External validation

The prediction of life expectancy (LE) for 65 year old women without screening was 19.01 years and 15.74 years for women aged 69 years (average: 17.38 years). Our model prediction was similar to the World Health Organization (WHO) audit whereas the mean LE for Chinese women aged 65-69 years is 17.15 years [53]. Osteoporosis prevalence rates for ages 60-69, 70- 79 and 80+ years and older were predicted at 14.4%, 26.3% and 39.9% respectively, closely matching the respective prevalence rate from the literature of 14.2%, 26.8% and 39.2% [28]. Our model predicted the lifetime osteoporotic hip, clinical vertebral and wrist fracture risks for Chinese women aged 50 years to be 7.9% (95% CI: 7.2%, 8.6%), 29.8% (95% CI: 27.8%, 31.9%) and 18.7% (95% CI: 17.2%, 20.1%) respectively. The lifetime risk of all osteoporosis- related fractures for Chinese women aged 50 years was predicted to be 56.3% (95% CI: 52.1%, 60.6%). The predictions were comparable to the corresponding values from a Korean study, where residual lifetime risk for hip, distal radius and all osteoporotic fractures were reported as 12.3%, 21.7% and 59.5% respectively [52]. These predictions were consistent with the results of a study on fracture risk across different ethnicities, in which the hip fracture rate for Asian women was estimated to be less than half that of Caucasians, but the vertebral fracture rate was higher in Asians than Caucasians [31]. The residual lifetime hip, clinical vertebral and any common osteoporotic fracture risks in a Caucasian population at age 50 years were estimated at 23%, 15% and 46% respectively [50]. By setting the simulation initiation age at 65 years, the 10-year risk of any major osteoporotic fractures (i.e. hip, clinical vertebral and wrist fractures) was predicted to be 13.7% (95% CI: 12.5%, 15.2%), which is comparable to the value of 17% observed in a Hong Kong population aged 65 years and older with a total hip BMD T-score ≤-2.5 [51]. The regression line slope was 0.952, which was close to 1.00, and the R2 was 0.994. The collective results for the external validation are shown in Appendix 4B.2.

Figure 4.3 Cost-effectiveness acceptability curves of screening initiated from age 65 years at different levels of willingness-to-pay (WTP) per quality-adjusted life year (QALY) gained versus no screening. Given the WTP threshold of $20,000 per QALY gained, the probability of screening being cost- effective versus no screening is 99% if screening is initiated at age 65 years.

4.6 Discussion

The application of health technology assessment has increased remarkably over the past decades and is expected to grow in the future [10, 11]. A successful health analytic model should be acceptable to healthcare providers, health policy decision makers and healthcare payers. Moreover, it should be transparent and validated against real-life data [47]. Our osteoporosis model was constructed using updated epidemiological and economic data. The model structure and functionality has been documented, and the validation analyses revealed the accuracy of reproduction of input data. Moreover, our model was constructed independently, without bias to any specific medication, intervention, or funding body, avoiding the implicit inherent bias potentially associated with external funding. The flexibility and adaptability of the model was considered throughout the model construction process. The model can be used for evaluating the costs, effectiveness and cost-effectiveness of screening for, prevention and medical treatment of osteoporotic fractures. It was designed not only for economic evaluations for primary fracture prevention (i.e. prevention of a first osteoporotic fracture) but can also be adapted for secondary fracture prevention (i.e. preventions targeted at the population who have already sustained a fracture). Further, it was not restricted to a specific

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 10,000 20,000 30,000 40,000 50,000 P ro ba bil it y o f being co st ef fec tiv e

Willingness-to-pay per QALY gained (in 2013 US dollars)

No screening

country or ethnic population, but can be applied to different populations and countries with population- and country-specific data. The model was constructed in TreeAge software which is widely used in cost-effectiveness modelling studies. In the future, a user-friendly web-based interface will make the model publically available. Finally, the R2 values are all close to 1 for the internal and external validations, which indicates our model has good internal and external validity: the R2 values are even greater than some validated cost-effectiveness models that have been extensively used to assist the submissions of new pharmaceutical products for reimbursement around the world such as CORE diabetes model (R2_{=0.9574 for the internal}

validation and R2=0.9023 for the external validation) [47] and the Archimedes diabetes model (R2_{=0.99 for the external validation) [48].}

Medication adherence and persistence have consistently been found to impact on medication effectiveness and healthcare costs, as well as cost-effectiveness of health interventions [6, 25, 55, 56]. In our study, we confirmed the previous findings in terms of the substantial impact on cost-effectiveness of osteoporosis screening (Table 4.3). However, the change of effectiveness in screening strategy was relatively small in one-way sensitivity analyses. The reasons are two- fold: first, this study incorporated screening of the whole population of both osteoporotic and non-osteoporotic people, therefore the changes in medication adherence impacted only to a minor degree on effectiveness, as most of the simulated individuals were non-osteoporotic at baseline. This differs from the findings reported in a cost-effectiveness study of fracture preventions where all simulated patients were osteoporotic [56]. In that Belgian study, medication non-adherence decreased the effectiveness (expressed in QALY gained) by 59% comparing with the full adherence scenario [56]. In another study of the cost-effectiveness of screening a Belgian population, effectiveness of the screening strategy with real-world medication adherence only decreased 0.34% from that with full adherence (12.95 QALY gained for real-world adherence and 13.00 QALY gained for full adherence compared with no screening) [6]. Second, impact of poor medication adherence and persistence was offset by residual medication effects after treatment discontinuation. Assuming no offset time effects, the effectiveness of screening decreased by 0.014 QALY compared to the base-case analysis (Table 4.3). These results further demonstrate the importance of accounting for medication adherence and persistence in health economic evaluations of osteoporosis management strategies.

