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The D-score: a metric for interpreting the early development of infants and toddlers across global settings
  1. Ann M Weber1,2,
  2. Marta Rubio-Codina3,
  3. Susan P Walker4,
  4. Stef van Buuren5,6,
  5. Iris Eekhout5,
  6. Sally M Grantham-McGregor7,
  7. Maria Caridad Araujo3,
  8. Susan M Chang4,
  9. Lia CH Fernald8,
  10. Jena Derakhshani Hamadani9,
  11. Charlotte Hanlon10,11,
  12. Simone M Karam12,
  13. Betsy Lozoff13,
  14. Lisy Ratsifandrihamanana14,
  15. Linda Richter15,
  16. Maureen M Black16,17
  17. Global Child Development Group collaborators
    1. 1School of Community Health Sciences, University of Nevada Reno, Reno, Nevada, USA
    2. 2Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA
    3. 3Inter-American Development Bank, Washington, District of Columbia, USA
    4. 4Caribbean Institute for Health Research, University of the West Indies, Kingston, Jamaica
    5. 5Netherlands Organization for Applied Scientific Research TNO, Leiden, Netherlands
    6. 6Methodology & Statistics, Utrecht University, Utrecht, Netherlands
    7. 7Institute of Child Health, University College London, London, UK
    8. 8School of Public Health, University of California Berkeley, Berkeley, California, USA
    9. 9Maternal and Child Health Division, icddr,b, Dhaka, Bangladesh
    10. 10Institute of Psychiatry, Psychology and Neuroscience, Health Service and Population Research Department, Centre for Global Mental Health, King's College London, London, UK
    11. 11Department of Psychiatry, WHO Collaborating Centre for Mental Health Research and Capacity Building, School of Medicine, and Centre for Innovative Drug Development and Therapeutic Trials for Africa (CDT-Africa), College of Health Sciences, Addis Ababa University, Addis Ababa, Ethiopia
    12. 12Department of Pediatrics, Federal University of Rio Grande, Rio Grande, Brazil
    13. 13Center for Human Growth and Development, University of Michigan, Ann Arbor, Michigan, USA
    14. 14Centre Médico-Educatif "Les Orchidées Blanches", Antananarivo, Madagascar
    15. 15Centre of Excellence in Human Development, University of the Witwatersrand, Johannesburg, South Africa
    16. 16Department of Pediatrics, University of Maryland School of Medicine, Baltimore, Maryland, USA
    17. 17International Education, RTI International, Research Triangle Park, North Carolina, USA
    1. Correspondence to Dr Ann M Weber; annweber{at}unr.edu

    Abstract

    Introduction Early childhood development can be described by an underlying latent construct. Global comparisons of children’s development are hindered by the lack of a validated metric that is comparable across cultures and contexts, especially for children under age 3 years. We constructed and validated a new metric, the Developmental Score (D-score), using existing data from 16 longitudinal studies.

    Methods Studies had item-level developmental assessment data for children 0–48 months and longitudinal outcomes at ages >4–18 years, including measures of IQ and receptive vocabulary. Existing data from 11 low-income, middle-income and high-income countries were merged for >36 000 children. Item mapping produced 95 ‘equate groups’ of same-skill items across 12 different assessment instruments. A statistical model was built using the Rasch model with item difficulties constrained to be equal in a subset of equate groups, linking instruments to a common scale, the D-score, a continuous metric with interval-scale properties. D-score-for-age z-scores (DAZ) were evaluated for discriminant, concurrent and predictive validity to outcomes in middle childhood to adolescence.

    Results Concurrent validity of DAZ with original instruments was strong (average r=0.71), with few exceptions. In approximately 70% of data rounds collected across studies, DAZ discriminated between children above/below cut-points for low birth weight (<2500 g) and stunting (−2 SD below median height-for-age). DAZ increased significantly with maternal education in 55% of data rounds. Predictive correlations of DAZ with outcomes obtained 2–16 years later were generally between 0.20 and 0.40. Correlations equalled or exceeded those obtained with original instruments despite using an average of 55% fewer items to estimate the D-score.

    Conclusion The D-score metric enables quantitative comparisons of early childhood development across ages and sets the stage for creating simple, low-cost, global-use instruments to facilitate valid cross-national comparisons of early childhood development.

    • child development
    • global health
    • psychometrics
    • item response theory

    This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.

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    Footnotes

    • Handling editor Seye Abimbola

    • Global Child Development Group collaborators Orazio Attanasio; Gary L. Darmstadt; Bernice M. Doove; Emanuela Galasso; Pamela Jervis; Girmay Medhin; Ana M. B. Menezes; Helen Pitchik; Sarah Reynolds; Norbert Schady.

    • Contributors All authors contributed to item mapping and to analysis decisions during three investigator meetings. SvB and IE conducted the data harmonisation and analyses to derive the model and estimate D-score and DAZ values. AMW and MRC conducted the validation analyses. AMW led the drafting of the paper with guidance from SPW, SGM, MRC, SvB, IE, and MMB. SPW and MMB obtained funding. All authors reviewed the manuscript, provided critical input, and approved submission.

    • Funding The Global Child Development Group (https://www.globalchilddevelopment.org/) was funded by the Bill and Melinda Gates Foundation, OPP1138517, to perform this study. The Bernard van Leer Foundation supported the initial meeting of investigators to establish the Advisory Board and conduct the instrument mapping. CH (King’s College London and AAU) is funded by the National Institute of Health Research (NIHR) Global Health Research Unit on Health System Strengthening in Sub-Saharan Africa, King’s College London (GHRU 16/136/54) using UK aid from the UK Government. The views expressed in this publication are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care, or of the Inter-American Development Bank, their Board of Directors, or the countries they represent. CH additionally receives support from the African Mental Health Research Initiative (AMARI) as part of the DELTAS Africa Initiative [DEL-15–01]. The original data collected in Ethiopia was funded by the Wellcome Trust (project grant 093559).

    • Competing interests CH receives support from the African Mental Health Research Initiative (AMARI) as part of the Wellcome Trust-funded DELTAS Africa Initiative [DEL-15-01]. The original data collected in Ethiopia was funded by the Wellcome Trust (project grant 093559).

    • Patient consent for publication Not required.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Data availability statement Data may be obtained from a third party and are not publicly available.

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