Determining the spatial distribution of environmental and socio-economic suitability for human leptospirosis in the face of limited epidemiological data

Abstract

Background: Leptospirosis is among the leading zoonotic causes of morbidity and mortality worldwide. Knowledge about spatial patterns of diseases and their underlying processes have the potential to guide intervention efforts. However, leptospirosis is often an underreported and misdiagnosed disease and consequently, spatial patterns of the disease remain unclear. In the absence of accurate epidemiological data in the urban agglomeration of Santa Fe, we used a knowledge-based index and cluster analysis to identify spatial patterns of environmental and socioeconomic suitability for the disease and potential underlying processes that shape them.

Methods: We geocoded human leptospirosis cases derived from the Argentinian surveillance system during the period 2010 to 2019. Environmental and socioeconomic databases were obtained from satellite images and publicly available platforms on the web. Two sets of human leptospirosis determinants were considered according to the level of their support by the literature and expert knowledge. We used the Zonation algorithm to build a knowledge-based index and a clustering approach to identify distinct potential sets of determinants. Spatial similarity and correlations between index, clusters, and incidence rates were evaluated.

Results: We were able to geocode 56.36% of the human leptospirosis cases reported in the national epidemiological database. The knowledge-based index showed the suitability for human leptospirosis in the UA Santa Fe increased from downtown areas of the largest cities towards peri-urban and suburban areas. Cluster analysis revealed downtown areas were characterized by higher levels of socioeconomic conditions. Peri-urban and suburban areas encompassed two clusters which differed in terms of environmental determinants. The highest incidence rates overlapped areas with the highest suitability scores, the strength of association was low though (CSc r = 0.21, P < 0.001 and ESc r = 0.19, P < 0.001).

Conclusions: We present a method to analyze the environmental and socioeconomic suitability for human leptospirosis based on literature and expert knowledge. The methodology can be thought as an evolutive and perfectible scheme as more studies are performed in the area and novel information regarding determinants of the disease become available. Our approach can be a valuable tool for decision-makers since it can serve as a baseline to plan intervention measures.

Keywords: Cluster analysis; Environmental conditions; Knowledge-based index; Socioeconomic groups; Spatial epidemiology; Underreported misdiagnosed diseases.

Fig. 1

The urban agglomeration of Santa Fe. A Political boundaries of Argentina and its provinces are shown in black. The area of the province of Santa Fe is shown in red. B The political boundaries of the Santa Fe province are shown in black and the study area with a red squared. C The urban agglomeration of Santa Fe (UA of Santa Fe): the city of Santa Fe (blue boundaries), the city of Santo Tomé (pink), the city of San José del Rincón (orange), the city of Recreo (yellow), the township of Monte Vera (green), the township of Arroyo Leyes (red)

Fig. 2

The gradient of the environmental and socioeconomic suitability (hereafter “suitability”) for human leptospirosis across the urban agglomeration of Santa Fe. A The suitability for human leptospirosis according to the Conservative scenario. B The suitability for the human leptospirosis according to the Explorative scenario

Fig. 3

The Fuzzy Inference System comparison between the environmental and socio-economic suitability for human leptospirosis obtained from both the Conservative and Explorative scenarios

Fig. 4

Aggregation of sites according to environmental and socio-economic determinants for human leptospirosis obtained from the cluster analysis and both the Conservative (a) and Explorative (b) scenarios

Fig. 5

Biplot of environmental and socioeconomic determinants of human leptospirosis and sites of the Urban Agglomaration of Santa Fe (UA of Santa Fe) based on a Principal Component Analysis. Sites of the UA of Santa Fe are represented by points. Determinants for human leptospirosis are represented by arrows. Arrow orientation represent the direction of the steepest increase of the determinant. Arrow length indicates the relative importance of determinants in the model, the angle between arrows and axes indicates the degree of correlation between them. Socioeconomic determinants: Proportion of housings without indoor water supply (“indoor_water_sp”), solid roof (“roof”), latrine (“latrine”), and with the head of household with a high school education or more (“high_sch_more”). Environmental determinants: elevation (“elevation”), distance to channel (“dist_to_channel”), water bodies (“dist_to_wb”), proportion of water bodies (“prop_wb”), high vegetation (“high_veg”), impervious (built-up) surface, number of patches (“patches”) and Shannon diversity (“shan_div”)

Fig. 6

Correspondence biplot of clusters (“suitability profiles”) from both the Conservative and Explorative scenarios for the Urban Agglomeration of Santa Fe. Blue circles correspond to clusters from the Conservative scenario (Csc). Red triangles represent clusters from the Explorative scenario (Esc). Clusters of each scenario are differentiated with a number added as a suffix (1, 2 and 3)