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Monitoring Suicide Mortality: A Bayesian Approach

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  • Peter Congdon

    (Queen Mary and Westfield College
    Department of Public Health, Barking and Havering Health Authority)

Abstract

A significant fall in suicide mortality relative to England and Wales levels has occurred in London though with wide variation between its 33 constituent boroughs in the extent of mortality reduction. A Bayesian random effects approach is used is to model differential changes in suicide by borough and time over a 16 year period, 1979–94. Of particular concern in such modelling are persistent differences between boroughs in suicide risk (temporal correlation) and spatial clustering in relative risk. It is also important to represent the changing impact on suicide of socio-economic factors such as social deprivation. The data used are defined by deaths through de-jure suicide (ICD9 categories E950-E959) and those through undetermined injury, whether accidental or purposely inflicted (ICD E980-E989).

Suggested Citation

  • Peter Congdon, 2000. "Monitoring Suicide Mortality: A Bayesian Approach," European Journal of Population, Springer;European Association for Population Studies, vol. 16(3), pages 251-284, September.
    • Handle: RePEc:spr:eurpop:v:16:y:2000:i:3:d:10.1023_a:1026587810551
    DOI: 10.1023/A:1026587810551
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    References listed on IDEAS

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    Cited by:

    1. Agerbo, Esben & Sterne, Jonathan A.C. & Gunnell, David J., 2007. "Combining individual and ecological data to determine compositional and contextual socio-economic risk factors for suicide," Social Science & Medicine, Elsevier, vol. 64(2), pages 451-461, January.
    2. Tae-Ho Yoon & Maengseok Noh & Junhee Han & Kyunghee Jung-Choi & Young-Ho Khang, 2015. "Deprivation and suicide mortality across 424 neighborhoods in Seoul, South Korea: a Bayesian spatial analysis," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 60(8), pages 969-976, December.
    3. Tao Hu & Xinyan Zhu & Lian Duan & Wei Guo, 2018. "Urban crime prediction based on spatio-temporal Bayesian model," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-18, October.
    4. Allison Milner & Lay San Too & Matthew J. Spittal, 2018. "Cluster Suicides Among Unemployed Persons in Australia Over the Period 2001–2013," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 137(1), pages 189-201, May.
    5. Ranjita Pandey & Himanshu Tolani, 2022. "Crime patterns in Delhi: a Bayesian spatio-temporal assessment," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(6), pages 2971-2980, December.
    6. Riccardo Borgoni & Ulf-Christian Ewert & Alexia Fürnkranz-Prskawetz, 2002. "How important are household demographic characteristics to explain private car use patterns? A multilevel approach to Austrian data," MPIDR Working Papers WP-2002-006, Max Planck Institute for Demographic Research, Rostock, Germany.
    7. Miriam Marco & Antonio López-Quílez & David Conesa & Enrique Gracia & Marisol Lila, 2017. "Spatio-Temporal Analysis of Suicide-Related Emergency Calls," IJERPH, MDPI, vol. 14(7), pages 1-13, July.
    8. Rashidi, Parinaz & Wang, Tiejun & Skidmore, Andrew & Mehdipoor, Hamed & Darvishzadeh, Roshanak & Ngene, Shadrack & Vrieling, Anton & Toxopeus, Albertus G., 2016. "Elephant poaching risk assessed using spatial and non-spatial Bayesian models," Ecological Modelling, Elsevier, vol. 338(C), pages 60-68.
    9. Cheung, Yee Tak Derek & Spittal, Matthew J. & Pirkis, Jane & Yip, Paul Siu Fai, 2012. "Spatial analysis of suicide mortality in Australia: Investigation of metropolitan-rural-remote differentials of suicide risk across states/territories," Social Science & Medicine, Elsevier, vol. 75(8), pages 1460-1468.

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