Not Asked or Not Answered? Missingness Provenance in Youth Suicide-Attempt Prediction Using the Combined Youth Risk Behavior Survey, 2015–2023

Pooled multi-site surveys mix several reasons a predictor can be absent: a jurisdiction did not ask the item, the item was not on that year’s questionnaire, or a student did not answer. Prediction models usually treat all of these as one missing value. Here, we use the CDC Youth Risk Behavior Survey combined high-school data, 2015–2023 (1,008,297 records; 250 site-year surveys), and code each predictor cell by provenance using only what the released data identify. Ten models predicted past-year suicide attempt (684,688 students) and injurious suicide attempt (504,707 students) from 15 behavioural items and demographics, comparing conventional handling (complete-case analysis, mean imputation, missing indicators, chained-equations imputation and gradient boosting) with provenance-aware indicators and pattern submodels. Evaluation used held-out school clusters, survey weights and a cluster bootstrap, and a structure-only model and 12 sensitivity analyses tested whether gains reflected survey recognition. Weighted prevalence was 8.8–10.7% for any attempt and 3.1–3.4% for injurious attempt. Most absent predictor cells came from surveys not asking the item (10.5% of all cells) rather than missingness where asked (3.5%). Provenance-split indicators improved AUROC over a single missing indicator by 0.0037 (95% CI 0.0019–0.0057) for any attempt and 0.0057 (0.0023–0.0090) for injurious attempt; the best model reached 0.853 and 0.849, whereas a site-year-and-pattern-only model reached 0.55. The gain disappeared in the national survey, where sites do not omit items, and grew when whole jurisdictions were held out (0.0054 and 0.0104). In a contrasting adult test bed built from BRFSS 2023 adverse childhood experience items, provenance handling made no difference. Distinguishing “not asked” from “not answered” gives small, consistent and transportable gains in pooled youth-survey prediction that are not explained by survey recognition, and pooled-survey analyses should code the source of absence.