Is it possible to estimate the incidence of breast cancer from medico-administrative databases?

One approach to estimate cancer incidence in the French Départements is to quantify the relationship between data in cancer registries and data obtained from the PMSI (Programme de Médicalisation des Systèmes d'Information Médicale). This relationship may then be used in Départements without registries to infer the incidence from local PMSI data. We present here some methodological solutions to apply this approach. Data on invasive breast cancer for 2002 were obtained from 12 Départemental registries. The number of hospital stays was obtained from the National PMSI using two different algorithms based on the main diagnosis only (Algorithm 1) or on that diagnosis associated to a mention of "resection" (Algorithm 2). Considering registry data as gold standard, a calibration approach was used to model the ratio of the number of hospital stays to the number of incident cases. In Départements with registries, validation of the predictions was done through cross-validation. In Départements without registries, validation was done through a study of homogeneity of the mean number of hospital stays per patient. Cross-validation showed that the estimates predicted by the model were true with data extracted by Algorithm 1 but not by Algorithm 2. However, with Algorithm 1, there was an important heterogeneity between French Départements as to the mean number of hospital stays per patient, which had an important impact on the estimations. In the near future, the method will allow using medico-administrative data (after calibration with registry data) to estimate Départemental incidence of selected cancers.

[Role of hospital claims databases from care units for estimating thyroid cancer incidence in the Rhône-Alpes region of France]. / Place du Programme de médicalisation des systèmes d'information (PMSI) dans l'estimation de l'incidence du cancer de la thyroïde dans la région Rhône-Alpes.

BACKGROUND: Incidence measures are essentially based on the data collected by cancer registries. Hospital claims databases from care units (PMSI) can be used as a source of information for registries because they contain standard records of most cancer patients. Regarding thyroid cancer, we have evaluated the PMSI as a source of information for the Rhône-Alpes thyroid cancer registry and usefulness of PMSI as a tool for surveillance of thyroid cancer incidence. METHODS: Patients with incident thyroid cancer in 2002 were identified in the claims data of the Rhône-Alpes region using an algorithm based on DRG codes of thyroidectomy and on diagnosis codes of thyroid cancer in a principal or secondary position. The patients identified were compared to those in thyroid cancer registry of the Rhône-Alpes region regarding sex, age, ZIP code of residence, month of discharge and length of stay versus the diagnosis date. When the percentage of cases of claims data identified in the cancer registry and the percentage of cases of the cancer registry identified in claims data were obtained, the capture-recapture method was applied to estimate the number of missing cases and the total number of incident thyroid cancers in the region. RESULTS: 667 patients were identified in claims data while the cancer registry included 677 patients. 95.2% of patients identified in claims data were in the cancer registry and 82.3% of patients in the cancer registry were identified in claims data. Cases lacking in claims data mostly corresponded to micro-cancers which represented 41% of cases in the cancer registry. Regarding cancer above 1 cm, 92% of the cancer registry cases were identified in claims data. Sensitivity of combining information from cancer registry and claims data was 99.2%. Cases lacking in cancer registry, present in claims data base and considered as true cases after obtaining pathological confirmation represented 2% of the whole thyroid cancer population. CONCLUSION: Claims data obtained from anonymous regional or national bases can be helpful for checking the completeness of thyroid cancer registries and to provide a small amount of unknown cases. They can be considered an acceptable tool for surveillance of thyroid cancer incidence. The significance of the variations in incidence that could be observed from claims data remains to be evaluated in comparison with comparable data obtained from registries.

Estimates of the number of cancer patients hospitalized in a geographic area using claims data without a unique personal identifier.

OBJECTIVE: In French national claims databases, claims are currently anonymous i.e. not linked to individual patients. In order to improve our estimate of the medical activity related to cancer in one French region, a statistical method was developed to use claims data to assess the number of cancer patients hospitalized in acute care. METHODS: This method used the medical and administrative information available in the claims (i.e. age, primary site, length of stay) to predict an average number of stays per patient, followed by a number of patients. It was based on a two-phase study design using an internal dataset which contained personal identifiers to estimate the model parameters. RESULTS: The predicted number of acute care patients hospitalized in one or several health care centers in one French region was 38,109 with a 95% predictive interval (37,990; 38,228) for the first six months of 2002. A prediction error of 24 per thousand was found. CONCLUSION: We provide a good estimate of the morbidity in acute care hospitals using claims data that is not linked to individual patients. This estimate reflects the medical activity and can be used to anticipate acute care needs.

[Sensitivity and specificity of two methods used to identify incident breast cancer in specialized units using claims databases]. / Sensibilité et spécificité de deux méthodes d'identification des cancers du sein incidents dans les services spécialisés à partir des données médico-administratives.

BACKGROUND: Hospital claims databases from acute care units are available nationwide and contain most patients at the beginning of their cancer. The goal is to define the ability of these databases to provide a number of incident breast cancer cases using identification methods. Two identification methods were assessed in three specialized sections of a teaching hospital. METHODS: The first method identified women who had at least one stay with a principal diagnosis of breast cancer. The second, which is more restrictive, identified women who had at least one stay with a principal diagnosis of breast cancer and a breast cancer-specific surgical treatment code. Both methods were applied to 4588 women 20 Years of age or older hospitalized in three specialized sections of the Hospices Civils de Lyon in 2000. To categorize these women in two groups, incident breast cancer cases or non-incident breast cancer cases, 150 women were randomized in each of two groups, one for incident breast cancer cases and one for non-incident breast cancer cases. Their medical records were used as references. RESULTS: Sensitivity, specificity and their credibility intervals were respectively 99.4% (84-99.9) and 91.7% (90.3-93.3) for the first method and 93.8% (76.2-98.7) and 97.3% (96.1-98) for the second. Among women wrongly identified with an incident breast cancer in 2000, 75.4% (43/57) had a breast cancer that was not incident that Year with the first method, compared to 96% (24/25) with the second. Among these women wrongly identified with an incident breast cancer, coding errors of the principal diagnosis were found for 24.6% (14/57) of patients with the first method and for 4% (1/25) with the second. Their correction led to 99.2% (86.5-99.9) sensitivity and 92.9% (91.4-94.6) specificity for the first method and to 94.2% (76.5-98.7) sensitivity and 97.3% (96.2-98.1) specificity for the second. CONCLUSIONS: The second method using cancer-specific surgical codes appeared more specific with a slight loss in sensitivity. The use of identification methods to assess the number of incident cancer cases still have to be defined.

Method of correction to assess the number of hospitalized incident breast cancer cases based on claims databases.

Estimations of the number of hospitalized incident cancer cases show biases when claims databases are used. This is due to false reports of incident cancer because of a lack of specificity, and because of unrecorded cancers resulting from a lack of sensitivity. We present a statistical method to provide corrected estimations. This method is based on a two-phase study design using an external data set for sensitivity and specificity estimates. Inaccuracy of the corrected number of hospitalized incident cancer cases was assessed by a credibility interval determined by a Bayesian approach using a Monte Carlo method. Based on the population hospitalized in a large group of French University hospitals, 334 women were identified in the French claims database as having potential incident cases of breast cancer in 1997. According to our method, the corrected number was 565 (550-580). In absence of hospital-based cancer registries, our approach provides estimates and credibility intervals, and has many potential applications in defining hospital policies with its applicability to other diseases.