Anatomic Pathology Procedure Manual.
Surgical Pathology Reports, Vocabulary Listing: 60,000 words; 500,000 phrases.
Modal Logic Theory for Pathology Inference.
Spreadsheet Order Logic for Pathology Inference.
Infinite Papillomas: Model for Unbounded Tumor Growth.

The Johns Hopkins
Autopsy Resource
(JHAR)

G. William Moore, M.D., Ph.D,
Jules J. Berman, Ph.D., M.D.,
Grover M. Hutchins, M.D.
Robert E. Miller, M.D.
Source of over 50,000 indexed and searchable autopsy summaries.
From the Department of Pathology
of The Johns Hopkins Medical Institutions.

First Internet version, November 1995.
First Revised version, April 1996.
Second Revised version, July 1996.
Third Revised version, April 1999.
Fourth Revised version, September 1999.
The JHAR is Public Domain.

Sponsored by The Johns Hopkins Medical Institutions
Department of Pathology

Query the JHAR


NOTICE: Access to autopsy files has been temporarily suspended, in order to bring this website into compliance with HIPAA regulations.
HIPAA regulations went into effect April 14, 2003.
  1. The Johns Hopkins Autopsy Resource (JHAR) consists of human autopsy reports (text only), which have been computer-translated into concept-unique-identifiers Unified Medical Language System Metathesaurus of the United States National Library of Medicine.
  2. To begin a search, type in ONE OR TWO ENGLISH WORDS, and click on the SUBMIT button.
  3. The first twenty autopsy reports containing the desired term will be displayed.
  4. If you select a term which occurs only rarely, then you may have to wait a few minutes to obtain your results.
  5. Autopsy reports in the JHAR are presented in ascending order of age at death (in decades). Therefore, younger patients will appear first.
  6. If you select a term that appears only in older patients, then you may have to wait a few minutes to obtain your results.
  7. Each autopsy facesheet shows: age in decades, race, sex, and decade of autopsy for the patient.
  8. Some autopsy diagnoses may appear awkward, because they represent a computer-translation from the original autopsy report. In addition to word-translation errors, the computer-translator may also mistakenly divide sentences in the original autopsy report at the wrong positions.

JOHNS HOPKINS AUTOPSY COLLABORATIVE TISSUE RESOURCE


The Johns Hopkins Autopsy Collaborative Tissue Resource (JHACTR) provides access to formalin-fixed, paraffin-embedded human tissue, with associated clinical data for research studies, particularly studies focused on translating basic research findings into clinical applications. The JHACTR has a database that contains pathologic and clinical information about the large collection of human tissue specimens that are available for research. Through the JHACTR database, researchers can determine whether the JHACTR has the tissues and patient data they need for their individual research studies. It should be noted that some tissues in the JHACTR have long prefixation intervals or methods of fixation that might render them unsuitable for certain research protocols.

Eposters: Advancing Pathology Informatics, Imaging, Internet (APIII'2001).



Gödelization of a Pathology Database: Re-identification by Inference.
G. William Moore, MD, PhD Lawrence A. Brown, MD, Robert E. Miller, MD.
Arch Pathol Lab Med. 2002;:in press.

Goethe University Autopsy Register: Anonymized Bilingual Database.
W. Giere, MD. G. William Moore, MD, PhD Grover M. Hutchins, MD.
Arch Pathol Lab Med. 2002;:in press.

Eposters: Advancing Pathology Informatics, Imaging, Internet (APIII'2000).



Set Theory Definition and Algorithm for Medical De-Identification.
G. William Moore, MD, PhD Lawrence A. Brown, MD, Robert E. Miller, MD.
Arch Pathol Lab Med. 2001;:in press.

Web-based Free-Text Query System for Surgical Pathology Reports with Automatic Case De-Identification.
Robert E. Miller, MD, John K. Boitnott, MD, G. William Moore, MD, PhD.
Arch Pathol Lab Med. 2001;:in press.

