MAPPING SCHISTOSOMIASIS MANSONI IN THE STATE OF MINAS GERAIS, BRAZIL, USING SPATIAL REGRESSION

F. R. Fonseca1, C. C. Freitas1, L. V. Dutra1

1Instituto Nacional de Pesquisas Espaciais/INPE, Av. dos Astronautas, 1.758 Jd. Granja, CEP 12227-010, São

José dos Campos, SP, Brasil

ABSTRACT

The main goal of this paper is to develop spatial regression models to estimate the prevalence of schistosomiasis in the state of Minas Gerais, Brazil, using information on the disease spatial dependence through the network of roads and rivers, in addition to climate, socioeconomic and environmental variables. The results showed that the Schistosoma mansoni hosts mobility is an important factor for modeling and estimating the schistosomiasis prevalence distribution. Variables representing vegetation, temperature, precipitation, topography, sanitation and human development indexes, proved their importance in explaining the disease spreading, indicating favorable conditions for the disease development. The use of spatial regression showed meaningful results to the health management procedures and direction of activities, enabling a better detection of disease risk areas. Index Terms - Regression analysis, spatial analysis, schistosomiasis mansoni.

1. INTRODUCTION

One of the serious public health problems affecting millions of people worldwide is the schistosomiasis. In Brazil, schistosomiasis is caused by the etiologic agent Schistosoma mansoni, whose intermediate hosts are snails of the genus Biomphalaria (B. straminea, B. glabrata or B. tenagophila) [1]. The lifecycle of the disease is well defined and has water as a support used by the parasite to infect humans (definitive host). Man, through its contaminated feces in contact with water, infects the snail of genus Biomphalaria. Thus, the study of the transmission of schistosomiasis is due to the combination of environmental characteristics related to man and the snail.

The human beings and the snail depend on a transportation network to move on a larger scale. Human beings depend on roads in order to geographically move and the snail depends on river systems. Thus, to study the spread of schistosomiasis, it must be taken into account aspects related to the transport routes of the intermediate and definitive host. Previous works studied the prevalence of this disease due to environmental variables extracted from remote sensing. These studies provided the opportunity to better understand the disease distribution and thereby improve their knowledge on the ecological influence [2, 3, 4, 5].

Facing this reality and considering the scarcity of studies in Brazil that exploit the risk of the spread of schistosomiasis, taking into account its spatial effects, the main goal of this paper is to estimate the prevalence of schistosomiasis in the state of Minas Gerais, Brazil, using data that have information on the disease spatial dependence through the network of roads and rivers, in addition to variables such as climate, socioeconomic and environmental, using spatial regression models.

2. MATERIALS AND METHODS

The schistosomiasis prevalence data were provided

by the Secretary of Health in the State of Minas Gerais, from historical data collected over the years, from 1984 to 2005. Among the 853 municipalities of Minas Gerais, only 197 municipalities have information on the disease prevalence. Environmental data were derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and surface models generated by the Shuttle Radar Topography Mission (SRTM). Nine variables were selected from the MODIS products, with the data collected on two different dates, during summer (rainy season, from Jan.17th to Feb.1st, 2002) and during winter (drought season, from Jul.28th to Aug.12th,

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2002), and other two variables from SRTM. The MODIS variables are composed of blue, red, near-infrared, middle infrared bands; enhanced vegetation index, normalized difference vegetation index and the vegetation, soil and shade indexes derived from mixture models [6]. The variables derived from SRTM are: elevation and declivity. In addition, a water accumulation map (which measures at each point of a hydrographic basin, the number of possible paths that water can run before reaching this particular place) was generated from the digital elevation model [7]. Based on this map, two hydrographic variables were derived: the mean and the median of water accumulation inside each municipality. A water mobility index, based on declivity and average of water accumulation [8], for rainy (from Jan.17th to Feb.1st, 2002) and drought seasons (from Jul.28th to Aug.12th, 2002) were also included. The water mobility index is an indication of how fast and abundant the water collections are. The climatic data, accumulated precipitation, maximum and minimum temperature of the rainy season and drought seasons were obtained from the Center for Weather Forecasting and Climate Studies (CPTEC, acronym in Portuguese). Other climate input data are the daily difference between the maximum and minimum temperatures during summer and winter, as proposed by [9]. Eighteen socioeconomic variables provided by the Brazilian Institute of Geography and Statistics (IBGE) were used, which included data from the human development index (municipal, longevity, income, education), of the years of 1991 and 2000; three variables with information on water quality of the year of 2000, which refers to the percentage of households with access to the main water supply network, access to water through wells or springs and other form of access to water; and eight variables of the year 2000 related to sanitation conditions which are: the percentage of households with toilet connected to a river or lake, connected to a ditch, rudimentary cesspools, septic tanks, general sewage network, the other type of sewage, with toilet facility and without toilet facility.Three other socioeconomic variables were also used in this study, obtained by the João Pinheiro Foundation (FJP, acronym in Portuguese) in 2004, namely: health need index, index of economic size and allocation factor of investment resources for health care. Spatial regression models were used in order to evaluate the spatial distribution of schistosomiasis mansoni. The spatial regression is used to establish a relationship between a dependent variable and several independent

