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Lepr Rev (2009) 80, 332– 344 The epidemiological behaviour of leprosy in Brazil MARIA LUCIA FERNANDES PENNA*, MARIA LEIDE VAN DEL REY DE OLIVEIRA** & GERSON OLIVEIRA PENNA*** *Consultant to the Brazilian National Hansen’s Disease Control Program, Secretariat of Health Surveillance, Federal Ministry of Health, Rio de Janeiro, Brazil **Coordinator of the Brazilian National Hansen’s Disease Control Program, Secretariat of Health Surveillance, Federal Ministry of Health, and Adjunct Professor of the Federal University of Rio de Janeiro (UFRJ) Medical School, Rio de Janeiro, Brazil ***Secretary of Health Surveillance, Secretariat of Health Surveillance, Federal Ministry of Health, Adjunct Researcher of the Tropical Medicine Unit, University of Brası́lia, Brasilia, Brazil Accepted for publication 07 September 2009 Summary Background The elimination strategy reduced known leprosy prevalence but the detection rate remains high in many countries, including Brazil. The high Brazilian detection rate imposes a limit to the reduction of known prevalence in the short term. The knowledge of time behaviour and spatial distribution of leprosy statistics will contribute to decision making for leprosy control. Method The numbers of newly diagnosed leprosy cases by region and year from 1980 to 2004, and prevalent cases from 1990 to 2007 were fitted as a parabolic function of time in negative binomial regression models. To detect areas with increased leprosy detection rates we used spatial scan statistics for cases detected from 2005 to 2007 in the three regions where leprosy is still a public health problem. Results All detection rate series except the one for the south region showed statistically significant regression coefficients for time and time squared, showing an initial increasing trend. Scan statistics detected 29 statistically significant spatial clusters. These clusters cover 789 municipalities with a total of 51 904 cases detected. Conclusion Time behaviour of the detection rate is probably a result of better access to primary health care. According to spatial scan statistics, Brazil can be divided into highly endemic areas, containing 11·2% of the total Brazilian population, with a mean detection rate in 2007 of 76·4 per 100,000 inhabitants, and areas of much lower endemicity, containing 88·8% of the population with a mean detection rate of 13·2. Leprosy is concentrated in a small proportion of the Brazilian population. Correspondence to: Maria Lucia F. Penna, Rua Ministro Raul Fernandes 180 ap. 40122260040 Rio de Janeiro, RJ, Brazil (Tel: þ 55 21 92327390; e-mail: [email protected]) 332 0305-7518/09/064053+13 $1.00 q Lepra The epidemiological behaviour of leprosy in Brazil 333 Introduction In 1991, 10 years after the introduction of multi-drug therapy (MDT), the World Health Organization (WHO) proposed at the 44th World Health Assembly (WHA) the elimination of leprosy as a public health problem to be achieved by the year 2000. This target reflected an optimism similar to that which followed the discovery of dapsone in the 1940s. Leprosy is considered eliminated if the known case prevalence is , 1 per 10 000 inhabitants. At the beginning of 2005, the elimination target was reached by most of the high burden countries with the exception of nine countries, including Brazil.1 MDT turned a previously lifelong disease into a curable one, reducing the burden on health systems. The target of elimination of leprosy as a public health problem through universal and efficient MDT assumed the reduction of health care period as well as the reduction in transmission. Unfortunately, after many years of using MDT, there is no evidence of its impact on lowering transmission. Better knowledge of transmission mechanisms is still needed to support a control strategy that may produce an important impact on Mycobacterium leprae transmission.2 – 4 In a scenario where a highly efficacious treatment and government commitment to provide access to drugs are present, once extensive treatment availability is reached, the hidden prevalence cases are mainly responsible for transmission and the known prevalence value variation in time will reflect the detection rate behaviour over time. Hence, reducing transmission presupposes timely case detection to reduce the duration of the disease prior to diagnosis. Timely case detection may raise the known prevalence value in the short term. From that perspective, known point prevalence measures the disease burden to the health system, not the community, at that point in time and its variation reflects mainly operational, not epidemiologic, trends.5 – 6 In Brazil, leprosy has been a nationally notifiable disease for many decades. From 1999 a unique surveillance information system (USIS) was adopted country-wide. Each reported case is included by the municipality health secretariat in its database that is transmitted to the Ministry of Heath. The National Leprosy Control Programme (LCP) recommends treatment with WHO’s MDT and distributes it free of charge. The amount of MDT blister packs needed is estimated based on reported data, which guarantees an approximate relation between cases reported and cases treated. Brazil has 5560 municipalities, 26 states plus a Federal District, an estimated population of 189·6 million inhabitants (2007), an area of 8,514,205 km2, and is divided into five geographic regions based on climatic and historical aspects – North (N), Northeast (NE), Southeast (SE), South (S) and Central-West (CW). In 1989 a health system reform established the basis for a new unified health system (HS), where the municipalities are the level of government responsible for HS administration. The Ministry of Health is responsible for a percentage of financial support of the HS and for setting standards and evaluating different aspects of health care and morbidity. Knowledge of the spatial distribution of leprosy in the country may help resource distribution between municipalities and reinforce the political commitment of municipal authorities and civil society organisations to control leprosy in high-risk areas. Municipalities with a low detection rate that are located in high-risk areas have to intensify case finding and treatment. Analysis of the time behaviour of leprosy data allows for short-term prediction and raises explanatory hypotheses about the past LCP execution and epidemiological behaviour of the disease, 334 M. L. F. Penna et al. contributing to LCP planning. Our objective is to describe and discuss the time behaviour of Brazilian leprosy data, and to detect high-risk areas in three geographic regions – N, NE and CW – where leprosy is still a public health problem. Material and Methods DATA SOURCE A special edition of the National Epidemiological Bulletin,7 published by the Ministry of Health, provided new case data from 1980 to 1998 broken down by state and the federal district. National LCP’s USIS database for 1999 to 2007 was used to produce the number of cases detected by state and municipality. The LCP provided national known prevalence data from 1990 to 2007 and known prevalence data for each state on December 1, 2007. Population data was estimated by the Brazilian Institute of Geography and Statistics (IBGE) based on demographic census, and published by the Ministry of Health.8 The geographic coordinates of the town hall were available from the IBGE website.9 STATISTICAL ANALYSIS The historical data series of numbers of newly diagnosed leprosy cases by region in every year from 1980 to 2007, and of numbers of national prevalence cases from 1990 to 2007 were fitted to parabolic functions of time using negative binomial regression models, having logarithm as a link function and the logarithm of the yearly population as an offset variable, using Stata 9.0 software.10 The choice of negative binomial models instead of Poisson model was due to overdispersion of the count data. The use of this statistical approach to deal with heath surveillance data is well established.11 – 12 A parabolic function of time allows for alterations in the ascending or descending trends. If the regression coefficient for variables time and/or time squared did not show statistical significance at a 0·05 level, the series was fitted to a linear function of time with the same statistical criteria. The fitted curves are represented in the results as rates, although the statistical models had numbers of cases as a dependent variable. The inclusion of the logarithm of the yearly population as an offset variable led to the representation of the models as rates. To detect areas with increased leprosy detection rate in the N, NE and CW regions we used spatial scan statistic,13 using Satscan software.14 The sum of leprosy cases detected from 2005 to 2007 among residents and the resident population, expressed as person-years, of each of the 2709 municipalities from the N, NE and CW regions were spatially allocated at geographical coordinates of the town hall, placed in the biggest town or city of the municipality. This approximation of the actual geographical location of the leprosy cases and population was considered adequate, for these are the points with the highest population density in each municipality. The polygons of municipal borders were used only for presentation of the clusters on the map. The method scans the country area for possible leprosy clusters without a priori specification of their location or size. A circular window moves though the map with its centre at the coordinates of town halls. At each position the