UMLS Concordance for Human Embryology.
Gladys L. G. Alonsozana, MD, G. William Moore, MD, PhD, Grover M. Hutchins, MD.
Arch Pathol Lab Med. 2001;:in press.

UMLS Concordance for a Comprehensive Pathology Text.
John H. Sinard, MD, PhD, G. William Moore, MD, PhD.
Arch Pathol Lab Med. 2001;:in press.

Linguistic Inventory of the Johns Hopkins Surgical Pathology Database.
G. William Moore, MD, PhD, Robert E. Miller, MD.
Arch Pathol Lab Med. 2001;:in press.

Platform Presentations. Advancing Pathology Informatics, Imaging, Internet (APIII'2000).



UMLS Concordance for Pathology Text.
John H. Sinard, MD, PhD, Gladys L. G. Alonsozana, MD, Grover M. Hutchins, MD. G. William Moore, MD, PhD.

Free-Text Query System for Surgical Pathology Reports with Automatic Case De-Identification.
Robert E. Miller, MD, John K. Boitnott, MD, Lawrence A. Brown, MD, G. William Moore, MD, PhD.

Eposters: Advancing Pathology Informatics, Imaging, Internet (APIII'1999).



Automatic Indexing of a Pathology Image Archive using UMLS.
G. William Moore, M.D., Ph.D., David S. Brenner, M.D., Jules J. Berman, Ph.D., M.D.
Arch Pathol Lab Med. 2000;124:809.

Dermatopathology False Negative Terms in UMLS.
Grace F. Kao, M.D., G. William Moore, M.D, Ph.D.
Arch Pathol Lab Med. 2000 Jun;124:809.

Japanese Language Annotation of an Internet Pathology Image Archive.
Daisuke Nonaka, M.D, G. William Moore, M.D., Ph.D., Yoichi Satomura, M.D
Arch Pathol Lab Med. 2000 Jun;124:820.

Turkish Language Annotation of an Internet Pathology Image Archive.
G. William Moore, MD, PhD., Enver Vardar, MD. Yener S. Erozan, M.D., Fatih Durmusoglu, M.D.
Arch Pathol Lab Med. 2000 Jun;124:820.

INTERNET LINKS.


