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Gillett M, Brennan A, Watson P, et al. The cost-effectiveness of testing strategies for type 2 diabetes: a modelling study. Southampton (UK): NIHR Journals Library; 2015 May. (Health Technology Assessment, No. 19.33.)
The cost-effectiveness of testing strategies for type 2 diabetes: a modelling study.
Show detailsRising prevalence and burden of diabetes
The number of cases of diagnosed diabetes in the UK increased to 3.2 million in 2013, with an estimated 850,000 people having the disease without knowing it, and as many as 7 million more at high risk of developing it. By 2025, if current trends continue, an estimated 5 million people will have diabetes.1 Type 2 diabetes accounts for about 90% of cases of diabetes.2 Of the total cost of diabetes, 75–80% is incurred in the treatment of complications associated with poor preventative care (e.g. poor glycaemic control) and the long duration of diabetes.3 Identification of individuals at high risk of diabetes (HRD) can at least delay the onset of diabetes, and early intervention for diabetes can reduce or at least delay the onset of complications, which already account for around 10% of the total NHS budget.4 This percentage is projected to rise to 17% over the next 20 years4 as a result of increasing rates of obesity and an ageing population.
Scope and context of the evaluation
This evaluation is concerned with which blood test to use when screening in order to identify cases of undiagnosed type 2 diabetes. Implicit in any evaluation of screening for diabetes is the concurrent opportunity to identify individuals at HRD and subsequently manage them to reduce their risk. The two main blood tests concerned are glycated haemoglobin (HbA1c) and fasting plasma glucose (FPG), these being the options recommended by the National Institute for Health and Care Excellence (NICE) for blood testing for diabetes and HRD.5 A HbA1c test does not require an overnight fast and measures the amount of glucose that is being carried by the red blood cells in the body. The result indicates an individual’s average blood glucose levels over the previous 2–3 months. A FPG test directly measures glucose levels and is to be taken after an 8-hour overnight fast. A HbA1c or FPG measurement can be used both for the screening test and, where the first test is in the relevant diabetes range, for the confirmatory diagnostic test. The corresponding HbA1c and FPG definitions of diabetes are sufficient to make a diagnosis, the performance of HbA1c or FPG test no longer being assessed with reference to the oral glucose tolerance test (OGTT) as a gold standard test.
The starting context for the cost-effectiveness analysis is an individual attending an appointment at a general practice (GP) centre in England having been offered a NHS health check. From this point, a blood test may be offered either to all individuals or only to those exceeding a diabetes risk score threshold obtained from risk factor data.
The figure could be extended to include rescreening but we decided not to include this, as we have not modelled this as there was insufficient evidence on some key parameters that would be required. These issues are discussed in detail in Chapter 5, Secondary analyses of epidemiological studies.
There is the possibility that individuals offered a test will not accept the offer or not attend, as shown in Figure 1. NHS Health Checks is the Department of Health’s 5-year programme to reduce the vascular risk, especially for cardiovascular disease (CVD), of individuals between 40 and 74 years of age with elevated risk factors for these conditions. The evaluation, therefore, compares the cost-effectiveness of offering screening for diabetes and HRD using a HbA1c test versus a FPG test at the time of the health check appointment, as an addition to the other standard checks such as cholesterol and blood pressure levels.
The main objectives of the modelling were as follows:
- To assess which test is most cost-effective, following the 2012 NICE guidance on the identification of individuals with HRD as shown in Figure 2. Specifically, the recommended cut-off points for referral for preventative intervention (aimed at supporting diet and lifestyle changes) were adopted, that is 6.0% for HbA1c or 5.5 mmol/l for FPG.
- To determine if the results and conclusions might be different in cohorts other than the Leicester Ethnic Atherosclerosis and Diabetes Risk (LEADER) cohort, in which the relative prevalence rates of diabetes and HRD differ markedly from those in LEADER. We also simultaneously examined the impact of alternative assumptions regarding uptake of HbA1c tests and FPG tests.
- To assess which test is most cost-effective for screening strategies with lower HbA1c and FPG cut-off points than the NICE recommendations. These were included because some studies have suggested that intervention may be cost-effective at lower HbA1c cut-off points.6
- To determine if it is more cost-effective to use a random capillary glucose (RCG) test or the Leicester Practice Database Score (LPDS – see Chapter 2, Prescreening using the LPDS risk score) to prioritise who should receive the blood test (FPG or HbA1c).
Absence of a single gold standard definition of diabetes
There no longer exists a single gold standard definition of diabetes. An individual can receive a diagnosis based on either HbA1c-based criteria or FPG-based criteria [or 2-hour plasma glucose (2hPG) criteria, but the associated test, the OGTT, is outside the scope of this assessment].
The cohorts identified by the alternative tests only partially overlap; this raises some issues to address around prognosis of individuals with differing diagnoses according to the two tests. Evidence on this is lacking, although we can estimate risk of diabetes and CVD through individuals’ risk factors.
It should be noted that diabetes risk assessment should be undertaken in all eligible 40- to 74-year-olds; it is not conditional on having a high CVD risk.
- Background - The cost-effectiveness of testing strategies for type 2 diabetes: a...Background - The cost-effectiveness of testing strategies for type 2 diabetes: a modelling study
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