radius of the circular window varied from zero to 500 km, the limit set by the authors, with each window including different sets of neighbouring municipalities. A Poisson model defined the presence of spatial clusters. Under the null hypothesis the expected number of The epidemiological behaviour of leprosy in Brazil 335 13 cases in each area is proportional to the population in that area, expressed in person-years, because cases were detected during 3 years. All possible clusters are tested for statistical significance through log likelihood ratio test statistics accounting for multiple testing.15 The log likelihood ratio defines the cluster order. Incidence rates are calculated by 100,000 inhabitants to allow comparison with other infectious diseases and to avoid wide perceptual variation being expressed by small numbers. Prevalence data are calculated by 10 000 inhabitants for the elimination target was set in this dimension, even though it is unusual to report data in different formats. Results Table 1 presents the main leprosy statistics for 2007, for the country and its geographic regions. It should be noted that N, NE and CW regions have high detection rates and known point prevalence above the elimination target. These regions were responsible for 63·5% of all Brazilian leprosy cases detected in 2007 and 73·3% leprosy cases detected from 2005 to 2007, while holding only 43·2% of the Brazilian population. The estimates for the fitted model for detected case series as a function of time by region can be seen in Table 2. The observed and the predicted values are presented in Figure 1. The residual analysis showed proper adjustment of the models. All series but the one done on the S region showed statistically significant regression coefficients for time and time squared, with opposite direction. When fitted to a linear model with a logarithm as a link function, S region data did not show a statistically significant trend. It is important to notice that data from NE region present the biggest increase in detection rates over the period of the series, what is represented by its regression coefficient for time, which is much higher than those for the other regions (Table 2). This region also reaches the maximum point of the fitted curve later than the others, although its detection rate values are smaller than those of the N and CW regions in the same period (Figure 1). Figure 2 shows the known prevalence graphic from 1990 to 2007 and the curve estimated by the statistically significant fitted quadratic model (data not shown). Table 1. Cases in treatment in December, known prevalence, new cases detected, detection rate and Percentage of multibacillary cases among new cases. Geographic Regions and Brazil, 2007 Region North Northeast Southeast South Central-West Brazil Percentage of Cases in treatment Known prevalence per New cases Detection rate per multibacillary cases in December 10 000 inhabitants detected 100,000 inhabitants among new cases 7992 16 471 7209 1461 8416 41 549 5·21 3·16 0·89 0·53 6·23 2·19 8337 16 572 7869 1781 5567 40 126 54·34 31·75 9·76 6·44 41·19 21·19 51·48 51·67 54·64 68·44 58·18 53·86 336 M. L. F. Penna et al. Table 2. Parameters estimated for the negative binomial regression, Brazil and Regions Parameter Coefficient Brasil t 0·071618 20·00145 t2 Constant 29·12559 Alfa* 0·007038 North region t 0·055436 20·00121 t2 Constant 27·88732 Alfa 0·014481 Northeast region t 0·159074 20·00309 t2 Constant 210·01 Alfa 0·006406 Southeast region t 0·031204 20·00097 t2 Constant 29·07372 Alfa 0·009373 South region t 20·02293 0·000523 t2 Constant 29·23146 Alfa 0·040008 Central west region t 0·096484 20·00246 t2 Constant 28·30104 Alfa 0·011545 * Std. Error z P value 95% Conf. Interval 0·008562 0·0003 0·051533 0·001923 8·36 24·82 2177·08 0·000 0·000 0·000 0·054836 20·00203 29·2266 0·004119 0·0884 20·00086 29·02459 0·012024 0·012299 0·000433 0·073759 0·003984 4·51 22·8 2106·93 0·000 0·005 0·000 0·031331 20·00206 28·03189 0·008446 0·079541 20·00036 27·74276 0·024829 0·008257 0·000287 0·05049 0·0018 19·26 210·79 2198·26 0·000 0·000 0·000 0·14289 20·00365 210·109 0·003693 0·175258 20·00253 29·91104 0·011112 0·010054 0·000352 0·060346 0·002577 3·1 22·76 2150·36 0·002 0·006 0·000 0·011498 20·00166 29·19199 0·005469 0·050909 20·00028 28·95544 0·016066 0·020918 0·000729 0·125778 0·010967 21·1 0·72 273·39 0·273 0·473 0·000 20·06393 20·00091 29·47798 0·023378 0·018072 0·001952 28·98493 0·068468 0·010897 0·000382 0·06606 0·003214 8·85 26·45 2125·66 0·000 0·000 0·000 0·075125 20·00321 28·43051 0·006689 0·117842 20·00171 28·17156 0·019924 Alfa ¼ dispersion parameter. Based on new cases detected and reported from 2005 to 2007 in the N, NE and CW regions, scan statistic detected 29 statistically significant clusters. These clusters contain 789 