F. Olive

Breast cancer incidence using administrative data: correction with sensitivity and specificity

OBJECTIVE To estimate breast cancer incidence in the general population using a method that corrects for lack of sensitivity and specificity in the identification of incident breast cancer in inpatient claims data. STUDY DESIGN AND SETTINGS Two-phase study: phase 1 to identify incident cases in claims data, and phase 2 to estimate sensitivity and specificity in a subset of the population. Two algorithms (1: principal diagnosis; 2: principal diagnosis+specific surgery procedures) were used to identify incident cases in claims of women aged 20 years or older, living in a French district covered by a cancer registry. Sensitivity and specificity were estimated in one district and used to correct incident cases identified. RESULTS The sensitivity and specificity for algorithms 1 and 2 were 69.0% and 99.89%, and 64.4% and 99.93%, respectively. In contrast to specificity, the sensitivity for both algorithms was lower for women younger than 40 years and older than 65 years. Cases reported by cancer registries were closer to cases identified with algorithm 2 (-3.2% to +20.1%) and to corrected numbers with algorithm 1 (-1% to +15%). CONCLUSION To obtain reliable estimates of breast cancer incidence in the general population, sensitivity and specificity, which reflect medical and coding practice variations, are necessary.

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.

Analyse critique des données du PMSI pour l’épidémiologie des cancers : une approche longitudinale devient possible

BACKGROUND Use of French Diagnosis Related Groups (DRGs) program databases, apart from financial purposes, has recently been improved since a unique anonymous patient identification number has been created for each inpatient in administrative case mix database. Based on the work of the group for cancer epidemiological observation in the Rhône-Alpes area, (ONC-EPI group), we review the remaining difficulties in the use of DRG data for epidemiological purposes and we consider a longitudinal approach based on analysis of database over several years. We also discuss limitations of this approach. DIFFICULTIES The main problems are related to a lack of quality of administrative data, especially coding of diagnoses. These errors come from missing or inappropriate codes, or not being in accordance with prioritization rules (causing an over- or under-reporting or inconsistencies in coding over time). One difficulty, partly due to the hierarchy of coding and the type of cancer, is the choice of an extraction algorithm. In two studies designed to estimate the incidence of cancer cared in hospitals (breast, colon-rectum, kidney, ovaries), a first algorithm, including a code of cancer as principal diagnosis with a selection of surgical procedures less performed than the second one including a code of cancer as principal diagnosis only, for which the number of hospitalizations per patient ratio was stable across time and space. The chaining over several years allows, by tracing the trajectory of the patient, to detect and correct inaccuracies, errors and missing values, and for incidence studies, to correct incident cases by removing prevalent cases. DISCUSSION However, linkage, complete only since 2007, does not correct data in all cases. Ways of future improvement certainly pass through improved algorithms for case identification and especially by linking DRG data with other databases.

A Suitable Approach to Estimate Cancer Incidence in Area without Cancer Registry

Objective. Use of cancer cases from registries and PMSI claims database to estimate Département-specific incidence of four major cancers. Methods. Case extraction used principal diagnosis then surgery codes. PMSI cases/registry cases ratios for 2004 were modelled then Département-specific incidence for 2007 estimated using these ratios and 2007 PMSI cases. Results. For 2007, only colon-rectum and breast cancer estimations were satisfactorily validated for infranational incidence not ovary and kidney cancers. For breast, the estimated national incidence was 50,578 cases and the incidence rate 98.6 cases per 100,000 person per year. For colon-rectum, incidence was 21,172 in men versus 18,327 in women and the incidence rate 38 per 100,000 versus 24.8. For ovary, the estimated incidence was 4,637 and the rate 8.6 per 100,000. For kidney, incidence was 6,775 in men versus 3,273 in women and the rate 13.3 per 100.000 versus 5.2. Conclusion. Incidence estimation using PMSI patient identifiers proved encouraging though still dependent on the assumption of uniform cancer treatments and coding.

Identifying Prevalent Cases of Breast Cancer in the French Case-mix Databases

OBJECTIVES Little is known about cancer prevalence due to a lack of systematic recording of cancer patient follow-up data. To estimate the annual hospital prevalence of breast cancer in the general population of the Isère department (1.1 million inhabitants) in the Rhône-Alpes region, the second largest region in France (6 million inhabitants), we used the inpatient case-mix data, available in most European countries, to develop a method of cancer case identification. METHODS A selection process was applied to the acute care hospital datasets among women aged 18 years or older, living in the Isère department and treated for breast cancer between 2004 and 2007. The first step in case selection was based on the national anonymous unique patient identifier. The second step consisted of retrieving all hospital stays for each case. The third step was designed to detect inconsistencies in the coding of the primary localization. An algorithm based on ICD-10 code for the hospital admission diagnosis was used to rule out hospitalizations unrelated to breast cancer. Five possible models for estimating prevalence were created combining selection steps with the admission diagnosis algorithm. RESULTS Hospital prevalence over the four-year period varied from 6073 breast cancer cases for the simplest model (first selection step without the admission diagnosis algorithm) to 4951 when the first selection step was associated with the breast cancer code as admission diagnosis. The model combining the third selection step with a breast cancer-specific admission reason provided 5275 prevalent cases. CONCLUSION The last model seems more appropriate for case-mix-data coding. Selecting admission diagnosis improved specificity. Combining all hospital stays for each patient has improved diagnostic sensitivity.

Joint use of epidemiological and hospital medico-administrative data to estimate prevalence. Application to French data on breast cancer

BACKGROUND Estimate complete, limited-duration, and hospital prevalence of breast cancer in a French Département covered by a population-based cancer registry and in whole France using complementary information sources. METHODS Incidence data from a cancer registry, national incidence estimations for France, mortality data, and hospital medico-administrative data were used to estimate the three prevalence indices. The methods included a modelling of epidemiological data and a specific process of data extraction from medico-administrative databases. RESULTS Limited-duration prevalence at 33 years was a proxy for complete prevalence only in patients aged less than 70 years. In 2007 and in women older than 15 years, the limited-duration prevalence at 33 years rate per 100,000 women was estimated at 2372 for Département Isère and 2354 for whole France. The latter rate corresponded to 613,000 women. The highest rate corresponded to women aged 65-74 years (6161 per 100,000 in whole France). About one third of the 33-year limited-duration prevalence cases were diagnosed five years before and about one fourth were hospitalized for breast-cancer-related care (i.e., hospital prevalence). In 2007, the rate of hospitalized women was 557 per 100,000 in whole France. Among the 120,310 women hospitalized for breast-cancer-related care in 2007, about 13% were diagnosed before 2004. CONCLUSION Limited-duration prevalence (long- and short-term), and hospital prevalence are complementary indices of cancer prevalence. Their efficient direct or indirect estimations are essential to reflect the burden of the disease and forecast median- and long-term medical, economic, and social patient needs, especially after the initial treatment.