Corbin, MDenison, HJDouwes, JWhyte, MThompson, SGHarwood, MDavis, AFink, JNBarber, PAGommans, JHCadilhac, DALevack, WMMcNaughton, HKim, JFeigin, VLRanta, A2026-02-032026-02-032026-01-07Lancet Regional Health Western Pacific, ISSN: 2666-6065 (Print); 2666-6065 (Online), Elsevier BV, 66, 101768-. doi: 10.1016/j.lanwpc.2025.1017682666-60652666-6065http://hdl.handle.net/10292/20576Background: Using community-based incidence studies and clinical registries to assess stroke care and outcomes is resource intensive and often geographically limited. Linked administrative data are lower-cost and wider-reaching, but potentially less accurate and complete. This study compared administrative data to national hospital-based study data to assess whether administrative data represents a valid alternative. Methods: We linked and compared data from the REGIONS Care Study, a New Zealand nationwide observational study, with administrative data from Statistics New Zealand's Integrated Data Infrastructure (IDI). Sensitivity, specificity, positive predictive value, and Cohen's kappa coefficient were used to assess case identification, risk factors, post-stroke outcomes, and interventions as applicable. Additional audits explored the validity of IDI ‘true false positives.’ Findings: From May to July 2018, 1719 patients with stroke were captured in REGIONS Care and 1833 in the IDI. Using REGIONS Care as the reference standard, the sensitivity of the IDI for stroke case identification was 83% and the positive predictive value 77%. There were 300 false-negatives and 414 false positives. The audit of two hospitals showed that some cases identified in IDI but excluded by REGIONS were actual strokes. For stroke risk factors, the IDI showed high sensitivity and specificity for diabetes (93% and 91%, respectively), atrial fibrillation (87% and 90%), and smoking (71% and 97%) but lower specificity for hypertension (61%), and dyslipidaemia (52%). A derived IDI favourable outcome measure showed good agreement with the modified Rankin Scale (sensitivity 88%, specificity 82%, kappa 0.67). The IDI accurately identified post-stroke medication use (sensitivities 81%–94%, specificities 78%–91%) and thrombectomy interventions (sensitivity 88%, kappa 0.91). Interpretation: The use of administrative data to ascertain stroke cases, risk factors, interventions and outcomes was feasible and compared well with manual hospital data collection making an administrative data based national stroke register possible, although supplementary data collection for comprehensive care evaluation may be required. Funding: The study was funded by the NZ Health Research Council (HRC 17/037).© 2025 The Authors. Published by Elsevier Ltd. This is an open access article distributed under the terms of the Creative Commons CC-BY license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. You are not required to obtain permission to reuse this article.Genetic panelsHereditary cancer predisposition syndromesPersonalized medicine4206 Public Health42 Health SciencesPreventionCerebrovascularClinical ResearchBrain DisordersStrokeAgingStroke3 Good Health and Well Being3202 Clinical sciences4203 Health services and systems4206 Public healthJournal ArticleOpenAccess10.1016/j.lanwpc.2025.101768