title: How Consistent is the Relationship between Improved Glucose Control and Modelled Health Outcomes for People with Type 2 Diabetes Mellitus? a Systematic Review.
abstract: There are an increasing number of studies using simulation models to conduct cost-effectiveness analyses for type 2 diabetes mellitus.
  
  
  
  To evaluate the relationship between improvements in glycosylated haemoglobin (HbA1c) and simulated health outcomes in type 2 diabetes cost-effectiveness studies.
  
  
  
  A systematic review was conducted on MEDLINE and EMBASE to collect cost-effectiveness studies using type 2 diabetes simulation models that reported modelled health outcomes of blood glucose-related interventions in terms of quality-adjusted life-years (QALYs) or life expectancy (LE). The data extracted included information used to characterise the study cohort, the intervention's treatment effects on risk factors and model outcomes. Linear regressions were used to test the relationship between the difference in HbA1c(∆HbA1c) and incremental QALYs (∆QALYs) or LE (∆LE) of intervention and control groups. The ratio between the ∆QALYs and ∆LE was calculated and a scatterplot between the ratio and ∆HbA1cwas used to explore the relationship between these two.
  
  
  
  Seventy-six studies were included in this research, contributing to 124 pair of comparators. The pooled regressions indicated that the marginal effect of a 1% HbA1cdecrease in intervention resulted in an increase in life-time QALYs and LE of 0.371 (95% confidence interval 0.286-0.456) and 0.642 (95% CI 0.494-0.790), respectively. No evidence of heterogeneity between models was found. An inverse exponential relationship was found and fitted between the ratio (∆QALY/∆LE) and ∆HbA1c.
  
  
  
  There is a consistent relationship between ∆HbA1cand ∆QALYs or ∆LE in cost-effectiveness analyses using type 2 diabetes simulation models. This relationship can be used as a diagnostic tool for decision makers.
authors.affiliation: School of Population and Global Health, University of Melbourne, Level 4, 207 Bouverie Street, Carlton, VIC, 3053, Australia.
authors.forename: Xinyang
authors.initials: X
authors.lastname: Hua
authors.affiliation: School of Population and Global Health, University of Melbourne, Level 4, 207 Bouverie Street, Carlton, VIC, 3053, Australia.|The George Institute for Global Health, University of Sydney, Lidcombe, NSW, Australia.
authors.forename: Thomas Wai-Chun
authors.initials: TW
authors.lastname: Lung
authors.affiliation: Menzies Research Institute, University of Tasmania, Hobart, TAS, Australia.
authors.forename: Andrew
authors.initials: A
authors.lastname: Palmer
authors.affiliation: Menzies Research Institute, University of Tasmania, Hobart, TAS, Australia.
authors.forename: Lei
authors.initials: L
authors.lastname: Si
authors.affiliation: School of Public Health, University of Michigan, Ann Arbor, MI, USA.
authors.forename: William H
authors.initials: WH
authors.lastname: Herman
authors.affiliation: School of Population and Global Health, University of Melbourne, Level 4, 207 Bouverie Street, Carlton, VIC, 3053, Australia. philip.clarke@unimelb.edu.au.
authors.forename: Philip
authors.initials: P
authors.lastname: Clarke
mesh_terms: D001786:Blood Glucose / Q000187:drug effects; D003198:Computer Simulation; D003362:Cost-Benefit Analysis; D003924:Diabetes Mellitus, Type 2 / Q000188:drug therapy* / Q000191:economics; D006442:Glycated Hemoglobin / Q000378:metabolism; D006801:Humans; D007004:Hypoglycemic Agents / Q000008:administration & dosage* / Q000191:economics; D016014:Linear Models; D018803:Models, Economic; D019057:Quality-Adjusted Life Years; D012307:Risk Factors
chemical_list: D001786:Blood Glucose; D006442:Glycated Hemoglobin; D007004:Hypoglycemic Agents