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Prediction models and questionnaires developed to predict vitamin D status in adults: a systematic review

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Abstract

A systematic review of prediction models/questionnaires developed to identify people with deficient/insufficient vitamin D status shows the potential of self-reported information to estimate vitamin D status. The objective is to identify and compare existing screening tools, developed to identify vitamin D deficiency or insufficiency in adults. A systematic search of literature was conducted using MEDLINE, Scopus, Web of Science and CINAHL databases. Risk of bias and applicability concerns were assessed by quality assessment of diagnostic accuracy studies (QUADAS-2). Data were extracted on socio-demographic, anthropometric, risk factors, serum 25 hydroxyvitamin D [25(OH)D] levels, statistical methods and predictive ability. A total of 12 studies were considered for inclusion for this systematic review after screening of 4851 abstracts and 15 full-text articles. Ten of twelve studies developed prediction models and 2 studies developed questionnaires. The majority of studies had low risk of bias and applicability as assessed by QUADAS-2. All studies included only self-reported predictors of vitamin D status in their final models and development of scores. Sunlight exposure and related factors were important significant contributors to the predictive ability of the models and/or questionnaires. Sensitivity and specificity of the prediction models or questionnaires ranged from 55 to 91% and 35 to 84%, respectively. Six out of twelve studies converted final models to scores associated with vitamin D status. There was no evidence that any of these existing tools have been translated into clinical practice. The prediction models or questionnaires identified in this systematic review were moderately sensitive and specific for identifying people with vitamin D deficiency or insufficiency. The substantial contribution of sunlight exposure to the prediction of vitamin D status highlights the importance of including this information when developing vitamin D screening tools.

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Acknowledgements

We acknowledge Kathryn Duncan, Resource Management Librarian at Swinburne University of Technology (Australia), for her support in systematic search of literature through various databases.

Funding

This research was not supported by any funding from agencies in the public, commercial or not-for-profit sectors. DS is supported by an NHMRC RD Wright Biomedical Career Development Fellowship (GNT1123014) and an NHMRC Investigator Grant (GNT1174886). SLB-O is supported by a NHMRC Career Development (GNT1107510) and a University of Melbourne MDHS Faculty Research Fellowship (2020). GN is supported by an Educational Grant from Sanofi Australia.

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  1. Level 3, Department of Medicine-Western Health, The University of Melbourne, 176 Furlong Road, St Albans, VIC, 3021, Australia

    G. Naureen, K. M. Sanders, J. Talevski, C. Connaughton & S. L. Brennan-Olsen

  2. Australian Institute for Musculoskeletal Science (AIMSS), The University of Melbourne and Western Health, St Albans, VIC, Australia

    G. Naureen, D. Scott, J. Talevski & S. L. Brennan-Olsen

  3. School of Public Health and Preventive Medicine, Monash University, St Kilda, VIC, Australia

    L. Busija

  4. School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia

    D. Scott

  5. Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Burwood, VIC, Australia

    D. Scott

  6. Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, VIC, Australia

    K. Lim

  7. Academic Centre for Health, Royal Women’s Hospital, The University of Melbourne, Parkville, VIC, Australia

    C. Connaughton

  8. School of Health and Social Development, Deakin University, Melbourne, VIC, Australia

    S. L. Brennan-Olsen

  9. Institute for Health Transformation, Deakin University, Melbourne, VIC, Australia

    S. L. Brennan-Olsen

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Ghazala Naureen, Kerrie M Sanders, Lucy Busija, David Scott, Karen Lim, Jason Talevski, Catherine Connaughton and Sharon L. Brennan-Olsen declare that they have no conflict of interest.

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Naureen, G., Sanders, K.M., Busija, L. et al. Prediction models and questionnaires developed to predict vitamin D status in adults: a systematic review. Osteoporos Int 31, 2287–2302 (2020). https://doi.org/10.1007/s00198-020-05539-1

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