Use of geographic information system as a tool for schistosomiasis surveillance in an endemic Municipality in Eastern Samar, The Philippines
Published: 14 May 2021
Abstract Views: 4662
PDF: 1513
HTML: 372
HTML: 372
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
This study aimed to demonstrate the use of geographic information systems (GIS) in identifying factors contributing to schistosomiasis endemicity and identifying high-risk areas in a schistosomiasis- endemic municipality in the Philippines, which was devastated by Typhoon Haiyan in 2013. Data on schistosomiasis determinants, obtained through literature review, the Philippine Department of Health, and concerned local government units, were standardized and incorporated into a GIS map using ArcGIS. Data gathered included modifiable [agriculture, poverty, sanitation, presence of intermediate and reservoir hosts, disease prevalence and mass drug administration (MDA) coverage] and nonmodifiable (geography and climate) determinants for schistosomiasis. Results showed that most barangays (villages) are characterized by favourable conditions for schistosomiasis transmission which include being located in flood-prone areas, presence of vegetation, low sanitary toilet coverage, presence of snail intermediate host, high carabao (water buffalo) population density, previously reported ‰¥1% prevalence using Kato-Katz technique, and low MDA coverage. Similarly, barangays not known to be endemic for schistosomiasis but also characterized by the same favourable conditions for schistosomiasis as listed above and may therefore be considered as potentially endemic, even if not being high-risk areas. This study demonstrated the importance of GIS technology in characterizing schistosomiasis transmission. Maps generated through application of GIS technology are useful in guiding program policy and planning at the local level for an effective and sustainable schistosomiasis control and prevention.
Altmetrics
Downloads
Download data is not yet available.
Citations
Belizario VY, delos Trinos JPCR, Silawan B, de Veyra CM, Hornido AB, Amoguis H, Basalo D, Dema-Ala C, Mantilla I, Layan R, 2017. The use of geographic information system as a tool for schistosomiasis surveillance in the province of Davao del Norte, the Philippines. Geospat Health 12:540. DOI: https://doi.org/10.4081/gh.2017.540
Belizario VJY, Molina VB, Miranda E, Ladia MA, Sison OT, Durano LP, Gerali PDR, Dejardin JTK, Lacuna JD, Cubarrubias DLP, 2018. War on worms and water, sanitation, and hygiene (WOW-A-WASH): integration of Helminthiasis control with water, sanitation, and hygiene in Haiyan-Stricken Areas in the Philippines. Technical Report.
Chen YY, Huang XB, Xiao Y, Jiang Y, Shan XW, Zhang J, Cai SX, Liu JB, 2015. Spatial analysis of Schistosomiasis in Hubei Province, China: a GIS-based analysis of Schistosomiasis from 2009 to 2013. PLoS One 10:e0118362. DOI: https://doi.org/10.1371/journal.pone.0118362
Ciddio M, Mari L, Sokolow S, De Leo G, Casagrandi R, Gatto M, 2016. The spatial spread of schistosomiasis: A multidimensional network model applied to Saint-Louis region, Senegal. Adv Water Resour 108:406-15. DOI: https://doi.org/10.1016/j.advwatres.2016.10.012
DOH, 2007. Administrative Order 2007-0015: Revised guidelines in the management and prevention of schistosomiasis.
DOH, 2009. Administrative Order 2009-0013: Declaring the month of July every year as the mass treatment and awareness month for schistosomiasis in the established endemic areas in the Philippinesitle.
DOH, 2010. Administrative Order 2010-0021: Sustainable sanitation as a national policy and a national priority program of the Department of Health.
DOH, 2016. Department Memorandum Order 2016-0212: Guidelines on the implementation of harmonized schedule and combined mass drug administration for the prevention and control of lymphatic Filariasis, Schistosomiasis, and soil-transmitted Helminthiasis.
DOH, 2018. Schistosomiasis control program. Available from: https://www.doh.gov.ph/schistosomiasis-control-program
DOH RO VIII, 2015. Malacological surveillance results summary.
DOH RO VIII, 2016. Schistosomiasis surveillance results and mass drug administration coverage summary.
Deardorff KV, Rubin Means A, Ãsbjörnsdóttir KH, Walson J, 2018. Strategies to improve treatment coverage in community-based public health programs: a systematic review of the literature. PLoS Neglect Trop Dis 12:e0006211. DOI: https://doi.org/10.1371/journal.pntd.0006211
Eastern Samar Provincial Health Office, 2016. LGU score card of Eastern Samar.
