The aim of this section is to describe the conceptual modelling, model implementation and model validation phases of the Sheffield Type 1 Diabetes Policy Model development process. The model is highly flexible and has broad potential application to evaluate the DAFNE programme, other diabetes structured education programmes and other interventions for type 1 diabetes.
Conceptual modelling
The conceptual modelling phase included two workshops with clinical and social science experts in diabetes, a systematic review of published models of type 1 diabetes and structured decision-making by researchers at the University of Sheffield. An initial workshop (workshop 1) was held in June 2009 with invited clinical diabetes specialists (including a nurse specialist) and the University of Sheffield DAFNE health economics team to understand the natural history of type 1 diabetes. The next stage of the conceptual modelling process was a systematic review of previously published cost-effectiveness models of type 1 diabetes. A total of 65 papers, relating to 32 individual cost-effectiveness models, were selected for inclusion in the review (details available from the authors on request). A draft model structure including all potential diabetes-related complications was then developed based on the systematic review of previous cost-effectiveness models. In July 2010, the University of Sheffield DAFNE health economics team conducted a second workshop (workshop 2) with clinical experts to discuss the results of the review and the proposed conceptual model. The final conceptual model was developed after discussions in the workshop and is shown inFigure 7.
Model description
The Sheffield Type 1 Diabetes Policy Model is a flexible and comprehensive long-term simulated patient-level Markov model incorporating up-to-date methodologies [such as capturing parameter uncertainty and the time profile of patient characteristics (i.e. updating the time-dependent characteristics at the beginning of each annual cycle) and including patient behaviour] to allow a number of cost-effectiveness evaluations.
Initial patient cohort
Updated patient characteristics
No
nephropathy Microalbuminuria
Macroalbuminuria
Death from ESRD
ESRD Nephropathy No peripheral neuropathy Clinical neuropathy P AD with amputation No retinopathy Retinopathy Background retinopathy Proliferative retinopathy Macular oedema Blindness Hypos/DKA Severe hypoglycaemia No hypos or DKA DKA Death from HF Other -cause mortality Other -cause mortality Patient alive HF
Calculate patient costs and utilities
Stroke No CVD MI Angina Death from MI CVD Death from stroke Nephropathy 7 Structure of the Sheffield Type 1 Diabetes Policy Model (from Thokala et al. 2013 183 with permission). ESRD, end-stage renal disease; HF, heart failure; hypoglycaemic attack; PAD, peripheral arterial disease.
The Sheffield Type 1 Diabetes Policy Model consists of a series of submodels simulating the progression of each of the diabetes-related complications, acute complications and mortality in a given population with type 1 diabetes, dependent on their characteristics, by using annual cycles. The individual patient characteristics include demographics (age, gender and duration of diabetes), clinical variables (HbA1c,
smoking status, blood pressure, HDL and total cholesterol), existing diabetes-related complications and treatment status. The complications included in the model are nephropathy, retinopathy, neuropathy, severe hypoglycaemia, MI, stroke, heart failure and angina whereas the adverse events included are severe hypoglycaemia and DKA, as shown inFigure 7. Each health state is associated with an annual cost and a utility value, which is combined with the number of annual time cycles that the patient spends in that health state to estimate costs and quality-adjusted life-years (QALYs). Costs and QALYs are summed across time and patients for use in cost-effectiveness analyses.Table 28provides a comparison between the two models widely used in the cost-effectiveness analysis of type 1 diabetes [CORE184and Economic
Assessment of Glycemic Control and Long-Term Effects of diabetes (EAGLE)185] and the Sheffield Type 1
Diabetes Policy Model.
