PLoS Negl Trop Dis plos plosntds PLOS Neglected Tropical Diseases 1935-2735 Public Library of Science San Francisco, CA USA 10.1371/journal.pntd.0009154 PNTD-D-20-01818 Research Article Biology and life sciencesOrganismsEukaryotaAnimalsVertebratesAmniotesMammalsRodents Biology and life sciencesZoologyAnimalsVertebratesAmniotesMammalsRodents Medicine and health sciencesMedical conditionsInfectious diseasesBacterial diseasesLeptospirosis Medicine and health sciencesMedical conditionsTropical diseasesNeglected tropical diseasesLeptospirosis Medicine and health sciencesMedical conditionsInfectious diseasesZoonosesLeptospirosis Medicine and health sciencesEpidemiologyMedical risk factors Research and analysis methodsAnimal studiesExperimental organism systemsModel organismsRats Research and analysis methodsModel organismsRats Research and analysis methodsAnimal studiesExperimental organism systemsAnimal modelsRats Biology and life sciencesOrganismsEukaryotaAnimalsVertebratesAmniotesMammalsRodentsRats Biology and life sciencesZoologyAnimalsVertebratesAmniotesMammalsRodentsRats Ecology and environmental sciencesTerrestrial environmentsUrban environments Biology and life sciencesOrganismsBacteriaLeptospira Biology and life sciencesMicrobiologyMedical microbiologyMicrobial pathogensBacterial pathogensLeptospira Medicine and health sciencesPathology and laboratory medicinePathogensMicrobial pathogensBacterial pathogensLeptospira People and placesGeographical locationsSouth AmericaBrazil Biology and life sciencesOrganismsBacteriaLeptospiraLeptospira interrogans Biology and life sciencesMicrobiologyMedical microbiologyMicrobial pathogensBacterial pathogensLeptospiraLeptospira interrogans Medicine and health sciencesPathology and laboratory medicinePathogensMicrobial pathogensBacterial pathogensLeptospiraLeptospira interrogans Household rat infestation in urban slum populations: Development and validation of a predictive score for leptospirosis Development and validation of a predictive score for leptospirosis https://orcid.org/0000-0001-6951-2336 Costa Federico Conceptualization Data curation Funding acquisition Investigation Methodology Project administration Resources Software Writing – original draft Writing – review & editing 1 2 3 https://orcid.org/0000-0002-0490-4395 Zeppelini Caio Graco Investigation Resources Software Visualization Writing – original draft 3 4 * https://orcid.org/0000-0002-6798-2059 Ribeiro Guilherme S. Conceptualization Methodology Writing – original draft 1 5 https://orcid.org/0000-0002-3146-7813 Santos Norlan Investigation Resources 1 Reis Renato Barbosa Formal analysis Investigation Resources Validation Writing – original draft 1 https://orcid.org/0000-0003-0295-9998 Martins Ridalva D. Methodology Visualization Writing – original draft 1 https://orcid.org/0000-0003-4788-0319 Bittencourt Deborah Investigation Resources Validation Writing – original draft 1 Santana Carlos Investigation Resources Visualization 6 https://orcid.org/0000-0003-2248-9102 Brant Jonas Formal analysis Investigation Resources 7 https://orcid.org/0000-0002-3051-9060 Reis Mitermayer G. Conceptualization Data curation Funding acquisition Methodology Project administration Supervision Writing – review & editing 1 5 https://orcid.org/0000-0001-9023-2339 Ko Albert I. Conceptualization Data curation Funding acquisition Methodology Project administration Software Supervision Writing – review & editing 1 8 Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Ministério da Saúde, Salvador, Brazil Instituto de Saúde Coletiva, Universidade Federal da Bahia, Salvador, Bahia, Brazil Programa de Pós-Graduação em Ecologia: Teoria, Aplicação e Valores, Universidade Federal da Bahia, Salvador, Brazil Laboratório de Mamíferos, Universidade Federal da Paraíba, João Pessoa, Brazil Faculdade de Medicina, Universidade Federal da Bahia, Salvador, Bahia, Brazil Centro de Controle de Zoonoses, Ministério da Saúde, Salvador, Brazil Programa de Treinamento em Epidemiologia Aplicada aos Serviços do SUS, Secretaria de Vigilância em Saúde, Ministério da Saúde, Brasília, Brazil Yale School of Public Health, Yale University, New Heaven, United States of America Coburn Jenifer Editor Medical College of Wisconsin, UNITED STATES

The authors have declared no competing interest.

 * E-mail: czeppelini@gmail.com 3 3 2021 3 2021 15 3 e0009154 19 10 2020 15 1 2021 2021 Costa et al 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.

Domestic rats are the principal reservoir for urban leptospirosis. However, few studies have identified infestation markers in slums and evaluated their predictivity for leptospirosis risk. We compared households with leptospirosis cases in Salvador, Brazil between 2007 and 2009 and their neighbors using a case control design, surveying for rodent infestation signs and environmental characteristics. With the 2007–2008 data, a conditional logistic regression modeling identified the peridomiciliar presence of rodent burrows (OR, 3.30; 95% CI, 1.50–7.26), rat feces (2.86; 1.24–6.59), runs (2.57; 1.06–6.22), households bordering abandoned houses (2.48; 1.04–6.02), and unplastered walls (2.22; 1.02–6.02) as risk factors and developed a predictive score for leptospirosis. With an independent data set from 2009, a receiver operating characteristic (ROC) curve analysis evaluated the prediction score performance, with the area under the curve being 0.70 (95% CI, 0.64–0.76) for score development and 0.71 (0.65–0.79) for validation. Results indicate that high proportions of urban slum households are infested with R. norvegicus. The score performed well when identifying high-risk households within slums. These findings need confirmation in other urban centers, but suggest that community-based screening for rodent infestation can allow to target rodent and environmental control measures in populations at highest risk for leptospirosis.

