IDEAS home Printed from https://ideas.repec.org/a/aph/ajpbhl/10.2105-ajph.72.2.133_5.html
   My bibliography  Save this article

On the distribution of underlying causes of death

Author

Listed:
  • Gittelsohn, A.M.

Abstract

The feasibility of applying surveillance techniques to large health data is being explored through study of a national mortality data base encompassing 21 million United States death records for the period 1968-1978. Through the development of efficient file structures and information recovery techniques, it is possible to pose a series of questions and follow-up questions of the entire data set within budgetary constraints. Initial screening of the mortality data base reveals that major changes have occurred over the 11 years with marked declines for diseases of cardiovascular, respiratory, digestive and renal systems and maternal and perinatal mortality. There is a tendency for increased usage of non-specific terminology. The occurrence of unlikely and unusual causes in the data set is documented and reasons for their inclusion discussed in terms of underlying cause of death logic. Problems in the study of geographic distribution of cause specific mortality are outlined with illustrations of the dispersion of standardized mortality ratios for major causes of death over areas of the country. Clusters of high mortality areas require interpretation in terms of underlying dispersion and possible reporting artifacts arising out of geographic differentials in diagnostic labeling practice.

Suggested Citation

  • Gittelsohn, A.M., 1982. "On the distribution of underlying causes of death," American Journal of Public Health, American Public Health Association, vol. 72(2), pages 133-140.
  • Handle: RePEc:aph:ajpbhl:10.2105/ajph.72.2.133_5
    DOI: 10.2105/AJPH.72.2.133
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.2105/AJPH.72.2.133
    Download Restriction: no

    File URL: https://libkey.io/10.2105/AJPH.72.2.133?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Smirnova, Ekaterina & Ivanescu, Andrada & Bai, Jiawei & Crainiceanu, Ciprian M., 2018. "A practical guide to big data," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 25-29.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:aph:ajpbhl:10.2105/ajph.72.2.133_5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Christopher F Baum (email available below). General contact details of provider: https://www.apha.org .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.