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Space–Time Clustering Characteristics of Malaria in Bhutan at the End Stages of Elimination

Author

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  • Kinley Wangdi

    (Research School of Population Health, Australian National University, Canberra, ACT 2601, Australia)

  • Kinley Penjor

    (Vector-Borne Diseases Control Program, Department of Public Health, Ministry of Health, Gelephu 31101, Bhutan)

  • Tobgyal

    (Vector-Borne Diseases Control Program, Department of Public Health, Ministry of Health, Gelephu 31101, Bhutan)

  • Saranath Lawpoolsri

    (Department of Tropical Hygiene, Faculty of Tropical Medicine, Mahidol University, Bangkok 10400, Thailand)

  • Ric N. Price

    (Global and Tropical Health Division, Menzies School of Health Research, Charles Darwin University, Darwin, NT 0810, Australia
    Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford OX1 2JD, UK
    Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok 10400, Thailand)

  • Peter W. Gething

    (Faculty of Health Sciences, Curtin University, Bentley, WA 6102, Australia
    Telethon Kids Institute, Nedlands, WA 6009, Australia)

  • Darren J. Gray

    (Research School of Population Health, Australian National University, Canberra, ACT 2601, Australia)

  • Elivelton Da Silva Fonseca

    (Institute of Geography, Federal University of Uberlândia, Uberlândia, MG 38408-100, Brazil)

  • Archie C. A. Clements

    (Faculty of Health Sciences, Curtin University, Bentley, WA 6102, Australia
    Telethon Kids Institute, Nedlands, WA 6009, Australia)

Abstract

Malaria in Bhutan has fallen significantly over the last decade. As Bhutan attempts to eliminate malaria in 2022, this study aimed to characterize the space–time clustering of malaria from 2010 to 2019. Malaria data were obtained from the Bhutan Vector-Borne Disease Control Program data repository. Spatial and space–time cluster analyses of Plasmodium falciparum and Plasmodium vivax cases were conducted at the sub-district level from 2010 to 2019 using Kulldorff’s space–time scan statistic. A total of 768 confirmed malaria cases, including 454 (59%) P. vivax cases, were reported in Bhutan during the study period. Significant temporal clusters of cases caused by both species were identified between April and September. The most likely spatial clusters were detected in the central part of Bhutan throughout the study period. The most likely space–time cluster was in Sarpang District and neighboring districts between January 2010 to June 2012 for cases of infection with both species. The most likely cluster for P. falciparum infection had a radius of 50.4 km and included 26 sub-districts with a relative risk (RR) of 32.7. The most likely cluster for P. vivax infection had a radius of 33.6 km with 11 sub-districts and RR of 27.7. Three secondary space–time clusters were detected in other parts of Bhutan. Spatial and space–time cluster analysis identified high-risk areas and periods for both P. vivax and P. falciparum malaria. Both malaria types showed significant spatial and spatiotemporal variations. Operational research to understand the drivers of residual transmission in hotspot sub-districts will help to overcome the final challenges of malaria elimination in Bhutan.

Suggested Citation

  • Kinley Wangdi & Kinley Penjor & Tobgyal & Saranath Lawpoolsri & Ric N. Price & Peter W. Gething & Darren J. Gray & Elivelton Da Silva Fonseca & Archie C. A. Clements, 2021. "Space–Time Clustering Characteristics of Malaria in Bhutan at the End Stages of Elimination," IJERPH, MDPI, vol. 18(11), pages 1-12, May.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:11:p:5553-:d:560144
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    References listed on IDEAS

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    1. Kinley Wangdi & Ayodhia Pitaloka Pasaribu & Archie C.A. Clements, 2021. "Addressing hard‐to‐reach populations for achieving malaria elimination in the Asia Pacific Malaria Elimination Network countries," Asia and the Pacific Policy Studies, Wiley Blackwell, vol. 8(2), pages 176-188, May.
    2. Sehwi Kim & Inkyung Jung, 2017. "Optimizing the maximum reported cluster size in the spatial scan statistic for ordinal data," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-15, July.
    3. Shaowei Sang & Shaohua Gu & Peng Bi & Weizhong Yang & Zhicong Yang & Lei Xu & Jun Yang & Xiaobo Liu & Tong Jiang & Haixia Wu & Cordia Chu & Qiyong Liu, 2015. "Predicting Unprecedented Dengue Outbreak Using Imported Cases and Climatic Factors in Guangzhou, 2014," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 9(5), pages 1-12, May.
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    Cited by:

    1. Koh Kawaguchi & Elorm Donkor & Aparna Lal & Matthew Kelly & Kinley Wangdi, 2022. "Distribution and Risk Factors of Malaria in the Greater Accra Region in Ghana," IJERPH, MDPI, vol. 19(19), pages 1-13, September.

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