DOI: 10.1590/1413-812320152012.01672015 3935 Space-time analysis of visceral leishmaniasis FREE THEMES in the State of Maranhão, Brazil Aline Santos Furtado 1 Flavia Baluz Bezerra de Farias Nunes 1 Alcione Miranda dos Santos 2 Arlene de Jesus Mendes Caldas 1 Abstract This study analyzed the spatial and temporal distribution of cases of visceral leish- maniasis in the State of Maranhão in the peri- od from 2000 to 2009. Based on the number of reported cases, thematic maps were prepared to show the evolution of the geographical distribu- tion of the disease in the state. The MCMC meth- od was used for estimating the parameters of the Bayesian model for space-time identification of risk areas. From 2000 to 2009 there were 5389 re- ported cases of visceral leishmaniasis, distributed in all 18 Regional Health Units in the state, with the highest indices in the cities of Caxias, Impera- triz, Presidente Dutra and Chapadinha. The Re- gional Health Units with the highest relative risks per biennium were: Caxias and Barra do Corda (2000-2001), Imperatriz and President Dutra (2002-2003), Imperatriz and Caxias (2004- 2005), Presidente Dutra and Codó (2006-2007) and Imperatriz and Caxias (2008-2009). There was considerable geographic expansion of visceral 1 Departamento de leishmaniasis in Maranhão, thus highlighting the Enfermagem, Centro need to adopt more effective measures for preven- de Ciências da Saúde, Universidade Federal tion and control of the disease in the state. do Maranhão. Av. dos Key words Visceral leishmaniasis, Space-time Portugueses 1966, Bacanga. analysis, Bayesian model 65080-805 São Luís Maranhão Brasil. [email protected] ² Departamento de Saúde Pública, Centro de Ciências da Saúde, Universidade Federal do Maranhão. 3936 et al. Caldas AJM Caldas Introduction ficial to use information from surrounding areas to estimate the relative risks. Visceral Leishmaniasis is a serious public health The use of these models in the area of epide- problem that is widely prevalent in the world. It miology have been present in a number of stud- is currently amongst the seven endemics consid- ies. Vieira et al.12 described the spatial-temporal ered to be a priority in relation to health actions behavior of LVH in Birigui (SP). Other studies in the world. It is on the list of neglected trop- used the same model for other endemic diseas- ical diseases that should be eradicated by 2015 es, such as that of Souza et al.13. They analyzed according to the World Health Organization the spatial distribution of leprosy in the city of (WHO)1,2. Recife. This was also done by Nobre et al.14. They Brazil accounts for 90% of the LV cases in analyzed the number of malaria cases in Pará be- Latin America and it is considered the country tween 1996 and 1998. with the third highest rates of the disease in the In this present study the aim is to analyze spa- world3. In Brazil, between 1999 and 2008, more tial-temporal distribution of LV cases in Maran- than one third of districts registered autochtho- hão between 2000 and 2009. nous cases and in these districts the disease has not been considered a priority. Also between 1980 and 2008 70 thousand cases of LV were Methods registered in the country which resulted in the deaths of more than 3,800 people4,5. This is an ecological study looking back at the Amongst all of the Federal states in Brazil, history of LV cases that were registered according Maranhão has recorded the highest number of to the residents in the state of Maranhão between cases of LV. From 1999 to 2005 this state was the 2000 to 2009. leader in the number of confirmed cases of the The state of Maranhão, that has as its cap- disease in Brazil. By 2009, 9,972 cases were reg- ital São Luís, is located in the western region istered with the majority being registered in the in the north east of Brazil. It covers an area of districts that make up the island of São Luís: São 331.933,3 km², and it has a population estimated Luís, São José de Ribamar, Paço do Lumiar e Ra- at around 6.184.538 inhabitants15,16. Maranhão is posa6,7. bordered by the following: the Atlantic ocean to Although there has been some recognition the north, the state of Piauí to the east, the state of the magnitude of the problem in Maranhão of Tocantins to the south and south east and the no studies have been done over the last decade state Pará to the west. The state has: 217 districts, covering the scale of the problem and the num- 5 mesoregions, 21 microregions and 18 region ber of new cases. It also should be remembered health units (URS). that many studies6-8 were done with a view to In this study the spatial units that were an- understanding LV’s epidemiology in the districts alyzed were the regional health units represent- that make