PLoS Neglected Tropical Diseases
Space-time risk cluster of visceral leishmaniasis in Brazilian endemic region with high social vulnerability: An ecological time series study
DOI 10.1371/journal.pntd.0009006 , Volume: 15 , Issue: 1
Article Type: research-article, Article History
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### Notes

Abstract

Visceral leishmaniasis (VL) remains a worldwide health issue, with increasing rates of mortality being observed. Brazil has an epidemiological scenario of expanding VL transmission, especially in the Northeast region. In the present study, we analysed spatiotemporal dynamics of VL cases and its association with social vulnerability in Brazilian Northeast. Briefly, data was analysed of all VL confirmed cases during the years of 2000 to 2017 and the Social Vulnerability Index (SVI) from 1,794 municipalities of Brazilian Northeast. Results revealed that VL continues to spread heterogeneously, with space-time high-risk clusters in the most socially vulnerable areas. We observed increasing trends of new cases among male subjects ≥ 40 years of age and urban residents. Our study represents the first investigation that demonstrates associations between VL and social vulnerability in the Northeast region of Brazil. These findings could contribute to VL prevention, surveillance, and control through better understanding of disease distribution, affording effective prioritization of municipalities with higher vulnerability. Thus, reduction of social inequality and better living conditions should be part of the planning of public health policies related to VL control.

Ribeiro, dos Santos, Lima, da Silva, Ribeiro, Duque, Peixoto, dos Santos, de Oliveira, Lipscomb, de Araújo, de Moura, and Ajjampur: Space-time risk cluster of visceral leishmaniasis in Brazilian endemic region with high social vulnerability: An ecological time series study

## Introduction

Despite the epidemiologic transition in Brazil, infectious diseases, especially neglected tropical diseases (NTDs), are still a major public health problem [1], such as visceral leishmaniasis (VL) that can be life-threatening if not properly treated [2].

It is estimated that one billion people live in VL-endemic regions worldwide, with 300,000 new cases and 20,000 deaths per year due to the disease. However, 94% of the diagnosed cases are concentrated in only six countries; among them, Brazil [3] was responsible for 97% of the recorded incidents in South America in 2017 [4]. In this continent, Leishmania infantum protozoan is the disease-causing agent transmitted by bites from female phlebotomies, and domestic dogs are the main urban reservoir [5].

The epidemiology of VL has changed dynamically due to a myriad of interactions among environmental, socioeconomic, demographic and immunological factors [6]. In Brazil, VL was previously considered a rural endemic disease. However, in recent decades, because of urbanization, the majority of cases have occurred in large cities and the surrounding urban areas [7].

In the 1990s, approximately 90% of recorded VL cases occurred in the Northeast region of Brazil. By the 2000s, the disease spread to other Brazilian urban regions [8]. Furthermore, a recent study demonstrated that 56% of VL deaths between 2000 to 2011 occurred within the Northeast region [9].

VL incidence has been connected to social inequality and poor living conditions [1013]. Brazil is considered one of the most unequal countries in terms of wealth distribution. Further compounding the situation, the Northeast has important socioeconomic disparities, which are represented by the highest social vulnerability index (SVI) and the lowest human development index (HDI) in the country [14]. This is in addition to being endemic for several NTDs [15]. The SVI shows relative access, absence or insufficiency of services, which include some basic needs that should be ensured for all citizens [16].

Therefore, investigating VL cases associated with the social vulnerability factors could support appropriate health interventions for specific regional conditions. Thus, this research aimed to analyse the spatiotemporal dynamics of VL cases to identify the temporal trends and high-risk areas for VL transmission, as well as the association of the disease with social vulnerability in Brazilian Northeast.

## Methods

### Ethics statement

This study used public-domain aggregate secondary data and followed national and international ethical recommendations, as well as the rules of the Helsinki Convention. All data analysed were anonymized. The research project was approved by the Research Ethics Committee of Federal University of Sergipe (CEP/UFS), registered under the approval number 2,537,671.

### Study design

This is an ecological time series study that used spatial analysis techniques including all confirmed VL cases in the Northeast region of Brazil between 2000 and 2017. Units of the analysis were the 1,794 municipalities in the region.

### Study area description

The Northeast region of Brazil (latitude: 01°02’30” N/18° 20’ 07” S; longitude: 34°47’30” /48°45’24” O) is divided into four subregions (meio-norte, sertão, agreste and zona da mata ). This corresponds to 18% of the national territory (Fig 1) with an estimated population of 57 million inhabitants [17].

Fig 1
(A) Northeast–NE, Brazil. (B) Maranhão–MA. (C) Piauí–PI. (D) Ceará–CE. (E) Rio Grande do Norte–RN. (F) Paraíba–PB. (G) Pernambuco–PE. (H) Alagoas–AL. (I) Sergipe–SE. (J) Bahia–BA. SVI: Social vulnerability index.Study area.

### Data sources

Morbidity data were collected from Sistema de Informação de Agravos de Notificação (SINAN) of the Departamento de Informática do Sistema Único de Saúde (Datasus) [18]. The Brazilian Northeastern population estimates and cartographic base (shapefile extension), presented in the latitude/longitudinal system (SIRGAS 2000), were obtained from Instituto Brasileiro de Geografia e Estatística (IBGE) [17]. SVI was taken from Instituto de Pesquisa Econômica Aplicada (IPEA) database (www.ipea.gov.br). The data used in this study is available in S1 Data.

### Variables and measures

The primary measurement of study was the VL incidence rate in municipality level. This rate was obtained by dividing the average of cases by the central population of each municipality and multiplying by 100,000. We used VL transmission risk stratification of the Brazilian Guide to Health Surveillance (2019) as follows: sporadic transmission (<2.4 cases/100,000 inhabitants), moderate transmission (≥2.4 and <4.4 cases/100,000 inhabitants) and intense transmission (≥4.4 cases/100,000 inhabitants) [5]. VL incidence rates was also calculated in state and regional levels. The classification and description of all variables used in our analysis are shown in Table 1.

