Abstracts

Introduction

Arbovirus infections including dengue, chikungunya, and Zika are important emerging and re-emerging diseases. They are considered serious global public health problems due to their heavy morbidity and mortality ¹1. Weaver SC, Reisen WK. Present and future arboviral threats. Antiviral Res 2010; 85:328-45.^,22. Honório NA, Câmara DCP, Calvet GA, Brasil P. Chikungunya: uma arbovirose em estabelecimento e expansão no Brasil. Cad Saúde Pública 2015; 31:906-8.^,33. Lowe R, Barcellos C, Brasil P, Cruz O, Honório N, Kuper H, et al. The Zika virus epidemic in Brazil: from discovery to future implications. Int J Environ Res Public Health 2018; 15:E96.. Dengue is endemic in more than 100 countries in the world’s tropical and subtropical regions. The disease totals approximately 100 million reported cases per year ⁴4. Gurugama P, Garg P, Perera J, Wijewickrama A, Seneviratne SL. Dengue viral infections. Indian J Dermatol 2010; 55:68-78.. Estimates using statistical modeling have suggested that dengue infects approximately 390 million persons per year in the world, of whom 96 million present detectable symptoms with different levels of severity ⁵5. Bhatt S, Gething PW, Brady OJ, Messina JP, Farlow AW, Moyes CL, et al. The global distribution and burden of dengue. Nature 2013; 496:504-7..

Dengue arbovirus infection has four antigenically distinct subtypes (DENV-1, DENV-2, DENV-3, and DENV-4), transmitted by infected female mosquitoes of the genus Aedes⁶6. Teixeira MG, Siqueira, JB, Ferreira GLC, Bricks L, Joint G. Epidemiological trends of dengue disease in Brazil (2000-2010): a systematic literature search and analysis. PLoS Negl Trop Dis 2013; 7:e2520.^,77. Honório NA, Castro MG, Barros FSM, Magalhães MAFM, Sabroza PC. The spatial distribution of Aedes aegypti and Aedes albopictus in a transition zone, Rio de Janeiro, Brazil. Cad Saúde Pública 2009; 25:1203-14.. The culicid mosquitoes Aedes aegypti and Aedes albopictus are widely dispersed around the world and are still undergoing geographic expansion ⁸8. Lounibos LP. Invasions by insect vectors of human disease. Annu Rev Entomol 2002; 47:233-66.. Both species are adapted to the peridomicile, where they feed and perform oviposition in different artificial and natural breeding places ⁷7. Honório NA, Castro MG, Barros FSM, Magalhães MAFM, Sabroza PC. The spatial distribution of Aedes aegypti and Aedes albopictus in a transition zone, Rio de Janeiro, Brazil. Cad Saúde Pública 2009; 25:1203-14.^,99. Lima-Camara TN. Emerging arboviruses and public health challenges in Brazil. Rev Saúde Pública 2016; 50:36..

Since Brazil’s re-infestation by Ae. aegypti in the 1970s and later the appearance of its first dengue epidemics in 1986 (which have lasted to date), the country has experienced successive epidemic outbreaks. Total reported dengue cases in Brazil by 2017 have been estimated at 12 million ¹⁰10. Secretaria de Vigilância em Saúde, Ministério da Saúde. Monitoramento dos casos de dengue, febre de chikungunya e febre pelo vírus Zika até a Semana Epidemiológica 53, 2017. Boletim Epidemiológico 2018; 49(2).. In 2017, according to Brazilian Ministry of Health data, 252,054 suspected dengue cases were reported, distributed across all 27 states of the country ¹⁰10. Secretaria de Vigilância em Saúde, Ministério da Saúde. Monitoramento dos casos de dengue, febre de chikungunya e febre pelo vírus Zika até a Semana Epidemiológica 53, 2017. Boletim Epidemiológico 2018; 49(2).. The state of Rio de Janeiro has been historically presented as being among the highest dengue incidence rates in Brazil. The state has suffered epidemics since 1986, when DENV-1 was isolated in the city of Nova Iguaçu ¹¹11. Schatzmayr H, Nogueira RMR, Rosa A. An outbreak of dengue virus at Rio de Janeiro - 1986. Mem Inst Oswaldo Cruz 1986; 81:245-6.^,1212. Nogueira RMR, Araújo JMG, Schatzmayr HG. Dengue viruses in Brazil, 1986-2006. Rev Panam Salud Pública 2007; 22:358-63.^,1313. Xavier DR, Magalhães MAFM, Gracie R, Reis IC, Matos VP, Barcellos C. Difusão espaço-tempo do dengue no Município do Rio de Janeiro, Brasil, no período de 2000-2013. Cad Saúde Pública 2017; 33:e00186615.. The city of Rio de Janeiro and its metropolitan region have historically recorded frequent dengue epidemics over the years ¹⁴14. Gibson G, Souza-Santos R, Brasil P, Pacheco AG, Cruz OG, Honório NA, Kubelka C, et al. From primary care to hospitalization: clinical warning signs of severe dengue fever in children and adolescents during an outbreak in Rio de Janeiro, Brazil. Cad Saúde Pública 2013; 29:82-90.. Rio de Janeiro is considered the “dengue metropolis”, since it is one of the most receptive areas in the country for the maintenance and spread of DENV serotypes ¹⁴14. Gibson G, Souza-Santos R, Brasil P, Pacheco AG, Cruz OG, Honório NA, Kubelka C, et al. From primary care to hospitalization: clinical warning signs of severe dengue fever in children and adolescents during an outbreak in Rio de Janeiro, Brazil. Cad Saúde Pública 2013; 29:82-90.^,1515. Catão RC. Dengue no Brasil: abordagem geográfica na escala nacional [Masters Thesis]. Presidente Prudente: Universidade Estadual Paulista; 2011.. In addition, the state and city of Rio de Janeiro display high Ae. aegypti density in the urban areas, which are highly susceptible to the dengue virus ⁷7. Honório NA, Castro MG, Barros FSM, Magalhães MAFM, Sabroza PC. The spatial distribution of Aedes aegypti and Aedes albopictus in a transition zone, Rio de Janeiro, Brazil. Cad Saúde Pública 2009; 25:1203-14.^,1616. Secretaria Municipal de Saúde do Rio de Janeiro. Apresentação do centro de vigilância epidemiológica sobre situação das arboviroses na cidade do Rio de Janeiro. Rio de Janeiro: Secretaria Municipal de Saúde do Rio de Janeiro; 2017..

