Mapeamento de áreas de risco à saúde pública por meio de métodos geoestatísticos

Lourenço, Roberto Wágner; Landim, Paulo Milton Barbosa

doi:10.1590/s0102-311x2005000100017

Cited by 16 publications

(13 citation statements)

References 12 publications

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“…The free software "R" was used in the geostatistical calculations. 5,6,11 Table 1 presents the epidemiological and demographic measurements, and Figure 1 shows the proportional distribution of the AIDS transmission categories for each DIR. It was observed that there was high incidence of AIDS cases with unknown transmission category, in the heavily populated DIRs (regions I, II, III, V and XII).…”

Section: Methodsmentioning

confidence: 99%

Distribuição espacial da co-infecção M. tuberculosis/HIV no Estado de São Paulo, 1991-2001

2006

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OBJECTIVE:To assess the spatial pattern of tuberculosis incidence in relation to the AIDS epidemic, with the aim of investigating the geographical influence on causality. METHODS:All AIDS cases from State of São Paulo, notified to the Brazilian Ministry of Health between 1991 and 2001, were included. The cases were stratified by municipality, by administrative health regions, AIDS transmission categories, gender and years since diagnosis. A Gaussian geostatistical model was used to construct a thematic risk map, utilizing the tuberculosis incidence among AIDS cases as the response variable. RESULTS:Exploratory analysis showed two patterns of AIDS incidence: one for the state capital, and another, with increasing risk, for the other municipalities. The more populous regions presented higher risk of tuberculosis transmission, with a pattern that matched the land occupation pattern, from east to west. The health regions with the highest AIDS incidence coefficients (per 10,000 inhabitants) were Santos (53.5), São José do Rio Preto (43.1), Ribeirão Preto (42.4) and São Paulo (40.3). The health regions with greatest tuberculosis incidence among AIDS cases were Santos (44.9%), Franco da Rocha (39.9%), Osasco (39.6%) and São Paulo (38.9%). CONCLUSIONS:The results allow the conclusion that geographical coordinates presented an association with tuberculosis risk, but not with AIDS risk.

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Section: Methodsmentioning

confidence: 99%

Distribuição espacial da co-infecção M. tuberculosis/HIV no Estado de São Paulo, 1991-2001

2006

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“…Since the beginning of this century, several authors have started to use these techniques also towards advancing human health (Campos et al, 2002;Fortes et al, 2004;Lourenço and Landim, 2005;Castro and Singer, 2007;Cardim et al, 2013;Samy et al, 2014). In particular, these techniques have been used to model diseases such as schistosomiasis in studies developed by Guimarães et al (2009Guimarães et al ( , 2010Guimarães et al ( , 2012 to estimate the spatial distribution of Biomphalaria snails in the state of Minas Gerais.…”

Section: Geospatial Healthmentioning

confidence: 99%

Modelling of the distribution of Biomphalaria glabrata and Biomphalaria straminea in the metropolitan region of Recife, Pernambuco, Brazil

Barbosa¹,

Guimarães²,

Loyo³

et al. 2016

Geospat Health

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The occurrence of schistosomiasis is directly linked to the presence of its snail intermediate host Biomphalaria spp. Knowledge of geographical distribution, habitats and behaviour of these snails in relation to the climate is essential for guiding measures for disease prevention and control. This study aims to model the distribution of B. glabrata and B. straminea in schistosomiasis non-endemic areas of the metropolitan region of Recife (MRR) based on environmental data and estimates of snail distributions in endemic and neighbouring areas. We applied Kriging with the aim of determining the spatial distribution of these two snail species and MaxEnt for modelling their ecological behaviour. Kriging showed that the North and the Centre of the MRR were generally either snail-free or contained only B. straminea, while both snail species could be found in the South. MaxEnt supported our observation that the northern and southern coastal regions were favoured by B. glabrata and diurnal mean temperature variation; July rainfall and November rainfall were the three variables favouring Biomphalaria breeding sites that contributed the most in the predictive model we developed. The study showed the location of areas suitable to Biomphalaria spp. and therefore at potential risk, first for invasion of these snails and later for the development of new schistosomiasis-endemic areas. This information should be useful, not only to estimate expansion possibilities of this disease in the MRR, but also to point out the climatic variables that would contribute to this expansion, thereby allowing timely application of prevention and control measures.

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“…The Índice Paulista de Responsabilidade Social (IPRS -Social Responsibility Index of the state of São Paulo) is an indicator that refl ects human development in the municipalities of the state of São Paulo, including, beside the Human Development Index (HDI), municipal administrative information updated in years between two demographic censuses. 16 Statistical methods contribute to the evaluation of these epidemiological landscapes: stochastic models of time series analysis 13,15 are used to estimate trend and seasonality parameters, and geostatistical models 5,8,14 generate risk zonings or identify groupings. An exploratory analysis was carried out with age group-specific detection coefficients 6 in the DRS, adopting the following stratifi cation: "up to 4 years of age" (pre-school phase), "from 5 to 19 years of age" (basic education phase), "from 20 to 34 years of age" (initial productive phase), "from 35 to 49 years of age" (intermediate productive phase), "from 50 to 64 years of age" (tardy productive phase), and "above 65 years of age" (retirement phase).…”

Section: Introductionmentioning

confidence: 99%

“…5,8 The kriging interpolation method involves the adjustment of the semivariogram and the estimation of parameters called "nugget", "sill" and "range", besides the identifi cation of the directionality of the spatial correlations of the semivariogram (Lourenço & Landim,8 2005). The northwest direction was adopted to model the semivariogram, because it corresponds to the direction in which the historical occupation of the territory occurred: from the coast to the interior of the country in the azimuthal direction 135º.…”

Section: Introductionmentioning

confidence: 99%

Estudo espacial e temporal da hanseníase no estado de São Paulo, 2004-2006

Rodrigues-Júnior

Ó²,

Motti³

2008

Rev. Saúde Pública

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METHODS:This is an ecological-social study that used the number of leprosy cases reported to the Brazilian Ministry of Health from January 2004 to December 2006. Monthly series were generated in each regional health department, and their sequences were adjusted by a Markovian model for the leprosy detection coeffi cients. The detection coeffi cient with the number of cases accumulated in the period in each municipality was used to produce the spatial distribution of the endemy; a correlation analysis was carried out with the leprosy detection coeffi cients and the components of the Social Responsibility Index of the state of São Paulo. RESULTS:Of the 645 municipalities of the state of São Paulo, 22 did not detect leprosy cases in the period. In the majority of regions, the endemy showed a decreasing trend; the time series presented random fl uctuation around expected values. The decline was infl uenced by a generalized decrease in the detection coeffi cients at the end of 2005. There was a positive correlation between the detection coeffi cients and the components "schooling" and "longevity", of the Social Responsibility Index of the state of São Paulo, and a negative correlation with "wealth", another component of the same Index. CONCLUSIONS:The result of the time series analysis suggests that the endemy is on the decline in the majority of regions of the state of São Paulo, while the spatial analysis shows that the coeffi cients are high in the northern part of the state.

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Mapeamento de áreas de risco à saúde pública por meio de métodos geoestatísticos

Cited by 16 publications

References 12 publications

Distribuição espacial da co-infecção M. tuberculosis/HIV no Estado de São Paulo, 1991-2001

Distribuição espacial da co-infecção M. tuberculosis/HIV no Estado de São Paulo, 1991-2001

Modelling of the distribution of Biomphalaria glabrata and Biomphalaria straminea in the metropolitan region of Recife, Pernambuco, Brazil

Estudo espacial e temporal da hanseníase no estado de São Paulo, 2004-2006

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