A Conceptual Epidemiological and Economic Model to Predict Obesity in Low- and Middle-Income Countries: A Meta-Synthesis
Author(s)
Francis Fatoye, BSc, MBA, MSc, PhD1, Chidozie Emmanuel Mbada, PhD1, Faatihah Niyi-Odumosu, PhD2, Clara Toyin Fatoye, BSc, MA1, Ushotanefe Useh, PhD3, Tadesse Gebrye, MPH, MSc4.
1Manchester Metropolitan University, Manchester, United Kingdom, 2University of the West of England, Bristol, United Kingdom, 3North West University, Mahikeng, South Africa, 4Research Associate, Manchester Metropolitan University, Manchester, United Kingdom.
1Manchester Metropolitan University, Manchester, United Kingdom, 2University of the West of England, Bristol, United Kingdom, 3North West University, Mahikeng, South Africa, 4Research Associate, Manchester Metropolitan University, Manchester, United Kingdom.
Presentation Documents
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.
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.
Conference/Value in Health Info
2025-05, ISPOR 2025, Montréal, Quebec, CA
Value in Health, Volume 28, Issue S1
Code
EE336
Topic
Economic Evaluation
Disease
SDC: Diabetes/Endocrine/Metabolic Disorders (including obesity)