DENV transmission patterns are determined by a combination of social and environmental factors that include the human host, the virus, and the vector ¹⁷17. Kuno G. Review of the factors modulating dengue transmission. Epidemiol Rev 1995; 17:321-35.. The city of Rio de Janeiro is characterized by great spatial heterogeneity and complexity. The city has a high population density, disorderly urban growth, lack of infrastructure, environmental degradation, and major socio-spatial inequality ¹⁸18. Luz PM, Codeço CT, Massad E, Struchiner CJ. Uncertainties regarding dengue modeling in Rio de Janeiro, Brazil. Mem Inst Oswaldo Cruz 2003; 98:871-8.^,1919. Freitas MGR, Tsouris P, Reis IC, Magalhães MAFM, Nascimento TFS, Honório NA. Dengue and land cover heterogeneity in Rio de Janeiro. Oecologia Australis 2010; 14:641-67.. These territorial characteristics together with the local climate conditions contribute to establishing and maintaining the epidemics, hindering dengue control and prevention measures ²⁰20. Braga IA, Valle D. Aedes aegypti: histórico do controle no Brasil. Epidemiol Serv Saúde 2007; 16:113-8.^,2121. Gubler DJ. Dengue, urbanization and globalization: the unholy trinity of the 21st century. Trop Med Health 2011; 39:S3-11.^,2222. Bouzid M, Colón-González FJ, Lung T, Lake IR, Hunter PR. Climate change and the emergence of vector-borne diseases in Europe: case study of dengue fever. BMC Public Health 2014;14:781..

A major portion of ecological studies in epidemiology view the territory homogeneously for the detection of transmission patterns, failing to represent the heterogeneity involved in the epidemiological dynamics of given diseases. Thus, territorial stratification in areas with increased risk through cluster analysis seeks spatial patterns of events and characterization of homogeneous areas ²³23. Resendes APC, Silveira NAPR, Sabroza PC, Souza-Santos R. Determinação de áreas prioritárias para ações de controle da dengue. Rev Saúde Pública 2010; 44:274-82..

