The model was developed and analysed using TreeAge Pro 2005 (Treeage Software Inc, Williamstown, MA, USA). Disease prevalence and screening efficacy parameters (table 1) were all obtained from a study assessing the efficacy of automated and manual grading on a test set of 14
406 images from 6722 patients.9
The cohort was almost entirely Caucasian, 55% of the patients were male, the median age was 63 (IQR 19) years, 88.5% were over the age of 45, and 52.8% over 65. These demographics are similar to those in the rest of Scotland.
Table 1Key efficacy variables used in the model
Patients undergoing screening fall within one of four disease categories based on their underlying retinopathy status according to the Scottish Diabetic Retinopathy Grading Scheme:10
no retinopathy, mild background retinopathy, observable retinopathy (observable background retinopathy and/or observable maculopathy), or referable retinopathy (referable maculopathy, referable background retinopathy, and/or proliferative retinopathy). Details of the grading scheme and assignment of grades are provided in the paper reporting the efficacy findings on which this analysis is based.9
Patients within each disease category either experience successful image capture or image capture failure leading to ungradeable images (technical failures). The image sets of all patients are first graded by either manual level 1 graders or the new automated system (fig 1). Patients are then either recalled 1 year later, or their images are referred to a level 2 grader based on efficacy data for the alternative level 1 grading approaches (table 1). Following referral to level 2, patients can either be recalled 1 year later, recalled 6 months later, referred for a slit‐lamp examination, or have their images referred to a level 3 grader. The probabilities of each of these four outcomes are reported in table 1, for each underlying category of retinopathy. The detection rates presented reflect the actual decisions made at each level (recall or refer), rather than the retinopathy grades assigned. For the base case analysis, level 3 grading and slit‐lamp grading were assumed to be 100% sensitive and specific.
Costs per patient for grading at each of the three levels were estimated from a survey of the diabetic retinopathy screening programme in Grampian. Table 2 outlines how these unit costs were calculated. In addition, the extra cost of quality assuring the manual grading strategy was estimated at £32
340 per annum (see Appendix for full details). Table 3 presents all the unit costs that were incorporated in the model and the plausible ranges assessed through sensitivity analysis.
Table 2Model grading cost variables, assumptions and values
Table 3Cost parameters and ranges used in the model
A cost per patient for the automated system, was calculated under the assumption that software would run from an existing central server covering all of Scotland. Implementation costs were estimated as the cost of further software development (12 months of a grade 6 computer analyst's time (£31
542)), and the cost of integration with the current system (£60
000) plus ongoing support and maintenance at £12
000 per annum (estimation from Siemens Medical Solutions). The non‐recurrent costs were annuitised over a period of 10 years (the assumed useful lifespan of the software) using a discount rate of 3.5%. The total annual equivalent implementation cost (£23
007) was divided by the number of patients screened annually in Scotland (160
000) to give a cost per patient of £0.14. Given the uncertainty surrounding this cost estimate, the impact of halving and doubling the implementation cost was also assessed. All unit costs were estimated in sterling for the year 2005/2006. All other costs relating to the screening programme (eg, IT equipment, office space and overheads) were assumed not to vary between the two grading strategies.
Cost‐effectiveness was calculated by estimating the grading cost, and number of appropriate outcomes and referable cases detected, for Scotland's diabetic population, assuming 100% coverage and uptake of screening. Appropriate outcomes were defined as final decisions (recalls and referrals) appropriate to actual grade of retinopathy present. The outcomes reported reflect the overall sensitivity and specificity of the three‐level grading system (fig 1). The cost per appropriate outcome and cost per referable case detected were calculated for each strategy, as were the corresponding incremental cost‐effectiveness ratios.
To characterise uncertainty in base case estimates, alternative cost and effectiveness estimates were computed by varying all individual parameters, one at a time, within their 95% confidence intervals or plausible ranges presented in tables 1 and 3. Following this, the impact of changing several key assumptions was assessed. First, changes were made to annual throughput. Secondly, as the automated system would result in more patient referrals to level 2 graders, and thus possibly more patients being recalled at 6 months, the impact of including the cost of extra screening visits was assessed. Finally, the impact of reducing the sensitivity and specificity of level 3 graders was calculated.
In addition, probabilistic sensitivity analysis using Monte Carlo simulation was employed.12
Values were simultaneously selected for each parameter from an assigned distribution and the results recorded. The process was repeated one thousand times to give a distribution of cost and effect differences between the two strategies. Beta distributions were assigned to all the probability parameters as is recommended where the data informing parameters are binomial.12
Gamma distributions were chosen to represent variation in cost variables, as health care costs most frequently follow this type of distribution (ie, skew to the right and constrained to be non‐negative). Finally, due to the greater uncertainty surrounding the cost of automated grading, we halved and doubled this cost and assigned a uniform distribution (table 3).