Table 1
Description of study variables.
ClassificationVariableDescriptionAnalysis
DependentPrimaryIncidence rate (per 100,000 inhabitants)Calculated in municipality, state, and regional levelsTime trends Spatial cluster Spatiotemporal cluster Bivariate spatial cluster
SecondaryPrevalence rate (per 100,000 inhabitants)Calculated in state and regional levelsTime trends
Mortality rate (per 100,000 inhabitants)Calculated in regional level
LethalityCalculated in regional level
IndependentSocial vulnerability index (SVI)Very low (0 to 0.200), low (0.201 to 0.300), medium (0.301 to 0.400), high (0.401 to 0.500), and very high (≥ 0.501)Bivariate spatial cluster
Year of occurrence2000 to 2017Time trends
ExplanatoryState of residenceAlagoas, Bahia, Ceará, Maranhão, Paraíba, Pernambuco, Piauí, Rio Grande do Norte, and SergipeDescriptive epidemiological characterization
SexMale and female
Age group≤ 4 years, 5–19 years, 20–39 years, 40–59 years, and ≥ 60 years
Ethnicity/skin colourWhite and non-white
Residence zoneRural, urban and periurban
VL-HIV co-infection
Level of education< 8 years and ≥ 8 years
Case typeNew case, relapse, and transference
Clinical outcomeCure, abandonment, death, and transference

SVI was employed as an independent variable for the occurrence of VL transmission in the municipalities of Brazilian Northeast. This index estimates exclusion and vulnerability beyond insufficient monetary resources and is composed of 16 indicators from data of Census 2010 grouped into three dimensions (urban infrastructure–SVI-UI; human capital–SVI-HC; and income/work–SVI-I/W) (S1 Table). Each dimension has the same weight for calculating the global SVI. Furthermore, all indicators were normalized into a scale varying from 0 to 1, in which 0 corresponds to an ideal situation and 1, to the worst situation. The complete construction methodology is described in the official report from IPEA [14].

The SVI-UI dimension reflects the conditions that affect quality of life, such as access basic sanitation services and urban mobility. Health conditions and access to education were used to determine individual prospects and are composed of the SVI-HC dimension. Insufficient family income, adult unemployment, informal employment of poorly educated adults, family dependence on elderly income and child labour indicate household income security, which is presented through the SVI-I/W dimension [14]. The SVI ranges from 0 to 1; the closer the index is to 1, the greater the social vulnerability of a municipality, which is classified as very low (0 to 0.200), low (0.201 to 0.300), medium (0.301 to 0.400), high (0.401 to 0.500) and very high (≥ 0.501) [16].

### Time trends analysis

Time trends were examined by segmented linear regression (Joinpoint), based on the calculation of the annual percentage changes (APCs), calculated for each segment, and average annual percentage changes (AAPCs) for the entire period when there was more than one significant inflexion in a study period, with their respective 95% confidence interval (95%CI). Monte Carlo permutation test was used to obtain the statistical significance, applying 999 permutations, and choose the best number of significant segments. APCs and AAPCs were significative when p<0.05 and their 95%CIs did not include zero. The selected final model was the most adjusted, allowing the best representation of trend, with the fewest number of inflexion points [19]. The results were interpreted as follows: positive and significant APCs/AAPCs were considered increasing trends, negative and significant APCs/AAPCs were considered decreasing trends; on the other hand, when there was no significance, the trend was considered stable [20,21].

### Spatial cluster analysis

First, crude VL incidence rates were smoothed by applying the local Bayesian empirical method to correct the random fluctuations and provide more stability to the incidence rates [22]. Crude and smoothed rates were represented on maps stratified by the risk of VL transmission [5].

The global Moran’s I index was computed to analyse the spatial autocorrelation using a first order proximity matrix, which was expanded upon using contiguity criterion. This index ranges from -1 to +1, with positive values indicating positive spatial autocorrelation and negative values indicating negative autocorrelation. Additionally, values close to zero point the lack of spatial autocorrelation [23]. Statistical significances were identified using Monte Carlo simulations with 999 permutations.

Once the autocorrelation was identified, the local Moran’s index (LISA) was used to indicate the occurrence of spatial clusters of municipalities with high VL transmission [24] and to generate a scatter plot with four quadrants: Q1 (municipalities with high VL incidence rates and high incidence rates in neighbouring municipalities), Q2 (municipalities with low VL incidence rate and low incidence rate in neighbouring municipalities), Q3 (municipalities with high VL incidence rates and low incidence rates in neighbouring municipalities) and Q4 (municipalities with low VL incidence rates and high incidence rates in neighbouring municipalities). The diagram was depicted through Moran maps, in which only the statistically significant results were considered (p<0.05).

### Spatiotemporal cluster analysis

Kulldorff’s retrospective space-time scan statistical analysis was performed to identify high-risk spatiotemporal clusters for VL transmission and to estimate the relative risks (RRs) of VL occurrence for each cluster in relation to its neighbours [25]. The Poisson’s discrete probability model was used for scanning since the single events under analysis (VL cases) are counts and considered rare [21], under the null hypothesis that the expected number of cases in each area is proportional to its population size [26]. We established the following conditions for the model, according to previous studies on infectious diseases [27,28]: aggregation time of 1 year, no geographical overlap of clusters, circular clusters, maximum spatial cluster size of 50% of the at-risk population, and a maximum temporal cluster of 50% of the study period. The primary (or most likely) and secondary clusters were detected using the log likelihood ratio (LLR) test and were represented through choropleth maps. The results were statistically significant when p<0.05 using 999 Monte Carlo simulations.

### Bivariate spatial cluster analysis

Initially, we represented the spatial distribution of the social vulnerability in Northeast region of Brazil through choropleth maps for SVI and its dimensions (SVI-IU, SVI-HC, and SVI-I/W). Subsequently, we performed the univariate global Moran’s I index to analyse the spatial autocorrelation of social vulnerability, using a first order proximity matrix. Univariate LISA analysis was applied to identify clustering and the significant results (p<0.05) were depicted in Moran maps, which were visually compared with the results of spatial and spatiotemporal cluster analysis of VL incidence rates [24].

In order to verify the association between the occurrence of VL and social vulnerability, Spearman’s correlation test was performed to examine correlation(s) between the VL incidence rate and SVI and its dimensions (SVI-IU, SVI-HC, and SVI-I/W). As there was positive correlation between VL incidence rate and SVI, we investigated the existence of spatial correlation between VL transmission and social vulnerability using a bivariate analysis of global Moran’s index and LISA.

