Cancer Prediction Accounting for Geographic Disparity in Incidence and Survival

Transcription

1 Cancer Prediction Accounting for Geographic Disparity in Incidence and Survival Stefan Peterson (RCC Syd, Region Skåne) Ulf Strömberg (Lund University and FoUU Halland) Anders Holmén (FoUU Halland) May 14

2 Introduction RCC Syd regional cancerplan Blekinge, Halland, 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 influential factors On the residential area level such factors may correlate with e.g. socio-economy To aid decision-making: combining prediction models with geo-maps, visualize correlations and expected prediction effects by interventions potential to enhance the understanding of which interventional variables to prioritize in planning.

3 Methods Data from the regional Cancer-registret Approx cases per year Geo-mapping of individuals to regions at time of cancer diagnosis Blekinge, Halland, Kronoberg and Skåne (with and without Malmö) Ten boroughs of Malmö A region-specific relative risk (RR) was calculated as the observed-to-expected ratio, where the expected number of individuals is the total populations in the regions RRs were estimated using the free software Rapid Inquiry Facility (RIF) RIF:

4 Methods (contd.) The cancer risk maps show the smoothed RRs (SmRR) for each region, obtained by running the Bayesian hierarchical mapping model in RIF/WinBUGS For notably elevated RRs this Bayesian smoothing gives downward adjustment of a RR for regions with few study persons, that is, those estimated with relatively high uncertainty (shrinkage) With smoothed cancer risk geo-maps, rational adjustments of the conventional (region-specific) RRs are taken into account (Beale et al) Adjustment for age (5-year classes) and sex Beale L, Abellan JJ, Hodgson S, Jarup L: Methodologic issues and approaches to spatial epidemiology. Environ Health Perspect 2008, 116:

5 Regional differences Educational levels (>12y): Halland 37% Malmö 39% Income (of avg.): Halland +3% Malmö -18% Immigrant background: Halland 14% Malmö 41%

6 Geographical Lung cancer variation in cancer incidence, the finer area-level Lung cancer Malmö % % 56% % 34% % 28/33% (20/24%) Daily smoking M/F (M/F) % /23% (23/32%) 32% 24% % 17/16% (12/7%) Observed/expected cases < > 1.09 Educational level > 12y (%) 5-6 years difference in mean life expectancy

7 Lung cancer Smoothing by per decretum Observed/expected cases < > 1.09

8 Predicting future cancer incidence Data from the regional Cancer-registret Geo-mapping of individuals to regions at time of cancer diagnosis Blekinge, Halland, Kronoberg, Skane without Malmö, Malmö Adjustment for age, period and cohort Age classes by 5 year Periods by 5 year Separate model for men and women Age Period Cohort model used Used for the NORDCAN-IARC site (Association of the Nordic Cancer Registries)

9 Predicting cancer incidence - model 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 = non-linear component of period p C c = non-linear cohort component Estimated by Poisson regression (link-function) 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 (2003) Statistics in medicine, 22:

10 Prediction based on data up to 2012 Malign Melanoma Halland Red: Women Blue: Men

11 Incidence of melanoma per 10^5 (ASR) Predictions of future regional variations Inc Prediction Halland Malmo Year

12 Summary Geographic differences displayed Direction seems to be diagnosis dependent Average socio-economic factors confirm but need to be further quantified Prediction of future incidence indicate an increasing spatial difference (malign melanoma)