Linking rattiness, geography and environmental degradation to spillover Leptospira infections in marginalised urban settings: An eco-epidemiological community-based cohort study in Brazil

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1. 

   Version published to 10.7554/elife.73120 on eLife

   Sep 16, 2022
2. 

   eLife

   Aug 20, 2022

   Author Response

   Reviewer #1 (Public Review):

   In their manuscript, these authors present a novel geostatistical framework for modelling the complex animal-environment-human interaction underlying Leptospira infections in a marginalised urban setting in Salvador, Brazil.

   In their work, the authors combine human infection data and the rattiness framework of Eyre et al. (Journal of the Royal Society Interface, 2020) . They use seroconversion defined as an MAT titer increase from negative to over 1:50 or a four-fold increase in titer for either serovar between paired samples from cohort subjects. Whereas this is a commonly used measure of infection; the work would benefit from answering the question about how robust results are related to this definition of seroconversion.

   Thank you for your comment. We have acknowledged this on line … More

   Author Response

   Reviewer #1 (Public Review):

   In their manuscript, these authors present a novel geostatistical framework for modelling the complex animal-environment-human interaction underlying Leptospira infections in a marginalised urban setting in Salvador, Brazil.

   In their work, the authors combine human infection data and the rattiness framework of Eyre et al. (Journal of the Royal Society Interface, 2020) . They use seroconversion defined as an MAT titer increase from negative to over 1:50 or a four-fold increase in titer for either serovar between paired samples from cohort subjects. Whereas this is a commonly used measure of infection; the work would benefit from answering the question about how robust results are related to this definition of seroconversion.

   Thank you for your comment. We have acknowledged this on line 534 in the discussion by adding the following text: “A possible limitation of this study is the titre rise cut-off values used for classifying seroconversion and reinfection in the cohort that determine the sensitivity and specificity of the infection criteria. However, these criteria were used because they are the standard definitions for serological determination of infection that are commonly applied for leptospirosis and a wide range of other infections, and they enable the comparison of results with other previous leptospirosis studies.”

   The model framework relies on the concept of 'rattiness' previously defined by Eyre et al. (JRSI, 2020) and assumes conditional independence within its built up (equation (1)). Whereas this is a reasonable assumption, it would be good to discuss situations in which this assumption is questionable and what the implications are for applying the modelling framework to other settings.

   We have added the following text immediately after “is shown schematically in Figure 2” following equation (1) on line 225: “The conditional independence assumption in (1) is reasonable for a vector-borne disease or one that is transmitted indirectly, in which context the observed rat indices are to be considered as noisy indicators of the unobservable spatial variation in the extent to which the environment is contaminated with rat-derived pathogen. It would be more questionable for applications in which the disease of interest is spread by direct transmission from rat to human.”

   The authors provide an extensive model building exercise and investigate, in different ways, whether the model captures the necessary complexity (GAM smoothers - testing linearity, spatial correlation, etc). I believe the work would benefit from (1) a formal diagnostic investigation, if feasible; (2) providing guidelines on how model building should be performed.

   We have added a new Appendix 7 with diagnostic plots of randomized quantile residuals to check the rattiness-infection model fit with the human infection data and included the following text in Section 2.4 of the main text: “A formal diagnostic investigation of randomized quantile residuals is included in Appendix 7. We found no evidence in the diagnostic plots to suggest that there were issues with our modelling approach.”

   To supplement the R code that is publicly available for repeating all of the steps in this analysis, we have now also included a detailed step-by-step explanation of the model building process in Appendix 8 that outlines the key steps for building the rat and infection components of the model (variable selection and evaluation of residual spatial autocorrelation) and fitting and examining the joint rattiness-infection model. We have added the following text in Section 2.6 of the main text: “We also include a step-by-step explanation of the model building process to guide future users of the rattiness-infection framework in Appendix 8.”

   The authors are to be acknowledged for providing an extensive and thorough discussion of the different aspects of their work. Whereas the discussion is complete, I wonder whether the authors can give a brief example about how this model can be applied in a different setting.

   Thank you. We have added the following text on line 551 in the discussion: “The framework may have important applications beyond the study of zoonotic spillover, with the rattiness component replaced by other exposure measures e.g. mosquito density or ecological indices (such as pollution, where there are multiple, related measures of air or groundwater quality) to model associations with human or animal health outcomes.”

