Methods with consequences: analysing demographic diversity in global health governance and knowledge production

Structural inequities remain deeply embedded in global health governance and knowledge production, shaping which voices are heard and whose expertise and priorities are valued. Attempts to understand patterns of systematic exclusion have inspired numerous initiatives to assess the demographic diversity of those producing knowledge and governing global health. However, in the absence of systematically reported demographic data, scholars often rely on proxy self-identification (eg, cued language in online biographies assumed to be authored or endorsed by the person themself) or external inference methods (eg, based on name, photo, or language) for demographic characteristics. This Review critically examines the strengths, limitations, and ethical concerns of these different approaches, and proposes guidance based on five pillars that support their more responsible use: (1) practising critical refusal; (2) prioritising self-reported methods; (3) aligning methods with purpose and context; (4) embedding safeguards in data storage, reporting, and sharing; and (5) ensuring transparency and reflexivity. Ultimately, the challenges that these quantitative assessments seek to address, and the pitfalls of their methods, can be avoided altogether with the fundamental transformation of the norms and practices underpinning global health.