 Author summary

Leptospirosis is a rodent-borne zoonosis associated to impoverished areas, where inadequate infrastructure and urban services expose the population to infection through contaminated water and soil. Given the difficulty of early diagnosis and of implementation of large-scale sanitary and rodent control interventions, predictive score that can assess the risk of infection could be useful to define priority areas for local interventions that mitigate the risks. The authors combined data from hospital surveillance for severe leptospirosis cases, domiciliary follow-up visits and environmental surveys to identify environmental and structural characteristics associated to severe leptospirosis incidence (using nearby households without severe cases as matched controls). Signs of rodent infestation in the peridomicile (burrows, fecal pellets, rodent run marks), bordering abandoned houses and non-plastered walls were associated to severe leptospirosis cases and were used to develop the predictive score. The score was able to identify the households with severe leptospirosis cases, and could be applied to define targeted control measures to reduce risk of infection.

 AIK acknowledges financial support for the project through the National Institutes of Health (grants R01 AI052473, U01 AI088752, R01 TW009504, R24 TW007988, R25 TW009338 and D43 TW00919). FC has been granted funds by the Wellcome trust for the study [102330/Z/13/Z; 218987/Z/19/Z] and a CAPES doctorate scholarship (Coordination for the Improvement of Higher Education Personnel/ Ministry of Education/ Brazil; https://www.gov.br/capes/pt-br), CGZ has received a doctorate scholarship from FAPESB (Foundation for Research Assistance of Bahia, http://www.fapesb.ba.gov.br/). GSR received a research scholarship from the Brazilian National Research Council (CNPq – www.cnpq.br). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. PLOS Publication Stage vor-update-to-uncorrected-proof Publication Update 2021-03-15 Data Availability All data used in this study is deposited in Zenodo under the DOI: https://doi.org/10.5281/zenodo.4537463 and is freely available. Introduction

Pathogenic Leptospira infection produces a broad spectrum of manifestations ranging from mild and self-limited illness to life-threatening diseases–Weil’s disease and severe pulmonary hemorrhage syndrome–with fatality rates greater than 10% and 50% respectively [1]. Every year, over one million cases of leptospirosis, and 58000 deaths are reported worldwide [2,3]. In developing countries, leptospirosis is an emerging health problem in urban slums [4], with epidemics during the rainy season, especially in flooding lands [5]. In poor communities, leptospirosis has been associated with increased flood risk, inadequate sewage systems and poor refuse collection services [6].

Urban transmission occurs with rats shedding leptospires in water and soil [7]. In Brazil, Rattus norvegicus is considered the principal reservoir of leptospirosis, as specimens are found carrying L. interrogans serovar Copenhageni (the serovar responsible for most of human infection) in areas of occurrence of incident cases [8]. In addition, subjective rat infestation markers, such as peridomiciliary sighting of rats by residents and residence in proximity of a rat preferential environment like open sewers, were independent risk factors for both severe leptospirosis [9] and subclinical Leptospira infection, determined by the presence of agglutinating antibodies [4]. Objective signs of rodent infestation (i.e. feces of R. norvegicus and rat burrows) were also identified as a risk factors for subclinical/asymptomatic Leptospira infection [10]. Based on those risk factors, the authors proposed an environmental score to identify households at high risk of Leptospira transmission and potentially focus rodent control interventions [7,10]. However, no studies have shown that objective signs of rodent infestation can predict environmental risk for severe leptospirosis, which is the ultimate target of rodent control strategies.

Effective control of urban leptospirosis is hampered by the challenges of introducing large-scale sanitation programs in slums, the difficulty of early diagnosis in the absence of a point-of-care diagnostic test [11], and the nonexistence of an effective human vaccine [1]. Use of boots or protective clothing [12] and antibiotic prophylaxis [13,14] are difficult interventions to implement in large, chronically at-risk populations. Currently, the principal strategy to prevent urban leptospirosis is rodent management via chemical rodenticides and environmental approaches to reduce rodent food, water and harborage [15]. Urban rodent management is based on surveys of households’ exterior areas to obtain information on rodent infestations and infrastructural deficiencies that support rodent populations [16]. However, these strategies are costly and have not been standardized for use in slum areas of developing countries. Additionally, no studies have systematically examined whether variables assessed during rodent surveillance can be used as predictive markers for risk of developing severe leptospirosis. Targeted and cost-effective interventions specific to households with high risk for leptospirosis occurrence could improve both leptospirosis prevention and rodent management. Therefore, we examined household environments to determine if signs of rodent infestation are associated with the occurrence of severe leptospirosis in household subjects.

 Methods Ethics statement

Ethical clearance for this study was granted by the Ethical Committee in Research of the Oswaldo Cruz Foundation (approval number 535/2003), and the National Committee of Ethics in Research (CONEP, approval number 7528). Consent for participation was declared by signing an Informed Consent Agreement Form for legal adults, and an Informed Agreement to Consent Form signed by a parent or legal guardian for participants under 18 years of age, and was obtained during the hospital surveillance activity.

 Surveillance site

Salvador, capital of the state of Bahia, has more than 2.9 million inhabitants and is the fourth most populous Brazilian city [17]. Couto Maia Hospital is the reference center for infectious disease in the state, providing medical assistance for 98% of the leptospirosis cases from Salvador metropolitan region [9]. Beginning in January 2007, trained personnel conducted active surveillance for 36 months (until December 2009) in this hospital, to consecutively identify patients from Salvador that fulfilled a clinical case definition for severe leptospirosis defined as a hospitalized patient with acute undifferentiated fever associated with either bleeding, acute renal failure, jaundice, or acute liver injury with transaminases <1,000 U/L [5]. Additionally, acute and convalescent-phase serum samples were collected and evaluated by microscopic agglutination test (MAT) for laboratorial confirmation [5]. Using panels including the most common Leptospira species in Salvador [8], L. interrogans serogroup Icterohaemorrhagiae serovar Copenhageni (strain Fiocruz L130), a laboratory-confirmed case of leptospirosis was defined as the presence of a four-fold rise in the MAT titer between paired acute and convalescent-phase serum samples or a titer of 1:800 in a single sample [8].

