Time trend and identification of risk areas for physical disability due to leprosy in Brazil: An ecological study, 2001-2022

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This study analysed the temporal trend in the rate of new leprosy cases with grade 2 physical disability in Brazil and identified spatial clusters of risk for physical disability. METHODS This is an ecological study of all new cases of leprosy with grade 2 physical disability (G2D) at diagnosis between 2001 and 2022 in Brazil, macro-regions, states and municipalities. Data were extracted from the National System of Notifiable Diseases and the Brazilian Institute of Geography and Statistics. Sociodemographic and clinical variables such as age, sex, race/colour, education and clinical form of the disease were analysed, as well as the rate of new cases of leprosy with grade 2 physical disability at the time of diagnosis per million inhabitants. Data analysis was divided into three stages: 1- Descriptive analysis of the socio-demographic and clinical profile; 2- Time series analysis: In this stage, the joint-point regression model was used; 3- Spatial analysis, using global and local Moran statistics. RESULTS There were 50,466 new cases of leprosy with G2D during the period studied, 97.4% aged > 14 years, 70.2% male, 45.7% brown, 87.6% with low education, 90.2% multibacillary and 47.5% dimorphic clinical form. There was a downward trend in the detection of leprosy with physical disability grade 2, with an average annual reduction of 2.8% (13.6/1 million in 2001 and 9.1/1 million in 2022). The southeast, south and north regions showed a downward trend. A total of 14 states and 4 capitals showed a decrease in the rate, the most significant being Roraima (-11.0%/year). Tocantins was the only state with an upward trend (3.2%/year). 635 (14.1%) municipalities were in the high-high quadrant. These municipalities belong to 12 Brazilian states and accounted for 30% (n = 15,139) of all cases recorded in the country over the 22 years analysed. CONCLUSIONS These results suggest an overall decrease in the detection of leprosy with physical disability in Brazil and highlight geographical disparities in the occurrence of leprosy disability. Interventions to promote early diagnosis should be targeted at areas with the highest rate of new cases with grade 2 disability. Leprosy Physical disability grade 2 Clusters of risk areas Ecological study Analysed the temporal trend Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Leprosy is a chronic infectious dermato-neurological disease caused by Mycobacterium leprae . It is an acid-fast bacillus with an affinity for skin cells and peripheral nerves. The pathophysiology and immune response to neural invasion by M. leprae can result in partial or total damage to nerve trunks, affecting autonomic, sensory and motor fibres innervating structures in the dermis, epidermis and muscles, leading to functional changes and physical disability [ 1 – 6 ]. In 2022, 174,000 new cases of leprosy were reported worldwide. Brazil remains the world's second most affected country, with 19,000 new cases in 2022, accounting for 92% of the new cases diagnosed in the Americas, making it a World Health Organization priority country for leprosy [ 7 ]. Worldwide, 9,554 new cases with grade 2 physical disability (G2D) were detected, which corresponds to a rate of 1.2 cases per million population. In Brazil, the proportion of new cases with G2D among those assessed at diagnosis increased from 7.1% in 2011 to 11.5% in 2022, an increase of 62.0% [ 8 ]. Despite the significant reduction in the global prevalence of leprosy in recent decades, mainly due to the introduction of multidrug therapy in the early 1980s and advances in scientific research, leprosy remains an important public health problem, especially in places exposed to a context of social vulnerability [ 9 – 11 ]. In addition, leprosy belongs to the group of neglected tropical diseases [ 12 – 14 ]. In 2021, WHO launched the Global Leprosy Strategy 2021–2030 - "Towards Zero Leprosy". The new strategy focuses on interrupting transmission and achieving zero autochthonous cases of leprosy. One of the pillars of the strategy is the need to treat leprosy and its complications and to prevent new disabilities. To achieve this goal, research is needed, particularly on the time trends of physical disability indicators and the identification of risk areas [ 15 ]. Nerve impairment is a critical aspect of leprosy, as it is closely related to the disabling potential of the disease. Currently, physical disability (PD) is graded on a scale of 0 to 2, with 0 indicating no PD, grade 1 indicating sensory loss, and grade 2 indicating the presence of permanent PD in the eyes, hands and feet, such as lagophthalmos, trichiasis, clawing of hands and feet, plantar perforation, bone resorption, and others [ 16 , 17 ]. It is estimated that between 3 and 4 million people may be living with visible physical disabilities due to leprosy [ 15 ]. These disabilities contribute to social stigma, reduced social participation, increased vulnerability, greater risk of mental disorders, and social and economic losses for the individual and their family. In addition, the indicator rate of new cases of Grade 2 physical disability per million population has been recommended by WHO for regional monitoring of the risk of physical disability in the population [ 18 – 24 ]. Based on the above, this study analysed the temporal trend in the rate of new cases of leprosy with grade 2 physical disability in Brazil and identified spatial clusters of risk for physical disability. Methods Study design, population and period This is an ecological study involving all new cases of leprosy with grade 2 physical disability at the time of diagnosis between 2001 and 2022. Study area The study area is Brazil, its macro-regions, states and municipalities. Brazil currently consists of five macro-regions (North, Northeast, South, Southeast and Centre-West), 27 federal entities (including the Federal District) and 5,568 municipalities. The size of Brazil presents a challenge to any scientific study because of the regional differences in economic, demographic, political and environmental characteristics. Brazil has a land area of approximately 8.5 million square kilometers, making it the fifth largest country in the world in terms of area, and a resident population of approximately 203 million people, with a population density of 23.86 inhabitants per square kilometer [ 25 ]. Data source The data were extracted from the National System of Notifiable Diseases (SINAN), available in the database of the Department of Informatics of the Unified Health System (DATASUS) ( http://datasus.saude.gov.br/ ). SINAN collects records of leprosy and other notifiable diseases throughout the country. The population data needed to calculate the indicators are obtained from the Brazilian Institute of Geography and Statistics (IBGE). The following criteria were used to extract the data Year of diagnosis (2001 to 2022); discharge (unfulfilled, cure, transfer to the same municipality, transfer to another municipality, transfer to another state, transfer to another country, death, abandonment), type of entry (new case), assessment of physical disability at the time of notification (grade 2). Study variables Two sets of variables were included. The first group consisted of socio-demographic and clinical variables: age, sex, race/colour, education, operational classification and clinical form of the disease. The second group consisted of an epidemiological indicator - the rate of new leprosy cases with grade 2 physical disability at the time of diagnosis per 1 million inhabitants. This indicator allows the disability caused by leprosy to be assessed in the general population. For the calculation, the numerator is the number of new cases with grade 2 physical disability at diagnosis, living in a particular place and diagnosed in the year of assessment, and the denominator is the population living in the same place and during the same period. In addition, there is no established assessment parameter. However, for the purposes of analysis, we have arbitrarily adopted the following classification 10 per 1 million inhabitants. Study Steps Step 1 - Descriptive analysis of socio-demographic and clinical profile: In this stage, the socio-demographic and clinical variables were described in absolute value and relative frequency (%). Step 2 - Time series analysis: In this stage, the inflection point regression model was used to determine the time trend in the rate of new leprosy cases with grade 2 physical disability at the time of diagnosis per million population. The inflection point regression model was used. This model examines whether a line with multiple segments is statistically better at describing the time trend of a data set than a straight line or a line with fewer segments [ 26 ]. The joinpoint regression model for the observations: (𝑥1, 𝑦1),..., (𝑥𝑛, 𝑦𝑛), where 𝑥1 ≤ ... ≤ 𝑥𝑛 represents the time variable, and 𝑦𝑖, 𝑖 = 1, 2,..., n is the response variable, being given by: $$\sum {y}_{i}\left|{x}_{i}\right]= {\beta }_{0}+{\beta }_{1}{x}_{1}+{\gamma }_{1}\left({x}_{i}-{\tau }_{1}\right)+\dots +{\gamma }_{n}\left({x}_{i}-{\tau }_{n}\right)$$ Where β0, β1, y1,..., γn are regression coefficients and yk, K = 1, 2,…, n, n 0$$ = 0, otherwise. In this way, the model makes it possible to check the trend of the indicator (whether it is stationary, increasing or decreasing) and the points at which this trend changes (joins), which makes it possible to identify the annual percentage change (APC) and that of the whole period (average annual percentage change - AAPC). The following parameters were used in the analyses: minimum of 0 joins and maximum of 4; model selection: test with 4,499 permutations; 5% significance; 95% confidence interval and autocorrelation of errors based on the data. Analyses were performed in the Joinpoint regression program, version 4.6.0.1 (National Cancer Institute, Bethesda, MD, USA). The analysis was performed in four spatial units: Brazil, five regions, 27 states and their respective capitals. Step 3- Spatial analysis: Global and local Moran's statistics (Moran's Index and p-value) were used to analyse the spatial dynamics. The global Moran's index (MI) measures the degree of autocorrelation in a given set, based on the product of the deviations from the global mean. The MI values can oscillate within a spectrum between − 1 and + 1, with values close to -1 indicating negative spatial autocorrelation, values close to + 1 indicating positive spatial autocorrelation and values close to zero indicating no autocorrelation. The pseudo significance test is used to determine whether or not the null hypothesis is rejected (H0: spatial homogeneity; H1: spatial heterogeneity) [ 27 , 28 ]. Global Moran's Index (Global I) is given by the equation: $$I=\frac{\sum _{i=1}^{n}\sum _{j=1}^{n}{w}_{ij}\left({z}_{i}-\stackrel{-}{z}\right)\left({z}_{j}-\stackrel{-}{z}\right)}{\sum _{i=1}^{n}\left({z}_{i}-\stackrel{-}{z}\right)²}$$ where n is the number of areas, zi is the value of the attribute considered in area i, z is the average value of the attribute in the study region and wij, the elements of the normalised spatial proximity matrix. Once global autocorrelation (spatial heterogeneity) has been confirmed, we move to the second stage, which is characterized by the use of the Local Indicator of Spatial Association (LISA). LISA aims to quantify the degree of spatial association to which each location in the sample set is subject as a function of a neighbourhood model. The result is the local pattern of the spatial distribution of the variable under analysis [ 27 ]. In this case, the following equation is used: $${I}_{i}=\frac{{z}_{i}\sum _{j=1}^{n}{w}_{ij}{z}_{j}}{\sum _{j=1}^{n}{z}_{j}^{2}}$$ where n is the number of areas, zi is the value of the attribute considered in area i, z is the average value of the attribute in the study region and wij are the elements of the normalised spatial proximity matrix. While Q1 (positive values, positive averages) and Q2 (negative values, negative averages) indicate points of positive spatial association, in the sense that a location has neighbors with similar values; Q3 (positive values, negative averages) and Q4 (negative values, positive averages) indicate points of negative spatial association, in the sense that a location has neighbors with different values [ 27 ]. Municipalities located in Q1 are considered priorities for intervention. Ethical aspects By using secondary data in the public domain, the study does not need to be evaluated by the Research Ethics Committee, in accordance with Resolution 510/2016 CEP/CONEP. Results Between 2001 and 2022, 50,466 new cases of leprosy with G2D were reported in Brazil, an average of 2,293.9 cases/year. In terms of demographics, 97.4% (n = 49,139) of the cases analyzed were individuals older than 14 years, 70.2% (n = 35,428) of the population studied was male. Blacks (brown and black) represented 45.7 percent (n = 23,039) and 12.9 percent (n = 6,505), respectively. In addition, 87.6 percent (n = 44,197) had little schooling (≤ eight years of schooling). The clinical profile of the population was characterized by the multibacillary operational classification (90.2%; n = 45,515), with the dimorphic clinical form accounting for 47.5% (n = 23,982) ( Table 1 ) . Table 1 Epidemiologic characterization of leprosy cases with grade 2 physical disability. Brazil, 2001–2022. Variable Population (n = 50,466) Age* % (n) ≤ 14 years 2.6 (1,316) > 14 years 97.4 (49,139) Sex** Male 70.2 (35,428) Female 29.8 (15,030) Race/color Ignored/Blank 10.3 (5,218) White 29.6 (14,957) Black 12.9 (6,505) Yellow 1.1 (528) Brown 45.7 (23,039) Indigenous 0.4 (219) Education ≤ 8 year 87.6 (44,197) < 8 years 12.1 (6,107) Not applicable 0.3 (162) Operational classification Paucibacillary 9.7 (4,912) Multibacillary 90.2 (45,515) Unclassified/Ignored/Blank 0.1 (39) Clinical form Undetermined 2.9 (1,450) Tuberculoid 8.3 (4,179) Dimorphic 47.5 (23,982) Virchowian 33.3 (16,794) Unclassified/Ignored/Blank 8.0 (4,061) Legend * 0.02% (n = 11 ) records with unknown age. **0.02% (n = 8) records with unknown sex. In Brazil, the rate of new cases with G2D decreased from 13.6/1 million in 2001 to 9.1/ 1 million in 2022, with a downward trend (AAPC: -2.8%; CI: -3.6 to -2.1; p < 0.001). Three regions show a downward trend: i. Southeast, where the rate decreases from 10.4/1 million in 2001 to 5.0/1 million in 2022 (AAPC: -4.9 percent; CI: -5.6 to -4.1; p < 0.001); ii. Southern region, rate from 6.3/1 million in 2001 to 2.7/1 million in 2022 (AAPC: -4.8 percent; CI: -7.1 to -2.6; p < 0.001); and iii. Northern region, rate from 32.5/1 million in 2001 to 19.4/1 million in 2022 (AAPC: -3.0%; CI: -3.9 to -2.0; p < 0.001). The stationary trend was observed in the Northeast and Central-West regions (Fig. 1 ). During the period analyzed, 14 states showed a downward trend, five in the north, three in the northeast, two in the central-west, three in the southeast and one in the south. The state with the highest rate of decline was Roraima, which fell from 54.9/1 million in 2001 to 9.4/1 million in 2022 (AAPC: -11.0%; p < 0.001). The only state with an upward trend was Tocantins, from 26.3/1 million in 2001 to 64.1/1 million in 2022 (AAPC: 3.2 percent; p < 0.001) (Table 2 ). Table 2. Average Annual Percentage Change (AAPC) and rate of grade 2 physical disability in Brazilian Regions and States, 2001-2022. Spatial unit Rate 1 AAPC (CI 95%) p-value 2001 2022 North 32.5 19.4 -3.0* (-3.9 to -2.0); p<0.001 RO 29.3 19.6 -4.6* (-6.5 to -2.7); p<0.001 AC 46.6 21.6 -3.7 (-10.3 to 3.4); p=0.301 AM 33.6 7.8 -5.6* (-6.8 to -4.4); p<0.001 RR 54.9 9.4 -11.0* (-14.2 to -7.7); p<0.001 PA 32.9 18.3 -3.1* (-4.1 to -2.1); p<0.001 AP 11.6 8.1 -3.8* (-6.1 to -1.5); p<0.001 TO 26.3 64.1 3.2* (1.3 to 5.2); p<0.001 Northeast 15.4 12.5 -1.2 (-2.7 to 0.3); p=0.103 MA 42.3 27.1 -2.0 (-4.0 to 0.0); p=0.112 PI 28.3 17.7 -2.7* (-4.1 to -1.3); p<0.001 CE 20.1 11.3 -2.7* (-3.8 to -1.7); p<0.001 RN 4.1 4.5 0.3 (-3.9 to 4.8); p=0.916 PB 14.5 6.5 -2.4* (-3.9 to -1.0); p<0.001 PE 12.1 15.8 0.5 (-3.1 to 4.1); p=0.808 AL 4.4 11.1 1.2 (-1.3 to 3.8); p=0.326 SE 6.9 6.3 -0.7 (-3.9 to 2.7); p=0.709 BA 6.4 7.9 1.0 (-1.0 to 3.2); p=0.301 Central-West 20.5 19.8 -1.0 (-2.0 to 0.0); p=0.179 MS 14.7 9.0 -1.9 (-5.4 to 1.6); p=0.364 MT 36.9 52.4 1.4 (-0.1 to 2.9); p=0.121 GO 17.7 13.3 -2.6* (-4.9 to -0.1); p<0.001 DF 13.0 4.6 -4.3* (-6.8 to -1.7); p<0.001 Southeast 10.4 5.0 -4.9* (-5.6 to -4.1); p<0.001 MG 16.0 8.4 -4.5* (-6.9 to -2.1); p<0.001 ES 25.1 12.7 -3.7 (-8.5 to 1.3); p=0.173 RJ 13.8 3.6 -5.6* (-7.4 to -3.8); p<0.001 SP 5.2 3.3 -3.1* (-4.1 to -2.1); p<0.001 South 6.3 2.7 -4.8* (-7.1 to -2.6); p<0.001 PR 14.1 3.7 -6.4* (-9.1 to -3.7); p<0.001 SC 1.4 2.8 -1.8 (-4.2 to 0.6); p=0.152 RS 1.5 1.6 -1.6 (-3.6 to 0.5); p=0.181 Brazil 13.6 9.1 -2.8* (-3.6 to -2.1); p<0.001 Legend : * Statistical significance (p<0.05); 1- Rate of grade 2/ 1 million inhabitants; AAPC: Average Annual Percent Change ; RO: Rondônia; AC: Acre; AM: Amazonas; RR: Roraima; PA: Pará; AP: Amapá; TO: Tocantins; MA: Maranhão; PI: Piauí; CE: Ceará; RN: Rio Grande do Norte; PB: Paraíba; PE: Pernambuco; AL: Alagoas; SE: Sergipe; BA: Bahia; MG: Minas Gerais; ES: Espírito Santo; RJ: Rio de Janeiro; SP: São Paulo; PR: Paraná; SC: Santa Catarina; RS: Rio Grande do Sul; MS: Mato Grosso do Sul; MT: Mato Grosso; GO: Goiás; e DF: Distrito Federal. Regarding the state capitals, a downward trend was observed in four capitals: Manaus/AM (AAPC: -14.5%; p < 0.001) and Macapá/PA (AAPC: -5.8%; p < 0.001) in the North, Brasília/DF (AAPC: -11.9%; p < 0.001) in the Central-West and São Paulo/SP (AAPC: -7.0%; p < 0.001) in the Southeast. No capital city showed an upward trend in G2D during the period analyzed (Table 3 ). Table 3. Average Annual Percentage Change (AAPC) in the Rate of New Leprosy Cases with Grade 2 Physical Disability at Diagnosis, in the Capital Cities of Brazil. 2001-2022. Spatial unit Rate 1 AAPC (CI 95%) p value 2001 2022 North Porto Velho 19.7 17.3 -5.0 (-11.4 to 1.8); p=0.102 Rio Branco 51.7 8.2 -13.7 (-50.1 to 49.2); p=0.616 Manaus 27.0 7.2 -14.5* (-4.0 to -26.0); p<0.001 Boa Vista 46.6 4.8 0.3 (-15.8 to 19.4); p=1.005 Belém 19.5 15.3 5.5 (-2.2 to 13.7); p=0.235 Macapá 16.3 4.5 -5.8* (-1.4 to -10.4); p<0,001 Palmas 13.2 102.4 -0.3 (-5.5 to 5.1); p=0.967 Northeast São Luís 26.7 29.8 -3.6 (-9.3 to 2.4); p=0.246 Teresina 61.9 17.3 -4.4 (-8.7 to 0.0); p=0.193 Fortaleza 23.2 7.4 1.1 (-3.8 to 6.2); p=0.775 Natal 2.6 3.9 0.7 (-4.1 to 5.6); p=0.828 João Pessoa 9.5 3.5 6.5 (-2.1 to 15.9); p=0.134 Recife 14.2 20.1 -1.3 (-5.2 to 2.7); p=0.516 Maceió 2.2 13.5 -1.3 (-6.8 to 4.4); p=0.673 Aracaju 6.1 1.6 -2.0 (-8.0 to 4.4); p=0.581 Salvador 4.3 5.7 -3.7 (-10.3 to 3.5); p=0.394 Central-West Campo Grande 8.6 10.0 -3.2 (-10.3 to 4.4); p=0.442 Cuiabá 33.7 24.5 -4.1 (-9.4 to 1.5); p=0.159 Goiânia 25.3 4.1 4.0 (-0.4 to 8.6); p=0.131 Brasília 13.0 4.2 -11.9* (-0.5 to -24.7); p<0.001 Southeast Belo Horizonte 6.9 1.7 -6.3 (-13.8 to 1.9); p=0.113 Vitória 13.5 18.5 -16.4 (-39.9 to 16.5); p=0.387 Rio de Janeiro 13.7 2.2 2.9 (-3.1 to 9.4); p=0.394 São Paulo 2.7 1.0 -7.0* (-11.5 to -2.4); p<0.001 South Curitiba 8.2 3.9 -3.8 (-8.3 to 0.9); p=0.164 Florianópolis 0.0 0.0 -5.1 (-56.0 to 104.5); p=0.993 Porto Alegre 0.0 0.7 27.8 (-6.0 to 73.7); p=0.172 Legend : * Statistical significance (p 10/million were mainly concentrated in the northern and central-western regions of Brazil. It should be noted that in the years of the Covid-19 pandemic, the number of municipalities in this stratum of endemicity was lower than in previous years. For example, in 2019, 14.5% (n = 807) of the municipalities had a G2D rate > 10/1 million. In 2020, this percentage was 10.0% (n = 559) (Fig. 2 ). Moran's local statistics showed spatial dependence in all the years analyzed (p < 0.05). The local statistics indicated the existence of spatial clusters, concentrated in the North and Central-West regions. The number of municipalities in quadrant Q1 (High-high) varied between the years, ranging from 170 (3.1%) in 2001 to 118 (2.1%) in 2019. It should be noted that in the years of the Covid-19 pandemic, the number of municipalities in the High-High quadrant was lower than in the pre-pandemic years: 91 (1.6%) in 2020, 88 (1.6%) in 2021 and 59 (1.1%) in 2022. In addition, there was a change in the spatial pattern over the time series, with a reduction in municipalities in the North and an increase in municipalities in the Central-West in the Q1 quadrant (Fig. 3 ). Considering the rate for the period 2001–2022, 40.1% (n = 2234) of Brazilian municipalities had a G2D rate > 10/1 million. Although there was a concentration in the North and Central-West regions of Brazil, clusters were found in the other regions. In the Moran map, 635 (14.1%) municipalities were in the high-high quadrant. These municipalities belong to 12 Brazilian states, one in the Northeast (Maranhão), one in the South (Paraná), two in the Southeast (Minas Gerais and Espírito Santo), all three in the Central-West (Goiás, Mato Grosso and Mato Grosso do Sul) and five in the North (Acre, Rondônia, Amazonas, Pará and Tocantins). In addition, these municipalities together accounted for 30 percent (n = 15,139) of all cases recorded in the country during the 22 years analyzed (Fig. 4 ). In addition, three spatial clusters were observed: the first consisting of five states in the north, three in the central-west and one in the northeast; the second observed in the southeast, between Minas Gerais and Espírito Santo and a third located in the state of Paraná (Fig. 4 ). Discussion This study described the temporal trend and identified the areas at risk of developing physical disability due to leprosy in Brazil over a twenty-one year time series. A variation in the trend was observed over the period, with three regions showing a decreasing trend and two showing a stationary trend in the coefficient of Grade 2 physical disability, suggesting the persistence of late-diagnosed cases. Despite