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Campos AA U20 2. FC Milsami 3. All Rights Reserved. Photographs are copyright by law. If you wish to use or buy a photograph contact the photographer directly. If you have any questions about advertising or editorial then please contact our team. If you encounter any technical issues then please email techsupport sail-world. The main objective of this study was to describe the trends and spatial distribution of leprosy in the state of Bahia, Brazil, between and This was a mixed ecological study, since it brought together temporal and spatial dynamics.
Its backdrop was the state of Bahia and its municipalities. We chose a time series of 15 years comprising the period Bahia is the largest state in Northeast Brazil and the fifth largest in Brazil in terms of its territorial extent, accounting for We selected ten indicators for analysis in order to i monitor and evaluate the magnitude of the problem of leprosy in Brazil and ii evaluate the quality of services provided to patients, all of which are recommended by the Ministry of Health and provided for by Ministerial Ordinance No.
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Figure 1. Thumbnail Figure 1 Monitoring and evaluation indicators for leprosy magnitude, and evaluation indicators for the quality of services provided to leprosy patients in Brazil. The methodology used to calculate the indicator for each year of the series followed the recommendations of Ordinance No. Data treatment was done in two stages. The first consisted of analyzing the trends of the ten indicators selected, using the joinpoint regression model, i. This model tests whether a line of multiple segments is statistically better for describing the temporal evolution of data when compared to a straight line or a line with fewer segments.
Permutation tests for joinpoint regression with applications to cancer rates. Stat Med [Internet]. We used Joinpoint software version 4. The second stage consisted of spatial analysis of three indicators overall detection coefficient, detection for those aged under 15 and coefficient for grade II physical disability to detect clusters of municipalities with high risk of leprosy transmission. To this end we applied spatial scan statistics using the Poisson discrete probability model.
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This model enables not only identification of spatial clusters but also calculation of the relative risk of each of them. Kulldorff MA. Spatial scan statistic. Commun Stat Theory Methods [Internet]. The test used to identify clusters is based on the maximum likelihood method, the alternative hypothesis of which is that there is high risk inside the window compared to outside the window. Monte Carlo simulations we used permutations were used to obtain the p-values. The analysis was conducted based on SatScan version 9. The thematic maps were prepared using QGIS 2.
The prevalence coefficient enabled mean endemicity to be classified for the state of Bahia throughout the entire period studied Table 1. Thumbnail Table 1 - Epidemiological indicators for monitoring the process of leprosy elimination and evaluation of the quality of services provided to leprosy patients, Bahia, With regard to the leprosy incidence coefficient for the general population, endemicity was classified as very high for the period and high in the remaining years.
When comparing incidence in and in , the rates are very similar In relation to the leprosy detection coefficient in those under 15 years old, in , , and , endemicity was classified as high in this population. In the remaining years, endemicity in the state was classified as very high Table 1. Notwithstanding, it must be highlighted that the detection coefficient at the beginning of the series was 4.
Tables 1 and 2. Analysis of the coefficient for new cases with grade II disability showed little variation over the years, being in the region of 0. Whereas in the year the coefficient was 0. Table 1. When comparing the indicator at the beginning and the end of the series, we found growth from 4. Stationary patterns were found in the trend analysis, although there was greater variation in the indicator Table 2.
The proportion of females with leprosy varied little over the time series. At the beginning and end of the series, the proportions found were The proportion of cured cases varied little between the beginning and the end of the time series, altering from Between the years and and again between and , this indicator was considered to be precarious.
The proportion of leprosy treatment dropout reduced substantially over the period studied. Whereas at the beginning of the series treatment dropout was 9. Throughout the entire time series this indicator was classified as good Table 1. Taking the period as a whole, AAPC was Finally, the proportion of contacts examined was the most irregular indicator over the period studied. In , the proportion of contacts examined was This indicator was considered precarious in all the years of the series. Spatial scan statistics for the general population detection coefficient identified 15 statistically significant spatial clusters Table 3 , ten of which were considered to be hyperendemic, while five had very high endemicity.
Together, these two clusters accounted for It is noteworthy that they are small municipalities, with the exception of Juazeiro, where the population was just over , inhabitants in Figure 2. Thumbnail Figure 2 - Spatial scan statistics of epidemiological leprosy monitoring indicators in Bahia, With regard to the detection coefficient for those under 15 years old, we identified 11 spatial clusters Table 3.