• View in gallery

    Univariate plots of leptospirosis incidence vs. six predictor variables: (A) Total population, (B) gross domestic product (GDP) per capita, (C) annual temperature, (D) temperature variability, (E) annual precipitation, and (F) precipitation variability. Leptospirosis is in per 100,000 individuals and is on a log (x + 1) scale in all plots, population and per capita GDP are shown on a log scale, temperature variability is the standard deviation of the monthly values of temperature, and precipitation variability is the coefficient of variation in monthly precipitation. Although not all of these univariate plots reveal strong relationships, all six predictors were significant in a multiple regression (leptospirosis incidence = 6.96–0.38 (log population size) − 0.57 (log GDP per capita) − 0.11 (annual temperature) − 0.23 (temperature variability) + 0.0004 (annual precipitation) + 0.01 (precipitation variability); R2 = 0.57, N = 46, P < 0.0001, leave-one-out cross-validation [LOOCV] = 0.35).

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    Univariate plot of angiostrongyliasis incidence vs. population. Angiostrongyliasis is in per 100,000 individuals and on a log scale, and population size is log-transformed. The model was highly significant (angiostrongyliasis incidence = 6.36–1.16 (log population size); R2 = 0.96, N = 11, P < 0.0001, leave-one-out cross-validation [LOOCV] = 0.94).

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    Univariate plots of seroprevalence of Toxoplasma gondii infection vs. (A) total population and (B) gross domestic product (GDP) per capita. Seroprevalence is shown on an arcsine square root scale, and GDP per capita and total population are shown on a log scale. Both predictors were significant in a multiple regression model (seroprevalence of T. gondii infection = 2.32–0.25 (log per capita GDP) − 0.11 (log population size); R2 = 0.53, N = 18, P = 0.003, leave-one-out cross-validation [LOOCV] = 0.32).

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    Univariate plot of toxocariasis seroprevalence and annual precipitation. Toxocariasis is arcsine square root transformed. The model was highly significant (toxocariasis seroprevalence = 0.09 + 0.0003 (annual precipitation); R2 = 0.77, N = 9, P = 0.002, leave-one-out cross-validation [LOOCV] = 0.67).

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    Predicted incidence of leptospirosis. Circles show predicted values and squares show extrapolated values. Leptospirosis is reported as incidence per 100,000 people.

  • View in gallery

    Predicted seroprevalence of Toxoplasma gondii infection. Circles show predicted values and squares show extrapolated values. Seroprevalence of T. gondii infection is reported as a percentage.

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Estimating Burdens of Neglected Tropical Zoonotic Diseases on Islands with Introduced Mammals

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  • 1 Department of Ecology and Evolutionary Biology, University of California, Santa Cruz, California.
  • 2 Island Conservation, Santa Cruz, California.

Many neglected tropical zoonotic pathogens are maintained by introduced mammals, and on islands the most common introduced species are rodents, cats, and dogs. Management of introduced mammals, including control or eradication of feral populations, which is frequently done for ecological restoration, could also reduce or eliminate the pathogens these animals carry. Understanding the burden of these zoonotic diseases is crucial for quantifying the potential public health benefits of introduced mammal management. However, epidemiological data are only available from a small subset of islands where these introduced mammals co-occur with people. We examined socioeconomic and climatic variables as predictors for disease burdens of angiostrongyliasis, leptospirosis, toxoplasmosis, toxocariasis, and rabies from 57 islands or island countries. We found strong correlates of disease burden for leptospirosis, Toxoplasma gondii infection, angiostrongyliasis, and toxocariasis with more than 50% of the variance explained, and an average of 57% (range = 32–95%) predictive accuracy on out-of-sample data. We used these relationships to provide estimates of leptospirosis incidence and T. gondii seroprevalence infection on islands where nonnative rodents and cats are present. These predicted estimates of disease burden could be used in an initial assessment of whether the costs of managing introduced mammal reservoirs might be less than the costs of perpetual treatment of these diseases on islands.

