Hanseníase, condições sociais e desmatamento na Amazônia brasileira

Silva, Diego Ricardo Xavier; Ignotti, Eliane; Souza-Santos, Reinaldo; Hacon, Sandra de Souza

doi:10.1590/s1020-49892010000400005

Cited by 44 publications

(46 citation statements)

References 18 publications

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“…Table 1, Table 2 and Table 3 show the articles' summaries according to the scale used: two evaluated regions/ mesoregions 14,15 ; 13 used the scale of municipalities 10,[16][17][18][19][20][21][22][23][24][25][26][27] ; 4 evaluated districts [28][29][30][31] ; 6 studied neighborhoods [32][33][34][35][36][37] ; 7 analyzed the census tracts [38][39][40][41][42][43][44] ; and 3 investigated households [45][46][47] . Within each scale, the articles were organized by location: Brazil, Northern, Northeast, Midwest, and Southeast regions.…”

Section: Resultsmentioning

confidence: 99%

“…Within each scale, the articles were organized by location: Brazil, Northern, Northeast, Midwest, and Southeast regions. In all, 4 approached Brazil 10,16-18 , 7 studied the Northern region or the states of the Amazon region 14,19,20,[38][39][40]45 , 11 focused on the Northeast region [21][22][23]28,[32][33][34][35][36]40,41 , 4 the Midwest region 15,29,37,46 , and 12 the Southeast region [24][25][26][27]30,31,36,37,[42][43][44]47 .…”

Section: Resultsmentioning

confidence: 99%

“…Regarding demographic conditions, population density was studied (on the scales of neighborhoods and census sectors), as well as the level of urbanization. The distance between households was also studied, and as an environmental condition, deforestation was evaluated in two articles 14,15 .…”

Section: Resultsmentioning

confidence: 99%

“…Kriging methods and scan tests were also used. The two studies 14,15 that covered mesoregions and microregions have shown a correlation between the evolution of deforestation and an increase in NCDR, besides the effect of migration movements on coefficients of detection and focuses (new and old) of the disease.…”

Section: Resultsmentioning

confidence: 99%

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Spatial distribution of leprosy in Brazil: a literature review

Silva

Fonseca

Kawa

et al. 2017

Rev. Soc. Bras. Med. Trop.

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Leprosy remains a public health problem in developing countries. Among communicable diseases, it is one of the leading causes of permanent disability. Brazil had not reached the goal of reducing cases to less than 1 per 10,000 population. This study aimed to analyze the spatial distribution of leprosy cases in Brazil, using a literature review. The search strategy included the LILACS and MEDLINE databases with no language or period restriction. Ecological studies with spatial data analysis were considered as a criterion for the inclusion. We found 38 studies for review after the selection criteria. Among the epidemiological indicators of the disease, the most common was the new case detection rate. Several articles have explored the association between spatial distribution of leprosy and socioeconomic, demographic, and environmental factors. The most common unit of analysis was the municipality. The spatial distribution methods mostly used were: empirical Bayesian method, autocorrelation (Moran's I index) and Kernel estimates. The distribution of leprosy was very heterogeneous, independent of t h e unit of analysis. There was a decrease in the rate of detection and among under-15-year-olds, but some regions maintained high endemicity during the study period. The distribution and risk of illness were directly related to living conditions of the population. Improved access to health services was associated with increased detection rate in some regions. Spatial analysis seems to be a very useful tool to study leprosy and to guide interventions and surveillance.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Spatial distribution of leprosy in Brazil: a literature review

Silva

Fonseca

Kawa

et al. 2017

Rev. Soc. Bras. Med. Trop.

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“…This method is useful because it captures spatial effects due to the influences of neighboring towns, rendering the analysis of the existence of a high transmission potential in determined areas possible, as well as the possibility of the under-registration of cases from the deduction of the random fluctuation of cases [12][13][14] .…”

Section: Discussionmentioning

confidence: 99%

Spatial analysis of new cases of leprosy in the State of Espírito Santo, Brazil, between 2004 and 2009

Sampaio

Rossi

Cerutti

et al. 2012

Rev. Soc. Bras. Med. Trop.

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INTRODUCTION: Leprosy in Brazil is a public health issue, and there are many regions in the State of Espírito Santo with high endemic incidence levels of leprosy, characterizing this state as a priority for leprosy programs. The aim of this study was to determine the spatial distribution of coefficients of new cases of leprosy in the State of Espírito Santo, Brazil. METHODS: We conducted a descriptive and ecologic study based on the spatial distribution of leprosy in the State of Espírito Santo between 2004 and 2009. Data were gathered from the available records of the Espírito Santo State Health Secretary. The global and local Bayesian empirical methods were used to produce an estimate of leprosy risk, smoothing the fluctuation effects of the detection coefficients. RESULTS: The study resulted in a coefficient adjustment of new cases in 10 towns that changed their classification, among which, 2 went from low to medium, 4 from medium to high, 3 from high to very high, and 1 from very high to hyper-endemic. An average variation of 1.02, fluctuating between 0 and 12.39 cases/100,000 inhabitants, was found in a comparative calculation between the Local Ebest value and the average coefficient of new leprosy cases in the State of Espírito Santo. CONCLUSIONS: The spatial analysis of leprosy favors the establishment of control strategies with a better cost-benefit relationship since it reveals specific and priority regions, thereby enabling the development of actions that can interfere in the transmission chain.

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