  1. Internet-Based Quality Improvement Documentation at the VAMHCS.
  2. Over 5,000 image-legends from the U. S. Armed Forces Institute of Pathology Electronic Fascicles.
  3. U. S. Department of Health & Human Services: Standards for Privacy of Individually Identifiable Health Information.
  4. Translations of medical vocabulary into foreign languages.
  5. Free computer translation of short texts.
  6. Pathology-to-UMLS Translator, Surgical Pathology Examples.
  7. Pathology-to-UMLS Translator, Autopsy Examples.
  8. Pathology-to-UMLS Translator, Congenital Heart Disease Examples.
  9. U. S. Natl Library Medicine Unified Medical Language System (UMLS).
  10. U. S. Natl Library Medicine UMLS Metathesaurus Documentation.
  11. U. S. Natl Library Medicine Medical Subject Headings (MeSH).
  12. U. S. Natl Library Medicine UMLS Knowledge Sources.
  13. U. S. Natl Cancer Institute Human Tissue Archive.
    Prospective procurement of human tissues for research.
  14. U. S. Natl Cancer Institute Breast Cancer Tissue Resource.
    Prospective procurement of human breast tissue for research.
  15. U. S. Natl Cancer Institute Human Tissue Resource.
    Prospective procurement of human tissue for research.
  16. Systematized Nomenclature of Human and Veterinary Medicine (SNOMED).
  17. College of American Pathologists (CAP).
  18. Bibliography of Studies on Staged Human Embryos.
  19. Bibliography of Studies on JHAR Autopsies.
  20. http://grants.nih.gov/grants/guide/rfa-files/RFA-CA-01-006.html The objective of this initiative for a SHARED PATHOLOGY INFORMATICS NETWORK is to create a model Web-based system to access data related to archived human specimens at multiple institutions.
  21. Vanderbilt, Hopkins, Pittsburgh Shared Pathology Informatics Network: Appendix Six: Demographic and Linguistic Inventory of the Johns Hopkins Surgical Pathology Database.
  22. Student Lecture on Computer Privacy of Individually Identifiable Medical Information. Presented: December 6, 2000, Baltimore City College High School, Baltimore, MD.
  23. SNOMED is the Systematized Nomenclature of Human and Veterinary Medicine, and consists of over 280,000 medical terms. For further information related to SNOMED, please visit the College of American Pathologists website. .
  24. Moore GW, Berman JJ.
    Anatomic pathology data mining. Chapter 4. In: Cios KJ. Medical Data Mining and Knowledge Discovery. Published, December 4, 2000, within the series: "Studies in Fuzziness and Soft Computing", Physica-Verlag Heidelberg, a Springer-Verlag Company. 2001. XVIII, 502 pp. 98 figs., 98 tabs. Hardcover. ISBN: 3-7908-1340-0.
    Copyright Springer-Verlag: Berlin/Heidelberg 1999.
  25. Prof. R. L. Rivest's cryptography and security page.
    http://theory.lcs.mit.edu/~rivest/crypto-security.html
    Prof. Rivest is the R in the RSA (Rivest-Shamir-Adelman) public-private cryptography algorithm, one of the intellectual masterpieces of this century.
  26. USNLM Publications on Ethical Issues in Research involving Human Subjects, including autopsy research.
    http://www.nlm.nih.gov/pubs/cbm/hum_exp.html
  27. U. S. Code of Federal Regulations, 45 CFR Subtitle A (10-1-95 Edition), part 46.101 (b) (4). The complete Common Rule document (45CFR46), on human subjects research, at URL:
    http://www.uaf.edu/oar/irb/45cfr46.html
    or at URL:
    http://ohrp.osophs.dhhs.gov/humansubjects/guidance/45cfr46.htm
  28. U. S. Department of Health and Human Services. Standards for Privacy of Individually Identifiable Health Information.
    http://aspe.hhs.gov/admnsimp/
  29. Human subjects research, including autopsies: National Bioethics Advisory Commission (NBAC). Executive Order 12975, October 3, 1995. Federal Register: October 5, 1995. v. 60.; no. 193. pp. 52063-52065.
    http://bioethics.gov/general.html
  30. National Bioethics Advisory Commission (NBAC), Recommendations to the Common Rule:
    http://bioethics.gov/pubs.html
  31. U. S. Government Printing Office, Superintendent of Documents, including Federal Register.
    http://www.access.gpo.gov/su_docs/
  32. The University of Mississippi Multiple Project Assurance Document for human subjects research, at URL:
    http://www.olemiss.edu/depts/research/irb/assurance.htm
  33. National Cancer Institute's Confidentiality Brochure, at URL:
    http://www-cdp.ims.nci.nih.gov/policy.html
  34. Office of Human Research Protections (OHRP), within OPHS, DHHS (formerly, Office for Protection from Research Risks (OPRR)), at URL:
    http://ohrp.osophs.dhhs.gov
