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Spatio-Temporal Analysis of Suicide-Related Emergency Calls

Author

Listed:
  • Miriam Marco

    (Department of Social Psychology, University of Valencia, Avda. Blasco Ibáñez, 21, 46010 Valencia, Spain)

  • Antonio López-Quílez

    (Department of Statistics and Operations Research, University of Valencia, Dr. Moliner, 50, 46100 Burjassot, Spain)

  • David Conesa

    (Department of Statistics and Operations Research, University of Valencia, Dr. Moliner, 50, 46100 Burjassot, Spain)

  • Enrique Gracia

    (Department of Social Psychology, University of Valencia, Avda. Blasco Ibáñez, 21, 46010 Valencia, Spain)

  • Marisol Lila

    (Department of Social Psychology, University of Valencia, Avda. Blasco Ibáñez, 21, 46010 Valencia, Spain)

Abstract

Considerable effort has been devoted to incorporate temporal trends in disease mapping. In this line, this work describes the importance of including the effect of the seasonality in a particular setting related with suicides. In particular, the number of suicide-related emergency calls is modeled by means of an autoregressive approach to spatio-temporal disease mapping that allows for incorporating the possible interaction between both temporal and spatial effects. Results show the importance of including seasonality effect, as there are differences between the number of suicide-related emergency calls between the four seasons of each year.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jijerp:v:14:y:2017:i:7:p:735-:d:103866
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    References listed on IDEAS

    as
    1. 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.
    2. Haining, Robert & Law, Jane & Griffith, Daniel, 2009. "Modelling small area counts in the presence of overdispersion and spatial autocorrelation," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 2923-2937, June.
    3. Hsu, Chia-Yueh & Chang, Shu-Sen & Lee, Esther S.T. & Yip, Paul S.F., 2015. "“Geography of suicide in Hong Kong: Spatial patterning, and socioeconomic correlates and inequalities”," Social Science & Medicine, Elsevier, vol. 130(C), pages 190-203.
    4. 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.
    5. Enrique Gracia & Antonio López-Quílez & Miriam Marco & Silvia Lladosa & Marisol Lila, 2014. "Exploring Neighborhood Influences on Small-Area Variations in Intimate Partner Violence Risk: A Bayesian Random-Effects Modeling Approach," IJERPH, MDPI, vol. 11(1), pages 1-17, January.
    6. 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.
    7. Craig Anderson & Louise M. Ryan, 2017. "A Comparison of Spatio-Temporal Disease Mapping Approaches Including an Application to Ischaemic Heart Disease in New South Wales, Australia," IJERPH, MDPI, vol. 14(2), pages 1-16, February.
    8. Jane Law & Matthew Quick, 2013. "Exploring links between juvenile offenders and social disorganization at a large map scale: a Bayesian spatial modeling approach," Journal of Geographical Systems, Springer, vol. 15(1), pages 89-113, January.
    9. Hempstead, Katherine, 2006. "The geography of self-injury: Spatial patterns in attempted and completed suicide," Social Science & Medicine, Elsevier, vol. 62(12), pages 3186-3196, June.
    10. Jong-Min Woo & Olaoluwa Okusaga & Teodor T. Postolache, 2012. "Seasonality of Suicidal Behavior," IJERPH, MDPI, vol. 9(2), pages 1-17, February.
    11. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
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    Cited by:

    1. David Payares-Garcia & Javier Platero & Jorge Mateu, 2023. "A Dynamic Spatio-Temporal Stochastic Modeling Approach of Emergency Calls in an Urban Context," Mathematics, MDPI, vol. 11(4), pages 1-28, February.
    2. Fatima Khalique & Shoab Ahmed Khan & Wasi Haider Butt & Irum Matloob, 2020. "An Integrated Approach for Spatio-Temporal Cholera Disease Hotspot Relation Mining for Public Health Management in Punjab, Pakistan," IJERPH, MDPI, vol. 17(11), pages 1-18, May.
    3. O'Connell, Katherine L. & Jacobson, Samantha V. & Ton, Andrew T. & Law, Keyne C., 2022. "Association between race and socioeconomic factors and suicide-related 911 call rate," Social Science & Medicine, Elsevier, vol. 306(C).

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