Spatial patterns of new leprosy cases in a northeastern state of Brazil, 2011-2021.

OBJECTIVE: To analyze the spatial patterns of leprosy in Pernambuco from 2011 to 2021. METHODS: This is an ecological epidemiological study, carried out with data from the Notifiable Diseases Information System, based on new cases of leprosy among inhabitants of Pernambuco, between 2011-2021. An empirical Bayesian analysis of local and spatial dependence was performed with the global and local Moran indices. RESULTS: 25,008 new cases of leprosy were registered with an annual case detection rate in the general population of 16.51 cases/100,000 inhabitants - which is considered high. Among those younger than 15 years of age, there were 5.16 cases/100,000 inhabitants (high) and 0.89/100,000 inhabitants with degree II of physical disability (low); there were also many high-risk cases with an overall Moran index of 0.33 (p<0.001), active transmission (0.26; p<0.001), and subsequent diagnosis of the disease (0.12; p<0.006), as well as distribution in macro-region 1 and macro-region 4. CONCLUSION: There was a heterogeneous spatial distribution in the state, showing two overviews, the first being the presence of municipalities with high risk of disease transmission and the second with clusters of silent municipalities, reinforcing the character of leprosy neglect as a major public health problem. This study brings reflections for leprosy control actions, due to the identification of priority areas to combat this disease in Pernambuco.

Establishing a standard method for analysing case detection delay in leprosy using a Bayesian modelling approach.

BACKGROUND: Leprosy is an infectious disease caused by Mycobacterium leprae and remains a source of preventable disability if left undetected. Case detection delay is an important epidemiological indicator for progress in interrupting transmission and preventing disability in a community. However, no standard method exists to effectively analyse and interpret this type of data. In this study, we aim to evaluate the characteristics of leprosy case detection delay data and select an appropriate model for the variability of detection delays based on the best fitting distribution type. METHODS: Two sets of leprosy case detection delay data were evaluated: a cohort of 181 patients from the post exposure prophylaxis for leprosy (PEP4LEP) study in high endemic districts of Ethiopia, Mozambique, and Tanzania; and self-reported delays from 87 individuals in 8 low endemic countries collected as part of a systematic literature review. Bayesian models were fit to each dataset to assess which probability distribution (log-normal, gamma or Weibull) best describes variation in observed case detection delays using leave-one-out cross-validation, and to estimate the effects of individual factors. RESULTS: For both datasets, detection delays were best described with a log-normal distribution combined with covariates age, sex and leprosy subtype [expected log predictive density (ELPD) for the joint model: -1123.9]. Patients with multibacillary (MB) leprosy experienced longer delays compared to paucibacillary (PB) leprosy, with a relative difference of 1.57 [95% Bayesian credible interval (BCI): 1.14-2.15]. Those in the PEP4LEP cohort had 1.51 (95% BCI: 1.08-2.13) times longer case detection delay compared to the self-reported patient delays in the systematic review. CONCLUSIONS: The log-normal model presented here could be used to compare leprosy case detection delay datasets, including PEP4LEP where the primary outcome measure is reduction in case detection delay. We recommend the application of this modelling approach to test different probability distributions and covariate effects in studies with similar outcomes in the field of leprosy and other skin-NTDs.

Performance of the Health System Network in Formosa, Argentina, in the Diagnosis of Leprosy.