 Study design

Based on the laboratory-confirmed leptospirosis cases detected by the active surveillance, a matched case-control study was conducted in Salvador. A household was regarded as a case household if there was at least one severe leptospirosis patient among permanent household members (i.e., people who spent three or more nights in the household). For each case household, two neighborhood-matched control households were selected from the same slum communities, according to the sampling scheme used in two previous investigations [9,18]. Briefly, controls were sampled from households according to distance of 50m (approximately five households away from the original case households), lack of household members with clinically diagnosed leptospirosis any time in the past, and consent (declared by signing an Informed Consent Agreement Form for legal adults, and an Informed Agreement to Consent Form signed by a parent or legal guardian for participants under 18 years of age) to participate in the study [18]. This strategy was selected in order to avoid overmatching for rodent infestation characteristics between case and control households. The 50m distance between cases and controls was chosen to minimize rat infestations overlapping as the home range of R. norvegicus varies between 30–50 m in urban areas [19]. Two control households were selected for each of the case households by sampling domiciles in opposite directions. The study team identified, recruited, and surveyed the case and their matched control households during the same community site visit.

The case control study design was used as the basis for developing and validating an environmental score for detection of households at risk for clinical leptospirosis. Case and control households from the years 2007 and 2008 were included in the score development group, while households from 2009 were used as the score validation group.

 Data collection and definitions

Domiciliary visits for case and control households from patients identified through 2007 were performed retrospectively during December 2007 and January 2008. For patients identified in 2008 and 2009, case and control household visits were performed prospectively within three weeks of clinical leptospirosis confirmation. During the visits, the research team, which included experienced rodent control specialists from the Zoonosis Control Center of Salvador, conducted environmental surveys of case and control households. The survey team was guided by an outdoor inspection form, adapted from the CDC manual [10,20]. We define rodent marks as black grease marks with a soft appearance, rodent runs were well-cleared paths following walls, possibly associated to rodent marks, fecal droppings were recognized as small dark pellets, with Norway rat ones recognized by being larger in size (about 19mm) and with blunt ends, black rats had medium-sized feces (about 12mm) with fusiform pellets, and house mice feces were smaller (6mm) and fusiform. Because of the environmental and socioeconomic differences found in Salvador, some variables from the CDC manual needed to be excluded or modified and additional variables were incorporated [10]. Abandoned vehicle was one of the variables maintained from the CDC form. To measure possible environmental variations between the date of hospitalization and the environmental survey, we asked the head-of-household (cases and controls) if domicile structure, peridomestic area, drainage systems, or accumulated refuse had changed from the date of hospitalization.

 Statistical analysis

Epidemiological and laboratory data were double-entered and validated using the Epi-Info for Windows software (Centers for Disease Control and Prevention, Atlanta, GA) database. A chi-square for matched data (McNemar’s chi-square) and conditional regression logistic were used to compare categorical and continuous data, respectively, in bivariable analysis to investigate: a) differences in domicile structure between case households and control groups and b) the association of case and control status with exposure to different environmental household characteristics (listed in Table 1) in the development group, with α = 0.010. In the development group, variables that attained a p-value<0.1 in the bivariate analysis were retained for multivariable analysis using conditional logistic regression. A backward elimination strategy was performed to obtain the final model (p-value<0.05).

10.1371/journal.pntd.0009154.t001 Rodent-related and environmental risk factors for severe leptospirosis among 95 case and 184 control households in Salvador, Brazil.
Household characteristics Case* (n = 95) Control* (n = 184)
No. (%) or median (IQR)† P‡
Demographics
No. of inhabitants 4 (3–5) 4 (2–5) -
Male sex 33 (33–50) 36 (25–50) -
Per capita income, US$/d 2.6 (1.3–4.1) 2.8 (1.6–4.6) -
Premise type and details§
Residential use only§ 92 (97) 176 (96) -
Borders on a vacant lot 18 (19) 37 (20) -
Open sewer <10m distance 30 (32) 31 (17) <0.05
Borders on an abandoned house 22 (23) 24 (13) <0.05
Access to food sources§
Exposed garbage§ 78 (82) 174 (73) <0.05
Animal food§ 45 (47) 66 (36) <0.05
Other food & plants§ 64 (67) 101 (55) <0.05
Open stores of human food 52 (55) 74 (40) <0.01
Access to water§
Standing water§ 24 (25) 45 (24) -
Leaks§ 34 (36) 41 (22) <0.01
Harborage for rodents§
Abandoned vehicles§ 1 (0) 0 (0) -
Abandoned appliances§ 94 (99) 183 (99) -
Lumber/clutter on ground§ 67 (70) 112 (61) -
Other large rubbish§ 44 (46) 94 (51) -
Outbuildings/Privies§ 19 (20) 31 (17) -
Dilapidated fences & walls§ 19 (20) 32 (17) -
Plant-related§ 75 (79) 147 (80) -
    Bushes or shrubbery 42 (44) 61 (33) <0.05
    Ornamental plants 65 (68) 168 (70) -
Presence of exposed earth 61 (64) 102 (55) -
Built on earthen slope 50 (53) 88 (48) -
Entry/Access§
Structural deficiencies§ 64 (67) 96 (52) <0.05
    Hole(s) in roof 50 (52) 75 (41) -
    Hole(s) in wall 29 (30) 38 (21) -
    Hole(s) in floor 19 (20) 18 (10) <0.05
Un-plastered walls# 64 (67) 99 (53) <0.05
Rodent active signs§
Active signs§ 60 (63) 64 (35) <0.001
    Rodent burrows 53 (56) 51 (27) <0.001
    Rodent runs 33 (35) 22 (12) <0.001
    R. norvegicus feces 28 (29) 20 (11) <0.001
    R. rattus feces 0 (0) 0 (0) -
    M. musculus feces 1 (1) 3 (2) -
Domestic animals
Dogs 39 (45) 62 (33) -
Cats 13 (13) 18 (10) -
Chickens 13 (13) 12 (6) <0.05

* Case and control households comprised of respectively, households in which laboratory-confirmed cases of leptospirosis resided and neighborhood households which were located within 35 to 50m of case households and did not have a member who developed leptospirosis during the study period.

† Median and inter-quartile range (IQR) values are shown for continuous variables.

‡ Values are not shown for non-significant associations in matched analyses.

§ Categories and variable defined in the CDC form (20).

¶ Presence of exposed earth slope (>45°) within 10m of the household.

