Denominator bias in MENA MASLD epidemiology: A call for decision-grade surveillance

Abstract

Metabolic dysfunction-associated steatotic liver disease (MASLD) is highly prevalent in the Middle East and North Africa (MENA) region, yet published estimates of prevalence and outcomes remain uncertain because the underlying denominators are inconsistently defined. This perspective argues that MENA MASLD epidemiology is systematically biased by three interacting mechanisms: distorted sampling frames, referral pathway selection, and structural undercapture of rural and displaced populations. Much of the current evidence is derived from convenience cohorts concentrated in urban, tertiary care settings where diagnostic availability and follow-up are greater than in the general population, leading to directional rather than random error. In parallel, risk stratification pathways that rely on two-step testing can funnel case detection toward specialty rich settings, overrepresenting advanced disease while missing earlier stages managed outside hepatology services. MASLD nomenclature change and incomplete alignment of coding and clinical documentation may further introduce artefactual inflection points that complicate trend interpretation. We highlight how underdiagnosis and under-recording in primary care propagate bias across downstream estimates and how validation of administrative algorithms and text-based ascertainment can quantify hidden disease reservoirs within routine data systems. Building on regional priority settings, we propose denominator-focused actions: probability-based sampling embedded in noncommunicable disease surveys; purposeful inclusion of rural and displaced groups; linkable data across primary care, laboratories, hospitals, and mortality registries; and harmonized coding and terminology. By decision-grade denominators, we refer to population denominators that are sufficiently representative, transparent, and linkable to support national surveillance, resource allocation, and trial-readiness decisions.