Closing the Surveillance-to-Stewardship Gap in antimicrobial resistance: a narrative review of global lessons from WHO GLASS, UK ESPAUR, and low- and middle-income countries.

BACKGROUND

Antimicrobial resistance (AMR) surveillance has expanded faster than any other component of the global response, yet resistance continues to rise. In a growing number of settings, the principal constraint is the failure to translate available data into action, a disconnect we describe as the Surveillance-to-Stewardship (S2S) Gap.

OBJECTIVES

To define the S2S Gap and its component gaps, examine where it binds across health systems of different maturity, analyse why it persists, and propose an operationalisable, testable framework for closing it.

SOURCES: A cross-sectional analysis of WHO GLASS participation across all 55 South Centre member states; the UK ESPAUR programme (2019-2024); published implementation studies from India, Nigeria, and Bangladesh, read alongside three sector-specific systematic syntheses conducted by our group, all now published; and comparative governance analyses and implementation-science frameworks.

CONTENT: Adopting the GLASS conception of surveillance (resistance plus antimicrobial use data), the gap recurs but binds at different stages by system maturity: as incomplete surveillance in many low- and middle-income countries (27% (15/55) of the states report both data types), as absent operational stewardship machinery despite near-universal awareness and policy, and as uncertain policy-to-outcome translation in the mature UK system, where a 2% fall in use has not yet been accompanied by falling resistance, for reasons that current data cannot distinguish. Six structural drivers (multi-level governance, diagnostics, workforce, financing, behaviour, and implementation capacity) explain persistence. South Africa illustrates process-level narrowing. The S2S Framework, a five-stage pathway from surveillance to outcomes aligned with CFIR, RE-AIM, and a One Health perspective, enables stage diagnosis and targeted intervention.

IMPLICATIONS: Closing the gap requires funding translation as deliberately as data generation, with actions for WHO, governments, health systems, and funders, and an explicit agenda for empirically evaluating the framework itself.