Validation of Canproj for projecting Canadian cancer incidence data

Introduction Cancer projections can provide key information to help prioritize cancer control strategies, allocate resources and evaluate current treatments and interventions. Canproj is a cancer-projection tool that builds on the Nordpred R-package by adding a selection of projection models. The objective of this project was to validate the Canproj R-package for the short-term projection of cancer rates. Methods We used national cancer incidence data from 1986 to 2014 from the National Cancer Incidence Reporting System and Canadian Cancer Registry. Cross-validation was used to estimate the accuracy of the projections generated by Canproj and relative bias (RB) was used as validation measure. The Canproj automatic model selection decision tree was also assessed. Results Five of the six models had mean RB between 5% and 10% and median RB around 5%. For some of the cancer sites that were more difficult to project, a shorter time period improved reliability. The Nordpred model was selected 79% of the time by Canproj automatic model selection although it had the smallest RB only 24% of the time. Conclusion The Canproj package was able to provide projections that closely matched the real data for most cancer sites.

Bivariate spatiotemporal disease mapping of cancer of the breast and cervix uteri among Iranian women

Cervical cancer in women is one of the most common cancers and breast cancer has grown dramatically in recent years. The purpose of this study was to map the incidence of breast and cervix uteri cancer among Iranian women over a 6-year period (2004-2009) searching for trend changes and risk factors. Cancer incidence data were extracted from the annual reports of the National Cancer Registry in Iran. Hierarchical Bayesian models, including random spatial and temporal effects was utilized together with bivariate, spatio-temporal shared component modelling. The provinces Tehran, Isfahan, Mazandaran and Gilan were found to have the highest relative risk (RR) of breast cancer, while the highest RR of cervix uteri cancer was observed in Tehran, Golestan, Khuzestan and Khorasan Razavi. Shared risk factors (smoking component) between the two cancers were seen to have the highest influence in Tehran, Khorasan Razavi, Yazd, Isfahan, Golestan, Khuzestan, Fars and Mazandaran, while the least were observed in Kohgiluyeh Boyerahmad. Apparent differences and distinctions between high-risk and low-risk provinces reveal a pattern of obvious dispersion for these cancers in Iran that should be considered when allocating healthcare resources and services in different areas.

On Reconstitution of Smooth Distributions from Grouped Data

In the paper, proposed is a simple nonparametric method of reconstitution of smooth distributions of additive quantities from grouped data. The method is based on the requirement of minimization of the norm of non-smoothness measure of the solution under the condition of exact equality of the group sums, which reduces the problem to the quadratic programming problem. The method was tested on the age-at-death data; its precision was shown to be comparable to and exceeding the precision of a method of other authors. After testing it on the cancer incidence data, some drawbacks and limitations of the nonparametric approach were determined. The advantages of the proposed method are algorithmic and computational simplicity, good flexibility of the mathematical model.

Public domain small-area cancer incidence data for New York State, 2005-2009

There has long been a demand for cancer incidence data at a fine geographic resolution for use in etiologic hypothesis generation and testing, methodological evaluation and teaching. In this paper we describe a public domain dataset containing data for 23 anatomic sites of cancer diagnosed in New York State, USA between 2005 and 2009 at the census block group level. The dataset includes 524,503 tumours distributed across 13,823 block groups with an average population of about 1400. In addition, the data have been linked with race/ethnicity and with socioeconomic indicators such as income, educational attainment and language proficiency. We demonstrate the application of the dataset by confirming two well-established relationships: that between breast cancer and median household income and that between stomach cancer and Asian race. We foresee that this dataset will serve as the basis for a wide range of spatial analyses and as a benchmark for evaluating spatial methods in the future.

Estimating Cancer Latency Times Using a Weibull Model

Mathematical models can be useful tools in exploring population disease trends over time and can be used to gain insight into the fundamental mechanisms of cancer development. In this paper, we provide a systematic comparison between the exact and the approximate solutions for estimating the length of time between the biological initiation of cancer and diagnosis through the development of a Weibull-like survival model. A total of 1,608,484 malignant primary cancers were used in the analysis using cancer incidence data obtained from the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) program. We find that the approximate solution provides a reliable comparison of the latency periods for different types of cancer and has no significant effect on the estimation accuracy, which differs from the exact solution by 0% to 11.3%. Thirty-five of the 44 cancers in this analysis were found to progress silently for 10 years or longer prior to detection representing 89% of the patients in this analysis. The results of this analysis differentiate cancer types that progress undetected over a period of years to identify new opportunities for early detection which increases the likelihood of successful treatment and alleviates the ever-growing cancer burden.