Leprosy is a chronic, neglected tropical infectious disease, currently endemic in Formosa, a province in northwestern Argentina. To analyze the performance, distribution, and effectiveness of the health system in leprosy diagnosis in Formosa, we estimated the trend of the number of new cases of leprosy diagnosed between 2002 and 2019 and estimated a forecast for 2022 at the primary health care centers (PHCCs) of at the first level of care (1stLC), at district hospitals (DHs) of the second level of care (2ndLC), high-complexity hospitals at the third level of care (3rdLC), and in rural and urban areas. The general trend was calculated based on the new cases detection rate (NCDR) using the autoregressive-moving average model (ARMA). The 1stLC, 2ndLC, and 3rdLC and the rural/urban variables were assessed using a proportional Bayesian trend ARMA (TrARMA) model. A predictive model was used for estimated forecasts. Markov-Monte Carlo chains were applied with A Metropolis-Hastings's algorithm. The highest median proportion (Mp) of new cases of leprosy was diagnosed at the 2ndLC (Mp, 0.67; 97.5% credibility interval [CI] [0.56-0.77]), at the 3rdLC (Mp, 0.11; 97.5% CI [0.08-0.15]), and in urban areas (urban median proportion (uMp), 0.86; 97.5% CI [0.83- 0.88]), whereas the lowest proportion of new cases was diagnosed at the 1stLC (Mp, 0.082; 97.5% CI [0.061-0.108]) and in rural areas (rural median proportion (rMp), 0.13; 97.5% CI [0.11-0.16]). Our model predicts for 2022 that a median number of new cases of leprosy of 19.70 will be diagnosed in urban areas (97.5% CI [15.94-23.80]), and will continue to be diagnosed at the 2ndLC (median number of cases, 15.33; 97.5% CI [12.40-10.52]) and 3rdLC (median number of cases, 2.43; 97.5% CI [1.97-2.94]).

DNA databases of an important tropical timber tree species Shorea leprosula (Dipterocarpaceae) for forensic timber identification.

International timber trade communities are increasingly demanding that timber in the wood supply chain be sourced from sustainably harvested forests and certified plantations. This is to combat illegal logging activities to prevent further depletion of our precious forests worldwide. Hence, timber tracking tools are important to support law enforcement officials in ensuring only sustainably harvested timbers are traded in the market. In this study, we developed chloroplast DNA (cpDNA) and simple sequence repeat (SSR) databases as tracking tools for an important tropical timber tree species, Shorea leprosula from Peninsular Malaysia. A total of 1410 individual trees were sampled from 44 natural populations throughout Peninsular Malaysia. Four cpDNA regions were used to generate a cpDNA haplotype database, resulting in a haplotype map comprising 22 unique haplotypes derived from 28 informative intraspecific variable sites. This cpDNA database can be used to trace the origin of an unknown log at the regional level. Ten SSR loci were used to develop the SSR allele frequency database. Bayesian cluster analysis divided the 44 populations into two genetic clusters corresponding to Region A and Region B. Based on conservativeness evaluation of the SSR databases for individual identification, the coancestry coefficients (θ) were adjusted to 0.1900 and 0.1500 for Region A and B, respectively. These databases are useful tools to complement existing timber tracking systems in ensuring only legally sourced timbers are allowed to enter the wood supply chain.

Correlated geometric models of order k and its application to intensive care unit and leprosy data.

Geometric models are used to analyse the discrete time until the occurrence of an event of interest (success or consecutive successes). In two real data sets, named leprosy and intensive care unit (ICU), the events correspond, respectively, to abandoning the clinical treatment of leprosy, where abandonment corresponds to four consecutive patient absences from treatment, and the patient's discharge from the ICU. The distribution proposed in this article, called the correlated geometric distribution of order k (or correlated k-order geometric distribution), k≥1 , consists of including a correlation parameter in the geometric distribution of order k, thus considering the dependence between patient responses until the occurrence of the event. This model proves to be a better option for real data analysis where the effect of individual correlation is considered. The model is applied to real leprosy data to estimate the treatment abandonment probability. Bayesian methods are used to determine the parameter estimators of the models and to evaluate regression models. The covariates are related to the probability of the event by an appropriate link function chosen by Bayesian selection criteria. A diagnostic analysis evaluates the models fit by posterior randomized quantile residuals and influential observations by ψ -divergence measures. This methodology is illustrated by simulation studies and real ICU admission data analysis. Studies show a good fit of the proposed model. Real data analyses also find that the probabilities of the event of interest can be overestimated or underestimated when modeled without considering the effect of dependency on the model.

