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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
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
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