The joinpoint-jump and joinpoint-comparability ratio model for trend analysis with applications to coding changes in health statistics

Huann Sheng Chen, Sarah Zeichner, Robert N. Anderson, David K. Espey, Hyune Ju Kim, Eric J. Feuer

Research output: Contribution to journalArticle

Abstract

Analysis of trends in health data collected over time can be affected by instantaneous changes in coding that cause sudden increases/decreases, or "jumps," in data. Despite these sudden changes, the underlying continuous trends can present valuable information related to the changing risk profile of the population, the introduction of screening, new diagnostic technologies, or other causes. The joinpoint model is a well-established methodology for modeling trends over time using connected linear segments, usually on a logarithmic scale. Joinpoint models that ignore data jumps due to coding changes may produce biased estimates of trends. In this article, we introduce methods to incorporate a sudden discontinuous jump in an otherwise continuous joinpoint model. The size of the jump is either estimated directly (the Joinpoint-Jump model) or estimated using supplementary data (the Joinpoint-Comparability Ratio model). Examples using ICD-9/ICD-10 cause of death coding changes, and coding changes in the staging of cancer illustrate the use of these models.

Original languageEnglish (US)
Pages (from-to)49-62
Number of pages14
JournalJournal of Official Statistics
Volume36
Issue number1
DOIs
StatePublished - Mar 1 2020

Keywords

  • Cancer staging system
  • Coding change
  • Comparability ratio
  • International classification of diseases (icd)
  • Joinpoint model
  • Trend analysis

ASJC Scopus subject areas

  • Statistics and Probability

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