# Table 6 Predictive variables

Outcome variable Predictor Odds ratio (95 % CI)
Weight management BMI at baselinea 1.03 (1.02, 1.05)
Agea 1.02 (1.01, 1.03)
BMI or waist circumference measured Sex (male)b 0.70 (0.57, 0.87)
Waist circumference [cm]a 1.01 (1.01, 1.02)
Referral to external weight loss services Weight at baseline [kg]a 1.01 (1.01, 1.02)
Internal weight management Sex (male)b 1.09 (0.99, 1.20)
Ethnicity (mixed)c 0.71 (0.51, 0.99)
Agea 1.01 (1.00, 1.02)
Lifestyle assessment BMI at baselinea 1.02 (1.01, 1.03)
Weight loss of at least 1 kg Ethnicity (mixed)c 0.45 (0.20, 1.00)
Weight at baselinea 1.02 (1.01, 1.02)
BMI at baselinea 1.03 (1.01, 1.06)
Agea 1.02 (1.01, 1.02)
Increase in outcome (95 % CI)
BMI Ethnicity (South Asian)c −0.47 (−0.97, 0.02)
BMI at baselined 0.74 (0.66, 0.82)
Aged −0.01 (−0.02, −0.00)
Weight BMId −0.17 (−0.22, −0.12)
Weight at baseline [kg]d 0.95 (0.92, 0.97)
Aged −0.07 (−0.08, −0.06)
1. aComparison between a binary outcome and continuous predictor. For the proportion of patients offered a weight loss intervention (outcome) and BMI (predictor). The odds ratio of 1.03 implies that an increase in BMI by a unit of 1 leads to a 3 % increase in the odds of receiving a weight management intervention
2. bComparison between a binary outcome and categorical predictor. For example, the odds of having a BMI or waist circumference measured is 30 % lower in men compared to women
3. cComparison between a continuous outcome and categorical predictor. For example, being of a South Asian ethnicity leads to an increase in BMI of −0.47 (i.e. a decrease of 0.47) in a South Asian when compared to a White European
4. dComparison between a continuous outcome and continuous predictor. For example, an increase in BMI at baseline of 1 leads to an increase in BMI at follow-up of 0.74