Análise espacial da ocorrência de leishmaniose visceral no estado do Tocantins, Brasil

Abstract

O Brasil e outros quatro países detêm 90% dos casos de leishmaniose visceral, que é uma doença grave que acarreta óbito, se não tratada. Este estudo teve por objetivo identificar padrões espaciais de distribuição da leishmaniose visceral no estado do Tocantins, Brasil, de 2008 a 2011. Trata-se de estudo ecológico e exploratório com dados obtidos do Datasus e realizada análise por município. Foram estimados os índices de Moran global e construídos mapas temáticos das taxas por 100 mil habitantes, mapa de Moran e de Kernel. Foram georreferenciados 1778 casos de leishmaniose visceral representando uma taxa de 31,75 casos/100 mil habitantes sendo a taxa variou entre 0,00 e 343,16 por 100.000 habitantes com a microrregião de Araguaína, no norte do estado, a mais atingida. O índice de Moran foi Im = 0,20 (p-valor < 0,01). Constatou-se que as microrregiões com maior necessidade de intervenção são as de Araguaína e do Bico do Papagaio, onde foi identificada maior densidade de casos notificados por local de residência, assim como os municípios vizinhos a Juarina, pertencentes à microrregião de Miracema do Tocantins.

Socio-environmental factors and diarrheal diseases in under five-year old children in the state of Tocantins, Brazil

“…Its territory covers 277,620 km 2 . Fig 1 shows the map of Tocantins divided into microregions and the counties numbered in a growing sequence from North to South, and its location in Brazil [ 23 , 25 ].…”

“…The Global Moran’s index and Local Moran’s index—LISA were used to evaluate spatial correlation and local autocorrelation, allowing the identification of subregions with spatial dependence. A first-order neighborhood criterion was used to make estimates, where neighbor counties were defined as those bordering each other [ 23 , 25 ]. The Global Moran’s index varies between -1 and 1; values nearing zero indicate no correlation, and values nearing 1 represent positive spatial dependence with greater similarity between neighboring counties (clustering) and negative spatial dependence is indicated as -1, indicating dissimilarity (dispersion).…”

“…Maps were constructed based on rates (number of cases/population x 100,000 inhabitants: data on diarrhea in under 5-year-old children, variables related to garbage "dumped on vacant areas and/or in the home environment" and counties "lacking sanitary installations" [ 25 , 31 ]. Results were defined in quantiles, which was the format that best represented the data, using the following intervals: > 0.0–0.1 (absence of cases or insignificant rate), > 0.1–5.0 (low rate), > 5.0–10.0 (intermediate rate), > 10.0 (high rate) ( Fig 3 ).…”

“…Data for preparation of the Moran Map are only generated if any level of significance at the interface (> 95%) is detected, indicating priority locations for intervention [ 30 ]. The criterion was: "0"—white (not significant = NS); 1- red (Q1—high-high = high priority for intervention), positive values and negative means, with significance < 0.05; and 2—dark gray (Q2—low-low = low priority), negative rates and negative means—counties with positive spatial association or similar to neighbors; 3—medium gray (Q3—high-low = intermediate priority), positive values and negative means and 4—light gray (Q4—low-high = intermediate priority), negative values and negative means are considered counties with negative spatial association, that is, low and high rates of diarrhea, respectively [ 23 , 25 ]. It can be said that "1" and "2" represent areas of agreement and "3" and "4" areas of transition [ 23 , 25 ].…”

“…The use of spatial analysis and Geographic Information System (GIS) tools allows the creation of maps that help in the analysis and better understanding of spatial patterns of epidemiological data distribution, making it possible to detect areas of greater risk, as well as associated factors to indicate points with greater and smaller need for intensification and/or prioritization of control measures [ 20 , 22 – 25 ]. Although geographic distribution models have been widely applied to analyze the spatial distribution of other diseases such as visceral leishmaniasis [ 20 , 23 , 25 , 26 ], dengue [ 22 ], leprosy [ 27 ], neonatal mortality [ 24 ] and malaria [ 28 ], they have been little used in the context of diarrheal diseases [ 1 ].…”