Prediction of high-risk areas for visceral leishmaniasis using socioeconomic indicators and remote sensing data

International Journal of Health Geographics

Guilherme L Werneck²  Andréa S Almeida¹ 

[1] Instituto de Medicina Social, Universidade do Estado do Rio de Janeiro, Rua São Francisco Xavier, 524, Pavilhão João Lyra Filho, 7º andar/blocos D e E, e 6º andar/bloco E, Maracanã, CEP 20550-013 Rio de Janeiro, Brazil;Instituto de Estudos em Saúde Coletiva, Universidade Federal do Rio de Janeiro, Avenida Horácio Macedo, S/N - Próximo a Prefeitura Universitária da UFRJ, Ilha do Fundão - Cidade Universitária, CEP 21941-598 Rio de Janeiro, RJ, Brazil

关键词: Remote sensing;    Predictive models;    Leishmaniasis;   

Others  :  804711
DOI  :  10.1186/1476-072X-13-13

 received in 2013-12-27, accepted in 2014-03-18,  发布年份 2014

【 摘 要 】

Spatial heterogeneity in the incidence of visceral leishmaniasis (VL) is an important aspect to be considered in planning control actions for the disease. The objective of this study was to predict areas at high risk for visceral leishmaniasis (VL) based on socioeconomic indicators and remote sensing data. We applied classification and regression trees to develop and validate prediction models. Performance of the models was assessed by means of sensitivity, specificity and area under the ROC curve. The model developed was able to discriminate 15 subsets of census tracts (CT) with different probabilities of containing CT with high risk of VL occurrence. The model presented, respectively, in the validation and learning samples, sensitivity of 79% and 52%, specificity of 75% and 66%, and area under the ROC curve of 83% and 66%. Considering the complex network of factors involved in the occurrence of VL in urban areas, the results of this study showed that the development of a predictive model for VL might be feasible and useful for guiding interventions against the disease, but it is still a challenge as demonstrated by the unsatisfactory predictive performance of the model developed.