up the island of São Luís (São Luís, ed by a group of neighboring districts that had São José de Ribamar, Paço do Lumiar e Raposa), similarities in relation to their: geography, cli- however we do not have any data regarding the mate and socio-economic position. They all also progression of the disease in the state of Mara- had a political/administrative office of the state nhão. Therefore we believe that a good grasp of Ministry for Health in Maranhão with the task of the nature of the problem or the risk hot spots improving health care in the state. These regio- within the regions is essential for designating ad- nal health units were: Açailândia, Bacabal, Balsas, equate resources to combat the disease and for Barra do Corda, Caxias, Chapadinha, Codó, Im- taking appropriate health care actions. peratriz, Itapecuru-Mirim, Pedreiras, Pinheiro, Different Bayesian saptio-temporal models Presidente Dutra, Rosário, Santa Inês, São Luís, have been used to get a better understanding of São João dos Patos, Viana and Zé Doca15,16. the dynamics involved in the transmission of LV. The population in the state of Maranhão be- This type of modeling allows for the obtaining tween 2000 and 2009 was affected by all of the of stable estimates of the spacial variations of the cases of Visceral Leishmaniasis. The information relative risks or the disease incidents9-11. However was collected from the Information System for when the number of cases and the population at the Notification of Diseases (SINAN) database. risk in an area over a specified period of time are This was from the Secretary of State for Health low, it becomes difficult to obtain risk estimates in Maranhão. We excluded all information that that are near to reality. But it may be more bene- could possibly be used to identify participants in 3937 Ciência & Saúde Coletiva, 20(12):3935-3942, 2015 20(12):3935-3942, Coletiva, & Saúde Ciência the study in order to respect their privacy. We also poral effort bit.. Apart from this, the random er- took out information which was: inconsistent, ror vt is normally distributed with an average of 2 imprecise, incomplete or duplicated (i.e. two or zero and an unknown variant σ v , designated by 2 σ 2 more registers of the same case). We included vt ~Normal(0, σ v ), and β1 ~Normal(0, β ). cases where the individual presented symptoms With the Bayesian approach is it necessary to of LV such as: a fever for more than two weeks, specify the a priori distribution for the model pa- hepatoesplenomegaly, and/or a confirmed diag- rameters in the study. For the parameter bit, an a nosis of the disease through a bone marrow as- priori autoagressive conditional distribution was 2 piration showing positive for Leishmania sp. We primarily used (CAR) with a variant σ t , as pro- 17 also considered the area for URS, suggesting au- posed by Besag and Kooperberg , denoted by bit 2 tochthony for the disease. ~ CAR ( σ t ), which determines the structure of Information from the Brazilian Institute for the spacial correlation, given by: Geography and Statistics16 was used to estimate and calculate the density of the population in w b 2 Σ jϵδ ij jt σ each period for URSs. b | b , j ≠ i ~ Normal ( i , t ) it jt w b j wij j ij jt The number of incidents of LV was calculated Σ ϵδi Σ ϵδi based on the total amount of cases registered in 2 each URS. We then divided the estimated densi- Where δi is the entirety of an area consid- ty population in each URS and multiplied this ered to be neighboring the i-ésima area accord- 2 number by 100,000. In this way we were able to ing to the criteria for neighbors; σ b represents ascertain the number of those stuck down by the the variability in the t and wij corresponds to the disease per 100,000 inhabitants. weight of the neighbor from the area j and for the We used the bayesian spatial-temporal model area i, which defines the matrix for the neighbor- 18,19 in order to obtain relative risk estimates for LV. hood . Sometime afterwards bit was considered Y represented the variable which was the num- for normal distribution with an average of zero 2 ber of LV registered cases at the URSs (i-ésima) and variants σ b , in other words, without con- during t year. i = 1,...,18 and t = 1,..., 10 (years sidering the spatial correlation structure. 2000 to 2009). For yit we used the Poisson distri- In order to define neighboring areas in spe- bution with an average µit = eitρρit or in a logarith- cific areas i, and its respective weight, we used the mic form log(µit)= log eit + log ρit, being eit which adjacency criteria that allows for the consider- was the number cases expected at the URSs i in ation of neighbors from other areas i limited to time t and ρit o the relative risk at the URSs i in that bordered.