Introduction

Introduced mammals are reservoirs of zoonotic pathogens and are also well-known drivers of decline and extinction of native species and changes to ecosystems on islands.14 Rodents (Mus spp., Rattus spp.), cats (Felis catus), and dogs (Canis familiaris) are the most common introduced mammals.5,6 These introduced mammals co-occur with over 470 million people on at least 560 islands that also harbor Critically Endangered and Endangered vertebrates as defined by the International Union for Conservation of Nature (IUCN).7,8 Peri-domestic rodents and domestic, stray, and feral cats and dogs are reservoirs for many neglected zoonotic diseases, including angiostrongyliasis, some strains of leptospirosis and rabies, as well as toxoplasmosis, toxocariasis, and echinococcosis (Supplemental Table 1).9,10 Although these diseases have a worldwide distribution, they have disproportionate impacts on marginalized human populations with limited access to health-care services.1113 Treatment of these diseases includes antibiotics, antiprotozoals, and vaccinations, but treatment is challenging to administer to remote populations. In mainland areas, controlling the reservoir host populations can reduce the source of infection,1416 but the ongoing costs for control can be prohibitively expensive.1719 In contrast, on islands where immigration of introduced mammals is low and potentially preventable, management actions to control (reduce populations) or eradicate (completely remove) introduced mammals represent significant public health opportunities to reduce or potentially eliminate these diseases.

Management of introduced mammals on islands is a common practice to protect native biodiversity. For many islands, complete eradication of introduced rodents, and feral cats and dogs is possible, with island size and human population size being key limiting factors on where that can occur.6,20,21 Where this is not currently feasible, controlling rodents, and feral dogs and cats on islands utilizes techniques to reduce populations of these introduced mammals to a desired state, ideally an outcome state for a native species.22,23

Islands represent a potential opportunity for reducing zoonotic disease burden because control or eradication of introduced mammal populations could result in disease alleviation or disease elimination, particularly if reintroductions of reservoirs can be prevented. Determining whether control or eradications are a cost-effective approach to controlling zoonotic diseases requires weighing the potential ecological, economic, and social costs of management against the human health and ecological benefits. The costs include the economic cost of control or eradication, economic and cultural benefits of the introduced mammal, and nontarget ecological impacts of removal.6,20 The benefits potentially include reduction of zoonotic disease in human populations, enhanced economic growth due to alleviation of economic costs of zoonotic disease on human livelihoods and local economies,24 reduced costs of disease control, and recovery of impacted native diversity.25 A first step in quantifying the potential benefits of management of introduced mammals is an assessment of the burden of zoonotic diseases on island communities.

Although there has been a considerable effort to estimate the global burden of neglected zoonotic diseases such as rabies,26 congenital toxoplasmosis,13 and leptospirosis,27 very little is known about the burden of zoonotic diseases transmitted by introduced mammals on islands. Only a fraction of the 560 islands where people co-occur with introduced mammal host populations7 has information on disease burdens for any zoonotic disease. However, if disease burdens could be estimated using other readily available ecological and socioeconomic data, these estimates could then be used to inform preliminary cost-effectiveness analyses for management of introduced mammal populations on islands, and identify where local disease burden assessments could then be conducted.

The goals of our study were to examine whether a suite of climatic and socioeconomic factors can be predictive of burden of zoonotic pathogens transmitted by introduced mammals on islands, and thereby to provide a tool that can be used in cost-effectiveness analyses to identify where control of introduced mammals could be applied as a public health mitigation tool. Several readily available socioeconomic and climatic variables could serve as useful predictors of disease burdens through direct, indirect, or correlated influences on pathogen transmission.

