Environmental risk of leptospirosis infections in the Netherlands: Spatial modelling of environmental risk factors of leptospirosis in the Netherlands

Abstract

Leptospirosis is a globally emerging zoonotic disease, associated with various climatic, biotic and abiotic factors. Mapping and quantifying geographical variations in the occurrence of leptospirosis and the surrounding environment offer innovative methods to study disease transmission and to identify associations between the disease and the environment. This study aims to investigate geographic variations in leptospirosis incidence in the Netherlands and to identify associations with environmental factors driving the emergence of the disease. Individual case data derived over the period 1995-2012 in the Netherlands were geocoded and aggregated by municipality. Environmental covariate data were extracted for each municipality and stored in a spatial database. Spatial clusters were identified using kernel density estimations and quantified using local autocorrelation statistics. Associations between the incidence of leptospirosis and the local environment were determined using Simultaneous Autoregressive Models (SAR) explicitly modelling spatial dependence of the model residuals. Leptospirosis incidence rates were found to be spatially clustered, showing a marked spatial pattern. Fitting a spatial autoregressive model significantly improved model fit and revealed significant association between leptospirosis and the coverage of arable land, built up area, grassland and sabulous clay soils. The incidence of leptospirosis in the Netherlands could effectively be modelled using a combination of soil and land-use variables accounting for spatial dependence of incidence rates per municipality. The resulting spatially explicit risk predictions provide an important source of information which will benefit clinical awareness on potential leptospirosis infections in endemic areas.

Fig 1. Kernel density estimates.

(A) kernel density estimate and point location of Lyme cases and (B) spatial distribution of incidence rates across the Netherlands. Hotpots or clusters of municipalities with increased incidences as identified using the Local Indicators of Spatial Autocorrelation (LISA) analysis are indicated.

Fig 2. Correlogram showing the spatial auto-correlation of leptospirosis incidence rates.

(A) before and (B) after fitting the multivariate model (e.g. residual variation) between municipalities over incremental distances. Lag orders correspond to the order of municipality adjacency ranging from 1^st to 15^th order neighboring municipalities.

Fig 3. Leptospirosis disease maps of the Netherlands.

(A) the estimated leptospirosis incidence and (B) residual variation after fitting the multivariate model to the data.