Abstract
There are many misunderstandings about databases. Database is a commonly misused term in reference to any set of data entered into a computer. However, true databases serve a main purpose, organising data. They do so by establishing several layers of relationships; databases are hierarchical. Databases commonly organise data over different levels and over time, where time can be measured as the time between visits, or between treatments, or adverse events, etc. In this sense, medical databases are closely related to longitudinal observational studies, as databases allow the introduction of data on the same patient over time. Basically, we could establish four types of databases in medicine, depending on their purpose: (1) administrative databases, (2) clinical databases, (3) registers, and (4) study-oriented databases. But a database is a useful tool for a large variety of studies, not a type of study itself. Different types of databases serve very different purposes, and a clear understanding of the different research designs mentioned in this paper would prevent many of the databases we launch from being just a lot of work and very little science.
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References
Hulley SB, Cummings S, Browner WS, Grady DB, Newman TB (2007) Designing clinical research, 3rd edn. Lippincott Williams & Wilkins, Philadelphia
Ludwig KA, Kosinski LA (2013) The risk-benefit ratio. Does the administrative database help? JAMA Surg 148(4):322
Beaudet N, Courteau J, Sarret P, Vanasse A (2013) Prevalence of claims-based recurrent low back pain in a Canadian population: a secondary analysis of an administrative database. BMC Musculoskelet Disord 14:151
Patkar NM, Curtis JR, Teng GG, Allison JJ, Saag M, Martin C, Saag KG (2009) Administrative codes combined with medical records based criteria accurately identified bacterial infections among rheumatoid arthritis patients. J Clin Epidemiol 62(3):321–327, 327 e321–327
Wolfe F (1999) Critical issues in longitudinal and observational studies: purpose, short versus long term, selection of study instruments, methods, outcomes, and biases. J Rheumatol 26(2):469–472
Silman A, Symmons D (1999) Reporting requirements for longitudinal observational studies in rheumatology. J Rheumatol 26(2):481–483
Symmons DP (2004) Methodological issues in conducting and analyzing longitudinal observational studies in rheumatoid arthritis. J Rheumatol Suppl 69:30–34
Kremer JM (2006) The CORRONA database. Autoimmun Rev 5(1):46–54
Ceballos M, Lopez-Revuelta K, Saracho R, Garcia Lopez F, Castro P, Gutierrez JA, Martin-Martinez E, Alonso R, Bernabeu R, Lorenzo V et al (2005) Dialysis and transplant patients Registry of the Spanish Society of Nephrology. Nefrologia 25(2):121–124, 126–129
Stel VS, Tomson C, Ansell D, Casino FG, Collart F, Finne P, Ioannidis GA, De Meester J, Salomone M, Traynor JP et al (2010) Level of renal function in patients starting dialysis: an ERA-EDTA Registry study. Nephrol Dial Transplant 25(10):3315–3325
Zurriaga Llorens O, Martinez Garcia C, Arizo Luque V, Sanchez Perez MJ, Ramos Aceitero JM, Garcia Blasco MJ, Ferrari Arroyo MJ, Perestelo Perez L, Ramalle Gomara E, Martinez Frias ML et al (2006) Disease registries in the epidemiological researching of rare diseases in Spain. Rev Esp Salud Publica 80(3):249–257
Dixon WG, Carmona L, Finckh A, Hetland ML, Kvien TK, Landewe R, Listing J, Nicola PJ, Tarp U, Zink A et al (2010) EULAR points to consider when establishing, analysing and reporting safety data of biologics registers in rheumatology. Ann Rheum Dis 69(9):1596–1602
Mann CJ (2003) Observational research methods. Research design II: cohort, cross sectional, and case–control studies. Emerg Med J 20(1):54–60
Listing J, Strangfeld A, Kekow J, Schneider M, Kapelle A, Wassenberg S, Zink A (2008) Does tumor necrosis factor alpha inhibition promote or prevent heart failure in patients with rheumatoid arthritis? Arthr Rheum 58(3):667–677
Askling J, Fored CM, Geborek P, Jacobsson LT, van Vollenhoven R, Feltelius N, Lindblad S, Klareskog L (2006) Swedish registers to examine drug safety and clinical issues in RA. Ann Rheum Dis 65(6):707–712
Dixon WG, Symmons DP, Lunt M, Watson KD, Hyrich KL, Silman AJ (2007) Serious infection following anti-tumor necrosis factor alpha therapy in patients with rheumatoid arthritis: lessons from interpreting data from observational studies. Arthr Rheum 56(9):2896–2904
Pratt AG, Lorenzi AR, Wilson G, Platt PN, Isaacs JD (2013) Predicting persistent inflammatory arthritis amongst early arthritis clinic patients in the UK: is musculoskeletal ultrasound required? Arthr Res Ther 15(5):R118
Carstensen B (2007) Age-period-cohort models for the Lexis diagram. Stat Med 26(15):3018–3045
Kaess BM, Gona P, Larson MG, Cheng S, Aragam J, Kenchaiah S, Benjamin EJ, Vasan RS (2013) Secular trends in echocardiographic left ventricular mass in the community: the Framingham Heart Study. Heart 99(22):1693–1698
Rao JK, Callahan LF (1995) Systems for data analysis. Rheum Dis Clin N Am 21(2):359–378
Pincus T, Sokka T (2003) Uniform databases in early arthritis: specific measures to complement classification criteria and indices of clinical change. Clin Exp Rheumatol 21(5 Suppl 31):S79–S88
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Curbelo, R.J., Loza, E., de Yébenes, M.J.G. et al. Databases and registers: useful tools for research, no studies. Rheumatol Int 34, 447–452 (2014). https://doi.org/10.1007/s00296-014-2954-x
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DOI: https://doi.org/10.1007/s00296-014-2954-x