IDEAS home Printed from https://ideas.repec.org/a/eee/socmed/v256y2020ics0277953620302811.html
   My bibliography  Save this article

Spatiotemporal trends and influence factors of global diabetes prevalence in recent years

Author

Listed:
  • Li, Junming
  • Wang, Sixian
  • Han, Xiulan
  • Zhang, Gehong
  • Zhao, Min
  • Ma, Ling

Abstract

Diabetes is one of the most widespread global epidemics and has become the main component of the global disease burden. Based on data regarding the prevalence of diabetes in 203 countries and territories from 2013 to 2017, we employed the Bayesian space-time model to investigate the spatiotemporal trends in the global diabetes prevalence. The factors influencing the diabetes prevalence were assessed by the Bayesian LASSO regression model. We identified 77 (37.9%) hotspots with a higher diabetes prevalence than the global average, 10 (0.4%) warm spots with global average level and 116 (57.1%) cold spots with lower level than global average. Of the 203 countries and territories, 68 (33.5%), including 31 hotspots, 5 warm spots and 32 cold spots, exhibited an increasing trend. Of these, 60 experienced an annual increase of more than 0.25%, and 8 showed an increasing trend. Three populous countries, namely China, the USA and Mexico, exhibited a high prevalence and an increasing trend simultaneously. Three socioeconomic factors, body mass index (BMI), urbanization rate (UR) and gross domestic product per capita (GDP-PC), and PM2.5 pollution were found to significantly influence the prevalence of diabetes. BMI was the strongest factor; for every 1% increase in BMI, the prevalence of diabetes increased by 2.371% (95% confidence interval (95% CI): 0.957%, 3.890%) in 2013 and by 3.045% (95% CI: 1.803%, 4.397%) in 2015 and 2017. PM2.5 pollution could be a risk factor, and its influencing magnitude gradually increased as well. With an annual PM2.5 concentrations increase of 1.0% in a country, the prevalence of diabetes increased by 0.196% (95% CI: 0.020%, 0.356%). The UR, on the other hand, was found to be inversely associated with the prevalence of diabetes; with each UR increase of 1%, the prevalence of diabetes decreased by 0.006% (95% CI: 0.001%, 0.011%).

Suggested Citation

  • Li, Junming & Wang, Sixian & Han, Xiulan & Zhang, Gehong & Zhao, Min & Ma, Ling, 2020. "Spatiotemporal trends and influence factors of global diabetes prevalence in recent years," Social Science & Medicine, Elsevier, vol. 256(C).
    • Handle: RePEc:eee:socmed:v:256:y:2020:i:c:s0277953620302811
    DOI: 10.1016/j.socscimed.2020.113062
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0277953620302811
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.socscimed.2020.113062?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. E. G. Knox, 1964. "The Detection of Space‐Time Interactions," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 13(1), pages 25-29, March.
    2. Vanesa Bellou & Lazaros Belbasis & Ioanna Tzoulaki & Evangelos Evangelou, 2018. "Risk factors for type 2 diabetes mellitus: An exposure-wide umbrella review of meta-analyses," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-27, March.
    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. Goryakin, Yevgeniy & Rocco, Lorenzo & Suhrcke, Marc, 2017. "The contribution of urbanization to non-communicable diseases: Evidence from 173 countries from 1980 to 2008," Economics & Human Biology, Elsevier, vol. 26(C), pages 151-163.
    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. Wang, Shaobin & Ren, Zhoupeng & Liu, Xianglong & Yin, Qian, 2022. "Spatiotemporal trends in life expectancy and impacts of economic growth and air pollution in 134 countries: A Bayesian modeling study," Social Science & Medicine, Elsevier, vol. 293(C).

    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. Katherine 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. Tadadjeu, Sosson & Njangang, Henri & Asongu, Simplice A. & Kamguia, Brice, 2023. "Natural resources, child mortality and governance quality in African countries," Resources Policy, Elsevier, vol. 83(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. Shreosi Sanyal & Thierry Rochereau & Cara Nichole Maesano & Laure Com-Ruelle & Isabella Annesi-Maesano, 2018. "Long-Term Effect of Outdoor Air Pollution on Mortality and Morbidity: A 12-Year Follow-Up Study for Metropolitan France," IJERPH, MDPI, vol. 15(11), pages 1-8, November.
    5. 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.
    6. Gil, Guilherme Dôco Roberti & Costa, Marcelo Azevedo & Lopes, Ana Lúcia Miranda & Mayrink, Vinícius Diniz, 2017. "Spatial statistical methods applied to the 2015 Brazilian energy distribution benchmarking model: Accounting for unobserved determinants of inefficiencies," Energy Economics, Elsevier, vol. 64(C), pages 373-383.
    7. Vanessa Santos-Sánchez & Juan Antonio Córdoba-Doña & Javier García-Pérez & Antonio Escolar-Pujolar & Lucia Pozzi & Rebeca Ramis, 2020. "Cancer Mortality and Deprivation in the Proximity of Polluting Industrial Facilities in an Industrial Region of Spain," IJERPH, MDPI, vol. 17(6), pages 1-15, March.
    8. Berti, Patrizia & Dreassi, Emanuela & Rigo, Pietro, 2014. "Compatibility results for conditional distributions," Journal of Multivariate Analysis, Elsevier, vol. 125(C), pages 190-203.
    9. Louise Choo & Stephen G. Walker, 2008. "A new approach to investigating spatial variations of disease," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(2), pages 395-405, April.
    10. Young‐Geun Choi & Lawrence P. Hanrahan & Derek Norton & Ying‐Qi Zhao, 2022. "Simultaneous spatial smoothing and outlier detection using penalized regression, with application to childhood obesity surveillance from electronic health records," Biometrics, The International Biometric Society, vol. 78(1), pages 324-336, March.
    11. Seunghan Kim & Byungyoon Yun & Seunghyun Lee & Changyoung Kim & Juho Sim & Ara Cho & Yeonsuh Oh & Jiho Lee & Jinha Yoon, 2021. "Occupational Noise Exposure and Incidence of High Fasting Blood Glucose: A 3-Year, Multicenter, Retrospective Study," IJERPH, MDPI, vol. 18(17), pages 1-12, September.
    12. 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.
    13. 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.
    14. 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.
    15. Sanjay Chaudhuri & Debashis Mondal & Teng Yin, 2017. "Hamiltonian Monte Carlo sampling in Bayesian empirical likelihood computation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(1), pages 293-320, January.
    16. Dolores Catelan & Annibale Biggeri & Corrado Lagazio, 2009. "On the clustering term in ecological analysis: how do different prior specifications affect results?," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 18(1), pages 49-61, March.
    17. 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.
    18. 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.
    19. 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).
    20. Edgar Santos‐Fernandez & Erin E. Peterson & Julie Vercelloni & Em Rushworth & Kerrie Mengersen, 2021. "Correcting misclassification errors in crowdsourced ecological data: A Bayesian perspective," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(1), pages 147-173, January.

    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:eee:socmed:v:256:y:2020:i:c:s0277953620302811. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/315/description#description .

    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.