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. 2017 Mar;96(3):749-757.
doi: 10.4269/ajtmh.16-0573. Epub 2017 Apr 6.

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

Luz A de Wit  1 , Donald A Croll  1 , Bernie Tershy  1 , Kelly M Newton  1 , Dena R Spatz  2   1 , Nick D Holmes  2 , A Marm Kilpatrick  1
Affiliations

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

Luz A de Wit et al. Am J Trop Med Hyg. 2017 Mar.

Abstract

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.

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Figures

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).
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).
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).
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).
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.
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.
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