IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0096556.html
   My bibliography  Save this article

An Exploratory Spatial Analysis of ALS Incidence in Ireland over 17.5 Years (1995 – July 2013)

Author

Listed:
  • James Rooney
  • Mark Heverin
  • Alice Vajda
  • Arlene Crampsie
  • Katy Tobin
  • Susan Byrne
  • Anthony Staines
  • Orla Hardiman

Abstract

Introduction: There has been much interest in spatial analysis of ALS to identify potential environmental or genetically caused clusters of disease. Results to date have been inconclusive. The Irish ALS register has been recently geocoded, presenting opportunity to perform a spatial analysis on national prospectively gathered data of incident cases over an 18-year period. Methods: 1,645 cases of ALS in Ireland from January 1995 to July 2013 were identified from the Irish ALS register. 1,638 cases were successfully geocoded. Census data from four censuses: 1996, 2002, 2006 & 2011 were used to calculate an average population for the period and standardized incidence rates (SIRs) were calculated for 3,355 areas (Electoral Divisions). Bayesian conditional auto-regression was applied to produce smoothed relative risks (RR). These were then mapped for all cases, males & females separately, and those under 55 vs over 55 at diagnosis. Bayesian and linear regression were used to examine the relationship between population density and RR. Results: Smoothed maps revealed no overall geographical pattern to ALS incidence in Ireland, although several areas of localized increased risk were identified. Stratified maps also suggested localized areas of increased RR, while dual analysis of the relationship between population density and RR of ALS yielded conflicting results, linear regression revealed a weak relationship. Discussion: In contrast to some previous studies our analysis did not reveal any large-scale geographic patterns of incidence, yet localized areas of moderately high risk were found in both urban and rural areas. Stratified maps by age revealed a larger number of cases in younger people in the area of County Cork - possibly of genetic cause. Bayesian auto-regression of population density failed to find a significant association with risk, however weighted linear regression of post Bayesian smoothed Risk revealed an association between population density and increased ALS risk.

Suggested Citation

  • James Rooney & Mark Heverin & Alice Vajda & Arlene Crampsie & Katy Tobin & Susan Byrne & Anthony Staines & Orla Hardiman, 2014. "An Exploratory Spatial Analysis of ALS Incidence in Ireland over 17.5 Years (1995 – July 2013)," PLOS ONE, Public Library of Science, vol. 9(5), pages 1-11, May.
    • Handle: RePEc:plo:pone00:0096556
    DOI: 10.1371/journal.pone.0096556
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0096556
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0096556&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0096556?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
    ---><---

    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. Wickham, Hadley, 2007. "Reshaping Data with the reshape Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 21(i12).
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. Augustinus, Benno A. & Blum, Moshe & Citterio, Sandra & Gentili, Rodolfo & Helman, David & Nestel, David & Schaffner, Urs & Müller-Schärer, Heinz & Lensky, Itamar M., 2022. "Ground-truthing predictions of a demographic model driven by land surface temperatures with a weed biocontrol cage experiment," Ecological Modelling, Elsevier, vol. 466(C).
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. Bondo, Kristin J. & Rosenberry, Christopher S. & Stainbrook, David & Walter, W. David, 2024. "Comparing risk of chronic wasting disease occurrence using Bayesian hierarchical spatial models and different surveillance types," Ecological Modelling, Elsevier, vol. 493(C).
    11. 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.
    12. 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.
    13. 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.
    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. Miller, Christine M.F. & Waterhouse, Hannah & Harter, Thomas & Fadel, James G. & Meyer, Deanne, 2020. "Quantifying the uncertainty in nitrogen application and groundwater nitrate leaching in manure based cropping systems," Agricultural Systems, Elsevier, vol. 184(C).
    16. Johnson, Blair T. & Sisti, Anthony & Bernstein, Mary & Chen, Kun & Hennessy, Emily A. & Acabchuk, Rebecca L. & Matos, Michaela, 2021. "Community-level factors and incidence of gun violence in the United States, 2014–2017," Social Science & Medicine, Elsevier, vol. 280(C).
    17. 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.
    18. 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.
    19. 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.
    20. 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.

    More about this item

    Statistics

    Access and download statistics

    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:plo:pone00:0096556. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.