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

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Space–Time Clustering Characteristics of Malaria in Bhutan at the End Stages of Elimination International Journal of Environmental Research and Public Health Article Space–Time Clustering Characteristics of Malaria in Bhutan at the End Stages of Elimination Kinley Wangdi 1,* , Kinley Penjor 2 , Tobgyal 2, Saranath Lawpoolsri 3 , Ric N. Price 4,5,6 , Peter W. Gething 7,8, Darren J. Gray 1, Elivelton Da Silva Fonseca 9 and Archie C. A. Clements 7,8 1 Research School of Population Health, Australian National University, Canberra, ACT 2601, Australia; [email protected] 2 Vector-Borne Diseases Control Program, Department of Public Health, Ministry of Health, Gelephu 31101, Bhutan; [email protected] (K.P.); [email protected] (T.) 3 Department of Tropical Hygiene, Faculty of Tropical Medicine, Mahidol University, Bangkok 10400, Thailand; [email protected] 4 Global and Tropical Health Division, Menzies School of Health Research, Charles Darwin University, Darwin, NT 0810, Australia; [email protected] 5 Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford OX1 2JD, UK 6 Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok 10400, Thailand 7 Faculty of Health Sciences, Curtin University, Bentley, WA 6102, Australia; [email protected] (P.W.G.); [email protected] (A.C.A.C.) 8 Telethon Kids Institute, Nedlands, WA 6009, Australia 9 Institute of Geography, Federal University of Uberlândia, Uberlândia, MG 38408-100, Brazil; [email protected] Citation: Wangdi, K.; Penjor, K.; * Correspondence: [email protected] Tobgyal; Lawpoolsri, S.; Price, R.N.; Gething, P.W.; Gray, D.J.; Da Silva Fonseca, E.; Clements, A.C.A. Abstract: Malaria in Bhutan has fallen significantly over the last decade. As Bhutan attempts to Space–Time Clustering eliminate malaria in 2022, this study aimed to characterize the space–time clustering of malaria from Characteristics of Malaria in Bhutan 2010 to 2019. Malaria data were obtained from the Bhutan Vector-Borne Disease Control Program at the End Stages of Elimination. Int. data repository. Spatial and space–time cluster analyses of Plasmodium falciparum and Plasmodium J. Environ. Res. Public Health 2021, 18, vivax cases were conducted at the sub-district level from 2010 to 2019 using Kulldorff’s space–time 5553. https://doi.org/10.3390/ scan statistic. A total of 768 confirmed malaria cases, including 454 (59%) P. vivax cases, were reported ijerph18115553 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 Academic Editor: Paul B. Tchounwou 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 Received: 14 April 2021 with both species. The most likely cluster for P. falciparum infection had a radius of 50.4 km and Accepted: 17 May 2021 P. vivax Published: 22 May 2021 included 26 sub-districts with a relative risk (RR) of 32.7. The most likely cluster for infection had a radius of 33.6 km with 11 sub-districts and RR of 27.7. Three secondary space–time clusters Publisher’s Note: MDPI stays neutral were detected in other parts of Bhutan. Spatial and space–time cluster analysis identified high-risk with regard to jurisdictional claims in areas and periods for both P. vivax and P. falciparum malaria. Both malaria types showed significant published maps and institutional affil- spatial and spatiotemporal variations. Operational research to understand the drivers of residual iations. transmission in hotspot sub-districts will help to overcome the final challenges of malaria elimination in Bhutan. Keywords: Bhutan; malaria; space; time; clustering; SaTScan Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and 1. Introduction conditions of the Creative Commons Historically, malaria in Bhutan has been reported from seven out of twenty districts: Attribution (CC BY) license (https:// Chukha, Dagana, Pemagatshel, Samdrup Jongkhar, Samtse, Sarpang and Zhemgang [1]. creativecommons.org/licenses/by/ These districts lie in the southern foothills of the Himalayas bordering the Indian states of 4.0/). Int. J. Environ. Res. Public Health 2021, 18, 5553. https://doi.org/10.3390/ijerph18115553 https://www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2021, 18, 5553 2 of 12 Assam and West Bengal. The main malaria parasite species are Plasmodium falciparum and Plasmodium vivax, and the main suspected vector species are Anopheles pseudowillmori and Anopheles culicifacies [2]. Malaria cases have been falling in recent years. As a result, Bhutan announced a national strategy to eliminate malaria by 2022 [3,4]. The success of malaria control in Bhutan can be