Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on medication adherence

LIVE: Thursday 27th June 2024, 18:00-19:30 (CEST)
Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.
ADVANCED SEARCH
Log in
Top
BMC Medical Research Methodology
Published in: BMC Medical Research Methodology 1/2008

Open Access 01-12-2008 | Research article

Challenges of self-reported medical conditions and electronic medical records among members of a large military cohort

Authors: Besa Smith, Laura K Chu, Tyler C Smith, Paul J Amoroso, Edward J Boyko, Tomoko I Hooper, Gary D Gackstetter, Margaret AK Ryan, the Millennium Cohort Study Team

Published in: BMC Medical Research Methodology | Issue 1/2008

Login to get access

Abstract

Background

Self-reported medical history data are frequently used in epidemiological studies. Self-reported diagnoses may differ from medical record diagnoses due to poor patient-clinician communication, self-diagnosis in the absence of a satisfactory explanation for symptoms, or the "health literacy" of the patient.

Methods

The US Department of Defense military health system offers a unique opportunity to evaluate electronic medical records with near complete ascertainment while on active duty. This study compared 38 self-reported medical conditions to electronic medical record data in a large population-based US military cohort. The objective of this study was to better understand challenges and strengths in self-reporting of medical conditions.

Results

Using positive and negative agreement statistics for less-prevalent conditions, near-perfect negative agreement and moderate positive agreement were found for the 38 diagnoses.

