A new method of estimating prevalence of childhood cancer survivors (POCCS): example of the 20-year prevalence in The Netherlands

International journal of epidemiology(2023)

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摘要
Background Estimating the number of childhood cancer survivors is crucial for cancer control, including clinical guidelines. To compare estimates across countries despite data sharing restrictions, we propose a new method of computing limited-duration prevalence of childhood cancer survivors (POCCS) using aggregated data.Methods We developed a Markov model that simulates, for each calendar year and birth cohort in a population, the proportion of individuals in the following health states: healthy, newly diagnosed with cancer, surviving with cancer, and deceased. Transitions between health states were informed using annual sex- and age-specific incidence rates, conditional 1-year net survival probabilities from the Netherlands Cancer Registry (1989-2011), and annual mortality probability by sex and age group for The Netherlands from the Human Mortality Database. Applying a Markov model, we computed 20-year prevalence of childhood cancer survivors. The resulting POCCS estimates, stratified by sex, were compared with SEER*Stat estimates derived from individual cancer records from the same registry.Results In 2011, POCCS predicted 654 males [95% confidence interval (95% CI): 637-672] and 539 females (95% CI: 523-555) per million persons living in The Netherlands after childhood cancer diagnosed within the previous 20 years. Using SEER*Stat, the 20-year prevalence was 665 males (95% CI: 647-683) and 544 females (95% CI: 529-560) per million persons on 1 July 2011.Conclusions Using the POCCS model and aggregated cancer data, our estimates of childhood cancer survivors limited-duration prevalence were consistent with those computed by a standard method requiring individual cancer records. The POCCS method provides relevant information for planning follow-up and care for childhood cancer survivors.
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关键词
Childhood cancer survivors,prevalence,Markov models
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