As described in Block ²⁴24. Block R. Scanning for clusters in space and time: a tutorial review of SaTScan. Soc Sci Comput Rev 2007; 25:272-8., SaTScan (http:\\www.satscan.org) is free software that allows the identification of temporal, spatial, or space-time clusters through the spatial scan statistic (Scan) developed by Kulldorff & Nagarwalla ²⁵25. Kulldorff M, Nagarwalla N. Spatial disease clusters: detection and inference. Stat Med 1995; 14:799-810.. Pioneering studies demonstrating the feasibility of the use of SaTScan for space-time analyses in health can be found in Hjalmars et al. ²⁶26. Hjalmars U, Kulldorff M, Gustafsson G, Nagarwalla N. Childhood leukaemia in Sweden: using GIS and a spatial scan statistic for cluster detection. Stat Med 1996; 15:707-15., Kulldorff et al. ²⁷27. Kulldorff M, Feuer EJ, Miller BA, Freedman LS. Breast cancer clusters in the northeast United States: a geographic analysis. Am J Epidemiol 1997; 146:161-70. and Park et al. ²⁸28. Park R, O'Brien TF, Huang SS, Baker MA, Yokoe DS, Kulldorff M, et al. Statistical detection of geographic clusters of resistant Escherichia coli in a regional network with WHONET and SaTScan. Expert Rev Anti Infect Ther 2016; 14:1097-107.. SaTScan is currently a well-established methodology employed in space-time analyses of various diseases and health conditions, as illustrated in the studies on leishmaniasis by Schweiger & de Freitas ²⁹29. Schweiger MCC, Freitas YM, Alves AJS, Kuroda RBS, Souza VAF. Análise de aglomerado espaço-temporal da leishmaniose visceral americana no Estado de São Paulo, 2011-2015. Atas de Saúde Ambiental (São Paulo) 2016; 4:61-7., tuberculosis by Santos Neto et al. ³⁰30. Santos Neto M, Yamamura M, Garcia MCC, Popolin MP, Rodrigues LBB, Chiaravalloti Neto F, et al. Pulmonary tuberculosis in São Luis, State of Maranhão, Brazil: space and space-time risk clusters for death (2008-2012). Rev Soc Bras Med Trop 2015; 48:69-76., schistosomiasis by Cardim et al. ³¹31. Cardim LL, Ferraudo AS, Pacheco STA, Reis RB, Silva MMN, Carneiro DDMT, et al. Análises espaciais na identificação das áreas de risco para a esquistossomose mansônica no município de Lauro de Freitas, Bahia, Brasil. Cad Saúde Pública 2011; 27:899-908., transportation accidents by Morais Neto et al. ³²32. Morais Neto OL, Montenegro MMS, Monteiro RA, Siqueira Júnior JB, Silva MMA, Lima CM, et al. Mortalidade por acidentes de transporte terrestre no Brasil na última década: tendência e aglomerados de risco. Ciênc Saúde Colet 2012; 17:2223-36., and leprosy by Penna et al. ³³33. Penna MLF, Wand-Del-Rey-de-Oliveira ML, Penna G. Spatial distribution of leprosy in the Amazon Region of Brazil. Emerg Infect Dis 2009; 15:650-2..

Space-time scan analysis has also been used to study dengue. Schmidt et al. ³⁴34. Schmidt W-P, Suzuki M, Dinh Thiem V, White RG, Tsuzuki A, Yoshida L-M, et al. Population density, water supply, and the risk of dengue fever in Vietnam: cohort study and spatial analysis. PLoS Med 2011; 8:e1001082. studied the interaction between population density and lack of running water as a cause of dengue outbreaks in Vietnam. In northern Argentina, Rotela et al. ³⁵35. Rotela C, Fouque F, Lamfri M, Sabatier P, Introini V, Zaidenberg M, et al. Space-time analysis of the dengue spreading dynamics in the 2004 Tartagal outbreak, Northern Argentina. Acta Trop 2007; 103:1-13. studied the spread of a dengue outbreak based on space-time grouping of cases. Space-time analyses of dengue cases have been performed in Thailand ³⁶36. Jeefoo P, Tripathi NK, Souris M. Spatio-temporal diffusion pattern and hotspot detection of dengue in Chachoengsao Province, Thailand. Int J Environ Res Public Health 2010; 8:51-74., Vietnam ³⁷37. Toan do TT, Hu W, Quang Thai P, Hoat LN, Wright P, Martens P. Hot spot detection and spatio-temporal dispersion of dengue fever in Hanoi, Vietnam. Glob Health Action 2013; 6:18632., and Colombia ³⁸38. Fuentes-Vallejo M. Space and space-time distributions of dengue in a hyper-endemic urban space: the case of Girardot, Colombia. BMC Infect Dis 2017; 17:512.. In Brazil, three publications were identified that discuss dengue using SaTScan: one analyzes dengue cases in the city of Lavras, Minas Gerais State, between 2007 and 2010 ³⁹39. Ferreira LM. Mapeamento dos casos de dengue na cidade de Lavras - MG, no período de 2007 [Masters Thesis]. Brasília: Universidade de Brasília; 2012.; the second detects clusters based on dengue seasonality in Brazilian municipalities from 2007 to 2011 ⁴⁰40. Lewkowicz R. Detecção de clusters?: uma análise de sazonalidade de surtos de dengue nos municípios do Brasil de 2007 a 2011 [Monography]. Brasília: Universidade de Brasília; 2013.; and the third, by Vicente et al. ⁴¹41. Vicente CR, Herbinger K-H, Cerutti Junior C, Malta Romano C, de Souza Areias Cabidelle A, Fröschl G. Determination of clusters and factors associated with dengue dispersion during the first epidemic related to Dengue virus serotype 4 in Vitória, Brazil. PLoS One 2017; 12:e0175432., analyzes the determination of clusters and factors associated with the spread of dengue during the first epidemic involving DENV-4 in the city of Vitória, Espírito Santo State.