  35. Joint Commission on Accreditation of Healthcare Organizations.
    http://www.jcaho.org
  36. National Committee for Clinical Laboratory Standards (NCCLS).
    http://www.nccls.org
  37. College of American Pathologists.
    http://www.cap.org
  38. United States and Canadian Academy of Pathology.
    http://www.uscap.org
  39. The Johns Hopkins Autopsy Resource.
    http://www.medparse.com/
  40. Description of the Veterans Affairs VistA computer system.
    http://www.hardhats.org/
  41. How to obtain a nominal-cost CD of the non-confidential parts of the Veterans Affairs VistA computer system, through the Freedom of Information Act (FOIA).
    http://www.hardhats.org/foia.html
  42. Surgical Pathology Reports: Vocabulary Listing.
    http://www.medparse.com/jharsprw.htm
  43. Autopsy Reports: Vocabulary Listing.
    http://www.medparse.com/jharaurw.htm
  44. Dr. Ed Friedlander's Introduction to the Autopsy.
    http://www.pathguy.com/autopsy.htm
  45. Dr. G. William Moore's Pathology Informatics Bookshelf.
    http://www.medparse.com/jharbksf.htm
  46. Dr. Jules J. Berman's Lightning Hypertext of Disease.
    http://www.pathinfo.com/
  47. Dr. Ed O. Uthman's Introduction to the Autopsy.
    http://www.neosoft.com/~uthman/
  48. Dr. Shawn E. Cowper's Pathology Education Websites.
    http://www.pathmax.com/
  49. University of Rochester Pathology Resources.
    http://www.urmc.rochester.edu/smd/pathres/long.html
  50. University of Michigan Pathology Resources.
    http://141.214.5.219/pathresourceak/path_resources.html
  51. Armed Forces Institute of Pathology Autopsy Diagrams.
    http://www.afip.org/homes/oafme/diagrams.html
  52. Tulane University Autopsy Pathology Images.
    http://www.som.tulane.edu/classware/pathology/medical_pathology/McPath
  53. University of Leicester Autopsy Cases.
    http://www.le.ac.uk/pathology/teach/va2/titlpag1.html
  54. Internet Pathology Laboratory for Medical Education.
    http://www-medlib.med.utah.edu/WebPath/webpath.html
  55. PubMed Stop Words (U. S. National Library of Medicine):
    http://www.ncbi.nlm.nih.gov/entrez/query/static/help/pmhelp.html#Stopwords
  56. PubMed Help (U. S. National Library of Medicine):
    http://www.ncbi.nlm.nih.gov/entrez/query/static/help/pmhelp.html
  57. PubMed Stop Words: Local Copy.
    http://www.medparse.com/umlsstop.htm
  58. Synonyms for UMLS Concept Unique Identifiers:
    http://www.medparse.com/umlspsdo.htm
  59. German Language Stop Words.
    http://www.medparse.com/deutbarr.htm
  60. German Language Collocations (Frankfurt Autopsy Resource).
    http://www.medparse.com/deutcoll.htm
  61. General Information about Pathology and Autopsies.
    http://www.medparse.com/neta0405.htm
  62. Thoughts about Pathology as a Career.
    http://www.medparse.com/billgrow.htm
  63. Dr. G. William Moore's Speculations about Mathematics.
    http://www.medparse.com/jharempr.htm
  64. Dr. G. William Moore's Introduction to the Internet.
    http://www.medparse.com/whatnett.htm
  65. Dr. G. William Moore's Introduction to Calculus.
    http://www.medparse.com/whatcalc.htm
  66. Dr. G. William Moore's Introduction to Medical Differential Diagnosis.
    http://www.medparse.com/whatdfdx.htm
  67. Dr. G. William Moore's Introduction to Artificial Intelligence.
    http://www.medparse.com/whatisai.htm
  68. Dr. G. William Moore's Introduction to Medical Ontologies.
    http://www.medparse.com/whatonto.htm
  69. Dr. G. William Moore's Introduction to Cryptography.
    http://www.medparse.com/whatcryp.htm
  70. Dr. G. William Moore's Introduction to Pathology Informatics.
    http://www.medparse.com/whatpinf.htm
  71. Practice Guidelines for Autopsy Pathology
    Hutchins GM, Berman JJ, Moore GW, Hanzlick R, and the Autopsy Committee of the College of American Pathologists.
    Archives of Pathology and Laboratory Medicine. 1999; 123:1085-1092.
  72. Moore GW, Berman JJ, Sydnor DL.
    Fractal dimension for pathology images, a repeatable and quantitative measurement of nuclear rim irregularity.
    Am J Clin Pathol 102:538, 1994.
  73. Moore GW, Berman JJ, Moore GW, Brown LA.
    Software for image segmentation and analysis in pathology (ISAP): public domain image software and source code developed at the Baltimore VA Medical Center.
    Am J Clin Pathol 102:538-539, 1994.
  74. Moore GW, Berman JJ, Sydnor DL.
    Automated edge detection in image analysis: distinguishing the nucleus from the cytoplasm without a user's threshold estimate.
    Am J Clin Pathol 102:539, 1994.

