4716 (2015).

  67. 67.

    Munafò, M. R. et al. A manifesto for reproducible science. Nat. Hum. Behav. 1, 0021 (2017).

  68. 68.

    Camerer, C. F. et al. Evaluating the replicability of social science experiments in Nature and Science between 2010 and 2015. Nat. Hum. Behav. 2, 637–644 (2018).

  69. 69.

    Duckworth, A. L. & Kern, M. L. A meta-analysis of the convergent validity of self-control measures. J. Res. Pers. 45, 259–268 (2011).

  70. 70.

    Zhou, Q., Chen, S. H. & Main, A. Commonalities and differences in the research on children’s effortful control and executive function: a call for an integrated model of self-regulation. Child Dev. Perspect. 6, 112–121 (2012).

  71. 71.

    Kelley, T. L. Interpretation of Educational Measurement (World Books, New York, 1927).

  72. 72.

    Credé, M., Tynan, M. C. & Harms, P. D. Much ado about grit: a meta-analytic synthesis of the grit literature. J. Pers. Soc. Psychol. 11, 492–511 (2017).

  73. 73.

    Ponitz, C. C., McClelland, M. M., Matthews, J. & Morrison, F. J. A structured observation of behavioral self-regulation and its contribution to kindergarten outcomes. Dev. Psychol. 45, 605–619 (2009).

  74. 74.

    Cameron, C. E. et al. Fine motor skills and executive function both contribute to kindergarten achievement. Child Dev. 83, 1229–1244 (2012).

  75. 75.

    Grindal, T. et al. The added impact of parenting education in early childhood education programs: a meta-analysis. Child. Youth Serv. Rev. 70, 238–249 (2016).

  76. 76.

    Olds, D. et al. Effects of home visits by paraprofessionals and by nurses: age 4 follow-up results of a randomized trial. Pediatrics 114, 1560–1568 (2004).

  77. 77.

    Iglehart, J. K. Prioritizing comparative-effectiveness research—IOM recommendations. N. Engl. J. Med. 361, 325–328 (2009).

  78. 78.

    Fiore, L. D. & Lavori, P. W. Integrating randomized comparative effectiveness research with patient care. N. Engl. J. Med. 374, 2152–2158 (2016).

  79. 79.

    Blair, C. & Raver, C. C. Closing the achievement gap through modification of neurocognitive and neuroendocrine function: results from a cluster randomized controlled trial of an innovative approach to the education of children in kindergarten. PLoS ONE 9, e112393 (2014).

  80. 80.

    Knol, M. J. & VanderWeele, T. J. Recommendations for presenting analyses of effect modification and interaction. Int. J. Epidemiol. 41, 514–520 (2012).

  81. 81.

    Egger, M., Davey Smith, G., Schneider, M. & Minder, C. Bias in meta-analysis detected by a simple, graphical test. BMJ 315, 629–634 (1997).

  82. 82.

    Weiss, M. J., Bloom, H. S. & Brock, T. A conceptual framework for studying the sources of variation in program effects. J. Policy Anal. Manag. 33, 778–808 (2014).

  83. 83.

    Higgins, J. P. T. et al. The Cochrane Collaboration’s tool for assessing risk of bias in randomised trials. BMJ 343, d5928 (2011).

  84. 84.

    Kaplan, R. M. & Irvin, V. L. Likelihood of null effects of large NHLBI clinical trials has increased over time. PLoS ONE 10, e0132382 (2015).

  85. 85.

    Leyrat, C., Morgan, K., Leurent, B. & Kahan, B. Cluster randomized trials with a small number of clusters: which analyses should be used? Int. J. Epidemiol. 47, 321–331 (2018).

  86. 86.

    Smaldino, P. E & McElreath, R. The natural selection of bad science. R. Soc. Open Sci. 3, 160384 (2016).

  87. 87.

    Gertler, P., Galiani, S. & Romero, M. How to make replication the norm. Nature 554, 417–419 (2018).

  88. 88.

    Munafo, M. & Davey Smith, G. Repeating experiments is not enough. Nature 553, 399–401 (2018).

  89. 89.

    Lawlor, D. A., Tilling, K. & Davey Smith, G. Triangulation in aetiological epidemiology. Int. J. Epidemiol. 45, 1866–1886 (2016).

  90. 90.

    Shrout, P. E. & Rodgers, J. Psychology, science and knowledge construction: broadening perspectives from the replication crisis. Annu. Rev. Psychol. 69, 487–510 (2018).

  91. 91.

    Higgins, J. P. T. & Green, S. (eds) Cochrane Handbook for Systematic Reviews of Interventions Version 5.1.0 (The Cochrane Collaboration, 2011).

  92. 92.

    Cohen, J. Statistical Power Analysis for the Behavioral Sciences 2nd edn (Lawrence Erlbaum Associates, Hillsdale, 1988).

  93. 93.

    Greenland, S., Maclure, M., Schlesselman, J. J., Poole, C. & Morgenstern, H. Standardized regression coefficients: a further critique and review of some alternatives. Epidemiology 2, 387–392 (1991).

  94. 94.