No study on the detection of clusters using SaTScan was found for the city of Rio de Janeiro. In this context, the city is an important scenario for understanding the factors affecting dengue transmission dynamics and the exacerbation of the disease, since Rio de Janeiro has considerable socioeconomic and demographic differences between its administrative regions, in addition to displaying high incidence rates over the years.

The way geographic space is shaped significantly affects human mobility patterns, generally giving rise to highly local clusters of mobility ⁴²42. Vazquez-Prokopec GM, Bisanzio D, Stoddard ST, Paz-Soldan V, Morrison AC, Elder JP, et al. Using GPS technology to quantify human mobility, dynamic contacts and infectious disease dynamics in a resource-poor urban environment. PLoS One 2013; 8:e58802.. The use of population groups that differ in their mobility can be a major tool for the choice of population groups to address specific objectives and diseases ⁴³43. Perkins TA, Paz-Soldan VA, Stoddard ST, Morrison AC, Forshey BM, Long KC, et al. Calling in sick: impacts of fever on intra-urban human mobility. Proc Biol Sci 2016; 283:20160390.. Both human mobility and immunity to the different DENV serotypes are considered confounding factors in the definition of priority areas for dengue interventions ⁴⁴44. Resendes APC, Silveira NAPR, Sabroza PC, Souza-Santos R. Determinação de áreas prioritárias para ações de controle da dengue. Rev Saúde Pública 2010; 44:274-82..

The current dengue control strategies, especially those targeted to the primary vector, Ae. aegypti, are known to produce limited results ⁴⁵45. Codeço CT, Lima AWS, Araújo SC, Lima JBP, Maciel-de-Freitas R, Honório NA, et al. Surveillance of Aedes aegypti: comparison of house index with four alternative traps. PLoS Negl Trop Dis 2015; 9:e0003475., since health systems have used across-the-board methodologies in the entire territory, failing to take different local realities into account. Defining areas with increased risk is thus a way of rationalizing resources and optimizing the interventions’ results.

This study thus sought to investigate the presence of persistent space-time dengue clusters in the city of Rio de Janeiro in populations with distinct mobility and immunity profiles, based on dengue incidence between 2008 and 2014. We used incidence data for the population 5 years and older (with greater mobility and more exposed to the DENV over time) and the population of children under 5 years (with less mobility and lower probability of having been exposed to different DENV serotypes), in addition to defining the regions with persistent clusters, that is, those that repeat themselves over the years in both population groups.

Materials and methods

Study area

The city of Rio de Janeiro, capital of the state of Rio de Janeiro, is located in the Southeast Region of Brazil and has an area of approximately 1,197km² and a population of 6,320,446 as of 2010 ⁴⁶46. Instituto Brasileiro de Geografia e Estatística. Censo Demográfico 2010: resultados do universo por setor censitário. 2011. https://censo2010.ibge.gov.br/ (accessed on 01/Jan/2017).
https://censo2010.ibge.gov.br/... . The city is divided into 10 Planning Areas, 33 Administrative Regions, and 160 neighborhoods (Figure 1). The climate is hot humid tropical, with local variations due to the city’s geomorphology. Mean annual temperature is 22ºC, and annual rainfall varies from 1,200 to 1,800mm ⁴⁷47. Instituto Pereira Passos. Sistema Municipal de Informações Urbanas. http://www.rio.rj.gov.br/web/ipp/siurb (accessed on 01/May/2017).
http://www.rio.rj.gov.br/web/ipp/siurb... .

The city of Rio de Janeiro displays great geographic complexity: its topographic characteristics, peculiar coastline, and spatial heterogeneity generated by the process of urban land use and occupation make the city a mosaic of landscapes and social contrasts (Figure 1).

Epidemiological data

The current study used reported dengue cases from 2008 to 2014 from the city of Rio de Janeiro, Brazil. Data were obtained from the Information System for Notificable Diseases (SINAN) through the National Dengue Control Program of the Ministry of Health. The data were tabulated by month and year for each neighborhood both for the population 5 years and older and the preschool population. Crude population data were obtained from the Population Censuses of 2000 and 2010 conducted by the Brazilian Institute of Geography and Statistics (IBGE) ⁴⁶46. Instituto Brasileiro de Geografia e Estatística. Censo Demográfico 2010: resultados do universo por setor censitário. 2011. https://censo2010.ibge.gov.br/ (accessed on 01/Jan/2017).
https://censo2010.ibge.gov.br/... ^,4848. Instituto Brasileiro de Geografia e Estatística. Censo Demográfico 2000: agregado por setores censitários dos resultados do universo. 2003. https://ww2.ibge.gov.br/home/estatistica/populacao/censo2000/ (accessed on 01/Feb/2017).
https://ww2.ibge.gov.br/home/estatistica... . For the inter-census years, population estimates were calculated for the city of Rio de Janeiro and its neighborhoods with use of the geometric method, using the populations of 2000 and 2010 as the basis.

