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

Background: Decision analytical models (DAMs) are used to develop an evidence base that is used in impact and health economic evaluations, including to evaluate interventions to improve diabetes care and health service—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. Compared with large-scale real-life randomised control trials and cohort studies, DAMs can produce evidence of the health and economic benefits of interventions in an affordable and timely manner, while overcoming ethical issues, by using mathematical and logical relationships to abstract vital aspects of reality/systems for analysis.This study examines how DAMs–in particular, Markov, system dynamic, agent-based, discrete event simulation and hybrid models–have been applied to investigate non-pharmaceutical (NP) policy interventions and how to advance their adoption to diabetes research in LMICs. Methods:  We conducted a systematic search of peer-reviewed articles published in English between 2000 and 2020 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. Results: : Thirteen studies were included (six Markov, six microsimulation and one agent-based model), most of which modelled interventions in Asian sub-population. Except for one study (which examined tax increment), all studies assessed interventions within the health sector. Seven studies reported health and economic outcomes and the rest reported health outcomes only. Twelve studies reported on uncertainty analysis. Almost half of the studies did not report on model validation, whereas two studies combined multiple validation techniques to increase confidence in models. Four studies modelled policy interventions among diabetes patients; the rest concentrated on at-risk populations. All studies reported limitations with obtaining sufficient data for modelling. Conclusions: This review summarises the application of DAMs to NP diabetes interventions in LMICs. Our purpose is to identify gaps in their adoption and advance their appropriate use for assessing NP policies intervention aimed at controlling diabetes. Our study recommends that LMICs should leverage the usefulness of DAMs to support economic evaluation of population-wide NP policy interventions, particularly fiscal policies, to control diabetes.