Over 5,000 pathology image links!

Visit our tumor image site, and query over 5,000 images related to skin, breast, uterus, cervix, cns, eye, bone marrow, thyroid, parathyroid, kidney, bone and lung.

Gene Links!

Visit our Genbank link page and query over 57,000 indexed molecular species.

What is The Johns Hopkins Autopsy Resource (JHAR)?

The The Johns Hopkins Autopsy Resource (JHAR) is a collection of over 50,000 autopsy facesheets, contributed by the Department of Pathology of The Johns Hopkins Medical Institutions. An autopsy facesheet is the summary of final diagnoses, which typically appears as the first page in an autopsy report. For each facesheet record in the JHAR, there is a DEMOGRAPHIC LINE , followed by DIAGNOSES . In order to maintain confidentiality, no names of patients, or care-givers are present in the demographic line. Confidentiality is protected by a double-brokered encryption of patient identifiers, which requires the participation of both the . JHAR administrator, . and the contributing institution to decrypt. The only demographic information is: age in decades, race, sex, decade of autopsy, and key-number. The DIAGNOSES in the original autopsy facesheet have been stripped of names of persons, locations, and institutions; and diagnoses have been automatically translated into SNOMED-compatible terms. This plan of anonymization of medical records follows guidelines recently discussed in the United States Senate.

      Contributions of autopsy facesheets are being accepted from academic medical centers worldwide. For additional information, send queries to the JHAR administrator, at: webmaster@medparse.com .

JHAR Scientific Directors

G. William Moore, MD, PhD
Jules J. Berman, PhD, MD
Grover M. Hutchins, MD
Robert E. Miller, MD.
Selected Full Text Publications of Drs. Berman, Moore and Hutchins, and Miller.

Confidentiality and Institutional Approval.

The autopsy facesheets in The Johns Hopkins Autopsy Collaborative Tissue Resource represent confidential medical records of deceased patients. Every reasonable effort has been made to protect the identity of the patient and care-givers, particularly in placing a component of the medical record in such a public venue as the Internet. The only mechanism for obtaining more information regarding an individual JHAR facesheet is to correspond with the database administrator, who will forward your letter to the appropriate official in the Department of Pathology of The Johns Hopkins Medical Institutions. The Johns Hopkins Medical Institutions will respond in accordance with policies set by the Institutional Review Board (IRB).

Quick Overview of the JHAR.


To gain a quick overview of the autopsy facesheet files available in the JHAR, translated into the Unified Medical Language System (UMLS), you should download ONE of the autopsy facesheet files.
NOTICE: Access to autopsy files has been temporarily suspended, in order to bring this website into compliance with HIPAA regulations.
HIPAA regulations go into effect April 14, 2003.
ALL FILES ARE IN XML FORMAT, BUT MAY BE DISPLAYED ON AN ORDINARY (HTML) INTERNET BROWSER, SUCH AS NETSCAPE OR INTERNET EXPLORER.
NOTICE: Access to autopsy files has been temporarily suspended, in order to bring this website into compliance with HIPAA regulations.
HIPAA regulations went into effect April 14, 2003.

NOTICE: The Johns Hopkins Autopsy Resource (JHAR) website has Institutional Review Board (IRB) approval from The Johns Hopkins Medical Institutions, and is in compliance with applicable U. S. Federal Guidelines [1,2]. The JHAR consists of DE-IDENTIFIED MEDICAL INFORMATION (DIMI). Compliance is insured through the following steps:
  • The DEMOGRAPHICS are reduced to four packets of information: age in decades, race, sex, and decade of autopsy. These minimal public demographics are sufficient for determining the general clinical context of a case, but are insufficient to reveal the identity of the individual patient. Exact identifiers noted by the Health Insurance Portability and Accountability Act include:
    "Name; address, including street address, city, county, zip code, or equivalent geocodes; names of relatives and employers; birth date; telephone and fax numbers; e-mail addresses; social security number; medical record number; health plan beneficiary number; account number; certificate/license number; any vehicle or other device serial number; web URL; Internet Protocol (IP) address; finger or voice prints; photographic images; and any other unique identifying number, characteristic, or code (whether generally available in the public realm or not) that the covered entity has reason to believe may be available to an anticipated recipient of the information...."