Space-time analysis

Space-time analysis requires that the spatial unit, in this case each neighborhood, be transformed into a single point, the centroid. Oftentimes during the process of allocating centroids with the geometric method, the centroids end up being defined in areas outside their own demographic references. The territory’s representation thus loses precision, failing to accurately portray how the space is occupied (Figure 2a). Seeking to mitigate this issue, the centroids’ location was adjusted to the areas with the highest population density in each neighborhood. First, supervised classification of the Landsat 8 satellite image of 2014 ⁴⁹49. Instituto Brasileiro de Geografia e Estatística. Manual técnico de uso da terra. 3ª Ed. Rio de Janeiro: Instituto Brasileiro de Geografia e Estatística; 2013. was performed in order to demarcate areas occupied by the human population (Figure 2b). Next, using the population data for each census tract, kernel analysis of the density was performed, thus allowing allocation of the centroids into the areas with the greatest population density within the occupied area in each neighborhood (Figures 2c and 2d). Comparison of the two methods clearly evidenced a superior fit to the territorial reality with use of the method that uses the occupied area and population density rather than the method that merely uses formal political-administrative boundaries (Figures 2e and 2f).

We then performed retrospective space-time analysis with Poisson probability distribution to identify the most likely clusters with high risk for dengue in each year from 2008 to 2014, for each of the two population groups used in the study. The scan statistic proposed by Kulldorff ⁵⁰50. Kulldorff M. A spatial scan statistic. Commun Stat Theory Methods 1997; 26:1481-96. was used for the detection and identification of space-time clusters with increased relative risk, using neighborhoods as spatial units and months as temporal units.

In order to avoid unwanted effects associated with different sizes of scan windows ⁵¹51. Chen J, Roth RE, Naito AT, Lengerith EJ, MacEachren AM. Geovisual analytics to enhance spatial scan statistic interpretation: an analysis of U.S. cervical cancer mortality. Int J Health Geogr 2008; 7:57., we used 20% of the total population, which allowed the neighborhood with the largest population to form a cluster. As for the time factor, we used the clusters that occurred in up to 50% of the study period, which is standard value for the statistical package and proved valid for this study.

In order to test the null hypothesis that no difference exists in the relative risk (RR) of dengue between the neighborhoods over time, we used the Monte Carlo approach with 9,999 repetitions, with significance set at 0.05. Log-likelihood ratio (LLR) was used to test the formation of clusters. The most likely cluster is the one with the highest LLR.

To analyze spatial persistence in both population groups, the clusters defined for each year were mapped and later overlaid and added through the map algebra methodology. A summary map was constructed, indicating how many times in the study period each neighborhood belonged to a cluster.

The scan methodology was performed with use of the SaTScan software package v9.4.2 (http://www.satscan.org), and geoprocessing, mapping, and map algebra were performed with use of the ArcGis software package, version 10.2 (http://www.esri.com/software/arcgis/index.html).

Results

In the city of Rio de Janeiro as a whole, 464,500 dengue cases were reported, of which 32,749 were in the population group under 5 years of age and 431,751 in the population 5 years and older. Dengue rates showed the same profile over the years in both population groups, with four observed epidemic peaks, in 2008, 2011, 2012, and 2013. The predominant serotypes were DENV-2 in 2008, DENV-1 in 2011, and DENV-4 in 2012 and 2013 (Table 1).

According to the space-time analysis for 2008 to 2014, the most probable clusters in both the preschool and five-and-over population groups were identified in the West and North Zones of the city in the years 2008, 2009, 2010, 2011, 2012, and 2014. However, only in 2013 were probable clusters for both populations located in the South and Central Zones of the city.

In the years 2011, 2012, and 2013, the defined clusters for the two study populations showed similar location and spatial conformation in the city. In the years 2008, 2009, 2010, and 2014, the defined clusters for the two populations did not coincide spatially. Both clusters over the years had the same defined timeframes in the first semester, except for the year 2010 (Tables 2 and 3; Figure 3).

In the under-five population group, the West Zone of the city showed the largest area of persistent risk, followed by the North Zone. The area of persistent risk located in the West Zone consists of 7 neighborhoods in the administrative regions of Bangu, Campo Grande and Guaratiba. Those in the North Zone consist of 11 neighborhoods located in the administrative regions of Tijuca, Ramos, Alemão, and Inhaúma (Figure 4).

