IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v15y2018i4p593-d138046.html
   My bibliography  Save this article

Exploring Geographic Variation of Mental Health Risk and Service Utilization of Doctors and Hospitals in Toronto: A Shared Component Spatial Modeling Approach

Author

Listed:
  • Jane Law

    (School of Planning, University of Waterloo, Waterloo, ON N2L 3G1, Canada
    School of Public Health and Health Systems, University of Waterloo, Waterloo, ON N2L 3G1, Canada)

  • Christopher Perlman

    (School of Public Health and Health Systems, University of Waterloo, Waterloo, ON N2L 3G1, Canada)

Abstract

Mental Health has been known to vary geographically. Different rates of utilization of mental health services in local areas reflect geographic variation of mental health and complexity of health care. Variations and inequalities in how the health care system addresses risks are two critical issues for addressing population mental health. This study examines these issues by analyzing the utilization of mental health services in Toronto at the neighbourhood level. We adopted a shared component spatial modeling approach that allows simultaneous analysis of two main health service utilizations: doctor visits and hospitalizations related to mental health conditions. Our results reflect a geographic variation of both types of mental health service utilization across neighbourhoods in Toronto. We identified hot and cold spots of mental health risks that are common to both or specific to only one type of health service utilization. Based on the evidence found, we discuss intervention strategies, focusing on the hotspots and provision of health services about doctors and hospitals, to improve mental health for the neighbourhoods. Limitations of the study and further research directions are also discussed.

Suggested Citation

  • Jane Law & Christopher Perlman, 2018. "Exploring Geographic Variation of Mental Health Risk and Service Utilization of Doctors and Hospitals in Toronto: A Shared Component Spatial Modeling Approach," IJERPH, MDPI, vol. 15(4), pages 1-13, March.
    • Handle: RePEc:gam:jijerp:v:15:y:2018:i:4:p:593-:d:138046
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/15/4/593/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/15/4/593/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Leonhard Knorr‐Held & Nicola G. Best, 2001. "A shared component model for detecting joint and selective clustering of two diseases," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 164(1), pages 73-85.
    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. Getayeneh Antehunegn Tesema & Zemenu Tadesse Tessema & Stephane Heritier & Rob G. Stirling & Arul Earnest, 2023. "A Systematic Review of Joint Spatial and Spatiotemporal Models in Health Research," IJERPH, MDPI, vol. 20(7), pages 1-24, March.
    2. Abu Yousuf Md Abdullah & Jane Law & Zahid A. Butt & Christopher M. Perlman, 2021. "Understanding the Differential Impact of Vegetation Measures on Modeling the Association between Vegetation and Psychotic and Non-Psychotic Disorders in Toronto, Canada," IJERPH, MDPI, vol. 18(9), pages 1-25, April.

    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. 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.
    2. 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.
    3. Qingyun Du & Mingxiao Zhang & Yayan Li & Hui Luan & Shi Liang & Fu Ren, 2016. "Spatial Patterns of Ischemic Heart Disease in Shenzhen, China: A Bayesian Multi-Disease Modelling Approach to Inform Health Planning Policies," IJERPH, MDPI, vol. 13(4), pages 1-14, April.
    4. Ping Yin & Lan Mu & Marguerite Madden & John Vena, 2014. "Hierarchical Bayesian modeling of spatio-temporal patterns of lung cancer incidence risk in Georgia, USA: 2000–2007," Journal of Geographical Systems, Springer, vol. 16(4), pages 387-407, October.
    5. Rodrigues, E.C. & Assunção, R., 2012. "Bayesian spatial models with a mixture neighborhood structure," Journal of Multivariate Analysis, Elsevier, vol. 109(C), pages 88-102.
    6. Xiaoping Jin & Sudipto Banerjee & Bradley P. Carlin, 2007. "Order‐free co‐regionalized areal data models with application to multiple‐disease mapping," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(5), pages 817-838, November.
    7. Quick, Matthew & Li, Guangquan & Brunton-Smith, Ian, 2018. "Crime-general and crime-specific spatial patterns: A multivariate spatial analysis of four crime types at the small-area scale," Journal of Criminal Justice, Elsevier, vol. 58(C), pages 22-32.
    8. Kassahun Abere Ayalew & Samuel Manda & Bo Cai, 2021. "A Comparison of Bayesian Spatial Models for HIV Mapping in South Africa," IJERPH, MDPI, vol. 18(21), pages 1-10, October.
    9. 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.
    10. 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.
    11. 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.
    12. Ying C. MacNab, 2018. "Some recent work on multivariate Gaussian Markov random fields," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(3), pages 497-541, September.
    13. 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.
    14. 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.
    15. F. Corpas-Burgos & P. Botella-Rocamora & M. A. Martinez-Beneito, 2019. "On the convenience of heteroscedasticity in highly multivariate disease mapping," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(4), pages 1229-1250, December.
    16. Alexandra Schmidt & Ajax Moreira & Steven Helfand & Thais Fonseca, 2009. "Spatial stochastic frontier models: accounting for unobserved local determinants of inefficiency," Journal of Productivity Analysis, Springer, vol. 31(2), pages 101-112, April.
    17. Maike Tahden & Juliane Manitz & Klaus Baumgardt & Gerhard Fell & Thomas Kneib & Guido Hegasy, 2016. "Epidemiological and Ecological Characterization of the EHEC O104:H4 Outbreak in Hamburg, Germany, 2011," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-19, October.
    18. Marc Marí-Dell’Olmo & Miguel Ángel Martínez-Beneito, 2015. "A Multilevel Regression Model for Geographical Studies in Sets of Non-Adjacent Cities," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-12, August.
    19. Peter Congdon, 2011. "The Spatial Pattern of Suicide in the US in Relation to Deprivation, Fragmentation and Rurality," Urban Studies, Urban Studies Journal Limited, vol. 48(10), pages 2101-2122, August.
    20. Shadi Rahimzadeh & Beata Burczynska & Alireza Ahmadvand & Ali Sheidaei & Sara Khademioureh & Forough Pazhuheian & Sahar Saeedi Moghaddam & James Bentham & Farshad Farzadfar & Mariachiara Di Cesare, 2021. "Geographical and socioeconomic inequalities in female breast cancer incidence and mortality in Iran: A Bayesian spatial analysis of registry data," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-16, March.

    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:gam:jijerp:v:15:y:2018:i:4:p:593-:d:138046. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.