Abstract
Patient registries are fundamental to the research process. Registries provide consistent data for defined populations and can support the study of the distribution and determinants of various diseases. One advantage of registries is the ability to observe caseload and population characteristics over time, which might facilitate the evaluation of disease incidence, disease etiology, planning, operation and evaluation of services, evaluation of treatment patterns, and diagnostic classification. Registries can be developed for many different needs, including research recruitment, study planning, public health, and observational research. Any registry program must collect high-quality data to be useful for its stated purpose. We describe the methodological issues, limitations, and ideal features of registries to support various purposes. The future impact of registries on our understanding and interventions for many diseases will depend upon technological and political solutions for global collaborations to achieve consistent data (via standards) and regulations for various registry applications. The development, implementation, interpretation, and evaluation of registries are areas that can benefit from informatics expertise and coordination.
*Adapted from Richesson RL, Vehik K. Patient Registries: Utility, Validity and Inference. In: Posada M and Groft SC (ed). Rare Diseases Epidemiology, The Netherlands, Springer, 2010, with kind permission of Springer Science + Business Media.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Solomon DJ, et al. Evaluation and implementation of public health registries. Public Health Rep. 1991;106:142–50.
WHO. Epidemiological methods in the study of chronic diseases. Eleventh report of the WHO expert committee on health statistics. Geneva: World Health Organization; 1967.
EPPOSI. EPPOSI workshop on patients registries for rare disorders. 2009. Available from http://rbdd.org/index.php?option=com_content&view=article&id=102. Cited 22 July 2009.
Goldberg J, Gelfand HM, Levy PS. Registry evaluation methods: a review and case study. Epidemiol Rev. 1980;2:210–20.
Bellows MT. Case registers. Public Health Rep. 1949;64:1148–58.
Muilu J, Peltonen L, Litton JE. The federated database – a basis for biobank-based post-genome studies, integrating phenome and genome data from 600,000 twin pairs in Europe. Eur J Hum Genet. 2007;15:718–23.
Nakamura Y. The BioBank Japan project. Clin Adv Hematol Oncol. 2007;5:696–7.
Ollier W, Sprosen T, Peakman T. UK Biobank: from concept to reality. Pharmacogenomics. 2005;6:639–46.
Sandusky G, Dumaual C, Cheng L. Review paper: Human tissues for discovery biomarker pharmaceutical research: the experience of the Indiana University Simon Cancer Center-Lilly Research Labs Tissue/Fluid BioBank. Vet Pathol. 2009;46:2–9.
Irgens LM, Bjerkedal T. Epidemiology of leprosy in Norway: the history of The National Leprosy Registry of Norway from 1856 until today. Int J Epidemiol. 1973;2:81–9.
Groth-Petersen E, Knudsen J, Wilbek E. Epidemiological basis of tuberculosis eradication in an advanced country. Bull World Health Organ. 1959;21:5–49.
Wirth HE, Locke BZ. Analyzing the tuberculosis case register. Public Health Rep. 1957;72:151–7.
Drolet BC, Johnson KB. Categorizing the world of registries. J Biomed Inform. 2008;41:1009–20.
Parkin DM. The evolution of the population-based cancer registry. Nat Rev Cancer. 2006;6:603–12.
The Genetic Alliance. Discovering openness in health systems. In: The Genetic Alliance 2009 annual conference, Bethesda; 2009.
FDA. Guidance for industry and FDA staff. Procedures for handling post-approval studies imposed by PMA order. U.S. DHHS., FDA, Center for Devices and Radiological Health Rockville; 2007.
AHRQ. Registries for Evaluating Patient Outcomes: A User’s Guide. Gliklich RE, editor. Rockville: Agency for Healthcare Research and Quality; 2007
Pedersen E. Some uses of the cancer registry in cancer control. Br J Prev Soc Med. 1962;16:105–10.
Brooke EM. The current and future use of registers in health information systems. Geneva: World Health Organization; 1974.
USPHS. The automation of rheumatic fever registries; report of a seminar, May 21 and 22, 1968. Public Health Service, United States Department of Health, Education and Welfare: Washington, DC; 1969.
Sekar CC, Deming WE. On a method of estimating birth and death rates and extent of registration. J Am Stat Assoc. 1949;44:101–15.
Sekar CC, Deming WE. On a method of estimating birth and death rates and the extent of registration (excerpt). Am Stat. 2004;58:13–5.
Cochi SL, et al. Congenital rubella syndrome in the United States, 1970–1985. On the verge of elimination. Am J Epidemiol. 1989;129:349–61.
Tilling K. Capture-recapture methods – useful or misleading? Int J Epidemiol. 2001;30:12–4.
Schlesselman JJ. Case–control studies: design, conduct and analysis. New York: Oxford University Press; 1982.
Rothman K, Greenland S. Modern epidemiology. 2nd ed. Hagerstown: Lippincott Williams and Wilkins; 1998.
