Modelling the Economic Impact of Next Generation Sequencing and Precision Medicine on Childhood Cancer Management—a Microsimulation Approach

2.1. Base population

The model is developed with inputs from multiple datasets (Figure 1). The base population in the model is sourced from the New South Wales (NSW) Central Cancer Registry which contains records of people who have had cancer in NSW (Cancer Institute, 2018). The study cohort comprised children aged <18 years, who were registered in the NSW Central Cancer Registry as cancer patients with a date of diagnosis between 1 July 2001 and 31 December 2012. Characteristics of the base population are presented in Table 1.

2.2. PeCanMOD structure

Globally, there were multiple large-scale precision medicine clinical trials designed for treating high-risk paediatric cancer (Chang et al., 2016; Harttrampf et al., 2017; Khater et al., 2019; Mody et al., 2015; Wong et al., 2020; Worst et al., 2016). Patients who had previous treatment failure, experiencing cancer relapsed, or were diagnosed with high-risk cancer (less than 30% 5-year survival rate) were the major participants in these precision medicine studies.

To reflect the current practice, the model assumed that individuals who were eventually decreased due to their illness would be simulated to have been eligible for precision medicines in our simulation prior to their death. These individuals were assumed to be high risk patients, who were unlikely to be cured with the current treatment regime. We identified these individuals from the NSW Central Cancer Registry by linking the dataset to other administrative datasets. Each individual in the NSW Central Cancer Registry dataset was assigned a unique identifier by the Centre for Health Record Linkage (Centre for Health Record Linkage, 2018), and data for these individuals were extracted from the other administrative datasets such as the NSW Registry of Births, Deaths, Marriages, and NSW Cause of Death Unit Record File. Records were then linked based on the unique identifier for each patient. The NSW Registry of Births, Deaths, and Marriages death registrations and the NSW Cause of Death Unit Record File records date of death, and cause of death (Figure 1).

2.3. Imputation of genomic variants

Understanding the prevalence of genomic variants responsible for cancer development is critical to estimate the effectiveness of a precision medicine programme (Subbiah et al., 2018). We imputed genomic variants responsible for cancers using published data from the Foundation Medicine Pediatric Portal (Chmielecki et al., 2016; Chmielecki et al., 2017). The dataset consists of the molecular profiles of over 1200 paediatric tumours sequenced by the Foundation Medicine. We estimated the prevalence of genomic variants in each cancer type from this dataset. Imputation of having specific genomic variant was carried out based on the distribution of genomic variants in each cancer type (55 categories) and allocated using Monte Carlo simulation method to the matched cancer type. We have considered other data sources (Gröbner et al., 2018; Ma et al., 2018; Rusch et al., 2018), but none were as comprehensive as the Foundation Medicine dataset in terms of cancer types (Table 2).

2.4. Simulation

To model genomic variants, we assigned a random value between 0 to 1 drawn from a uniform distribution to individuals in the cancer registry. Controlling for cancer types, if the value falls between the upper and lower bounds for a gene, then the individual would be assigned the associated gene in this simulation. The simulation process was repeated 1,000 times.

The treatment protocol was mostly based on one of the largest paediatric cancer precision medicine trials, NCI-COG Pediatric MATCH (MATCH) (Table 3) (Allen et al., 2017). Due to limited evidence of the effectiveness of precision medicine as well as limited understanding of the distribution of actionable variants within the patient population, we have made several assumptions in our current model, and the model will be updated as results from the precision medicine trials become available. Treatment response rate and survival duration used in this model were sourced from clinical trials results on adult cancers (Tables 4 and 5) as there were no reported outcomes of these medications for childhood cancer cohort. It is possible that children’s response to these medicines may be different to their adult counterpart’s (Joseph et al., 2015). A one-way sensitivity analysis on response rate and duration of response will be conducted to estimate resulting cost and effectiveness in best- and worst-case scenarios (range of input parameters are described in Tables 4–6). Method for sensitivity analysis is described in 2.10.

The model simulated the number of individuals eligible for precision medicine, and the cost of treatment per life year gained if precision medicine was introduced to late stage cancer patients as final treatment options (Figure 2). The probability of responding to precision medicine for each genomic variant and duration of response were estimated based on published literature or reports from the U.S. Food and Drug Administration (Table 4). They were used to estimate the incremental life years that would have been gained for each individual in our base population if they had had one of the 10 targeted therapies from the MATCH trial for their specific simulated genomic variant, with the assumption that the patients died if they failed to respond, and that if they did respond, the patients would only survive as long as the duration of response (Table 5).

2.5. Cost of Next-Generation Sequencing (NGS)

The model assumes that each patient receives Whole-Genome Sequencing (WGS) at a cost of AU$4,926 per cancer patient (range: AU$2,991-AU$45,333) (reported costs were inflated to 2019 values by the consumer price index in origin country, and converted to Australian dollars using Purchasing Power Parities) (Table 6) (Gordon et al., 2020; Schwarze et al., 2020; Schwarze et al., 2018; Weymann et al., 2017) . Note that the cost of WGS included all steps in the sequencing pathway, including the costs of bioinformatic analysis and returning results. We also model the cost and effectiveness if each patient receives targeted multi-gene panel sequencing versus WGS. The cost of targeted multi-gene panel sequencing is assumed to be AU$1,433 (range: AU$437-AU$10,178) per sample (Gordon et al., 2020; Hamblin et al., 2017; van Amerongen et al., 2016; Yu et al., 2018).

