Same Dataset, Same Conclusion, Different in Detail: Who's Doing What from Large Public Database?
Authors: Anis Fuad, Chien-Yeh Hsu
Journal: Journal of Thoracic Oncology (2012)

We read with great interest the article by Yu et al.1Yu YH Liao CC Hsu WH et al.Increased lung cancer risk among patients with pulmonary tuberculosis: a population cohort study.J Thorac Oncol. 2011; 6: 32-37Abstract Full Text Full Text PDF PubMed Scopus (164) Google Scholar on the association of tuberculosis with lung cancer. This retrospective cohort study indispensably proved the positive relationship, thanks to the nationwide scale and large number of cases they analyzed from the Taiwan National Health Insurance Database. This administrative database has been widely used to produce scientific knowledge. From 2000 until 2009, no less than 383 articles have been published, and 29 (7.5%) of them are related to cancer.2Chen Y-C Yeh H-Y Wu J-C et al.Taiwan's National Health Insurance Research Database: administrative health care database as study object in bibliometrics.Scientometrics. 2011; : 365-380Crossref Scopus (244) Google Scholar However, because it is publicly available, there is a chance that people from different groups work on the same topics from the same dataset at the same period. It coincidentally occurred when similar research byWu et al.3Wu CY Hu HY Pu CY et al.Pulmonary tuberculosis increases the risk of lung cancer: a population-based cohort study.Cancer. 2011; 117: 618-624Crossref PubMed Scopus (140) Google Scholar was published in the February 2011 issue of Cancer journal (Epublication by September 30, 2010). Although both longitudinal studies likewise showed the increased risk of lung cancer among patients with tuberculosis, they were different in defining eligibility, case and control group, and follow-up duration (Table 1). This resulted in different incident rate ratio and hazard ratio. In this case, according to International Committee of Medical Journal Editors, “there may even be a good reason to publish more than one manuscript because different analytical approaches may be complementary and equally valid.”4International Committee of Medical Journal Editors.. Uniform requirements for manuscripts submitted to biomedical journals: writing and editing for biomedical publication.Available at http://www.icmje.orgGoogle Scholar However, this implies readers, particularly those who perform meta-analysis, to carefully consider different approach used in those articles while taking into account their result.TABLE 1Comparison of Two Articles on Tuberculosis with Lung CancerArticle by Yu et al.Article by Wu et al.Dataset Source and number of original recordOne million individuals from National Health Insurance ResearchDatabase, 5% of total health insurance members in TaiwanMethods Eligibility (number)Individuals without cancer (716,872)All NHIRD individuals (1,000,000) TB case definitionICD-9-CM code 011 and A code A020ICD-9-CM code 010–012, 018 or A-code A020, A021 plus prescriptions of at least two antituberculosis medications for >28 d No. of cases44805657 Control definitionAll non-TB casesFour non-TB cases matched to 1 TB case by age and sex No. of controls712,39223,984 Lung cancer definitionICD-9-CM code 162 or A code A101ICD-9-CM code 162 or A code A101 Follow-up (yr)7–91–12Results Incident rate ratio (CI)10.9 (NA)1.76 (1.33–2.32) Hazard ratio (CI)3.32 (2.70–4.09)1.64 (1.24–2.15)ConclusionIncreased lung cancer among individuals with pulmonary TBTB, tuberculosis; NHIRD, National Health Insurance Research Database; CI, confidence interval. Open table in a new tab TB, tuberculosis; NHIRD, National Health Insurance Research Database; CI, confidence interval. With regard to more publication from the same dataset, Wright et al.5Wright E Seed PT Schofield P et al.Bisphosphonates and cancer. More data using same database.BMJ. 2010; 341: c5315Crossref PubMed Scopus (4) Google Scholar have reported three groups working on similar topic from the same source: UK General Practice Research Database, another large public database in the world. Those groups are not known to each other before publication. Considering that it could recur, we propose to the owner of any large public database to develop a real-time tool to check who is doing what from that resource during publication process period. We believe that this tool will be of benefit to the researchers and substantially contribute to the knowledge discovery from that large database.