# Walls composed of exposed bricks without external application of stucco or plastering.

To develop a practical prognostic score, we assigned weights (proportional to the β regression coefficient values, rounded to the nearest integer) to the independent risk factors identified by the multivariable analysis performed in the development group [21]. A risk score was then calculated for each household, and the population was divided into three categories by comparing differences in sensitivity-specificity: households at low risk, households at intermediate risk, and households at high risk for leptospirosis. For both the development and the validation groups we assessed the discriminative power of the score by using c-statistics generated by the receiver operating characteristic (ROC) curve, sensitivity and false positivity rates. C-statistics greater than 0.80, 0.70 to 0.79, 0.60 to 0.69, and 0.50 to 0.59 indicate excellent, good, fair, and poor predictive ability, respectively.

 Results Household characteristics

From 2007 to 2009, our surveillance identified 179 patients who met the clinical and laboratorial definition for leptospirosis. During the study period, in all laboratory-confirmed cases the highest agglutination titers were directed against L. interrogans serogroup Icterohaemorrhagiae serovar Copenhageni (strain Fiocruz L130). During the domiciliary visits, we were not able to survey 15 case households because they could not be located (13) or could not be appropriately surveyed due to potential violence and unsafety in the neighborhood (2). We also excluded two case households for whom we could not find matched control households satisfying the selection criteria. Thus, the final number of case and control households were 162 and 315 respectively. Most case households (153) had two matched control households and 9 cases households had only one matched control that fulfilled the selection criteria.

From the total number of case and control households, 95 case and 184 control households were identified in 2007/08 and were assigned in the development group. Case and control households identified in 2009, 67 and 131 respectively, were used for the validation group. Case household characteristics of the development and validation groups are shown in S1 Table. Both case groups (development and validation) presented similar characteristics; only 3 of the 38 variables presented statistically significant inter-group differences, and case households in the validation group presented proportionately fewer abandoned appliances but had more holes in the floor and rodent burrows than case households in the development group (S1 Table).

 Bivariate results

Within the development group, several characteristics were statistically associated with severe leptospirosis in a household (Table 1). Premise characteristics, such as the presence of an open sewer <10m and household border on an abandoned house were found to be associated with case households (P<0.05 for both). Food and water sources for rodent (exposed garbage, animal food, other food and plants, open stores of human food, and water leaks) were also associated with case households (P<0.05 for all). Bushes or shrubbery were sources of harborage for rodents more often observed in case households (P<0.05). In addition, case households more frequently had with structural deficiencies, such as holes in the floor and un-plastered walls, which may facilitate rodents’ ability to enter the building (P<0.05 for both).

A larger percentage of case households (63% of cases and 35% of control households) had at least one rodent sign and were considered infested (P<0.001) (Table 1). Major active rodent signs were rodent burrows, runs, and R. norvegicus fecal droppings, and all rodent signs together were more often found in case household compared to control households (P<0.001 for the three) (Table 1). Nevertheless, R. norvegicus fecal droppings had a poor concordance with rodent burrows (kappa = 0.31; CI = 0.22–0.40) and a marginally good concordance with rodent runs (kappa = 0.41; CI = 0.29–0.53). Of note, of the 29 case households with fecal droppings, 28 (96.6%) and 1 (3.4%) had the droppings classified as R. norvegicus and as M. musculus feces, respectively. Similarly, of the 23 control households with fecal droppings, 20 (87.0%) and 3 (13.0%) had them classified as R. norvegicus and as M. musculus feces, respectively. No R. rattus feces were detected.

Statistically significant associations were not found for indicators of low socioeconomic status, such as per capita income, number of inhabitants in the house and proportion of males. Additionally, the risk of acquiring leptospirosis in a household was positively associated with domestic animals, specifically the presence of chickens.

 Multivariable results and development of prediction models

We performed multivariable analysis to identify independent predictors of a case household within the development group (Table 2). The final model retained five variables: three variables from the group of rodent infestation factors, one from the group of premise characteristics, and one other from the rodent access group. Rodent burrows had the strongest association with case households in the model (OR = 3.30, 95% CI = 1.50–7.26). R. norvegicus fecal droppings (OR = 2.86, 95% CI = 1.24–7.26) and rodent runs (OR = 2.57, 95% CI = 1.06–6.22) were additional independent risk factors related to rodent infestation. Households bordering on an abandoned house or un-plastered exterior wall surface were the premise characteristics associated with case household (OR = 2.48 (95% CI = 1.02–6.02) and OR = 2.22 (95% CI = 1.02–6.02), respectively). An additional model was created replacing the variables fecal droppings, rodent burrows, and runs with the combined variable of any sign of rat infestation. This model retained only the variable of any sign of rat infestation (OR = 4.91, 95% CI = 2.69–9.75).

10.1371/journal.pntd.0009154.t002 Logistic regression analysis of rodent-related and environmental risk factors for severe leptospirosis and scoring system.
Variables Matched OR (95% CI)* β regression coefficient Points†
Unadjusted Adjusted
Rodent burrows 5.81 (2.79–12.11) 3.30 (1.50–7.26) 1.19 3
Rodent runs 5.83 (2.61–13.00) 2.57 (1.06–6.22) 0.94 2
R. norvegicus feces 3.76 (1.84–7.64) 2.86 (1.24–6.59) 1.05 3
Borders abandoned house 2.50 (1.16–5.36) 2.48 (1.02–6.02) 0.90 2
Unplastered walls‡ 2.11 (1.12–3.98) 2.22 (1.02–6.02) 0.79 2

* Mantel-Haentzel odds ratios (OR) and 95% confidence intervals (CI) are shown for matched analyses. Conditional logistic regression was performed to obtain estimates for odds ratios which were adjusted for covariates in the final model.

† Assignment of points to risk factors was based on a linear transformation of the corresponding β regression coefficient. The coefficient of each variable was divided by 0.79 (the lowest β value, corresponding to un-plastered walls), multiplied by two, and rounded to the nearest integer.

‡ Walls composed of exposed bricks without external application of stucco or plastering.

To calculate a risk score based on the development group, we assigned each of the five prognostic variables weights that were proportional to its regression coefficient (Table 2). A score was calculated for each household by adding together the points corresponding to its risk factors (minimal score: 0; maximum score: 12). The households were then divided into 11 subgroups based on the scores. There were no households in the point subgroups 1 and 11, and. 32% of the case households and 63% of the control households had a score of 0.