Socioeconomic factors such as gross domestic product (GDP) per capita and population size are often correlated with zoonotic disease burden possibly due to their inherent connection to public health-care services and infrastructure. Infrastructure in public services is predicted to scale exponentially with increasing population size through economies of scale.28 In particular, efficiency in public services such as solid waste management and access to waste disposal centers lead to decreased rodent population sizes, and decreased contact with commensal rodents and rodent-borne pathogens.2931 This in turn may have a cascading effect on cat abundance through bottom-up processes,32 and in consequence affect the environmental load of parasites like Toxoplasma gondii.33,34 However, this pattern has only been studied in large metropolitan areas, and whether it persists at smaller population sizes is currently unknown. Climate can influence disease dynamics by affecting host and pathogen population fluctuations via availability of resources in the form of vegetation or prey, or by creating suitable environmental conditions for free-living stages of pathogens.35

We searched the literature and other sources for data on population size, economic development, and climatic variables. We correlated these variables with disease burden data for five important zoonotic diseases: angiostrongyliasis, leptospirosis, toxoplasmosis, toxocariasis, and rabies. We focused on these diseases because of their public health importance and neglected status,11,13,3638 as well as on the availability of epidemiological information.

Methods

We collected disease data for angiostrongyliasis, leptospirosis, toxoplasmosis, toxocariasis, rabies, and echinococcosis from the Global Infectious Disease and Epidemiology Network (GIDEON) online database39 and by searching Google Scholar and PubMed for published epidemiological studies from islands, island countries, or countries within islands. We found incidence data for cystic echinococcosis and alveolar echinococcosis for only eight islands and one island, respectively (Supplemental Table 2). Given the limited availability of incidence data for these diseases, we did not pursue further analysis.

For islands that do not harbor native felids, T. gondii can be exclusively maintained by introduced cats,40 and both introduced cats and dogs can maintain Toxocara spp.41 However, some species and serotypes of Leptospira and rabies can be maintained by native mammal species in islands.42,43 For these two diseases, we only included cases of species or serotypes associated with rodents or dogs, respectively.

We obtained the majority of disease information for leptospirosis and rabies from the GIDEON online database (Supplemental Table 2), which reports number of cases per year. We calculated incidence using the average number of cases reported between 2005 and 2015 to account for variability between years. We calculated the average yearly incidence of these diseases by dividing the number of cases by the island's population. We estimated countrywide incidence for archipelagos when cases were not reported for a specific island. We obtained the majority of information for angiostrongyliasis from epidemiological studies (Supplemental Table 2). To meet assumptions of normality, we log-transformed leptospirosis and rabies incidence data after adding one to account for islands that reported zero cases, and log-transformed angiostrongyliasis incidence data.

Reporting of cases is thought to be heterogeneous, with underreporting being higher in rural populations. As a result, we performed analyses on estimates of incidence, one using total population size, and another using urban population size to account for underreporting in rural areas. We primarily used The World Bank Group (World Bank)44 and Central Intelligence Agency (CIA) World Factbook45 to obtain information on total and urban population size. However, when data were not available in these databases, we searched the country profiles of the UNdata,46 United Nations Children's Emigration Fund Migration Profiles,47 and the Insee (Institut national de la statistique et des etudes économiques).48

We obtained data for prevalence of T. gondii infection and toxocariasis from epidemiological studies (Supplemental Tables 3 and 4). These diseases were reported as seroprevalence, and for both diseases the age classes for which data were collected for each island differed. To compare seroprevalence between islands, we only included data from islands with disease information stratified by age classes. For seroprevalence of T. gondii infection, we imputed missing values using pairwise multivariate correlations.49 We analyzed adjusted estimates for the 31- to 40-year age class for seroprevalence of T. gondii infection, and the 5- to 15-year age class for toxocariasis, which were the most commonly reported age classes in the epidemiological studies (Supplemental Tables 3 and 4). Seroprevalence was arcsine square-root transformed to normalize the data.

For islands with disease information, we obtained data on climate, population size, and per capita GDP. We obtained climatic data from Bioclim World Climate,50 and focused on mean annual temperature, temperature variability, annual precipitation, and precipitation variability. This database reports temperature variability as the standard deviation of the monthly values of temperature, and precipitation variability as the coefficient of variation in monthly precipitation. We used ESRI ArcMap version 10.251 to obtain the climate values for each island by extracting the corresponding BioClim layers using the 30 arc-seconds spatial resolution. We primarily used the World Bank and CIA World Factbook to obtain information on per capita GDP and population size that corresponded to the year the epidemiological study was done. We log-transformed population size as well as per capita GDP to equalize leverage in the analyses.

