Cancer Prevalence Statistics:  Approaches to Estimation

Counting Method is used to estimate prevalence based on tumor registry data. Cases still alive on the desired prevalence date are simply counted, while adjustments must be made to estimate the proportion of cases lost to follow-up who would have made it to the prevalence date. The expected number of cases lost to follow-up who make it to the prevalence date is computed using conditional survival curves for specified cohorts. For example, for 20-year SEER prevalence estimates, computations are done using 8 survival cohorts (4 calendar year groupings by 2 age groupings). Because people can be diagnosed with more than one tumor, there are different methods that can be used to determine which tumors to include in the counting method (See Feldman et al., 1986; Gail et al., 1999).

Standard Error for the Counting Method is based on the Poisson method. (See Clegg et al., 2002)

Cross-Sectional Population-Based Surveys can be used to estimate prevalence using self reporting; however, one must be concerned with underreporting and misclassification of disease. (See Byrne et al., 1987).

Completeness Index is a statistical model which estimates complete prevalence from limited-duration prevalence. (See Capocaccia & De Angelis, 1997; Merrill et al., 2000).

Transition Rate Method estimates prevalence using a three-state stochastic process:  1) alive and cancer free, 2) alive with cancer, 3) dead. After estimating the transition rates between these states, this stochastic model then is allowed to run to simulate cancer prevalence under a set of specified conditions (e.g. constant transition rates over time). (See Gail et al., 1999).

Back Calculation Methods have been widely applied to estimate the incidence and prevalence of HIV infection from the reported incidence of AIDS and information about the duration of HIV infection prior to the appearance of AIDS. Back-calculation methods similar to those used for AIDS can be applied to calculate the incidence and prevalence of cancer from mortality and survival. Since mortality data are available for the entire nation, and survival for known areas can be extrapolated to other areas, these methods can be used to calculate regional and national estimates of incidence and prevalence. (See Verdecchia et al., 1989; De Angelis et al., 1994; Verdecchia et al., 2002).

Estimation of Childhood Cancer Prevalence
Childhood cancer prevalence is the prevalence of people who were diagnosed with cancer between ages 0 and 19 or ages 0 and 14. Since people diagnosed with childhood cancer can live for a very long time limited-duration prevalence of childhood cancer can be zero by definition for some age groups. For example, when we consider childhood cancers (0 - 19 years), the 20-year prevalence includes only cases who are 39 years or younger at the prevalence date. Survivors older than 39 years at the prevalence date would have been diagnosed prior to the 20 years of incidence data used to estimate prevalence. Thus the 20-year prevalence is zero by definition for age groups 40-44, 45-49,... Research is underway to adapt the complete index methodology in this particular situation. Other approaches, such as the back-calculation or the transition rates methods, can be used to estimate complete prevalence of childhood cancers.