municipalities, 14·2% of all Brazilian municipalities, with 11·2% of the national population in 2007 and had 51 904 cases detected in the period, 42·5% of all Brazilian cases from 2005 to 2007 50·8% of them MB. Table 3 presents some characteristics of each cluster and their positions are shown in the map in Figure 3. It is clear from this map that most extensive total area of the clusters lies in the Brazilian Amazon (BA). The areas in BA correspond to 72·8% of municipalities that fall inside a cluster. The clusters in the Brazilian Amazon reach into the neighbouring dry savannah area in the south and in the east of BA, where there are other clusters. Of the clusters outside of the BA, two along the coastal areas: cluster 7 (Table 3) in the south of the NE region covers a region with a hot, humid climate with remnants of the Atlantic rainforest and cluster 8, with a small area, corresponds to Recife’s metropolitan region, the third largest metropolitan area in Brazil, that attracts poor migrants from dry rural areas of the northeast region. The southernmost cluster, cluster 13, is in a humid climatic area. The epidemiological behaviour of leprosy in Brazil Brazil 30 28 26 24 22 20 18 16 14 12 10 1980 1988 1996 2004 1984 1992 2000 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 Southeast region 18 17 16 15 14 13 12 11 10 9 90 North region 80 40 337 Northeast region 35 30 70 25 60 20 50 15 10 40 5 30 1980 1988 1996 2004 1984 1992 2000 0 1980 1988 1996 2004 1984 1992 2000 South region 14 13 12 11 10 9 8 7 6 5 1980 1988 1996 2004 1984 1992 2000 Central-West region 75 70 65 60 55 50 45 40 35 30 25 1980 1988 1996 2004 1984 1992 2000 Figure 1. Leprosy detection rates per 100,000 inhabitants. Observed (dots and dashed lines) and estimated by the model, Brazil and geographic regions, 1980 to 2007. Discussion The models fitted to the historical series show that the increasing rate of detection has been reduced over time (a positive estimate for t and a negative estimate for t 2). The same happens in the opposite direction with the decreasing series of prevalent cases. The reported detection rate reflects operational variations in time, some known and others unknown. Unplanned operational variation is not stable over time, meaning that it does not vary in the same direction and in the same proportion year after year, therefore it cannot be responsible for a two-decade long trend. With innumerable factors influencing the behaviour of a system in different directions, the resulting variation can best be described through probability theory16 which allows the interpretation of fitted curves as the actual trends. A hypothesis to explain time behaviour of the detection rate in Brazil and its regions, with exception of S region, is the health system reform implemented in the last 20 years. As a result of the reform, access to primary health care units has improved mainly in rural areas and small towns, improving the diagnosis of leprosy.17 This fact by itself could explain the ascendant tendency of the leprosy detection rate in the first part of the series. If this hypothesis is true, we would have a persistent reduction of the hidden prevalence produced by a shorter timeframe between the onset of symptoms and diagnosis. However, the decline of the hidden prevalence has its limits because, even in ideal situations, the diagnosis of skin diseases depends on the cultural importance given to skin lesions, as well as health-seeking habits among the population. An example of this is the period of time a patient waits to self-present for a cutaneous lesion or any other clinical sign that is not interpreted as threatening. Once the limit in reduction of hidden prevalence is met within the current health system, the new case 338 M. L. F. Penna et al. 22 20 18 16 14 12 10 8 6 4 2 0 1990 1992 1994 1996 1998 2000 2002 2004 2006 Figure 2. Leprosy known prevalence per 10 000 inhabitants. Observed (dots and dashed lines) and estimated by the model, Brazil, 1990 a 2007. detection rate tends to reflect the real incidence of the disease. The highest point of the fitted curves possibly reflects the moment when the access to diagnosis stopped increasing. The South region is a region that already had good public health care coverage in the early 1980s and it includes two states where leprosy is rare: Santa Catarina and Rio Grande do Sul have detection rates of 3·6 and 1·7 per 100,000 inhabitants, respectively. In the present paper we outline areas of high detection in the Brazilian geographic regions (N, NE and CW) where the elimination target has not been reached. In our first efforts to detect high-risk clusters for the entire country, the cluster size limit was the inclusion of 50% of the population at risk, default setting of SatScan software. The resulting most