2023
DOI: 10.1192/bjo.2023.616
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Impact of the Japanese Government's ‘General Principles of Suicide Prevention Policy’ on youth suicide from 2007 to 2022

Ryusuke Matsumoto,
Eishi Motomura,
Takashi Shiroyama
et al.

Abstract: Background The Japanese Government programme ‘General Principles of Suicide Prevention Policy' (GPSPP) contributed to decreasing suicide mortality rates (SMRs) before the COVID-19 pandemic, but they increased after the pandemic. Aims To identify risk factors for youth suicide and the impact of GPSPP on youth suicide. Method Annual suicide numbers during 2007–2022 were obtained from government databases. SMRs of student and non-student youths were analysed with a linear … Show more

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Cited by 6 publications
(12 citation statements)
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“…Joinpoint regression and interrupted time series analyses are well-established time series statistical methods used for analyzing temporal fluctuations in SMRs. As has been noted, suicide is a temporally and fundamentally complicated phenomenon comprising various risk factors [16,20,23,32,40,41,53]. Interrupted time series analysis is known as one of the most effective/powerful statistical methods for detecting the impacts of the COVID-19 pandemic outbreak on SMRs via the correlation between the periods before and after the pandemic outbreak [13,22,[54][55][56].…”
Section: Statistical Analysesmentioning
confidence: 99%
See 4 more Smart Citations
“…Joinpoint regression and interrupted time series analyses are well-established time series statistical methods used for analyzing temporal fluctuations in SMRs. As has been noted, suicide is a temporally and fundamentally complicated phenomenon comprising various risk factors [16,20,23,32,40,41,53]. Interrupted time series analysis is known as one of the most effective/powerful statistical methods for detecting the impacts of the COVID-19 pandemic outbreak on SMRs via the correlation between the periods before and after the pandemic outbreak [13,22,[54][55][56].…”
Section: Statistical Analysesmentioning
confidence: 99%
“…Interrupted time series analysis can incorporate various options, including parametric/non-parametric regressions, seasonal variation and panel data analyses [15,[54][55][56], but it cannot detect unknown joinpoints (changing trends periods) during observation periods. Indeed, previous reports have suggested, when the intervention is set at the COVID-19 pandemic outbreak alone, interrupted time series analysis tends to overestimate the positive impacts of the pandemic outbreak on SMRs due to the attenuation of decreasing trends of male SMRs before the pandemic (in the late 2010s) [15,23,39,41]. In contrast, joinpoint regression analysis has been evaluated to be an appropriate statistical method, which can detect unknown joinpoints, where trends change via fitting the simplest joinpoint model that the trend data allow [57,58].…”
Section: Statistical Analysesmentioning
confidence: 99%
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