Title: Seasonal effects on suicide rates - Wikipedia
Headings: Seasonal effects on suicide rates
Seasonal effects on suicide rates
Contents
General effects
Popular belief
Related factors
Gender
Biochemistry
Environment
Psychology
See also
References

Content: Seasonal effects on suicide rates - Wikipedia
Seasonal effects on suicide rates
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This article's lead section may be too short to adequately summarize the key points. Please consider expanding the lead to provide an accessible overview of all important aspects of the article. ( November 2018)
Research on seasonal effects on suicide rates suggests that the prevalence of suicide is greatest during the late spring and early summer months, despite the common belief that suicide rates peak during the cold and dark months of the winter season. Contents
1 General effects
2 Popular belief
3 Related factors
3.1 Gender
3.2 Biochemistry
3.3 Environment
3.4 Psychology
4 See also
5 References
General effects
The Center for Disease Control and Prevention and the National Center for Health Statistics report that suicide rates in the United States are lowest during the winter months and highest in the spring and summer. Stephen Bridges asserts that there is "a high incidence in early spring (April and May) and also a low incidence in winter" of suicide rates. Bridges's study focused on seasonal effects on suicide risk by examining the monthly distribution of suicides in the 1970s, 1980s, and 1990s via a "harmonic time series model" with six observable harmonic cycles within each consecutive year. The results show a consistent pattern of suicide risk with most suicides occurring in the spring/summer and the fewest during the winter months. Specifically, Bridges found that in the 1970s "about 47% of the total variances can be explained by the seasonal components" within that given decade (with more suicides in spring/summer). In the 1980s, "the seasonal component of suicide incidence for the time period 1981 through 1990 is clearly significant and records about a 27.7% increase in seasonal contribution by comparison with the previous period". In the 1990s, "the significant seasonal rhythm were explained in 63% of the total variation".