Similar to univariate analysis, the bivariate global Moran’s does not reveal spatial clustering [13,29]. Thus, the bivariate LISA analysis was employed to determine the degree of spatial correlation of the data in relation to its neighbours [23], generating a scatter plot with four quadrants [30]: Q1 (municipalities with high VL incidence rates and high social vulnerability in neighbouring municipalities), Q2 (municipalities with low VL incidence rate and low social vulnerability in neighbouring municipalities), Q3 (municipalities with high VL incidence rates and low social vulnerability in neighbouring municipalities) and Q4 (municipalities with low VL incidence rates and high social vulnerability in neighbouring municipalities). These clusters were depicted in Moran maps and only the statistically significant results were considered (p<0.05).

### Softwares

Microsoft Office Excel 2016 (Microsoft Corporation; Redmond, WA, EUA) was used to store and prepare the data. QGis 3.4.11 (QGIS Development Team; Open Source Geospatial Foundation Project) was used to produce choropleth maps. TerraView 4.2.2 (www.inpe.br) and GeoDa 1.14 [30] were employed to perform spatial analysis. Joint Point Regression 4.6 (US National Cancer Institute, Bethesda, MD, EUA) were used to time trend analysis. SaTScan 9.6 (Harvard Medical School, Boston and Information Management Service Inc., Silver Spring, MD, EUA) was used to analyse spatiotemporal clusters.

## Results

A total of 36,514 VL cases were confirmed in Brazilian Northeast between 2000 and 2017. Fig 2 describes the number of cases per federative unit/state and the respective annual frequencies and average prevalence rates. Maranhão state had a high number of registered cases in this period, corresponding to 28.86% of the total records.

Fig 2
$\stackrel{-}{Tx}$: average prevalence (cases/100,000 inhabitants/year).Flowchart of study population describing the number of cases, average frequencies, and annual prevalence rates per state.

Table 2 shows the baseline characteristics of the VL epidemiologic indicators. The predominant characteristics of the VL cases in Brazilian Northeast were males (62.71%), <5 years old (40.77%), non-white (69.75%), urban residents (62.58%) with a low education level (27.01%) and residents who were cured (71.15%). Only in the federative unit/state of Alagoas were there more prevalent cases in rural areas (69.19%). The epidemiological characterization per state is available in S2 Table.

Table 2
Baseline characteristics.
Variablesn = 36,498%
Federative unit / state
Alagoas1,2273.36
Bahia6,58918.05
Ceará7,95321.79
Maranhão10,53428.86
Paraíba7402.03
Pernambuco2,3666.48
Piauí4,28911.75
Rio Grande do Norte1,7914.91
Sergipe1,0092.76
Case type
New cases32,86290.03
Relapse1,4043.85
Transference4471.22
Miss data1,7854.90
Sex
Male22,88862.71
Female13,58737.23
Miss data230.06
Age
0–4 years14,88040.77
5–19 years8,21122.5
20–39 years7,53920.65
40–59 years4,25211.65
≥ 60 years1,5994.38
Miss data170.05
Ethinicity / skin colour
White3,4469.44
Nonwhite25,45869.75
Miss data7,59420.81
Zone
Urban22,84062.58
Rural12,19033.4
Periurban4001.09
Miss data1,0682.93
Level of education
< 8 years9,85927.01
≥ 8 years3,0668.4
Miss data/N.A.23,57364.59
Outcome
Cure25,97071.15
Abandonment1440.39
Death2,6907.37
Transference1,8495.07
Miss data5,84516.01
N.A. not applicable

### Time trends analysis

Until 2010, the proportion of municipalities with VL transmission remained stable, but since then, an annual increase of 3.6 (95%CI: 1.0 to 6.4; p<0.05) was observed (S1 Fig). Table 3 highlights the inflexion points for trend changes of VL epidemiological indicators. The crude prevalence rate in the general population ranged from 5.57 in 2000 to 3.36 cases per 100,000 inhabitants in 2017, with an annual decrease of -1.4 (95%CI: -2.6 to -0.2; p<0.05), while the annual incidence remained stable, varying from 4.84 in 2000 to 3.52 cases per 100,000 inhabitants in 2017 (p>0,05). In turn, Alagoas and Rio Grande do Norte showed decreasing trends in the numbers of new cases, whereas there was an increasing trend in Ceará (Table 2). The incidence rates showed increasing trends in males (APC: 1.4; 95%CI: 0.8 to 2.0; p<0.05) and groups of individuals between ages of 40–50 years old (APC: 3.8; 95%CI: 2.2 to 5.4) and ≥ 60 years old (APC: 5.9; 95%CI: 4.2 to 7.6 p<0.05). Furthermore, there were statistically significant increases in the percentages of cases with VL-HIV co-infections and in the crude mortality rate (AAPC: 2.5; 95%CI: 0.6 to 4.4; p<0.05) and lethality (APC: 3.9; 95%CI: 3.0 to 4.9; p<0.05) (Table 3).