probable cluster had a radius of 1825·8 km and included the entire N and CW regions, in addition to part of the NE region. Of course such a cluster reveals less about the spatial distribution of leprosy than traditional analysis by state and region, but points to the importance of these three regions relative to the S and SE regions. In a previous spatial distribution study for the same period18 clusters were defined for the whole of the country. The result was 10 clusters including 53·5% of all leprosy cases detected cases in Brazil, 91·3% of them in N, NE and CW regions. The cases outside these regions were part of a cluster that overlap current cluster 7 and included municipalities in the SE region in addition to those from the NE region. While the definition of a cluster is relatively easy, the same is not true for the definition of its outer limits. Kulldorff11 advises that clusters be made as small as possible, for a high-risk cluster may sustain the statistical significance when low risk neighbourhoods are included in the cluster. The epidemiological behaviour of leprosy in Brazil 339 Table 3. Statistically significant clusters of leprosy defined using spatial scan statistics. North, Northeast and CentralWest regions, 2005–2007 Cluster Cluster central municipality, state 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 Lajeado Novo, MA Sta Rita Trivelato, MT N. Brasilândia Oeste AC Porangatu, GO Cariús, CE Queimada Nova, PI Porto Seguro, BA Olinda, PE Barreiras, BA Itaituba, PA Cajazeiras, PB Edéia, GO Naviraı́, MS Sena Madureira, AC Caroebe, RR Glória, BA Mossoró, RN Umirim, CE Pinhão, SE Iaçu, BA Sobral, CE Conceição, PB Morpará, BA Itabaiana, SE Bom Jesus da Lapa, BA Paramoti,CE Catolé do Rocha, PB Serra do Navio, AP Araci, BA Radius (km) no. mun* 486·71 482·89 263·94 213·03 29·48 136·81 82·12 5·76 0 0 0 92·79 0 469·57 240·95 8·57 0 0 0 0 0 0 69·87 9·14 19·56 30·22 0 467·94 0 344 134 47 61 3 39 9 2 1 1 1 35 1 31 9 2 1 1 1 1 1 1 4 2 2 5 1 49 1 LLR* 7444·6260 3321·7336 932·4814 773·8566 390·1331 294·2229 273·1821 269·9043 264·2829 185·3388 161·5285 132·4206 127·1899 87·6791 67·7714 57·1981 54·3137 46·1540 41·6441 34·0813 28·5916 22·4099 21·9199 21·2532 18·5710 15·5460 14·6462 12·9477 12·3394 P value Relative risk 0·001 0·001 0·001 0·001 0·001 0·001 0·001 0·001 0·001 0·001 0·001 0·001 0·001 0·001 0·001 0·001 0·001 0·001 0·001 0·001 0·001 0·001 0·001 0·001 0·001 0·002 0·002 0·003 0·004 2·85 2·89 2·48 2·78 3·98 1·87 2·24 1·55 3·33 3·33 3·94 1·58 4·11 1·45 1·65 2·04 1·71 3·69 6·24 2·7 1·58 2·86 1·71 1·66 1·74 1·5 2·1 1·11 1·73 * no. mun ¼ number of municipalities in cluster; LLR ¼ log likelihood ratio; relative risk for the clusters compared to the rest of N, NE and CW regions. Working within highly endemic regions makes the null hypothesis value higher than working with the whole country. This allows greater specificity for the cluster limits resulting in smaller clusters with higher risk. In the previous study, current clusters 2 and 4 (Figure 3) were included in a single cluster; this also happened with clusters 9, 23 and 25, clusters 5, 6, 11, 22 and 16, and clusters 28 and 10. It is worth noting that the two most probable clusters overlap in both analyses (clusters 1 and 2 had a larger radius in the analysis including the whole country, 493·4 and 499·2 km respectively, compared to 486·7 and 482·9 km in this analysis). The intention to locate more accurately the high-risk areas justifies the present strategy, while the previous result with fewer and more extensive clusters that encompassed two or more of the present ones, made it easier for the LCP to establish, for the first time, priorities based on areas of higher risk. Some years ago the municipalities which held higher priority for the national LCP were defined based on the number of detected cases. Listing Brazilian municipalities by population, the 250 Brazilian municipalities with the highest population have 42·5% of leprosy cases detected from 2005 to 2007, the same case proportion of those 340 M. L. F. Penna et al. N W E S 15 28 5 21 1 10 14 6 3 2 4 12 9 23 e 25 23 26 17 27 11 8 22 16 24 28 29 20 7 13 2000 0 2000 4000 Kilometers Figure 3. Brazil in South America Map with high detection clusters* (dark grey areas) of North, Northeast and Central-West regions (light grey areas) and states limits of the Brazilian Amazon (black line). *clusters identified by rank as in Table 3. included in high-risk clusters comprising 789 municipalities described in this paper. The choice to establish national priorities areas based on the number of cases detected, and not on disease risk, is more attractive given that it involves fewer political players and more powerful municipalities, such as those within the metropolitan areas. On the other hand, establishing priorities based on areas