A Patient-Level Model to Estimate Lifetime Health Outcomes of Patients With Type 1 Diabetes.

OBJECTIVE: To develop a patient-level simulation model for predicting lifetime health outcomes of patients with type 1 diabetes and as a tool for economic evaluation of type 1 diabetes treatment based on data from a large, longitudinal cohort. RESEARCH DESIGN AND METHODS: Data for model development were obtained from the Swedish National Diabetes Register. We derived parametric proportional hazards models predicting the absolute risk of diabetes complications and death based on a wide range of clinical variables and history of complications. We used linear regression models to predict risk factor progression. Internal validation was performed, estimates of life expectancies for different age-sex strata were computed, and the impact of key risk factors on life expectancy was assessed. RESULTS: The study population consisted of 27,841 patients with type 1 diabetes with a mean duration of follow-up of 7 years. Internal validation showed good agreement between the predicted and observed cumulative incidence of death and 10 complications. Simulated life expectancy was ∼13 years lower than that of the sex- and age-matched general population, and patients with type 1 diabetes could expect to live with one or more complications for ∼40% of their remaining life. Sensitivity analysis showed the importance of preventing renal dysfunction, hypoglycemia, and hyperglycemia as well as lowering HbA1c in reducing the risk of complications and death. CONCLUSIONS: Our model was able to simulate risk factor progression and event histories that closely match the observed outcomes and to project events occurring over patients' lifetimes. The model can serve as a tool to estimate the impact of changing clinical risk factors on health outcomes to inform economic evaluations of interventions in type 1 diabetes.

Recent trends in life expectancy for people with type 1 diabetes in Sweden.

AIMS/HYPOTHESIS: People with type 1 diabetes have reduced life expectancy (LE) compared with the general population. Our aim is to quantify mortality changes from 2002 to 2011 in people with type 1 diabetes in Sweden. METHODS: This study uses health records from the Swedish National Diabetes Register (NDR) linked with death records. Abridged period life tables for those with type 1 diabetes aged 20 years and older were derived for 2002-06 and 2007-11 using Chiang's method. Cox proportional hazard models were used to assess trends in overall and cause-specific mortality. RESULTS: There were 27,841 persons aged 20 years and older identified in the NDR as living with type 1 diabetes between 2002 and 2011, contributing 194,685 person-years of follow-up and 2,018 deaths. For men with type 1 diabetes, the remaining LE at age 20 increased significantly from 47.7 (95% CI 46.6, 48.9) in 2002-06 to 49.7 years (95% CI 48.9, 50.6) in 2007-11. For women with type 1 diabetes there was no significant change, with an LE at age 20 of 51.7 years (95% CI 50.3, 53.2) in 2002-06 and 51.9 years (95% CI 50.9, 52.9) in 2007-11. Cardiovascular mortality significantly reduced, with a per year HR of 0.947 (95% CI 0.917, 0.978) for men and 0.952 (95% CI 0.916, 0.989) for women. CONCLUSIONS/INTERPRETATION: From 2002-06 to 2007-11 the LE at age 20 of Swedes with type 1 diabetes increased by approximately 2 years for men but minimally for women. These recent gains have been driven by reduced cardiovascular mortality.

A meta-analysis of the relative risk of mortality for type 1 diabetes patients compared to the general population: exploring temporal changes in relative mortality.

AIMS: Type 1 diabetes has been associated with an elevated relative risk (RR) of mortality compared to the general population. To review published studies on the RR of mortality of Type 1 diabetes patients compared to the general population, we conducted a meta-analysis and examined the temporal changes in the RR of mortality over time. METHODS: Systematic review of studies reporting RR of mortality for Type 1 diabetes compared to the general population. We conducted meta-analyses using a DerSimonian and Laird random effects model to obtain the average effect and the distribution of RR estimates. Sub-group meta-analyses and multivariate meta-regression analysis was performed to examine heterogeneity. Summary RR with 95% CIs was calculated using a random-effects model. RESULTS: 26 studies with a total of 88 subpopulations were included in the meta-analysis and overall RR of mortality was 3.82 (95% CI 3.41, 3.4.29) compared to the general population. Observations using data prior to 1971 had a much larger estimated RR (5.80 (95% CI 4.20, 8.01)) when compared to: data between; 1971 and 1980 (5.06 (95% CI 3.44, 7.45)); 1981-90 (3.59 (95% CI 3.15, 4.09)); and those after 1990 (3.11 (95% CI 2.47, 3.91)); suggesting mortality of Type 1 diabetes patients when compared to the general population have been improving over time. Similarly, females (4.54 (95% CI 3.79-5.45)) had a larger RR estimate when compared to males (3.25 (95% CI 2.82-3.73) and the meta-regression found evidence for temporal trends and sex (p<0.01) accounting for heterogeneity between studies. CONCLUSIONS: Type 1 diabetes patients' mortality has declined at a faster rate than the general population. However, the largest relative improvements have occurred prior to 1990. Emphasis on intensive blood glucose control alongside blood pressure control and statin therapy may translate into further reductions in mortality in coming years.