Table 3
Time trends of VL epidemiologic indicators.
Indicator/variableSegmented periodEntire period
PeriodAPC (95%CI)TrendAAPC (95%CI)Trend
Crude prevalence rate (per 100,000 inhab.)
2000–2017-1.4 (-2.6 to 0.2)Decreasing
Crude incidence rate (per 100,000 inhab.)
General2000–2017-1.22 (-2.5 to 0.1)Stable
Federative Unit/State
Alagoas2000–2007-26.2 (-33.5 to -18.1)Decreasing-10.6 (-16.3 to -4.6)Decreasing
2007–20172.2 (-7.5 to 12.9)Stable
Bahia2000–2017-1.1 (-3.6 to 1.4)Stable
Ceará2000–200624.8 (8.5 to 43.6)Increasing5.5 (0.2 to 10.9)Increasing
2006–2017-3.8 (-7.6 to 0.2)Stable
Maranhão2000–2009-8.2 (-13.1 to -3.0)Decreasing-1.8 (-5.5 to 2.1)Stable
2009–20176.0 (-0.8 to 13.3)Stable
Paraíba2000–2017-2.2 (-6.2 to 2.0)Stable
Pernambuco2000–2003-43.7 (-60.9 to -18.7)Decreasing-6.2 (-12.3 to 0.2)Stable
2003–20174.6 (0.3 to 9.1)Increasing
Piauí2000–200428.5 (5.8 to 56.0)Increasing3.4 (-3.2 to 10.4)Stable
2004–2009-13.2 (-26.9 to 3.1)Stable
2009–20173.4 (-3.3 to 10.5)Stable
Rio Grande do Norte2000–2003-42.8 (-50.8 to -33.4)Decreasing-7.8 (-11.3 to -4.2)Decreasing
2003–20117.4 (1.5 to 13.5)Increasing
2011–2017-4.5 (-10.2 to 1.6)Stable
Sergipe2000–2002-31.6 (-68.6 to 48.9)Stable-1.7 (-9.9 to 7.4)Stable
2002–20173.2 (-0.4 to 7.0)Stable
Sex
Male2000–20171.4 (0.8 to 2.0)Increasing
Female2000–2017-2.6 (-3.9 to -1.3)Decreasing
Age
≤ 4 years2000–2017-1.4 (-3.0 to 0.3)Stable
5–19 years2000–2002-23.1 (-41.6 to 1.3)Stable-3.6 (-6.6 to -0.5)Decreasing
2002–2017-0.7 (-2.2 to 0.8)Stable
20–39 years2000–20170.6 (-0.6 to 1.8)Stable
40–59 years2000–20173.8 (2.2 to 5.4)Increasing
≥ 60 years2000–20175.9 (4.2 to 7.6)Increasing
Percentage of LV-HIV co-infection
2000–201124.98 (15.8 to 34.9)Increasing17.3 (11.3 to 23.7)Increasing
2011–20174.5 (-4.2 to 13.9)Stable
Crude mortality rate (per 100,000 inhab.)
2000–2009-0.9 (-3.8 to 2.0)Stable2.5 (0.6 to 4.4)Increasing
2009–20176.5 (3.4 to 9.7)Increasing
Lethality
2000–20173.9 (3.0 to 4.9)Increasing
Proportion of municipalities with transmission
2000–2010-0.8 (-2.4 to 0.8)Stable1 (-0.3 to 2.3)Stable
2010–20173.6 (1.0 to 6.4)Increasing

### Spatial cluster analysis

VL transmission was broadly distributed, as shown in Fig 3A. Almost a quarter of municipalities (429) had intense transmission (≥ 4.4 cases per 100,000 inhabitants). When the smoothed rates were considered through the local empirical Bayesian method (Fig 3B), results revealed that this amount was up to 30% in the study area (542 municipalities). New cases were concentrated mainly in the sertão and meio-norte sub-regions, which comprise the states of Bahia, Maranhão, Pernambuco and Piauí. The global Moran’s I index showed a significant spatial autocorrelation (0.338, p = 0.001), highlighting spatial dependence of new VL cases in municipalities with similar patterns. Fig 3C presents the municipalities identified through the LISA analysis. The high-risk clusters were detected in 269 municipalities of Maranhão (96), Piauí (67), Bahia (60), Ceará (29), Alagoas (12) and Pernambuco (5).

Fig 3
(A) VL crude incidence rates. (B) VL smoothed incidence rates. (C) Univariate LISA cluster analysis. (D) Space-time scan statistical analysis.Spatial and spatiotemporal distribution of VL, Northeast, Brazil (2000–2017). The maps show high-risk clusters for VL transmission mainly in sertão and meio-norte sub-regions.

### Spatiotemporal cluster analysis

The space-time scan statistics identified 12 significant spatiotemporal clusters of new VL cases in the general population (p<0.001), as shown in Table 4 and illustrated in Fig 3D. The primary cluster included 465 municipalities and the highest number of cases (8,245), from 2000 to 2008, in the federative units/states of Piauí (200), Maranhão (119), Bahia (97), Ceará (28) and Pernambuco (21), with a crude incidence rate of 9.5 per 100,000 inhabitants (RR = 3.35; p<0.001). Importantly, Bahia’s municipalities contained 7 of 12 identified clusters, with one municipality from Bahiahaving the highest annual incidence rate (81.6 cases/100,000 inhabitants) and the highest relative risk (RR = 23.61; p<0.001).

Table 4
Space-time clusters of annual crude incidence rate of LV per 100,000 general population.
ClusterTime periodNumber of municipalitiesStatesNumber of new casesExpected number of new casesAnnual incidence rateaRRLLR
12000–2008465Maranhão, Piauí, Ceará, Pernambuco, Bahia8,2452,9919.53.353598.54
22006–2014108Ceará, Rio Grande do Norte3,2101,9575.71.71361.24
32000–200232Pernambuco, Alagoas3736320.45.94353.06
42003–20111Bahia94481.623.61207.07
52000–200162Paraíba, Pernambuco35510511.73.42184.20
62004–200633Bahia2727213.13.80162.16
72000–200148Rio Grande do Norte1914315.34.44136.30
82009–20137Bahia882512.33.5548.28
92000–20002Pernambuco, Alagoas22161.117.6642.41
102000–20001Bahia20153.015.3135.87
112002–20071Bahia31522.86.6032.18
122000–20003Bahia15144.012.7224.32
RR: relative risk for the cluster compared with the rest of the region; LLR: likelihood ratio.
a LV incidence rate (per 100,000 inhabitants) during the clustering time.

### Bivariate spatial analysis

Fig 4A shows the distribution of social vulnerability in Brazilian Northeast through SVI (SVI-UI, SVI-HC, and SVI-I/W). Approximately 76% of municipalities had high to very high social vulnerability (858 and 502, respectively), and a quarter (340) had intense VL transmission (Table 5).

