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Related Experiment Videos

Multiple cause-of-death data as a tool for detecting artificial trends in the underlying cause statistics: a

B I Lindahl1, L A Johansson

  • 1Department of Geriatric Medicine, Karolinska Institute, Stockholm, Sweden.

Scandinavian Journal of Social Medicine
|June 1, 1994
PubMed
Summary

Coder interpretation significantly impacted cause-of-death statistics trends between 1970-1988. Changes in how coders registered underlying causes of death were identified as a key factor influencing these trends.

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Area of Science:

  • Public Health
  • Medical Statistics
  • Epidemiology

Background:

  • Cause-of-death statistics are crucial for public health surveillance.
  • Trends in these statistics can be influenced by coding practices, not just disease prevalence.
  • Understanding coding changes is essential for accurate interpretation of mortality data.

Purpose of the Study:

  • To identify trends in underlying cause-of-death statistics attributable to coder selection and coding changes.
  • To determine shifts in coder registration principles explaining observed statistical trends.

Main Methods:

  • Analysis of 31 Basic Tabulation List categories from the Swedish national cause-of-death register (1970-1988).
  • Estimation of the underlying cause ratio (coder tendency to register a condition as the underlying cause).

Related Experiment Videos

  • Comparison of underlying cause rate trends with multiple cause rate trends and coder interpretation practice.

    • Main Results:

    • Trends in thirteen categories (fourteen trends) were explained by changes in coder interpretation practices.
    • Five upward, four insignificant, and five downward trends were identified due to coder interpretation.
    • Three categories showed trends explained by new explicit International Classification of Diseases, 9th Revision (ICD-9) rules.

    Conclusions:

    • Coder interpretation practices significantly influenced cause-of-death statistics trends from 1970 to 1988.
    • Changes in coding rules and interpretation led to observable shifts in underlying cause registration.
    • Accurate interpretation of mortality data requires consideration of evolving coding practices.