treatment of osteoporosis [7-9, 13, 49, 57-59]. Other than Mueller’s model, all models were patient-level (microsimulation) based. Most of models were of good quality and satisfied the recommendations from a recent systematic review with regard to the progression of osteoporosis models [10]. Nevertheless, limitations existed in these models. For example, rescreening intervals for patients diagnosed as non-osteoporotic in the last screening were not considered in Mueller’s model [8, 58]; medication adherence and offset time effects were not considered in the model from Nshimyumukiza [59]; medication persistence was not included in Kingkaew’s model [13]. Medication persistence, adherence and offset time are recommended to be incorporated in osteoporosis models due to the demonstrated impact of these parameters on the cost-effectiveness of osteoporosis interventions [6, 10]. In the model from Hiligsmann [6], medication persistence and adherence was considered in the “screening and treating” arm but not in the “no intervention” arm.

There are a number of major strengths to our modelling approach. First, Bayes’ revision was adopted in our model structure, which calculated the posterior probabilities using prior and likelihood probabilities. Bayes’ revision has not been adopted in previous screening models for osteoporosis but is recommended [6, 7, 9, 13, 60]. Second, the model used patient-level (microsimulation) techniques rather than cohort analysis. Microsimulation models using a lifetime horizon account for the evolution of patient characteristics over time, such as fracture history, time since treatment initiation and time since fracture, and are therefore preferred by healthcare decision makers [61]. Third, medication adherence, persistence and offset time effect were all thoroughly accounted for in the model.

There are potential limitations to our model. First, adverse events (i.e. events from the medication side effects) were not recorded. Side effects of alendronate such as gastrointestinal adverse events and osteonecrosis of the jaw were not included because the health-state utility impact of adverse events is unclear, and skeletal side effects occur rarely at the doses used in osteoporosis treatment [62-64]. However, medication adverse events should ideally be considered in health economic evaluation on medications when good epidemiological data are available on such adverse events. Second, our model assumed a fixed rescreening interval for those not diagnosed with osteoporosis. Several organizations provided different recommendations that varied from at least six months to 5 years [20, 65, 66]. Recent studies suggest that the interval of repeat BMD testing should be determined by age, the patient’s clinical risk factors as well as baseline bone density [67-69]. With limited prospective data,

frequency of bone densitometry is controversial. Our study used a 5-year rescreening interval in the base-case analysis and was varied in sensitivity analysis, we expect to adopt a flexible rescreening interval in the future when country-specific clinical trial data are available. Third, patients in the “no screening” arm who sustained an osteoporotic fracture ideally should be prescribed medication to prevent subsequent fractures. However, the current pattern of secondary fracture prevention is unknown in China, therefore we only assumed inpatient costs one year after fracture in the “no screening” arm. Finally, because of the paucity of data on “other fractures” in Chinese studies, we were unable to validate our “other fracture” rates generated by the model against published Chinese data. In addition, some of the data we used in this study were from Caucasian populations, such as the probability of residing in a nursing home after a hip fracture, and treatment efficacy. Future external validation and updates of the model are expected when new evidence becomes available.

Osteoporosis models have been used extensively in cost-effectiveness studies on osteoporosis since the first model was published in 1980 [70]. Good cost-effectiveness models are expected to be consistent with a coherent theory of the objective health condition [71], so that the structure should represent all important disease outcomes and the transition parameters should be consistent with the most convincing evidence from clinical trials or meta-analyses. Evolution in osteoporosis model structures over time is possibly a reflection of the adoption and implementation of good practice in modelling studies. Microsimulation models are preferable over cohort based models, which lack comprehensive memory integrity, but they require more sophisticated data from clinical trials. Many of the previous models estimated the specificity and sensitivity of the screening approach by relying on bone mineral densitometry without incorporating other clinical risk factors such as history of fracture, glucocorticoids use and smoking. Clinical risk factors were found to contribute substantially to the risk of fracture and were included in many fracture risk models such as FRAX [72]. FRAX not only incorporated BMD at the femoral neck but other clinical risk factors as an indicator of medication intervention threshold. However, the recommended intervention threshold is not applicable to the Chinese population [73, 74]. Our model is able to include these assessment tools and we would like to define the threshold for the Chinese population from a health economic perspective when relevant epidemiological data are available. With multiple osteoporosis models developed around the world, and the ongoing evolution of modelling techniques, osteoporosis health economics computer modellers should be encouraged to meet regularly to compare their models against each other and against data from clinical trials and

other high quality studies and to optimize the future development of modelling techniques. This approach has been successful in cross-validation and improvement of models in other disease areas, e.g.: the Mount Hood Challenge series in diabetes modelling to compare model projections with the best available clinical and epidemiological outcomes and to discuss avenues of research to improve future models [75-77]. It is recommended that a similar series of meetings in the field of osteoporosis health economics modelling should be established. A new cost-effectiveness state-transition microsimulation model of screening for and treatment of osteoporosis was constructed that implements a unique combination of modern-day modelling techniques. It is a flexible model with good internal and external validity that closely reproduces clinical input data and epidemiological studies. This new model provides an important tool for researchers and policy makers to test the cost-effectiveness of osteoporosis screening and treatment strategies. Nevertheless, further external validation and updates of the model will constantly be needed as new evidence and more advanced modelling techniques become available.

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