We used R version 3.2.152 to perform regression analyses. We performed stepwise backward regression for leptospirosis and T. gondii infection, starting with six predictor variables (annual temperature, temperature variability, annual precipitation, precipitation variability, population size, and per capita GDP), until only significant (P < 0.05) predictors remained in the model. Models obtained through stepwise regression have been shown to have higher predictive power than regression trees and similar predictive power to models obtained through exhaustive subset search and stepwise elimination using Akaike information criterion.53 Although stepwise regression can lead to bias in coefficients when predictor variables are correlated, the alternative, including nonsignificant and possibly spurious variables can lead to overfitting. In our analyses, coefficients of predictors in reduced models were very similar to those estimated for the full models, suggesting that bias in coefficients due to variable selection was minor. Sample sizes of islands for rabies, angiostrongyliasis, and toxocariasis were too small to examine in multiple regression analyses. Thus, we performed univariate regression analyses for these diseases. We included population size as a predictor variable in our analyses, and although very low incidence can result in spurious negative relationships with population, analyses in which we excluded islands that reported zero and one cases were qualitatively identical to the results reported below. The residuals of all analyses did not significantly deviate from normality (Shapiro–Wilks tests, P > 0.05) and inspection of residual plots did not reveal any clear nonlinearities.

For seroprevalence of T. gondii infection and toxocariasis, we also fit the nontransformed seroprevalence data with a generalized linear model with a binomial distribution and logit link. This analysis weights each seroprevalence estimate by the sample size in the study, rather than giving each data point equal weight, as in the Gaussian regression.

We tested the prediction accuracy of the Gaussian models by using leave-one-out cross-validation (LOOCV). We used prediction accuracy to determine whether total population or urban population was more appropriate for estimating incidence. In addition, we calculated the correlation coefficients of the socioeconomic predictors with the residuals of the final model of each disease to test for potential reverse causation between disease data and the socioeconomic variables, which would indicate a need to add instrumental variables to our models.54 For all models, we did not find any correlation between residuals and the socioeconomic predictor variables, suggesting that there was no need to use instrumental variables.

We used the Gaussian regression models for leptospirosis and T. gondii infection, which were cross-validated, and were based on the largest sample size of islands, to predict disease burdens on a subset of islands lacking local disease data to illustrate the potential use of the fitted models. We used the Threatened Island Biodiversity database, which is a global dataset of islands based on the presence of breeding populations of IUCN Critically Endangered and Endangered vertebrates to identify islands with human population, and introduced rodents and/or cats.7,8 We used the Threatened Island Biodiversity database to obtain population size, and the sources described above to obtain climate data and per capita GDP. We primarily focused on islands with known per capita GDP, or that form part of an island country or state with known per capita GDP. However, when GDP per capita was not available for the island or island country, we assigned the GDP per capita value of the sovereign country of the island.

To predict estimates of leptospirosis and T. gondii infection we used the “sp” package in R52 to create bivariate polygon regions to identify islands with values that fell within the range of the predictor values that were used to fit the models (Supplemental Figures 1 and 2). We expect the average prediction accuracy for these estimates to be close to that reported in our analyses. We did not predict disease for islands with values that fell outside the range of these predictors, except for population size. We extrapolated disease estimates for islands with small population sizes (and clearly identify them as extrapolations) because population size is a limiting factor for management of introduced mammals. Specifically eradications are currently feasible on islands with population sizes less than approximately 1,000 people.55 A substantial fraction of the islands that are therefore suitable for management were less populous than the range of islands in the dataset we used to fit the models. As a result, these extrapolated predictions should be used in planning with extreme caution and, as with all our predicted estimates, should be verified by on-the-ground measurement before any rigorous cost-benefit analyses are undertaken.