The five-and-over population group showed a similar pattern to that of the preschoolers, with the West Zone as the part of the city with the largest area of persistent risk, consisting of 22 neighborhoods from the administrative regions of Bangu, Campo Grande, and Guaratiba, followed by the North Zone with 3 neighborhoods located in the administrative region of Madureira (Figure 4).

Discussion

The results of this study showed that the Scan methodology was able to define the areas of greatest risk for dengue through the identification of incidence clusters, as well as the identification of persistent areas over the years analyzed by means of the map algebras methodology. The areas of persistently increased risk of dengue (clusters) in the city of Rio de Janeiro were shown to be concentrated in the West Zone of the city.

The results also showed the probable influence of the population’s immunological status on the geographic distribution of the incidence and risk areas of dengue, only demonstrating spatial agreement between the two target populations when they presented the same immunological status with regard to the predominant circulating serology for the cases reported during the period in question.

Different social groups tend to occupy the territory in more or less grouped ways as a function of their similarities. In order to understand the dynamics of the territory to which these different groups belong, it is essential to understand the spatial distribution of health-disease processes ¹³13. Xavier DR, Magalhães MAFM, Gracie R, Reis IC, Matos VP, Barcellos C. Difusão espaço-tempo do dengue no Município do Rio de Janeiro, Brasil, no período de 2000-2013. Cad Saúde Pública 2017; 33:e00186615.. The city of Rio de Janeiro is a heterogeneous mosaic of territorial realities, and analysis on a larger geographic scale such as a Zone allows for the analysis of common structural characteristics that can help explain the persistence of risk in some areas or zones.

The West Zone of Rio de Janeiro was the area with the most neighborhoods displaying clusters throughout the study and in both population groups analyzed. However, in the preschool population, the number of neighborhoods and the total area were lower than in the population 5 years and older, which could be explained by the difference in mobility between these populations ⁴³43. Perkins TA, Paz-Soldan VA, Stoddard ST, Morrison AC, Forshey BM, Long KC, et al. Calling in sick: impacts of fever on intra-urban human mobility. Proc Biol Sci 2016; 283:20160390.. Thus, the West Zone of Rio was the one with the most areas of persistent dengue risk in the city.

The repetition of areas comprising clusters throughout the study period thus characterizes a level of persistent risk in certain areas or zones. Understanding the structuring processes in these persistent areas through variables that allow grasping the changes in the geographic space can help identify the determinants of dengue in the territory ⁵²52. Cavallieri F, Lopes GP. Índice de Desenvolvimento Social - IDS: comparando as realidades microurbanas da cidade do Rio de Janeiro. Coleção Estudos Cariocas 2008; 8:1-12.^,5353. Kawa H, Sabroza PC. Espacialização da leishmaniose tegumentar na cidade do Rio de Janeiro. Cad Saúde Pública 2002; 18:853-65..

Historically, the West Zone of Rio de Janeiro was the last to be incorporated into the city’s urban fabric. The transformation of farmlands into urban areas occurred largely due to the stagnation of agricultural properties, which were cut up into housing lots. This new mode of occupation reflected the diverse rationale of social agents shaping the urban space, leading to a constant process of spatial reorganization ⁵³53. Kawa H, Sabroza PC. Espacialização da leishmaniose tegumentar na cidade do Rio de Janeiro. Cad Saúde Pública 2002; 18:853-65.^,5454. Almeida AS, Medronho RA, Valencia LIO. Análise espacial da dengue e o contexto socioeconômico no município do Rio de Janeiro, RJ. Rev Saúde Pública 2009; 43:666-73.. The West Zone is the area of the city with the greatest recent alteration in its landscape, and its territory has undergone an accelerated process of growth and increased population density, but lacking a corresponding increase in urban infrastructure ⁵⁴54. Almeida AS, Medronho RA, Valencia LIO. Análise espacial da dengue e o contexto socioeconômico no município do Rio de Janeiro, RJ. Rev Saúde Pública 2009; 43:666-73..