global reductions in the detection rates of new leprosy cases [ 29 – 31 ], Brazil still reported more than fifty thousand new cases with grade 2 physical disability between 2001 and 2022. The highest rates of new cases of leprosy with G2D were concentrated among adult males with black skin, low levels of education, and living in the northern and central-western regions of Brazil. National and international studies show a higher prevalence of the disease among men with lower levels of education, which may reflect social and economic inequalities, especially in neglected populations [ 32 – 39 ]. In general, men are more vulnerable to the disease and its more severe forms, often due to behaviours that make it difficult for them to seek appropriate health care, combined with a lack of specialized services to meet their specific needs [ 32 , 40 ]. Patients with the multibacillary clinical form are more likely to develop physical disabilities. In our study, they accounted for 80.8 percent of the reported cases. The presence of leprosy disabilities is an indicator of late diagnosis and treatment [ 41 – 43 ]. This underscores the need to define and strengthen public health interventions aimed at early diagnosis and comprehensive care of people affected by M. leprae , especially those with severe forms of the disease, in order to reduce disability, mitigate associated stigma, and reduce disease transmission [ 42 , 44 – 46 ]. The results show that there has been a decrease in the rate of new cases with G2D in Brazil. The Southeast, South and North regions showed a decreasing trend, while the Northeast and Central-West regions showed a stable trend. These results are consistent with other studies of leprosy conducted in Brazil, which have generally shown a reduction in the burden of leprosy in Brazil [ 45 – 47 ]. Roraima was the state with the highest rate of decline, from 54.9/1 million in 2001 to 9.4/1 million in 2022. A study conducted in this state between 2001 and 2017 showed a decrease in prevalence and case detection rates, demonstrating a trend toward elimination of the disease [ 48 ]. In an analysis measuring the severity of the disease in Brazil, Roraima, Paraíba, Rio Grande do Sul and Espírito Santo have better results in the fight against leprosy [ 49 ]. This decrease can be attributed to the implementation of the National Leprosy Control Programme in Brazil, which promoted and facilitated the decentralization of leprosy control to primary health care [ 46 ]. None of Brazil's state capitals showed an upward trend. Manaus/AM, Macapá/PA, Brasília/DF and the capital São Paulo/SP showed a downward trend. This finding may be related to two factors: (i) the ability of large centers to have better infrastructure and health care, which may facilitate early diagnosis [ 15 ], (ii) the higher hidden prevalence of the disease (50–52). This second factor seems to be the most appropriate to explain the stationary trend in the vast majority of capital cities. In São Paulo, for example, a study showed the hidden prevalence of the disease, whose real prevalence was six times higher than the observed prevalence [ 50 ]. The other Brazilian capitals show stationary behavior and some remain hyperendemic for leprosy, requiring additional efforts in the process of eliminating the disease [ 53 , 54 ]. When analyzing the municipalities, endemicity fluctuated over the period analyzed, especially during the Covid-19 pandemic, where there was a decrease in the rates of new leprosy cases with G2D compared to previous years in the series. For example, in 2019, 14.5% (n = 807) of the municipalities had a rate > 10/million. In 2020, this percentage was 10.0% (n = 559). The Covid-19 pandemic has had an impact on diagnoses, increasing the hidden prevalence of the disease, as health systems in Brazil and around the world have turned their attention to controlling the pandemic, affecting the ability to maintain service provision and ensure adequate care for the population, especially for chronic and neglected diseases [ 15 , 55 – 57 ]. The Covid-19 pandemic has created challenges in the follow-up and treatment of leprosy patients in primary health care centers throughout Brazil. This has led to delays in appointments, the need to reschedule appointments, treatment extensions due to drug shortages, and barriers to contact assessment and active case finding. These factors have had a negative impact on the leprosy program in all regions of the country [ 15 , 55 , 56 , 58 , 59 ]. The impact of the pandemic has been observed worldwide: the World Health Organization published a report showing a decrease in the number of countries and territories sharing leprosy statistics, from 166 (out of 221) in 2019 to 127 in 2020 and 143 in 2021, as well as a 37% global decrease in the detection of new cases of the disease [ 60 ]. Our study also noted the impact of the pandemic on high-risk areas: the number of municipalities in the high-high quadrant was lower than in the pre-pandemic years: 91 (1.6%) in 2020, 88 (1.6%) in 2021, and 59 (1.1%) in 2022. Despite all the methodological care taken, this study has important limitations. First, it used secondary data from SINAN, which is directly influenced by the capacity of municipalities to detect and report new leprosy cases. In this way, the capacity of local surveillance services directly influences the records and thus the results of this study. Second, assessing the degree of physical disability is still a challenge for many municipalities, especially smaller ones with few human resources. Thus, the quality of the neurological examination and the degree of disability may influence the results. Analysis and interpretation of these results are important for the development and evaluation of leprosy surveillance activities in Brazil. It suggests the need to strengthen active case finding and continuous monitoring of leprosy patients. These measures are essential to ensure an effective approach to control and to prevent the spread of the disease and its disabilities. Our results show spatial inequalities in the incidence of leprosy disability, which may be related to differences in services between health regions and in the implementation of disease control interventions, particularly with regard to timely diagnosis and treatment. In addition, these findings highlight the importance of discussing the control of neglected diseases such as leprosy and the development of new technologies for their diagnosis and treatment. These initiatives are essential to improve the effectiveness of public health strategies to control this disease. Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable. Availability of data and materials The data were extracted from the National System of Notifiable Diseases (SINAN), available in the database of the Department of Informatics of the Unified Health System (DATASUS) (http://datasus.saude.gov.br/) Competing interests Not applicable. Funding Not applicable. Authors' contributions TSM and CDF: Data collection, interpretation of findings, analysis, interpretation, drafting of the manuscript and study supervision. TRMOF, MBS, RJVCB, DUSM, RFC, and TFAS: Data collection, interpretation of findings, analysis and drafting of the manuscript. Acknowledgements Not applicable. References Brasil. Ministério da Saúde. Secretaria de Vigilância em Saúde. Departamento de Vigilância das Doenças Transmissíveis. Diretrizes para vigilância, atenção e eliminação da hanseníase como problema de saúde pública: manual técnico-operacional. Brasília: Ed. do Ministério da Saúde; 2016. 58 p. (Série C: projetos, programas e relatórios). https://portalarquivos2.saude.gov.br/images/pdf/2016/fevereiro/04/diretrizes-eliminacao-hanseniase-4fev16-web.pdf . Brasil. Ministério da Saúde. Secretaria de Vigilância em Saúde. Departamento de Vigilância das Doenças Transmissíveis. Guia prático sobre a hanseníase [recurso eletrônico]. Brasília: Ministério da Saúde; 2017. 68 p. http://bvsms.saude.gov.br/bvs/publicacoes/guia_pratico_hanseniase.pdf . Brasil. Ministério da Saúde. Secretaria de Atenção Primária à Saúde (SAPS). 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Petrolina","correspondingAuthor":false,"prefix":"","firstName":"Rodrigo","middleName":"José Videres Cordeiro","lastName":"Brito","suffix":""},{"id":304172662,"identity":"85c4b644-1ae2-4344-bf1f-1438e6cbd4d6","order_by":5,"name":"Diogo Uester Silva Matos","email":"","orcid":"","institution":"Federal University of the São Francisco Valley. Petrolina","correspondingAuthor":false,"prefix":"","firstName":"Diogo","middleName":"Uester Silva","lastName":"Matos","suffix":""},{"id":304172663,"identity":"9e434758-1328-4ba8-a6e3-3ff0d0cd9512","order_by":6,"name":"Rodrigo Feliciano Carmo","email":"","orcid":"","institution":"Federal University of the São Francisco Valley. Petrolina","correspondingAuthor":false,"prefix":"","firstName":"Rodrigo","middleName":"Feliciano","lastName":"Carmo","suffix":""},{"id":304172664,"identity":"4daee0da-3f42-4ac6-a13a-68d440ed5fe4","order_by":7,"name":"Tarcísio Fulgêncio Alves Silva","email":"","orcid":"","institution":"University of Pernambuco (UPE)","correspondingAuthor":false,"prefix":"","firstName":"Tarcísio","middleName":"Fulgêncio Alves","lastName":"Silva","suffix":""}],"badges":[],"createdAt":"2024-04-23 14:55:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4312843/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4312843/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12879-025-10586-2","type":"published","date":"2025-03-06T15:57:02+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":56887822,"identity":"693ceb89-2920-4a9f-b339-e255bf01e358","added_by":"auto","created_at":"2024-05-21 18:59:20","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":524019,"visible":true,"origin":"","legend":"\u003cp\u003eTime trend analysis of the rate of new leprosy cases with grade 2 physical disability at diagnosis, in Brazil and regions, 2001 - 2022.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4312843/v1/b3c57c0532cd0dfe6c5484c2.jpeg"},{"id":56887823,"identity":"9aca8138-8f1a-43a8-a62c-cfe1ed72d5fe","added_by":"auto","created_at":"2024-05-21 18:59:20","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1414094,"visible":true,"origin":"","legend":"\u003cp\u003eRate of new leprosy cases with grade 2 physical disability at diagnosis, by municipality of residence and year of diagnosis. Brazil, 2001-2022.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4312843/v1/525494364f62b45dc4dbb738.jpeg"},{"id":56887824,"identity":"99e9b3bf-9622-430a-8677-25f81b0d1a6e","added_by":"auto","created_at":"2024-05-21 18:59:20","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1147017,"visible":true,"origin":"","legend":"\u003cp\u003eMoran's map of the rate of new leprosy cases with grade 2 physical disability at diagnosis, by municipality of residence and year of diagnosis. Brazil, 2001-2022.