Results

Data existed for at least one disease on 57 populated islands (including island countries or countries within islands) that were also inhabited by introduced rodents, cats, and/or dogs.7 We found significant associations for four of the five diseases with 53–95% of the variance explained. Based on the average predictive accuracy values of the regression models, incidence of leptospirosis and angiostrongyliasis were better described when estimated using total population size than urban population size (Supplemental Tables 5 and 6, respectively).

The best fitting model for incidence of leptospirosis was the full model. Leptospirosis decreased with increasing values of population size, GDP per capita, temperature variability, and annual temperature, and decreasing values of annual precipitation and precipitation variability, explaining 57% of the variability with 35% out-of-sample average predictive accuracy (Figure 1, Supplemental Table 5). When incidence was estimated using the most recent (2013–2015) reported number of cases, the same model showed an even better fit with higher predictive accuracy (R2 = 0.64, P < 0.0001, LOOCV = 0.44). Island population size and per capita GDP were not correlated with the residuals in the models.

Figure 1.
Figure 1.

Univariate plots of leptospirosis incidence vs. six predictor variables: (A) Total population, (B) gross domestic product (GDP) per capita, (C) annual temperature, (D) temperature variability, (E) annual precipitation, and (F) precipitation variability. Leptospirosis is in per 100,000 individuals and is on a log (x + 1) scale in all plots, population and per capita GDP are shown on a log scale, temperature variability is the standard deviation of the monthly values of temperature, and precipitation variability is the coefficient of variation in monthly precipitation. Although not all of these univariate plots reveal strong relationships, all six predictors were significant in a multiple regression (leptospirosis incidence = 6.96–0.38 (log population size) − 0.57 (log GDP per capita) − 0.11 (annual temperature) − 0.23 (temperature variability) + 0.0004 (annual precipitation) + 0.01 (precipitation variability); R2 = 0.57, N = 46, P < 0.0001, leave-one-out cross-validation [LOOCV] = 0.35).

Citation: The American Society of Tropical Medicine and Hygiene 96, 3; 10.4269/ajtmh.16-0573

Incidence of angiostrongyliasis decreased significantly with population size, explaining 95% of the variability with 94% out-of-sample average predictive accuracy (Figure 2, Supplemental Table 6). Island population was not correlated with the residuals in the models.

Figure 2.
Figure 2.

Univariate plot of angiostrongyliasis incidence vs. population. Angiostrongyliasis is in per 100,000 individuals and on a log scale, and population size is log-transformed. The model was highly significant (angiostrongyliasis incidence = 6.36–1.16 (log population size); R2 = 0.96, N = 11, P < 0.0001, leave-one-out cross-validation [LOOCV] = 0.94).

Citation: The American Society of Tropical Medicine and Hygiene 96, 3; 10.4269/ajtmh.16-0573

Seroprevalence of T. gondii infection decreased with increasing per capita GDP and increasing population size in both Gaussian and binomial regression models (Supplemental Table 7). The predictors in the Gaussian model explained 53% of the variability with 32% average out-of-sample predictive accuracy (Figure 3, Supplemental Table 7). Island population and per capita GDP were not correlated with the residuals in the Gaussian model.

Figure 3.
Figure 3.

Univariate plots of seroprevalence of Toxoplasma gondii infection vs. (A) total population and (B) gross domestic product (GDP) per capita. Seroprevalence is shown on an arcsine square root scale, and GDP per capita and total population are shown on a log scale. Both predictors were significant in a multiple regression model (seroprevalence of T. gondii infection = 2.32–0.25 (log per capita GDP) − 0.11 (log population size); R2 = 0.53, N = 18, P = 0.003, leave-one-out cross-validation [LOOCV] = 0.32).

Citation: The American Society of Tropical Medicine and Hygiene 96, 3; 10.4269/ajtmh.16-0573

Seroprevalence of toxocariasis was significantly and positively correlated with annual precipitation in both the Gaussian and binomial regression models (Supplemental Table 8). Annual precipitation explained 77% of the variability with 67% out-of-sample average predictive accuracy (Figure 4, Supplemental Table 8).

