When Trends Change: Joinpoint Regression in Public Health Surveillance

Thomas M. Menino Convention & Exhibition Center 

Joinpoint regression fits segmented linear models to time-series data, identifying "joinpoints" where trends change significantly. We applied it to annual stimulant prescription rates in Oregon (2014–2022). Trends were summarized with segment-specific annual percent change (APC) and average APC (AAPC).
We detected several significant joinpoints. For example, a joinpoint was detected in 2020, when rates shifted from moderate (APC ≈5%) to accelerated (>13%), with subgroup APCs >20% among young/middle-aged adults and stimulant-naïve patients. Average prescriptions per patient remained stable, showing growth was driven by new initiations.
This approach is valuable for noisy public health data: it identifies meaningful shifts invisible to simple linear trends, quantifies speed of change, and helps link trends to policy, clinical, or social events. Our session will describe this application, compare it to other contemporary piecewise and spline regression approaches, and illustrate its utility in pharmacoepidemiology and public health surveillance.

Joinpoint regression

Segmented trend detection

Public health research 

Mental Health Statistics Section