Estimating underreporting of leprosy in Brazil using a Bayesian approach.

BACKGROUND: Leprosy remains concentrated among the poorest communities in low-and middle-income countries and it is one of the primary infectious causes of disability. Although there have been increasing advances in leprosy surveillance worldwide, leprosy underreporting is still common and can hinder decision-making regarding the distribution of financial and health resources and thereby limit the effectiveness of interventions. In this study, we estimated the proportion of unreported cases of leprosy in Brazilian microregions. METHODOLOGY/PRINCIPAL FINDINGS: Using data collected between 2007 to 2015 from each of the 557 Brazilian microregions, we applied a Bayesian hierarchical model that used the presence of grade 2 leprosy-related physical disabilities as a direct indicator of delayed diagnosis and a proxy for the effectiveness of local leprosy surveillance program. We also analyzed some relevant factors that influence spatial variability in the observed mean incidence rate in the Brazilian microregions, highlighting the importance of socioeconomic factors and how they affect the levels of underreporting. We corrected leprosy incidence rates for each Brazilian microregion and estimated that, on average, 33,252 (9.6%) new leprosy cases went unreported in the country between 2007 to 2015, with this proportion varying from 8.4% to 14.1% across the Brazilian States. CONCLUSIONS/SIGNIFICANCE: The magnitude and distribution of leprosy underreporting were adequately explained by a model using Grade 2 disability as a marker for the ability of the system to detect new missing cases. The percentage of missed cases was significant, and efforts are warranted to improve leprosy case detection. Our estimates in Brazilian microregions can be used to guide effective interventions, efficient resource allocation, and target actions to mitigate transmission.

High-risk transmission clusters of leprosy in an endemic area in the Northeastern Brazil: A retrospective spatiotemporal modelling (2001-2019).

OBJECTIVES: To analyse and map the leprosy risk areas in the state of Alagoas, an endemic region in the Northeastern Brazil, between 2001 and 2019. METHODS: Ecological and time series study, using spatial analysis techniques. First, we analyse the epidemiological aspects of leprosy cases, using the data available in the Notifiable Diseases Information System; then, we used the segmented log-linear regression model to assess time trends. Spatial distribution was analysed by the Local Empirical Bayesian Estimator and by calculating the Global and Local Moran Index. Finally, spatiotemporal clusters were identified through scanning statistics, using the Kulldorf method of retrospective analysis. RESULTS: We observed that Alagoas showed an average new case detection rate of 14.43/100,000 inhabitants between 2001 and 2019, being classified as highly endemic. The area of highest risk was the 9th health region (state hinterland), with increasing time trend (Annual Percentage Change/APC = 7.2; p-value < 0.05). Several clusters of high risk of leprosy transmission were verified in Alagoas, including the state capital and hinterland municipalities. CONCLUSIONS: Our data indicate that active M. leprae transmission persists in Alagoas; that diagnosis is delayed and that there are high-risk areas, especially in inland municipalities.

New species and findings of emPagastia/em Oliver (Diptera: Chironomidae: Diamesinae) from Central Asia, with DNA barcoding of known species of the genus.

Chironomids of the genus Pagastia Oliver (Diamesinae, Diamesini) from the mountains of Central Asia are revised using both morphological characters and molecular data. Illustrated descriptions of the adult male Pagastia (P.) caelestomontana sp. nov. from Kirgizstan and Tajikistan, P. (P.) hanseni sp. nov. from Tajikistan, and record of a finding apparently a new species P. (P.) aff. lanceolata (Tokunaga) from Tajikistan as well as an updated a key to the determination of the adult males of all known species of Pagastia are provided. A phylogenetic framework is reconstructed based on two mitochondrial genes cytochrome oxidase subunit I (COI) sequences of 34 samples belonging to 7 species of the genus Pagastia and cytochrome oxidase subunit II (COII) available for most samples. Phylogenetic trees of some known species of the genus Pagastia were reconstructed using the combined dataset and Bayesian inference (BI) and Maximum Likelihood (ML) methods. The interspecific K2P distances between seven Pagastia species including P. (P.) caelestomontana sp. nov., P. (P.) hanseni sp. nov. and undescribed P. (P.) aff. lanceolata (Tokunaga) are 6.3-13.2 which corresponding to species level.