The areas of persistent risk of dengue (clusters) in the city of Rio de Janeiro are concentrated in the Planning Areas 5.1 and 5.2 located in the West Zone of the city (Figure 4), which mostly presents low socioeconomic development ⁵²52. Cavallieri F, Lopes GP. Índice de Desenvolvimento Social - IDS: comparando as realidades microurbanas da cidade do Rio de Janeiro. Coleção Estudos Cariocas 2008; 8:1-12.^,5454. Almeida AS, Medronho RA, Valencia LIO. Análise espacial da dengue e o contexto socioeconômico no município do Rio de Janeiro, RJ. Rev Saúde Pública 2009; 43:666-73. and no fully consolidated urban occupation. Planning Areas 5.1 and 5.2, identified as areas of persistent dengue risk, displayed low Social Development Indices (SDI) in 2010 (0.57 and 0.56, respectively) ⁵²52. Cavallieri F, Lopes GP. Índice de Desenvolvimento Social - IDS: comparando as realidades microurbanas da cidade do Rio de Janeiro. Coleção Estudos Cariocas 2008; 8:1-12.^,5555. Carvalho S, Magalhães MAFM, Medronho RDA. Analysis of the spatial distribution of dengue cases in the city of Rio de Janeiro, 2011 and 2012. Rev Saúde Pública 2017; 51:79.. The SDI is a composite indicator that measures the degree of social development of a given geographic area compared to other areas: the closer to 1, the higher the level of social development ⁵²52. Cavallieri F, Lopes GP. Índice de Desenvolvimento Social - IDS: comparando as realidades microurbanas da cidade do Rio de Janeiro. Coleção Estudos Cariocas 2008; 8:1-12.. These areas of the city also showed high vector density between 2009 and 2014, with building infestation indices nearly always between 1% and 4% according to the Rapid Index Survey for Aedes aegypti (LIRAa) conducted by the Rio de Janeiro Municipal Health Department ¹⁶16. Secretaria Municipal de Saúde do Rio de Janeiro. Apresentação do centro de vigilância epidemiológica sobre situação das arboviroses na cidade do Rio de Janeiro. Rio de Janeiro: Secretaria Municipal de Saúde do Rio de Janeiro; 2017..

The clusters identified for each year of the study period showed spatial variability at the neighborhood level over the years in both population groups. The majority of the neighborhoods defined as clusters in a given year were not repeated in the following year, and so on successively. This spatial variability might be related to uncontrolled local situational factors such as the population dynamics of the mosquito vector, the population’s immunity, mobility, and changing priorities in vector for control measures ⁵⁶56. Sabroza PC, Leal MC. Saúde, ambiente e desenvolvimento: alguns conceitos fundamentais. In: Leal MC, Sabroza PC, Rodriguez RH, Buss PM, editors. Saúde, ambiente e desenvolvimento: uma análise interdisciplinar. Rio de Janeiro: Editora Hucitec/Abrasco; 1992. p. 45-93..

Following outbreaks and epidemics, dengue antibody seroprevalence can reach 80% of the population, and herd immunity is a determinant factor for new infections ⁵⁷57. Siqueira-Junior JB, Maciel IJ, Barcellos C, Souza WV, Carvalho MS, Nascimento NE, et al. Spatial point analysis based on dengue surveys at household level in central Brazil. BMC Public Health 2008; 8:361.. Dengue has four antigenically distinct serotypes (DENV-1, DENV-2, DENV-3 and DENV-4) that produce lasting immunity specifically for each serotype. However, this immunity is specific and does not protect against other serotypes. As such, the stock of individuals susceptible to a certain serotype ends up having a determining role in the epidemic process and in the spatial distribution of dengue cases in the territory ⁵⁸58. Teixeira MG, Barreto ML, Guerra Z. Epidemiologia e medidas de prevenção do dengue. Inf Epidemiol SUS 1999; 8:5-33..

The persistence of dengue epidemics in the human populations of a given territory only occurs in urban spaces that maintain significant indexes of vector infestation and large population densities, which, together with birth rates, will restore the stock of individuals susceptible to infection. This is because the vector’s main or perhaps only source of infection is human viremia, which persists for only seven days during the acute phase of infection, and recurrent viremia with the same serotype has never been demonstrated ⁵⁹59. Rodhain F, Rosen L. Mosquito vectors and dengue virus-vector relations ships. In: Gubler DJ, Kuno G, editors. Dengue and dengue hemorragic fever. New York: CAB International; 1997. p. 45-60..

As a result, the spatial distribution of dengue cases can vary according to the study population’s immune status at any given moment. In this study, most areas defined as clusters did not coincide between both population groups when analyzed year by year. This result is explained by the differing immunological profile of the two study populations with regard to the different serotypes prevalent in the years 2008, 2009, 2010 and 2014 ¹⁶16. Secretaria Municipal de Saúde do Rio de Janeiro. Apresentação do centro de vigilância epidemiológica sobre situação das arboviroses na cidade do Rio de Janeiro. Rio de Janeiro: Secretaria Municipal de Saúde do Rio de Janeiro; 2017. (Table 1).

However, in 2011, 2012, and 2013, when the immune profile concerning the year’s predominant serotype was similar between both groups, and the clusters defined by the modeling displayed major spatial coincidence that was even identical in 2012 and extremely similar in 2013 ¹⁶16. Secretaria Municipal de Saúde do Rio de Janeiro. Apresentação do centro de vigilância epidemiológica sobre situação das arboviroses na cidade do Rio de Janeiro. Rio de Janeiro: Secretaria Municipal de Saúde do Rio de Janeiro; 2017. (Table 1).

