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dc.contributor.authorLuo, Da-Peng
dc.date.accessioned2019-10-22T11:27:37Z
dc.date.available2019-10-22T11:27:37Z
dc.date.created2016-09-05 23:25
dc.date.issued2000
dc.identifieroai:researchonline.lshtm.ac.uk:682300
dc.identifierhttp://researchonline.lshtm.ac.uk/682300/1/394512.pdf
dc.identifierLuo, Da-Peng; (2000) Spatial prediction of malaria in the Red River basin, Yunnan, China using geographical information systems and remote sensing. PhD thesis, London School of Hygiene & Tropical Medicine.
dc.identifier.urihttp://hdl.handle.net/20.500.12424/713621
dc.description.abstractThis study aims to identify risk 
 factors for 
 malaria 
 related 
 to landscape, 
 environmental, and socio-economic and 
 human 
 behaviour 
 variables 
 in the 
 Red 
 River 
 basin area, Yunnan, China, to develop a predictive 
 model of malaria 
 spatial 
 distribution, and to utilise these to improve malaria 
 surveillance and control 
 in the 
 basin area. 
 Yunnan is one of the most endemic areas 
 for 
 malaria 
 in 
 China today, 
 particularly 
 in 
 the Red River basin and its border areas. 
 Chloroquine-resistant falciparum 
 malaria 
 is 
 continuing to increase, partly due to immigration 
 and 
 socio-economic 
 development 
 for agriculture in the region. Traditional intensive 
 surveillance systems 
 for 
 malaria are 
 becoming unreliable. The terrain shows considerable 
 variation 
 and some 
 of 
 it is 
 relatively inaccessible. The environment, particularly 
 its land 
 use pattern 
 keeps 
 changing. The present study has used geographical 
 information 
 systems 
 (GIS) 
 and 
 satellite imagery data to identify malaria risk 
 factors 
 related to landscape 
 and 
 environmental data and to develop a predictive model 
 in hope 
 of predicting the 
 risk 
 of 
 malaria transmission and outbreaks and guiding malaria 
 control 
 in the 
 Red River 
 basin area. 
 The work is in two phases: 
 Phase I was a retrospective ecological study. 
 It 
 used 
 basic 
 GIS techniques to 
 analyse 
 routine malaria data, and existing environmental and 
 ecological 
 data in the 
 Red River 
 basin area primarily to identify major 
 determinants 
 of malaria 
 spatial 
 distribution 
 related to landscape and environmental variables. 
 The 
 malaria 
 data 
 were those 
 were 
 reported from villages through health care 
 systems. 
 In 
 view 
 of their limitations 
 of 
 accuracy and coverage, phase II was undertaken. 
 However, 
 The 
 work 
 of the 
 phase 
 I 
 study helped to formulate the specific 
 hypotheses for 
 phase 
 II 
 study. 
 Phase II study was a prospective study. 
 Malaria incidence 
 data 
 were 
 collected 
 in 
 a 
 field survey of the whole study population 
 by 
 our 
 own 
 research team. 
 Malaria 
 incidence data were integrated with altitude 
 data derived from terrain 
 maps 
 and a 
 land 
 use map derived from SPOT 4 imagery data into the 
 GIS. 
 Multilevel 
 Poisson regression modelling were used to model 
 landscape 
 and 
 land 
 use 
 determinants 
 of 
 malaria. 
 The phase I study was carried out 
 in 
 one 
 county of the 
 Red River 
 basin 
 area, 
 Yuanyang County, Yunnan, China. Malaria 
 and population 
 data 
 at the level 
 of 
 administrative village were collected 
 in 
 131 
 administrative 
 villages of the 
 county. 
 Terrain maps, the land use map, soil map and 
 administrative 
 boundaries 
 of 
 Yuanyang 
 as well as malaria risk maps were integrated within 
 GIS. 
 The 
 data 
 were analysed 
 by 
 modelling the risk of malaria in the administrative villages 
 and their landscape 
 and 
 environmental variables generated from GIS. 
 The 
 results of the 
 analysis 
 revealed that 
 spatial distribution of malaria was determined by the landscape 
 and 
 land 
 use 
 patterns 
 in the administrative villages. Malaria was 
 negatively correlated 
 with 
 mean altitudes 
 of administrative villages, but more paddy and 
 forest 
 would 
 increase the 
 risk of 
 malaria in the villages. 
 Phase II study was carried out in the Feng Chun 
 Ling Township 
 of 
 Yuanyang 
 County 
 from May to December 1998. The entire population 
 of 
 24,280 in 
 5,007 households 
 was included in the study. Around 14% 
 of the 
 study 
 cohort, 
 mostly 
 from the 
 mountains, however, had a history of temporary 
 migration to the lowlands 
 where 
 malaria is highly endemic during the study 
 period. 