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4312843/v1/0adf0ce4a6138f1552a411e7.jpeg"},{"id":56887825,"identity":"64115c5e-e9fa-4b4c-9ade-09f3f380db39","added_by":"auto","created_at":"2024-05-21 18:59:20","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":945246,"visible":true,"origin":"","legend":"\u003cp\u003eMoran's map of the rate of new leprosy cases with grade 2 physical disability at diagnosis, by municipality of residence and year of diagnosis. Brazil, 2001-2022.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4312843/v1/5529be110de7a3000522644a.jpeg"},{"id":78191247,"identity":"3e5d143a-7c68-4c4f-9117-7b8df2e78acc","added_by":"auto","created_at":"2025-03-10 19:54:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5082872,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4312843/v1/1111383d-f17f-482b-8f23-16b2553de58e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Time trend and identification of risk areas for physical disability due to leprosy in Brazil: An ecological study, 2001-2022","fulltext":[{"header":"Background","content":"\u003cp\u003eLeprosy is a chronic infectious dermato-neurological disease caused by \u003cem\u003eMycobacterium leprae\u003c/em\u003e. It is an acid-fast bacillus with an affinity for skin cells and peripheral nerves. The pathophysiology and immune response to neural invasion by \u003cem\u003eM. leprae\u003c/em\u003e can result in partial or total damage to nerve trunks, affecting autonomic, sensory and motor fibres innervating structures in the dermis, epidermis and muscles, leading to functional changes and physical disability [\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn 2022, 174,000 new cases of leprosy were reported worldwide. Brazil remains the world's second most affected country, with 19,000 new cases in 2022, accounting for 92% of the new cases diagnosed in the Americas, making it a World Health Organization priority country for leprosy [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Worldwide, 9,554 new cases with grade 2 physical disability (G2D) were detected, which corresponds to a rate of 1.2 cases per million population. In Brazil, the proportion of new cases with G2D among those assessed at diagnosis increased from 7.1% in 2011 to 11.5% in 2022, an increase of 62.0% [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite the significant reduction in the global prevalence of leprosy in recent decades, mainly due to the introduction of multidrug therapy in the early 1980s and advances in scientific research, leprosy remains an important public health problem, especially in places exposed to a context of social vulnerability [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In addition, leprosy belongs to the group of neglected tropical diseases [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn 2021, WHO launched the Global Leprosy Strategy 2021\u0026ndash;2030 - \"Towards Zero Leprosy\". The new strategy focuses on interrupting transmission and achieving zero autochthonous cases of leprosy. One of the pillars of the strategy is the need to treat leprosy and its complications and to prevent new disabilities. To achieve this goal, research is needed, particularly on the time trends of physical disability indicators and the identification of risk areas [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNerve impairment is a critical aspect of leprosy, as it is closely related to the disabling potential of the disease. Currently, physical disability (PD) is graded on a scale of 0 to 2, with 0 indicating no PD, grade 1 indicating sensory loss, and grade 2 indicating the presence of permanent PD in the eyes, hands and feet, such as lagophthalmos, trichiasis, clawing of hands and feet, plantar perforation, bone resorption, and others [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIt is estimated that between 3 and 4\u0026nbsp;million people may be living with visible physical disabilities due to leprosy [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. These disabilities contribute to social stigma, reduced social participation, increased vulnerability, greater risk of mental disorders, and social and economic losses for the individual and their family. In addition, the indicator rate of new cases of Grade 2 physical disability per million population has been recommended by WHO for regional monitoring of the risk of physical disability in the population [\u003cspan additionalcitationids=\"CR19 CR20 CR21 CR22 CR23\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBased on the above, this study analysed the temporal trend in the rate of new cases of leprosy with grade 2 physical disability in Brazil and identified spatial clusters of risk for physical disability.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design, population and period\u003c/h2\u003e \u003cp\u003eThis is an ecological study involving all new cases of leprosy with grade 2 physical disability at the time of diagnosis between 2001 and 2022.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy area\u003c/h2\u003e \u003cp\u003eThe study area is Brazil, its macro-regions, states and municipalities. Brazil currently consists of five macro-regions (North, Northeast, South, Southeast and Centre-West), 27 federal entities (including the Federal District) and 5,568 municipalities. The size of Brazil presents a challenge to any scientific study because of the regional differences in economic, demographic, political and environmental characteristics. Brazil has a land area of approximately 8.5\u0026nbsp;million square kilometers, making it the fifth largest country in the world in terms of area, and a resident population of approximately 203\u0026nbsp;million people, with a population density of 23.86 inhabitants per square kilometer [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData source\u003c/h2\u003e \u003cp\u003eThe data were extracted from the National System of Notifiable Diseases (SINAN), available in the database of the Department of Informatics of the Unified Health System (DATASUS) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://datasus.saude.gov.br/\u003c/span\u003e\u003cspan address=\"http://datasus.saude.gov.br/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). SINAN collects records of leprosy and other notifiable diseases throughout the country. The population data needed to calculate the indicators are obtained from the Brazilian Institute of Geography and Statistics (IBGE).\u003c/p\u003e \u003cp\u003eThe following criteria were used to extract the data Year of diagnosis (2001 to 2022); discharge (unfulfilled, cure, transfer to the same municipality, transfer to another municipality, transfer to another state, transfer to another country, death, abandonment), type of entry (new case), assessment of physical disability at the time of notification (grade 2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStudy variables\u003c/h2\u003e \u003cp\u003eTwo sets of variables were included. The first group consisted of socio-demographic and clinical variables: age, sex, race/colour, education, operational classification and clinical form of the disease. The second group consisted of an epidemiological indicator - the rate of new leprosy cases with grade 2 physical disability at the time of diagnosis per 1\u0026nbsp;million inhabitants. This indicator allows the disability caused by leprosy to be assessed in the general population.\u003c/p\u003e \u003cp\u003eFor the calculation, the numerator is the number of new cases with grade 2 physical disability at diagnosis, living in a particular place and diagnosed in the year of assessment, and the denominator is the population living in the same place and during the same period. In addition, there is no established assessment parameter. However, for the purposes of analysis, we have arbitrarily adopted the following classification\u0026thinsp;\u0026lt;\u0026thinsp;2 per 1\u0026nbsp;million inhabitants, 2 to 4.99 per 1\u0026nbsp;million inhabitants, 5 to 9.99 per 1\u0026nbsp;million inhabitants and \u0026gt;\u0026thinsp;10 per 1\u0026nbsp;million inhabitants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStudy Steps\u003c/h2\u003e \u003cp\u003eStep 1 - Descriptive analysis of socio-demographic and clinical profile: In this stage, the socio-demographic and clinical variables were described in absolute value and relative frequency (%).\u003c/p\u003e \u003cp\u003eStep 2 - Time series analysis: In this stage, the inflection point regression model was used to determine the time trend in the rate of new leprosy cases with grade 2 physical disability at the time of diagnosis per million population. The inflection point regression model was used. This model examines whether a line with multiple segments is statistically better at describing the time trend of a data set than a straight line or a line with fewer segments [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe joinpoint regression model for the observations: (\u0026#119909;1, \u0026#119910;1),..., (\u0026#119909;\u0026#119899;, \u0026#119910;\u0026#119899;), where \u0026#119909;1 \u0026le; ... \u0026le; \u0026#119909;\u0026#119899; represents the time variable, and \u0026#119910;\u0026#119894;, \u0026#119894; = 1, 2,..., n is the response variable, being given by:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\sum {y}_{i}\\left|{x}_{i}\\right]= {\\beta }_{0}+{\\beta }_{1}{x}_{1}+{\\gamma }_{1}\\left({x}_{i}-{\\tau }_{1}\\right)+\\dots +{\\gamma }_{n}\\left({x}_{i}-{\\tau }_{n}\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere β0, β1, y1,..., γn are regression coefficients and yk, K\u0026thinsp;=\u0026thinsp;1, 2,\u0026hellip;, n, n\u0026thinsp;\u0026lt;\u0026thinsp;N, is the k-th unknown joinpoints where:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\left({x}_{i}-{\\tau }_{k}\\right)=\\left({x}_{i}-{\\tau }_{k}\\right)if\\left({x}_{i}-{\\tau }_{k}\\right)\u0026gt;0$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e=\u0026thinsp;0, otherwise.\u003c/p\u003e \u003cp\u003eIn this way, the model makes it possible to check the trend of the indicator (whether it is stationary, increasing or decreasing) and the points at which this trend changes (joins), which makes it possible to identify the annual percentage change (APC) and that of the whole period (average annual percentage change - AAPC). The following parameters were used in the analyses: minimum of 0 joins and maximum of 4; model selection: test with 4,499 permutations; 5% significance; 95% confidence interval and autocorrelation of errors based on the data. Analyses were performed in the Joinpoint regression program, version 4.6.0.1 (National Cancer Institute, Bethesda, MD, USA). The analysis was performed in four spatial units: Brazil, five regions, 27 states and their respective capitals.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStep 3-\u003c/b\u003e Spatial analysis: Global and local Moran's statistics (Moran's Index and p-value) were used to analyse the spatial dynamics. The global Moran's index (MI) measures the degree of autocorrelation in a given set, based on the product of the deviations from the global mean.