environmental factors associated with the risk of

Annals of Tropical Medicine & Parasitology, Vol. 104, No. 1, 35–53 (2010)

The use of remote sensing in the identification of the eco– environmental factors associated with the risk of human visceral leishmaniasis (kala-azar) on the Gangetic plain, in north–eastern India G. S. BHUNIA, V. KUMAR, A. J. KUMAR, P. DAS and S. KESARI Department of Vector Biology and Control, Rajendra Memorial Research Institute of Medical Sciences (ICMR), Agamkuan, Patna – 800 007, Bihar, India Received 24 June 2009, Revised 29 October 2009, Accepted 3 November 2009

Human visceral leishmaniasis (VL) or kala-azar remains a major cause of mortality, particularly in the developing world. The disease is common in the internal regions of north–eastern India, which have a tropical or sub-tropical climate. In a recent study on VL in this region, the relationship between the incidence of VL and certain physio– environmental factors was explored, using a combination of a geographical information system (GIS), satellite imagery and data collected ‘on the ground’. Some eco–environmental parameters were then used to map and describe the spatial heterogeneity seen in the transmission of the parasite (Leishmania donovani) that causes VL in India, and to identify those habitats, on the Gangetic plain, where the sandfly vectors might thrive. It was found that the presence of waterbodies, woodland and urban, built-up areas, soil of the fluvisol type, air temperatures of 25.0–27.5uC, relative humidities of 66%–75%, and an annual rainfall of 100–,160 cm were all positively associated with the incidence of VL. A VL map was created and stratified into areas of ‘risk’ and ‘non-risk’ for the disease, based on calculations of risk indices.

Human visceral leishmaniasis (VL) or kalaazar, caused by Leishmania donovani, occurs in many parts of India but is especially common in the north–east of the country. The disease is by no means a new problem in India. For example, a major epidemic, which began in Assam and rapidly spread to West Bengal and Bihar, occurred more than 100 years ago (Thakur, 2007). The current global estimate for the incidence of VL, of about 0.5 million cases/year (WHO, 1998), fails to describe the large geographical variations seen in the disease’s incidence, both between and within countries. The incidence of VL in India is among the highest in the world, and 90% of all Indian cases occur in just one state, Bihar, which Reprint requests to: S. Kesari. E-mail: [email protected]. # The Liverpool School of Tropical Medicine 2010 DOI: 10.1179/136485910X12607012373678

lies on the Gangetic plain (Desjeux, 1992; Bora, 1999). The primary Indian vector of L. donovani is Phlebotomus argentipes and all of the VL in the country is anthroponotic, with no vertebrate hosts other than humans (WHO, 1990). The distribution of the vector appears particularly sensitive to environmental and meteorological factors but it remains unclear how ecological, environmental and climatic characteristics determine the level of transmission and thus the local incidence of VL. Life-tables indicate that most Indian sandflies live 25– 35 days, although diapausing species can survive longer (Kumar and Kishore, 1991). On the plains in the north–east of the country, where the climate is relatively warm, adult sandflies spend most of the day in cracks, crevices, burrows and tree

36

BHUNIA ET AL.

holes, where there is shade and humidity and, often, the organic matter on which the flies’ larvae feed. As flowing water, very dense vegetation, very dry soils, low temperatures (,15uC) and high rainfall can all reduce sandfly survival and breeding, climate, land cover and landscape patterns are all important epidemiological determinants in the distribution of the vectors and VL (Randolph, 1993; Frank et al., 1998; Lindgren et al., 2000). Although the idea of using landscape ecology in the context of epidemiology was proposed by Pavlovsky (1966) more than 40 years ago, it is only in the last couple of decades that remote sensing and the development of geographical information systems (GIS) have enabled some of the links between landscape ecology and disease to be elucidated (O’Neill et al., 1999). Such technology has allowed the geographical distribution of several vector-borne diseases to be understood and predicted (Jacquez, 1998), although its successful use depends on a good understanding of the spatial and temporal changes that occur in the distribution of the vectors and vector-borne diseases (Rogers and Williams, 1993; Thomson et al., 1997). Landscape features can now be mapped, with relative ease, and used as predictors of the abundance of a vectorborne pathogen and its vectors and reservoir hosts (Daniel and Kolar, 1990; Dister et al., 1997; Kitron and Kazmierczak, 1997). Because an accurate understanding of the spatial distribution of both the pathogen and the vector is integral to strategies for the prevention of vector-borne diseases, spatially explicit models founded on basic ecological principles are invaluable tools in the fields of epidemiology and public health. A useful model must, however, allow for the changes in climatic conditions and habitats, agricultural development, and human demographics and behaviours that can all have a major effect on the epidemiology of such diseases. The main aims of the present study were to explore the usefulness of satellite data for

