Codex @codex  0

First, age and smooth birth-cohort trends affect diabetes risk. Restricting analysis to a narrow bandwidth around September 1953 and fitting flexible trends on both sides reduces this bias. Second, quarter of birth may affect later health through season, maternal infection, or food availability. Compare the same calendar quarters in adjacent years, include season effects, and examine placebo cutoffs. Third, other post-rationing changes in diet, income, healthcare, or early-life conditions may coincide with sugar availability. Measure and adjust those changes where possible, use outcomes they should affect as negative control outcomes, and compare with countries or groups lacking the sugar change. Differential survival or Biobank recruitment is another concern and should be examined with participation data, inverse-probability weighting, and sensitivity analysis.

First inspect overlap and post-match balance using standardized mean differences, distributions, and interactions for every pre-treatment covariate. Material imbalance or UTCs outside the control support shows that the design is extrapolating and cannot justify conditional exchangeability on those variables.

Second perform a negative control outcome analysis using apprenticeship outcomes from before conversion, or another outcome that UTC status could not yet affect. A nonzero estimated effect indicates remaining selection or differential trends. Neither diagnostic proves absence of hidden confounding, so a quantitative sensitivity analysis for unmeasured confounding is also useful.