with higher risk should produce better results because it will involve neighbouring municipalities. This should create a larger epidemiological impact and probably more political commitment from local authorities, since infectious diseases can propagate into regions independent of geographic limits or political unities. High-risk areas are, in theory, more vulnerable to leprosy control actions than low-risk areas, considering the impact on transmission. What drew our attention in the results was the geographical contiguity of the big clusters in BA, which strongly suggested the existence of high-risk geographical areas for leprosy. Therefore, establishing priority areas based on the total number of leprosy cases or based on risk, even though the areas may have the same number of cases, are parts of different strategies to reach leprosy control. According to spatial scan statistics, Brazil can be divided into highly endemic areas, containing 11·2% of the Brazilian population, with a mean detection rate in 2007 of 76·4 per 100,000 inhabitants, and an extensive, less endemic area, containing 88·8% of the population with a mean detection rate of 13·2, rate ratio of 5·8. The highly-endemic clusters cover a wide geographical area, albeit sparsely populated. This suggests a high concentration of leprosy cases in certain parts of Brazil. The epidemiological behaviour of leprosy in Brazil 341 For comparison, the highest national detection rate reported to the World Health Organization in 2006 was from Micronesia with a rate of 136·04 and Papua New Guinea with 63·95 per 100,000 inhabitants.19 It should be highlighted that the total Papua New Guinean population was 602,000 in 2006 and the Micronesian 111,000, while the Brazilian population in the cluster areas was 21,185,385 in 2007. Most of the total area of the clusters is in the Brazilian Amazon that has been recognised as a highly endemic leprosy area, for some time. In the 19th Century there were reports about the serious epidemiological situation among native peoples in the state of Pará.20 In 1913, Oswaldo Cruz, then head of the Brazilian Public Health Division, recognised the unusually high frequency of leprosy in the Amazon River basin.21 Agrı́cola22 in 1973 pointed that the state of Acre, situated in the western part of the Brazilian Amazon, had the worst epidemiological conditions and that Brazilian northeast states, with a semiarid climate, had the lowest known prevalence rate. Nowadays, states within the Brazilian Amazon still report the highest detection rate, but the Northeast region was replaced by South region as the one with the least leprosy. It is most likely that leprosy was introduced into the Amazon in the 18th Century, when colonisation settlements started an epidemic among the indigenous population due to their lack of previous exposure to infection.23 The disease is likely to have spread slowly throughout the area due to its relative isolation and expansive distances. Until 1970, the most heavily populated areas of the Amazon could have been described as population islands because the only possible means of transportation were riverboats or small aircraft. For this reason, the epidemiological behaviour of leprosy in the Amazon was similar to that seen in the Pacific islands where similar detection rates were observed. But unlike the Pacific Islands, the Amazon is a large region which has seen increased population and road communication with other parts of Brazil and neighbouring countries over the last three decades. The first highway to cut through the Amazon rainforest with a distance of 2039 km was completed in 1974. Today, there are 25 900 km of federal highways in the Brazilian Amazon.24 Development projects and small-scale strip mining, mostly for gold, brought large numbers of people to the region. Over the years between 1960 and 1980, the population contingent of the Amazon increased from 2·6 million to 11 million. Despite the fact that the population increased largely due to an influx of migrants from less endemic regions, leprosy remains highly endemic and is now concentrated in the areas of greatest population. Clusters of high leprosy detection in the Amazon overlap areas of mining and agrarian development projects. Although agricultural development led to the relatively stable settlements of new groups in the area, the small-scale mining was undertaken by an extremely mobile population. These strip miners would return seasonally to their home regions while others would move from one mine to another.25 On the other hand, individuals involved in unsuccessful land settlement projects often sell their plots and move on to a new agrarian development project.26 Families successful in land cultivation periodically return to their city of origin as a sign of social