@article{23005, keywords = {Bayes Theorem, Brazil, Humans, Incidence, leprosy, Space-Time Clustering}, author = {Sampaio PB and Rossi TL and Cerutti Junior C and Zandonade E}, title = {Spatial analysis of new cases of leprosy in the State of Esp{\'\i}rito Santo, Brazil, between 2004 and 2009.}, abstract = {

INTRODUCTION: Leprosy in Brazil is a public health issue, and there are many regions in the State of Esp{\'\i}rito 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 Esp{\'\i}rito Santo, Brazil.

METHODS: We conducted a descriptive and ecologic study based on the spatial distribution of leprosy in the State of Esp{\'\i}rito Santo between 2004 and 2009. Data were gathered from the available records of the Esp{\'\i}rito 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 Esp{\'\i}rito 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.

}, year = {2012}, journal = {Revista da Sociedade Brasileira de Medicina Tropical}, volume = {45}, pages = {380-4}, month = {2012 Jun}, publisher = {Scielo}, address = {Uberaba}, issn = {1678-9849}, url = {http://www.scielo.br/pdf/rsbmt/v45n3/a19v45n3.pdf}, doi = {10.1590/s0037-86822012000300019}, language = {eng}, }