Social exclusion modifies climate and deforestation impacts on a vector-borne disease

Abstract

Background: The emergence of American Cutaneous Leishmaniasis (ACL) has been associated with changes in the relationship between people and forests, leading to the view that forest ecosystems increase infection risk and subsequent proposal that deforestation could reduce re-emergence of this disease.

Methodology/principal findings: We analyzed county-level incidence rates of ACL in Costa Rica (1996-2000) as a function of social and environmental variables relevant to transmission ecology with statistical models that incorporate breakpoints. Once social marginality was taken into account, the effect of living close to a forest on infection risk was small, and diminished exponentially above a breakpoint. Forest cover was associated with the modulation of temporal effects of El Niño Southern Oscillation (ENSO) at small spatial scales, revealing an additional complex interplay of environmental forces and disease patterns.

Conclusions/significance: Social factors, which previously have not been evaluated rigorously together with environmental and climatic factors, appear to play a critical role that may ultimately determine disease risk.

Figure 1. Patterns of clustering and Schmalhausen's law.

(A) Quinquennial (1996–2000) cutaneous leishmaniasis case rates (cases/population) in Costa Rica at the county level. Colors indicate clustering in monthly rates per 10,000 inhabitants obtained using the Scan method: blue corresponds to the most likely cluster, comprised of the Talamanca county, with a monthly rate 308 per 10,000 from January 1999 to December 2000 (loglikelihood ratio = 3020.06, P<0.001); green depicts the second most likely cluster, comprised of the counties of Osa, Buenos Aires, Aguirre, Perez Zeledon, Golfito, Coto Brus, Aguirre y Corredores, with a rate of 7 per 10,000 from June 1996 to November 1999 (loglikelihood ratio = 515, P<0.001); and red corresponds to the third most likely cluster, comprised of the county of Limon with a rate of 12 per 10,000 from April 1997 to May 2000 (loglikelihood ratio = 265, P<0.001). (B) The county marginalization index (See Protocol S1 for details). Red and blue indicate clusters with high and low marginality, respectively, found using the LISA method with weights based on the 4 nearest neighbors (overall I = 0.7096, P<0.05). (C) County rate as a function of the marginalization index. Black dots represent counties with less than 2 cases in the five years. This pattern, which we call Schmalhausen's pattern, shows a significant positive correlation between marginality and the rate of the disease (r = 0.39, t = 3.8221, df = 79, P<0.0002), where a qualitative change in the relationship is apparent after and before a value of 4 in the marginalization index. Specifically, the variance increase for larger values of social marginalization, consistent with the prediction that new or anomalous conditions modify the system's sensitivity to other drivers.

Figure 2. Breakpoints and discontinuous patterns of association.

A schematic representation of the breakpoint in marginalization (MI) and people living close to the forest (%close), when minimum elevation (ME) is set to 500 m and rainfall (log(MinRfll)) is set at its breakpoint. The surface illustrates major qualitative differences in disease risk as a function of the covariates. Specifically, risk increases exponentially as the proportion of people living close to the forest decreases above the breakpoint. The change has the opposite sign and decreases in magnitude for smaller values below the breakpoint. Marginality exacerbates this difference above its own breakpoint. Parameters are those of the model selected as best. This model has 7 parameters (AIC = 5768.7) and fits the data satisfactorily (Residual deviance = 79.718, df = 72, P>0.24), explains 71.34% of the deviance (null deviance = 278.108) and is not different from the more complex models presented in Table 1, values for the coefficients are presented in Table S6.

Figure 3. Cutaneous leishmaniasis in Costa Rica: Deforestation and El Niño Southern Oscillation (ENSO).

(A) Local Effects of ENSO. Linear model results for a model testing for localized effects of ENSO in the counties where the disease was clustered. Color indicates clusters found with the spatio-temporal scan analysis of Figure 1, characters are used for the data in each individual county (For parameter values, see table in the appendix). For representation purposes, a small amount of noise was added in the x (ENSO) axis. The ANCOVA for this model showed the interaction of ENSO*County to be statistically significant (P<0.0113, for more details see Tables S7, S8). The model has a high goodness of fit (R² = 0.85) that outperforms a similar model with the same number of parameters but that uses a first order autoregressive structure (R² = 0.26) instead of ENSO. (B) Differences in forest cover for counties where the incidence diminishes or increases with ENSO. In the boxplot, 1 stands for the counties where the annual rate decreases with ENSO (Talamanca, Limón, Golfito, Buenos Aires & Coto Brus) and 2 for those where the incidence increases with ENSO (Aguirre, Corredores, Osa & Pérez-Zeledón). The difference is statistically significant as shown by a one tail Welch's t-test (a test robust to differences in variance) in which the alternative hypothesis is that the difference in forest cover between 1 and 2 is larger than 0 (Welch's t =  2.14, d.f. = 5.9, p<0.038).