TABLE 28 Comparison of the Sheffield Type 1 Diabetes Policy Model, the IMS CORE model and the EAGLE model
Feature Sheffield model CORE model184 EAGLE model185
Scope Type 1 diabetes Type 1 and type 2 diabetes Type 1 and type 2 diabetes Model type Patient-level simulation with
Markov submodels
Patient-level simulation with Markov submodels
Patient-level simulation with Markov submodels Diabetes-related
complications included
Retinopathy, nephropathy, neuropathy, macular oedema, MI, stroke, angina, heart failure, severe hypoglycaemia, ketoacidosis
Retinopathy, nephropathy, neuropathy, macular oedema, cataract, MI, stroke, angina, heart failure, severe hypoglycaemia, ketoacidosis
Retinopathy, nephropathy, neuropathy, macular oedema, cataract, MI, stroke, angina, heart failure, severe hypoglycaemia, ketoacidosis
Risk engine for microvascular complications
Developed own transition probabilities from published data
Developed own transition probabilities from published data
UKPDS,186WESDR XXII,187
DCCT188
Risk engine for macrovascular complications
Type 1-specific transition probabilities based on DCCT189 and Cederholmet al.190 UKPDS186 (type 2 diabetes) and Framingham (general population) risk equations191
UKPDS,186
WESDR XXII,187
DCCT,188
ARIC,192
EURODIAB193
Dynamic nature of HbA1c HbA1cincluded in risk engines as a continuous variable updated over time (e.g. based on long-term effects of structured education)
HbA1clevels are controlled solely by exogenous insulin
HbA1cis simulated over time with regard to predefined target HbA1cvalues for each individual patient and updated annually according to user input
Inclusion of psychosocial factors
Can incorporate psychological and behavioural predictors of treatment response, e.g. self-efficacy/self-care behaviours
Not included Not included
ARIC, Atherosclerosis Risk in Communities; EURODIAB, Childhood Diabetes in Europe; UKPDS, UK Prospective Diabetes Study; WESDR, Wisconsin Epidemiologic Study of Diabetic Retinopathy.
Microvascular complications
For each microvascular complication (retinopathy, neuropathy and nephropathy), patients progress to the more severe health states within each annual time cycle according to the probabilities reported inTable 29. These probabilities of transitioning between states within a particular complication were estimated by combining data from multiple sources (full details of these methods are reported elsewhere183,196).
The probabilities were estimated at the reference HbA1clevel of 10% and the method of Eastmanet al.197
TABLE 29 Annual probability of microvascular events
Annual transition probabilities for microvascular complications
Parameter Base-case value Source(s)
Neuropathy
Healthy to clinically confirmed neuropathya 0.0354 DCCT,188Mosset al.194(WESDR)
Healthy to PAD with amputation 0.0003 Clinically confirmed neuropathy to PAD with amputation 0.0154 Nephropathy
Healthy to microalbuminuriab 0.0436 DCCT,188Wonget al.195(WESDR),
UKPDS 33186
Healthy to macroalbuminuriac
0.0037
Healthy to ESRD 0.0002
Healthy to death from ESRD 3.3e-6 Microalbuminuria to macroalbuminuriac 0.1565
Microalbuminuria to ESRD 0.0133 Microalbuminuria to death from ESRD 0.0004 Macroalbuminuria to ESRD 0.1579 Macroalbuminuria to death from ESRD 0.0070 ESRD to death from ESRD 0.0884 Retinopathy and macular oedema
Healthy to background retinopathyd
0.0454 Kleinet al.187
(WESDR XXII) Healthy to proliferative retinopathye 0.0013
Healthy to macular oedemaf 0.0012
Healthy to blindness 1.9e-6
Background retinopathy to proliferative retinopathye
0.0595 Background retinopathy to macular oedemaf 0.0512
Background retinopathy to blindness 0.0001 Proliferative retinopathy to blindness 0.0038 Macular oedema to blindness 0.0016
ESRD, end-stage renal disease; PAD, peripheral arterial disease; UKPDS, UK Prospective Diabetes Study; WESDR, Wisconsin Epidemiologic Study of Diabetic Retinopathy.
a β-coefficient for neuropathy=5.30. b β-coefficient for microalbuminuria=3.25. c β-coefficient for macroalbuminuria=7.95. d β-coefficient for background retinopathy=10.10. e β-coefficient for proliferative retinopathy=6.30. f β-coefficient for macular oedema=1.20.
was used to adjust the risk of background retinopathy, macular oedema, proliferative retinopathy, microalbuminuria, macroalbuminuria and neuropathy for patients with different HbA1clevels (PHbA1c) using
the formula:
PHbA1c¼PHbA1c=10(HbA1c=10)^β (1)
where the baseline probabilities (PHbA1c=10) are as shown inTable 29and theβcoefficients are as shown
in the footnote ofTable 29. The rest of the transition probabilities are assumed to be independent of HbA1clevel.
