Covariates
  1. Age in years
  2. Sex
  3. Financial year of admission
  4. Hospital Frailty Risk Score (HFRS)9
  5. Complex ESS: Complex surgery was defined using any occurrence of any of the OPCS-4 codes E143, E147, E148, E151, E152 (seeSupplementary material Table S1 for descriptions).
  6. Diagnosis during index admission of obesity (ICD-10 code: E66-)
  7. Diagnosis of any of the 17 conditions that make up the Charlson comorbidity index10
  8. Likely complications of surgery identified during the index admission. Complications were defined as described above for the outcome measures. This covariate was chosen in recognition of the fact that a complication identified during the index procedure would, in many cases, preclude same-day discharge and be associated with poorer post-discharge outcomes.
Data management and statistical analyses
Data were analysed using standard statistical software: Microsoft Excel (Microsoft Corp, Redmond, WA, USA), Stata (Stata Corp LLC, College Station, TX, USA) and Alteryx (Alteryx Inc, Irvine, CA, USA).
Age data were broadly normally distributed on visual inspection and summarised using the mean and standard deviation. All other data were categorical and were summarised using frequency and percentage. Multilevel (hierarchical) logistic regression models were constructed. All variables were treated as categorical in model building except age, which was modelled as a continuous variable using restricted cubic splines; knots (at the 10th, 50thand 90th percentile) were found to give optimal model fit for the primary outcome based on Akaike’s Information Criterion.11 Adjusted outcomes were calculated based on fixed-effects within a conditional framework. Confidence intervals (CIs) were used to draw inference, with a 95% CI for an odds ratio (OR) not including the value 1 taken to indicate significance.