\u003c/p\u003e \u003cp\u003eThe MI values can oscillate within a spectrum between \u0026minus;\u0026thinsp;1 and +\u0026thinsp;1, with values close to -1 indicating negative spatial autocorrelation, values close to +\u0026thinsp;1 indicating positive spatial autocorrelation and values close to zero indicating no autocorrelation. The pseudo significance test is used to determine whether or not the null hypothesis is rejected (H0: spatial homogeneity; H1: spatial heterogeneity) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGlobal Moran's Index (Global I) is given by the equation:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$I=\\frac{\\sum _{i=1}^{n}\\sum _{j=1}^{n}{w}_{ij}\\left({z}_{i}-\\stackrel{-}{z}\\right)\\left({z}_{j}-\\stackrel{-}{z}\\right)}{\\sum _{i=1}^{n}\\left({z}_{i}-\\stackrel{-}{z}\\right)\u0026sup2;}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere n is the number of areas, zi is the value of the attribute considered in area i, z is the average value of the attribute in the study region and wij, the elements of the normalised spatial proximity matrix.\u003c/p\u003e \u003cp\u003eOnce global autocorrelation (spatial heterogeneity) has been confirmed, we move to the second stage, which is characterized by the use of the Local Indicator of Spatial Association (LISA). LISA aims to quantify the degree of spatial association to which each location in the sample set is subject as a function of a neighbourhood model. The result is the local pattern of the spatial distribution of the variable under analysis [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this case, the following equation is used:\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$${I}_{i}=\\frac{{z}_{i}\\sum _{j=1}^{n}{w}_{ij}{z}_{j}}{\\sum _{j=1}^{n}{z}_{j}^{2}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere n is the number of areas, zi is the value of the attribute considered in area i, z is the average value of the attribute in the study region and wij are the elements of the normalised spatial proximity matrix.\u003c/p\u003e \u003cp\u003eWhile Q1 (positive values, positive averages) and Q2 (negative values, negative averages) indicate points of positive spatial association, in the sense that a location has neighbors with similar values; Q3 (positive values, negative averages) and Q4 (negative values, positive averages) indicate points of negative spatial association, in the sense that a location has neighbors with different values [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Municipalities located in Q1 are considered priorities for intervention.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEthical aspects\u003c/h2\u003e \u003cp\u003eBy using secondary data in the public domain, the study does not need to be evaluated by the Research Ethics Committee, in accordance with Resolution 510/2016 CEP/CONEP.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eBetween 2001 and 2022, 50,466 new cases of leprosy with G2D were reported in Brazil, an average of 2,293.9 cases/year. In terms of demographics, 97.4% (n\u0026thinsp;=\u0026thinsp;49,139) of the cases analyzed were individuals older than 14 years, 70.2% (n\u0026thinsp;=\u0026thinsp;35,428) of the population studied was male. Blacks (brown and black) represented 45.7 percent (n\u0026thinsp;=\u0026thinsp;23,039) and 12.9 percent (n\u0026thinsp;=\u0026thinsp;6,505), respectively. In addition, 87.6 percent (n\u0026thinsp;=\u0026thinsp;44,197) had little schooling (\u0026le;\u0026thinsp;eight years of schooling). The clinical profile of the population was characterized by the multibacillary operational classification (90.2%; n\u0026thinsp;=\u0026thinsp;45,515), with the dimorphic clinical form accounting for 47.5% (n\u0026thinsp;=\u0026thinsp;23,982) \u003cstrong\u003e(\u003c/strong\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEpidemiologic characterization of leprosy cases with grade 2 physical disability. Brazil, 2001\u0026ndash;2022.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePopulation (n\u0026thinsp;=\u0026thinsp;50,466)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAge*\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e% (n)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;14 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.6 (1,316)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;14 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97.4 (49,139)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70.2 (35,428)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.8 (15,030)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace/color\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIgnored/Blank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.3 (5,218)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.6 (14,957)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.9 (6,505)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYellow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.1 (528)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBrown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45.7 (23,039)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndigenous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4 (219)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;8 year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e87.6 (44,197)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;8 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.1 (6,107)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot applicable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3 (162)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOperational classification\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePaucibacillary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.7 (4,912)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMultibacillary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e90.2 (45,515)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnclassified/Ignored/Blank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1 (39)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical form\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUndetermined\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.9 (1,450)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTuberculoid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.3 (4,179)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDimorphic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47.5 (23,982)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVirchowian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.3 (16,794)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnclassified/Ignored/Blank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.0 (4,061)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eLegend\u0026nbsp;\u003c/strong\u003e* 0.02% (n\u0026thinsp;=\u0026thinsp;11 ) records with unknown age. **0.02% (n\u0026thinsp;=\u0026thinsp;8) records with unknown sex.\u003c/p\u003e\n\u003cp\u003eIn Brazil, the rate of new cases with G2D decreased from 13.6/1 million in 2001 to 9.1/ 1 million in 2022, with a downward trend (AAPC: -2.8%; CI: -3.6 to -2.1; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Three regions show a downward trend: i. Southeast, where the rate decreases from 10.4/1 million in 2001 to 5.0/1 million in 2022 (AAPC: -4.9 percent; CI: -5.6 to -4.1; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001); ii. Southern region, rate from 6.3/1 million in 2001 to 2.7/1 million in 2022 (AAPC: -4.8 percent; CI: -7.1 to -2.6; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001); and iii. Northern region, rate from 32.5/1 million in 2001 to 19.4/1 million in 2022 (AAPC: -3.0%; CI: -3.9 to -2.0; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The stationary trend was observed in the Northeast and Central-West regions (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eDuring the period analyzed, 14 states showed a downward trend, five in the north, three in the northeast, two in the central-west, three in the southeast and one in the south. The state with the highest rate of decline was Roraima, which fell from 54.9/1 million in 2001 to 9.4/1 million in 2022 (AAPC: -11.0%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The only state with an upward trend was Tocantins, from 26.3/1 million in 2001 to 64.1/1 million in 2022 (AAPC: 3.2 percent; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Average Annual Percentage Change (AAPC) and rate of grade 2 physical disability in Brazilian Regions and States, 2001-2022.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"90%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.36363636363637%\" rowspan=\"3\"\u003e\n \u003cp\u003eSpatial unit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"63.63636363636363%\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.984126984126984%\" colspan=\"2\"\u003e\n \u003cp\u003eRate\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"73.01587301587301%\" rowspan=\"2\"\u003e\n \u003cp\u003eAAPC (CI 95%) p-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e2001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.734693877551024%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNorth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u003cstrong\u003e32.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u003cstrong\u003e19.4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.93877551020408%\"\u003e\n \u003cp\u003e\u003cstrong\u003e-3.0* (-3.9 to -2.0); p\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.734693877551024%\"\u003e\n \u003cp\u003eRO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e29.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e19.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.93877551020408%\"\u003e\n \u003cp\u003e-4.6* (-6.5 to -2.7); p\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.734693877551024%\"\u003e\n \u003cp\u003eAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e46.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e21.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.93877551020408%\"\u003e\n \u003cp\u003e-3.7 (-10.3 to 3.4); p=0.301\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.734693877551024%\"\u003e\n \u003cp\u003eAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e33.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.93877551020408%\"\u003e\n \u003cp\u003e-5.6* (-6.8 to -4.4); p\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.734693877551024%\"\u003e\n \u003cp\u003eRR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e54.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e9.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.93877551020408%\"\u003e\n \u003cp\u003e-11.0* (-14.2 to -7.7); p\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.734693877551024%\"\u003e\n \u003cp\u003ePA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e32.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e18.