IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i10p5921-d814676.html
   My bibliography  Save this article

Spatial and Temporal Analysis of COVID-19 in the Elderly Living in Residential Care Homes in Portugal

Author

Listed:
  • Felipa De Mello-Sampayo

    (Business Research Unit (BRU-IUL), Lisbon University Institute (ISCTE-IUL), 1649-026 Lisbon, Portugal)

Abstract

Background: The goal of this study is to identify geographic areas for priority actions in order to control COVID-19 among the elderly living in Residential Care Homes (RCH). We also describe the evolution of COVID-19 in RHC throughout the 278 municipalities of continental Portugal between March and December 2020. Methods: A spatial population analysis of positive COVID-19 cases reported by the Portuguese National Health Service (NHS) among the elderly living in RCH. The data are for COVID-19 testing, symptomatic status, comorbidities, and income level by municipalities. COVID-19 measures at the municipality level are the proportion of positive cases of elderly living in RCH, positive cases per elderly living in RCH, symptomatic to asymptomatic ratio, and the share of comorbidities cases. Spatial analysis used the Kernel density estimation (KDE), space-time statistic Scan, and geographic weighted regression (GWR) to detect and analyze clusters of infected elderly. Results: Between 3 March and 31 December 2020, the high-risk primary cluster was located in the regions of Braganca, Guarda, Vila Real, and Viseu, in the Northwest of Portugal (relative risk = 3.67), between 30 September and 13 December 2020. The priority geographic areas for attention and intervention for elderly living in care homes are the regions in the Northeast of Portugal, and around the large cities, Lisbon and Porto, which had high risk clusters. The relative risk of infection was spatially not stationary and generally positively affected by both comorbidities and low-income. Conclusion: The regions with a population with high comorbidities and low income are a priority for action in order to control COVID-19 in the elderly living in RCH. The results suggest improving both income and health levels in the southwest of Portugal, in the environs of large cities, such as Lisbon and Porto, and in the northwest of Portugal to mitigate the spread of COVID-19.