Gordon CA, Kurscheid J, Williams GM, Clements ACA, Li Y, Zhou X, Utzinger J, Mcmanus DP, Gray DJ, 2019. Asian schistosomiasis: current status and prospects for control leading to elimination. Trop Med Infect Dis 4:40. DOI: https://doi.org/10.3390/tropicalmed4010040
Gray DJ, Williams GM, Li Y, Chen H, Forsyth SJ, Li RS, Barnett AG, Guo J, Ross AG, Feng Z, McManus DP, 2009. A cluster-randomised intervention trial against Schistosoma japonicum in the Peoples’ Republic of China: bovine and human transmission. PLoS One 4:e5900. DOI: https://doi.org/10.1371/journal.pone.0005900
Grimes JE, Croll D, Harrison WE, Utzinger J, Freeman MC, Templeton MR, 2015. The roles of water, sanitation and hygiene in reducing schistosomiasis: a review. Parasite Vectors 8:1-16. DOI: https://doi.org/10.1186/s13071-015-0766-9
Higgs G, 2004. A literature review of the use of GIS-based measures of access to health care services. Health Serv Outcomes Res Methodol 5:119-39. DOI: https://doi.org/10.1007/s10742-005-4304-7
Hu Y, Li R, Bergquist R, Lynn H, Gao F, Wang Q, Zhang S, Sun L, Zhang Z, Jiang Q, 2015. Spatio-temporal transmission and environmental determinants of Schistosomiasis Japonica in Anhui Province, China. PLoS Negl Trop Dis 9:e0003470. DOI: https://doi.org/10.1371/journal.pntd.0003470
Karunamoorthi K, Almalki M, Ghailan K, 2018. Schistosomiasis: a neglected tropical disease of poverty: a call for intersectoral mitigation strategies for better health. J Health Res Rev 5:1-12. DOI: https://doi.org/10.4103/jhrr.jhrr_92_17
Katanha A, Masocha V, 2014. Schistosomiasis an issue in flood prone area of DambakurimaWard 1, Muzarabani District of Zimbabwe.
Kerr K, 2004. Zoonoses: infectious diseases transmissible from animals to humans. J Clin Pathol 57:1120. DOI: https://doi.org/10.1136/jcp.2004.019646
King C, 2010. Parasites and poverty: the case of Schistosomiasis. Acta Tropica 113:95-104. DOI: https://doi.org/10.1016/j.actatropica.2009.11.012
King CH, Dickman K, Tisch DJ, 2005. Reassessment of the cost of chronic helmintic infection: a meta-analysis of disability-related outcomes in endemic schistosomiasis. Lancet 365:1561-9. DOI: https://doi.org/10.1016/S0140-6736(05)66457-4
Kouadio IK, Aljunid S, Kamigaki T, Hammad K, Oshitani H, 2012. Infectious diseases following natural disasters: Prevention and control measures. Exp Rev Anti-Infect Ther 10:95-104. DOI: https://doi.org/10.1586/eri.11.155
Leonardo LR, Rivera PT, Crisostomo B, Sarol JN, Bantayan NC, Tiu WU, Bergquist NR, 2005. A study of the environmental determinants of malaria and schistosomiasis in the Philippines using Remote Sensing and Geographic Information Systems. Parassitologia 47:105-14.
McManus DP, Gray DJ, Li Y, Feng Z, Williams GM, Stewart D, Rey-Ladino J, Ross AG, 2010. Schistosomiasis in the People’s Republic of China: The era of the three Gorges Dam. Clin Microbiol Rev 23:442-66. DOI: https://doi.org/10.1128/CMR.00044-09
MPDO (Oras Municipal Planning and Development Office), 2015. Topographic map and comprehensive land use in Oras, Eastern Samar.
NEDA (National Economic Development Authority), 2017. Philippine Development Plan 2017-2022. Available from: http://pdp.neda.gov.ph/wp-content/uploads/2017/01/PDP-2017-2022-07-20-2017.pdf
Oras Rural Health Unit, 2016. Sanitary toilet coverage in Oras, Eastern Samar.
PAGRO (Eastern Samar Provincial Agricultural Office), 2016. Carabao population in Eastern Samar.