Fig 4
(A) Spatial distribution of SVI. (B) Univariate LISA cluster analysis of SVI. (C) Bivariate LISA cluster analysis of VL incidence rate and SVI.Association between VL transmission and social vulnerability, Northeast, Brazil (2000–2017).
Table 5
Distribution of Northeastern municipalities of Brazil according to social vulnerability and VL transmission.
SVIMunicipalities with VL transmission n (%)Total
Very low1 (0.1)--1 (0.1)
Low33 (1.8)3 (0.2)9 (0.5)45 (2.5)
Medium268 (14.9)40 (2.2)80 (4.5)388 (21.6)
High497 (27.7)158 (8.8)203 (11.3)858 (47.8)
Very high287 (16.0)78 (4.3)137 (7.6)502 (28.0)
Total1,086 (60.5)279 (15.6)429 (23.9)1,794 (100.0)

Table 6 demonstrates that the global Moran’s I index revealed significant spatial autocorrelation in the socially vulnerable municipalities (Fig 4B). A cluster of high social vulnerability (Q1) for all SVI domains was formed in Maranhão. Social vulnerability clusters related to human capital were evidenced in 7 of 9 states in the region. In Bahia, there was a clustering of municipalities with social vulnerability related to income-work aspects.

Table 6
Association between VL transmission and social vulnerability in Northeast region of Brazil.
Social vulnerabilitySpearman’s testGlobal Moran’s index
Rhop-valueUnivariatep-valueBivariatep-value
SVI0.0780.0010.530.0010.100.001
SVI-UI0.0830.0010.560.0010.130.001
SVI-HC0.0200.4060.460.0010.030.005
SVI-I/W0.0810.0010.350.0010.040.001

There was a positive correlation between VL incidence rate and all SVI domains (Table 6). The bivariate analyses of the global Moran’s I index and LISA were significant for all aspects of social vulnerability (Fig 4C). In the SVI-UI, SVI-HC and SVI-I/W dimensions, 114, 119 and 107 municipalities, respectively, were VL high-risk clusters. A significant positive correlation was also observed in the general SVI with the clustering of 119 municipalities in Maranhão (Fig 4D). Smaller high-risk clusters were observed in Alagoas, Bahia, Pernambuco and Piauí.

## Discussion

Despite the international, national, and local efforts in recent decades to control and eliminate VL, this disease remains a worldwide health problem. To the best of our knowledge, this is the first study that describes the VL transmission dynamics in Brazilian Northeast and its association with social vulnerability using techniques of spatiotemporal clustering analyses. VL had a heterogeneous geographical distribution with clusters strongly associated with social vulnerability. Several studies have investigated the VL trends and/or their spatial distributions in different states and municipalities in the Northeast region of Brazil, but few have clarified the association between VL and social vulnerability [3135]. Considering this gap, we carried out an integrative approach, including time trends, spatiotemporal, univariate, and bivariate spatial cluster analysis, in order to strengthen the methodology.

The spatial distributions of infectious diseases in the Brazilian Northeast have been reported, such as those for tuberculosis [36,37], leprosy [28], schistosomiasis [38], zika, dengue [39] and chikungunya [40]. This scenario shows that NTDs are a major health issue in the region and that their characterization is a difficult challenge for public health management. VL incidence remained stable during the 18 years analysed in this study (i.e. 2000–2017). Nevertheless, in the last decade there was a territorial expansion of the disease coupled with an increase in the proportion of municipalities with reported cases.

Along these lines, the states of Maranhão and Ceará are highlighted, with the latter having an increasing trend in the number of new cases. Some regions of Maranhão compound the Legal Brazilian Amazon, which is a strategic place for agrobusiness interests and has been suffering profound anthropic modifications in the ecosystem [41]. Similarly, recent study carried out in Ceará pointed out that the highest VL incidences occur in Sobral and Cariri, where there are rapid and unplanned urbanizing, intense anthropic action and migration [42]. Thus, we hypothesize that the association between the environmental modifications and the deterioration of living conditions of population from Maranhão and Ceará states can partially explain the VL expansion.

In the general population, mortality and lethality were increased, as reported in a nationwide analysis of epidemiology, trends and spatial patterns of VL case fatality [9]. A possible explanation could be related to the higher prevalence among children under 5 years old and the increasing trend in the number of cases among older adults, especially the elderly given that VL can lead to more severe consequences in these extreme age groups [43,44]. In 2016, the Global Burden Disease’s study (GBD) [45] pointed out that VL caused approximately 21 years of life lost (YLL), which reinforces the need for more attention and intervention to reduce fatalities. In addition, after an increase in the number of cases with VL-HIV co-infection, this trend remained stable. The coexistence of both diseases complicates the handling of the case and increases mortality, therapeutic failure and relapse rates [32,43,4651].

Increasing trends in the numbers of new cases were observed among males and urban residents. The higher male susceptibility to VL has been described in animal and human studies, which hypothesize that men are more frequently exposed to outdoor sandflies and higher levels of testosterone [32,52]. In recent decades, VL urbanization has been consolidating in Brazil because of the rural exodus that started between the 1950s and 1980s and population growth in the suburbs with poor living conditions [7]. The higher VL prevalence observed among non-white and less educated subjects supports the hypothesis that social determinants of health, whether at the population or individual level, can structurally influence the disease patterns [53]. The historical heritage of slavery and ineffective public policies of social inclusion mark the Brazilian territory and may partially explain the regional health disparities [54].

There have been investigations into how and why social iniquities affect population health [55]. Therefore, the association between NTDs and poverty has been strongly established [56], since both are socially stigmatizing conditions that create feedback loops [57]. In Bihar, India, it was shown that households with the worst socioeconomic indicators were more affected by VL than households with better socioeconomic indicators [10,58]. Similarly, in Brazil, a significant association between VL and social vulnerability has been reported. In Araguaína, Tocantins, bivariate LISA analysis revealed high-risk cluster for VL incidence in zone with worst vulnerability indicators [13]. Moreover, social determinants have been related to VL mortality, especially in the North and Northeast regions. Recent ecological study highlighted high mortality rates of VL associated with unplanned urbanization and precariousness of households, where both reservoirs and breeding sites for disease vectors are present [59].

Local studies carried out in the Northeast region of Brazil also demonstrate this alarming reality. In Rio Grande do Norte, there is an association between VL incidence and households with no garbage collection or piped water supply [32]. In Aracaju, Sergipe, it was observed that VL is heterogeneously distributed, with higher concentration of cases in outskirts, where there are risk factors for transmission of vector borne diseases [33]. Yet, a spatial analysis conducted in São Luís, Maranhão, pointed out high prevalence of infection in canine reservoirs in areas of recent occupation with poor sanitary conditions [60].

