Temporal generalized linear mixed models for monitoring national health behavior using subnational observational data

Since 2025, administrative changes have resulted in the dismissal of federal workers responsible for implementing US national health surveys. While national data are crucial to understanding trends at the national level, it may be possible to complement such data sources using state-level observational data by using the latter to predict the former in statistical models. This study presents a proof-of-concept approach for estimating the national prevalence of health behaviors in the United States based on partial state-level prevalence estimates.

Focusing on US adolescent e-cigarette use, the prevalence of which informs regulatory efforts by the US Food and Drug Administration's Center for Tobacco Products, we implemented temporal generalized linear mixed models (GLMMs; with binomial or beta response distributions, a logit link function, and a spatial power covariance structure to account for autocorrelation). Models were fitted to national e-cigarette use prevalence from the National Youth Tobacco Survey and state-level e-cigarette use prevalence from 38 state-specific surveys administered by state health and education departments and other organizations from 2011 to 2025.

Beta models performed similarly to binomial models in year-level predictive performance, with modest mean absolute error (MAE) for in-sample predictions (beta: 1.5-1.8 percentage points [pp]; binomial: 1.4-2.6 pp) and out-of-sample predictions (beta: 2.3 pp; binomial: 2.2 pp). MAEs were generally higher for simpler comparator models: time-trend extrapolation models, 4.7-5.5 pp; 1-year-lag persistence models, 4.1-4.7 pp; and naïve mean models, 1.8-2.0 pp.

Temporal GLMMs may be useful tools for monitoring national trends in substance use behaviors when subnational data, but not national data, are available.