The Joinpoint-Jump and Joinpoint-Comparability Ratio Model for Trend Analysis with Applications to Coding Changes in Health Statistics
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.
Additional Information
© 2020 Statistics Sweden. Attribution-NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0) Received March 2019; Revised September 2019; Accepted November 2019; Published online: 17 Mar 2020. The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention or the National Institutes of Health.Attached Files
Accepted Version - nihms-1605240.pdf
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Additional details
- PMCID
- PMC7380682
- Eprint ID
- 102426
- Resolver ID
- CaltechAUTHORS:20200409-091534034
- Created
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2020-04-09Created from EPrint's datestamp field
- Updated
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2021-11-16Created from EPrint's last_modified field