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Table 4 Logistic regression models for fractures in control group (development) at 12 months

From: Falls and fracture risk screening in primary care: update and validation of a postal screening tool for community dwelling older adults recruited to UK Prevention of Falls Injury Trial (PreFIT)

Predictors

Control group (model 1) 12 months, n = 3078

Coefficient

95% CI

p

Age

0.080

0.040, 0.121

 < 0.001

Female gender

0.897

0.360, 1.434

 < 0.001

BMI

-0.050

-0.105, 0.005

0.073

Practice deprivation 4 -7

-0.076

-0.784, 0.631

0.832

Practice deprivation 8 -10

0.344

-0.362, 1.051

0.339

Clock Draw Test

-0.022

-0.257, 0.212

0.851

High risk of falling

0.534

0.030, 1.038

0.038

Intercept

-9.641

-13.806, -5.477

 < 0.001

  1. Equation:\(logit\left[P\left(fracutre=1\right)\right]={\beta }_{0}+{\beta }_{1}age+{\beta }_{2} sex {+{\beta }_{3}BMI+\beta }_{4}Dep+{\beta }_{5}CDT+{\beta }_{6} High risk\)
  2. Adjusted for these variables only due to predictor collinearity. Reference values: GP deprivation score vs. 1–3; female vs. male; baseline high vs. low risk. Example e.g. female, aged 73, BMI kg/m2 = 25, GP deprivation code 1–4, clock draw test 6 = fall risk is low. The equation gives log odds. Probability can be calculated by taking the antilogit of the log odds
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BMC Geriatrics

ISSN: 1471-2318

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