Validation of an algorithm for identifying incident cancer cases based on long‐term illness and diagnosis related group program data from the French National Health Insurance Information System (SNDS)

• Published in: Pharmacoepidemiology and drug safety Vol. 33; no. 1; pp. e5709 - n/a
• Main Authors: Goulard, Hélène, Homère, Julie, Maurisset, Sylvain, Coureau, Gaëlle, Defossez, Gautier, d’Almeida, Tania, Lapôtre‐Ledoux, Bénédicte, Guizard, Anne‐Valérie, Bouvier, Véronique, Bara, Simona, Plouvier, Sandrine, Monnereau, Alain
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Inc 01.01.2024; Wiley Subscription Services, Inc; Wiley
• Subjects: administrative health data; Algorithms; Cancer; cancer registry; claims‐based algorithm; Databases, Factual; Diagnosis; Diagnosis related groups; DRGs; Epidemiology; Esophageal carcinoma; Gastric cancer; Humans; Illnesses; incident case; Information systems; Life Sciences; National health insurance; National Health Programs; Neoplasms - diagnosis; Neoplasms - epidemiology; Population studies; Predictive Value of Tests; incident case; cancer registry; cancer; claims-based algorithm; administrative health data
• ISSN: 1053-8569; 1099-1557; 1099-1557
• DOI: 10.1002/pds.5709

Purpose Three generic claims‐based algorithms based on the Illness Classification of Diseases (10th revision‐ ICD‐10) codes, French Long‐Term Illness (LTI) data, and the Diagnosis Related Group program (DRG) were developed to identify retirees with cancer using data from the French national health insurance information system (Système national des données de santé or SNDS) which covers the entire French population. The present study aimed to calculate the algorithms' performances and to describe false positives and negatives in detail. Methods Between 2011 and 2016, data from 7544 participants of the French retired self‐employed craftsperson cohort (ESPrI) were first matched to the SNDS data, and then toFrench population‐based cancer registries data, used as the gold standard. Performance indicators, such as sensitivity and positive predictive values, were estimated for the three algorithms in a subcohort of ESPrI. Results The third algorithm, which combined the LTI and DRG program data, presented the best sensitivities (90.9%–100%) and positive predictive values (58.1%–95.2%) according to cancer sites. The majority of false positives were in fact nearby organ sites (e.g., stomach for esophagus) and carcinoma in situ. Most false negatives were probably due to under declaration of LTI. Conclusion Validated algorithms using data from the SNDS can be used for passive epidemiological follow‐up for some cancer sites in the ESPrI cohort.