IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v17y2020i4p1413-d323742.html
   My bibliography  Save this article

Space-Time Clustering Characteristics of Tuberculosis in Khyber Pakhtunkhwa Province, Pakistan, 2015–2019

Author

Listed:
  • Sami Ullah

    (Department of Fundamental & Applied Sciences, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia)

  • Hanita Daud

    (Department of Fundamental & Applied Sciences, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia)

  • Sarat C. Dass

    (School of Mathematical and Computer Sciences, Heriot-Watt University Malaysia, Putrajaya 62200, Malaysia)

  • Hadi Fanaee-T

    (Center for Applied Intelligent Systems Research (CAISR), Halmstad University, SE-301 18 Halmstad, Sweden)

  • Husnul Kausarian

    (Department of Geological Engineering, Universitas Islam Riau, Pekanbaru 28284, Indonesia)

  • Alamgir

    (Department of Statistics, University of Peshawar, Peshawar 25120, Pakistan)

Abstract

The number of tuberculosis (TB) cases in Pakistan ranks fifth in the world. The National TB Control Program (NTP) has recently reported more than 462,920 TB patients in Khyber Pakhtunkhwa province, Pakistan from 2002 to 2017. This study aims to identify spatial and space-time clusters of TB cases in Khyber Pakhtunkhwa province Pakistan during 2015–2019 to design effective interventions. The spatial and space-time cluster analyses were conducted at the district-level based on the reported TB cases from January 2015 to April 2019 using space-time scan statistics (SaTScan). The most likely spatial and space-time clusters were detected in the northern rural part of the province. Additionally, two districts in the west were detected as the secondary space-time clusters. The most likely space-time cluster shows a tendency of spread toward the neighboring districts in the central part, and the most likely spatial cluster shows a tendency of spread toward the neighboring districts in the south. Most of the space-time clusters were detected at the start of the study period 2015–2016. The potential TB clusters in the remote rural part might be associated to the dry–cool climate and lack of access to the healthcare centers in the remote areas.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:4:p:1413-:d:323742
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/17/4/1413/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/17/4/1413/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bin Zhu & Jinlin Liu & Yang Fu & Bo Zhang & Ying Mao, 2018. "Spatio-Temporal Epidemiology of Viral Hepatitis in China (2003–2015): Implications for Prevention and Control Policies," IJERPH, MDPI, vol. 15(4), pages 1-17, April.
    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. Sami Ullah & Hanita Daud & Sarat C Dass & Hadi Fanaee-T & Alamgir Khalil, 2018. "An Eigenspace approach for detecting multiple space-time disease clusters: Application to measles hotspots detection in Khyber-Pakhtunkhwa, Pakistan," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-13, June.
    4. Muhammad Farooq Umer & Shumaila Zofeen & Abdul Majeed & Wenbiao Hu & Xin Qi & Guihua Zhuang, 2018. "Spatiotemporal Clustering Analysis of Malaria Infection in Pakistan," IJERPH, MDPI, vol. 15(6), pages 1-15, June.
    5. Martin Kulldorff & Ulf Hjalmars, 1999. "The Knox Method and Other Tests for Space-Time Interaction," Biometrics, The International Biometric Society, vol. 55(2), pages 544-552, June.
    6. Sebsibe Tadesse & Fikre Enqueselassie & Seifu Hagos, 2018. "Spatial and space-time clustering of tuberculosis in Gurage Zone, Southern Ethiopia," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-14, June.
    7. Kulldorff, M. & Athas, W.F. & Feuer, E.J. & Miller, B.A. & Key, C.R., 1998. "Evaluating cluster alarms: A space-time scan statistic and brain cancer in Los Alamos, New Mexico," American Journal of Public Health, American Public Health Association, vol. 88(9), pages 1377-1380.
    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. 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.
    2. 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.
    3. Miao, Congcong & Chen, Xiang & Zhang, Chuanrong, 2024. "Assessing network-based traffic crash risk using prospective space-time scan statistic method," Journal of Transport Geography, Elsevier, vol. 119(C).
    4. Lan Huang & Martin Kulldorff & David Gregorio, 2007. "A Spatial Scan Statistic for Survival Data," Biometrics, The International Biometric Society, vol. 63(1), pages 109-118, March.
    5. Sami Ullah & Hanita Daud & Sarat C Dass & Hadi Fanaee-T & Alamgir Khalil, 2018. "An Eigenspace approach for detecting multiple space-time disease clusters: Application to measles hotspots detection in Khyber-Pakhtunkhwa, Pakistan," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-13, June.
    6. Thais Paiva & Renato Assunção & Taynãna Simões, 2015. "Prospective space–time surveillance with cumulative surfaces for geographical identification of the emerging cluster," Computational Statistics, Springer, vol. 30(2), pages 419-440, June.
    7. Shixin Wang & Wenjun Li & Yi Zhou & Fuli Yan & Futao Wang & Wenliang Liu, 2015. "Space–time evolution of historical drought hazards in eastern China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 77(3), pages 2027-2047, July.
    8. Edith Gabriel & Peter J. Diggle, 2009. "Second‐order analysis of inhomogeneous spatio‐temporal point process data," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 63(1), pages 43-51, February.
    9. Linus Schiöler & Marianne Fris�n, 2012. "Multivariate outbreak detection," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(2), pages 223-242, April.
    10. 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.
    11. Hua Zhu & Han Zhao & Rong Ou & Haiyan Xiang & Ling Hu & Dan Jing & Manoj Sharma & Mengliang Ye, 2019. "Epidemiological Characteristics and Spatiotemporal Analysis of Mumps from 2004 to 2018 in Chongqing, China," IJERPH, MDPI, vol. 16(17), pages 1-14, August.
    12. Ken Silver & Richard Clapp, 2006. "Environmental Surveillance at Los Alamos: An Independent Reassessment of Historical Data," Risk Analysis, John Wiley & Sons, vol. 26(4), pages 893-906, August.
    13. 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.
    14. 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.
    15. Kamran Zandi & Ali Tavakoli Kashani & Atsuyuki Okabe, 2022. "Influence of Traffic Parameters on the Spatial Distribution of Crashes on a Freeway to Increase Safety," Sustainability, MDPI, vol. 15(1), pages 1-20, December.
    16. Hoje Kang, 2010. "Detecting agglomeration processes using space–time clustering analyses," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 45(2), pages 291-311, October.
    17. 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.
    18. 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.
    19. Zhezhe Cui & Dingwen Lin & Virasakdi Chongsuvivatwong & Jinming Zhao & Mei Lin & Jing Ou & Jinghua Zhao, 2019. "Spatiotemporal patterns and ecological factors of tuberculosis notification: A spatial panel data analysis in Guangxi, China," PLOS ONE, Public Library of Science, vol. 14(5), pages 1-15, May.
    20. 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.

    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:17:y:2020:i:4:p:1413-:d:323742. 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.