Literature DB >> 1776052

Predictions of epidemiology and the evaluation of cancer control measures and the setting of policy priorities.

T Hakulinen1, M Hakama.   

Abstract

Cancer incidence predictions may be constructed for administrative and scientific purposes. For administrative purposes it is often important that the predictions come true. The resources planned on the basis of the predictions and allocated on the diagnostics, treatment and rehabilitation can then be optimally utilized. However, predictions that do not materialize can also be useful. The effects of intervention or early detection programmes express themselves as failures of predictions that have been made in the absence of such programmes. Predictions of cancer incidence in Finland are used as examples. The prerequisite for the predictions is a well-functioning population-based cancer registry. The predictions were constructed using time trends and differentials in cancer incidence with or without the aetiological or other risk factors. Short-term, 10-15 year predictions with no explicit use of risk factors, have proven successful with most cancers, e.g. those of the colon, rectum, pancreas and urinary organs, and lymphomas. The marked prediction failures have occurred for cancers of the lung and breast. Predictions for these cancers have been improved by taking aetiological or other risk factors explicitly into account. The cancer consequences of the preventive cardiovascular programme in North Karelia have been evaluated using predictions. The effectiveness of screening for cervical cancer at population level was predicted on the basis of estimated parameters of the natural history of the disease.(ABSTRACT TRUNCATED AT 250 WORDS)

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Year:  1991        PMID: 1776052     DOI: 10.1016/0277-9536(91)90282-h

Source DB:  PubMed          Journal:  Soc Sci Med        ISSN: 0277-9536            Impact factor:   4.634


  3 in total

1.  RiskDiff: a web tool for the analysis of the difference due to risk and demographic factors for incidence or mortality data.

Authors:  Joan Valls; Ramon Clèries; Jordi Gálvez; Victor Moreno; Rosa Gispert; Josep M Borràs; Josepa Ribes
Journal:  BMC Public Health       Date:  2009-12-18       Impact factor: 3.295

2.  Empirical evaluation of prediction intervals for cancer incidence.

Authors:  Bjørn Møller; Harald Weedon-Fekjaer; Tor Haldorsen
Journal:  BMC Med Res Methodol       Date:  2005-06-10       Impact factor: 4.615

3.  REGSTATTOOLS: freeware statistical tools for the analysis of disease population databases used in health and social studies.

Authors:  Laura Esteban; Ramon Clèries; Jordi Gálvez; Laura Pareja; Josep Maria Escribà; Xavier Sanz; Angel Izquierdo; Jaume Galcerán; Josepa Ribes
Journal:  BMC Public Health       Date:  2013-03-07       Impact factor: 3.295

  3 in total

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