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.93877551020408%\" valign=\"top\"\u003e\n \u003cp\u003e-3.1* (-4.1 to -2.1); p\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.734693877551024%\"\u003e\n \u003cp\u003eAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e11.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e8.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.93877551020408%\" valign=\"top\"\u003e\n \u003cp\u003e-3.8* (-6.1 to -1.5); p\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.734693877551024%\"\u003e\n \u003cp\u003eTO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e26.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e64.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.93877551020408%\" valign=\"top\"\u003e\n \u003cp\u003e3.2* (1.3 to 5.2); p\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.734693877551024%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNortheast\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u003cstrong\u003e15.4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u003cstrong\u003e12.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.93877551020408%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-1.2\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(-2.7 to 0.3); 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p\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.734693877551024%\"\u003e\n \u003cp\u003eES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e25.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e12.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.93877551020408%\" valign=\"top\"\u003e\n \u003cp\u003e-3.7 (-8.5 to 1.3); p=0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.734693877551024%\"\u003e\n \u003cp\u003eRJ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e13.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.93877551020408%\" valign=\"top\"\u003e\n \u003cp\u003e-5.6* (-7.4 to -3.8); p\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.734693877551024%\"\u003e\n \u003cp\u003eSP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.93877551020408%\" valign=\"top\"\u003e\n \u003cp\u003e-3.1* (-4.1 to -2.1); p\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.734693877551024%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSouth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.93877551020408%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-4.8* (-7.1 to -2.6); p\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.734693877551024%\"\u003e\n \u003cp\u003ePR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e14.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.93877551020408%\" valign=\"top\"\u003e\n \u003cp\u003e-6.4* (-9.1 to -3.7); p\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.734693877551024%\"\u003e\n \u003cp\u003eSC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.93877551020408%\" valign=\"top\"\u003e\n \u003cp\u003e-1.8 (-4.2 to 0.6); p=0.152\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.734693877551024%\"\u003e\n \u003cp\u003eRS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.93877551020408%\" valign=\"top\"\u003e\n \u003cp\u003e-1.6 (-3.6 to 0.5); p=0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.734693877551024%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBrazil\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u003cstrong\u003e13.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u003cstrong\u003e9.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.93877551020408%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-2.8* (-3.6 to -2.1); p\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eLegend\u003c/strong\u003e: \u003csup\u003e*\u003c/sup\u003e Statistical significance (p\u0026lt;0.05); \u003csup\u003e1-\u0026nbsp;\u003c/sup\u003eRate of grade 2/ 1 million inhabitants; AAPC: \u003cem\u003eAverage Annual Percent Change\u003c/em\u003e; RO: Rond\u0026ocirc;nia; AC: Acre; AM: Amazonas; RR: Roraima; PA: Par\u0026aacute;; AP: Amap\u0026aacute;; TO: Tocantins; MA: Maranh\u0026atilde;o; PI: Piau\u0026iacute;; CE: Cear\u0026aacute;; RN: Rio Grande do Norte; PB: Para\u0026iacute;ba; PE: Pernambuco; AL: Alagoas; SE: Sergipe; BA: Bahia; MG: Minas Gerais; ES: Esp\u0026iacute;rito Santo; RJ: Rio de Janeiro; SP: S\u0026atilde;o Paulo; PR: Paran\u0026aacute;; SC: Santa Catarina; RS: Rio Grande do Sul; MS: Mato Grosso do Sul; MT: Mato Grosso; GO: Goi\u0026aacute;s; e DF: Distrito Federal.\u003c/p\u003e\n\u003cp\u003eRegarding the state capitals, a downward trend was observed in four capitals: Manaus/AM (AAPC: -14.5%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and Macap\u0026aacute;/PA (AAPC: -5.8%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in the North, Bras\u0026iacute;lia/DF (AAPC: -11.9%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in the Central-West and S\u0026atilde;o Paulo/SP (AAPC: -7.0%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in the Southeast. No capital city showed an upward trend in G2D during the period analyzed (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003eAverage Annual Percentage Change (AAPC) in the Rate of New Leprosy Cases with Grade 2 Physical Disability at Diagnosis, in the Capital Cities of Brazil.\u0026nbsp;2001-2022.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"86%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.37373737373738%\" rowspan=\"3\"\u003e\n \u003cp\u003eSpatial unit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"62.62626262626262%\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.508196721311474%\" colspan=\"2\"\u003e\n \u003cp\u003eRate\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"70.49180327868852%\" rowspan=\"2\"\u003e\n \u003cp\u003eAAPC (CI 95%) p value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.05882352941177%\"\u003e\n \u003cp\u003e2001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.94117647058823%\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNorth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.329896907216494%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\" valign=\"top\"\u003e\n \u003cp\u003ePorto Velho\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e19.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e17.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.329896907216494%\"\u003e\n \u003cp\u003e-5.0 (-11.4 to 1.8); p=0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\" valign=\"top\"\u003e\n \u003cp\u003eRio Branco\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e51.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.329896907216494%\" valign=\"top\"\u003e\n \u003cp\u003e-13.7 (-50.1 to 49.2); p=0.616\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\" valign=\"top\"\u003e\n \u003cp\u003eManaus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e27.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.329896907216494%\" valign=\"top\"\u003e\n \u003cp\u003e-14.5* (-4.0 to -26.0); p\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\" valign=\"top\"\u003e\n \u003cp\u003eBoa Vista\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e46.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.329896907216494%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.3 (-15.8 to 19.4); p=1.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\" valign=\"top\"\u003e\n \u003cp\u003eBel\u0026eacute;m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e19.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e15.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.329896907216494%\" valign=\"top\"\u003e\n \u003cp\u003e5.5 (-2.2 to 13.7); p=0.235\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\" valign=\"top\"\u003e\n \u003cp\u003eMacap\u0026aacute;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e16.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.329896907216494%\" valign=\"top\"\u003e\n \u003cp\u003e-5.8* (-1.4 to -10.4); p\u0026lt;0,001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\" valign=\"top\"\u003e\n \u003cp\u003ePalmas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e13.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e102.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.329896907216494%\" valign=\"top\"\u003e\n \u003cp\u003e-0.3 (-5.5 to 5.1); p=0.967\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNortheast\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.329896907216494%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\" valign=\"top\"\u003e\n \u003cp\u003eS\u0026atilde;o Lu\u0026iacute;s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e26.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e29.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.329896907216494%\" valign=\"top\"\u003e\n \u003cp\u003e-3.6 (-9.3 to 2.4); p=0.246\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\" valign=\"top\"\u003e\n \u003cp\u003eTeresina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e61.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e17.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.329896907216494%\" valign=\"top\"\u003e\n \u003cp\u003e-4.4 (-8.7 to 0.0); p=0.193\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\" valign=\"top\"\u003e\n \u003cp\u003eFortaleza\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e23.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e7.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.329896907216494%\" valign=\"top\"\u003e\n \u003cp\u003e1.1 (-3.8 to 6.2); p=0.775\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\" valign=\"top\"\u003e\n \u003cp\u003eNatal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.329896907216494%\" valign=\"top\"\u003e\n \u003cp\u003e0.7 (-4.1 to 5.6); p=0.828\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\" valign=\"top\"\u003e\n \u003cp\u003eJo\u0026atilde;o Pessoa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e9.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.329896907216494%\" valign=\"top\"\u003e\n \u003cp\u003e6.5 (-2.1 to 15.9); p=0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\" valign=\"top\"\u003e\n \u003cp\u003eRecife\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e14.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e20.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.329896907216494%\" valign=\"top\"\u003e\n \u003cp\u003e-1.3 (-5.2 to 2.7); p=0.516\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\" valign=\"top\"\u003e\n \u003cp\u003eMacei\u0026oacute;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e13.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.329896907216494%\" valign=\"top\"\u003e\n \u003cp\u003e-1.3 (-6.8 to 4.4); p=0.673\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\" valign=\"top\"\u003e\n \u003cp\u003eAracaju\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.329896907216494%\" valign=\"top\"\u003e\n \u003cp\u003e-2.0 (-8.0 to 4.4); p=0.581\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\" valign=\"top\"\u003e\n \u003cp\u003eSalvador\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.329896907216494%\" valign=\"top\"\u003e\n \u003cp\u003e-3.7 (-10.3 to 3.5); p=0.394\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCentral-West\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.329896907216494%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\" valign=\"top\"\u003e\n \u003cp\u003eCampo Grande\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e10.