01976nas a2200265   4500000000100000008004100001653001200042653002100054653002400075653002300099653002500122653001100147100001400158700001500172700001500187700001200202700001400214700001700228245011500245856007700360300001300437490000700450520123900457022001401696       2018                            d10aleprosy10aSpatial analysis10aInformation Systems10aEcological studies10aSpatial distribution10aBrazil1 aSouza CDF1 aSantos FGB1 aMarques CS1 aLeal TC1 aPaiva JPS1 aAraújo EMCF00aSpatial study of leprosy in Bahia, Brazil, 2001-2012: an approach based on the local empirical Bayesian model.  uhttp://www.scielo.br/pdf/ress/v27n4/en_2237-9622-ress-27-04-e2017479.pdf  ae20174790 v273 a<p><strong>OBJECTIVE: </strong>to compare the temporal evolution and spatial distribution of epidemiological indicators of leprosy, both crude and also corrected using the empirical Bayesian model, Bahia, Brazil, 2001-2012.</p>

<p><strong>METHODS: </strong>this was an ecological study using data from the Notifiable Diseases Information System; all 417 municipalities in Bahia were included and the following indicators per 100,000 inhabitants were analyzed - detection rate of new cases in the general population, in those &lt;15 years old, and in those with grade 2 physical disability -; the local empirical Bayesian model was used to smoothen the indicators, and Student's t-test was used to compare means.</p>

<p><strong>RESULTS: </strong>indicators estimated by the model were higher than crude indicators; estimated detection rates in the general population and in those &lt;15 years old were higher than crude rates in 253 (60.7%) and 209 (50.1%) municipalities, respectively; areas of greatest risk were concentrated in the northwestern and southern regions of the state.</p>

<p><strong>CONCLUSION: </strong>spatial distribution of the disease was heterogeneous and there was possible underreporting of cases.</p>
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