  • A unique, DOUBLE-BROKERED KEY-NUMBER [6] is assigned to each case by an unbreakable encryption system, the so-called ONE-TIME PAD METHOD [24]. As an additional security measure, in some cases, THE KEY-NUMBER MAY CORRESPOND TO UP TO TEN INDIVIDUAL PATIENTS. This means that, even if you guess that a particular autopsy facesheet in the JHAR might correspond to a particular patient, you still do not know whether this key-number corresponds to other patients, as well. The identity of a given key-number, and whether this key-number corresponds to more than one patient, can only be obtained through negotiation with The Johns Hopkins Medical Institutions Department of Pathology and Institutional Review Board, exclusively for legitimate medical research projects.

  • The NUMERIC INFORMATION contained in each patient-record consists EXCLUSIVELY of two numbers, which cannot be traced to an individual patient, namely: age in decades and decade of autopsy.

  • In each record, there are NO NAMES, NO DATES, NO INSTITUTIONS, NO LOCATIONS, and NO QUANTITIES, except for age in decades and decade of autopsy.

  • All diagnoses correspond to UMLS CODES, not necessarily to the exact language of the original autopsy report. The UMLS codes are expressed in plain English, corresponding to the generic medical language used in The Johns Hopkins Medical Institutions, NOT to the language of specific, proprietary coding systems. English terms that happen to match to those proprietary coding systems are purely coincidental.

  • NO PATIENTS DECEASED SINCE 1997 (WITHIN THE LAST TWO YEARS) are listed in the JHAR, in compliance with U. S. Federal guidelines [2].

  • The proposed new law goes into force in year 2001, at the earliest.

  • In this time interval, NO ADDITIONAL PATIENTS WILL BE ADDED TO THE JHAR, until further testing of the system is completed.

  • PENDING STATISTICAL STUDIES of the JHAR show that patients are NOT uniquely identified by demographics, since numerical demographics are grouped by age in decades and decade of autopsy; and since each keynumber may match up to ten actual patients.

  • ATTEMPTS TO RE-IDENTIFY PATIENTS in the JHAR medical research resource are UNLAWFUL, and subject to civil and criminal penalties [2], as follows:
    "Section 1177 establishes penalties for any person that knowingly uses a unique health identifier, or obtains or discloses individually identifiable health information in violation of the part. The penalties include: (1) A fine of not more than $50,000 and/or imprisonment of not more than 1 year; (2) if the offense is ``under false pretenses,'' a fine of not more than $100,000 and/or imprisonment of not more than 5 years; and (3) if the offense is with intent to sell, transfer, or use individually identifiable health information for commercial advantage, personal gain, or malicious harm, a fine of not more than $250,000 and/ or imprisonment of not more than 10 years. We note that these penalties do not affect any other penalties that may be imposed by other federal programs."


    • PRIVACY AND CRYPTOGRAPY REFERENCES.

           1. U. S. Code of Federal Regulations, 45 CFR Subtitle A (10-1-95 Edition), part 46.101 (b) (4). The complete Common Rule document (45CFR46), at URL:
    http://www.uaf.edu/oar/irb/45cfr46.html
    or at URL:
    http://ohrp.osophs.dhhs.gov/humansubjects/guidance/45cfr46.htm

           2. U. S. Department of Health and Human Services. Standards for Privacy of Individually Identifiable Health Information.
    Fed Regist. 1999 Nov 3;64(212):59917-59966. http://aspe.hhs.gov/admnsimp/

           3. Protection of human subjects: categories of research that may be reviewed by the Institutional Review Board (IRB) through an expedited review procedure--FDA. Notice.
    Fed Regist. 1998 Nov 9;63(216 Pt 1):60353-60356.
    PMID: 10187395; UI: 99080910.

