A prediction tool for vitamin D deficiency in New Zealand adults

• Published in: Archives of osteoporosis Vol. 15; no. 1; p. 172
• Main Authors: Narang, Ravi K., Gamble, Greg G., Khaw, Kay-Tee, Camargo, Carlos A., Sluyter, John D., Scragg, Robert K. R., Reid, Ian R.
• Format: Journal Article
• Language: English
• Published: London Springer London 31.10.2020
• Subjects: Adult; Aged; Aged, 80 and over; Cohort Studies; Endocrinology; Exercise; Female; Humans; Medicine; Medicine & Public Health; Middle Aged; New Zealand - epidemiology; Original Article; Orthopedics; Vitamin D; Vitamin D Deficiency - diagnosis; Vitamin D Deficiency - epidemiology; New Zealand; Vitamin D; Osteomalacia; Vitamin D deficiency
• ISSN: 1862-3522; 1862-3514
• DOI: 10.1007/s11657-020-00844-y

Purpose This study aims to develop a model for predicting vitamin D deficiency in New Zealand adults using easily accessible clinical characteristics. Methods Data were derived from the Vitamin D Assessment (ViDA) study dataset. Included participants in the main analysis were aged 50–84 years and resided in Auckland, New Zealand. The dataset was split into a discovery dataset in which the prediction model was developed ( n = 2036) and a validation dataset in which it was tested ( n = 2037). The prediction model was developed using clinical characteristics in a logistic regression analysis with deseasonalised serum 25OHD (DS-25OHD) as the dependent variable. Results DS-25OHD < 40 nmol/L was found in 8.2% of European participants, 18.8% of Māori participants, 23.1% of Pacific participants and 52.2% of South Asian participants. Predictors for DS-25OHD < 40 nmol/L in the European sub-cohort included increasing age, female sex, higher body mass index, current smoking, no alcohol intake, lower self-reported general health status, lower physical activity hours, lower outdoor hours and no use of vitamin D–containing supplementation. The area under the curve in the discovery dataset was 0.73, and in the validation dataset was 0.71. Of those with a prediction score ≥ 10 (total risk score range 0–21.5), the sensitivity and specificity for predicting vitamin D deficiency was 0.90 and 0.41, respectively. Conclusion Non-European ethnicity is an important risk factor for vitamin D deficiency. Our vitamin D deficiency prediction model performed well and demonstrates its potential as a tool that can be integrated into clinical practice for the prediction of vitamin D deficiency.