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.329896907216494%\" valign=\"top\"\u003e\n \u003cp\u003e-3.2 (-10.3 to 4.4); p=0.442\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\" valign=\"top\"\u003e\n \u003cp\u003eCuiab\u0026aacute;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e33.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e24.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.329896907216494%\" valign=\"top\"\u003e\n \u003cp\u003e-4.1 (-9.4 to 1.5); p=0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\" valign=\"top\"\u003e\n \u003cp\u003eGoi\u0026acirc;nia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e25.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.329896907216494%\" valign=\"top\"\u003e\n \u003cp\u003e4.0 (-0.4 to 8.6); p=0.131\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\" valign=\"top\"\u003e\n \u003cp\u003eBras\u0026iacute;lia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e13.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.329896907216494%\" valign=\"top\"\u003e\n \u003cp\u003e-11.9* (-0.5 to -24.7); p\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSoutheast\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.329896907216494%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\" valign=\"top\"\u003e\n \u003cp\u003eBelo Horizonte\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.329896907216494%\" valign=\"top\"\u003e\n \u003cp\u003e-6.3 (-13.8 to 1.9); p=0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\" valign=\"top\"\u003e\n \u003cp\u003eVit\u0026oacute;ria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e13.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e18.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.329896907216494%\" valign=\"top\"\u003e\n \u003cp\u003e-16.4 (-39.9 to 16.5); p=0.387\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\" valign=\"top\"\u003e\n \u003cp\u003eRio de Janeiro\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e13.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.329896907216494%\" valign=\"top\"\u003e\n \u003cp\u003e2.9 (-3.1 to 9.4); p=0.394\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\" valign=\"top\"\u003e\n \u003cp\u003eS\u0026atilde;o Paulo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.329896907216494%\" valign=\"top\"\u003e\n \u003cp\u003e-7.0* (-11.5 to -2.4); p\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSouth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.329896907216494%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\" valign=\"top\"\u003e\n \u003cp\u003eCuritiba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.329896907216494%\" valign=\"top\"\u003e\n \u003cp\u003e-3.8 (-8.3 to 0.9); p=0.164\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\" valign=\"top\"\u003e\n \u003cp\u003eFlorian\u0026oacute;polis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.329896907216494%\" valign=\"top\"\u003e\n \u003cp\u003e-5.1 (-56.0 to 104.5); p=0.993\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.144329896907216%\" valign=\"top\"\u003e\n \u003cp\u003ePorto Alegre\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.329896907216494%\" valign=\"top\"\u003e\n \u003cp\u003e27.8 (-6.0 to 73.7); p=0.172\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eLegend\u003c/strong\u003e: \u003csup\u003e*\u003c/sup\u003e Statistical significance (p\u0026lt;0.05); \u003csup\u003e1-\u0026nbsp;\u003c/sup\u003eRate of grade 2/ 1 million inhabitants; AAPC: \u003cem\u003eAverage Annual Percent Change\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eMunicipalities with rates of new leprosy cases with G2D\u0026thinsp;\u0026gt;\u0026thinsp;10/million were mainly concentrated in the northern and central-western regions of Brazil. It should be noted that in the years of the Covid-19 pandemic, the number of municipalities in this stratum of endemicity was lower than in previous years. For example, in 2019, 14.5% (n\u0026thinsp;=\u0026thinsp;807) of the municipalities had a G2D rate\u0026thinsp;\u0026gt;\u0026thinsp;10/1 million. In 2020, this percentage was 10.0% (n\u0026thinsp;=\u0026thinsp;559) (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eMoran\u0026apos;s local statistics showed spatial dependence in all the years analyzed (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The local statistics indicated the existence of spatial clusters, concentrated in the North and Central-West regions. The number of municipalities in quadrant Q1 (High-high) varied between the years, ranging from 170 (3.1%) in 2001 to 118 (2.1%) in 2019. It should be noted that in the years of the Covid-19 pandemic, the number of municipalities in the High-High quadrant was lower than in the pre-pandemic years: 91 (1.6%) in 2020, 88 (1.6%) in 2021 and 59 (1.1%) in 2022. In addition, there was a change in the spatial pattern over the time series, with a reduction in municipalities in the North and an increase in municipalities in the Central-West in the Q1 quadrant (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eConsidering the rate for the period 2001\u0026ndash;2022, 40.1% (n\u0026thinsp;=\u0026thinsp;2234) of Brazilian municipalities had a G2D rate\u0026thinsp;\u0026gt;\u0026thinsp;10/1 million. Although there was a concentration in the North and Central-West regions of Brazil, clusters were found in the other regions. In the Moran map, 635 (14.1%) municipalities were in the high-high quadrant. These municipalities belong to 12 Brazilian states, one in the Northeast (Maranh\u0026atilde;o), one in the South (Paran\u0026aacute;), two in the Southeast (Minas Gerais and Esp\u0026iacute;rito Santo), all three in the Central-West (Goi\u0026aacute;s, Mato Grosso and Mato Grosso do Sul) and five in the North (Acre, Rond\u0026ocirc;nia, Amazonas, Par\u0026aacute; and Tocantins). In addition, these municipalities together accounted for 30 percent (n\u0026thinsp;=\u0026thinsp;15,139) of all cases recorded in the country during the 22 years analyzed (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eIn addition, three spatial clusters were observed: the first consisting of five states in the north, three in the central-west and one in the northeast; the second observed in the southeast, between Minas Gerais and Esp\u0026iacute;rito Santo and a third located in the state of Paran\u0026aacute; (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study described the temporal trend and identified the areas at risk of developing physical disability due to leprosy in Brazil over a twenty-one year time series. A variation in the trend was observed over the period, with three regions showing a decreasing trend and two showing a stationary trend in the coefficient of Grade 2 physical disability, suggesting the persistence of late-diagnosed cases. Despite global reductions in the detection rates of new leprosy cases [\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], Brazil still reported more than fifty thousand new cases with grade 2 physical disability between 2001 and 2022.\u003c/p\u003e \u003cp\u003eThe highest rates of new cases of leprosy with G2D were concentrated among adult males with black skin, low levels of education, and living in the northern and central-western regions of Brazil. National and international studies show a higher prevalence of the disease among men with lower levels of education, which may reflect social and economic inequalities, especially in neglected populations [\u003cspan additionalcitationids=\"CR33 CR34 CR35 CR36 CR37 CR38\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. In general, men are more vulnerable to the disease and its more severe forms, often due to behaviours that make it difficult for them to seek appropriate health care, combined with a lack of specialized services to meet their specific needs [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePatients with the multibacillary clinical form are more likely to develop physical disabilities. In our study, they accounted for 80.8 percent of the reported cases. The presence of leprosy disabilities is an indicator of late diagnosis and treatment [\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. This underscores the need to define and strengthen public health interventions aimed at early diagnosis and comprehensive care of people affected by \u003cem\u003eM. leprae\u003c/em\u003e, especially those with severe forms of the disease, in order to reduce disability, mitigate associated stigma, and reduce disease transmission [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe results show that there has been a decrease in the rate of new cases with G2D in Brazil. The Southeast, South and North regions showed a decreasing trend, while the Northeast and Central-West regions showed a stable trend. These results are consistent with other studies of leprosy conducted in Brazil, which have generally shown a reduction in the burden of leprosy in Brazil [\u003cspan additionalcitationids=\"CR46\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRoraima was the state with the highest rate of decline, from 54.9/1\u0026nbsp;million in 2001 to 9.4/1\u0026nbsp;million in 2022. A study conducted in this state between 2001 and 2017 showed a decrease in prevalence and case detection rates, demonstrating a trend toward elimination of the disease [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. In an analysis measuring the severity of the disease in Brazil, Roraima, Para\u0026iacute;ba, Rio Grande do Sul and Esp\u0026iacute;rito Santo have better results in the fight against leprosy [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. This decrease can be attributed to the implementation of the National Leprosy Control Programme in Brazil, which promoted and facilitated the decentralization of leprosy control to primary health care [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNone of Brazil's state capitals showed an upward trend. Manaus/AM, Macap\u0026aacute;/PA, Bras\u0026iacute;lia/DF and the capital S\u0026atilde;o Paulo/SP showed a downward trend. This finding may be related to two factors: (i) the ability of large centers to have better infrastructure and health care, which may facilitate early diagnosis [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], (ii) the higher hidden prevalence of the disease (50\u0026ndash;52). This second factor seems to be the most appropriate to explain the stationary trend in the vast majority of capital cities. In S\u0026atilde;o Paulo, for example, a study showed the hidden prevalence of the disease, whose real prevalence was six times higher than the observed prevalence [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. The other Brazilian capitals show stationary behavior and some remain hyperendemic for leprosy, requiring additional efforts in the