Quantifying Life expectancy in people with Type 2 diabetes

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1 School of Public Health University of Sydney Quantifying Life expectancy in people with Type 2 diabetes Alison Hayes School of Public Health University of Sydney

2 The evidence Life expectancy reduced by 8 years at age 40 (Roper et al BMJ 2001:322; Life expectancy reduced by 5-12 years if diagnosed between years; 2-5 years shorter if diagnosed at years. Diabetes Natural History Study, Joslin Clinic , up to 25 year follow-up Schneider et al 29 year follow-up study, East Germany average patient age 63 years. Average loss of life expectancy 5.3 years (men) 6.4 years (women)

3 Hazard ratios by age and sex for all cause mortality for people with diabetes compared to no diabetes Mulnier et al Diabetic Medicine 2006 May;23(5):

4 Age standardized mortality rates of diabetic men and women compared with general population men women Gulliford and Charlton Am J Epidemiol 2009; 169:

5

6 Conceptual Model School of Public Health University of Sydney R 3 R T2D 1 R 2 population Complications DEAD

7 Aspects of increased risk CVD risk is 2-4 times than of non-diabetic population Copenhagen heart study risk of having MI or stroke increased 2-3 fold, risk of death increased 2-fold (Almdal et al 2004) UK GP database - age adjusted risk of stroke 2.19 (diabetes/no diabetes) Mulnier et al 2006 MONICA project Perth in 1 year survivors of MI, HR death 2.5 (diabetes/no diabetes) (Briffa BMJ 2009) Diabetes associated with increased mortality after acute MI (HR 1.7). Mukamal et al Diabetes Care 2001

8 Risk factors for vascular events Age Sex Body mass index Smoking status Duration of diabetes Systolic blood pressure HbA1 c Total cholesterol : hdl ratio Prior clinical history Self rated health

9 Self rated health Cumulative hazard (%) Vascular events p< Time (years) since EQ5D survey VAS <70 VAS 70 -<80 VAS 80 -<90 VAS >=90 Hayes et al, Diabetes Care 2008

10 Estimates of LE for Australians with diabetes Large prospective cohort study? Clinical trial data, e.g. ADVANCE, FIELD, UKPDS Registry data, NDR Administrative data e.g WA linked data Modelling

11 Trials vs administrative data Trial data Adjudicated outcomes (<1000 events) Detailed risk factor data (e.g. HbA1c measure) Selection criteria (generally healthier general population) Most trials do not have information on older patients Administrative data Outcomes identified through hospital admissions (1000s of events observed) Minimal risk factor data (e.g. age sex Whole population Include people of all ages

12 Population counts for Swedish & Australian data 30,000 WA data Swedish Registry Persons with diabetes 25,000 20,000 15,000 10,000 5, UKPDS Age group FIELD ADVANCE

13 6,000 5,000 Trials vs admin data - numbers of Counts of events events WA data UKPDS Counts 4,000 3,000 2,000 1,000 0 MI stroke IHD Heart failure Amputation Renal failure Blindness Type of Complication

14 UKPDS outcomes model Discrete time simulation model Based on patient level data (n=3462) from UKPDS Predicts occurrence of major diabetes related complications and death Based on 20 years of data from Provides life expectancy and health economic outcomes Clarke et al Diabetologia. 2004; 47(10):

15 Components of UKPDS outcomes Model Epidemiological module Relate risk-factors to outcomes Non-fatal Outcomes; Diabetes-related deaths; Other deaths (competing risks) Risk factor time paths Predict risk factor values over time HbA1c Systolic Blood pressure Lipids Smoking Health Economic Outcomes Provide economic meaningful outcomes Event based costs Event based Utilities

16 Summary of UKPDS model equations Ischaemic Heart Disease (IHD) AGE 1.03 FEMALE 0.62 HbA1c 1.13 SBP 1.10 Ln (TOTAL:HDL) 4.47 (Eq.1, 231 events) Blindness (BLIND) AGE 1.07 HbA1c 1.25 (Eq. 6, 104 events) Renal failure (RENAL) SBP 1.50 BLIND 8.02 (Eq. 7, 24 events) Amputation (AMP) PVD HbA1c 1.55 SBP 1.26 BLIND 6.12 (Eq. 5, 40 events ) Fatal and non-fatal Heart failure (CHF) AGE 1.10 myocardial infarction (MI) HbA1c 1.17 AGE 1.06 SBP 1.12 FEMALE 0.44 BMI 1.07 AC 0.27 (Eq. 3, n = 97) SMOK 1.41 HbA1c 1.13 SBP 1.11 Ln (TOTAL:HDL) 3.29 IHD 2.49 STROKE CHF 4.75 AGE 1.09 (Eq. 2, n = 495) FEMALE 0.60 SMOK 1.43 OTHER DEATH ATRFIB 4.17 HbA1c 1.12 (In force at all times) SBP 1.32 AGE FEMALE 1.08 TOTAL:HDL 1.12 AGE (1-FEMALE) 1.11 CHF 5.71 SMOK 1.36 (Eq. 4, n = 157) (Eq. 10, 250 deaths) Diabetes related mortality EVENT FATALITY (odds ratios) (In year of first event) DIABETES MORTALITY (In subsequent years) Ln (AGE_EVENT) Ln (AGE_EVENT) HbA1c 1.12 TOTAL:HDL 1.12 MI_EVENT MI_EVENT STROKE 2.85 MI_POST 3.06 RENAL AMP CHF STROKE_EVENT STROKE_POST 1.00 CHF 1.00 AMP 2.81 RENAL 4.88 (Eq. 8, 717 deaths) (Eq. 9, 100 deaths)

17 Risk models Probability of event in the next year conditional on survival to present time Risk of complications usually based on clinical risk factors, age, smoking Risk of death post event less dependent on clinical risk factors

18 Survival analysis Survival Years Median survival = 20.5 years Life expectancy = 19.7 years

19 Life expectancy tables Leal et al European Heart Journal epub 2008

20 Conceptual Model School of Public Health University of Sydney R 3 R T2D 1 R 2 population Complications DEAD

21 Post event survival Australian data WA administrative data set 70,340 persons with diabetes 10 years Average 4.5 years follow-up Linked to death and hospital records 5 complications which elevate death MI, stroke, heart failure, amputation, renal failure Covariates age, sex, prior history

22 Kaplan Meier survival Kaplan-Meier survival estimates analysis time fspk = 1 fspk = 2 fspk = 3 fspk = 4 fspk = 5

23 Two-part model for death post event Logistic model for death within first month Gompertz model for survival post 1 month Covariates include age, sex, type of event, prior medical history

24 Survival post MI - men Survival years 60 years 70 years 80 years Time since event (years)

25 Survival post CHF - men Survival years 60 years 70 years 80 years Time since event (years)

26 Life expectancy estimates yr model 80yr 1month survivor Survival Time (years) LE for 80 yr old immediately after MI = 2.6 years LE for 1 month survivors = 5.2 years

27 Continuing work Develop life expectancy tables following major diabetic complications Derive risk equation in suitable form to incorporate into UKPDS Risk tables based on clinical risk factors

28 Prediction is very difficult, especially if it is about the future Niels Bohr, Danish physicist 1922 Nobel Prizewinner

29 Acknowledgements NHMRC Research Grant Development and validation of an Australian Diabetes Health Policy Simulation Model Millennium Award DART Research Grant Development and validation of risk tables for quantifying life expectancy in people with type 2 diabetes

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