    King, G. How not to lie with statistics: avoiding common mistakes in quantitative political science. Am. J. Polit. Sci. 30, 666–687 (1986).

  95. 95.

    Cheung, A. C. K. & Slavin, R. E. How methodological features affect effect sizes in education. Educ. Res. 45, 283–292 (2016).

  96. 96.

    Lipsey, M. W. et al. Translating the Statistical Representation of the Effects of Education Interventions into More Readily Interpretable Forms (US Department of Education, 2012).

  97. 97.

    Watts, D. J. Should social science be more solution-oriented? Nat. Hum. Behav. 1, 0015 (2017).

  98. 98.

    Blair, C. & Diamond, A. Biological processes in prevention and intervention: the promotion of self-regulation as a means of preventing school failure. Dev. Psychol. 20, 899–911 (2008).

  99. 99.

    Blair, C. & Raver, C. C. School readiness and self-regulation: a developmental psychobiological approach. Annu. Rev. Psychol. 66, 711–731 (2015).

  100. 100.

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

  101. 101.

    Little, R. J. & Rubin, D. B. Causal effects in clinical and epidemiological studies via potential outcomes: concepts and analytical approaches. Annu. Rev. Public Health 21, 121–145 (2000).

  102. 102.

    Altman, D. G. & Bland, J. M. How to obtain the confidence interval from a P value. BMJ 343, d2090 (2011).

  103. 103.

    Higgins, J. P. T., Thompson, S. G. & Spiegelhalter, D. J. A re-evaluation of random-effects meta-analysis. J. R. Stat. Soc. Ser. A Stat. Soc. 172, 137–159 (2009).

  104. 104.

    Borenstein, M., Higgins, J. P. T., Hedges, L. V. & Rothstein, H. R. Basics of meta-analysis: I 2 is not an absolute measure of heterogeneity. Res. Synth. Methods 8, 5–18 (2017).

  105. 105.

    VandenBos, G. R. (ed.) APA Concise Dictionary of Psychology (APA, Washington DC, 2009).

  106. 106.

    Corsini, R. The Dictionary of Psychology (Taylor Francis, Philadelphia, 1999).

  107. 107.

    Eisenberg, N. Encyclopedia on Early Childhood Development (Centre of Excellence for Early Childhood Development and Strategic Knolwedge Cluster on Early Child Development, Montreal, 2012);

  108. 108.

    Nock, M., Wedig, M., Holmberg, E. & Hooley, J. The emotion reactivity scale: development, evalution and relation to self-injurious thoughts and behaviours. Behav. Ther. 39, 107–116 (2008).

  109. 109.

    Barkley, R. Behavioural inhibition, sustained attention, and executive functions: constructing a unifying theory of ADHD. Psychol. Bull. 121, 65–94 (1997).

Download references


We thank J. Grant, T. Nuske and T. Goodwin for their research assistance in collecting, and initially screening eligibility, and in the preparation of tables and figures. J.L. is funded by a National Health and Medical Research Council of Australia Partnership Project Grant (1056888) and Centre of Research Excellence (1099422). N.D. is supported by the Economics and Social Research Council (ESRC) via a Future Research Leaders Fellowship (ES/N000757/1). The Medical Research Council (MRC) and the University of Bristol fund the MRC Integrative Epidemiology Unit (MC_UU_12013). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. All authors will have access to the data and will take responsibility for the integrity and accuracy of the review.

Author information

Author notes

  1. These authors contributed equally: Lisa G. Smithers, Alyssa C. P. Sawyer.


  1. School of Public Health, University of Adelaide, Adelaide, South Australia, Australia

    • Lisa G. Smithers
    • , Alyssa C. P. Sawyer
    • , Catherine R. Chittleborough
    •  & John W. Lynch
  2. Robinson Research Institute, University of Adelaide, Adelaide, South Australia, Australia

    • Lisa G. Smithers
    • , Alyssa C. P. Sawyer
    • , Catherine R. Chittleborough
    •  & John W. Lynch
  3. Bristol Medical School: Population Health Sciences, University of Bristol, Bristol, UK

    • Neil M. Davies
    • , George Davey Smith
    •  & John W. Lynch
  4. Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK

    • Neil M. Davies
    •  & George Davey Smith


  1. Search for Lisa G. Smithers in:

  2. Search for Alyssa C. P. Sawyer in:

  3. Search for Catherine R. Chittleborough in:

  4. Search for Neil M. Davies in:

  5. Search for George Davey Smith in:

  6. Search for John W. Lynch in:


L.G.S., A.C.P.S., C.R.C., G.D.S. and J.W.L. conceived the study. L.G.S., A.C.P.S., C.R.C., N.M.D. and J.W.L. screened the literature and extracted the data. L.G.S., A.C.P.S., C.R.C. and N.M.D. analysed the data. J.W.L. led the drafting of the manuscript, with all authors contributing to the interpretation of the findings and writing of the final manuscript.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to John W. Lynch.

Supplementary information

  1. Supplementary Information

    Supplementary Methods, Supplementary Figures 1–32, Supplementary Tables 1–8 and Supplementary References

  2. Reporting Summary

About this article

Publication history