Because score values were not normally distributed within case and control households, we used Wilcoxon ran-sun test to compare the scores by case status. The median risk score for case households was statistically different from that of control households (5 and 2, respectively; P<0.001). Score cumulative sensitivity and false positivity rate for the 11 subgroups were used to define 3 groups with significantly different risks: a low-risk group (0 to 2 points), an intermediate-risk group (3 to 5 points), and a high-risk group (6 to 12 points) (Table 3). The development group yielded a c statistic of 0.70 (95% CI: 0.63–0.76).

10.1371/journal.pntd.0009154.t003 Score system sensitivity and false positivity rate in the development cohort.
Risk categories (Score points) Development Group Validation Group
Case household (% total) Control household (% total) Cumulative sensitivity (95% CI) Cumulative false positive rate (95% CI) Case household (% total) Control household (% total) Cumulative sensitivity (95% CI) Cumulative false positive rate (95% CI)
Low (0–2 points) 30 (32) 116 (63) 100 100 28 (42) 88 (78) 100 100
Intermediate (3–5 points) 24 (24) 43 (24) 68 (58–77) 37 (30–45) 22 (33) 20 (18) 58 (45–70) 22 (15–31)
High (6–12 points) 42 (44) 24 (13) 44 (34–54) 14 (9–19) 17 (25) 5 (4) 25 (15–37) 4 (2–10)
Model validation

The score was calculated for the validation group, for whom 12% of the case households and 40% of the control households had a point value of 0. As the validation group, the median risk score for case households was also statistically different from that for control households (3 and 0, respectively; P<0.001). The validation group yielded a c statistic of 0.71 (95% CI: 0.65–0.79). There were no differences in the two scores’ ability to discriminate between case and control households (p = 0.38). ROC curves were plotted in the Fig 1.

10.1371/journal.pntd.0009154.g001 Receiver operating characteristic curve for based logistic regression model score system.

Area under the curves for the developing cohort 0.70 (95% CI 0.63–0.76) and for the validation cohort 0.71 (95% CI 0.65–0.79).

 Discussion

Efforts to implement and improve rodent management interventions for urban leptospirosis have been hampered by the lack of readily available epidemiologically-based markers that allow identification and monitoring of households at increased risk for leptospirosis. Our study demonstrates that the presence of five variables related to objective signs of rodent infestation and environmental features can be used to predict households located in urban slums with increased risk for occurrence of severe leptospirosis case. These variables were rodent burrows, R. norvegicus fecal droppings, rodent runs, borders on an abandoned house and un-plastered walls. A risk score derived by combining points for each of these features classified households into subgroups at low, medium, and high risk for occurrence of leptospirosis. Households in the medium or high-risk groups could potentially benefit from the most intense chemical rodent control, environmental interventions, and educational measures.

In the process to develop the risk score, we found specific markers of rodent infestation that were strongly associated with the occurrence of severe leptospirosis in a household. While 63% of the case households had signs of rodents, only 35% of control households had these signs. Those results support previous findings from our group [10], showing that the same rodent infestation markers are also associated with Leptospira infection. Although none of these studies had been specifically designed to evaluate rodent infestation level, our findings suggest significant infestation in the sampled neighborhoods.

Based on the fecal dropping characteristics, R. norvegicus was found to be the predominant rodent species in the study households’ peridomicile, mirroring previous work [10,22–29]. R. norvegicus fecal droppings and rat burrows had been previously identified as independent risk factors for Leptospira transmission in a household [10] and now were also associated with severe disease. The high R. norvegicus infestation in association with Leptospira infection and disease in Salvador, together with the corresponding high levels of Leptospira serovar Copenhagen carriage within R. norvegicus specimens captured throughout the city [30] provide additional evidence linking R. norvegicus and leptospirosis. Findings of a unique predominant rodent reservoir for leptospirosis transmission in urban areas highlight the importance of implementing targeted management interventions based on R. norvegicus ecology.

Bordering an abandoned house was an independent risk factor for leptospirosis infection, as these locales provide harborage for rodents [31]. Furthermore, abandoned houses indicate environmental deterioration because, due to rapid population growth in the study area, only structures that are almost destroyed would be abandoned. Open sewer proximity, which was not retained in the model, could be the rodents’ source of water in an infestation and thus a risk factor for Leptospira infection. It has been shown that R. norvegicus prefers environments with open water and has been associated to sewers [32–34]. Although the proximity to an open sewer was a risk factor for severe leptospirosis household status, it did not remain in the final model that was used to build the predictive score, likely because of the strong linkage between R. norvegicus and sewers and the potential correlation between them.

We observed a large availability of concomitant food, harborage, and access sources for rodents in the neighborhoods studied. These variables that favor household rodent infestation were not appropriate to predict leptospirosis high-risk households given their non-specific abundance throughout the urban slum. The only independent risk factor belonging to these groups was the presence of an un-plastered wall, a variable initially proposed for rodent access. This variable was also associated with Leptospira infection in a previous study [7] and we believe un-plastered walls may be a proxy for socioeconomic status not captured by the income variable.

This study proposes an environmental score based on five independent risk factors to stratify urban households into distinct risk groups for leptospirosis infection. The score performed well in stratification of severe leptospirosis and was successfully validated in an independent sample, showing no decrease in discrimination. Our score is an easily accessible research tool, appliable by virtually anyone following quick training, while maintaining moderate prognostic accuracy of 0.7. At the intermediate risk level, point value 3, score sensitivity and specificity were 68% and 63% respectively. The aim of environmental survey tools is to identify the highest sensitivity that can be combined with an operating point in order to provide the lowest proportion of false negative results. Previous environmental survey tools have defined the lower limit of specificity as 50% in order to decrease false classification of risk and prevent unnecessary deployment of control measures [35].

