J Korean Med Sci.  2024 Apr;39(13):e125. 10.3346/jkms.2024.39.e125.

Understanding the Fluctuations in Korea’s Suicide Rates: A Change-Point Analysis and Interrupted Time Series Analysis

• Ryu S
• Nam HJ
• Lee JY
• Kim JM
• Kim SW

• KMID: 2554579
• DOI: http://doi.org/10.3346/jkms.2024.39.e125

Abstract

Background
Korea has witnessed significant fluctuations in its suicide rates in recent decades, which may be related to modifications in its death registration system. This study aimed to explore the structural shifts in suicide trends, as well as accidental and ill-defined deaths in Korea, and to analyze the patterns of these changes.
Methods
We analyzed age-adjusted death rates for suicides, deaths due to transport accidents, falls, drowning, fire-related incidents, poisonings, other external causes, and ill-defined deaths in Korea from 1997 to 2021. We identified change-points using the ‘breakpoints’ function from the ‘strucchange’ package and conducted interrupted time series analyses to assess trends before and after these change-points.
Results
Korea’s suicide rates had three change-points in February 2003, September 2008, and June 2012, characterized by stair-step changes, with level jumps at the 2003 and 2008 change-points and a sharp decline at the 2012 change-point. Notably, the 2003 and 2008 spikes roughly coincided with modifications to the death ascertainment process. The trend in suicide rates showed a downward slope within the 2003–2008 and 2008–2012 periods. Furthermore, ill-defined deaths and most accidental deaths decreased rapidly through several change-points in the early and mid-2000s.
Conclusion
The marked fluctuations in Korea’s suicide rate during the 2000s may be largely attributed to improvements in suicide classification, with potential implications beyond socio-economic factors. These findings suggest that the actual prevalence of suicides in Korea in the 2000s might have been considerably higher than officially reported.

Keyword

Suicide; Suicide; Mortality; Mortality; Death Certificates; Death Certificates; Social Stigma; Social Stigma; Interrupted Time Series Analysis; Interrupted Time Series Analysis

Figure

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  Fig. 1 Change-points and interrupted time series analysis of suicide rate (per month per 100,000 population) in Korea. The blue line represents the segmented regression models. In the quasi-Poisson regression model, the following equation was employed to assess the suicide rate (Y): Y = b0 + b1 × Time + b2 × Change-point + b3 × Time since change-point + e. The red dashed line indicates the trend line estimated by a locally estimated scatterplot smoothing.

• 

  Fig. 2 Change-points and interrupted time series analyses of accidental death rates (per month per 100,000 population) in Korea. The blue line represents the segmented regression models. In the quasi-Poisson regression models, the following equation was employed to assess the accidental death rates (Y): Y = b0 + b1 × Time + b2 × Change-point + b3 × Time since change-point + e. The red dashed line represents the trend line estimated by a locally estimated scatterplot smoothing. The arrows on the x-axis indicate the timing of the change-points in the suicide rate.

• 

  Fig. 3 Change-points and interrupted time-series analysis of ill-defined death rate (per month per 100,000 population) in Korea. The blue line represents the segmented regression models. In the quasi-Poisson regression models, the following equation was employed to assess the ill-defined death rate (Y): Y = b0 + b1 × Time + b2 × Change-point + b3 × Time since change-point + e. The red dashed line represents the trend line estimated by a locally estimated scatterplot smoothing. The arrows on the x-axis indicate the timing of the change-points in the suicide rate.

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