           4. Berman JJ, Moore GW, Hutchins GM.
    Maintaining patient confidentiality in the public domain Internet Autopsy Database (IAD).
    Proc AMIA Annu Fall Symp. 1996;:328-332.
    PMID: 8947682; UI: 97103310.

           5. Berman JJ, Moore GW, Hutchins GM.
    U. S. Senate Bill 422. The Genetic Confidentiality and Nondiscrimination Act of 1997.
    Diagn Mol Pathol. 1998 Aug;7(4):192-196.
    PMID: 9917128; UI: 99114200.

           6. Sweeney L.
    Computational Disclosure Control:     A Primer on Data Privacy Protection.
    PhD Thesis. Massachusetts Institute of Technology. Spring, 2001. Draft.
    Summary of several current systems for computational disclosure control, used in the USA and in the European Community.

           7. Sweeney L.
    Three computational systems for disclosing medical data in the year 1999.
    Medinfo. 1998;9 Pt 2:1124-1129.
    PMID: 10384634; UI: 99312628.

           8. Sweeney L.
    Privacy and medical-records research.
    N Engl J Med. 1998 Apr 9;338(15):1077; discussion 1077-1078.
    PMID: 9537887; UI: 98181820.

           9. Sweeney L.
    Guaranteeing anonymity when sharing medical data, the Datafly System.
    Proc AMIA Annu Fall Symp. 1997;:51-55.
    PMID: 9357587; UI: 98020458.

           10. Sweeney L.
    Replacing personally-identifying information in medical records, the Scrub system.
    Proc AMIA Annu Fall Symp. 1996;:333-337.
    PMID: 8947683; UI: 97103311.

           11. Moore GW, Berman JJ, Hanzlick RL, Buchino JJ, Hutchins GM.
    A prototype internet autopsy database: 1625 consecutive fetal and neonatal autopsy facesheets spanning twenty years.
    Arch Pathol Lab Med. 1996; 120:782-785.

           12. Berman JJ, Moore GW, Hutchins GM.
    Internet Autopsy Database.
    Human Pathol. 1997; 28:393-394.

           13. Carter JR, Nash NP, Cechner RL, Platt RD.
    Proposal for a national autopsy data bank. A potential major contribution of pathologists to the health care of the nation.
    Am J Clin Pathol. 76 (Suppl): 597-617, 1981.

           14. Peery TM.
    The autopsy data bank. A proposal for pathologists to contribute to the health care of the nation.
    Am J Clin Pathol 69 (Suppl): 258-259, 1978.

           15. Wagner BM.
    The future of environmental and toxicologic pathology.
    Human Pathol. 27:1003-1004, 1996.

           16. Mullick F.
    The Center for Environmental Pathology and Toxicology at the Armed Forces Institute of Pathology.
    Human Pathology 52: 752-753, 1997.

           18. U. S. Government Documents: http://thomas.loc.gov

           19. National Bioethics Advisory Commission (NBAC).
    http://bioethics.gov/general.html
    Executive Order 12975, October 3, 1995.
    Federal Register: October 5, 1995. v. 60.; no. 193. pp. 52063-52065

           20. National Bioethics Advisory Commission (NBAC), Recommendations to the Common Rule:
    http://bioethics.gov/pubs.html

           21. U.S. National Library of Medicine.
    Unified Medical Language System.
    http://www.nlm.nih.gov/research/umls/

           22. Schneier B.
    Applied Cryptography, Second Edition. Protocols, Algorithms, and Source Code in C.
    New York: John Wiley & Sons, 1996.

           23. Moore GW, Brown LA, Miller RE.
    Set Theory Definition and Algorithm for Medical De-Identification.
    Arch Pathol Lab Med. 2001;:in press.