The rationale and the procedure followed to choose the sources is provided inConceptual modelling. The baseline values are obtained from three main source papers.
For neuropathy, the DCCT188is a multicentre RCT involving 29 US and Canadian clinical centres and
includes 1441 patients aged 13–39 years, of whom 726 had had type 1 diabetes mellitus for 1–5 years and had no retinopathy at baseline (primary prevention cohort), and 715 had had diabetes for
1–15 years and had minimal to moderate non-proliferative retinopathy at baseline.
For retinopathy, the Wisconsin Epidemiologic Study of Diabetic Retinopathy (WESDR XXII)187is a
population-based study that examined the 25-year cumulative progression and regression of diabetic retinopathy and its relation to various risk factors, based on a total of 955 insulin-taking patients living in an 11-county area in southern Wisconsin with type 1 diabetes diagnosed before the age of 30 years. For nephropathy, a mix of evidence from the DCCT,188WESDR195and UK Prospective Diabetes Study
(UKPDS)186was utilised. The UKPDS compared, in a RCT, intensive blood glucose control with either
sulphonylurea or insulin with conventional treatment with regard to the risk of microvascular and
macrovascular complications in 3867 newly diagnosed patients with type 2 diabetes (median age 54 years).
Macrovascular complications
The risks of fatal and non-fatal macrovascular complications (MI, stroke, heart failure and angina)
are modelled in three stages. First, the annual probability of experiencing any cardiovascular event, P_CVD, is estimated based on patient characteristics, as per the 5-year cardiovascular risk model of
Cederholmet al.:190
P CVD¼1−exp(−(−(ln(1−5 year CVD risk))=5)1), (2) where 5 year_CVD_risk is given by the equation:
5 year CVD risk¼1−0:97136exp½H, (3)
where
H¼ ½0:08426(duration−28:014)þ0:04742(age−duration−16:601)þ0:80050
(log(total cholesterol:HDL)−1:1470)þ1:27275(log(HbA1c(DCCT))−2:0605)þ1:20050 (log(systolic blood pressure)−4:8598)þ0:56688(smoker−0:1483)þ0:41995
(macroalbuminuria−0:1237)þ1:25506(previous CVD−0:0612): (4) The risk equations for macrovascular complications are obtained from Cederholmet al.190and
Palmer’s 2012 thesis.198Cederholmet al.190assessed the association between risk factors and CVD in
an observational study with a derivation sample of 3661 patients (aged 30–65 years) with type 1 diabetes from the Swedish National Diabetes Register; they used a separate validation data set of 4484 patients. The study by Cederholmet al.190was chosen because it is a recent large European study of the risk of
macrovascular complications in type 1 diabetes patients from the Swedish National Diabetes Register. Palmer198developed a microsimulation model consisting of a series of Markov health states that could
predict both CVD and end-stage renal disease (ESRD) based on data from the DCCT/Epidemiology of Diabetes Interventions and Complications (EDIC) study.189
Second, if the patient is deemed to experience a cardiovascular event, the type of event (MI, stroke, heart failure or angina) is determined using methods outlined in Palmer,198based on data from the DCCT/EDIC
study.189Given a cardiovascular event, there is a 53% chance that it is a MI, a 28% chance that it is
angina, a 12% chance that it is heart failure and a 7% chance that it is stroke, as shown inTable 30. These values were obtained from Nathanet al.,189who studied whether the use of intensive therapy
compared with conventional therapy during the DCCT affected the long-term incidence of CVD. Third is the issue of fatality. If the event experienced is a MI, stroke or heart failure, it is determined whether the event is fatal using methods outlined in Palmer198and as shown inTable 31. The values in
Table 31are obtained from a number of source papers including Sonkeet al.199and Malmberget al.200
Sonkeet al.199investigated if the reported higher case fatality in hospital after an acute cardiac event in
women can be explained by sex differences in mortality before admission and baseline risk factors, by analysing data from a community-based coronary heart disease register in the Auckland region, New Zealand, with 5106 patients (aged 25–64 years) with an acute cardiac event leading to coronary death or definite MI within 28 days of onset, occurring between 1986 and 1992. Malmberget al.200
investigated how insulin–glucose infusion followed by MDI treatment in diabetic patients with acute MI affected mortality during the subsequent 12 months of follow-up by studying 620 patients admitted to the coronary care units of 19 Swedish hospitals in the Diabetes Mellitus Insulin–Glucose Infusion in Acute Myocardial Infarction (DIGAMI) study.
