IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v15y2018i4p671-d139506.html
   My bibliography  Save this article

An Ecological Study on the Spatially Varying Relationship between County-Level Suicide Rates and Altitude in the United States

Author

Listed:
  • Hoehun Ha

    (Department of Sociology, Anthropology and Geography, Auburn University at Montgomery, 7041 Senators Drive, Montgomery, AL 36117, USA)

  • Wei Tu

    (Department of Geology and Geography, Georgia Southern University, P.O. Box 8149, Statesboro, GA 30460, USA)

Abstract

Suicide is a serious but preventable public health issue. Several previous studies have revealed a positive association between altitude and suicide rates at the county level in the contiguous United States. We assessed the association between suicide rates and altitude using a cross-county ecological study design. Data on suicide rates were obtained from a Web-based Injury Statistics Query and Reporting System (WISQARS), maintained by the U.S. National Center for Injury Prevention and Control (NCIPC). Altitude data were collected from the United States Geological Survey (USGS). We employed an ordinary least square (OLS) regression to model the association between altitude and suicide rates in 3064 counties in the contiguous U.S. We conducted a geographically weighted regression (GWR) to examine the spatially varying relationship between suicide rates and altitude after controlling for several well-established covariates. A significant positive association between altitude and suicide rates (average county rates between 2008 and 2014) was found in the dataset in the OLS model ( R 2 = 0.483, p < 0.001). Our GWR model fitted the data better, as indicated by an improved R 2 (average: 0.62; range: 0.21–0.64) and a lower Akaike Information Criteria (AIC) value (13,593.68 vs. 14,432.14 in the OLS model). The GWR model also significantly reduced the spatial autocorrelation, as indicated by Moran’s I test statistic (Moran’s I = 0.171; z = 33.656; p < 0.001 vs. Moran’s I = 0.323; z = 63.526; p < 0.001 in the OLS model). In addition, a stronger positive relationship was detected in areas of the northern regions, northern plain regions, and southeastern regions in the U.S. Our study confirmed a varying overall positive relationship between altitude and suicide. Future research may consider controlling more predictor variables in regression models, such as firearm ownership, religion, and access to mental health services.

Suggested Citation

  • Hoehun Ha & Wei Tu, 2018. "An Ecological Study on the Spatially Varying Relationship between County-Level Suicide Rates and Altitude in the United States," IJERPH, MDPI, vol. 15(4), pages 1-16, April.
    • Handle: RePEc:gam:jijerp:v:15:y:2018:i:4:p:671-:d:139506
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/15/4/671/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/15/4/671/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Siegel, M. & Rothman, E.F., 2016. "Firearm ownership and suicide rates among US men and women, 1981-2013," American Journal of Public Health, American Public Health Association, vol. 106(7), pages 1316-1322.
    2. Jalal Poorolajal & Nahid Darvishi, 2016. "Smoking and Suicide: A Meta-Analysis," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-18, July.
    3. Yee Leung, 2010. "Discovery of Spatial Relationships in Spatial Data," Advances in Spatial Science, in: Knowledge Discovery in Spatial Data, chapter 0, pages 223-276, Springer.
    4. Mary C. Daly & Daniel J. Wilson & Norman J. Johnson, 2013. "Relative Status and Well-Being: Evidence from U.S. Suicide Deaths," The Review of Economics and Statistics, MIT Press, vol. 95(5), pages 1480-1500, December.
    5. Carla Shoff & Tse-Chuan Yang, 2012. "Spatially varying predictors of teenage birth rates among counties in the United States," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 27(14), pages 377-418.
    6. David Wheeler & Michael Tiefelsdorf, 2005. "Multicollinearity and correlation among local regression coefficients in geographically weighted regression," Journal of Geographical Systems, Springer, vol. 7(2), pages 161-187, June.
    7. A. Stewart Fotheringham & Taylor M. Oshan, 2016. "Geographically weighted regression and multicollinearity: dispelling the myth," Journal of Geographical Systems, Springer, vol. 18(4), pages 303-329, October.
    8. Colin D Mathers & Dejan Loncar, 2006. "Projections of Global Mortality and Burden of Disease from 2002 to 2030," PLOS Medicine, Public Library of Science, vol. 3(11), pages 1-20, November.
    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. Marco Helbich, 2018. "Mental Health and Environmental Exposures: An Editorial," IJERPH, MDPI, vol. 15(10), pages 1-4, October.

