Pre-Deployment Audit of Actionability and Equity in Severe Tooth Loss Prediction Using Constrained Algorithmic...

Predictive performance does not establish whether model-identified risks correspond to feasible, equitable pathways. We assessed the actionability and equity of a severe tooth loss prediction model using constrained algorithmic recourse and same-source temporal validation.

An Explainable Boosting Machine was trained using 2022 Behavioral Risk Factor Surveillance System data from 433,772 adults. High-risk adults underwent recourse auditing incorporating immutability locks, behavioral directionality, physiological safety floors, and at most three feature changes. Recourse represented hypothetical movement within model space, not treatment advice, causal risk reduction, or reversal of tooth loss. Primary reachability was the unweighted analytic-cohort proportion for which the frozen engine identified a feasible pathway; a BRFSS survey-weighted domain sensitivity analysis used final weights, strata, and primary sampling units. The audit separated reachability from conditional burden among reachable adults. Equity was evaluated across Social Indicators of Disparity Index (SIDI) and income strata using Oaxaca-Blinder decomposition. The frozen specification was evaluated in a 2022 holdout and the 2024 BRFSS cohort (N = 448,213) without retraining.

Survey-weighted areas under the receiver operating characteristic curve were 0.858, 0.855, and 0.858 in the 2022 full, 2022 holdout, and 2024 cohorts. Primary unweighted reachability was 10.61%, 10.54%, and 9.82%; corresponding survey-weighted estimates were 12.69% (95% design-aware CI, 12.33-13.07%), 12.31% (11.54-13.12%), and 11.30% (10.95-11.65%), respectively. Thus, reachability remained limited under both estimands. The high-SIDI group showed poorer calibration. Under the prespecified primary SIDI-neutral additive-cost specification, residual conditional cost differences across SIDI strata were small; alternative burden metrics were direction-dependent. The income residual attenuated from the 2022 holdout to 2024, although its confidence intervals were fixed-pipeline row-bootstrap intervals rather than fully design-based intervals.

Stable predictive performance masked limited model-space actionability. Integrating explicitly labeled reachability estimands, conditional burden, equity, and temporal transport can strengthen pre-deployment evaluation. Because severe tooth loss is irreversible, recourse pathways should be interpreted as a stress test of model-implied modifiable factors rather than evidence of reversibility.