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Development of a Microsimulation Model to Project the Future Prevalence of Childhood Cancer in Ontario, Canada

Author

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  • Alexandra Moskalewicz

    (The Hospital for Sick Children Research Institute, Child Health Evaluative Sciences program, Toronto, ON, Canada
    Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, ON, Canada)

  • Sumit Gupta

    (The Hospital for Sick Children Research Institute, Child Health Evaluative Sciences program, Toronto, ON, Canada
    Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, ON, Canada
    Cancer Research Program, ICES, Toronto, ON, Canada
    Division of Haematology/Oncology, The Hospital for Sick Children, Toronto, ON, Canada)

  • Paul C. Nathan

    (The Hospital for Sick Children Research Institute, Child Health Evaluative Sciences program, Toronto, ON, Canada
    Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, ON, Canada
    Cancer Research Program, ICES, Toronto, ON, Canada
    Division of Haematology/Oncology, The Hospital for Sick Children, Toronto, ON, Canada)

  • Petros Pechlivanoglou

    (The Hospital for Sick Children Research Institute, Child Health Evaluative Sciences program, Toronto, ON, Canada
    Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, ON, Canada
    Cancer Research Program, ICES, Toronto, ON, Canada)

Abstract

Background Estimates of the future prevalence of childhood cancer are informative for health system planning but are underutilized. We describe the development of a pediatric oncology microsimulation model for prevalence (POSIM-Prev) and illustrate its application to produce projections of incidence, survival, and limited-duration prevalence of childhood cancer in Ontario, Canada, until 2040. Methods POSIM-Prev is a population-based, open-cohort, discrete-time microsimulation model. The model population was updated annually from 1970 to 2040 to account for births, deaths, net migration, and incident cases of childhood cancer. Prevalent individuals were followed until death, emigration, or the last year of simulation. Median population-based outcomes with 95% credible intervals (CrIs) were generated using Monte Carlo simulation. The methodology to derive model inputs included generalized additive modeling of cancer incidence, parametric survival modeling, and stochastic population forecasting. Individual-level data from provincial cancer registries for years 1970 to 2019 informed cancer-related model inputs and internal validation. Results The number of children (aged 0–14 y) diagnosed with cancer in Ontario is projected to rise from 414 (95% CrI: 353–486) in 2020 to 561 (95% CrI: 481–653) in 2039. The 5-y overall survival rate for 2030–2034 is estimated to reach 90% (95% CrI: 88%–92%). By 2040, 24,088 (95% CrI: 22,764–25,648) individuals with a history of childhood cancer (diagnosed in Ontario or elsewhere) are projected to reside in the province. The model accurately reproduced historical trends in incidence, survival, and prevalence when validated. Conclusions The rising incidence and prevalence of childhood cancer will create increased demand for both acute cancer care and long-term follow-up services in Ontario. The POSIM-Prev model can be used to support long-range health system planning and future health technology assessments in jurisdictions that have access to similar model inputs. Highlights This article describes the development of a population-based, discrete-time microsimulation model that can simulate incident and prevalent cases of childhood cancer in Ontario, Canada, until 2040. Use of an open cohort framework allowed for estimation of the potential impact of net migration on childhood cancer prevalence. In addition to supporting long-term health system planning, this model can be used in future health technology assessments, by providing a demographic profile of incident and prevalent cases for model conceptualization and budget impact purposes. This modeling framework is adaptable to other jurisdictions and disease areas where individual-level data for incidence and survival are available.

Suggested Citation

  • Alexandra Moskalewicz & Sumit Gupta & Paul C. Nathan & Petros Pechlivanoglou, 2025. "Development of a Microsimulation Model to Project the Future Prevalence of Childhood Cancer in Ontario, Canada," Medical Decision Making, , vol. 45(3), pages 245-256, April.
    • Handle: RePEc:sae:medema:v:45:y:2025:i:3:p:245-256
    DOI: 10.1177/0272989X251314031
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    1. Eline M. Krijkamp & Fernando Alarid-Escudero & Eva A. Enns & Hawre J. Jalal & M. G. Myriam Hunink & Petros Pechlivanoglou, 2018. "Microsimulation Modeling for Health Decision Sciences Using R: A Tutorial," Medical Decision Making, , vol. 38(3), pages 400-422, April.
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