An economic evaluation for prevention of diabetes mellitus in a developing country: a modelling study - PubMed (original) (raw)

An economic evaluation for prevention of diabetes mellitus in a developing country: a modelling study

Xiaoqian Liu et al. BMC Public Health. 2013.

Abstract

Background: The serious consequences of diabetes mellitus, and the subsequent economic burden, call for urgent preventative action in developing countries. This study explores the clinical and economic outcomes of strategies that could potentially prevent diabetes based on Chinese circumstances. It aims to provide indicators for the long-term allocation of healthcare resources for authorities in developing countries.

Methods: A representative sample of Chinese adults was used to create a simulated population of 20,000 people aged 25 years and above. The hybrid decision tree Markov model was developed to compare the long-term clinical and economic outcomes of four simulated diabetes prevention strategies with a control group, where no prevention applied. These preventive strategies were the following: (i) one-off screening for undiagnosed diabetes and impaired glucose tolerance (IGT), with lifestyle interventions on diet, (ii) on exercise, (iii) on diet combined exercise (duo-intervention) respectively in those with IGT, and (iv) one-off screening alone. Independent age-specific models were simulated based on diverse incidences of diabetes, mortalities and health utilities. The reported outcomes were the following: the remaining survival years, the quality-adjusted life years (QALYs) per diabetes or IGT subjects, societal costs per simulated subject and the comparisons between preventions and control over 40 years. Sensitivity analyses were performed based on variations of all assumptions, in addition to the performance and the compliance of screening.

Results: Compared with the control group, all simulated screening programmes prolonged life expectancy at the initiation ages of 25 and 40 years, postponed the onset of diabetes and increased QALYs at every initiation age. Along with an assumption of six years intervention, prevention programmes were associated with cost-saving compared with the control group, especially in the population aged 25 years. The savings were at least US$2017 per subject, but no statistically significant difference was observed among the intervention strategies within each age groups. The cost savings were reduced when screening was affected by poor performance and noncompliance.

Conclusions: Developing countries have few effective strategies to manage the prevention of diabetes. One-off screening for undiagnosed diabetes and IGT, with appropriate lifestyle interventions for those with IGT are cost saving in China, especially in young adults.

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Figures

Figure 1

Figure 1

Progression of individuals screened and intervened for diabetes. The hybrid tree combined a decision tree and Markov models. The decision tree (the left side) consisted of five main arms representing five scenarios. The first three scenarios involved screening for undiagnosed diabetes and IGT followed by any of the three active lifestyle interventions (diet, exercise, and duo-intervention), which were applied to the IGT subjects. The fourth scenario involved screening for undiagnosed diabetes and IGT, but without formal interventions, and the fifth scenario involved control group. Nine Markov models represented the nature history of diabetes (the lower right side). Each of them consisted of eight states: IGT, normal glucose tolerance, onset of diabetes, four diabetes complication states and death. The IGT states were tunnel states that included six temporary ones representing 6 years lifestyle interventions. Transition probability, costs, benefits were required for each state. Three separate models were performed for strategies starting at age of 25, 40 and 60 respectively. “2-hour PG” means 2-hour plasma glucose after breakfast. “DM” means diabetes mellitus. “OGTT” means oral glucose tolerance test. “IGT” means impaired glucose tolerance. “NORMAL GT” represented normal glucose tolerance state. “DIABETES COMPLICATIONS” included four different diabetes complications states: cardiovascular disease, retinopathy, nephropathy, and overt neuropathy disease. We numbered the transition paths corresponding to the main transition parameters in Table 1. (#The complement probabilities of one branch. *The life-table information used to model competing causes of death. @The proportion of individuals with normal PG. ¢, £, ¤ Transition parameters which determined whether a subject would receive interventions. §, ¶, ß, ð,&: Transition parameters applied to the Markov models: ð1 to ð4 determined transitions from onset of diabetes state to complications states respectively; &1 and &2 determined transitions from CVD or nephropathy to death state. We did not include the neuropathy-specific and retinopathy-specific mortalities, since these complications are not fatal).

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