Spatial analysis of epidemiological and quality indicators of health services for leprosy in hyperendemic areas in Northeastern Brazil.

Leprosy is a public health problem due to the physical disabilities and deformities it causes. This study aimed to describe new leprosy cases using an operational classification and analyzing spatial patterns by means of epidemiological and quality indicators of health services in Pernambuco State, Brazil, between 2005 and 2014. This was an ecological study performed in 184 municipalities grouped into 12 health regions units for analysis. To analyze spatial patterns, the Bayesian local empirical method and Moran's spatial autocorrelation indicator were applied and box and Moran maps were used. Individuals aged ≥15 years old, grade zero physical disability and complete remission as the treatment outcome were predominant in both paucibacillary and multibacillary cases, the only difference was the predominance of females (n=9,286; 63.00%) and males (n=8,564; 60.70%), respectively. These variables were correlated (p<0.05) with the operational classification. The overall detection rate showed three high-priority areas; the indicator rate of grade 2 physical disability revealed clusters in regions IV, V, and VI; and the indicator rate of cases with some degree of disability showed precarious municipalities in seven health regions. Pernambuco maintains an active chain of transmission and ongoing endemicity of leprosy. Therefore, spatial analysis methods allow the identification of priority areas for intervention, thereby supporting the disease elimination strategy.

[Spatial modeling of leprosy in the state of Bahia, Brazil, (2001-2015) and social determinants of health]. / Modelagem espacial da hanseníase no estado da Bahia, Brasil, (2001-2015) e determinantes sociais da saúde.

This work analyzes the spatial distribution of leprosy in Bahia and associated social determinants. It is an ecological study, with leprosy data from 2001-2015. Three epidemiological indicators were selected: coefficient of detection in the general population and in children under 15 and the rate of new cases with grade II physical disability. These indicators were flattened by the Local Empirical Bayesian Model and Global and Local Moran statistics were applied. The independent variables were selected from the IBGE-2010 Census. Multivariate regressions were employed, followed by spatial regression. Leprosy exhibited a heterogeneous distribution in the state, with concentration in the north-west axis and the south region. For the general detection coefficient, five variables composed the final model: demographic density, urban population proportion, per capita income, proportion of extremely poor and households with over three people per dormitory. The illiteracy proportion made up the final model for the grade II rate of physical disability. No determinants of the occurrence of the disease were identified in children under 15. The modeling used contributed to demonstrate the spatial heterogeneity and social determinants of the disease in Bahia, revealing the complexity of the problem.

O trabalho analisa a distribuição espacial da hanseníase na Bahia e os determinantes sociais relacionados. Estudo ecológico com dados de hanseníase do período 2001-2015. Três indicadores epidemiológicos foram selecionados: coeficiente de detecção na população geral e em menores de 15 anos e a taxa de casos novos com grau II de incapacidade. Os indicadores foram suavizados pelo Modelo Bayesiano Empírico Local e aplicou-se estatística de Moran Global e Local. As variáveis independentes foram selecionadas a partir do Censo IBGE-2010. Regressões multivariadas foram empregadas, seguidas de regressão espacial. Observou-se distribuição heterogênea no estado, com concentração no eixo norte-oeste e região sul. Para o coeficiente de detecção geral, cinco variáveis compuseram o modelo: densidade demográfica, proporção da população urbana, renda per capita, proporção de extremamente pobres e domicílios com mais de três pessoas por dormitório. A proporção de analfabetismo compôs o modelo final para a taxa de grau II de incapacidade física. Não foram identificados determinantes da ocorrência da doença em menores de 15 anos. A modelagem utilizada contribuiu para demonstrar a heterogeneidade espacial e os determinantes sociais da doença na Bahia, colocando em evidência a complexidade do problema.