Although Brazil is the 9th largest economy worldwide, social inequality is a persistent issue, and the Northeast region of Brazil is the most remarkable example of how the development process did not occur simultaneously throughout the country [61]. In this study, spatiotemporal clustering was observed in sertão and meio-norte , the Northeastern sub-regions with large scarcities of resources and higher social vulnerability. This result corroborates the findings of a study that showed clustering of leprosy cases in the same region [62], which could overwhelm the health system to handle with surveillance of multiple infectious diseases simultaneously. When compared to the national average, all Northeastern states have higher percentages of people living with no water supply or garbage collection, inadequate sanitary sewage, income inequality according to the Gini index, and worse municipal human development index (HDI-M) values. It is important to emphasize that almost a quarter of the residents in Maranhão live in poor conditions; this state had the strongest correlation between VL incidence and the SVI [63]. In case of Bahia state, its municipalities comprised 7 out of 12 spatiotemporal clusters, beyond the remarkable social vulnerability, the broad dispersion of Lutzomyia longipalpis throughout the state can be related to the intense VL transmission [64].

In examining our results and previous studies, it is possible to infer that surveillance and control actions have been failing to reduce VL incidence and lethality in Brazil [1], since an epidemiologic scenario of transmission expansion has been maintained [4,65]. Meanwhile, there is a Brazilian policy called the VL Surveillance and Control Program that conducts vector control strategies, reservoir eradication, early diagnosis and timely treatment of cases [5,8]. Unfortunately, the program lacks scientific evidence to prove its cost effectiveness [66,67]. Therefore, we believe that planning and implementation of public policies for VL control should consider the reduction in social iniquities since most of the Northeastern populations live without basic sanitation nor access to health services. In this sense, spatiotemporal techniques could be useful for monitoring and prioritizing high-risk areas, as demonstrated in a research carried out in Belo Horizonte, Minas Gerais, Brazil. The authors investigated the spatiotemporal distribution of VL incidence in humans (HVL) and prevalence of canine VL (CVL) in order to identify priority areas for control actions through Bayesian empirical method, univariate (HVL) and bivariate (HVL versus CVL) LISA analysis, and space-time scan statistics [29].

In 2011, the Pan American Health Organization instituted a VL regional program to optimize the surveillance, prevention, and control of leishmaniasis in the Americas. The action plan for the period of 2017–2022 set goals of lethality reduction throughout the continent and a decrease of 50% in incidence rates of countries with expanding VL transmission [65]. According to our findings, Brazil may not achieve this goal. Combating VL and other NTDs should be of high priority to decision makers because their elimination could contribute to reducing disparities of health and socioeconomic statuses in Brazil, thereby improving overall quality of life for its citizens. Further evaluative studies are necessary to identify the critical points in the Brazilian VL program that have hindered the reduction in VL incidence and lethality in Northeast region. Moreover, there is a need to conduct investigations with spatial regression and prospective spatiotemporal cluster analysis to identify the epidemiological and social determinants related to intense clustering of VL cases in states in the Northeastern sub-regions sertão and meio-norte.

This study has some limitations. First, the ecological design with the use of secondary data did not enable the establishment of casual links. Along those lines, it was only possible to interpret that there was a significant association between VL and social vulnerability. It is important consider the potential underreporting of cases and missingness of some data (i.e. level of education, outcome, HIV serology, etc.), possibly due to the weaknesses of the health system in the poorest municipalities of Northeast region. Furthermore, SVI has been elaborated with data from the last census performed in 2010 and it is known that Brazilian social scenario has been changing because of the neoliberal policies and current global crisis. So, the poverty and social vulnerability could be more profound than the SVI demonstrates.

Despite of these limitations, our study demonstrates a living VL epidemiologic scenario of 18 years and the impactful use and integration of spatial, temporal, and spatiotemporal analysis for disease surveillance and control, as they allow the prioritization of areas with higher transmission risks and the understanding of the potential association of the disease dynamics with the social phenomena in the territory. Additionally, we recognize that the SVI can be an important tool for future health research in Brazil because it is a robust and multidimensional index developed for the purpose of guiding decision making of public managers and help researchers to better understand the different aspects of social vulnerability in Brazil [14]. Some studies applied SVI or its indicators in spatial analysis of leprosy [28,62,68] and VL mortality [59], but they showed promising insights for using the index in epidemiological research.

In conclusion, the results of this study revealed that VL is a persistent health problem in the Brazilian Northeast and that the disease has a strong correlation with social vulnerability. The spatiotemporal clusters indicated the high priorities areas for disease control and suggest interventions for the prevention of VL dissemination to susceptible municipalities or to those with sporadic transmission. In a country such as Brazil, with so much social inequality in health, NTD eradication can only be possible through intersectoral policies that focus on the reduction in inequality and improvements to the living conditions for its population.

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19 Nov 2019

8 May 2020

Dear Dr. Rodrigues de Moura,

Thank you very much for submitting your manuscript "Space-time risk cluster of visceral leishmaniasis in Brazilian endemic region with high social vulnerability: an ecological and time series study" for consideration at PLOS Neglected Tropical Diseases. As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. In light of the reviews (below this email), we would like to invite the resubmission of a significantly-revised version that takes into account the reviewers' comments.

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***********************

Reviewer's Responses to Questions

Key Review Criteria Required for Acceptance?

As you describe the new analyses required for acceptance, please consider the following:

Methods

-Are the objectives of the study clearly articulated with a clear testable hypothesis stated?

-Is the study design appropriate to address the stated objectives?

-Is the population clearly described and appropriate for the hypothesis being tested?

-Is the sample size sufficient to ensure adequate power to address the hypothesis being tested?

-Were correct statistical analysis used to support conclusions?

-Are there concerns about ethical or regulatory requirements being met?

Reviewer #2: First of all I would like to say thanks for your contributions on this very sensitive issue visceral leishmaniosis in Brazil.

How can you calculate and measure the VL incidence rate in municipality level?

Study Variable classifications and definitions is not clear? (Response variable? Explanatory variables? Please summarize in tabular form.