 A total 
 of 
 649 
 malaria 
 cases 
 (including 3 mixed infections) were identified in the 
 study cohort 
 during the 
 7-month 
 period. Of the 649 malaria cases, 400 cases were 
 from the 
 population 
 with 
 a 
 history 
 of 
 temporary migration during study period. 
 The 
 overall 
 risk 
 of people 
 with 
 and 
 without 
 temporary migration history were 118.6 and 
 12.1 
 per 
 1000 
 persons, 
 respectively, 
 during the 7-month study period. The relative 
 risk 
 of the 
 migrated 
 population 
 against 
 non-migrated population were 9.8, suggesting the 
 migrated 
 population 
 had 
 around 
 10-fold higher risk of malaria than those of 
 non-migrated 
 population. 
 Only 
 334 
 indigenous malaria cases out of all malaria cases 
 (649) in the 
 study 
 were 
 used 
 for 
 further analyses and model building. The risk 
 for 
 P. vivax 
 indigenous 
 malaria 
 was 
 17.8 
 per 1,000 person years at risk and for P. falciparum 
 was 
 6.9 
 per 
 1,000 
 person 
 years 
 at 
 risk. Malaria data were integrated with 
 household locations identified 
 by 
 Global 
 Position Systems (GPS), a land use map 
 derived from 
 a 
 SPOT 
 4 image 
 and terrain 
 maps into GIS. The results of multilevel Poisson regression modelling 
 in 
 phase 
 II 
 study revealed 
 that 
 indigenous malaria is negatively correlated with 
 altitude for P. vivax 
 and 
 P. falciparum. Paddy and forest would increase the 
 risk 
 of 
 malaria, 
 but the 
 effect 
 of 
 forest on malaria reached a plateau at certain 
 level 
 of 
 the 
 forest coverage. 
 The 
 mosquito 
 net is protective for indigenous malaria 
 in the 
 analysis. 
 The 
 protective efficacy 
 for 
 P. 
 vivax is 40% and P. falciparum 29%, respectively. 
 In conclusion, malaria transmission in the 
 study area 
 is 
 primarily 
 determined 
 by the 
 environmental variables particularly altitude, paddy 
 and 
 forest in the 
 Red 
 River 
 basin 
 area. But human behaviour such as temporary migration and 
 the 
 use of mosquito nets 
 play a very important role in determining the 
 malaria 
 spatial 
 pattern 
 in the 
 study 
 area. 
 Malaria control and surveillance should 
 focus 
 on the lower 
 altitude 
 areas 
 and the 
 mobile population in the Red River 
 basin 
 area. 
 The 
 overall temporarily 
 migrated 
 population plus population living 
 below 
 1,200 
 metres accounted 
 for 
 44.2% 
 (10734/24280) of total population, but accounted for 
 85.7% (559/652) 
 of 
 total 
 malaria 
 cases during the study period, while migrant 
 plus those living 
 under 
 800 metres accounted for 73.6% (480/652) of cases 
 in 
 only 
 21.2% 
 (5155/24280) 
 of the 
 population. 
 Future development of land should be aware of the 
 potential 
 for 
 malaria 
 and 
 other 
 vector borne disease risk arising from expansion 
 of paddy 
 field 
 and 
 deforestation, 
 which will provide breeding sites for mosquitoes, particularly 
 in the 
 middle 
 and 
 lower 
 altitude areas. Subsequently, they might result 
 in 
 malaria 
 and 
 other 
 vector 
 borne 
 disease outbreaks or epidemics. The first 
 priority 
 of 
 malaria 
 control 
 strategy 
 in the 
 immediate future is to encourage local residents 
 and 
 'downhill' 
 migrants to 
 use 
 mosquito nets and to ensure these are 
 regularly treated 
 with 
 insecticides. 
 Chemoprophylaxis and other control measures need to 
 be 
 explored 
 for the temporarily 
 migrating population in the Red River 
 basin 
 area.
dc.format.mediumtext
dc.languageen
dc.language.isoeng
dc.relation.ispartofhttp://researchonline.lshtm.ac.uk/682300/
dc.relation.ispartofuk.bl.ethos.394512
dc.rightscr_author
dc.titleSpatial prediction of malaria in the Red River basin, Yunnan, China using geographical information systems and remote sensing.
dc.typeThesis
ge.collectioncodeOAIDATA
ge.dataimportlabelOAI metadata object
ge.identifier.legacyglobethics:10251257
ge.identifier.permalinkhttps://www.globethics.net/gtl/10251257
ge.lastmodificationdate2016-09-05 23:25
ge.lastmodificationuseradmin@pointsoftware.ch (import)
ge.submissions0
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ge.oai.setnameStatus = Unpublished
ge.oai.setnameType = Thesis
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ge.linkhttp://researchonline.lshtm.ac.uk/682300/1/394512.pdf


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