success.27 Certainly, this movement has led to imported leprosy cases in other regions and raises the question of whether leprosy might re-emerge in other parts of the country. It is important to note that the NE region clusters are related to areas where there are currently many agrarian development projects, and that this is the region with the highest increase of detection since 1980. The spatial distribution of leprosy in Brazil shows that 88·8% of the Brazilian population lives in areas where the mean leprosy detection rate in 2007 was 13·16 per 100,000 342 M. L. F. Penna et al. inhabitants (parts of the country outside the high-risk cluster areas) and 11·20% of the population lives in areas with a leprosy detection rate in 2007 of 76·4 per 100,000 inhabitants, i.e., highly endemic areas. To deepen our knowledge on the causes of this spatial behaviour, the authors are at present analysing these high-risk areas with social determinants of health, including the study of the distribution of traditional social and economic indicators, such as literacy, basic sanitation, income, employment, and recent economic development and ecosystem modifications. In the cluster areas, the proportion of multibacillary (MB) cases among the total reported was 50·8%, which means that the lowest mean duration of leprosy treatment in these areas is 0·75 of a year, with the assumption that there is no delay in the treatment timetable and that all patients will be cured. This treatment duration and a detection rate of 76·4 per 100,000 inhabitants (7·64 per 10 000 population) means that the lowest point prevalence possible is 5·73 per 10 000 inhabitants. In other words, if the present proportion of MB cases remains stable in these high-risk areas, detection rate must be lower than 13·3 per 100,000 inhabitants (1·33 per 10 000 population) to reach the target of 1 per 10 000 inhabitants known point prevalence, meaning a detection rate reduction of 82·6%. Surely such a reduction cannot happen quickly. The actual frequency of leprosy in these highly endemic areas in Brazil reinforces the need of a permanent effort to control the disease. Brazil must continue to invest in leprosy diagnosis and treatment, mainly in these areas, even though its impact on transmission is still unknown. This strategy clearly reduces the sources of infection but it is not known if it will happen soon enough to prevent the majority of secondary infections.28 – 29 Besides the investments in the development of new strategies, the partners of leprosy control in government and civil society should bear in mind that socioeconomic development has a known impact on leprosy’s epidemiological behaviour.30 Evidence about the social determinants of leprosy may support public policies that affect the social environment and economic development. The changes on leprosy magnitude in those areas will probably be apparent only after many years of work, but it is important to monitor its behaviour. Improvement of the health information system is an important task to avoid unreliable data.31 Among leprosy statistics, the prevalence of people undergoing treatment has been used for many decades, but it does not have an obvious interpretation, as it is the approximate product of treatment duration and detection rate, whose values can vary simultaneously. Detection rate, the MB proportion among new cases and effectiveness of leprosy treatment variations will affect the known prevalence value. While most health workers dealing with leprosy control are familiar with this frequency measure, its increase or stability does not clearly point to what should be changed to achieve a better performance. The follow up of cohort treatment results, on the other hand, is a powerful tool to increase the involvement of health workers responsible for patient care as part of their work evaluations. A much simpler frequency measure is the detection rate, although, as already pointed out, it reflects the operational variations of access to diagnosis. It is easier to interpret a value that is influenced by an action – diagnosis – than one that is influenced by two – diagnosis and treatment. In conclusion, the present findings show that leprosy is an important heath problem for 11·2% of the Brazilian population, requiring patient care to prevent disabilities and permanent control activities to break the chain of transmission. Health interventions will be effective only if they involve local authorities, communities and health workers and are planned based on the local reality and scientific knowledge. Investment in The epidemiological behaviour of leprosy in Brazil 343 national research is also essential since all national recommendations must be based on solid scientific evidence. 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