Christovam Barcellos

The place behind the case: leptospirosis risks and associated environmental conditions in a flood-related outbreak in Rio de Janeiro

The environmental context in which a leptospirosis outbreak took place during the summer of 1996 in the Rio de Janeiro Western Region was examined by using spatial analysis of leptospirosis cases merged with socio-demographic data using Geographic Information System (GIS). Risk areas were mapped based on flood and solid waste accumulation information for the region. Incidence rates were calculated for each area by the division of number of cases per total population in the specific areas. Higher rates were observed for census tracts inside the flood risk area and in the vicinities of waste accumulation sites. These findings are in agreement with the expected risk of leptospirosis, evidencing the role of environmental and collective factors in the determination of the disease.

Geoprocessamento, ambiente e saúde: uma união possível?

The use of geoprocessing techniques allows one to gather socioeconomic, health, and environmental data on a spatial basis. However, interpretation of associations between epidemiological and environmental variables requires the geoprocessing system design. The study scale and object choices precede conception of the system, conditioning the possible statistical and visual results. This scale must be compatible with the phenomenon on which one intends to focus, aiming at internal homogeneity and external heterogeneity of spatial units. The interdependency of spatial processes, reflected in the spatial configuration of social, environmental, and epidemiological data distribution, affects interpretation of causes for simultaneous processes. Geoprocessing allows for knowledge of the context or situational surroundings in which the damage to health takes place.

Organização espacial, saúde e qualidade de vida: análise espacial e uso de indicadores na avaliação de situações de saúde

Space is a synthetic and convergent category in which the processes involved in the quality of life, environment and health conditions of populations are expressed. Through these processes, the results obtained by the analysis of indicators are sensitive to the choice of indicators, information sources, scale of analysis, spatial reference units and methods of spatial analysis. The objective of this work is to define the role of spatial analysis in the health situation assessment, which includes the description of the epidemiological profiles, quality of life and environmental conditions. The first step for characterizing the health situation is to describe the structure and spatial dynamics of the population. Spatial analysis permits the planning of control actions and resource allocation as it allows to describe and understand the social and enviromental processes in which health events occur.

Spatio-temporal modelling of climate-sensitive disease risk: Towards an early warning system for dengue in Brazil

This paper considers the potential for using seasonal climate forecasts in developing an early warning system for dengue fever epidemics in Brazil. In the first instance, a generalised linear model (GLM) is used to select climate and other covariates which are both readily available and prove significant in prediction of confirmed monthly dengue cases based on data collected across the whole of Brazil for the period January 2001 to December 2008 at the microregion level (typically consisting of one large city and several smaller municipalities). The covariates explored include temperature and precipitation data on a 2.5^^ox2.5^^o longitude-latitude grid with time lags relevant to dengue transmission, an El Nino Southern Oscillation index and other relevant socio-economic and environmental variables. A negative binomial model formulation is adopted in this model selection to allow for extra-Poisson variation (overdispersion) in the observed dengue counts caused by unknown/unobserved confounding factors and possible correlations in these effects in both time and space. Subsequently, the selected global model is refined in the context of the South East region of Brazil, where dengue predominates, by reverting to a Poisson framework and explicitly modelling the overdispersion through a combination of unstructured and spatio-temporal structured random effects. The resulting spatio-temporal hierarchical model (or GLMM-generalised linear mixed model) is implemented via a Bayesian framework using Markov Chain Monte Carlo (MCMC). Dengue predictions are found to be enhanced both spatially and temporally when using the GLMM and the Bayesian framework allows posterior predictive distributions for dengue cases to be derived, which can be useful for developing a dengue alert system. Using this model, we conclude that seasonal climate forecasts could have potential value in helping to predict dengue incidence months in advance of an epidemic in South East Brazil.