attributed to the implementation of a three-pronged approach, namely: (1) universal access to early diagnosis and prompt treatment with artemisinin- based combination therapy (ACT); (2) the protection of at-risk populations with indoor residual spraying (IRS) and long-lasting insecticide nets (LLINs), and (3) integrated vector management (IVM). As the malaria map shrinks, cases tend to be confined to hard-to-reach populations, often in border areas [5,6]. Identifying these last pockets of transmission is imperative for the program to eliminate the parasite and prevent its reintroduction. Spatial epidemiologi- cal tools (including geographical information systems (GIS) and spatial analytic methods) can be used to quantify spatial and spatiotemporal patterns of malaria, including cluster- ing [7–10]. The identification of malaria clusters can help in the delineation of problem areas and the application of targeted program interventions suitable in the elimination phase [11]. Focused interventions in areas with a higher risk of malaria are likely to be more cost-effective than uniform resource allocation, particularly in resource-constrained settings for sustainable elimination programs [9,12,13]. In addition, knowledge of seasonal patterns will aid in the estimation of time for malaria transmission for initiating suitable and appropriate control measures [14,15]. The aims of this study were to quantify the temporal, spatial and spatial–temporal patterns of both species of malaria in sub-districts in Bhutan using retrospective surveil- lance data. 2. Methods 2.1. Study Site Bhutan is a small Himalayan country in South Asia, neighboring India in the east, south and west, and China in the north. The country is divided into 20 districts and 205 sub-districts. The total population of Bhutan in 2017 was estimated at 681,720 [16]. There is a wide variation in the climate, with sub-tropical conditions in the southern foothills neighboring India to snow-capped mountains rising above 4500 m from sea level in the north. Malaria transmission occurs mainly in southern Bhutan in 82 sub-districts within the seven historically malaria-endemic districts (Figure1). 2.2. Data Source Malaria data for the study were obtained from the national malaria surveillance sys- tem, housed within the Bhutan Vector-borne Diseases Control Program (VDCP). Monthly malaria cases for 2010–2019 from 82 sub-districts were extracted, including those with zero cases. In these malaria-transmitting sub-districts, all febrile patients presenting to health facilities (hospitals and primary health centers) undergo a mandatory blood test with RDTs or microscopy. Malaria cases stratified by species as P. falciparum and P. vivax are reported through the national malaria surveillance system at the end of each month. All patients in public health facilities are offered treatment free of charge according to national guidelines. The yearly sub-district population was calculated using the district population growth rate (Chukha 1.7, Dagana 1.9, Pemagatshel 1.2, Samdrup Jongkhar 1.8, Samtse 1.6, Sarpang 2.0 and Zhemgang 1.4) as outlined by the National Statistical Bureau and the Office of the Census Commissioner of Bhutan [17,18]. An electronic map of sub-district boundaries in shapefile format was obtained from the Global Administrative Areas database (http://www.gadm.org/country, accessed on 21 December 2020). Int. J. Environ. Res. Public Health 2021, 18, 5553 3 of 12 Int. J. Environ. Res. Public Health 2021, 18, x 3 of 14 Figure 1. Map of Bhutan with malaria-transmittingmalaria-transmitting districts and sub-districts.sub-districts. 2.3. Exploration of Seasonal Patterns and Inter Inter-Annual-Annual Patterns TThehe m malariaalaria time series (January 2010 2010–December–December 2019) was decomposed using sea- sonal trend decomposition based on locally (STL) weightedweighted regression.regression. The STL modelmodel was structured as follows:follows: = + + <!-- MathType@Translator@5@5@MathML2Yt St Tt Rt (no namespace).tdl@MathML (1) where Yt represents numbers of local2.0 (no malaria namespace)@ cases with -- logarithmic> transformation, St is the additive seasonal component, Tt is the trend, and Rt is the “remainder component”; t is time in months [19–21]. <math> 2.4. Spatial and Spatiotemporal Cluster Analysis <semantics> Kulldorff’s spatial and space–time scan statistics were applied to identify malaria <mrow> incidence clusters by species (P. falciparum and P. vivax) at a sub-district level over the 10- TM year study period. The analysis was implemented <msub> using the
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