   Reviewer #2 (Public Review):

   Eyre et al. developed and applied a novel geostatistical framework for joint spatial modeling of multiple indices of pathogen (Leptospira) reservoir (rats) abundance and human infection risk. This framework enabled evaluation of infection risk at a fine spatial scale and accounted for uncertainty in the pathogen reservoir abundance estimates. The authors used data collected in two different field projects: (1) a rat ecology study in which three different approaches were used to detect rat presence "rattiness", and (2) a prospective community cohort study in which individuals were sampled during two different time periods to detect recent infections via seroconversion or a four-fold increase in anti-Leptospira antibody MAT titer. Univariable and then multivariable analyses were performed on these data to identify (1) the environmental variables that best predicted "rattiness", and (2) the demographic/social, environmental (household), occupational, and behavioral variables that best predicted human risk of infection. Once identified, the best predictors from (1) and (2) were included in a final, joint model to identify the significant predictors of both 'rattiness' and human infection risk. As a result of this study, the authors were able to detect spatial heterogeneity in leptospiral transmission to humans. They found that infection risk associated with increases in reservoir abundance differed by elevation, and that increases in reservoir abundance at high elevation were associated with a much higher odds ratio for infection than at low elevation. The authors suggest that this has to do with differences in how the infectious leptospires (shed by the rat reservoir) are dispersed in the environment. At high elevations, flooding is less frequent and thus rat shed leptospires are likely to stay where the rat deposited them. Whereas at lower elevations, flooding may play a large role in spreading leptospires more evenly across the landscape, reducing the importance of rat presence at smaller spatial scales. The final best model was then used to generate prediction maps of 'rattiness' as well as human infection risk at all locations within the study area (i.e. including those that lacked rat detection data and human infection data. This work represents an important advance in infection risk modeling as it explicitly incorporates estimates of reservoir abundance and the uncertainty surrounding these estimates into the infection risk assessment, and allows for modeling of infection risk at fine spatial scales. Findings from this study have important management implications at the authors' study site as it suggests that interventions directed at high elevations should be different from those designed to address infection risk at lower elevations. However these are broader implications, as this novel approach may be applied to other systems to enable identification of differences in infection risk for other pathogens at a fine spatial scale, predict infection risk more broadly, and facilitate intervention strategies targeted for the specific epidemiological and ecological conditions experienced by a population.

   This was a well-designed study. The field sampling approach was well balanced, well described and appropriate. Broadly the modeling framework is appropriate for the questions being asked and for the data being used. The variable and model selection approaches were clearly described and appropriate. Evaluation of the more detailed mathematical approach is outside of my area of expertise, so I am unable to comment on the validity of the approach.

   For the most part, the explanatory variables assessed in the different models were well described and justified, however there were some cases for which further explanation would have been helpful. For example, how did the authors determine which occupations to evaluate? Specifically, why traveling salesperson? What is the difference between open sewer within 10 m and unprotected from sewer?

   We have added the following additional text to Section 2.3.2 on line 297 to clarify the definition and reason for inclusion for these variables: “In the household environment domain, two variables were used to capture risk due to sewer flooding close to the household: i) the presence of an open sewer within 10 metres of the household location and ii) a binary `unprotected from open sewer' variable which identified those households within 10 metres of an open sewer that did not have any physical barriers erected to prevent water overflow. Three high-risk occupations were included in the occupational exposures domain as binary variables. Construction workers and refuse collectors have direct contact with potentially contaminated soil, building materials and refuse in areas that provide harbourage and food for rats. Travelling salespeople have regular and high levels of exposure to the environment (particularly during flooding events) as they move from house to house by foot. Two other binary occupational exposure variables were included that measured whether a participant worked in an occupation that involves contact with floodwater or sewer water.”

   I also had some concerns regarding the time-period of the rat ecology study used to determine abundance, potential fluctuations in rat abundance through time, and how this might align with sampling to detect infection in humans. Depending on the time scale of population fluctuation in rats as well as fluctuations in infection prevalence in rats, the abundances calculated from data from the ecology study may not be accurately reflecting true abundance (and therefore shedding and transmission risk) during the time period that a human may have been exposed. However, the authors do a nice job of addressing some of these issues in the discussion. They mention that infection prevalence in rats is consistently around 80% and that there don't appear to be seasonal fluctuations in human exposure risk in the study area.

   Thank you.

   Reviewer #3 (Public Review):

   The goal of the authors was to test how important local rat abundance is as a driver of Leptospira infection in humans.

   The authors approached this using a strong combination of datasets on human infection risk and rat abundance, across a spatial scale that is large enough to allow simultaneous assessment of multiple potentially important drivers of infection risk. This further enables the authors to develop infection prediction maps based on the fitted models.