2.6. Cost of drugs and managing toxicity

For the base case, the cost of hospital admission for precision medicine was assumed to be the same as the cost of hospital admission for chemotherapy less the direct cost relating to pharmacy, and was estimated based on Australian Refined Diagnosis Related Groups version 8.0 (AR-DRGs). AR-DRGs is a classification system to classify patient hospital admissions by connecting the number and type of patients treated in a hospital (known as hospital casemix) to the resources required by the hospital. For drugs that do not require inpatient care, we will refer to outpatient service cost for chemotherapy administration. Costs of managing toxicity or adverse events from treatment were assumed to be AU$5,890 per month per person (based on our (unpublished) analysis of the NSW Admitted Patient Data Collection linked to the NSW Central Cancer Registry) (Table 6).

The costs of drugs in the model are based on published costs from the Pharmaceutical Benefits Scheme (Pharmaceutical BenefitsScheme, 2019a; Pharmaceutical BenefitsScheme, 2019b; Pharmaceutical BenefitsScheme, 2019c), or online materials (Herper, 2018; Pagliarulo, 2019), or imputed as the mean of available costs (e.g. Tazemetostat, Samotolisib, Selumetinib Sulfate, Ensartinib, and Ulixertinib) (Table 6).

All costs were presented in 2019 Australian dollars. For costs not originally reported in Australian dollars or in 2019 cost base, we inflated the reported costs to 2019 by the consumer price index in origin country and converted to Australian dollars using Purchasing Power Parities.

2.7. Health utility

Health Related Quality of Life (HRQoL) measures the impact of health states on patient’s quality of life. In the context of cost-utility analysis, HRQoL is summarised into utility values ranging between 0 (death) and 1 (perfect health). Utility measurements allow for comparison of health outcomes across diseases as well as comparison between various health care interventions. In PeCanMOD, we impute utility based on cancer type, treatment phase, health outcome, age, and gender from published literature. A review by Tarride et al. (2010) has summarised the health utilities measured for Acute Lymphoblastic Leukaemia patients during treatment (range: 0.81-0.91), and survivors of various cancers . Yeh et al. (2016) measured and reported that health utility among childhood cancer survivors is significantly poorer than health utility for the general population. In addition, a systematic review and meta-analysis of child health utilities by Kwon et al. (2018) reported utilities for a wide range of health conditions, including cancers.

2.8. Disability adjusted life years (DALYs)

Health utility data is scarce for most childhood cancers, especially during the treatment phase. To model health outcomes we used Disability-Adjusted Life Year (DALY). DALYs is a standard metric used to describe burden of disease. This value is calculated using the Years of Life Lost (YLL) and the Years Lived With Disability (YLD). The Global Burden of Disease Study (Global Burden of Disease Collaborative Network, 2018) reported the DALY burden due to childhood cancers (GBD 2017 Childhood Cancer Collaborators, 2019). We attributed DALYs based on patients’ response to precision medicine during the microsimulation. The model will first determine YLD with estimated duration of response to precision medicine and corresponding disability weight for treatment phase and cancer types. As we assumed that once patient will only survive as long as the duration of response, we will determine YLL based on the life expectancy at the counterfactual age at death.

2.9. Budget impact analysis

The model assigns a multiplier to individuals to reflect the number of childhood cancer patients within the Australian population. The AIHW published childhood cancer incidence between 1982-2015 (Australian Institute of Health and Welfare, 2019a ), and the multipliers are assigned to the total costs incurred by each individual in the NSW Central Cancer Registry, by age group at diagnosis, cancer types, sex, and year of diagnosis.

2.10. Sensitivity and uncertainty analysis

We perform one-way sensitivity analysis to determine the parameters that have the biggest influence on the model outcome. This is achieved by changing one parameter at a time while keeping other parameters constant. The parameters of interest are varied between plausible extremes (input values range are described in Tables 4–6). Model outcome (incremental costs per life year gained) for each scenario is then compared against base case to identify the parameters that significantly affect model outcome.

We also conduct probabilistic sensitivity analysis to explore the robustness of model results on all model parameters such as costs of drugs and response rate to precision medicine using Monte Carlo simulation. We assumed lognormal distributions for cost of drugs and sequencing, binomial distributions for the response rates to drugs, and Weibull distributions for the duration of response.

2.11. Weighting

The PeCanMOD assigns weights to individuals to reflect an estimate of childhood cancers within the Australian population. The AIHW published national annual cancer incidence by cancer types and sex via its Australian Cancer Database (Australian Institute of Health and Welfare, 2019a ). Person weight was assigned to each individual in the NSW Central Cancer Registry, controlling for year of diagnosis, sex and cancer types.

2.12. Validation

We carried out internal validation including debugging via code walk through to ensure that the model does not have obvious construction and syntax errors. We also compared model output against external data where available. Model input and output are assessed by paediatric medical oncologist (authors TOB, and TT) for face validity.

To validate whether the imputed genomic variants distribution is comparable with the Foundation Medicine Pediatric Portal data, we performed Spearman’s rank correlation coefficient analysis and demonstrated that the distribution of genomic variants was significantly correlated between the reference population and NSW Central Cancer Registry simulation output (ρ=0.73, p<0.01) (Figure 3).

2.13. Ethics approval and consent to participate

Human Research Ethics approval has been obtained from the NSW Population & Health Services Research Ethics Committee (HREC/17/CIPHS/7). We have sought permission to waive consent from NSW Ministry of Health under the Health Records and Information Privacy Act 2002 (NSW).