Modelagem espacial da hanseníase no estado da Bahia, Brasil, (2001-2015) e determinantes sociais da saúde / Spatial modeling of leprosy in the state of Bahia, Brazil, (2001-2015) and social determinants of health

Resumo O trabalho analisa a distribuição espacial da hanseníase na Bahia e os determinantes sociais relacionados. Estudo ecológico com dados de hanseníase do período 2001-2015. Três indicadores epidemiológicos foram selecionados: coeficiente de detecção na população geral e em menores de 15 anos e a taxa de casos novos com grau II de incapacidade. Os indicadores foram suavizados pelo Modelo Bayesiano Empírico Local e aplicou-se estatística de Moran Global e Local. As variáveis independentes foram selecionadas a partir do Censo IBGE-2010. Regressões multivariadas foram empregadas, seguidas de regressão espacial. Observou-se distribuição heterogênea no estado, com concentração no eixo norte-oeste e região sul. Para o coeficiente de detecção geral, cinco variáveis compuseram o modelo: densidade demográfica, proporção da população urbana, renda per capita, proporção de extremamente pobres e domicílios com mais de três pessoas por dormitório. A proporção de analfabetismo compôs o modelo final para a taxa de grau II de incapacidade física. Não foram identificados determinantes da ocorrência da doença em menores de 15 anos. A modelagem utilizada contribuiu para demonstrar a heterogeneidade espacial e os determinantes sociais da doença na Bahia, colocando em evidência a complexidade do problema.

Abstract This work analyzes the spatial distribution of leprosy in Bahia and associated social determinants. It is an ecological study, with leprosy data from 2001-2015. Three epidemiological indicators were selected: coefficient of detection in the general population and in children under 15 and the rate of new cases with grade II physical disability. These indicators were flattened by the Local Empirical Bayesian Model and Global and Local Moran statistics were applied. The independent variables were selected from the IBGE-2010 Census. Multivariate regressions were employed, followed by spatial regression. Leprosy exhibited a heterogeneous distribution in the state, with concentration in the north-west axis and the south region. For the general detection coefficient, five variables composed the final model: demographic density, urban population proportion, per capita income, proportion of extremely poor and households with over three people per dormitory. The illiteracy proportion made up the final model for the grade II rate of physical disability. No determinants of the occurrence of the disease were identified in children under 15. The modeling used contributed to demonstrate the spatial heterogeneity and social determinants of the disease in Bahia, revealing the complexity of the problem.

DNA barcode and minibarcode identification of freshwater fishes from Cerrado headwater streams in Central Brazil.

The extraordinary species diversity of the Neotropical freshwater fish fauna is world renown. Yet, despite rich species diversity, taxonomic and genetic resources for its Cerrado ichthyofauna remain poorly developed. We provide a reference library of 149 DNA barcodes for 39 species/lineages of Cerrado headwater stream fishes from the Brazilian Distrito Federal and nearby areas and test the utility of distance-based criteria, tree-based criteria and minibarcodes for specimen identification. Mean Kimura 2-parameter genetic distances within species to orders ranged 1·8-12·1%. However, mean intraspecific v. congeneric-interspecific distances (0·9-1·3%) overlapped extensively and distance-based barcoding failed to achieve correct identifications due to c. 4-12·1% error rates and 19·5% ambiguous identifications related to the presence of singletons. Overlap was reduced and best-match success rates improved drastically to 83·5% when Characidium barcodes representing potential misidentifications or undescribed species were removed. Tree-based monophyly criteria generally performed similarly to distance methods, correctly differentiating up to c. 85% of species/lineages despite neighbour-joining and Bayesian tree errors (random lineage-branching events, long-branch attraction). Five clusters (Ancistrus aguaboensis, Characidium spp., Eigenmannia trilineata, Hasemania hanseni and Hypostomus sp. 2) exhibited deep intraspecific divergences or para-/polyphyly and multiple Barcode Index Number assignments indicative of putative candidate species needing taxonomic re-examination. Sliding-window analyses also indicated that a 200 bp minibarcode region performed just as well at specimen identification as the entire barcode gene. Future DNA barcoding studies of Distrito Federal-Cerrado freshwater fishes will benefit from increased sampling coverage, as well as consideration of minibarcode targets for degraded samples and next-generation sequencing.