   This study design is a major advance towards understanding link between rat abundance and human infection risk.

   Based on the top models tested in the study, the authors conclude that local rat abundance is indeed correlated with infection risk, and that this correlation is strongest at higher elevation.

   This is an impactful finding, but in my opinion it is not yet clear how robust and important this is, because of two reasons:

   (1) The infection risk data: while the actual infection risk data are not shown, the map shown in Figure 5B suggests that there is an infection hotspot that happens to be at high elevation. This raises the question of how strongly this single hotspot is driving the observed correlation between rat abundance and infection risk (which the authors find to be much stronger at high elevation than at lower elevations).

   We have added a new figure (Figure 4) earlier on in the article (we decided to add this here rather than to Figure 6 - formerly Figure 5 - to ensure that the map is large enough that points in Figure 4A are easily visible – please note that it is included as a larger and easier to view image in the main eLife template version) with the raw infection data overlaid on contour lines for the three elevation levels to provide the reader with a better overview of the raw data. This new Figure 4 shows that out of a total of 403 participants in the high elevation region there were 16 infections, of which only 5 (31%) were located in the large hotspot in Valley 3 (valleys are numbered 1 to 3 from west to east, see Figure 1A). In addition to the largest hotspot in the north of Valley 3, there are several other areas in the high elevation region with raised predicted infection risk values relative to their surroundings where there were also rattiness hotspots and infected participants in the raw data: fives cases (red and yellow infection risk areas in Figure 5B) on the western side of Valley 2; the two cases on the eastern edge of Valley 2; the two cases on the western edge of Valley 3; and the single case in the southwest of Valley 3. Other variables are also important drivers of infection risk and at several of these locations the contribution of rattiness increases infection risk significantly relative to the low-risk surrounding area (e.g. to 10% in areas where risk is closer to 1% or 2%) without reaching the more obviously visible high infection risk values closer to 20%. We believe that our statistical model provides a better test of whether there is a statistical association between rattiness and infection at high elevations than a visual examination, but that this is supported by the large number of observations in the high elevation area (403) and the distribution of infected and uninfected households, which demonstrates that the observed association is not only driven by the hotspot in Valley 2.

   (2) The statistical models: if I understand correctly, all tested models of infection risk include the variable rat abundance, and while the individual effect estimates for rat abundance are statistically significant (Table 3), the more important question of how the fit of a model without the rat abundance variables compares with those of the other tested models (shown in Supplementary Table S2) has not been addressed.

   These models were considered but were ranked outside of the top five models and for this reason were not reported in Table S2. We agree that showing the AIC of a model without rattiness in this table can more clearly demonstrate the improved fit of the model with rattiness. To do this we have added the highest ranked model without rattiness (M) to Table S2 and added a note to the table explaining the reason for its inclusion (“Model M was ranked outside of the top 5 models but is included here for reference to demonstrate the improvement in model fit when rattiness is included”). The AIC of M* was 532.13. This is substantially higher than the top five models (M1 = 523.14 and M5 = 525.04), justifying its inclusion in this model and in the joint rattiness-infection framework.

   Regardless of whether rat abundance is an important driver of human infection risk, this study is a major step in our understanding of the role of rats in the spread of leptospirosis, due to the strong combination of a unique combination of datasets and a spatial statistical modeling approach.

   Thank you.

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   Read the original source
3. 

   eLife

   Feb 7, 2022

   Evaluation Summary:

   Leptospirosis is an important zoonotic disease with a major global health impact. Although the role of rats as hosts is well known, it is less clear how important the fine-scale local and simultaneous presence of infected rats is relative to contact with water that could have been contaminated by rats elsewhere, or some time in the past. This study leverages a fine-scaled spatial dataset on human infection data and rat abundance to address this question, using a carefully developed statistical model that incorporates key variables and takes into account spatial variation. The models show that 'rattiness', a proxy for local rat abundance, might be an important driver of human infection risk, suggesting that rodent control measures might be an avenue for lowering the risk of infection with Leptospira bacteria.

   (This … More

   Evaluation Summary:

   Leptospirosis is an important zoonotic disease with a major global health impact. Although the role of rats as hosts is well known, it is less clear how important the fine-scale local and simultaneous presence of infected rats is relative to contact with water that could have been contaminated by rats elsewhere, or some time in the past. This study leverages a fine-scaled spatial dataset on human infection data and rat abundance to address this question, using a carefully developed statistical model that incorporates key variables and takes into account spatial variation. The models show that 'rattiness', a proxy for local rat abundance, might be an important driver of human infection risk, suggesting that rodent control measures might be an avenue for lowering the risk of infection with Leptospira bacteria.

