Strengthening data governance for disease surveillance using stewardship roles and accountability performance metrics

Abstract

Surveillance programs often face recurring disagreements over definitions, data corrections, and release decisions, leading to inconsistent indicators and delayed reporting. This article proposes a governance operating model that defines stewardship roles for critical datasets and introduces accountability metrics linked to operational outcomes. We describe decision workflows for schema changes, a shared glossary for core measures, and escalation pathways for unresolved data disputes. The model also recommends periodic governance reviews supported by measurable targets for completeness, timeliness, and error recurrence. By formalizing ownership and measurement, programs can sustain reliable surveillance outputs across changing stakeholders and systems.