Suicide mortality rates in Japan before and beyond the COVID-19 pandemic era

Statistical analyses from Japan reported increasing suicides in 2020, first in the world, proving the severity of the public health crisis during the COVID-19 pandemic; however, so far, international suicides have not been shown to be objectively increasing at population level. Followed studies reported the existence of a substantial heterogeneity of suicides among subgroups and time-lag impacts. Against public health crisis in Japan, policymakers, psychiatrists and public health personnel should prioritize improving suicide prevention programs following evidence-based policymaking. Understanding how/what factors relate to the COVID-19 pandemic and what other factors have shaped the increasing suicide numbers since 2020 through objectively well-controlled/fine-grained analyses of high-quality longitudinal/cross-sectional data at the individual, regional, and national levels is important for identifying the reasons for the recent trend. For this purpose, this study examined suicide statistics, statistical analysis methods, and their interpretations. Recent analyses suggest an increased suicide risk among females <50 years and males <30 years in 2020-2022. Notably, time-series analyses revealed that adolescent suicides began increasing before the pandemic, while working-age female suicides sharply increased synchronously with the pandemic outbreak. Causality analyses suggest that social issues facing Japan and recent global psychosocial and socioeconomic transformations are risk factors for suicide in high-risk groups. Finally, this report demonstrates the importance of providing appropriate support based on an objective understanding of individuals who are at risk for suicide, without being bound by traditional established knowledges. Deceasing trends of age-standardized suicide death rates (SDRs) were attenuated in late in 2010s. SDRs in Japan increased from 2020 compared with predicted SDRs detected by both autoregressive integrated moving average and interrupted time-series analyses models. image