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Breast cancer incidence using administrative data: correction with sensitivity and specificity.
Chantal Marie Couris1, Stephanie Polazzi, Frederic Olive
1Pole Information Médicale Evaluation Recherche, Hospices Civils de Lyon, Université de Lyon, Santé Individu Société, Lyon, France. ccouris@cihi.ca
Journal of Clinical Epidemiology
|December 17, 2008
Summary
Accurate breast cancer incidence estimation requires correcting inpatient claims data for sensitivity and specificity. This method improves the reliability of cancer registry data, reflecting real-world medical and coding variations.
Area of Science:
- Epidemiology
- Public Health
- Cancer Research
Background:
- Inpatient claims data are a valuable resource for estimating disease incidence.
- However, the accuracy of claims data for identifying incident breast cancer is limited by sensitivity and specificity issues.
- Existing methods may not fully account for these limitations, potentially leading to underestimation.
Purpose of the Study:
- To develop and validate a method for correcting breast cancer incidence estimates derived from inpatient claims data.
- To assess the sensitivity and specificity of algorithms used to identify incident breast cancer in claims data.
- To improve the reliability of population-based breast cancer incidence figures.
Main Methods:
- A two-phase study was conducted using French claims data linked to a cancer registry.
- Phase 1 involved developing two algorithms to identify incident breast cancer cases in claims data.
- Phase 2 estimated the sensitivity and specificity of these algorithms in a population subset and applied corrections.
Main Results:
- Algorithm 1 (principal diagnosis) showed 69.0% sensitivity and 99.89% specificity.
- Algorithm 2 (principal diagnosis + specific surgery) showed 64.4% sensitivity and 99.93% specificity.
- Sensitivity was lower for women under 40 and over 65; specificity remained high across age groups.
Conclusions:
- Reliable breast cancer incidence estimation necessitates accounting for data sensitivity and specificity.
- These parameters reflect variations in medical practice and coding, crucial for accurate epidemiological surveillance.
- The proposed correction method enhances the utility of claims data for public health monitoring.
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