Best practices in using secondary analysis as a method
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1 Best practices in using secondary analysis as a method Katharine Green, PhD(c), CNM University of Massachusetts Amherst, USA July, 2015 University of Massachusetts Amherst, U.S.A.
2 Secondary data analysis: why use? Uses prior data to obtain new information or interpretation. Frequently used, electronic medical records Cost effective, Efficient use of time Good for large studies, longitudinal data Good for comparing data across data bases Real life (example: pharmaceutics)
3 What are best practices with this method in health care? Literature review o CINAHL, GoogleScholar, Ovid Exploring Attributes of the method and considerations in health care
4 Fit of data: Best practices: design o Note initial strengths/ limitations and justify use, congruity of type of database and analysis packages Size of sample Measurement/ analysis Did database measure variable of interest? Does current conceptual framework fit into the theory that underpinned first study?
5 Data management Similar to any other study: data cleaning o Consistency, accuracy, evaluation of outliers Coding errors Missing data or improperly defined data Pilot any forms Inclusion and exclusion criteria in primary use and in secondary use o Do data points and methods from initial study match current study needs?
6 Statistical analysis Similar to doing any initial study: o Descriptive, predictive, and non-linear statistics o Standard error should be smaller as unlikely to be randomized to decrease risk of falsely rejecting null hypothesis
7 Think carefully before proceeding. For qualitative use o Usually unable to see or hear initial interviews o Interpretation issues o Sensitivity of data For meta analysis o Frequently use of outcomes, not initial data o Risk of bias Usually cannot establish causality: this method is almost always observational Who owns the data?
8 Other cautions Self reported data, missing data, unasked questions o Missing data may be bias- not just missing Changes in practice, diagnoses, treatment Societal, economic and political changes Time lapse Multiple researchers using database Was informed consent obtained for current work?
9 Data linkage If link pharmacy, ancillary health services, and electronic medical records during providers or hospital visits, may have fairly complete picture of people who did not consent. Takes surprisingly few data points to identify someone!
10 More on data linkage Takes only 4 data points from a cell phone provider to identify a person 90% adults can be identified through a social media file (Yves-Alexandre et al., 2013) Large companies forming aggregate data bases (Tucker, 2013) Takes only 5 medical record data points to link a newspaper to a person. (Sweeney, 2013)
11 HIPAA (Health Insurance Portability and Accountability Act) Names. All geographic subdivisions smaller than a state, including street address, city, county, precinct, ZIP Code, and their equivalent geographical codes, except for the initial three digits of a ZIP Code if, according to the current publicly available data from the Bureau of the Census: o o The geographic unit formed by combining all ZIP Codes with the same three initial digits contains more than 20,000 people. The initial three digits of a ZIP Code for all such geographic units containing 20,000 or fewer people are changed to 000..
12 HIPAA All elements of dates (except year) for dates directly related to an individual, including birth date, admission date, discharge date, date of death; and all ages over 89 and all elements of dates (including year) indicative of such age, except that such ages and elements may be aggregated into a single category of age 90 or older. Telephone numbers. Facsimile numbers. Electronic mail addresses
13 HIPAA Social security numbers. Medical record numbers. Health plan beneficiary numbers. Account numbers. Certificate/license numbers. Vehicle identifiers and serial numbers, including license plate numbers. Device identifiers and serial numbers. Web universal resource locators (URLs).
14 HIPAA Internet protocol (IP) address numbers. Biometric identifiers, including fingerprints and voiceprints. Full-face photographic images and any comparable images. Any other unique identifying number, characteristic, or code, unless otherwise permitted by the Privacy Rule for re-identification.
15 What are best practices with this method in health care? Aggregated (combined) data De-identified data Revise consent forms to remove illusion of anonymity unless it is actually possible Minimizing individual data if an anonymous study: is it possible? Rethinking issues of privacy: is it generational?
16 References Alvarez, J., Canduela, J. and Raeside, R. (2012), Knowledge creation and the use of secondary data. Journal of Clinical Nursing, 21: Aponte, J. (2010). Key Elements of Large Survey Data Sets. Nursing Economic$, 28(1), Benningfield, M., Dietrich, M., Jones, H., Kaltenbach, K., Heil, S., Stine, S., & Martin, P. (2012), Opioid dependence during pregnancy: relationships of anxiety and depression symptoms to treatment outcomes. Addiction, 107: Berger, A. M., & Berger, C. R. (2004). Data mining as a tool for research and knowledge development in nursing. Computer Information in Nursing, 22, Clarke, S., & Cossette, S. (2000). Secondary analysis: theoretical, methodological, and practical considerations. Canadian Journal Of Nursing Research, 32(3), Doolan D. & Froelicher E. (2009) Using an existing data set to answer new research questions: a methodological review. Research Theory and Nursing Practice 23, Finlayson, M., Egan, M., & Black, C. (1999). Secondary analysis of survey data: a research method with potential for occupational therapists. Canadian Journal Of Occupational Therapy, 66(2), Gooding, B. (1988). Secondary analysis: A method for learning research activities. Journal of Nursing Education 27(5), Higgins JPT, Green S (editors) (2011). Cochrane Handbook for Systematic Reviews of Interventions Version [updated March 2011]. The Cochrane Collaboration. Retrieved from Health Insurance Portability and Accountability Act of 1996, 42 U.S.C. 1320d-9 (2010) Kneipp, S., & Yarandi, H. (2002). Complex sampling designs and statistical issues in secondary analysis. Western Journal Of Nursing Research, 24(5), Long-Sutehall, T., Sque, M., & Addington-Hall, J. (2011). Secondary analysis of qualitative data: a valuable method for exploring sensitive issues with an elusive population?. Journal Of Research In Nursing, 16(4), Magee, T., Lee, S., Giuliano, K., & Munro, B. (2006). Generating new knowledge from existing data: the use of large data sets for nursing research. Nursing Research, 55(2S), S Safron, C., Bloomrosen, M., Hammond, W., Labkoff, S., Markel-Fox, S., Tang, P., Detmer, D. (2007). Toward a National Framework for the Secondary Use of Health Data: An American Medical Informatics Association White Paper. Journal of the American Medical Informatics Association. 14, (1), 1 9. Saylor, J., Friedman, E., Lee, H. (2012). Navigating complex sample analysis using national survey data. Nursing Research, 61(3): Smith A, Ayanian J, Covinsky K, Landon B, McCarthy E, Wee C, Steinman M. (2011). Conducting high-value secondary dataset analysis: an introductory guide and resources. J Gen Intern Med Aug; 26(8): Sweeney, L (2013). Matching known patients to health records in Washington state data. Harvard University. dataprivacylab.org projects/wa. Retrieved from Talbert, S., Sole M. (2013). Too much information: research issues associated with large databases. Clinical Nurse Specialist, 27(2): Tucker, P. (2013). Business report: Has big data made anonymity impossible? MIT Technology Review. May 7. Retrieved from: Windle P. (2010) Secondary data analysis: is it useful and valid? Journal of Perianaesthesia Nursing, 25(5), Yiannakoulias, N. (2011). Understanding identifiability in secondary health data. Canadian Journal Of Public Health, 102(4), Yves-Alexandre de Montjoye, César A. Hidalgo, Michel Verleysen, and Vincent D. Blondel (2013). Unique in the crowd: The privacy bounds of human mobility." Scientific Reports. DOI: /srep01376
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