【 授权许可】

   
2014 Almeida and Werneck; licensee BioMed Central Ltd.

【 图 表 】

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【 参考文献 】

• [1]Ministério da Saúde: Manual de Vigilância e Controle da Leishmaniose Visceral. Brasília, DF: Série A. Normas e Manuais Técnicos; 2006.
• [2]Costa CHN, Tapety CMM, Werneck GL: Controle da leishmaniose visceral em meio urbano: estudo de intervenção randomizado fatorial. Rev Soc Bras Med Trop 2007, 40(4):415-419.
• [3]Werneck GL: Forum: geographic spread and urbanization of visceral leishmaniasis in Brazil. Introduction Cad Saúde Pública 2008, 24(12):2937-2940.
• [4]Koopman JS, Simon CP, Riolo CP: When to control endemic infections by focusing on high-risk groups. Epidemiology 2005, 16:621-627.
• [5]Zha Y, Gao J, Ni S: Use of normalized difference build-up index in automatically mapping urban areas from TM imagery. Int J Remote Sens 2003, 24(17):583-594.
• [6]Jacquin A, Misakova L, Gay M: A hybrid object-based classification approach for mapping urban sprawl in periurban environment. Landsc Urban Plan 2008, 84:152-165.
• [7]Breiman L, Friedman J, Olshen R, Stone C: Classification and Regression Trees. Wadsworth & Brooks/Cole: Pacific Grove, CA; 1984.
• [8]Thompson RA, Wellington de Oliveira Lima J, Maguire JH, Braud DH, Scholl DT: Climatic and demographic determinants of American visceral leishmaniasis in northeastern Brazil using remote sensing technology for environmental categorization of rain and region influences on leishmaniasis. Am J Trop Med Hyg 2002, 67(6):648-655.
• [9]Costa CHN, Werneck GL, Rodrigues L Jr, Santos MV, Araújo IB, Moura LS, Moreira S, Gomes RB, Lima SS: Household structure and urban services: neglected targets in the control of visceral leishmaniasis. Ann Trop Med Parasitol 2005, 99(3):229-236.
• [10]Sudhakar S, Srinivas T, Palit A, Kar SK, Battacharya SK: Mapping of risk prone areas of kala-azar (Visceral leishmaniasis) in parts of Bihar State, India: an RS and GIS approach. J Vector Borne Dis 2006, 43(3):115-122.
• [11]Oliveira CD, Diez-Roux A, César CC, Proietti FA: A case–control study of microenvironmental risk factors for urban visceral leishmaniasis in a large city in Brazil, 1999–2000. Rev Panam Salud Publica 2006, 20(6):369-376.
• [12]Cerbino Neto J, Werneck GL, Costa CH: Factors associated with the incidence of urban visceral leishmaniasis: an ecological study in Teresina, Piauí State, Brazil. Cad Saude Publica 2009, 25(7):1543-1551.
• [13]Boelaert M, Meheus F, Sanchez A, Singh SP, Vanlerberghe V, Picado A, Meessen B, Sundar S: The poorest of the poor: a poverty appraisal of households affected by visceral leishmaniasis in Bihar, India. Trop Med Int Health 2009, 14(6):639-644.
• [14]Bhunia GS, Kumar V, Kumar AJ, Das P, Kesari S: The use of remote sensing in the identification of the eco-environmental factors associated with the risk of human visceral leishmaniasis (kala-azar) on the Gangetic plain, in north-eastern India. Ann Trop Med Parasitol 2010, 104(1):35-53.
• [15]Werneck GL, Pereira TJCF, Farias GC, Silva FO, Chaves FC, Gouvêa MV, Costa CHNC, Carvalho FAA: Avaliação da efetividade das estratégias de controle da leishmaniose visceral na cidade de Teresina, estado do Piauí, Brasil: resultados do inquérito inicial – 2004. Epidemiologia e Serviços de Saúde 2008, 17(2):87-96.
• [16]Soares SSD: Distribuição de Renda no Brasil de 1976 a 2004 com Ênfase no Período Entre 2001 e 2004. Brasília: Ipea. Texto para Discussão no 1166; 2006. fev
• [17]Fundação CEPRO (Centro de Pesquisas Econômicas e Sociais do Piauí): Piauí em Números. 8th edition. Teresina: Fundação CEPRO; 2011.
• [18]PNUD (Programa das Nações Unidas para o Desenvolvimento): Atlas do desenvolvimento humano no brasil. 2003. Disponível em: http://www.pnud.org.br/atlas/ webcite
• [19]Lacerda JT, Calvo MCM, Freitas SFT: Diferenciais intra-urbanos no município de florianópolis, santa catarina, brasil: potencial de uso para o planejamento em saúde. Cad Saude Publica 2002, 18(5):1331-1338.
• [20]Gamarra CJ, Valente JG, Azevedo e Silva G: Magnitude da mortalidade por câncer do colo do útero na Região Nordeste do Brasil e fatores socioeconômicos. Rev Panam Salud Publica 2010, 28(2):100-106.
• [21]Van Benthem BHB, Vanwambeke SO, Khantikul N, Burghoorn-Maas C, Panart K, Oskam L, Lambin EF, Somboon P: Spatial patterns of and risk factors for seropositivity for dengue infection. Am J Trop Med Hyg 2005, 72(2):201-208.
• [22]Correia VRM, Monteiro AMV, Carvalho MS, Werneck GL: Uma aplicação do sensoriamento remoto para a investigação de endemias urbanas. Cad Saude Publica 2007, 23(5):1015-1028.
• [23]Leonardi F, Almeida CN, Fonseca LMG, Camargo FF: Avaliação Comparativa entre Classificação Supervisionada por Regiões e Orientada a Objeto para Imagens de Alta Resolução Espacial: Cbers 2B-HRC e QuickBird. Anais XIV Simpósio Brasileiro de Sensoriamento Remoto. Natal, Brasil: INPE; 2009:981-988. 25–30 abril
• [24]Werneck GL, Maguire JH: Spatial modeling using mixed models: an ecologic study of visceral leishmaniasis in Teresina, Piaui State, Brazil. Cad Saude Publica 2002, 18:633-637.
• [25]Werneck GL, Costa CHN, Walker AM, David JR, Wand M, Maguire JH: Multilevel modelling of the incidence of visceral leishmaniasis in Teresina, Brazil. Epidemiol Infect 2007, 135:195-201.
• [26]Souza IM, Alves CD, Almeida CM, Pinho CMD: Caracterização socioeconômica do espaço residencial construído utilizando imagens de alta resolução espacial e análise orientada a objeto. Geografia 2007, 16(1):119-142.