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Additional file 1: Table S1. Search words. Table S2. Summary of the characteristics of the studies included in this review (n = 30). Table S3. Risk of Bias Tool Assessments Across Studies (n = 30). Table S4. GRADE Evidence Profiles Across Studies in Meta-analysis (n = 24). Table S5. Estimates and parameters in studies that reported on the mean difference in blood pressure (n = 27). Table S6. Sensitivity analysis for systolic blood pressure (SBP) and diastolic blood pressure (DBP) in meta-analysis stratified by intervention type. Table S7. Sensitivity analysis for systolic blood pressure (SBP) and diastolic blood pressure (DBP) in meta-analysis stratified by intervention setting. Table S8. Sensitivity analysis for systolic blood pressure (SBP) and diastolic blood pressure (DBP) in meta-analysis stratified by intervention duration. Fig. S1. Methods to calculate mean differences (MD). Fig. S2. Methods to calculate standard errors (SE). Fig. S3. Forest plot stratified by intervention settings for blood pressure. (A) Forest plot stratified by intervention settings for systolic blood pressure (SBP). (B) Forest plot stratified by intervention settings for diastolic blood pressure (DBP). Fig. S4. Forest plot stratified by intervention duration for blood pressure. (A) Forest plot stratified by intervention duration for systolic blood pressure (SBP). (B) Forest plot stratified by intervention duration for diastolic blood pressure (DBP). Fig. S5. Funnel plot of systolic blood pressure (SBP), diastolic blood pressure (DBP) stratified by intervention types. Fig. S6. Funnel plot of systolic blood pressure (SBP), diastolic blood pressure (DBP) stratified by intervention settings. Fig. S7. Funnel plot of systolic blood pressure (SBP), diastolic blood pressure (DBP) stratified by intervention duration. Fig. S8. Sensitivity analysis of systolic blood pressure (SBP), diastolic blood pressure (DBP) stratified by intervention types. Fig. S9. Sensitivity analysis of systolic blood pressure (SBP), diastolic blood pressure (DBP) stratified by intervention settings. Fig. S10. Sensitivity analysis of systolic blood pressure (SBP), diastolic blood pressure (DBP) stratified by intervention duration

BackgroundIt is difficult to evaluate the effectiveness of interventions aimed at reducing early childhood obesity using randomized trials. Objective To illustrate how observational data can be analysed using causal inference methods to estimate the potential impact of behavioural 'interventions' on early childhood adiposity. Methods We used longitudinal data from 1054 children 1-5 years old enrolled in the Special Supplemental Nutrition Program for Women, Infants and Children and followed (WIC) from 2008 to 2010 for a mean duration of 23 months. The data came from a random sample of WIC families living in Los Angeles County in 2008. We used the parametric g-formula to estimate the impact of various hypothetical behavioural interventions. Results Adjusted mean weight-for-height Z score at the end of follow-up was 0.73 (95% CI 0.65, 0.81) under no intervention and 0.63 (95% CI 0.38, 0.87) for all interventions given jointly. Exclusive breastfeeding for 6 months or longer was the most effective intervention [population mean difference = -0.11 (95% CI -0.22, 0.01)]. Other interventions had little or no effect. Conclusions Compared with interventions promoting healthy eating and physical activity behaviours, breastfeeding was more effective in reducing obesity risk in children aged 1-5 years. When carefully applied, causal inference methods may offer viable alternatives to randomized trials in etiologic and evaluation research.

In the supplementary file, we presented two alternative ways for estimating ATT and ATU, and the detailed g-computation steps, and the corresponding SAS code for the illustration. (DOCX 81Â kb)

Sensitivity analysis. (DOCX 27Â kb)

In the supplementary file, we presented two alternative ways for estimating ATT and ATU, and the detailed g-computation steps, and the corresponding SAS code for the illustration. (DOCX 81Â kb)