Geografia social da AIDS no Brasil

The first of a series of papers concerning the evaluation of the dynamics of the AIDS epidemic in Brazil employing techniques of geographical analysis, is here presented. Results of research undertaken in the US (especially in New York City) are compared with those of a recent investigation carried out in the city of S.Paulo, Brazil (Grangeiro, 1994). In both, geographical patterns of socio-demographic variables correlate with different patterns of the spread of the AIDS epidemic through the transmission groups. Recent trends of the AIDS epidemic in Brazil: the displacement toward medium sized cities and expansion frontiers, increasing report of AIDS cases among the poor and underprivileged, changes in the pattern of transmission with proportional augmentation of heterosexual transmission and IDUs as transmission groups, are described and analysed. The geographical distribution of the AIDS cases registered between 1987-1993 in Brazil throughout the Brazilian States is evaluated by means of worksheets, maps, and non-parametric statistics. Results show that Gravimetric Centers (obtained by the use of the calculus spatial means) of AIDS in Brazil are situated within a triangle the sides of which are formed lines joining the three main metropolitan areas of the wealthiest region of Brazil--the southeast, i.e. São Paulo, Rio de Janeiro and Belo Horizonte. These especially S. Paulo, function as points of attraction for these Gravimetric Centers (GCs) towards the south as compared with the GCs of the general population calculated ia accordance with data from the 1980 and 1991 censuses. It is possible to observe a displacement of the GCs toward the northwest over this period in accordance with the migration patterns of the Brazilian population in general, though with a dynamic of its own. These changes in the geographical, socio-demographic and transmission group patterns show the complex nature of the epidemic in Brazil and pose additional difficulties for the development of prevention strategies.

Spatial point analysis based on dengue surveys at household level in central Brazil

BackgroundDengue virus (DENV) affects nonimunne human populations in tropical and subtropical regions. In the Americas, dengue has drastically increased in the last two decades and Brazil is considered one of the most affected countries. The high frequency of asymptomatic infection makes difficult to estimate prevalence of infection using registered cases and to locate high risk intra-urban area at population level. The goal of this spatial point analysis was to identify potential high-risk intra-urban areas of dengue, using data collected at household level from surveys.MethodsTwo household surveys took place in the city of Goiania (~1.1 million population), Central Brazil in the year 2001 and 2002. First survey screened 1,586 asymptomatic individuals older than 5 years of age. Second survey 2,906 asymptomatic volunteers, same age-groups, were selected by multistage sampling (census tracts; blocks; households) using available digital maps. Sera from participants were tested by dengue virus-specific IgM/IgG by EIA. A Generalized Additive Model (GAM) was used to detect the spatial varying risk over the region. Initially without any fixed covariates, to depict the overall risk map, followed by a model including the main covariates and the year, where the resulting maps show the risk associated with living place, controlled for the individual risk factors. This method has the advantage to generate smoothed risk factors maps, adjusted by socio-demographic covariates.ResultsThe prevalence of antibody against dengue infection was 37.3% (95%CI [35.5–39.1]) in the year 2002; 7.8% increase in one-year interval. The spatial variation in risk of dengue infection significantly changed when comparing 2001 with 2002, (ORadjusted = 1.35; p < 0.001), while controlling for potential confounders using GAM model. Also increasing age and low education levels were associated with dengue infection.ConclusionThis study showed spatial heterogeneity in the risk areas of dengue when using a spatial multivariate approach in a short time interval. Data from household surveys pointed out that low prevalence areas in 2001 surveys shifted to high-risk area in consecutive year. This mapping of dengue risks should give insights for control interventions in urban areas.

Identificação de locais com potencial de transmissão de dengue em Porto Alegre através de técnicas de geoprocessamento

The social-environmental conditions that promote the continuity of Aedes aegypti in urban areas and its capacity for dengue transmission have yet to be determined. The aim of this work was to locate dengue cases and vector presence, then to identify social-environmental factors that characterize these sites, through Geographical Information Systems (GIS), with a view to developing a dengue fever prevention model. The vector was mainly located in south and east areas of the city, with a broad dispersion, while most of the cases were located in the central region of the city. The sectors with cases present characteristics of high income. Otherwise, sectors with vector present a predominance of houses and good sanitation infrastructure. The difference between cases and vector spatial patterns ensured for 2002 an absence of dengue transmission in the city.