Weddell JM. Registers and registries: a review. Int J Epidemiol. 1973;2:221–8.
Green SB, Byar DP. Using observational data from registries to compare treatments: the fallacy of omnimetrics. Stat Med. 1984;3:361–73.
Rockette HE, Redmond CK, Fisher B. Impact of randomized clinical trials on therapy of primary breast cancer: the NSABP overview. Control Clin Trials. 1982;3:209–25.
Miettinen OS. The need for randomization in the study of intended effects. Stat Med. 1983;2:267–71.
FDA. Guidance for industry. Establishing pregnancy exposure registries. U.S. Department of Health and Human Services, Food and Drug Administration, Center for Biologics Evaluation and Research (CBER); 2002.
Frost JH, et al. How the social web supports patient experimentation with a new therapy: the demand for patient-controlled and patient-centered informatics. AMIA Annu Symp Proc. 2008;2008:217–21.
European Medicines Agency (EMA). “International Conference on Harmonisation (ICH) Topic E 6 (R1). Guideline for good clinical practice; 2002.” Guideline # CPMP/ICH/135/95. P. 59. Available at: http://www.emea.europa.eu/docs/en_GB/document_library/Scientific_guideline/2009/09/WC500002874.pdf Accessed on 12 December 2011.
Murff HJ, Spigel DR, Syngal S. Does this patient have a family history of cancer? An evidence-based analysis of the accuracy of family cancer history. JAMA. 2004;292:1480–9.
Fridsma DB, et al. The BRIDG project: a technical report. J Am Med Inform Assoc. 2008;15:130–7.
Nadkarni PM, Brandt CA. The common data elements for cancer research: remarks on functions and structure. Methods Inf Med. 2006;45:594–601.
Richesson RL, Krischer JP. Data standards in clinical research: gaps, overlaps, challenges and future directions. J Am Med Inform Assoc. 2007;14:687–96.
Richesson RL, Andrew JE, Krischer JP. Use of SNOMED CT to represent clinical research data: a semantic characterization of data items on case report forms in vasculitis research. J Am Med Inform Assoc. 2006;13:536–46.
Andrews JE, Richesson RL, Krischer JP. Variation of SNOMED CT coding of clinical research concepts among coding experts. J Am Med Inform Assoc. 2007;14:497–506.
CHI. Consolidated Health Informatics. Standards adoption recommendation. Functioning and disability. U.S. DHHS, Consolidated Health Informatics; 2006.
White TM. Update on survey instruments and questions. In: Clinical LOINC® Meeting. Salt Lake City; 2004.
Carter J, et al. Making the “minimum data set” compliant with health information Technology standards. Executive Summary. U.S. Department of Health and Human Services; 2006.
Bakken S, et al. Evaluation of the clinical LOINC (Logical Observation Identifiers, Names, and Codes) semantic structure as a terminology model for standardized assessment measures. J Am Med Inform Assoc. 2000;7:529–38.
Feero WG, Bigley MB, Brinner KM. New standards and enhanced utility for family health history information in the electronic health record: an update from the American Health Information Community’s Family Health History Multi-Stakeholder Workgroup. J Am Med Inform Assoc. 2008;15:723–8.
Godard B, et al. Data storage and DNA banking for biomedical research: informed consent, confidentiality, quality issues, ownership, return of benefits. A professional perspective. Eur J Hum Genet. 2003;11:S88–122.
DuchenneConnect. 2009. DuchenneConnect. Available from https://www.duchenneconnect.org. Cited 24 Aug 2009.
TREAT-NMD. TREAT-NMD neuromuscular network. 2009. Available from http://www.treat-nmd.eu/home.php. Cited 27 July 2009.
Warren JL, et al. Overview of the SEER-Medicare data: content, research applications, and generalizability to the United States elderly population. Med Care. 2002;40:IV-3–18.
Brooks JM, et al. Information gained from linking SEER Cancer Registry Data to state-level hospital discharge abstracts. Surveillance, epidemiology, and end results. Med Care. 2000;38:1131–40.
McNally RJ, et al. Geographical and ecological analyses of childhood acute leukaemias and lymphomas in north-west England. Br J Haematol. 2003;123:60–5.
Sweeney L. Weaving technology and policy together to maintain confidentiality. J Law Med Ethics. 1997;25:98–110.
Sweeney L. Privacy-preserving surveillance using databases from daily life. IEEE Intell Syst. 2005;20:83–4.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag London Limited
About this chapter
Cite this chapter
Richesson, R.L., Vehik, K. (2012). Patient Registries* . In: Richesson, R., Andrews, J. (eds) Clinical Research Informatics. Health Informatics. Springer, London. https://doi.org/10.1007/978-1-84882-448-5_13
Download citation
DOI: https://doi.org/10.1007/978-1-84882-448-5_13
Published:
Publisher Name: Springer, London
Print ISBN: 978-1-84882-447-8
Online ISBN: 978-1-84882-448-5
eBook Packages: MedicineMedicine (R0)