TABLE 30 Probability of different cardiovascular events
Parameter Base-case value
Gamma distribution Source Alpha Beta MI 0.53 1 0.0053 Nathanet al.189 (DCCT/EDIC) Stroke 0.07 1 0.0007 Angina 0.28 1 0.0028 Heart failure 0.12 1 0.00126
TABLE 31 Probability of dying from cardiovascular events
Parameter Base-case value
Gamma distribution
Source Alpha Beta
MI death in hospital: men 0.3930 1 0.00393 Sonkeet al.199
MI death in hospital: women 0.3640 1 0.00364 Sonkeet al.199
MI death within 1 year: aged<65 years 0.1522 1 0.00152 Malmberget al.(DIGAMI)200
MI death within 1 year: aged 65–75 years 0.1860 1 0.00186 Malmberget al.(DIGAMI)200
MI death within 1 year: aged>75 years 0.2508 1 0.00250 Malmberget al.(DIGAMI)200
Stroke death within 30 days 0.1240 1 0.00124 Erikssonet al.201
Stroke death within 1 year 0.1063 1 0.00106 Nathanet al.(DCCT/EDIC)189
Heart failure death within 1 year 0.0570 1 0.00057 Anselminoet al.202
Acute complications
Two acute complications are simulated in the Sheffield Type 1 Diabetes Policy Model: severe
hypoglycaemia (defined as a hypoglycaemic event that the person with type 1 diabetes is unable to treat themselves) and DKA. The model parameters for the incidence of these two events were estimated from the DAFNE research database and the original DAFNE education compared with no DAFNE education RCT data set (DAFNE NIHR Research Group, Central DAFNE Administration Office, North Tyneside General Hospital, North Shields, Tyne and Wear, 2006, personal communication). Negative binomial models were developed to predict the annual rates of hypoglycaemia and DKA, and the results of the models are presented inTable 32. Full details of these methods are available from the authors on request.
Mortality
Patients can also die from other causes (than ESRD and CVD) and this other-cause mortality is modelled based on UK interim life tables from 2008 to 2010.203The model allows for other life tables to be selected,
for example there is an option to select US mortality data used in the CORE model184or mortality rates
from the EAGLE model.185
Treatment effectiveness in terms of change in glycated haemoglobin
Treatment effect is measured in terms of change in HbA1cfrom baseline over time.
The specific details of the evidence used for the various analyses undertaken in this research programme are outlined in the final three sections of this chapter. These evidence sources include the different components of the DAFNE programme, that is, the original RCT of DAFNE education compared with no DAFNE education, the RCT of 5-week compared with 1-week courses, the psychosocial study and the general database. In most cases the analysis of the individual-level data is carried out using a statistical regression. For example, to quantify the impact of DAFNE education after 12 months in the 5 × 1-day RCT we undertake a regression with:
12monthHbA1c=constantþCoeff1BaselineHbA1cþCoeff2Treatment: (5)
Change in HbA1c, as a result of an intervention, has an impact on the risk of developing several
microvascular complications and this effect is modelled based on Eastmanet al.’s197method of adjusting
the risk for changes in HbA1clevels. For macrovascular complications the coefficients for HbA1c, HDL,
smoking status, blood pressure and total cholesterol used in Cederholmet al.190were used to adjust the
probability of any cardiovascular event.