Large cities in Brazil (Salvador, Sao Paulo, Recife, and others) have implemented rodent control programs aimed to decrease leptospirosis incidence. Interventions vary, but most city programs focus on prioritizing areas identified as having high leptospirosis incidence. These areas are large (20,000 to 60,000 households per city) and include households with high variability in socioeconomic and environmental status. This heterogeneity makes difficult for rodent control specialists from the Zoonosis Control Center to systematically select priority households needing rodenticide. Rodent control programs are cost-time expensive because they require rodent infestation screening in all premises in those areas and rodenticide treatment three times a year in premises identified as priority. Due to the low visibility of leptospirosis and the relatively recent implementation of rodent control programs, these programs are frequently negatively affected during epidemics of other diseases, due to resource shortages. Using this score system on top of the mass rodent infestation screening that is already performed, interventions prioritizing rodent chemical control in households with score point values ≥3 could be more cost effective than conventional interventions. Additionally, classifying households by degree of risk for leptospirosis could help policymakers identify high risk areas and implement more focused and efficient interventions during outbreaks or in periods of inadequate resources.

Our model has several limitations. First, the study was limited by the time lag between initial occurrence of leptospirosis and our 2007 assessment of rodent-related risk factors in households. Excluding households with modifications in domicile and peridomicile structure or open sewer or refuse deposit could help control this bias, but other unregistered environmental factors could have changed during the study period. Second, we tried to control observational bias through a predefined structured questionnaire. Third, the sampling distance of 50 m (~5 households) between case and control households used to avoid overmatching related to rodent infestation variables may have inadvertently masked other risk factors. Fourth, the results of the present study may not be generalizable to other urban settings. Nonetheless, 37% of the Brazilian urban population reside in slums with equal or greater levels of poverty than the study neighborhoods [36]. Additionally, a large proportion of the world’s slum population resides in conditions of poverty and environmental degradation that support high levels of rat infestations similar to that in Salvador.

In conclusion, we developed and validated a risk score based on five variables related to objective signs of rodent infestation that predicts risk of severe leptospirosis occurrence in an urban slum household. These findings may be useful in developing rodent management programs to predict individual household risk, to direct control measures, and for policymakers to better allocate limited health care resources.

 Supporting information Rodent-related and environmental characteristics among 95 and 67 case households of development and validation groups in Salvador, Brazil.

(DOC)

We would like to thank the staff of Zoonosis Control Center from Salvador for their assistance in conducting the study; Ananda Nascimento, Ana Claudia da Silva Batista and Erica Sousa for database management; and Barbara Szonyi and Paula Ristow for their critical advice during the preparation of the manuscript.

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PLoS Neglected Tropical Diseases. 2015;9(6):e0003819. doi: 10.1371/journal.pntd.0003819 26047009 Ko AI, Galvao Reis M, Ribeiro Dourado CM, Johnson WD Jr., Riley LW. Urban epidemic of severe leptospirosis in Brazil. Salvador Leptospirosis Study Group. Lancet (London, England). 1999;354(9181):820–5. Sarkar U, Nascimento SF, Barbosa R, Martins R, Nuevo H, Kalafanos I, et al. Population-based case-control investigation of risk factors for leptospirosis during an urban epidemic. American Journal of Tropical Medicine and Hygiene. 2002;66(5):605–10. doi: 10.4269/ajtmh.2002.66.605 12201599 Costa F, Ribeiro GS, Felzemburgh RD, Santos N, Reis RB, Santos AC, et al. Influence of household rat infestation on leptospira transmission in the urban slum environment. PLoS Neglected Tropical Diseases. 2014;8(12):e3338. doi: 10.1371/journal.pntd.0003338 25474580 McBride AJA, Athanazio DA, Reis MG, Ko AI. Leptospirosis. Current Opinion in Infectious Diseases. 2005;18(5):376–86. doi: 10.1097/01.qco.0000178824.05715.2c 16148523 Phraisuwan P, Whitney EA, Tharmaphornpilas P, Guharat S, Thongkamsamut S, Aresagig S, et al. Leptospirosis: skin wounds and control strategies, Thailand, 1999. Emerg Infect Dis. 2002;8(12):1455–9. doi: 10.3201/eid0812.020180 12498663 Krick WK. Prophylaxis against leptospirosis with doxycycline. The New England journal of medicine. 1984;311(1):54. doi: 10.1056/NEJM198407053110115 6727968 Gonsalez CR, Casseb J, Monteiro FG, Paula-Neto JB, Fernandez RB, Silva MV, et al. Use of doxycycline for leptospirosis after high-risk exposure in Sao Paulo, Brazil. Rev Inst Med Trop Sao Paulo. 1998;40(1):59–61. doi: 10.1590/s0036-46651998000100012 9713140 BRAZIL. Manual de controle de roedores. Fundação Nacional de Saúde. Brasília: Funasa 2002. CDC. Integrated pest management: conducting urban rodent surveys. In: Centers for Disease Control and Prevention AUDoHaHS, editor. 2006. Instituto Brasileiro de Geografia e Estatística. Contagem da População 2007. Tabela 1.1.16—População recenseada e estimada, segundo os municípios—Bahia—2007. 2007. Maciel EAP, de Carvalho ALF, Nascimento SF, de Matos RB, Gouveia EL, al. e. Household Transmission of Leptospira Infection in Urban Slum Communities. PLoS neglected tropical diseases. 2008;2(e154 doi: 10.1371/journal.pntd.0000154 18357340 Nowak R. Walker’s Mammals of the world. Baltimore: The John Hopkins University Press; 1991. Centers for Disease Control and Prevention. Integrated pest management: conducting urban rodent surveys. In: Services AUDoHaH, editor. 2006. Rassi A Jr., Rassi A, Little WC, Xavier SS, Rassi SG, Rassi AG, et al. Development and validation of a risk score for predicting death in Chagas’ heart disease. The New England journal of medicine. 2006;355(8):799–808. doi: 10.1056/NEJMoa053241 16928995 Villafaña FM, RA.; Lagos GM.; Pérez MD. Efectividad en el uso del 4 rodenticide biológico Biorat en comparación com el rodenticida químico para el control de los roedores sinantrópicos en objetivos urbanos de la Provincia de Cienfuegos, Cuba. Boletín de Malariología y Salud Ambiental. 2000;11:3–8. Fernández MS, Cavia R., Cueto G.R., Suárez O.V.,. Implementation and evaluation of an integrated program for rodent control in a shanty town of Buenos Aires city, Argentina. EcoHealth. 2007;4:271–7. Cavia RC GR.; Suárez OV. Changes in rodent communities according to the landscape structure in an urban ecosystem. Landscape and Urban Planning. 2009;90 11–9. Childs JE, McLafferty S.L., Sadek R., Miller G.L., Khan A.S., DuPree E.R., Advani R., Mills J.N. & Glass G.E. Epidemiology of rodent bites and prediction of rat infestation in New York City. American Journal of Epidemiology. 1998;148:78–87. doi: 10.1093/oxfordjournals.aje.a009563 9663407 Easterbrook JD, Shields T, Klein SL, Glass GE. Norway rat population in Baltimore, Maryland, 2004. Vector borne and zoonotic diseases (Larchmont, NY). 2005;5(3):296–9. Bajomi D, Sasvári K.,. Results of eight years examination of the habitats of residual urban Norway rat populations after eradication. In: Paper presented at the Twelfth Vertebrate Pest Conference, San Diego, California. 1986. Ieradi LA, Cristaldi M., De Angelis R.,. Rodent pest management. Biological and anthropological aspects. In: Paper Presented at the Fifth International Conferenci Rodens and Spatium. Biodiversity and Adaptation, Maroc. 1996. Traweger D, Slotta-Bachmayr L.,. Introducing GIS-modelling into the management of a brown rat (Rattus norvegicus Berk.) (Mamm. Rodentia Muridae) population in an urban habitat. J Pest Sci. 2005;78(1):17–24. de Faria MT, Calderwood MS, Athanazio DA, McBride AJA, Hartskeerl RA, Pereira MM, et al. Carriage of Leptospira interrogans among domestic rats from a high endemic urban setting for leptospirosis in Brazil. Acta Trop. 2008;108(1):1–5. doi: 10.1016/j.actatropica.2008.07.005 18721789 Panti-May JA, Carvalho-Pereira TS, Serrano S, Pedra GG, Taylor J, Pertile AC, et al. A Two-Year Ecological Study of Norway Rats (Rattus norvegicus) in a Brazilian Urban Slum. PLoS One. 2016;11(3):e0152511. doi: 10.1371/journal.pone.0152511 27015422 Langton SC DP.; Meyer AN. The occurrence of comensal rodents in dwellings as revealed by the 1996 English House Conditions Survey. J Appl Ecol. 2001;38:699–709. Channon EC RT.; Haines R. Hotspots: Are some areas of sewer network prone to reinfestation by rats (Rattus norvegicus) year after year?. Epidemiol Infect. 2006;134:41–8. doi: 10.1017/S0950268805004607 16409649 de Masi E, Vilaça P, Razzolini MT. Environmental conditions and rodent infestation in Campo Limpo district, Sao Paulo municipality, Brazil. Int J Environ Health Res. 2009;19(1):1–16. doi: 10.1080/09603120802126670 19241243 Sanchez L, Vanlerberghe V, Alfonso L, Marquetti Mdel C, Guzman MG, Bisset J, et al. Aedes aegypti larval indices and risk for dengue epidemics. Emerg Infect Dis. 2006;12(5):800–6. doi: 10.3201/eid1205.050866 16704841 UN-HABITAT. Slums of the world: The face of urban poverty in the new millennium? Nairobi: UN-HABITAT. 94 p. 2003. 10.1371/journal.pntd.0009154.r001 Decision Letter 0 Coburn Jenifer Associate Editor Azman Andrew S. Deputy Editor 2021 Coburn, Azman 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. Submission Version 0