monitoring and and incidence of and to identify parameters that region.

mapping the distribution VL on the Gangetic plain, the main environmental affect the disease in this

MATERIALS AND METHODS Study Area The data analysed came from an area, defined by longitudes of 77u–89u E and latitudes of 20u–30u N, that covers the Gangetic plain, in north–eastern India (Shukla and Raju, 2008), where VL is a severe health concern (Fig. 1). Altitudes range only from sea level to approximately 50 m above sea level, and, in addition to the Ganges, there are several smaller rivers that flow across the study area, from the highlands to the Bay of Bengal. The general climate of the region is humid subtropical, with a mean annual rainfall of 100–120 cm. Most of the rain falls during the monsoon period that runs from July to September. Mean air temperatures range from 11–12uC in winter to 26– 28uC in summer. Satellite-data Processing and Land-use/ land-cover Analysis In investigating the land-use/land-cover characteristics of the study area, satellite data collected in 1998 using an advanced very-high-resolution radiometer (AVHRR) at a spatial resolution of 1 km (National Oceanic and Atmospheric Administration, Washington, DC) were used. The satellite images were processed, reprojected on to a Universal Transverse Mercator (UTM) projection (WGS84 model and North-45 zone), and then enhanced. For the initial classification into nine land-use/land-cover categories (waterbody/river, evergreen forest, dense forest, woodland, grassland, closed shrubland, open shrubland, crop/ agricultural land, or urban/built-up), all bands from the satellite images were

FIG. 1. Map of northern India, showing the area occupied by the Gangetic plain and the locations of those districts, on the plain, that had endemic visceral leishmaniasis (VL) in 2007.

REMOTE SENSING AND VISCERAL LEISHMANIASIS

37

38

BHUNIA ET AL.

included, and a supervised classification technique, based on the maximum-likelihood algorithm (MXL) and the nearestneighbourhood resampling method, was used. This technique, applied with the ERDAS ImagineH software package (ERDAS, Atlanta, GA), was found to give 85.43% classification accuracy when five sites assigned to each category were investigated on the ground (data not shown). Sandfly Trapping Adult sandflies were collected in households or cattle sheds in 20 villages in Bihar [Buxar, Ara, Koailbar, Manner, Danapur, Dighha, Jetly, Fatua, Baktiarpur, Raghopur, Bahahpur, Sawnima, Rupas, Barh, Pandrak, More, Mokama, Hathdar, Garhimore (Quil), and Borha], between the January and December of 2007, using CDC light traps placed 50–70 cm above the ground. Traps were run (between 18.00 hours and 06.00 hours) once a month, early in (in the first 10 days of) each month. The counts of P. argentipes in the traps were used to calculate mean monthly numbers of these sandflies/trap-night, as a measure of the density of the local vector population. Air temperature and relative humidity (two variables that appeared to have a marked effect on the numbers of sandflies in the traps) were recorded in each village on the day following each sandfly collection.

assess the probabilistic relationship between the various factors potentially affecting VL incidence and the local land-use/land-cover category. In this analysis, information values (Ij) were calculated as log10(category density/map density) — where ‘category density’ is the number of land-use/land-cover categories within a 1-km buffer zone, divided by the number of such categories within the district, and ‘map density’ is the proportion of the entire land-use/land-cover map covered by endemic VL. Values for Ij of .1.50, 1.00–1.50, 0.50–,1.00, 0.30– ,0.50, 0.00–,0.30 and ,0.00 were considered to indicate areas where the risk of VL was very high, high, moderate, moderate–low, low and very low, respectively. The number of pixels, for each land-use/ land-cover category, covering land with endemic VL, as well as the total number of pixels represented by each land-use/landcover category were evaluated.

