Cancer incidence estimation at a district level without a national registry: A validation study for 24 cancer sites using French health insurance and registry data

  • Uhry Z
  • Remontet L
  • Colonna M
 et al. 
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Abstract

Background: District-level cancer incidence estimation is an important issue in countries without a national cancer registry. This study aims to both evaluate the validity of district-level estimations in France for 24 cancer sites, using health insurance data (ALD demands - Affection de Longue Durée) and to provide estimations when considered valid. Incidence is estimated at a district-level by applying the ratio between the number of first ALD demands and incident cases (ALD/I ratio), observed in those districts with cancer registries, to the number of first ALD demands available in all districts. These district-level estimations are valid if the ratio does not vary greatly across the districts or if variations remain moderate compared with variations in incidence rates. Methods: Validation was performed in the districts covered by cancer registries over the period 2000-2005. The district variability of the ALD/I ratio was studied, adjusted for age (mixed-effects Poisson model), and compared with the district variability in incidence rate. The epidemiological context is also considered in addition to statistical analyses. Results: District-level estimation using the ALD/I ratio was considered valid for eight cancer sites out of the 24 studied (lip-oral cavity-pharynx, oesophagus, stomach, colon-rectum, lung, breast, ovary and testis) and incidence maps were provided for these cancer sites. Conclusion: Estimating cancer incidence at a sub-national level remains a difficult task without a national registry and there are few studies on this topic. Our validation approach may be applied in other countries, using health insurance or hospital discharge data as correlate of incidence. © 2012 Elsevier Ltd.

Author-supplied keywords

  • Cancer
  • Health insurance data
  • Incidence
  • Registries
  • Statistical modelling

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Authors

  • Zoé Uhry

  • Laurent Remontet

  • Marc Colonna

  • Aurélien Belot

  • Pascale Grosclaude

  • Nicolas Mitton

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