Figure 4.
Figure 4.

Univariate plot of toxocariasis seroprevalence and annual precipitation. Toxocariasis is arcsine square root transformed. The model was highly significant (toxocariasis seroprevalence = 0.09 + 0.0003 (annual precipitation); R2 = 0.77, N = 9, P = 0.002, leave-one-out cross-validation [LOOCV] = 0.67).

Citation: The American Society of Tropical Medicine and Hygiene 96, 3; 10.4269/ajtmh.16-0573

Rabies incidence was not significantly correlated with any of the predictor variables (Supplemental Table 9).

We used the fitted models for leptospirosis and T. gondii infection to generate preliminary estimates of disease burdens on islands lacking local information. Preliminary estimates included seven and 32 islands with values within the bivariate polygon regions of predictors of our fitted models for leptospirosis (Figure 5; Supplemental Table 10) and T. gondii infection (Figure 6; Supplemental Table 11), respectively, and 30 and 61 additional islands for which we extrapolated outside the range of population size.

Figure 5.
Figure 5.

Predicted incidence of leptospirosis. Circles show predicted values and squares show extrapolated values. Leptospirosis is reported as incidence per 100,000 people.

Citation: The American Society of Tropical Medicine and Hygiene 96, 3; 10.4269/ajtmh.16-0573

Figure 6.
Figure 6.

Predicted seroprevalence of Toxoplasma gondii infection. Circles show predicted values and squares show extrapolated values. Seroprevalence of T. gondii infection is reported as a percentage.

Citation: The American Society of Tropical Medicine and Hygiene 96, 3; 10.4269/ajtmh.16-0573

Discussion

Introductions of nonnative mammals have facilitated the establishment and persistence of several important zoonotic diseases.56 Efforts to reduce or eradicate introduced mammal hosts from islands could result in elimination or reduction of the zoonotic pathogens they transmit, particularly if they are the sole pathogen reservoirs on the island (i.e., cats for T. gondii, dogs and cats for Toxocara spp.). However, the costs of controlling or eradicating introduced mammals from islands is nontrivial and cost-effectiveness analyses are needed to assess whether management is worthwhile to attempt as a public health mitigation tool.20,57,58 Cost-effectiveness analyses require estimates of disease incidence or prevalence, which are lacking for most islands globally.

We developed models to provide initial estimates of incidence or seroprevalence on islands where diseases transmitted by rodents, cats, or dogs are present. We found that widely available island attributes explained most of the variation in incidence and seroprevalence for four zoonotic diseases. Although the socioeconomic and ecologic variables rarely directly influence the incidence of zoonotic disease, they appear to be useful correlates in providing an estimate of incidence when local data are lacking. Due to the correlative nature of our analyses, our estimates should be used in the initial identification stage. After prospective islands for management of introduced mammals are identified, local incidence estimates should be obtained before formal cost-effectiveness analyses are undertaken.

Determining whether management of introduced mammal populations offers a conclusive benefit to public health requires an assessment of the disease burden and a cost-effectiveness analysis of the proposed management action at an island scale.59 Disability-adjusted life years (DALYs) are commonly integrated into cost-effectiveness analyses as a value for healthy years of life lost through premature mortality or disability.60 The monetary value of DALYs can be estimated based on the region's income, the funds allocated toward disease alleviation, and the DALY value of the disease.61 The fitted models above could be used to provide an initial estimate of disease incidence or seroprevalence on islands, island countries, or countries within islands where this information is missing to identify prospective islands in which local data should be collected. Ultimately, these results could be converted into DALYs.60 The results can be used to estimate the number of DALYs potentially saved through management of introduced mammal populations on a particular island, and then be assigned a monetary value to be compared with the monetary costs of the management action.61

Management of introduced mammals on inhabited islands implies social, cultural, and economic challenges that should be considered when assessing the feasibility of management actions.58,62,63 For example, costs of eradication vary greatly, mostly as a function of population size, island area (e.g., US$3 to $20,000 per hectare), the type of eradication method (target species dependent), and other variable costs such as capacity building, environmental compliance, and mitigation of nontarget species.20,57,58 Incorporating disease burden estimates into feasibility assessments for introduced mammal eradications can potentially expand the rationale and utility of these actions.

