Application of decision analytical models to diabetes in low- and middle-income countries: a systematic review

Abstract

Background: Decision analytical models (DAMs) are used to develop an evidence base for impact and health eco nomic evaluations, including evaluating interventions to improve diabetes care and health services—an increasingly important area in low- and middle-income countries (LMICs), where the disease burden is high, health systems are weak, and resources are constrained. This study examines how DAMs–in particular, Markov, system dynamic, agent based, discrete event simulation, and hybrid models–have been applied to investigate non-pharmacological popula tion-based (NP) interventions and how to advance their adoption in diabetes research in LMICs. Methods: We systematically searched peer-reviewed articles published in English from inception to 8th August 2022 in PubMed, Cochrane, and the reference list of reviewed articles. Articles were summarised and appraised based on publication details, model design and processes, modelled interventions, and model limitations using the Health Economic Evaluation Reporting Standards (CHEERs) checklist. Results: Twenty-three articles were fully screened, and 17 met the inclusion criteria of this qualitative review. The majority of the included studies were Markov cohort (7, 41%) and microsimulation models (7, 41%) simulating non pharmacological population-based diabetes interventions among Asian sub-populations (9, 53%). Eleven (65%) of the reviewed studies evaluated the cost-efectiveness of interventions, reporting the evaluation perspective and the time horizon used to track cost and efect. Few studies (6,35%) reported how they validated models against local data. Conclusions: Although DAMs have been increasingly applied in LMICs to evaluate interventions to control diabetes, there is a need to advance the use of DAMs to evaluate NP diabetes policy interventions in LMICs, particularly DAMs that use local research data. Moreover, the reporting of input data, calibration and validation that underlies DAMs of diabetes in LMICs needs to be more transparent and credible