Algorithms-Based Monitoring of Vaccine Coverage in Chronic-Disease Cohorts: A Population-Based Evaluation of Regional Immunization Policy in Emilia-Romagna, Italy.
Abstract
Objectives
This study aims at estimating uptake of risk-based recommended adult vaccinations among major chronic-condition subgroups in Emilia-Romagna Region (Italy) and at quantifying sociodemographic and territorial determinants of vaccine uptake.
Methods
Retrospective, population-based record-linkage study including all residents aged ≥18 years registered on December 31, 2023 (N = 3 793 475). Chronic conditions (nonmutually exclusive: diabetes, cardiopathies, heart failure, chronic obstructive pulmonary disease [COPD], chronic kidney disease, cancer, and anatomical asplenia) were identified through multisource algorithms applied to regional administrative databases (2020-2024). Vaccination histories were retrieved from the Regional Vaccination Registry-Real Time (2000-2024); uptake was defined as ≥1 recorded dose. Uptake was modeled via subgroup-specific multivariable logistic regression (sex, age group, citizenship, Local Health Authority).
Results
Pneumococcal uptake ranged from 28.4% in cancer to 40.7% in COPD and was markedly higher in anatomical asplenia (85.1%). Composite uptake mirrored these patterns (32.6% cancer; 42.0% COPD; 85.5% asplenia). Uptake of other risk-indicated vaccines was low in large subgroups, whereas asplenia showed high meningococcal uptake (MenACWY 67.5%; MenB 65.8%). In multivariable models, Italian citizenship was consistently associated with statistically significant higher odds of composite uptake (OR 1.348-2.527), and male sex increased odds in all subgroups (P < .001) except for asplenia. Age effects were substantial but condition specific. Residual territorial heterogeneity persisted after adjustment.
Conclusions
Algorithm-based identification of chronic-disease populations linked to immunization registries enables scalable, risk-stratified monitoring applicable to any chronic-condition subgroup definable through administrative codes. This approach detects strong equity gradients and persistent between-Local-Health-Authority variation, supporting regression-based indicators for subregional benchmarking and targeted strengthening of integrated multivaccine delivery pathways.