   (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #3 agreed to share their name with the authors.)

   Less

   Read the original source
4. 

   eLife

   Feb 7, 2022

   Reviewer #1 (Public Review):

   In their manuscript, these authors present a novel geostatistical framework for modelling the complex animal-environment-human interaction underlying Leptospira infections in a marginalised urban setting in Salvador, Brazil.

   In their work, the authors combine human infection data and the rattiness framework of Eyre et al. (Journal of the Royal Society Interface, 2020) . They use seroconversion defined as an MAT titer increase from negative to over 1:50 or a four-fold increase in titer for either serovar between paired samples from cohort subjects. Whereas this is a commonly used measure of infection; the work would benefit from answering the question about how robust results are related to this definition of seroconversion.

   The model framework relies on the concept of 'rattiness' previously defined by Eyre … More

   Reviewer #1 (Public Review):

   In their manuscript, these authors present a novel geostatistical framework for modelling the complex animal-environment-human interaction underlying Leptospira infections in a marginalised urban setting in Salvador, Brazil.

   In their work, the authors combine human infection data and the rattiness framework of Eyre et al. (Journal of the Royal Society Interface, 2020) . They use seroconversion defined as an MAT titer increase from negative to over 1:50 or a four-fold increase in titer for either serovar between paired samples from cohort subjects. Whereas this is a commonly used measure of infection; the work would benefit from answering the question about how robust results are related to this definition of seroconversion.

   The model framework relies on the concept of 'rattiness' previously defined by Eyre et al. (JRSI, 2020) and assumes conditional independence within its built up (equation (1)). Whereas this is a reasonable assumption, it would be good to discuss situations in which this assumption is questionable and what the implications are for applying the modelling framework to other settings.

   The authors provide an extensive model building exercise and investigate, in different ways, whether the model captures the necessary complexity (GAM smoothers - testing linearity, spatial correlation, etc). I believe the work would benefit from (1) a formal diagnostic investigation, if feasible; (2) providing guidelines on how model building should be performed.

   The authors are to be acknowledged for providing an extensive and thorough discussion of the different aspects of their work. Whereas the discussion is complete, I wonder whether the authors can give a brief example about how this model can be applied in a different setting.

   Less

   Read the original source
5. 

   eLife

   Feb 7, 2022

   Reviewer #2 (Public Review):

   Eyre et al. developed and applied a novel geostatistical framework for joint spatial modeling of multiple indices of pathogen (Leptospira) reservoir (rats) abundance and human infection risk. This framework enabled evaluation of infection risk at a fine spatial scale and accounted for uncertainty in the pathogen reservoir abundance estimates. The authors used data collected in two different field projects: (1) a rat ecology study in which three different approaches were used to detect rat presence "rattiness", and (2) a prospective community cohort study in which individuals were sampled during two different time periods to detect recent infections via seroconversion or a four-fold increase in anti-Leptospira antibody MAT titer. Univariable and then multivariable analyses were performed on these data to … More

   Reviewer #2 (Public Review):

   Eyre et al. developed and applied a novel geostatistical framework for joint spatial modeling of multiple indices of pathogen (Leptospira) reservoir (rats) abundance and human infection risk. This framework enabled evaluation of infection risk at a fine spatial scale and accounted for uncertainty in the pathogen reservoir abundance estimates. The authors used data collected in two different field projects: (1) a rat ecology study in which three different approaches were used to detect rat presence "rattiness", and (2) a prospective community cohort study in which individuals were sampled during two different time periods to detect recent infections via seroconversion or a four-fold increase in anti-Leptospira antibody MAT titer. Univariable and then multivariable analyses were performed on these data to identify (1) the environmental variables that best predicted "rattiness", and (2) the demographic/social, environmental (household), occupational, and behavioral variables that best predicted human risk of infection. Once identified, the best predictors from (1) and (2) were included in a final, joint model to identify the significant predictors of both 'rattiness' and human infection risk. As a result of this study, the authors were able to detect spatial heterogeneity in leptospiral transmission to humans. They found that infection risk associated with increases in reservoir abundance differed by elevation, and that increases in reservoir abundance at high elevation were associated with a much higher odds ratio for infection than at low elevation. The authors suggest that this has to do with differences in how the infectious leptospires (shed by the rat reservoir) are dispersed in the environment. At high elevations, flooding is less frequent and thus rat shed leptospires are likely to stay where the rat deposited them. Whereas at lower elevations, flooding may play a large role in spreading leptospires more evenly across the landscape, reducing the importance of rat presence at smaller spatial scales. The final best model was then used to generate prediction maps of 'rattiness' as well as human infection risk at all locations within the study area (i.e. including those that lacked rat detection data and human infection data. This work represents an important advance in infection risk modeling as it explicitly incorporates estimates of reservoir abundance and the uncertainty surrounding these estimates into the infection risk assessment, and allows for modeling of infection risk at fine spatial scales. Findings from this study have important management implications at the authors' study site as it suggests that interventions directed at high elevations should be different from those designed to address infection risk at lower elevations. However these are broader implications, as this novel approach may be applied to other systems to enable identification of differences in infection risk for other pathogens at a fine spatial scale, predict infection risk more broadly, and facilitate intervention strategies targeted for the specific epidemiological and ecological conditions experienced by a population.