process of eliminating the disease [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhen analyzing the municipalities, endemicity fluctuated over the period analyzed, especially during the Covid-19 pandemic, where there was a decrease in the rates of new leprosy cases with G2D compared to previous years in the series. For example, in 2019, 14.5% (n\u0026thinsp;=\u0026thinsp;807) of the municipalities had a rate\u0026thinsp;\u0026gt;\u0026thinsp;10/million. In 2020, this percentage was 10.0% (n\u0026thinsp;=\u0026thinsp;559). The Covid-19 pandemic has had an impact on diagnoses, increasing the hidden prevalence of the disease, as health systems in Brazil and around the world have turned their attention to controlling the pandemic, affecting the ability to maintain service provision and ensure adequate care for the population, especially for chronic and neglected diseases [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan additionalcitationids=\"CR56\" citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe Covid-19 pandemic has created challenges in the follow-up and treatment of leprosy patients in primary health care centers throughout Brazil. This has led to delays in appointments, the need to reschedule appointments, treatment extensions due to drug shortages, and barriers to contact assessment and active case finding. These factors have had a negative impact on the leprosy program in all regions of the country [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe impact of the pandemic has been observed worldwide: the World Health Organization published a report showing a decrease in the number of countries and territories sharing leprosy statistics, from 166 (out of 221) in 2019 to 127 in 2020 and 143 in 2021, as well as a 37% global decrease in the detection of new cases of the disease [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Our study also noted the impact of the pandemic on high-risk areas: the number of municipalities in the high-high quadrant was lower than in the pre-pandemic years: 91 (1.6%) in 2020, 88 (1.6%) in 2021, and 59 (1.1%) in 2022.\u003c/p\u003e \u003cp\u003eDespite all the methodological care taken, this study has important limitations. First, it used secondary data from SINAN, which is directly influenced by the capacity of municipalities to detect and report new leprosy cases. In this way, the capacity of local surveillance services directly influences the records and thus the results of this study. Second, assessing the degree of physical disability is still a challenge for many municipalities, especially smaller ones with few human resources. Thus, the quality of the neurological examination and the degree of disability may influence the results.\u003c/p\u003e \u003cp\u003eAnalysis and interpretation of these results are important for the development and evaluation of leprosy surveillance activities in Brazil. It suggests the need to strengthen active case finding and continuous monitoring of leprosy patients. These measures are essential to ensure an effective approach to control and to prevent the spread of the disease and its disabilities.\u003c/p\u003e \u003cp\u003eOur results show spatial inequalities in the incidence of leprosy disability, which may be related to differences in services between health regions and in the implementation of disease control interventions, particularly with regard to timely diagnosis and treatment. In addition, these findings highlight the importance of discussing the control of neglected diseases such as leprosy and the development of new technologies for their diagnosis and treatment. These initiatives are essential to improve the effectiveness of public health strategies to control this disease.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data were extracted from the National System of Notifiable Diseases (SINAN), available in the database of the Department of Informatics of the Unified Health System (DATASUS) (http://datasus.saude.gov.br/)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTSM and CDF: Data collection, interpretation of findings, analysis, interpretation, drafting of the manuscript and study supervision. \u0026nbsp;TRMOF, MBS, RJVCB, DUSM, RFC, and TFAS: Data collection, interpretation of findings, analysis and drafting of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBrasil. 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PMID: 29538501.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. Global leprosy update. 2015: time for action, accountability and inclusion. Weekly Epidemiol Rec. 2016; 91:405\u0026thinsp;\u0026ndash;\u0026thinsp;20 [cited 2016 Nov 02]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.who.int/lep/resources/who_wer9135/en/\u003c/span\u003e\u003cspan address=\"http://www.who.int/lep/resources/who_wer9135/en/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eda Paz WS, Souza MDR, Tavares DDS, de Jesus AR, Dos Santos AD, do Carmo RF, et al. Impact of the COVID-19 pandemic on the diagnosis of leprosy in Brazil: An ecological and population-based study. Lancet Reg Health Am. 2022;9:100181. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.lana.2021.100181\u003c/span\u003e\u003cspan address=\"10.1016/j.lana.2021.100181\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2022 Jan 15. PMID: 35072147; PMCID: PMC8759948. Dispon\u0026iacute;vel em. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/35072147/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/35072147/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatos TS, do Nascimento VA, do Carmo RF, Fernandes TRMO, de Souza CDF, da Silva TFA. Impact of the COVID-19 pandemic on the diagnosis of new leprosy cases in Northeastern Brazil, 2020. 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Dispon\u0026iacute;vel em:. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/publications/i/item/who-wer9736-429-450\u003c/span\u003e\u003cspan address=\"https://www.who.int/publications/i/item/who-wer9736-429-450\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Leprosy, Physical disability grade 2, Clusters of risk areas, Ecological study, Analysed the temporal trend","lastPublishedDoi":"10.21203/rs.3.rs-4312843/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4312843/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBACKGROUND\u003c/h2\u003e \u003cp\u003eLeprosy is a chronic infectious disease that affects the peripheral nerves, leading to neurological damage and physical disability. This study analysed the temporal trend in the rate of new leprosy cases with grade 2 physical disability in Brazil and identified spatial clusters of risk for physical disability.\u003c/p\u003e\u003ch2\u003eMETHODS\u003c/h2\u003e \u003cp\u003eThis is an ecological study of all new cases of leprosy with grade 2 physical disability (G2D) at diagnosis between 2001 and 2022 in Brazil, macro-regions, states and municipalities. Data were extracted from the National System of Notifiable Diseases and the Brazilian Institute of Geography and Statistics. Sociodemographic and clinical variables such as age, sex, race/colour, education and clinical form of the disease were analysed, as well as the rate of new cases of leprosy with grade 2 physical disability at the time of diagnosis per million inhabitants. Data analysis was divided into three stages: 1- Descriptive analysis of the socio-demographic and clinical profile; 2- Time series analysis: In this stage, the joint-point regression model was used; 3- Spatial analysis, using global and local Moran statistics.\u003c/p\u003e\u003ch2\u003eRESULTS\u003c/h2\u003e \u003cp\u003eThere were 50,466 new cases of leprosy with G2D during the period studied, 97.4% aged\u0026thinsp;\u0026gt;\u0026thinsp;14 years, 70.2% male, 45.7% brown, 87.6% with low education, 90.2% multibacillary and 47.5% dimorphic clinical form. There was a downward trend in the detection of leprosy with physical disability grade 2, with an average annual reduction of 2.8% (13.6/1\u0026nbsp;million in 2001 and 9.1/1\u0026nbsp;million in 2022). The southeast, south and north regions showed a downward trend. A total of 14 states and 4 capitals showed a decrease in the rate, the most significant being Roraima (-11.0%/year). Tocantins was the only state with an upward trend (3.2%/year). 635 (14.1%) municipalities were in the high-high quadrant. These municipalities belong to 12 Brazilian states and accounted for 30% (n\u0026thinsp;=\u0026thinsp;15,139) of all cases recorded in the country over the 22 years analysed.\u003c/p\u003e\u003ch2\u003eCONCLUSIONS\u003c/h2\u003e \u003cp\u003eThese results suggest an overall decrease in the detection of leprosy with physical disability in Brazil and highlight geographical disparities in the occurrence of leprosy disability. Interventions to promote early diagnosis should be targeted at areas with the highest rate of new cases with grade 2 disability.\u003c/p\u003e","manuscriptTitle":"Time trend and identification of risk areas for physical disability due to leprosy in Brazil: An ecological study, 2001-2022","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-21 18:59:15","doi":"10.21203/rs.3.rs-4312843/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-12-19T19:55:40+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-18T22:08:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"271456299004891565029021998456346227330","date":"2024-08-12T11:23:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-08T17:55:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"23113847847902480937782562056739726980","date":"2024-08-03T17:05:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"193633628228289995864056636768151479045","date":"2024-08-02T17:38:59+00:00","index":"hide","fulltext":""},{"type":"editorInvited","content":"","date":"2024-06-25T13:06:46+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-27T14:29:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"52772648832074277713127614469391192533","date":"2024-05-18T15:44:14+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-16T02:26:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-10T06:20:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-09T16:33:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2024-04-23T14:26:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bdcdeb4b-62b6-4bd8-aeb3-a0914a06cf62","owner":[],"postedDate":"May 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-03-10T19:53:53+00:00","versionOfRecord":{"articleIdentity":"rs-4312843","link":"https://doi.org/10.1186/s12879-025-10586-2","journal":{"identity":"bmc-infectious-diseases","isVorOnly":false,"title":"BMC Infectious Diseases"},"publishedOn":"2025-03-06 15:57:02","publishedOnDateReadable":"March 6th, 2025"},"versionCreatedAt":"2024-05-21 18:59:15","video":"","vorDoi":"10.1186/s12879-025-10586-2","vorDoiUrl":"https://doi.org/10.1186/s12879-025-10586-2","workflowStages":[]},"version":"v1","identity":"rs-4312843","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4312843","identity":"rs-4312843","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}