title: Predicting vitamin D deficiency in older Australian adults.
abstract: OBJECTIVE
  There has been a dramatic increase in vitamin D testing in Australia in recent years, prompting calls for targeted testing. We sought to develop a model to identify people most at risk of vitamin D deficiency.
  
  
  METHODS
  This is a cross-sectional study of 644 60- to 84-year-old participants, 95% of whom were Caucasian, who took part in a pilot randomized controlled trial of vitamin D supplementation.
  
  
  METHODS
  Baseline 25(OH)D was measured using the Diasorin Liaison platform. Vitamin D insufficiency and deficiency were defined using 50 and 25 nmol/l as cut-points, respectively. A questionnaire was used to obtain information on demographic characteristics and lifestyle factors. We used multivariate logistic regression to predict low vitamin D and calculated the net benefit of using the model compared with 'test-all' and 'test-none' strategies.
  
  
  RESULTS
  The mean serum 25(OH)D was 42 (SD 14) nmol/1. Seventy-five per cent of participants were vitamin D insufficient and 10% deficient. Serum 25(OH)D was positively correlated with time outdoors, physical activity, vitamin D intake and ambient UVR, and inversely correlated with age, BMI and poor self-reported health status. These predictors explained approximately 21% of the variance in serum 25(OH)D. The area under the ROC curve predicting vitamin D deficiency was 0·82. Net benefit for the prediction model was higher than that for the 'test-all' strategy at all probability thresholds and higher than the 'test-none' strategy for probabilities up to 60%.
  
  
  CONCLUSIONS
  Our model could predict vitamin D deficiency with reasonable accuracy, but it needs to be validated in other populations before being implemented.
authors.affiliation: Population Health Division, Queensland Institute of Medical Research, Herston, Qld, Australia; Centre for Research Excellence in Sun and Health, Brisbane, Qld, Australia.
authors.forename: Bich
authors.initials: B
authors.lastname: Tran
authors.forename: Bruce K
authors.initials: BK
authors.lastname: Armstrong
authors.forename: Kevin
authors.initials: K
authors.lastname: McGeechan
authors.forename: Peter R
authors.initials: PR
authors.lastname: Ebeling
authors.forename: Dallas R
authors.initials: DR
authors.lastname: English
authors.forename: Michael G
authors.initials: MG
authors.lastname: Kimlin
authors.forename: Robyn
authors.initials: R
authors.lastname: Lucas
authors.forename: Jolieke C
authors.initials: JC
authors.lastname: van der Pols
authors.forename: Alison
authors.initials: A
authors.lastname: Venn
authors.forename: Val
authors.initials: V
authors.lastname: Gebski
authors.forename: David C
authors.initials: DC
authors.lastname: Whiteman
authors.forename: Penelope M
authors.initials: PM
authors.lastname: Webb
authors.forename: Rachel E
authors.initials: RE
authors.lastname: Neale
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D001315:Australia / Q000453:epidemiology; D003430:Cross-Sectional Studies; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D011795:Surveys and Questionnaires; D014807:Vitamin D / Q000097:blood*; D014808:Vitamin D Deficiency / Q000097:blood* / Q000453:epidemiology*
chemical_list: D014807:Vitamin D