   This was a well-designed study. The field sampling approach was well balanced, well described and appropriate. Broadly the modeling framework is appropriate for the questions being asked and for the data being used. The variable and model selection approaches were clearly described and appropriate. Evaluation of the more detailed mathematical approach is outside of my area of expertise, so I am unable to comment on the validity of the approach.

   For the most part, the explanatory variables assessed in the different models were well described and justified, however there were some cases for which further explanation would have been helpful. For example, how did the authors determine which occupations to evaluate? Specifically, why traveling salesperson? What is the difference between open sewer within 10 m and unprotected from sewer?

   I also had some concerns regarding the time-period of the rat ecology study used to determine abundance, potential fluctuations in rat abundance through time, and how this might align with sampling to detect infection in humans. Depending on the time scale of population fluctuation in rats as well as fluctuations in infection prevalence in rats, the abundances calculated from data from the ecology study may not be accurately reflecting true abundance (and therefore shedding and transmission risk) during the time period that a human may have been exposed. However, the authors do a nice job of addressing some of these issues in the discussion. They mention that infection prevalence in rats is consistently around 80% and that there don't appear to be seasonal fluctuations in human exposure risk in the study area.

   Less

   Read the original source
6. 

   eLife

   Feb 7, 2022

   Reviewer #3 (Public Review):

   The goal of the authors was to test how important local rat abundance is as a driver of Leptospira infection in humans. The authors approached this using a strong combination of datasets on human infection risk and rat abundance, across a spatial scale that is large enough to allow simultaneous assessment of multiple potentially important drivers of infection risk. This further enables the authors to develop infection prediction maps based on the fitted models.

   This study design is a major advance towards understanding link between rat abundance and human infection risk.

   Based on the top models tested in the study, the authors conclude that local rat abundance is indeed correlated with infection risk, and that this correlation is strongest at higher elevation.

   This is an impactful finding, but in my opinion … More

   Reviewer #3 (Public Review):

   The goal of the authors was to test how important local rat abundance is as a driver of Leptospira infection in humans. The authors approached this using a strong combination of datasets on human infection risk and rat abundance, across a spatial scale that is large enough to allow simultaneous assessment of multiple potentially important drivers of infection risk. This further enables the authors to develop infection prediction maps based on the fitted models.

   This study design is a major advance towards understanding link between rat abundance and human infection risk.

   Based on the top models tested in the study, the authors conclude that local rat abundance is indeed correlated with infection risk, and that this correlation is strongest at higher elevation.

   This is an impactful finding, but in my opinion it is not yet clear how robust and important this is, because of two reasons:

   (1) The infection risk data: while the actual infection risk data are not shown, the map shown in Figure 5B suggests that there is an infection hotspot that happens to be at high elevation. This raises the question of how strongly this single hotspot is driving the observed correlation between rat abundance and infection risk (which the authors find to be much stronger at high elevation than at lower elevations).

   (2) The statistical models: if I understand correctly, all tested models of infection risk include the variable rat abundance, and while the individual effect estimates for rat abundance are statistically significant (Table 3), the more important question of how the fit of a model without the rat abundance variables compares with those of the other tested models (shown in Supplementary Table S2) has not been addressed.

   Regardless of whether rat abundance is an important driver of human infection risk, this study is a major step in our understanding of the role of rats in the spread of leptospirosis, due to the strong combination of a unique combination of datasets and a spatial statistical modeling approach.

   Less

   Read the original source
7. 

   Version published to 10.1101/2021.09.21.21263884v1 on medRxiv

   Sep 22, 2021