Some experiences from regional geospatial mapping of cancer cases and incidences Predictions

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1 Some experiences from regional geospatial mapping of cancer cases and incidences Predictions Stefan Peterson (RCC Syd, Region Skåne) Ulf Strömberg (Lund University and FoUU ) Anders Holmén (FoUU ) June 4

2 Content Introduction and aim Methods Results Summary

3 Introduction and aim RCC Syd regional cancer plan Blekinge,, Kronoberg, Skåne Vision: Evidence and knowledge-based, equal, and costeffective care with a focus on the patient Efficient allocation of health resources will require an understanding of the current and the future situation Future cancer burden where and how to prevent

4 Methods Data from the regional Cancer-registret Approx. 25 cases per year Geo-mapping of individuals to regions at time of cancer diagnosis Blekinge,, Kronoberg, without Malmö, Malmö Adjustment for age, period and cohort Age classes by 5 year Periods by 5 year Separate model for men and women the NORDCAN-IARC site (Association of the Nordic Cancer Registries Demographic prognosis from SCB

5 Prediction model(s) 1) Age Period Cohort model with rate-model: (A a + Dp + P p +C c ) 5 = incidence rate in age group a in calendar period p A a = age component for age group a D = average drift parameter (modified if data permits) P p = period p component C c = cohort c component Estimated by Poisson regression (link-function) 2) Prediction by demographic trend only using current incidence (28-212) the Nordpred package Møller B., Fekjær H., Hakulinen T., Sigvaldason H, Storm H. H., Talbäck M. and Haldorsen T. "Prediction of cancer incidence in the Nordic countries: Empirical comparison of different approaches (23) Statistics in medicine, 22:

6 Model contd. Predictions by all types, and seven subtypes + other Normalised by constraining the sum to all types Prediction intervals available by the delta method Internal validation by comparing the actual outcome with a predicted outcome Prediction with data up to 1992 Potential modifiers (disrupters) Screening Exposure habits Smoking UV radiation

7 Population (% of yearly total) utan utan utan utan utan utan utan utan utan utan utan utan utan utan utan utan utan utan Demographic prognosis of age distribution kvinna man

8 Prediction based on data up to 212 Malign Melanoma Red: Women Blue: Men

9 alla typer cancer cases per year Total number of cancer cases kvinna ( ) kvinna (obs ) man ( ) man (obs ) utan

10 lunga cancer incidence per 1^5 Lung cancer incidence kvinna ( ) kvinna (obs ) man ( ) man (obs ) utan

11 melanom cancer incidence per 1^5 Melanoma incidence kvinna ( ) kvinna (obs ) man ( ) man (obs ) utan

12 bröst cancer incidence per 1^5 Breast cancer incidence kvinna ( ) kvinna (obs ) utan

13 prostata cancer incidence per 1^5 Prostate cancer incidence man ( ) man (obs ) utan

14 Incidence of melanoma per 1^5 (ASR) Predictions of future regional variations Inc. 212 Prediction

15 Summary A standard APC-model has been used Limitations due to interventions Breast cancer and prostate cancer Melanoma increase Evaluated by earlier predictions from Future work: incorporate uncertainty contrast with other models (splines Bendix Carstensen) Nobody wants a prediction that the future will be more or less like the present, even if that is, statistically speaking, an excellent prediction.

16 We ll meet again in 2 years to evaluate the predictions!

17 Demographic prognosis Population (1^4) kvinna man

18 hud exklusive malignt melanom cancer cases per year Skin cancer excluding malign melanoma kvinna ( ) kvinna (obs ) man ( ) man (obs ) utan

19 prostata cancer cases per year Prostate cancer man ( ) man (obs ) utan