We validated our Gaussian models using the LOOCV technique. For leptospirosis and T. gondii infection, prediction accuracy was 35% and 32%, respectively, and thus, for islands whose traits fall within the bivariate polygon regions of predictors, the predicted values should offer a useful initial estimate of disease burdens. However, disease data were particularly limited for islands with small populations and low GDP per capita (Supplemental Figures 1 and 2). The paucity of epidemiological information from islands with these traits limits the extent of our predictions, yet highlights islands where disease sur veillance could be relevant based on our model results. Furthermore, given that population size is currently a limiting factor for management of introduced mammals on islands, we extrapolated burden estimates of leptospirosis and T. gondii infection on islands in which eradication might be feasible, yet based on information from islands with larger population sizes. This may lead to less accurate predicted estimates of disease burden. Obtaining local data from the prospective islands for formal cost-effectiveness analyses is thus especially important for islands with population values outside those used to build the models described above.

Three recent studies have developed estimates of countrywide incidence for rabies,26 congenital toxoplasmosis,13 and leptospirosis,27 to guide surveillance, vaccination, and reservoir control programs. These studies include incidence estimates for some islands, the majority of which have large populations. Our estimates for predicted and extrapolated incidence of leptospirosis were similar to some of those developed by Costa and others27 (Supplemental Table 12). Unfortunately, we could not compare our estimates of T. gondii burden because we calculated seroprevalence estimates for the 31- to 40-year old age-group, whereas Torgerson and Mastroiacovo13 examined incidence of congenital toxoplasmosis. We did not develop estimates for rabies incidence that could be compared with Hampson and others,26 because none of our predictors reached statistical significance. However, as more data become available, all our models should be updated and estimated values should be compared with other estimates, and if possible, merged into a single dataset to identify islands for potential eradications of introduced mammals.

Improving public health and reducing threats from introduced species are recognized in global targets for sustainable development and biodiversity conservation.64,65 There has been substantial recent attention investigating the relationship between biodiversity and human health,26,66 but there are few concrete examples of the potential for synergistic interventions that benefit both.67,68 However, the biodiversity benefits of eradicating introduced mammals from islands has been well documented,55,69 and our study enables a way to connect these biodiversity benefits to improve public health.

ACKNOWLEDGMENTS

We are very grateful to Erin McCreless for sharing climatic data from inhabited islands.

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Author Notes

* Address correspondence to Luz A. de Wit, Department of Ecology and Evolutionary Biology, University of California Santa Cruz, 100 Shaffer Road, Santa Cruz, CA 95060. E-mail: ldewit@ucsc.edu

Financial support: This research was supported by a joint scholarship for LDW from the National Council of Science and Technology of Mexico (CONACyT) and UC MEXUS, and the Complementary Scholarship from the Ministry of Education (SEP) of Mexico. This research was also supported by the National Science Foundation (grant number DEB-1115895) for AMK, and grants from the David and Lucile Packard Foundation.

Authors' addresses: Luz A. de Wit, Donald A. Croll, Bernie Tershy, Kelly M. Newton, and A. Marm Kilpatrick, Department of Ecology and Evolutionary Biology, University of California Santa Cruz, Santa Cruz, CA, E-mails: ldewit@ucsc.edu, dcroll@ucsc.edu, tershy@ucsc.edu, newton@ucsc.edu, and akilpatr@ucsc.edu. Dena Spatz, Department of Ecology and Evolutionary Biology, University of California Santa Cruz, Santa Cruz, CA, and Island Conservation, Santa Cruz, CA, E-mail: dspatz@ucsc.edu. Nick D. Holmes, Island Conservation, Santa Cruz, CA, E-mail: nick.holmes@islandconservation.org.

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