Spatial modeling of leprosy in the state of Bahia and its social determinants: a study of health inequities.

BACKGROUND: Leprosy is a neglected disease caused by Mycobacterium leprae. Brazil has the second largest number of cases in the world. OBJECTIVES: To analyze the spatial distribution of leprosy in the state of BAHIA, Brazil, and the association between his occurrence and the synthetic indicators of municipal socioeconomic performance, social vulnerability and income inequality. METHODS: An ecological study with secondary data obtained from the National System of Notifiable Diseases. Dependent variables: coefficient of detection in the general population and in the population under 15 years old and the rate of grade II of physical disability. Independent variables: Synthetic indicators of socioeconomic performance, social vulnerability and income inequality. RESULTS: The highest coefficients of detection of new cases in the general population and in children under 15 years old are concentrated in the north-west axis and in the southern region of the state. On the other hand, the highest rates of degree II of physical incapacity are concentrated in the north, northeast and south regions. Only the Index of Social and Economic Performance(IPESE)-Economy and Finance composed the final regression model of the general detection coefficients and in children under 15 years old. The municipalities with the highest indexes had the highest detection coefficients, reflecting the capacity to diagnose new cases. STUDY LIMITATIONS: The use of synthetic indicators is a limitation of the study. CONCLUSIONS: Leprosy presents a heterogeneous spatial pattern in the state of BAHIA, and the IPESE-Economics and Finance indicator is the only one with explanatory potential of the disease.

Spatial modeling of leprosy in the state of Bahia and its social determinants: a study of health inequities

Abstract BACKGROUND: Leprosy is a neglected disease caused by Mycobacterium leprae. Brazil has the second largest number of cases in the world. OBJECTIVES: To analyze the spatial distribution of leprosy in the state of BAHIA, Brazil, and the association between his occurrence and the synthetic indicators of municipal socioeconomic performance, social vulnerability and income inequality. METHODS: An ecological study with secondary data obtained from the National System of Notifiable Diseases. Dependent variables: coefficient of detection in the general population and in the population under 15 years old and the rate of grade II of physical disability. Independent variables: Synthetic indicators of socioeconomic performance, social vulnerability and income inequality. RESULTS: The highest coefficients of detection of new cases in the general population and in children under 15 years old are concentrated in the north-west axis and in the southern region of the state. On the other hand, the highest rates of degree II of physical incapacity are concentrated in the north, northeast and south regions. Only the Index of Social and Economic Performance(IPESE)-Economy and Finance composed the final regression model of the general detection coefficients and in children under 15 years old. The municipalities with the highest indexes had the highest detection coefficients, reflecting the capacity to diagnose new cases. STUDY LIMITATIONS: The use of synthetic indicators is a limitation of the study. CONCLUSIONS: Leprosy presents a heterogeneous spatial pattern in the state of BAHIA, and the IPESE-Economics and Finance indicator is the only one with explanatory potential of the disease.

Spatial clustering, social vulnerability and risk of leprosy in an endemic area in Northeast Brazil: an ecological study.

