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Social Psychiatry and Psychiatric Epidemiology

, Volume 54, Issue 12, pp 1471–1482 | Cite as

Spatiotemporal clustering of suicides in the US from 1999 to 2016: a spatial epidemiological approach

  • Karla Therese L. SyEmail author
  • Jeffrey Shaman
  • Sasikiran Kandula
  • Sen Pei
  • Madelyn Gould
  • Katherine M. Keyes
Original Paper
First Online:

Abstract

Purpose

This study aims to describe and characterize the spatial and temporal clustering patterns of suicide in the ten states with the greatest suicide burden in the United States from 1999 to 2016.

Methods

All suicide deaths from January 1, 1999 to December 31, 2016 in the United States were identified using data from the Wide-ranging Online Data for Epidemiologic Research (WONDER) dataset. The ten states with the highest age-adjusted suicide rates were Montana, Alaska, Wyoming, New Mexico, Nevada, Utah, Idaho, Colorado, Arizona, and Oklahoma. A spatiotemporal scan statistic using a discrete Poisson model was employed to retrospectively detect spatiotemporal suicide clusters.

Results

From 1999 to 2016, a total of 649,843 suicides were recorded in the United States. Nineteen statistically significant spatiotemporal suicide mortality clusters were identified in the states with the greatest suicide rates, and 13.53% of the suicide cases within these states clustered spatiotemporally. The risk ratio of the clusters ranged from 1.45 to 3.64 (p < 0.001). All states had at least one cluster, with three clusters spanning multiple states, and four clusters were found in Arizona. While there was no clear secular trend in the average size of suicide clusters, the number of clusters increased from 1999 to 2016.

Conclusions

Hot spots for suicidal behavior in the United States warrant public health intervention and continued surveillance. As suicide rates in the US continue to increase annually, public health efforts could be maximized by focusing on regions with substantial clustering.

Keywords

Suicide Suicide clusters Epidemiology Spatial statistics Cluster analysis 
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Notes

Acknowledgements

Support for this work was provided by the National Institute of Alcohol Abuse and Alcoholism (K01AA021511, Keyes), the Center for Injury Epidemiology and Prevention at Columbia University, and the National Institute of General Medical Science (R01GM110748, Shaman).

Compliance with ethical standards

Conflict of interest

JS and Columbia University disclose partial ownership of SK Analytics. SK has consulted for SK Analytics. The other authors report no conflicts of interest and have no financial relationships with commercial interests.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of EpidemiologyBoston University School of Public HealthBostonUSA
  2. 2.Department of Environmental Health Sciences, Mailman School of Public HealthColumbia UniversityNew YorkUSA
  3. 3.Department of Epidemiology, Mailman School of Public HealthColumbia UniversityNew YorkUSA
  4. 4.Department of Psychiatry, New York State Psychiatric InstituteColumbia UniversityNew YorkUSA

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