IDEAS home Printed from https://ideas.repec.org/a/spr/eurpop/v16y2000i3d10.1023_a1026587810551.html
   My bibliography  Save this article

Monitoring Suicide Mortality: A Bayesian Approach

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1023/A:1026587810551
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1023/A:1026587810551?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. N. E. Breslow, 1984. "Extra‐Poisson Variation in Log‐Linear Models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 33(1), pages 38-44, March.
    2. Robert Haining, 1991. "Estimation With Heteroscedastic And Correlated Errors: A Spatial Analysis Of Intra‐Urban Mortality Data," Papers in Regional Science, Wiley Blackwell, vol. 70(3), pages 223-241, July.
    3. Julian Besag & Jeremy York & Annie Mollié, 1991. "Bayesian image restoration, with two applications in spatial statistics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 43(1), pages 1-20, March.
    4. Julian Besag & James Newell, 1991. "The Detection of Clusters in Rare Diseases," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 154(1), pages 143-155, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    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. Jianxiang Song & Jiafu Liu & Xinyue Zhang & Xin Chen & Yingtao Shang & Fengjie Gao, 2024. "Spatio-Temporal Fluctuation Analysis of Ecosystem Service Values in Northeast China over Long Time Series: Based on Bayesian Hierarchical Modeling," Land, MDPI, vol. 13(6), pages 1-20, June.
    8. 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.
    9. 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.
    10. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Peter Congdon, 1997. "Multilevel and Clustering Analysis of Health Outcomes in Small Areas," European Journal of Population, Springer;European Association for Population Studies, vol. 13(4), pages 305-338, December.
    2. Gene A. Pennello & Susan S. Devesa & Mitchell H. Gail, 1999. "Using a Mixed Effects Model to Estimate Geographic Variation in Cancer Rates," Biometrics, The International Biometric Society, vol. 55(3), pages 774-781, September.
    3. Ronald E. Gangnon & Murray K. Clayton, 2000. "Bayesian Detection and Modeling of Spatial Disease Clustering," Biometrics, The International Biometric Society, vol. 56(3), pages 922-935, September.
    4. Hosik Choi & Eunjung Song & Seung-sik Hwang & Woojoo Lee, 2018. "A modified generalized lasso algorithm to detect local spatial clusters for count data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 102(4), pages 537-563, October.
    5. Mabel Morales-Otero & Vicente Núñez-Antón, 2021. "Comparing Bayesian Spatial Conditional Overdispersion and the Besag–York–Mollié Models: Application to Infant Mortality Rates," Mathematics, MDPI, vol. 9(3), pages 1-33, January.
    6. Katie Wilson & Jon Wakefield, 2022. "A probabilistic model for analyzing summary birth history data," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 47(11), pages 291-344.
    7. Eibich, Peter & Ziebarth, Nicolas, 2014. "Examining the Structure of Spatial Health Effects in Germany Using Hierarchical Bayes Models," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 49, pages 305-320.
    8. Mayer Alvo & Jingrui Mu, 2023. "COVID-19 Data Analysis Using Bayesian Models and Nonparametric Geostatistical Models," Mathematics, MDPI, vol. 11(6), pages 1-13, March.
    9. Darcy Steeg Morris & Kimberly F. Sellers, 2022. "A Flexible Mixed Model for Clustered Count Data," Stats, MDPI, vol. 5(1), pages 1-18, January.
    10. Zhengyi Zhou & David S. Matteson & Dawn B. Woodard & Shane G. Henderson & Athanasios C. Micheas, 2015. "A Spatio-Temporal Point Process Model for Ambulance Demand," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 6-15, March.
    11. Eric C. Tassone & Marie Lynn Miranda & Alan E. Gelfand, 2010. "Disaggregated spatial modelling for areal unit categorical data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(1), pages 175-190, January.
    12. Junming Li & Xiulan Han & Xiao Li & Jianping Yang & Xuejiao Li, 2018. "Spatiotemporal Patterns of Ground Monitored PM 2.5 Concentrations in China in Recent Years," IJERPH, MDPI, vol. 15(1), pages 1-15, January.
    13. Massimo Bilancia & Giacomo Demarinis, 2014. "Bayesian scanning of spatial disease rates with integrated nested Laplace approximation (INLA)," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(1), pages 71-94, March.
    14. Douglas R. M. Azevedo & Marcos O. Prates & Dipankar Bandyopadhyay, 2021. "MSPOCK: Alleviating Spatial Confounding in Multivariate Disease Mapping Models," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(3), pages 464-491, September.
    15. Jonathan Wakefield & Taylor Okonek & Jon Pedersen, 2020. "Small Area Estimation for Disease Prevalence Mapping," International Statistical Review, International Statistical Institute, vol. 88(2), pages 398-418, August.
    16. Johnston, Robert J. & Ramachandran, Mahesh & Schultz, Eric T. & Segerson, Kathleen & Besedin, Elena Y., 2011. "Characterizing Spatial Pattern in Ecosystem Service Values when Distance Decay Doesn’t Apply: Choice Experiments and Local Indicators of Spatial Association," 2011 Annual Meeting, July 24-26, 2011, Pittsburgh, Pennsylvania 103374, Agricultural and Applied Economics Association.
    17. Lluís Bermúdez & Dimitris Karlis & Isabel Morillo, 2020. "Modelling Unobserved Heterogeneity in Claim Counts Using Finite Mixture Models," Risks, MDPI, vol. 8(1), pages 1-13, January.
    18. Francisca Corpas-Burgos & Miguel A. Martinez-Beneito, 2021. "An Autoregressive Disease Mapping Model for Spatio-Temporal Forecasting," Mathematics, MDPI, vol. 9(4), pages 1-17, February.
    19. Li Xu & Qingshan Jiang & David R. Lairson, 2019. "Spatio-Temporal Variation of Gender-Specific Hypertension Risk: Evidence from China," IJERPH, MDPI, vol. 16(22), pages 1-26, November.
    20. Isabel Martínez-Pérez & Verónica González-Iglesias & Valentín Rodríguez Suárez & Ana Fernández-Somoano, 2021. "Spatial Distribution of Hospitalizations for Ischemic Heart Diseases in the Central Region of Asturias, Spain," IJERPH, MDPI, vol. 18(23), pages 1-10, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:eurpop:v:16:y:2000:i:3:d:10.1023_a:1026587810551. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.