BACKGROUND: Despite the global decline in the detection of leprosy cases, its incidence has remained unchanged in certain settings and requires the determination of the factors linked to its persistence. We examined the spatial and space-time distribution of leprosy and the influence of social vulnerability on the occurrence of the disease in an endemic area of Northeast Brazil. METHODS: We performed an ecological study of all leprosy cases reported by Sergipe state, Northeast Brazil from 2001 to 2015, to examine the association of the Social Vulnerability Index and the prevalence and persistence of leprosy among the State's municipalities. Socio-economic and leprosy surveillance information was collected from the Brazilian information systems, and a Bayesian empirical local model was used to identify fluctuations of the indicators. Spatial and space-time clusters were identified using scan spatial statistic tests and to measure the municipalities' relative risk of leprosy. RESULTS: Leprosy clusters and burden of disease had a strong statistical association with the municipalities' Social Vulnerability Index. Municipalities with a high social vulnerability had higher leprosy incidence, multibacillary leprosy and newly diagnosed cases with grade 2 disability than areas with low social vulnerability. CONCLUSION: Social vulnerability is strongly associated with leprosy transmission and maintenance of disease incidence. Leprosy control programmes should be targeted to the populations with high social vulnerability.

Spatial approach of leprosy in the State of São Paulo, 2009-2012.

BACKGROUND: Leprosy is a chronic, infectious disease that affects the skin and the peripheral nervous system and can lead to permanent disability and/or deformity. OBJECTIVES: To identify the distribution and to quantify the spatial dependence of the detection rates of new cases of Hansen's disease in the State of São Paulo, correlating with socioeconomic variables. METHODS: Ecological and exploratory study with data on the detection rates of new cases of Hansen's disease among residents of São Paulo State municipalities between 2009-2012. The average rate per 10,0000 inhabitants was estimated. Information on the proportion of the population with low-income and the values of the Gini index were obtained. Thematic maps were constructed with the average rates and with those obtained by the Bayesian estimator; Moran and Kernel maps were also constructed. Spatial analysis by TerraView program. An alpha of 5% was adopted. RESULTS: In total, 7163 new cases of Hansen's disease were recorded throughout the state. The average rate per 10,000 inhabitants, was 0.71 (SD = 1.06), ranging from zero to 12.87, with higher rates in the west of the State, in the metropolitan area of the capital and the Paraiba Valley. Municipalities with high priority for intervention were located in the west and northwest of the State. There was no correlation between rates with Gini index and low income. STUDY LIMITATIONS: Possible inconsistency of Hansen's disease notification database regarding information quantity, quality and processing. CONCLUSIONS: This study identified the distribution and quantified the spatial dependence of the detection rates of new cases of Hansen's disease in the State of São Paulo, corroborating previous studies and serving as a subsidy to health managers.

Spatial approach of leprosy in the State of São Paulo, 2009-2012

Abstract: Background: Leprosy is a chronic, infectious disease that affects the skin and the peripheral nervous system and can lead to permanent disability and/or deformity. Objectives: To identify the distribution and to quantify the spatial dependence of the detection rates of new cases of Hansen's disease in the State of São Paulo, correlating with socioeconomic variables. Methods: Ecological and exploratory study with data on the detection rates of new cases of Hansen's disease among residents of São Paulo State municipalities between 2009-2012. The average rate per 10,0000 inhabitants was estimated. Information on the proportion of the population with low-income and the values of the Gini index were obtained. Thematic maps were constructed with the average rates and with those obtained by the Bayesian estimator; Moran and Kernel maps were also constructed. Spatial analysis by TerraView program. An alpha of 5% was adopted. Results: In total, 7163 new cases of Hansen's disease were recorded throughout the state. The average rate per 10,000 inhabitants, was 0.71 (SD = 1.06), ranging from zero to 12.87, with higher rates in the west of the State, in the metropolitan area of the capital and the Paraiba Valley. Municipalities with high priority for intervention were located in the west and northwest of the State. There was no correlation between rates with Gini index and low income. Study limitations: Possible inconsistency of Hansen's disease notification database regarding information quantity, quality and processing. Conclusions: This study identified the distribution and quantified the spatial dependence of the detection rates of new cases of Hansen's disease in the State of São Paulo, corroborating previous studies and serving as a subsidy to health managers.