The methodological part is not clear for instance spatiotemporal cluster analysis try to elaborate on the Poisson probability discrete model, bivariate spatial analysis, try to rewrite explicitly including relevant dynamic spatiotemporal models. Try to put also the detail statistical model in short and precise manner.

Reviewer #3: The authors provide a clear and concise overview of their study methodology, evident in their description of the SVI value range and its interpretation. Any mention of a statistical test should be met with a citation to help with reproducibility. Based on Figure 4C, the clustering does not appear to be circular, so what was the authors justification for each chosen scan statistic parameter?

--------------------

Results

-Does the analysis presented match the analysis plan?

-Are the results clearly and completely presented?

-Are the figures (Tables, Images) of sufficient quality for clarity?

Reviewer #2: The result presented are good as descriptive but try to modify the tables 1 for base line characteristics if it is possible you have to present in readable manner and put in the previous page.

In trend analysis you said it is stationary? How have you check test of stationarity? If so which test are used? If it is unit root what are you going to do with non-stationary trend /data? Please specify and define the test of Stationarity with its hypothesis testing procedures.

In the result section you said there was a statistical significance? (How and when we say statistically significant) please interpret your results in relation to p-vale. Again put table 2 in previous page. Also put figure 4 on previous page.

Moreover there are a lot of works are done on this area so try to include all recent and relevant works under discussion part including the dynamic spatiotemporal modeling in VL.

Reviewer #3: Reporting unadjusted statistics in Table 1 is important to report. Also reporting population adjusted statistics (e.g., using the population of Brazil an external reference group) would make comparing VL and other characteristics across the ten federative units/states. The authors include helpful detail to interpret the results, however, these details are best for the methods or discussion. Figure 1 does not seem to add any detail to the paper and can be omitted.

--------------------

Conclusions

-Are the conclusions supported by the data presented?

-Are the limitations of analysis clearly described?

-Do the authors discuss how these data can be helpful to advance our understanding of the topic under study?

-Is public health relevance addressed?

Reviewer #2: What are the limitations or gaps for this study? Please clearly state in precise manner. What are the key limitation of analysis? What is the contribution of this study in the area including public health relevance please update this part under discussion section.

Reviewer #3: Based on Appendix 1, Appendix 2, and Appendix 3 the SVIs are likely correlated. Presently, the study does neither assess this correlation, mention it in the discussion, or cite previous studies that may have done so already.

What characteristics of Maranhão and Ceará may explain why VL incidence is increasing?

The number of study limitations discussed are few and appear tucked away in paragraph starting on L521 with the study strengths. The ecological design of the study is not the only study limitation. Other examples the authors may want to consider are the potential limitations of using the SVIs, in general, or potential reporting issues of VL that may lead to uncertainty, or limitations of choices in statistical methodology.

Besides the public policy angle, what insight about VL epidemiology or natural history does the investigation demonstrate? Or what further questions does the investigation raise and what future directions can this study be taken. For example, a future direction could be to conduct spatial regression to further assess the relationship between VL and SVI beyond spatial- or spatio-temporal cluster detection.

--------------------

Editorial and Data Presentation Modifications?

Use this section for editorial suggestions as well as relatively minor modifications of existing data that would enhance clarity. If the only modifications needed are minor and/or editorial, you may wish to recommend “Minor Revision” or “Accept”.

Reviewer #1: O texto foi adequamente escrito e possui pontencial de publicação.

Reviewer #2: The paper needs minor modifications by considering the above comments.

Reviewer #3: Spell out “<“ when used in body of text

L72 “Ecological and time series”, remove the ‘and’

L253 typo

L117 use of semicolon

L102 typo

L321 the syntax in this section needs some tightening and double-check the formatting of all 95%CIs

L348 & L388 spell out “Fig” when in-line text

The sentence starting in L459 seems out of place

Paragraph starting on L473 and L478 can be combined

Table 2: Typo in a heading of a column

Figure 4: suggestion to have one common compass rose and scale bar like in Figure 6

Table 3: If indicated all clusters are statistically significant, then the authors may remove the p-value column.

Supplemental 2 Figure 1: Inclusion of an overall trend line combining all federative units/states would help with comparisons between federative units/states

--------------------

Summary and General Comments

Use this section to provide overall comments, discuss strengths/weaknesses of the study, novelty, significance, general execution and scholarship. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. If requesting major revision, please articulate the new experiments that are needed.

Reviewer #1: (No Response)

Reviewer #2: The paper generally good but the author try to update all the necessary comments particularly in methodology section including variable classifications, definitions and statistical model formulations. Some editorial problem the author must modify as per the format of the journal including tables and figures. Updating recent studies on this area under discussion part and also modify the conclusion.

Reviewer #3: The authors conducted a novel investigation of the relationship between the incidence of a neglected tropical disease and social vulnerability in a region of Brazil with the highest historical prevalence and high degree of social inequalities. The study of health disparities is an important topic and health research priority. The study is thorough and with some improvements it could be considered for publication.

--------------------

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Reviewer #1: No

Reviewer #2: Yes: Anteneh Asmare Godana (PhD)(BSc in Statistics, MSc in Applied Statistics, Ph.D. in Statistics)

Assistant Professor of Statistics,

University of Gondar College of Natural and Computational Sciences,

Department of Statistics

P.0.Box 196

Reviewer #3: Yes: Ian D. Buller, Ph.D., M.A.

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1 Jun 2020

16 Oct 2020

Dear Dr. Rodrigues de Moura,

Thank you very much for submitting your manuscript "Space-time risk cluster of visceral leishmaniasis in Brazilian endemic region with high social vulnerability: an ecological time series study" for consideration at PLOS Neglected Tropical Diseases. As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. The reviewers appreciated the attention to an important topic. Based on the reviews, we are likely to accept this manuscript for publication, providing that you modify the manuscript according to the review recommendations.

Thank you very much for resubmitting your manuscript. It is much improved and only requires a few minor changes. Please take into account the comments of Reviewer 3, Ian D. Buller, Ph.D., M.A., at this stage and resubmit.

Please prepare and submit your revised manuscript within 30 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email.

When you are ready to resubmit, please upload the following:

[1] A letter containing a detailed list of your responses to all review comments, and a description of the changes you have made in the manuscript.

Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out

[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).

Thank you again for your submission to our journal. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.

Sincerely,

Genevieve Milon

Deputy Editor

PLOS Neglected Tropical Diseases

Genevieve Milon

Deputy Editor

PLOS Neglected Tropical Diseases

***********************

Thank you very much for resubmitting your manuscript. It is much improved and only requires a few minor changes. Please take into account the comments of Reviewer 3, Ian D. Buller, Ph.D., M.A., at this stage and resubmit.

Reviewer's Responses to Questions

Key Review Criteria Required for Acceptance?

As you describe the new analyses required for acceptance, please consider the following:

Methods

-Are the objectives of the study clearly articulated with a clear testable hypothesis stated?

-Is the study design appropriate to address the stated objectives?

-Is the population clearly described and appropriate for the hypothesis being tested?

-Is the sample size sufficient to ensure adequate power to address the hypothesis being tested?

-Were correct statistical analysis used to support conclusions?

-Are there concerns about ethical or regulatory requirements being met?

Reviewer #3: Yes, sufficient revisions after previous review.

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Results

-Does the analysis presented match the analysis plan?

-Are the results clearly and completely presented?

-Are the figures (Tables, Images) of sufficient quality for clarity?

Reviewer #3: Yes, sufficient revisions after previous review.

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Conclusions

-Are the conclusions supported by the data presented?

-Are the limitations of analysis clearly described?

-Do the authors discuss how these data can be helpful to advance our understanding of the topic under study?

-Is public health relevance addressed?

Reviewer #3: L522: Great addition about data limitations. If there is evidence of underreporting at the health systems in Northeastern Brazil, then its citation would enhance your limitation section. If there is no evidence or study about health system underreporting then I would recommend adding “potential” before “underreporting of cases” and “missingness” replacing “the missing.” You could combine with the following sentence by removing “Maybe this situation occurs due the” with “possibly due to the.”

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Editorial and Data Presentation Modifications?

Use this section for editorial suggestions as well as relatively minor modifications of existing data that would enhance clarity. If the only modifications needed are minor and/or editorial, you may wish to recommend “Minor Revision” or “Accept”.

Reviewer #3: “global Moran’s index I” = “global Moran’s I index”

L436: Typo “trough” = “through”’

Table 2: “Miss data = “Missing data”

L369, L485: use of “whose” seems awkward here. Try “and” or removing

L456: Sentence “Similarly, in Brazil” is awkward, try “has been reported” at the end of the sentence.

L465: Sentence “In Rio Grande do Norte…” is awkward, try removing “it was found” for “there is”

L487: This is the first mention of Lutzomyia longipalpis so provide the full genus

L515: Suggestion to change “it is urgent to develop researches” to “there is a need to conduct investigations”

S2 Appendix 1: Typo in column 2 row 3 “inclued” = “included” (it appears correctly in track-changed document)

S2 Appendix 3: Typo “ie” = “i.e.,”

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Summary and General Comments

Use this section to provide overall comments, discuss strengths/weaknesses of the study, novelty, significance, general execution and scholarship. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. If requesting major revision, please articulate the new experiments that are needed.

Reviewer #3: The authors returned with an improved manuscript after considering all suggestions from a previous review. The study remains in the scope of the journal and is an important analysis health disparity. I thank the authors for their collegial and thorough responses. I have few substantive edits for consideration, primarily minor grammatical modification for clarity.

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Reviewer #3: Yes: Ian D. Buller, Ph.D., M.A.

Figure Files:

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org.

Data Requirements:

Please note that, as a condition of publication, PLOS' data policy requires that you make available all data used to draw the conclusions outlined in your manuscript. Data must be deposited in an appropriate repository, included within the body of the manuscript, or uploaded as supporting information. This includes all numerical values that were used to generate graphs, histograms etc.. For an example see here: http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001908#s5.

Reproducibility:

To enhance the reproducibility of your results, PLOS recommends that you deposit laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see http://journals.plos.org/plosntds/s/submission-guidelines#loc-materials-and-methods

20 Oct 2020

24 Nov 2020

Dear Dr. Rodrigues de Moura,

We are pleased to inform you that your manuscript 'Space-time risk cluster of visceral leishmaniasis in Brazilian endemic region with high social vulnerability: an ecological time series study' has been provisionally accepted for publication in PLOS Neglected Tropical Diseases.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests.

Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.

IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.

Should you, your institution's press office or the journal office choose to press release your paper, you will automatically be opted out of early publication. We ask that you notify us now if you or your institution is planning to press release the article. All press must be co-ordinated with PLOS.

Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Neglected Tropical Diseases.

Best regards,

Sitara SR Ajjampur

Guest Editor

PLOS Neglected Tropical Diseases

Genevieve Milon

Deputy Editor

PLOS Neglected Tropical Diseases

***********************************************************

14 Jan 2021

Dear Dr. Rodrigues de Moura,

We are delighted to inform you that your manuscript, "Space-time risk cluster of visceral leishmaniasis in Brazilian endemic region with high social vulnerability: an ecological time series study," has been formally accepted for publication in PLOS Neglected Tropical Diseases.

We have now passed your article onto the PLOS Production Department who will complete the rest of the publication process. All authors will receive a confirmation email upon publication.

The corresponding author will soon be receiving a typeset proof for review, to ensure errors have not been introduced during production. Please review the PDF proof of your manuscript carefully, as this is the last chance to correct any scientific or type-setting errors. Please note that major changes, or those which affect the scientific understanding of the work, will likely cause delays to the publication date of your manuscript. Note: Proofs for Front Matter articles (Editorial, Viewpoint, Symposium, Review, etc...) are generated on a different schedule and may not be made available as quickly.

Soon after your final files are uploaded, the early version of your manuscript will be published online unless you opted out of this process. The date of the early version will be your article's publication date. The final article will be published to the same URL, and all versions of the paper will be accessible to readers.

Thank you again for supporting open-access publishing; we are looking forward to publishing your work in PLOS Neglected Tropical Diseases.

Best regards,

co-Editor-in-Chief

PLOS Neglected Tropical Diseases

Paul Brindley

co-Editor-in-Chief

PLOS Neglected Tropical Diseases

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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