In 2011, the preschool population was practically entirely susceptible, since the predominant serotype that year was DENV-1, which had not predominated in the city for at least ten years, besides the introduction of DENV-4. The large time lapse since the last epidemic caused predominantly by DENV-1 also produced a large contingent of susceptible individuals in the population 5 years and older. In 2012 and 2013, the predominant serotype was DENV-4, which had been introduced into the city in 2011, so both populations had the same profile with regard to this serotype in those two years.

The fact that two populations with varying mobility showed the same location of clusters during years with similar immune profiles suggests that the spatial definition of clusters occurs more as a function of immunity or the reserve of susceptible individuals than as a function of mobility.

The extremely local profile of human movement networks ⁴²42. Vazquez-Prokopec GM, Bisanzio D, Stoddard ST, Paz-Soldan V, Morrison AC, Elder JP, et al. Using GPS technology to quantify human mobility, dynamic contacts and infectious disease dynamics in a resource-poor urban environment. PLoS One 2013; 8:e58802. and the reduced individual mobility when a person becomes ill with dengue ⁴³43. Perkins TA, Paz-Soldan VA, Stoddard ST, Morrison AC, Forshey BM, Long KC, et al. Calling in sick: impacts of fever on intra-urban human mobility. Proc Biol Sci 2016; 283:20160390. can help explain the similarity in the area of clusters identified between 2011 and 2013.

Use of the scan statistic to determine clusters (assigning statistical significance to them), the high georeferencing degree of reported cases at the neighborhood level, and the shifting of the neighborhoods’ centroids to their demographic centers via remote sensing increased the analyses’ robustness. In the specific case of shifting the neighborhoods’ centroids, this process adds great value to studies that take space into account, since it seeks a more trustworthy picture of the territory’s reality.

One limitation of this study was the exclusive use of incidence data in the statistical model. More robust statistical models that take explanatory variables into account could contribute to a better understanding of dengue determinants. However, the strategy of using only incidence data in the model can facilitate the model’s implementation and use in health services, since incidence is one of the few forms of data available practically in real time for health service managers.

Another limitation is that the reported dengue cases used in this study probably do not include all infection cases. Many dengue infections are asymptomatic, and differential diagnosis is difficult for dengue, since its symptoms are similar to those of various other diseases ⁶⁰60. Fares RCG, Souza KPR, Añez G, Rios M. Epidemiological scenario of dengue in Brazil. BioMed Res Int 2015; 2015:1-13.. In addition, data from SINAN include both suspected and confirmed cases. There was also a low percentage of serotyping tests performed, thus compromising the definition of the circulating serotypes.

We emphasize that the main objective of this study was to present the method for identifying areas of persistent risk through incidence data using the Scan statistic. These areas are always related to the period of the time series used. Thus, the results must be interpreted and possibly applied with caution because they have been defined with previous incidence data.

The use of maps in routine surveillance facilitates the identification of visible elements in the territory, thus aiding the understanding of local realities ⁶⁰60. Fares RCG, Souza KPR, Añez G, Rios M. Epidemiological scenario of dengue in Brazil. BioMed Res Int 2015; 2015:1-13.. An important strength of spatial analysis is that it allows easy and rapid identification and visualization of areas with different realities, which as such are exposed to different risk levels ⁶¹61. Barcellos C, Bastos FI. Geoprocessamento, ambiente e saúde: uma união possível? Cad Saúde Pública 1996; 12:389-97..

The fight against dengue is not a simple task, since its occurrence in both classic and severe form involves social, economic, and environmental factors, the supply of health services, and the population’s immune status ⁶²62. Souza-Santos R, Carvalho MS. Análise da distribuição espacial de larvas de Aedes aegypti na Ilha do Governador, Rio de Janeiro, Brasil. Cad Saúde Pública 2000; 16:31-42.. The use of spatial analysis techniques in the study, more specifically the scan statistic, allowed the identification of areas of increased risk that were repeated over time. Therefore, this method can be used to assist local dengue surveillance activities with customized strategies in the territory as a function of the risk and recurrence in each area.

We emphasize that the use of populations with a high percentage of susceptible individuals, such as young populations or total population in epidemic years with the entry of new serotypes, is theoretically more adequate for the territorial study of dengue. These populations are more likely to express the areas with increased risk of transmission, since the confounding factor of immunity is minimized in these cases.

Acknowledgments

This study was funded by the Rio de Janeiro State Research Foundation (Faperj; grant n. E-26/202.736/2018, to N.A.H.) and the Brazilian National Research Council (CNPq; grant n. 421884/2017-7, to A.A.N.). The authors thank to Rede Arboalvo and Rede Dentarget.

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