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Objective To analyse the Growing Up in Scotland cohort for predictors of obesity at age 12, present at school entry (age 5–6). Methods The initial model included literature-based risk factors likely to be routinely collected in high-income countries (HICs), as well as “Adverse/Protective Childhood Experiences (ACEs/PCEs)”. Missing data were handled by Multiple Chained Equations. Variable-reduction was performed using multivariable logistic regression with backwards and forwards stepwise elimination, followed by internal validation by bootstrapping. Optimal sensitivity/specificity cut-offs for the most parsimonious and accurate models in two situations (optimum available data, and routinely available data in Scotland) were examined for their referral burden, and Positive and Negative Predictive Values. Results Data for 2787 children with full outcome data (obesity prevalence 18.3% at age 12) were used to develop the models. The final “Optimum Data” model included six predictors of obesity: maternal body mass index, indoor smoking, equivalized income quintile, child’s sex, child’s BMI at age 5–6, and ACEs. After internal validation, the area under the receiver operating characteristic curve was 0.855 (95% CI 0.852–0.859). A cut-off based on Youden’s J statistic for the Optimum Data model yielded a specificity of 77.6% and sensitivity of 76.3%. 37.0% of screened children were “Total Screen Positives” (and thus would constitute the “referral burden”.) A “Scottish Data” model, without equivalized income quintile and ACEs as a predictor, and instead using Scottish Index of Multiple Deprivation quintile and “age at introduction of solid foods,” was slightly less sensitive (76.2%) but slightly more specific (79.2%), leading to a smaller referral burden (30.8%). Conclusion Universally collected, machine readable and linkable data at age 5–6 predict reasonably well children who will be obese by age 12. However, the Scottish treatment system is unable to cope with the resultant referral burden and other criteria for screening would have to be met.

AimsWe set out to investigate the potential sex differences in the association between diabetes and depressive symptoms by conducting an interaction analysis, and to investigate whether sex mediates the effect of diabetes on depressive symptoms.MethodsWe conducted analyses on cross-sectional data of adults aged 20 years or older in the Mexican National Health and Nutrition Survey 2018–2019 (ENSANUT 2018–2019). Diabetes was defined by self-reported medical diagnosis, and depressive symptoms were measured using the seven-item Centre for Epidemiologic Studies Depression scale. First, an unadjusted interaction analysis was conducted. Second, the inverse probability of treatment weighting was applied to account for imbalances and biases. Third, the four-way decomposition method was used to estimate the potential mediating effect of sex.ResultsIn the study population (N=43 074), the prevalence of diabetes was 9.3% for men and 11.7% for women. Depressive symptoms were more prevalent in women (19.0%) than in men (9.5%). Women with diabetes had the greatest odds of having depressive symptoms, compared with men without diabetes (ORwomen-diabetes3.49 (95% CI: 3.16 to 3.86)). The interaction analysis indicated that diabetes and sex interact on both, multiplicative and additive scales (ratio of ORs (95% CI) 1.22 (1.02 to 1.45), and relative excess risk due to interaction (95% CI) 0.99 (0.63 to 1.36)). The four-way decomposition analysis showed that the interaction effect between diabetes and sex is larger than the mediation effect.ConclusionsWe found a positive interaction between diabetes and sex in the odds of having depressive symptoms. Mental health and diabetes care services planning would benefit from adopting a sex-informed approach.