Peng WX, Tao B, Clements A, Jiang QL, Zhang ZJ, Zhou YB, Jiang QW, 2010. Identifying high-risk areas of schistosomiasis and associated risk factors in the Poyang Lake region, China. Parasitology 137:1099-107. DOI: https://doi.org/10.1017/S003118200999206X
Philippine Statistics Authority, 2015. Region 8 Profile. Available from: http://www.nscb.gov.ph/activestats/psgc/regview.asp?region=8
PPDO (Eastern Samar Provincial Planning and Development Office), 2015. Topographic map and comprehensive land use in Eastern Samar.
Qin ZQ, Xu J, Feng T, Lv S, Qian YJ, Zhang LJ, Li YL, Lv C, Bergquist R, Li SZ, Zhou XN, 2018. Field evaluation of a loop-mediated isothermal amplification (LAMP) platform for the detection of Schistosoma japonicum infection in Oncomelania hupensis Snails. Trop Med Infect Dis 3:124. DOI: https://doi.org/10.3390/tropicalmed3040124
Salazar MA, Law R, Pesigan A, Winkler V, 2017. Health consequences of Typhoon Haiyan in the Eastern Visayas Region using a syndromic surveillance database. PLoS Current Disasters doi:10.1371/currents.dis.4a3d3b4474847b2599aa5c5eefe3a621
Seto E, Xu B, Liang S, Gong P, Wu W, Davis G, Qiu D, Gu X, Spear R, 2002. The use of remote sensing for predictive modeling of Schistosomiasis in China. Photogram Engine Remote Sens 68:167-74
United Nations Children’s Fund, 2015. WASH pathway of change after Yolanda-one year on. Available from: https://www.communityledtotalsanitation.org/sites/communityledtotalsanitation.org/files/UNICEF_PhATS_Yolanda_One_year_on_0.pdf
van Dam GJ, Odermatt P, Acosta L, Bergquist R, de Dood CJ, Kornelis D, Muth S, Utzinger J, Corstjens PL, 2015. Evaluation of banked urine samples for the detection of circulating anodic and cathodic antigens in Schistosoma mekongi and S. japonicum infections: a proof-of-concept study. Acta Trop 141:198-203. DOI: https://doi.org/10.1016/j.actatropica.2014.09.003
World Clim, 2000. Global climate data. Available from: https://www.worldclim.org/data/worldclim21.html
WHO, 2006. Preventive chemotherapy in human Helminthiasis: coordinated use of anthelminthic drugs in control interventions: a manual for health professionals and programme managers. World Health Organization, Geneva, Switzerland.
WHO, 2012. Schistosomiasis fact sheet. World Health Organization, Geneva, Switzerland.
WHO, 2017. Expert consultation to accelerate elimination of Asian Schistosomiasis. World Health Organization, Geneva, Switzerland. Available from: https://iris.wpro.who.int/bitstream/handle/10665.1/13938/RS-2017-GE-36-CHN-eng.pdf
WHO, 2020. Ending the neglect to attain sustainable development goals - a road map for neglected tropical diseases 2021-2030. World Health Organization, Geneva, Switzerland.
Wu XH, Zhang SQ, Xu XJ, Huang YX, Steinmann P, Utzinger J, Wang TP, Xu J, Zheng J, Zhou XN, 2008. Effect of floods on the transmission of schistosomiasis in the Yangtze River valley, People’s Republic of China. Parasitol Int 57:271-6. DOI: https://doi.org/10.1016/j.parint.2008.04.004
Yang GJ, Utzinger J, Sun LP, Hong QB, Vounatsou P, Tanner M, Zhou XN, 2007. Effect of temperature on the development of Schistosoma japonicum within Oncomelania hupensis, and hibernation of O. hupensis. Parasitol Res 100:695-700. DOI: https://doi.org/10.1007/s00436-006-0315-8
Yang K, Li W, Sun LP, Huang YX, Zhang JF, Wu F, Hang DR, Steinmann P, Liang YS, 2013. Spatio-temporal analysis to identify determinants of Oncomelania hupensis infection with Schistosoma japonicum in Jiangsu province, China. Parasites Vectors 6:138. DOI: https://doi.org/10.1186/1756-3305-6-138
Zhang ZY, Xu DZ, Zhou XN, Zhou Y, Sun ZD, Zhang B, Gong ZL, Liu SJ, 2003. [Application of satellite image for surveillance of vegetation landscapes of Oncomelenia-snail habitats in marshland using unsupervised classification]. Zhonghua Liu Xing Bing Xue Za Zhi = Zhonghua Liuxingbingxue Zazhi 24:261-4 [In Chinese].