Suggested Citation

  • Felipa De Mello-Sampayo, 2022. "Spatial and Temporal Analysis of COVID-19 in the Elderly Living in Residential Care Homes in Portugal," IJERPH, MDPI, vol. 19(10), pages 1-14, May.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:10:p:5921-:d:814676
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/10/5921/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/10/5921/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Srivastava, Divya & McGuire, Alistair, 2015. "Patient access to health care and medicines across low-income countries," Social Science & Medicine, Elsevier, vol. 133(C), pages 21-27.
    2. Martin Kulldorff, 2001. "Prospective time periodic geographical disease surveillance using a scan statistic," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 164(1), pages 61-72.
    3. Felipa de Mello-Sampayo, 2020. "Spatial Interaction Model for Healthcare Accessibility: What Scale Has to Do with It," Sustainability, MDPI, vol. 12(10), pages 1-19, May.
    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. Linus Schiöler & Marianne Fris�n, 2012. "Multivariate outbreak detection," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(2), pages 223-242, April.
    2. Dong Ding & Axel Gandy & Georg Hahn, 2020. "A simple method for implementing Monte Carlo tests," Computational Statistics, Springer, vol. 35(3), pages 1373-1392, September.
    3. Zhenyu Sun & Ying Sun & Xueyi Liu & Yixue Tu & Shaofan Chen & Dongfu Qian, 2022. "A Refined Evaluation Analysis of Global Healthcare Accessibility Based on the Healthcare Accessibility Index Model and Coupling Coordination Degree Model," Sustainability, MDPI, vol. 14(16), pages 1-16, August.
    4. Sami Ullah & Hanita Daud & Sarat C. Dass & Hadi Fanaee-T & Husnul Kausarian & Alamgir, 2020. "Space-Time Clustering Characteristics of Tuberculosis in Khyber Pakhtunkhwa Province, Pakistan, 2015–2019," IJERPH, MDPI, vol. 17(4), pages 1-10, February.
    5. Costa, Marcelo Azevedo & Ruiz-Cárdenas, Ramiro & Mineti, Leandro Brioschi & Prates, Marcos Oliveira, 2021. "Dynamic time scan forecasting for multi-step wind speed prediction," Renewable Energy, Elsevier, vol. 177(C), pages 584-595.
    6. Yingqi Zhao & Donglin Zeng & Amy H. Herring & Amy Ising & Anna Waller & David Richardson & Michael R. Kosorok, 2011. "Detecting Disease Outbreaks Using Local Spatiotemporal Methods," Biometrics, The International Biometric Society, vol. 67(4), pages 1508-1517, December.
    7. Neely, Abigail H. & Ponshunmugam, Arunsrinivasan, 2019. "A qualitative approach to examining health care access in rural South Africa," Social Science & Medicine, Elsevier, vol. 230(C), pages 214-221.
    8. Ruth Benson & Jan Rigby & Christopher Brunsdon & Grace Cully & Lay San Too & Ella Arensman, 2022. "Quantitative Methods to Detect Suicide and Self-Harm Clusters: A Systematic Review," IJERPH, MDPI, vol. 19(9), pages 1-13, April.
    9. Hadeel AlQadi & Majid Bani-Yaghoub & Sindhu Balakumar & Siqi Wu & Alex Francisco, 2021. "Assessment of Retrospective COVID-19 Spatial Clusters with Respect to Demographic Factors: Case Study of Kansas City, Missouri, United States," IJERPH, MDPI, vol. 18(21), pages 1-15, November.
    10. Alexandre Rodrigues & Peter J. Diggle, 2012. "Bayesian Estimation and Prediction for Inhomogeneous Spatiotemporal Log-Gaussian Cox Processes Using Low-Rank Models, With Application to Criminal Surveillance," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(497), pages 93-101, March.
    11. Ibrahim Musa & Hyun Woo Park & Lkhagvadorj Munkhdalai & Keun Ho Ryu, 2018. "Global Research on Syndromic Surveillance from 1993 to 2017: Bibliometric Analysis and Visualization," Sustainability, MDPI, vol. 10(10), pages 1-20, September.
    12. Katarina Mostarac & Petar Mostarac & Zvonko Kavran & Dragana Šarac, 2022. "Determining Optimal Locations of Postal Access Points Based on Simulated Annealing," Sustainability, MDPI, vol. 14(14), pages 1-17, July.
    13. Neill, Daniel B., 2009. "Expectation-based scan statistics for monitoring spatial time series data," International Journal of Forecasting, Elsevier, vol. 25(3), pages 498-517, July.
    14. Frisén, Marianne, 2008. "Introduction to financial surveillance," Research Reports 2008:1, University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law.
    15. Jingnan Zhang & Yicheng Kang & Yang Yang & Peihua Qiu, 2015. "Statistical monitoring of the hand, foot and mouth disease in China," Biometrics, The International Biometric Society, vol. 71(3), pages 841-850, September.
    16. Fuyu Xu & Kate Beard, 2021. "A comparison of prospective space-time scan statistics and spatiotemporal event sequence based clustering for COVID-19 surveillance," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-23, June.
    17. Zhou, Ruoyu & Shu, Lianjie & Su, Yan, 2015. "An adaptive minimum spanning tree test for detecting irregularly-shaped spatial clusters," Computational Statistics & Data Analysis, Elsevier, vol. 89(C), pages 134-146.
    18. Chih-Chieh Wu & Chien-Hsiun Chen & Sanjay Shete, 2017. "Assessing current temporal and space-time anomalies of disease incidence," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-10, November.
    19. Kim, Tae Jun & Vonneilich, Nico & Lüdecke, Daniel & von dem Knesebeck, Olaf, 2017. "Income, financial barriers to health care and public health expenditure: A multilevel analysis of 28 countries," Social Science & Medicine, Elsevier, vol. 176(C), pages 158-165.
    20. Andrea J. Cook & Diane R. Gold & Yi Li, 2007. "Spatial Cluster Detection for Censored Outcome Data," Biometrics, The International Biometric Society, vol. 63(2), pages 540-549, June.

    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:19:y:2022:i:10:p:5921-:d:814676. 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.