2025/02/14 by Nina Deliu, Deliu, Nina, Piero Demetrio Falorsi +7 · 1 citation
Health Professions · Economics, Econometrics and Finance · #Global Health Workforce Issues #Primary Care and Health Outcomes #Healthcare Policy and Management
paper · pdf · doi:10.48550/arxiv.2502.10182
Official statistics are undergoing a significant transformation, as national statistical institutes transition from traditional single-source data production systems to integrated systems of statistical registers combining administrative, census, and survey data. The resulting multisource register-based estimates are prone to multiple interacting sources of error, yet rigorous scalable frameworks for quantifying their accuracy remain underdeveloped. This work discusses and validates a global measure of error assessment for such multisource register-based statistics. Focusing on two central sources of uncertainty, sampling and modelling, we derive an analytical solution that accurately approximates the global error of mass-imputation procedures under a multinomial logistic model. The proposed measure is interpretable, flexible, and computationally scalable, enabling on-the-fly accuracy quantification for user-defined, unplanned domain-specific statistics on population totals. Its validity is established theoretically and confirmed through simulation studies. An application to education data from the Italian National Institute of Statistics is presented.