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).
administrative health data
cancer
cancer registry
claims-based algorithm
incident case
Journal
Pharmacoepidemiology and drug safety
ISSN: 1099-1557
Titre abrégé: Pharmacoepidemiol Drug Saf
Pays: England
ID NLM: 9208369
Informations de publication
Date de publication:
26 Oct 2023
26 Oct 2023
Historique:
revised:
04
09
2023
received:
21
02
2023
accepted:
22
09
2023
medline:
26
10
2023
pubmed:
26
10
2023
entrez:
26
10
2023
Statut:
aheadofprint
Résumé
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. 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. 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. Validated algorithms using data from the SNDS can be used for passive epidemiological follow-up for some cancer sites in the ESPrI cohort.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2023 John Wiley & Sons Ltd.
Références
Goldberg M, Coeuret-Pellicer M, Ribet C, Zins M. Epidemiological studies based on medical and administrative databases: a potential strength in France. Med Sci. 2012;28(434):430-434.
Ganry O, Taleb A, Peng J, Raverdy N, Dubreuil A. Evaluation of an algorithm to identify incident breast cancer cases using DRGs data. Eur J Cancer Prev. 2003;12(4):295-299.
Schott AM, Hajri T, Colin C, et al. Usefulness of the French DRG based information system (PMSI) in the measurement of cancer activity in a multidisciplinary hospital: the Hospices Civils de Lyon. Bull Cancer. 2002;89(11):969-973.
Bousquet PJ, Caillet P, Coeuret-Pellicer M, et al. Using cancer case identification algorithms in medico-administrative databases: literature review and first results from the REDSIAM tumors group based on breast, colon, and lung cancer. Rev Epidemiol Sante Publique. 2017;65(Suppl 4):S236-S242.
Gallini A, Moisan F, Maura G, et al. Identification of neurodegenerative diseases in administrative databases in France: a systematic review of the literature. Rev Epidemiol Sante Publique. 2017;65(Suppl 4):S183-S197.
McBean AM, Babish JD, Warren JL. Determination of lung cancer incidence in the elderly using Medicare claims data. Am J Epidemiol. 1993;137(2):226-234.
Couris CM, Seigneurin A, Bouzbid S, et al. French claims data as a source of information to describe cancer incidence: predictive values of two identification methods of incident prostate cancers. J Med Syst. 2006;30(6):459-463.
Couris CM, Polazzi S, Olive F, et al. Breast cancer incidence using administrative data: correction with sensitivity and specificity. J Clin Epidemiol. 2009;62(666):660-666.
Remontet L, Mitton N, Couris CM, et al. Is it possible to estimate the incidence of breast cancer from medico-administrative databases? Eur J Epidemiol. 2008;23(10):681-688.
Clin B, Morlais F, Launoy G, et al. Cancer incidence within a cohort occupationally exposed to asbestos: a study of dose-response relationships. Occup Environ Med. 2011;68(11):832-836.
Mitton N, Colonna M, Trombert B, et al. A suitable approach to estimate cancer incidence in area without cancer registry. J Cancer Epidemiol. 2011;2011:418968.
Quantin C, Benzenine E, Hägi M, et al. Estimation of national colorectal-cancer incidence using claims databases. J Cancer Epidemiol. 2012;2012:298369.
Diop M, Strumpf EC, Datta GD. Measuring colorectal cancer incidence: the performance of an algorithm using administrative health data. BMC Med Res Methodol. 2018;18(1):38.
Oppelt KA, Luttmann S, Kraywinkel K, Haug U. Incidence of advanced colorectal cancer in Germany: comparing claims data and cancer registry data. BMC Med Res Methodol. 2019;19(1):142.
Chatignoux E, Uhry Z, Grosclaude P, Colonna M, Remontet L. How to produce sound predictions of incidence at a district level using either health care or mortality data in the absence of a national registry: the example of cancer in France. Int J Epidemiol. 2021;50(1):279-292.
Tuppin P, Rudant J, Constantinou P, et al. Value of a national administrative database to guide public decisions: from the systeme national d'information interregimes de l'Assurance Maladie (SNIIRAM) to the systeme national des donnees de sante (SNDS) in France. Rev Epidemiol Sante Publique. 2017;65(Suppl 4):S149-S167.
Rey B. What is PMSI? (program of medicalization of information systems). Rev Infirm. 2000;61:38-39.
Goulard H, Homère J. Programme de Surveillance Post-professionnelle des Artisans Ayant été Exposés à L'amiante (ESPrI). Retraités entre 2004 et 2008, Artisans du Régime Social des Indépendants. Institut de Veille Sanitaire; 2012.
Aboa-Eboule C, Mengue D, Benzenine E, et al. How accurate is the reporting of stroke in hospital discharge data? A pilot validation study using a population-based stroke registry as control. J Neurol. 2013;260(2):605-613.
Baan RA. Carcinogenic hazards from inhaled carbon black, titanium dioxide, and talc not containing asbestos or asbestiform fibers: recent evaluations by an IARC monographs working group. Inhal Toxicol. 2007;19(Suppl 1):213-228.
Defossez G, Le Guyader-Peyrou S, Uhry Z, et al. Estimations Nationales de L'incidence et de la Mortalité par Cancer en France Métropolitaine entre 1990 et 2018. Vol 1. Tumeurs solides. Santé Publique France SaintMaurice; 2019 372.
Baldi I, Vicari P, di Cuonzo D, et al. A high positive predictive value algorithm using hospital administrative data identified incident cancer cases. J Clin Epidemiol. 2008;61(4):373-379.
Kudjawu Y. Système multi sources de surveillance des cancers Croisement des données ALD et PMSI: Description des données 2006-2008 et construction d'indicateurs. Rapport. 2012;1(1). https://www.santepubliquefrance.fr/recherche/#search=cancer%20multi%20sources
Cooper GS, Yuan Z, Stange KC, Dennis LK, Amini SB, Rimm AA. The sensitivity of Medicare claims data for case ascertainment of six common cancers. Med Care. 1999;37(5):436-444.
Nordstrom BL, Whyte JL, Stolar M, Mercaldi C, Kallich JD. Identification of metastatic cancer in claims data. Pharmacoepidemiol Drug Saf. 2012;21(Suppl 2):21-28.
Whyte JL, Engel-Nitz NM, Teitelbaum A, Rey GG, Kallich JD. An evaluation of algorithms for identifying metastatic breast, lung, or colorectal cancer in administrative claims data. Med Care. 2015;53(7):49-57.
Goldsbury D, Weber M, Yap S, Banks E, O'Connell DL, Canfell K. Identifying incident colorectal and lung cancer cases in health service utilisation databases in Australia: a validation study. BMC Med Inform Decis Mak. 2017;17(1):23.
Bronson MR, Kapadia NS, Austin AM, et al. Leveraging linkage of cohort studies with administrative claims data to identify individuals with cancer. Med Care. 2018;56:e83-e89.
Olive F, Gomez F, Schott AM, et al. Critical analysis of French DRG based information system (PMSI) databases for the epidemiology of cancer: a longitudinal approach becomes possible. Rev Epidemiol Sante Publique. 2011;59(1):53-58.
Doat S, Thiébaut A, Samson S, Ricordeau P, Guillemot D, Mitry E. Elderly patients with colorectal cancer: treatment modalities and survival in France. National data from the ThInDiT cohort study. Eur J Cancer. 2014;50(7):1276-1283.
Deshpande AD, Schootman M, Mayer A. Development of a claims-based algorithm to identify colorectal cancer recurrence. Ann Epidemiol. 2015;25(4):297-300.