Socio-environmental determinants of the leptospirosis outbreak of 1996 in western Rio de Janeiro: a geographical approach

The environmental and social context in which a leptospirosis outbreak took place during the summer of 1996 in the Rio de Janeiro Western Region was examined by using spatial analysis of leptospirosis cases merged with population and environmental data in a Geographical Information System (GIS). Important differences were observed between places where residences of leptospirosis cases are concentrated and other places in the region. Water supply coverage, solid waste collection, sewerage system coverage and flood risk area were the main determining variables from an initial list of ten. The influence of these unfavorable social and environmental factors is verified hundreds of meters distant from the leptospirosis case residences, demonstrating a necessity to broaden the area of health surveillance practices. The geocoding indicated that some cases did not report contact with flood water, even though they were geographically adjacent to cases who did report this contact. Cases may only report exposures they believe are related to the disease. Geocoding is a useful tool for evaluating such bias in the exposure recall.

Influence of Deforestation, Logging, and Fire on Malaria in the Brazilian Amazon

Malaria is a significant public health threat in the Brazilian Amazon. Previous research has shown that deforestation creates breeding sites for the main malaria vector in Brazil, Anopheles darlingi, but the influence of selective logging, forest fires, and road construction on malaria risk has not been assessed. To understand these impacts, we constructed a negative binomial model of malaria counts at the municipality level controlling for human population and social and environmental risk factors. Both paved and unpaved roadways and fire zones in a municipality increased malaria risk. Within the timber production states where 90% of deforestation has occurred, compared with areas without selective logging, municipalities where 0–7% of the remaining forests were selectively logged had the highest malaria risk (1.72, 95% CI 1.18–2.51), and areas with higher rates of selective logging had the lowest risk (0.39, 95% CI 0.23–0.67). We show that roads, forest fires, and selective logging are previously unrecognized risk factors for malaria in the Brazilian Amazon and highlight the need for regulation and monitoring of sub-canopy forest disturbance.

Dengue outlook for the World Cup in Brazil: an early warning model framework driven by real-time seasonal climate forecasts

BACKGROUND With more than a million spectators expected to travel among 12 different cities in Brazil during the football World Cup, June 12-July 13, 2014, the risk of the mosquito-transmitted disease dengue fever is a concern. We addressed the potential for a dengue epidemic during the tournament, using a probabilistic forecast of dengue risk for the 553 microregions of Brazil, with risk level warnings for the 12 cities where matches will be played. METHODS We obtained real-time seasonal climate forecasts from several international sources (European Centre for Medium-Range Weather Forecasts [ECMWF], Met Office, Meteo-France and Centro de Previsão de Tempo e Estudos Climáticos [CPTEC]) and the observed dengue epidemiological situation in Brazil at the forecast issue date as provided by the Ministry of Health. Using this information we devised a spatiotemporal hierarchical Bayesian modelling framework that enabled dengue warnings to be made 3 months ahead. By assessing the past performance of the forecasting system using observed dengue incidence rates for June, 2000-2013, we identified optimum trigger alert thresholds for scenarios of medium-risk and high-risk of dengue. FINDINGS Our forecasts for June, 2014, showed that dengue risk was likely to be low in the host cities Brasília, Cuiabá, Curitiba, Porto Alegre, and São Paulo. The risk was medium in Rio de Janeiro, Belo Horizonte, Salvador, and Manaus. High-risk alerts were triggered for the northeastern cities of Recife (p(high)=19%), Fortaleza (p(high)=46%), and Natal (p(high)=48%). For these high-risk areas, particularly Natal, the forecasting system did well for previous years (in June, 2000-13). INTERPRETATION This timely dengue early warning permits the Ministry of Health and local authorities to implement appropriate, city-specific mitigation and control actions ahead of the World Cup. FUNDING European Commissions Seventh Framework Research Programme projects DENFREE, EUPORIAS, and SPECS; Conselho Nacional de Desenvolvimento Científico e Tecnológico and Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro.