A review of simulation models for the long-term management of type 2 diabetes in low-and-middle income countries

Mukonda, Elton; Cleary, Susan; Lesosky, Maia

doi:10.1186/s12913-021-07324-0

Cited by 5 publications

(7 citation statements)

References 50 publications

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“…However, it is difficult to obtain patient clinical data over extended periods of time due to logistical and privacy issues. 41 Our microsimulation is designed to simulate complex interactions between observed individual characteristics and associated health risk factors so that conclusions can be drawn about health and economic outcomes within a specific population. Combining findings from published clinical trials and original analysis, the model simulates probabilities of disease onset under alternative scenarios.…”

Section: Discussionmentioning

confidence: 99%

Impact of a Digitally Enhanced Diabetes Self-Management Program on Glycemia and Medical Costs

Chen¹,

Jasik²,

Dall³

et al. 2022

The Science of Diabetes Self-Management and Care

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Objective: To analyze economic savings and health impacts associated with a virtual digitally enhanced diabetes self-management education and support (DSMES) program. Research design and methods: Participants (n = 1,494) were nonpregnant adults with diagnosed type 2 diabetes and baseline body mass index (BMI) of 25 kg/m2 (23 kg/m2 if of Asian descent) or higher who enrolled in virtual DSMES between February 2019 and April 2020 for at least 4 months. Participants’ changes in glycated hemoglobin (A1C) and body weight were calculated as the difference between program start and last recorded values between months 4 and 6. Outcomes for all participants were analyzed; subanalyses were done on 628 participants with starting A1C >7% (53 mmol/mol), who could benefit most from DSMES. Markov-based microsimulation approach was used to model the potential reductions in diabetes sequalae and medical expenditures if observed improvements in A1C and BMI were maintained. Results: DSMES participants with starting A1C >7% experienced average reductions of 0.9% A1C and 2.1 kg of body weight (−1.7% of BMI) within 6 months. If these improvements were maintained, simulated outcomes include reduced 5-year onset of ischemic heart disease by 9.2%, myocardial infarction by 10.6%, stroke by 12.1%, chronic kidney disease by 16.5%, and reduced onset of other sequelae. Simulated cumulative reduction in medical expenditures is $1160 after 1 year, $4150 after 3 years, $7790 after 5 years, and $18 020 after 10 years. Conclusions: Participation in virtual DSMES improves A1C and body weight, with the potential to slow onset of diabetes sequelae and reduce medical expenditures.

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Section: Discussionmentioning

confidence: 99%

Impact of a Digitally Enhanced Diabetes Self-Management Program on Glycemia and Medical Costs

Chen¹,

Jasik²,

Dall³

et al. 2022

The Science of Diabetes Self-Management and Care

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“…Indeed, most research has been focused on producing estimates of the cost of diabetes in the short-run [8][9][10]. For example, a recent review reported the lack of studies modelling long-term economic and health effects of diabetes in low-and middle-income countries [11]. The purpose of this study was to estimate the expected cost and health outcomes in the long run for recently diagnosed diabetic patients in Chile.…”

Section: Introductionmentioning

confidence: 99%

Health and economic effects on patients with type 2 diabetes mellitus in the long run: predictions for the Chilean population

Espinoza

Abbott

Passi

et al. 2022

Diabetol Metab Syndr

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Background Diabetes is associated to a high financial and disease burden, explaining a large proportion of expenditure of the health system in one year. The purpose of this study was to estimate long-term costs and health outcomes of recently diagnosed patients with type 2 diabetes in Chile. Methods Cost and consequence study based on mathematical discrete event simulation (DES) model. We modelled expected costs (USD) and quality-adjusted life-years (QALYs) from diagnosis to death (or the age of 95) of a hypothetical cohort of 100,000 incident cases, simulated based on the Chilean National Health Survey 2018. The incidence of twelve complications was estimated assuming the hazard functions provided by the United Kingdom Prospective Diabetes Study. We explore heterogeneity across patients based on their baseline risk covariates and their impact on costs and QALYs. Results The expected cost and QALY of a recently diagnosed type 2 diabetes patient in Chile were USD 8660 and 12.44 QALYs. Both costs and QALYs were independently determined by baseline risk and the patient's life expectancy from the diagnosis. Length of life since diagnosis showed the major impact on costs (5.2% increase for every additional year). Myocardial infarction was the most frequent complication (47.4%) and the most frequent cause of death. Conclusion Diabetes type 2 determines a significant expenditure of the health system and substantial health losses. Although the control of cardiovascular risk factors and the metabolic control of the disease, both have an important impact on costs and outcomes, the main impact is achieved by postponing the age of onset of the disease.

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“…Whereas studies have appraised the application of DAMs to study diabetes interventions [ 7 – 11 ], few have focused on NP diabetes interventions in LMICs. Our study adds to this research.…”

Section: Introductionmentioning

confidence: 99%

“…Our study adds to this research. Mukonda, Cleary and Lesosky’s review on computer simulation models for type 2 diabetes in LMICs, like our present study, reports on model-based economic evaluations to support decisions in type 2 diabetes care, to assess their quality and validity [ 11 ]. However, unlike Mukonda and colleagues, we focus on DAMs for NP interventions and consider both type 1 and 2 diabetes.…”

Section: Introductionmentioning

confidence: 99%

“…NP interventions are important for reducing diabetes risk and improving its outcomes [ 3 ], providing benefits to both diabetic and non-diabetic populations. Our review focuses on NP interventions as they are understudied in the literature compared to pharmacological interventions [ 11 ]. Moreover, the methods (and outcomes reported) used to investigate NP interventions can be very different from those used to examine pharmacological interventions as the latter concentrates on biology, physiology, and medicines compared to NP intervention, for which local context, including culture, health system and infrastructure is so important [ 3 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

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

Tagoe

Nonvignon

Robert

et al. 2022

BMC Health Serv Res

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Background Decision analytical models (DAMs) are used to develop an evidence base for impact and health economic 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 population-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-effectiveness of interventions, reporting the evaluation perspective and the time horizon used to track cost and effect. 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.

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A review of simulation models for the long-term management of type 2 diabetes in low-and-middle income countries

Cited by 5 publications

References 50 publications

Impact of a Digitally Enhanced Diabetes Self-Management Program on Glycemia and Medical Costs

Impact of a Digitally Enhanced Diabetes Self-Management Program on Glycemia and Medical Costs

Health and economic effects on patients with type 2 diabetes mellitus in the long run: predictions for the Chilean population

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

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