Severe hypoglycemia and mortality after cardiovascular events for type 1 diabetic patients in Sweden.

OBJECTIVE: To examine whether previous severe hypoglycemic events were associated with the risk of all-cause mortality after major cardiovascular events (myocardial infarction [MI] or stroke) in patients with type 1 diabetes. RESEARCH DESIGN AND METHODS: This study is based on data from the Swedish National Diabetes Register linked to patient-level hospital records, prescription data, and death records. We selected patients with type 1 diabetes who visited a clinic during 2002-2010 and experienced a major cardiovascular complication after their clinic visit. We estimated a two-part model for all-cause mortality after a major cardiovascular event: logistic regression for death within the first month and a Cox proportional hazards model conditional on 1-month survival. At age 60 years, 5-year cumulative mortality risk was estimated from the models for patients with and without prior diabetes complications. RESULTS: A total of 1,839 patients experienced major cardiovascular events, of whom 403 had previously experienced severe hypoglycemic events and 703 died within our study period. A prior hypoglycemic event was associated with a significant increase in mortality after a cardiovascular event, with hazard ratios estimated at 1.79 (95% CI 1.37-2.35) within the first month and 1.25 (95% CI 1.02-1.53) after 1 month. Patients with prior hypoglycemia had an estimated 5-year cumulative mortality risk of 52.4% (95% CI 45.3-59.5) and 39.8% (95% CI 33.4-46.3) for MI and stroke, respectively. CONCLUSIONS: We have found evidence that patients with type 1 diabetes in Sweden with prior severe hypoglycemic events have increased risk of mortality after a cardiovascular event.

Simulating lifetime outcomes associated with complications for people with type 1 diabetes.

OBJECTIVES: The aim of this study was to develop a discrete-time simulation model for people with type 1 diabetes mellitus, to estimate and compare mean life expectancy and quality-adjusted life-years (QALYs) over a lifetime between intensive and conventional blood glucose treatment groups. METHODS: We synthesized evidence on type 1 diabetes patients using several published sources. The simulation model was based on 13 equations to estimate risks of events and mortality. Cardiovascular disease (CVD) risk was obtained from results of the DCCT (diabetes control and complications trial). Mortality post-CVD event was based on a study using linked administrative data on people with diabetes from Western Australia. Information on incidence of renal disease and the progression to CVD was obtained from studies in Finland and Italy. Lower-extremity amputation (LEA) risk was based on the type 1 diabetes Swedish inpatient registry, and the risk of blindness was obtained from results of a German-based study. Where diabetes-specific data were unavailable, information from other populations was used. We examine the degree and source of parameter uncertainty and illustrate an application of the model in estimating lifetime outcomes of using intensive and conventional treatments for blood glucose control. RESULTS: From 15 years of age, male and female patients had an estimated life expectancy of 47.2 (95 % CI 35.2-59.2) and 52.7 (95 % CI 41.7-63.6) years in the intensive treatment group. The model produced estimates of the lifetime benefits of intensive treatment for blood glucose from the DCCT of 4.0 (95 % CI 1.2-6.8) QALYs for women and 4.6 (95 % CI 2.7-6.9) QALYs for men. Absolute risk per 1,000 person-years for fatal CVD events was simulated to be 1.37 and 2.51 in intensive and conventional treatment groups, respectively. CONCLUSIONS: The model incorporates diabetic complications risk data from a type 1 diabetes population and synthesizes other type 1-specific data to estimate long-term outcomes of CVD, end-stage renal disease, LEA and risk of blindness, along with life expectancy and QALYs. External validation was carried out using life expectancy and absolute risk for fatal CVD events. Because of the flexible and transparent nature of the model, it has many potential future applications.

A meta-analysis of health state valuations for people with diabetes: explaining the variation across methods and implications for economic evaluation.

PURPOSE: To review published studies on the effect of diabetes and its complications on utility scores to establish whether there is systematic variation across studies and to examine the implications for the estimation of quality-adjusted life years (QALYs). METHODS: A systematic review was performed using studies reporting QALY measures elicited from people with diabetes including those with a history of complications. Meta-analysis was used to obtain the average utility, and meta-regression was employed to examine the impact of study characteristics and elicitation methods on these values. The effect of different utility scores on QALYs was examined using diabetes simulation models. RESULTS: In the meta-analysis based on 45 studies reporting 66 values, the average utility score was 0.76 (95% CI 0.75-0.77). A meta-regression showed significant variation due to age, method of elicitation and the proportion of males. The average utility score for individual complications ranged from 0.48 (95% CI 0.25, 0.71) for chronic renal disease to 0.75 (95% CI 0.73, 0.78) for myocardial infarction, and these differences produced meaningful changes in simulated QALYs. There was significant heterogeneity between studies. CONCLUSIONS: We provide summary utility scores for diabetes and its major complications that could help inform economic evaluation and policy analysis.