Tagliabue Giovanna, Maghini Anna, Fabiano Sabrina, Tittarelli Andrea, Frassoldi Emanuela, Costa Enrica, Nobile Silvia, Codazzi Tiziana, Crosignani Paolo, Tessandori Roberto, Contiero Paolo
Cancer Registry Division, Istituto Nazionale per lo Studio e la Cura dei Tumori, Via Venezian 1, 20133 Milan, Italy.
Popul Health Metr. 2006 Sep 28;4:10. doi: 10.1186/1478-7954-4-10.
Automated procedures are increasingly used in cancer registration, and it is important that the data produced are systematically checked for consistency and accuracy. We evaluated an automated procedure for cancer registration adopted by the Lombardy Cancer Registry in 1997, comparing automatically-generated diagnostic codes with those produced manually over one year (1997).
The automatically generated cancer cases were produced by Open Registry algorithms. For manual registration, trained staff consulted clinical records, pathology reports and death certificates. The social security code, present and checked in both databases in all cases, was used to match the files in the automatic and manual databases. The cancer cases generated by the two methods were compared by manual revision.
The automated procedure generated 5027 cases: 2959 (59%) were accepted automatically and 2068 (41%) were flagged for manual checking. Among the cases accepted automatically, discrepancies in data items (surname, first name, sex and date of birth) constituted 8.5% of cases, and discrepancies in the first three digits of the ICD-9 code constituted 1.6%. Among flagged cases, cancers of female genital tract, hematopoietic system, metastatic and ill-defined sites, and oropharynx predominated. The usual reasons were use of specific vs. generic codes, presence of multiple primaries, and use of extranodal vs. nodal codes for lymphomas. The percentage of automatically accepted cases ranged from 83% for breast and thyroid cancers to 13% for metastatic and ill-defined cancer sites.
Since 59% of cases were accepted automatically and contained relatively few, mostly trivial discrepancies, the automatic procedure is efficient for routine case generation effectively cutting the workload required for routine case checking by this amount. Among cases not accepted automatically, discrepancies were mainly due to variations in coding practice.
自动化程序在癌症登记中使用得越来越多，对所产生的数据进行系统的一致性和准确性检查很重要。我们评估了伦巴第癌症登记处1997年采用的一种癌症登记自动化程序，将自动生成的诊断编码与一年（1997年）内手动生成的编码进行比较。
自动生成的癌症病例由开放式登记算法生成。对于手动登记，经过培训的工作人员查阅临床记录、病理报告和死亡证明。在所有病例中均存在并在两个数据库中进行核对的社会保险代码用于匹配自动数据库和手动数据库中的文件。通过人工核对比较两种方法生成的癌症病例。
自动化程序生成了5027例病例：2959例（59%）被自动接受，2068例（41%）被标记需人工核对。在自动接受的病例中，数据项（姓氏、名字、性别和出生日期）的差异占病例的8.5%，国际疾病分类第九版（ICD-9）编码前三位数字的差异占1.6%。在标记的病例中，女性生殖道癌、造血系统癌、转移性和部位不明的癌症以及口咽癌占主导。常见原因是使用特定编码与通用编码、存在多个原发肿瘤以及淋巴瘤使用结外编码与结内编码。自动接受病例的百分比从乳腺癌和甲状腺癌的83%到转移性和部位不明癌症的13%不等。
由于59%的病例被自动接受且差异相对较少，大多是小问题，该自动化程序对于常规病例生成是有效的，有效地减少了常规病例核对所需的工作量。在未自动接受的病例中，差异主要是由于编码实践的差异。