TABLE 32 Negative binomial models of the annual number of severe hypoglycaemic and DKA episodes
Covariates Coefficient Standard error 95% CI
Total number of hypoglycaemic episodes that you were unable to treat yourself (in the last year)
Intercept B1H 0.928 0.553 –0.155 to 2.012
Before/after DAFNE education –1.291 0.142 –1.569 to–1.013
HbA1cB2H –0.113 0.064 –0.259 to–0.006
Total number of episodes of DKA requiring admission (in the last year)
Intercept B1K –8.108 1.097 –10.259 to–5.958 Before/after DAFNE education –0.965 0.351 –1.653 to–0.277
HbA1cB2K 0.617 0.115 0.392 to 0.842
Utilities
The model calculates lifetime QALYs for each patient based on his or her life expectancy and
corresponding annual utility given the health states experienced. Each health state is associated with a disutility value (negative) as reported inTable 33and these decrements are added to the baseline utility to estimate the absolute utility in the given health state. In case of multiple complications, the utility is estimated by aggregating the disutilities of the multiple complications with the baseline utility. As all three source papers170,174,176used to assign the utility decrements are based on statistical regression models that
are essentially assuming an additive model within the regression, it would be incorrect to use another structure for multiple complication utilities. The parameters themselves are estimated in an additive form. The model has the flexibility to use alternative utility values as inputted by the model user.
The base-case utility parameters reported inTable 33are based on four main source papers.170,172,174,176
To decide which estimates to use we considered both the merits of the evidence (size of the sample and appropriateness to a UK type 1 diabetic population) and the utility measure used (favouring the EQ-5D in line with NICE reference case guidance168) and also compared utilities across the studies and between the health
states within a comorbidity, favouring estimates that were similar to those in other studies and did not look to be outliers.
Alvaet al.174estimated the impact on QoL of six diabetes-related complications (MI, ischaemic heart disease,
stroke, heart failure, amputation and visual acuity) using longitudinal data (seven rounds) from EQ-5D questionnaires administered to 3380 individuals with type 2 diabetes in the UKPDS (11,684 questionnaires, a mean of 3.4 questionnaires completed per patient) between 1997 and 2007. This study both provides a very large sample of UK patients who experienced diabetes complications, including a long follow-up period compared with that used in other studies, and uses a fixed-effects longitudinal data analysis model that, as far as possible, estimates the change in EQ-5D utility in individuals who actually experienced changes in health state over time. We used this source whenever possible, including for CVD health states and amputation. Alvaet al.174did not report utilities for several of our health states and other sources were also
needed. We utilised UKPDS cross-sectional analyses from Clarkeet al.172for angina.
Our own analysis of DAFNE utilities data is used as one of the key sources of evidence (seeUtilities analysis: the impact of diabetes-related complications on utility-based measures of quality of life in adults with type 1 diabetesfor details as well as Peasgoodet al.170). The analysis showed broadly similar results to those of
Alvaet al.174when comparisons were able to be made. We used data from Peasgoodet al.170for EQ-5D
utility decrements related to hypoglycaemia, DKA, background retinopathy and proliferative retinopathy. For other health states we used the only other source of type 1 diabetes utility evidence we could find. Coffeyet al.176described the health utilities associated with diabetes and its treatments, complications and
comorbidities based on data collected using the Quality of Well-Being Scale–Self-Administered from 784 individuals with type 1 diabetes registered at the Michigan Diabetes Research and Training Center, USA. This is a US-based study and the measure used is also not a preference-based measure. We used these data for macroalbuminuria and ESRD in type 1 diabetics as well as for the three milder neuropathy health states.
Costs
The model calculates long-term costs by using health state cost values from the literature, as presented in