11 Nov 2020

Dear Mr. Zeppelini,

Thank you very much for submitting your manuscript "Household Rat Infestation in Urban Slum Populations: Development and Validation of a Predictive Score for Leptospirosis" 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 two independent reviewers. Both reviewers appreciated the importance of the study, but both suggested inclusion of additional detail as noted in the reviews. Based on these reviews, we request that you modify the manuscript according to the review recommendations, but acceptance is not guaranteed at this time.

In addition to the reviewer comments, please make sure the revised version of your manuscript includes the data, and ideally code, needed to reproduce the analyses. This can be in the form of supplemental files or a link to an online repository (e.g., osf.io, github).

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.  

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[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).

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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,

Jenifer Coburn, PhD

Associate Editor

PLOS Neglected Tropical Diseases

Andrew Azman

Deputy Editor

PLOS Neglected Tropical Diseases

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

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 #1: Line 114: You mention that the MAT was used for diagnosis of clinical cases. Can you confirm that only reactors to rat-associated serovars were included?

Line 125: Three or more nights is not intuitive as a definition for permanent household members. Can you justify why that number was chosen?

Line 152: I see there is a reference supporting this survey tool but can you provide the survey as a supplementary file or link to where a copy is publicly available?

Line 165: I believe you mean "...between case households in the development and validation groups."? Also, can you specify which variables were compared?

Line 167: It would be helpful to reference the Table 1 as this point so that the reader can figure out the full compliment of 'characteristics' that were compared.

Line 169: This is the one part of the manuscript that would like to see fleshed out. Specifically, I would like to know how collinearity among ALL variables was assessed and dealt with, particularly given that the discussion identifies that many variables are likely to be colinear. Indeed, results are presented for a collinearity analysis among burrows, runs, and droppings that is not described in the results. It will be important to also establish whether or not there is collinearity among the rat signs and the environmental variables and among the environmental variables. Additionally, it would be helpful to provide more information in the methods on the model selection strategy/metrics and how model fit was assessed. I am particularly interested to know why the more intuitive and parsimonious variable 'any sign of rat infestation' was discarded in favor of a more complex model.

Reviewer #2: The study is well designed especially the case households and control selection.

The rodent marks/runs needs further description especially on how they were identified considering that they could be confused with those of organisms if no further physical evidence was observed. It is worth mentioning where exactly these marks were checked e.g. indoors or outdoors. A more thorough evaluation of rodent presence or activities in an area is that which tiles or the floor is powdered with flour and rodent marks are determined next morning to estimate their presence and activeness in the area. Applying this approach in the two groups of the study cases would give a more reliable evidence.

