Estimation and projections of cancer prevalence from cancer registry data

Abstract

A method, PIAMOD (Prevalence, Incidence, Analysis MODel), which allows the estimation and projection of cancer prevalence patterns by using cancer registry incidence and survival data is presented. As a first step the method involves the fit of incidence data by an age, period and cohort model to derive incidence projections. Prevalence is then estimated from modelled incidence and survival estimates. Cancer mortality is derived as a third step from modelled incidence, prevalence and survival. An application to female breast cancer is given for the Connecticut State by using data from the Connecticut Tumor Registry (CTR), 1973-1993. The age, period and cohort model fitted incidence quite well and allowed us to derive long-term projections up to 2030. Patients' survival was also projected to future years according to a scenario approach based on two extreme hypotheses: steady, that is, no more improvements after 1993 (conservative), and continuously improving at the same rate as during the observation period. Age-standardized estimated incidence shows a changing trend around the year 2005, when it starts decreasing. Age-standardized prevalence is expected to increase and change trend at a later date. Breast cancer mortality is projected as decreasing, as the combined result of no further increase in incidence and improving cancer patients' survival. An easy-to-use PIAMOD software package, on which work is in progress, will be made available to individual cancer registries and/or health planning institutions or authorities once it is developed. The use of the PIAMOD method for cancer registries will allow them to provide results of paramount importance for the whole community involved in the assessment of future disease burden scenarios in an evolving society.

Trends in Radiation Therapy among Cancer Survivors in the United States, 2000–2030

“…We used the Prevalence Incidence Approach Model (PIAMOD) to estimate prevalence using trends in cancer incidence, cancer survival, and all-cause mortality (3,5). The PIAMOD approach uses incidence and relative cancer survival rates obtained from registry data to create projected estimates of incidence and survival beyond the range of known years.…”

“…Observed survival was determined from the SEER-9 data, and expected survival was calculated from all-cause mortality data from the Social Security Administration. To allow for survival projections after 2013, we fit a mixture survival model to the existing data (5). The mixture survival model is a parametric model that assumes a fraction of the cancer cohort will be "cured" of cancer and experience the same risk of death as the non-cancer population.…”

Projecting Cancer Incidence using Age-period-cohort Models Incorporating Restricted Cubic Splines

“…It is often proposed that assuming that the rates will stay the same as the last observation point is a suitable lower/upper bound for the projections (Verdecchia, Angelis, and Capocaccia (2002), Heinävaara and Hakulinen (2006)). Of course, calculations can be made for the uncertainty for the parameters in a given model, and prediction intervals can be put on the estimated rates (Elkum (2005), Møller, Weedon-Fekjaer, and Haldorsen (2005)).…”

Prognostic value of health‐related quality of life for death risk stratification in patients with unresectable glioblastoma

“…The incidence in Europe and North America is similar, at 2–3 per 100,000 adults per year 2. A minority of GBs are unresectable (UGB: RPA class V) 3.…”

“…This disease is frequently revealed by a neurological deficit, whereas health status at diagnosis is mostly preserved 1. Nevertheless, the survival prognosis of patients with UGB remains extremely poor 1, 2.…”