Am J Epidemiol. 2021 Dec 7:kwab285. doi: 10.1093/aje/kwab285. Online ahead of print.
ABSTRACT
There are unique challenges to identifying causes and developing strategies for prevention of rare cancers, driven by the difficulty in estimating incidence, prevalence, and survival due to their small numbers. Using a Poisson modeling approach, Salmerón et al. (Am J Epidemiol. 2021) built upon their previous work to estimate incidence rates of rare cancers in Europe using a Bayesian framework, establishing a uniform prior for a measure of variability for country-specific incidence rates. They offer a methodology with potential transferability to other settings with similar cancer surveillance infrastructure. However, the approach does not consider the spatio-temporal correlation of rare cancer case counts and other, potentially more appropriate, non-normal probability distributions. In this commentary, we discuss the implications of future work from cancer and spatial epidemiology perspectives. We describe the possibility of developing prediction models tailored to each type of rare cancer; incorporating the spatial heterogeneity in at-risk populations, surveillance coverage, and risk factors in these predictions; and considering a modeling framework to address the inherent spatio-temporal components of these data. We note that extension of this methodology to estimate sub-country rates at provincial, state, or smaller levels of geography would be useful but pose additional statistical challenges.
PMID:34875003 | DOI:10.1093/aje/kwab285