    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. Alexis Comber & Paul Harris, 2018. "Geographically weighted elastic net logistic regression," Journal of Geographical Systems, Springer, vol. 20(4), pages 317-341, October.
    2. Oshan, Taylor M., 2022. "Navigating the methodological landscape in spatial analysis: a comment on ‘A Route Map for Successful Applications of Geographically-Weighted Regression’," OSF Preprints rckzj, Center for Open Science.
    3. Christos Agiakloglou & Cleon Tsimbos & Apostolos Tsimpanos, 2019. "Evidence of spurious results along with spatially autocorrelated errors in the context of geographically weighted regression for two independent SAR(1) processes," Empirical Economics, Springer, vol. 57(5), pages 1613-1631, November.
    4. Stephen Matthews & Daniel M. Parker, 2013. "Progress in Spatial Demography," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 28(10), pages 271-312.
    5. Paul Harris & Bruno Lanfranco & Binbin Lu & Alexis Comber, 2020. "Influence of Geographical Effects in Hedonic Pricing Models for Grass-Fed Cattle in Uruguay," Agriculture, MDPI, vol. 10(7), pages 1-17, July.
    6. Qianyao Li & Junwu Wang & Judith Callanan & Binbin Lu & Zeng Guo, 2021. "The spatial varying relationship between services of the train network and residential property values in Melbourne, Australia," Urban Studies, Urban Studies Journal Limited, vol. 58(2), pages 335-354, February.
    7. Chacon-Hurtado, Davis & Kumar, Indraneel & Gkritza, Konstantina & Fricker, Jon D. & Beaulieu, Lionel J., 2020. "The role of transportation accessibility in regional economic resilience," Journal of Transport Geography, Elsevier, vol. 84(C).
    8. Geniaux, Ghislain & Martinetti, Davide, 2018. "A new method for dealing simultaneously with spatial autocorrelation and spatial heterogeneity in regression models," Regional Science and Urban Economics, Elsevier, vol. 72(C), pages 74-85.
    9. Jia Lu & Shabana Jamani & Joseph Benjamen & Eric Agbata & Olivia Magwood & Kevin Pottie, 2020. "Global Mental Health and Services for Migrants in Primary Care Settings in High-Income Countries: A Scoping Review," IJERPH, MDPI, vol. 17(22), pages 1-28, November.
    10. Matthijs van den Berg & Filip Smit & Theo Vos & Pieter H M van Baal, 2011. "Cost-Effectiveness of Opportunistic Screening and Minimal Contact Psychotherapy to Prevent Depression in Primary Care Patients," PLOS ONE, Public Library of Science, vol. 6(8), pages 1-7, August.
    11. Pulugurtha, Srinivas S. & Mathew, Sonu, 2021. "Modeling AADT on local functionally classified roads using land use, road density, and nearest nonlocal road data," Journal of Transport Geography, Elsevier, vol. 93(C).
    12. Ide, Hiroo & Mollahaliloglu, Salih, 2009. "How firms set prices for medical materials: A multi-country study," Health Policy, Elsevier, vol. 92(1), pages 73-78, September.
    13. Oded Stark & Wiktor Budzinski, 2021. "A social‐psychological reconstruction of Amartya Sen’s measures of inequality and social welfare," Kyklos, Wiley Blackwell, vol. 74(4), pages 552-566, November.
    14. Chih-Hao Wang & Na Chen, 2021. "A multi-objective optimization approach to balancing economic efficiency and equity in accessibility to multi-use paths," Transportation, Springer, vol. 48(4), pages 1967-1986, August.
    15. Eldon Spackman & Stewart Richmond & Mark Sculpher & Martin Bland & Stephen Brealey & Rhian Gabe & Ann Hopton & Ada Keding & Harriet Lansdown & Sara Perren & David Torgerson & Ian Watt & Hugh MacPherso, 2014. "Cost-Effectiveness Analysis of Acupuncture, Counselling and Usual Care in Treating Patients with Depression: The Results of the ACUDep Trial," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-12, November.
    16. Tsimpanos, Apostolos & Tsimbos, Cleon & Kalogirou, Stamatis, 2018. "Assessing spatial variation and heterogeneity of fertility in Greece at local authority level," MPRA Paper 100406, University Library of Munich, Germany.
    17. Peele, Morgan & Wolf, Sharon, 2020. "Predictors of anxiety and depressive symptoms among teachers in Ghana: Evidence from a randomized controlled trial," Social Science & Medicine, Elsevier, vol. 253(C).
    18. Carsten Hinrichsen & Vibeke Jenny Koushede & Katrine Rich Madsen & Line Nielsen & Nanna Gram Ahlmark & Ziggi Ivan Santini & Charlotte Meilstrup, 2020. "Implementing Mental Health Promotion Initiatives—Process Evaluation of the ABCs of Mental Health in Denmark," IJERPH, MDPI, vol. 17(16), pages 1-26, August.
    19. Diana Gutiérrez Posada & Fernando Rubiera Morollón & Ana Viñuela, 2018. "Ageing Places in an Ageing Country: The Local Dynamics of the Elderly Population in Spain," Tijdschrift voor Economische en Sociale Geografie, Royal Dutch Geographical Society KNAG, vol. 109(3), pages 332-349, July.
    20. Marco Helbich & Wolfgang Brunauer & Eric Vaz & Peter Nijkamp, 2014. "Spatial Heterogeneity in Hedonic House Price Models: The Case of Austria," Urban Studies, Urban Studies Journal Limited, vol. 51(2), pages 390-411, February.

    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:671-:d:139506. 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.