Comment on: Systematic review of the Lancet Commission on Global Surgery indicators with quality assessment of modelled estimates

Correspondence to: Greta L. Davis (email: Greta.Davis@ucsf.edu)

Center for Health Equity in Surgery and Anesthesia

University of California San Francisco

550 16th Street

Box 1224

San Francisco

California 94158

USA

BJS, https://doi.org/10.1093/bjs/znaf289, published 04 March 2026

We read with great interest the systematic review by Anyomih and colleagues, which represents a meaningful methodological advance over prior national surgical indicator reporting reviews.¹ The authors extend their search strategy to include government domains of National Surgical, Obstetric, and Anaesthesia Plan (NSOAP)-committed countries, addressing a key limitation we previously identified: reliance on academic literature underestimates reporting in low- and middle-income countries (LMICs), where data are often held within ministries of health (MOHs) and national statistical agencies.2

However, an important inconsistency remains. While the authors incorporate government sources, their indicator analysis appears largely restricted to scientific literature. If so, this risks perpetuating the same bias they aim to address, as routine surgical data are frequently collected, analyzed, and used within health systems without formal publication.

More fundamentally, the field remains constrained by an underdeveloped conception of “reporting.”1,3 Binary classifications that examine whether a country has ever reported (or repeatedly report) an indicator collapse distinct stages of data system development into a single metric. These single estimates may reflect a one-time analytic effort rather than a functioning and evolving data system, which is the key outcome of interest.

We suggest reframing the discussion from “reporting” toward data ecosystem development. Effective surgical surveillance depends not only on data availability, but on the full data lifecycle: collection, validation, integration within national systems (e.g., DHIS2), analysis, dissemination, and use for decision-making.4 Programmatic experience from the Democratic Republic of Congo demonstrates that strengthening surgical data requires coordinated interventions across governance, workforce, infrastructure, and feedback loops to ensure actionability.5

Without accounting for these dimensions, reporting estimates risk overstating progress. We argue that future work should adopt a tiered framework reflecting stages of data ecosystem maturity. Only then can reporting be interpreted as a proxy for surveillance readiness and, ultimately, for the capacity to improve surgical care.