Conclusion

This report highlights the challenges of using self-reported medical data and electronic medical records data, but illustrates that agreement between the two data sources increases with increased surveillance period of medical records. Self-reported medical data may be sufficient for ruling out history of a particular condition whereas prevalence studies may be best served by using an objective measure of medical conditions found in electronic healthcare records. Defining medical conditions from multiple sources in large, long-term prospective cohorts will reinforce the value of the study, particularly during the initial years when prevalence for many conditions may still be low.
Appendix
Available only for authorised users
Literature
1.
Colditz GA, Martin P, Stampfer MJ, Willett WC, Sampson L, Rosner B, et al: Validation of questionnaire information on risk factors and disease outcomes in a prospective cohort study of women. Am J Epidemiol. 1986, 123 (5): 894-900.PubMed
2.
Bush TL, Miller SR, Golden AL, Hale WE: Self-report and medical record report agreement of selected medical conditions in the elderly. Am J Public Health. 1989, 79 (11): 1554-6.CrossRefPubMedPubMedCentral
3.
Paganini-Hill A, Chao A: Accuracy of recall of hip fracture, heart attack, and cancer: a comparison of postal survey data and medical records. Am J Epidemiol. 1993, 138 (2): 101-6.PubMed
4.
Haapanen N, Miilunpalo S, Pasanen M, Oja P, Vuori I: Agreement between questionnaire data and medical records of chronic diseases in middle-aged and elderly Finnish men and women. Am J Epidemiol. 1997, 145 (8): 762-9.CrossRefPubMed
5.
Bergmann MM, Byers T, Freedman DS, Mokdad A: Validity of self-reported diagnoses leading to hospitalization: a comparison of self-reports with hospital records in a prospective study of American adults. Am J Epidemiol. 1998, 147 (10): 969-77.CrossRefPubMed
6.
Walker MK, Whincup PH, Shaper AG, Lennon LT, Thomson AG: Validation of patient recall of doctor-diagnosed heart attack and stroke: a postal questionnaire and record review comparison. Am J Epidemiol. 1998, 148 (4): 355-61.CrossRefPubMed
7.
Lampe FC, Walker M, Lennon LT, Whincup PH, Ebrahim S: Validity of a self-reported history of doctor-diagnosed angina. J Clin Epidemiol. 1999, 52 (1): 73-81. 10.1016/S0895-4356(98)00146-2.CrossRefPubMed
8.
Martin LM, Leff M, Calonge N, Garrett C, Nelson DE: Validation of self-reported chronic conditions and health services in a managed care population. Am J Prev Med. 2000, 18 (3): 215-8. 10.1016/S0749-3797(99)00158-0.CrossRefPubMed
9.
Horner RD, Cohen HJ, Blazer DG: Accuracy of self-reported stroke among elderly veterans. Aging Ment Health. 2001, 5 (3): 275-81. 10.1080/13607860120065041.CrossRefPubMed
10.
Okura Y, Urban LH, Mahoney DW, Jacobsen SJ, Rodeheffer RJ: Agreement between self-report questionnaires and medical record data was substantial for diabetes, hypertension, myocardial infarction and stroke but not for heart failure. J Clin Epidemiol. 2004, 57 (10): 1096-103. 10.1016/j.jclinepi.2004.04.005.CrossRefPubMed
11.
St Sauver JL, Hagen PT, Cha SS, Bagniewski SM, Mandrekar JN, Curoe AM, et al: Agreement between patient reports of cardiovascular disease and patient medical records. Mayo Clin Proc. 2005, 80 (2): 203-10.CrossRefPubMed
12.
Skinner KM, Miller DR, Lincoln E, Lee A, Kazis LE: Concordance between respondent self-reports and medical records for chronic conditions: experience from the Veterans Health Study. J Ambul Care Manage. 2005, 28 (2): 102-10.CrossRefPubMed
13.
Rahman A, Gibney L, Person SD, Williams OD, Kiefe C, Jolly P, et al: Validity of self-reports of reasons for hospitalization by young adults and risk factors for discordance with medical records: the Coronary Artery Risk Development in Young Adults (CARDIA) Study. Am J Epidemiol. 2005, 162 (5): 491-8. 10.1093/aje/kwi215.CrossRefPubMed
14.
Goldman N, Lin IF, Weinstein M, Lin YH: Evaluating the quality of self-reports of hypertension and diabetes. J Clin Epidemiol. 2003, 56 (2): 148-54. 10.1016/S0895-4356(02)00580-2.CrossRefPubMed
15.
Ryan MA, Smith TC, Smith B, Amoroso P, Boyko EJ, Gray GC, et al: Millennium Cohort: enrollment begins a 21-year contribution to understanding the impact of military service. J Clin Epidemiol. 2007, 60 (2): 181-91. 10.1016/j.jclinepi.2006.05.009.CrossRefPubMed
16.
Gray GC, Chesbrough KB, Ryan MA, Amoroso P, Boyko EJ, Gackstetter GD, et al: The Millennium Cohort Study: a 21-year prospective cohort study of 140,000 military personnel. Mil Med. 2002, 167 (6): 483-8.PubMed
17.
Wells TS, Jacobson IG, Smith TC, Spooner CN, Smith B, Reed RJ, et al: Prior health care utilization as a determinant to enrollment in a 22-year prospective study; the Millennium Cohort Study. Eur J Epidemiol. 2008, 23 (2): 79-87. 10.1007/s10654-007-9216-0.CrossRefPubMed
18.
Smith TC, Smith B, Jacobson IG, Corbeil TE, Ryan MA: Reliability of standard health assessment instruments in a large, population-based cohort study. Ann Epidemiol. 2007, 17 (7): 525-32. 10.1016/j.annepidem.2006.12.002.CrossRefPubMed
19.
LeardMann CA, Smith B, Smith TC, Wells TS, Ryan MA: Smallpox vaccination: comparison of self-reported and electronic vaccine records in the Millennium Cohort Study. Hum Vaccin. 2007, 3 (6): 245-51.CrossRefPubMed
20.
Smith B, Leard CA, Smith TC, Reed RJ, Ryan MA: Anthrax vaccination in the Millennium Cohort: validation and measures of health. Am J Prev Med. 2007, 32 (4): 347-53. 10.1016/j.amepre.2006.12.015.CrossRefPubMed
21.
Smith TC, Jacobson IG, Smith B, Hooper TI, Ryan MA: The occupational role of women in military service: validation of occupation and prevalence of exposures in the Millennium Cohort Study. Int J Environ Health Res. 2007, 17 (4): 271-84. 10.1080/09603120701372243.CrossRefPubMed
22.
Smith B, Wingard DL, Ryan MA, Macera CA, Patterson TL, Slymen DJ: U.S. military deployment during 2001–2006: comparison of subjective and objective data sources in a large prospective health study. Ann Epidemiol. 2007, 17 (12): 976-82. 10.1016/j.annepidem.2007.07.102.CrossRefPubMed
23.
Smith B, Smith TC, Gray GC, Ryan MAK: When epidemiology meets the Internet: Web-based surveys in the Millennium Cohort Study. Am J Epidemiol. 2007, 166 (11): 1345-54. 10.1093/aje/kwm212.CrossRefPubMed
24.
International classification of diseases, 9th revision, clinical modification: 2005, Salt Lake City: Medicode Publication
25.
National Center for Health Statistics: Conversion Table of New ICD-9-CM Codes. 2005
26.
Cicchetti DV, Feinstein AR: High agreement but low kappa: II. Resolving the paradoxes. J Clin Epidemiol. 1990, 43 (6): 551-8. 10.1016/0895-4356(90)90159-M.CrossRefPubMed
27.
American Medical Association: Ad Hoc Committee on Health Literacy for the Council on Scientific Affairs. JAMA. 1999, 281: 552-57. 10.1001/jama.281.6.552.CrossRef
28.
Riddle JR, Smith TC, Smith B, Corbeil TE, Engel CC, Wells TS, et al: Millennium Cohort: the 2001–2003 baseline prevalence of mental disorders in the U.S. military. J Clin Epidemiol. 2007, 60 (2): 192-201. 10.1016/j.jclinepi.2006.04.008.CrossRefPubMed
Metadata
Title
Challenges of self-reported medical conditions and electronic medical records among members of a large military cohort
Authors
Besa Smith
Laura K Chu
Tyler C Smith
Paul J Amoroso
Edward J Boyko
Tomoko I Hooper
Gary D Gackstetter
Margaret AK Ryan
the Millennium Cohort Study Team
Publication date
01-12-2008
Publisher
BioMed Central
Published in
BMC Medical Research Methodology / Issue 1/2008
Electronic ISSN: 1471-2288
DOI
https://doi.org/10.1186/1471-2288-8-37

Other articles of this Issue 1/2008

BMC Medical Research Methodology 1/2008 Go to the issue

Research article

Comparison of two dependent within subject coefficients of variation to evaluate the reproducibility of measurement devices

Research article

Engaging participants in a complex intervention trial in Australian General Practice

Research article

Maximising response rates in household telephone surveys

Research article

Methods for the thematic synthesis of qualitative research in systematic reviews

Research article

Assessing the order of magnitude of outcomes in single-arm cohorts through systematic comparison with corresponding cohorts: An example from the AMOS study

Research article

A comparison of two methods for estimating odds ratios: Results from the National Health Survey