With regard to predominant rodent species in the area: It is worth mentioning if Rattus rattus which was not recorded in this study through examination of faeces has been previously reported in the area.

Indicate how the faeces of the two species of Rattus (R. norvegicus and R. rattus) can be precisely used to conclude presence of one and absence of the other.

This will help clarify the results (Line 220-222)

Include in the methodology the abandoned vehicle listed in the supplementary info

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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 #1: The results are described very clearly. No suggestions for improvement.

Reviewer #2: Line 220: The methodology do not clearly show how this variable can be used to distinguish between the faecal droppings of the two species, mentioning where these faeces were looked for may help considering that the two species occupy slightly different habitats within human settlements (sewer vs inside houses per se)

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

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 #1: My main comment for the conclusion is that I would be more conservative with regard to describing the predictive power of the tool. Indeed, it is hard to say that the tool 'accurately classified' risk when 42% of case households in the validation group were 'misclassified' (and both sensitivity and specificity are relatively low). Furthermore, it would be beneficial to supplement the discussion in two ways. Firstly, I would like to know why the authors think that such as significant proportion of cases were misclassified according to environmental risk. What risk variables are missing and could the survey be supplemented to capture them? Secondly, it would be beneficial to more explicitly describe appropriate vs inappropriate uses of the tool. This is probably very obvious to the authors but potentially not to future users. For example, the relatively low sensitivity and specificity suggest that the tool may not be appropriate for more precise interventions where accurate household by household identification is needed. However, as the authors note, it can be used as a rough guide to direct existing efforts to maximize efficiency. Can the authors go even one step further and suggest how it could be used in practice? For example, would they recommend that an entire neighborhood be surveyed and interventions be focused on areas where > a specific proportion of households have high scores? Perhaps the utility of neighborhood scores to predict cumulative neighborhood cases could even been an avenue for further study?

Reviewer #2: (No Response)

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

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: Accept

Reviewer #2: (No Response)

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

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: This is a fantastic paper from a first class team that makes a novel and significant contribution to the field of rat-related research by developing a tool for the prediction of urban leptospirosis based on rat presence and environmental factors. I commend the authors for their elegant execution of the study and for the clarity with which they present their work. I have provided minor comments for improvement above, but overall believe that this paper is highly deserving of publication in this journal.

In my opinion the one addition that would take this paper to the 'next level' would be the independent modelling of the environmental factors, i.e., without the signs of rat presence. My concern is that, as the authors point out in the discussion, the colinear nature of the rat presence and many of the included environmental variables means that it is not possible for these variables to be retained in the same model. However, the environmental variables do provide a different piece of the puzzle. For example, they serve as points of intervention for IPM, i.e., the removal of sources of food and harborage vs. reliance on poison baits. Environmental predictors may also require less training and expertise to identify. Finally, given that environmental variables that foster rat infestations are also markers for an 'unhealthy' environment, a focus on these variables would yield resilience-based solutions (i.e., solutions that may protect residents against a plethora of health issues). Ideally, the authors might present both models (with and without rat presence) and compare the fit/predictive power. Overall, I don't think that this analysis (or the lack of it) should be a barrier to proceeding with publication. It is primarily a suggestion of what I would like to see in the literature and what might make this study even more impactful.

Reviewer #2: (No Response)

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

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 10.1371/journal.pntd.0009154.r002 Author response to Decision Letter 0 Submission Version 1

2 Jan 2021

Submitted filename: 31.12.20 When you are ready to resubmit.docx

 10.1371/journal.pntd.0009154.r003 Decision Letter 1 Coburn Jenifer Associate Editor Azman Andrew S. Deputy Editor 2021 Coburn, Azman 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. Submission Version 1

5 Jan 2021

Dear Mr. Zeppelini,

Thank you very much for submitting your manuscript "Household Rat Infestation in Urban Slum Populations: Development and Validation of a Predictive Score for Leptospirosis" for consideration at PLOS Neglected Tropical Diseases. Your resubmitted manuscript was reviewed by members of the editorial board, and we would like to suggest a few minor edits, detailed below with references to the version with changes highlighted. We are likely to accept this manuscript for publication, providing that you modify the manuscript according to the review recommendations.

1. Line 115: suggest replacing "with" with "including" as it is clearer that the most prevalent species was one of a group tested, as opposed to the only serovar/species tested.

2. Line 198: suggest replacing "laboratorial confirmed" with "laboratory-confirmed".

3. We did not see a reply to our request to make sure data (and ideally code) are available to reproduce analyses, as required by the PLoS Data Policy. Please include these data, with an explanation of what exactly is included, in the revised submission so we are able to proceed.

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

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Important additional instructions are given below your reviewer comments.

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,

Jenifer Coburn, PhD

Associate Editor

PLOS Neglected Tropical Diseases

Andrew Azman

Deputy Editor

PLOS Neglected Tropical Diseases

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

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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.

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 10.1371/journal.pntd.0009154.r004 Author response to Decision Letter 1 Submission Version 2

7 Jan 2021

Submitted filename: Dear editors.docx

 10.1371/journal.pntd.0009154.r005 Decision Letter 2 Coburn Jenifer Associate Editor Azman Andrew S. Deputy Editor 2021 Coburn, Azman 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. Submission Version 2

15 Jan 2021

Dear Mr. Zeppelini,

We are pleased to inform you that your manuscript 'Household Rat Infestation in Urban Slum Populations: Development and Validation of a Predictive Score for Leptospirosis' 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. In addition you will need to submit your data needed to reproduce analyses in order to comply with the PLOS Data Policy. 

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Jenifer Coburn, PhD

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PLOS Neglected Tropical Diseases

Andrew Azman

Deputy Editor

PLOS Neglected Tropical Diseases

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

 10.1371/journal.pntd.0009154.r006 Acceptance letter Coburn Jenifer Associate Editor Azman Andrew S. Deputy Editor 2021 Coburn, Azman 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.

26 Feb 2021

Dear Mr. Zeppelini,

We are delighted to inform you that your manuscript, "Household Rat Infestation in Urban Slum Populations: Development and Validation of a Predictive Score for Leptospirosis," has been formally accepted for publication in PLOS Neglected Tropical Diseases.

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PLOS Neglected Tropical Diseases