Revisiting area risk classification of visceral leishmaniasis in Brazil

被引:9
作者
Machado, Gustavo [1 ]
Alvarez, Julio [2 ,3 ]
Bakka, Haakon Christopher [4 ]
Perez, Andres [5 ]
Donato, Lucas Edel [6 ]
de Ferreira Lima Junior, Francisco Edilson [6 ]
Alves, Renato Vieira [6 ]
Vilas, Victor Javier Del Rio [7 ]
机构
[1] North Carolina State Univ, Coll Vet Med, Dept Populat Hlth & Pathobiol, 1060 William Moore Dr, Raleigh, NC 27607 USA
[2] Univ Complutense, VISAVET Hlth Surveillance Ctr, Avda Puerta Hierro S-N, E-28040 Madrid, Spain
[3] Univ Complutense, Fac Vet, Dept Sanidad Anim, Avda Puerta Hierro S-N, E-28040 Madrid, Spain
[4] King Abdullah Univ Sci & Technol, CEMSE Div, Trondheim, Saudi Arabia
[5] Univ Minnesota, Coll Vet Med, Dept Vet Populat Med, St Paul, MN 55108 USA
[6] Minist Saude SVS MH, Secretaria Vigilancia Saude, Brasilia, DF, Brazil
[7] Univ Surrey, Sch Vet Med, Guildford GU2 7A, Surrey, England
关键词
Visceral leishmaniasis; Brazil; Disease mapping; Bayesian; Risk classification; LUTZOMYIA-LONGIPALPIS; SPATIAL-ANALYSIS; PIAUI STATE; 1ST REPORT; SAO-PAULO; DO-SUL; TERESINA; EPIDEMIOLOGY; DISEASE; VECTOR;
D O I
10.1186/s12879-018-3564-0
中图分类号
R51 [传染病];
学科分类号
100401 ;
摘要
BackgroundVisceral leishmaniasis (VL) is a neglected tropical disease of public health relevance in Brazil. To prioritize disease control measures, the Secretaria de Vigilancia em Saude of Brazil's Ministry of Health (SVS/MH) uses retrospective human case counts from VL surveillance data to inform a municipality-based risk classification. In this study, we compared the underlying VL risk, using a spatiotemporal explicit Bayesian hierarchical model (BHM), with the risk classification currently in use by the Brazil's Ministry of Health. We aim to assess how well the current risk classes capture the underlying VL risk as modelled by the BHM.MethodsAnnual counts of human VL cases and the population at risk for all Brazil's 5564 municipalities between 2004 and 2014 were used to fit a relative risk BHM. We then computed the predicted counts and exceedence risk for each municipality and classified them into four categories to allow comparison with the four risk categories by the SVS/MH.ResultsMunicipalities identified as high-risk by the model partially agreed with the current risk classification by the SVS/MH. Our results suggest that counts of VL cases may suffice as general indicators of the underlying risk, but can underestimate risks, especially in areas with intense transmission.ConclusionAccording to our BHM the SVS/MH risk classification underestimated the risk in several municipalities with moderate to intense VL transmission. Newly identified high-risk areas should be further evaluated to identify potential risk factors and assess the needs for additional surveillance and mitigation efforts.
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页数:9
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