variables, taking into account the spatial interdependence between the observations. This spatial dependence is incorporated in the modeling, allowing the improvement of predictive power. There are some types of modeling that allows incorporating the spatial effect [10, 11]. The present study used spatial lag, which considers that the observation of the dependent variable in a certain location is correlated to the observations in neighboring localities. In this study, the dependent variable used was the prevalence of the disease and the independent variables were the climatic, socioeconomic, and environmental variables; as mentioned above. To include the spatial dependence in the spatial regression model, it was necessary to check and capture the distance extension of this dependence. For this, it was adopted the generalized neighborhood concept, implemented as a proximity matrix, defined from calculations of distances considering a transportation network among the municipalities. To study the effects of man's mobility and measure the impact of the snail occurrence in the disease transmission, two types of transport network were used to define the neighborhood: paved roads and rivers networks.

3. RESULTS AND CONCLUSIONS

From the 853 municipalities of the state of Minas Gerais, Brazil, there are 388 municipalities that have neighbors with prevalence information and are connected only by roads (group 1); 113 municipalities that have neighbors with prevalence information and with connectivity by both roads and rivers (group 2); and 352 municipalities that have neighbors with prevalence information that are not connected either by roads neither by rivers (group 3). A regression model was generated for each group.

After a variable selection process, the model for group 1 contained the following variables: median of water accumulation (WA), winter precipitation (Prec_w), and minimum temperature during the summer (Tmin_s). The model produced for group 1 included a spatial dependence (�=0.29), related to man’s mobility in the municipalities. Equation 1 shows the model generated by spatial regression for group 1.

����� = �� 0.29 ∗ � ����� − 3.39 − 5.63(ln(�� + 1))+ 0.18(ln(����_� + 1)) + 2.14������_��� − 1

(Eq. 1)

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Where WYRoads is the weighted average of the natural logarithm of prevalence of the neighboring municipalities, taking as criterion for construction of the proximity matrix, the paved roads network.

In group 2, the selected variables were: percentage of households with toilet connected to a ditch (TD), human development index of longevity in the year 2000 (HDIL_00), water mobility index in the summer (WM_s). The model generated for group 2 showed a more pronounced spatial dependence than that for group 1 (� = 0.51).This is due to the increase in the possibilities of the municipalities interconnection, either by roads or by rivers. This results show that the higher (lower) prevalence in neighboring municipalities connected by roads and rivers, the greater (smaller) will be the prevalence of the observed municipality. Equation 2 shows the model generated by spatial regression for group 2.

����� = ���0.51 ∗ � ������!"#$� + 3.17 − 0.004((�%)&)− 3.08(('%*,_00)&) − 0.002((�-_�)&)/ − 1

(Eq. 2)

Where WYRoadsRivers is the weighted average of the natural logarithm of prevalence of the neighboring municipalities, taking as criterion for construction of the proximity matrix, the network of roads and rivers.

For group 3, it was not possible to generate a spatial regression model since all of these municipalities do not carry information on the spatial dependence or human mobility, related to roads, or the snail movement along the rivers. Thus, it was generated a multiple linear regression model with no spatial component, which included the following variables: percentage of households with toilet connected to a river or lake (TRL), median of water accumulation (WA), fraction vegetation in the winter (Veg_w), minimum temperature in the summer (Tmin_s) and interaction of variables, declivity of the terrain (Dec) with fraction vegetation in the winter (Veg_w) [3]. Equation 3 shows the model generated by multiple linear regression for group 3.

����� = ���−3.30 + 0.01(�:,) − 2.54(��) + 0.07(;�<_�)− 0.003(%�� ∗ ;�<_�) + 0.37(����_�)/ − 1

(Eq. 3)

The accuracy analysis of the models generated by spatial regression, were performed through the distribution of residuals and the estimation of disease prevalence for the entire state of Minas Gerais. Figure 1a shows the distribution of observed prevalence, Figure 1b shows the estimated prevalence and Figure 1c shows the residuals of the obtained models.

Figure 1 - (a) Observed prevalence, (b) Estimated prevalence, (c) Residuals. The accuracy of the models can be evaluated according to Figure 1c, which shows where the models are more accurate (municipalities in orange tone), where the predicted values are overestimated (municipalities in light green and dark green) and where the predicted values are underestimated

(municipalities in light blue and dark blue). The neighborhood matrix via roads and rivers, used in the spatial regression models, reflected the movement of man and snail in neighboring municipalities through roads and rivers, respectively. The results showed that the Schistosoma mansoni hosts mobility is an important

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factor for modeling and estimating the prevalence of schistosomiasis. Variables representing vegetation, temperature, precipitation, topography, sanitation and human development indexes, proved their importance in explaining the disease spreading, indicating favorable conditions for the disease development. The use of spatial regression showed meaningful results to the health management procedures and direction of activities, enabling a better detection of disease risk areas. The methods used can be expanded to other studies regarding disease vectors, where these have a certain spatial dependence and whose hosts depend on some transportation network for displacement.

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