Zhou XN, Malone JB, Kristensen TK, Bergquist NR, 2001. Application of geographic information systems and remote sensing to schistosomiasis control in China. Acta Tropica 79:97-106. DOI: https://doi.org/10.1016/S0001-706X(01)00107-3
Zhou X-N, Yang K, Yang G-J, Wu X-H, Kristensen TK, Bergquist R, Utzinger J, 2009. Potential impact of, and adaptation to, climate change influence on schistosomiasis transmission in China - a) experiences from China. IOP Conf Ser Earth Environ Sci 6:142002 DOI: https://doi.org/10.1088/1755-1307/6/14/142002
How to Cite
Use of geographic information system as a tool for schistosomiasis surveillance in an endemic Municipality in Eastern Samar, The Philippines. (2021). Geospatial Health, 16(1). https://doi.org/10.4081/gh.2021.957
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
Similar Articles
- Anna-Sofie Stensgaard, Mark Booth, Grigory Nikulin, Nicky McCreesh, Combining process-based and correlative models improves predictions of climate change effects on Schistosoma mansoni transmission in eastern Africa , Geospatial Health: Vol. 11 No. s1 (2016): HEALTHY FUTURES
- Jean Pierre Bizimana, Stefan Kienberger, Michael Hagenlocher, Emmanuel Twarabamenye, Modelling homogeneous regions of social vulnerability to malaria in Rwanda , Geospatial Health: Vol. 11 No. s1 (2016): HEALTHY FUTURES
- Kristin Meseck, Marta M. Jankowska, Jasper Schipperijn, Loki Natarajan, Suneeta Godbole, Jordan Carlson, Michelle Takemoto, Katie Crist, Jacqueline Kerr, Is missing geographic positioning system data in accelerometry studies a problem, and is imputation the solution? , Geospatial Health: Vol. 11 No. 2 (2016)
- Yvonne Walz, Martin Wegmann, Benjamin Leutner, Stefan Dech, Penelope Vounatsou, Eliézer K. N'Goran, Giovanna Raso, Jürg Utzinger, Use of an ecologically relevant modelling approach to improve remote sensing-based schistosomiasis risk profiling , Geospatial Health: Vol. 10 No. 2 (2015)
- Nicola A. Wardrop, Matthew Geary, Patrick E. Osborne, Peter M. Atkinson, Interpreting predictive maps of disease: highlighting the pitfalls of distribution models in epidemiology , Geospatial Health: Vol. 9 No. 1 (2014)
- Joseph Leedale, Adrian M. Tompkins, Cyril Caminade, Anne E. Jones, Grigory Nikulin, Andrew P. Morse, Projecting malaria hazard from climate change in eastern Africa using large ensembles to estimate uncertainty , Geospatial Health: Vol. 11 No. s1 (2016): HEALTHY FUTURES
- Ernest O. Asare, Adrian M. Tompkins, Leonard K. Amekudzi, Volker Ermert, Robert Redl, Mosquito breeding site water temperature observations and simulations towards improved vector-borne disease models for Africa , Geospatial Health: Vol. 11 No. s1 (2016): HEALTHY FUTURES
- David Taylor, Michael Hagenlocher, Anne E. Jones, Stefan Kienberger, Joseph Leedale, Andrew P. Morse, Environmental change and Rift Valley fever in eastern Africa: projecting beyond HEALTHY FUTURES , Geospatial Health: Vol. 11 No. s1 (2016): HEALTHY FUTURES
- Ana Carolina Lopes Antunes, Els Ducheyne, Ward Bryssinckx, Sara Vieira, Manuel Malta, Yolanda Vaz, Telmo Nunes, Koen Mintiens, The dog and cat population on Maio Island, Cape Verde: characterisation and prediction based on household survey and remotely sensed imagery , Geospatial Health: Vol. 10 No. 2 (2015)
- David Gikungu, Jacob Wakhungu, Donald Siamba, Edward Neyole, Richard Muita, Bernard Bett, Dynamic risk model for Rift Valley fever outbreaks in Kenya based on climate and disease outbreak data , Geospatial Health: Vol. 11 No. 2 (2016)
You may also start an advanced similarity search for this article.