           24. The University of Mississippi has published its Multiple Project Assurance Document at URL:
    http://www.olemiss.edu/depts/research/irb/assurance.htm

           25. National Cancer Institute's Confidentiality Brochure, at URL:
    http://www-cdp.ims.nci.nih.gov/policy.html

           26. Office of Human Research Protections (OHRP), within OPHS, DHHS (formerly, Office for Protection from Research Risks (OPRR)), at URL:
    http://ohrp.osophs.dhhs.gov

    DEMOGRAPHICS DISTRIBUTION.
    AGE IN DECADES vs. DECADE OF AUTOPSY.
    SECURITY AGAINST PATIENT RE-IDENTIFICATION.
    AGE 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990
    0-9 267 339 1334 2731 2237 2171 2424 2040 1234 1100 801
    10-19 83 137 128 238 272 245 175 199 174 112 55
    20-29 200 282 238 335 404 336 207 178 167 217 79
    30-39 225 280 289 419 566 474 478 351 228 302 201
    40-49 266 305 305 480 630 650 858 772 484 341 282
    50-59 226 261 253 420 642 653 1138 1127 867 515 248
    60-69 114 153 157 336 450 581 1095 1342 999 712 337
    70-79 55 55 68 152 222 263 743 1008 698 547 287
    >80 14 16 9 23 51 59 261 483 273 239 192


          DISCUSSION: A patient is potentially identifiable by demographics alone if there is one and only one patient within a single demographic category.

          The number of cases in each demographic category (age in decades vs. decade of autopsy) is shown above. This table demonstrates that, for the most part, no single demographic category contains a unique element. Thus in general, demographic information alone on JHAR cases does not suffice to uniquely identify the patient.

          The demographic categories most vulnerable to re-identification are octogenarians from the end of the nineteenth and beginning of the twentieth century, when relatively few patients lived to that age. The single patient identifiable by demographics alone is an octagenarian female from the 1910 decade (see next panel). Patients deceased for over fifty years are least likely to have significant confidentiality issues, but may possibly contribute to significant research perspectives.

          It is the intention of the emerging U. S. Federal privacy guidelines to encourage epidemiologic research, even if there are possible privacy issues for long-deceased patients.

    DEMOGRAPHICS DISTRIBUTION.
    SEX / AGE IN DECADES vs. DECADE OF AUTOPSY.
    SECURITY AGAINST PATIENT RE-IDENTIFICATION.
    AGE 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990
    0-9M 194 189 740 1525 1257 1198 1309 1117 681 583 273
    0-9F 73 150 594 1206 980 973 1115 923 553 517 528
    10-19M 33 70 63 111 116 135 91 114 92 73 29
    10-19F 50 67 65 127 156 110 84 85 82 39 26
    20-29M 123 168 147 176 186 166 75 87 91 118 38
    20-29F 77 114 91 159 218 170 132 91 76 99 41
    30-39M 130 179 157 218 291 237 207 164 125 153 96
    30-39F 95 101 132 201 275 237 271 187 103 149 105
    40-49M 163 212 212 305 362 380 466 396 293 192 163
    40-49F 103 93 93 175 268 270 392 376 191 149 119
    50-59M 150 200 191 292 428 390 673 655 486 281 168
    50-59F 76 61 62 128 214 263 465 472 381 234 80
    60-69M 80 118 126 253 310 405 676 812 579 432 233
    60-69F 34 35 31 83 140 176 419 530 420 280 104
    70-79M 41 45 58 132 162 176 435 551 377 323 220
    70-79F 14 10 10 20 60 87 308 457 321 224 67
    >80M 8 12 8 19 37 34 143 242 126 119 154
    >80F 6 4 1 4 14 25 118 241 147 120 38

    With a single exception, namely the single >80F born in the 1910-1919 decade, the demographics in the JHAR satisfy Sweeney's definition of k-ANONYMOUS, for k=4, as described in:
    Sweeney L.
    Computational Disclosure Control. A Primer on Data Privacy Protection.
    MIT. PhD Thesis, Spring 2001. Draft.


    For additional information, send queries to the JHAR administrator, at URL: . George.Moore4@med.va.gov .


    Last Updated: July 13, 2005, G. William Moore, MD, PhD.