EE336 A Conceptual Epidemiological and Economic Model to Predict Obesity in Low- and Middle-Income Countries: A Meta-Synthesis

Fatoye, Francis ORCID: https://orcid.org/0000-0002-3502-3953, Mbada, Chidozie Emmanuel, Niyi-Odumosu, Faatihah, Fatoye, Clara Toyin, Useh, Ushotanefe and Gebrye, Tadesse ORCID: https://orcid.org/0000-0001-7976-2013 (2025) EE336 A Conceptual Epidemiological and Economic Model to Predict Obesity in Low- and Middle-Income Countries: A Meta-Synthesis. In: Value in Health, S123. Presented at ISPOR 2025, 13 - 16 May 2025, Montreal, Quebec, Canada.

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Abstract

Objectives The objective of this study was to develop a conceptual epidemiological and economic model to predict obesity in LMICs. Methods A Systematic review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines was carried out on MEDLINE, CINHAL and AMED, PubMed, and The Cochrane library and EconLit databases for publications in English from inception to August 28, 2024. Economic evaluation studies where they described an economic model of adult patients with/without obesity in which obesity progression was evaluated were eligible. Title and abstract, full text screening, and data extraction were conducted. Thematic analysis was applied to identify key domains, and this was helpful to develop a conceptual model of obesity in LMICs. Results A total of 825 studies were identified, of which 31 met the inclusion criteria in the meta-synthesis. The model type of the included studies was Markov model (n = 23), decision tree (n = 2), discrete event simulation (n = 4), and combination of Markov model and decision tree (n = 2). These studies were published between 1997 to 2024 and were developed based on cohort and patient data. Six health states which include diabetes (n = 19), stoke (n = 14), cardiovascular (n = 14), normal weight (n = 10), overweight (n = 9), and obesity (n = 8) were thematically identified. Conclusions This is the first study to develop a conceptual epidemiological and economic model to predict obesity in LMICs. The model will now be used in a future study to predict the prevalence and the economic burden associated with obesity. The findings can be used to inform healthcare decisions to facilitate resource allocation for prevention and management of the condition in LMICs. Thereby, reducing the significant burden associated with the condition in these regions.