Spatial study of leprosy in Bahia, Brazil, 2001-2012: an approach based on the local empirical Bayesian model. / Estudo espacial da hanseníase na Bahia, 2001-2012: abordagem a partir do modelo bayesiano empírico local.

OBJECTIVE: to compare the temporal evolution and spatial distribution of epidemiological indicators of leprosy, both crude and also corrected using the empirical Bayesian model, Bahia, Brazil, 2001-2012. METHODS: this was an ecological study using data from the Notifiable Diseases Information System; all 417 municipalities in Bahia were included and the following indicators per 100,000 inhabitants were analyzed - detection rate of new cases in the general population, in those <15 years old, and in those with grade 2 physical disability -; the local empirical Bayesian model was used to smoothen the indicators, and Student's t-test was used to compare means. RESULTS: indicators estimated by the model were higher than crude indicators; estimated detection rates in the general population and in those <15 years old were higher than crude rates in 253 (60.7%) and 209 (50.1%) municipalities, respectively; areas of greatest risk were concentrated in the northwestern and southern regions of the state. CONCLUSION: spatial distribution of the disease was heterogeneous and there was possible underreporting of cases.

Bayesian inference of ancestral dates on bacterial phylogenetic trees.

The sequencing and comparative analysis of a collection of bacterial genomes from a single species or lineage of interest can lead to key insights into its evolution, ecology or epidemiology. The tool of choice for such a study is often to build a phylogenetic tree, and more specifically when possible a dated phylogeny, in which the dates of all common ancestors are estimated. Here, we propose a new Bayesian methodology to construct dated phylogenies which is specifically designed for bacterial genomics. Unlike previous Bayesian methods aimed at building dated phylogenies, we consider that the phylogenetic relationships between the genomes have been previously evaluated using a standard phylogenetic method, which makes our methodology much faster and scalable. This two-step approach also allows us to directly exploit existing phylogenetic methods that detect bacterial recombination, and therefore to account for the effect of recombination in the construction of a dated phylogeny. We analysed many simulated datasets in order to benchmark the performance of our approach in a wide range of situations. Furthermore, we present applications to three different real datasets from recent bacterial genomic studies. Our methodology is implemented in a R package called BactDating which is freely available for download at https://github.com/xavierdidelot/BactDating.

Towards leprosy elimination by 2020: forecasts of epidemiological indicators of leprosy in Corrientes, a province of northeastern Argentina that is a pioneer in leprosy elimination.

BACKGROUND: Corrientes, a province of northeastern Argentina with endemic leprosy, has improved its epidemiological indicators, however, a study of the dynamics over time is lacking. OBJECTIVES: We analysed data of 1308 leprosy patients between 1991 to 2014, and the forecast for 2020. METHODS: Descriptive statistics and stepwise Bayesian model selection were performed. Forecasts were made using the median of 100,000 projections using the parameters calculated via Monte Carlo methods. RESULTS: We found a decreasing number of new leprosy cases (-2.04 cases/year); this decrease is expected to continue by an estimated 20.28 +/- 10.00 cases by 2020, evidenced by a sustained decline in detection rate (from 11 to 2.9/100,000 inhabitants). Age groups that were most affected were 15-44 (40.13%) and 45-64 (38.83%) year olds. Multibacillary forms (MB) predominated (70.35%) and while gradually declining, between 10 and 30% developed disability grade 2 (DG2) (0.175 (0.110 - 0.337) DG2/MB cases), with a time delay between 0 to 15 years (median = 0). The proportion of MB clinic forms and DG2 increased and will continuously increase in the short term (0.036 +/- 0.018 logit (MB/total of cases). MAIN CONCLUSIONS: Corrientes is on the way to eliminating leprosy by 2020, however the increased proportion of MB clinical forms and DG2 signals a warning for disease control efforts.