Disease Incidence The incidences of VL in each district within the study area, for the whole of 2007, were extracted from the detailed records of the district health offices and the Bihar State Health Society.

Weather-data Analysis Data on monthly rainfall, temperature and relative humidity for 2000–2005 were collected from the Indian Meteorological Department’s weather stations on the Gangetic plain (at Patna, Gaya, Bhagalpur, Ranchi, Dumka, Agra, Kolkata, Uttar Pradesh, Kanpur, Varanasi, Asansol, Darjeeling, Gauhati, Maldah and Midnapur) and used to calculate annual mean values. These data were subjected to spatial interpolation using the Thiessen polygon technique (Tabios and Salas, 1985). In GIS, maps of mean annual rainfall, temperature and relative humidity were created and then overlain with the locations of the new cases of VL in 2007, so that possible links between each climatic variable and disease incidence could be explored.

Information-value Computation and Risk Analysis by Land-use/land-cover Class Conditional analysis (Prakash, 1998; Yin and Yan, 1998; Suresh, 1999) was used to

Soil Characteristics A map of the soils of the Gangetic plain (at a scale of 1 : 100,000) was generated from data collected by the Bihar soil-survey office, soil types being divided into 12

716,148 150–180 Red and yellow Rainfed agriculture

5,843,897 .210 140–160 Hot sub-humid (moist)

Hot sub-humid Eastern Plateau

120–160

100–200 100–160 Warm sub-humid Hot sub-humid

Udic–ustic and hyperthermic .22

Rice

Alluvial

11,323,133 4,937,815 180–210 150–180 Podzol Red laterite Rainfed farming Rainfed farming

140–180 50–100 Hot sub-humid (moist) Hot semi-arid

Eastern Plain Northern Plain and Central Highlands Western Himalayas Eastern Plateau and Eastern Ghats Bengal and Assam Plain

12–20 .22

9,796,868 8,067,849 180–210 90–150 Alluvial Alluvial

289,479 150–180 Alluvial

Rainfed and irrigated agriculture Rainfed agriculture Irrigated agriculture Ustic and hyperthermic .22 Hyperthermic 100–120 Hot sub-humid (dry)

Area Growth period

Soil type Land use (uC)

Soil temperature Rainfall

(cm/year) Eco-region

Northern Plain

Eco–environmental Risk Model Variables such as temperature, relative humidity, precipitation, soil type, agro– ecological characteristics and land-use/

Region

Statistical Analysis Descriptive statistics (means, standard deviations or errors, measures of kurtosis and skewness) were calculated. A linearregression model, based on the maximumlikelihood method, was used to evaluate the level of correlation between mean monthly temperatures, relative humidities and rainfall and the corresponding abundance of sandflies (measured as the number of sandflies caught/trap-night). One-way analysis of variance was used to explore such correlations further.

TABLE 1. The characteristics of the seven agro–ecological regions forming the Gangetic plain

Agro–ecological Characteristics Another map, again at a scale of 1 : 100,000, was obtained from the Bihar Agricultural Department. This divided the Gangetic plain into seven ‘agro–ecological’ regions: Northern Plain, Eastern Plain, Northern Plain/Central Highlands, Western Himalayas, Eastern Plateau/Eastern Ghats, the Bengal and Assam Plain, and the Eastern Plateau (Table 1 and Figure 2). Again, a GIS was used to overlay the locations of VL-endemic districts on this map, so that the risk of endemic VL in each agro– ecological region could be determined. The zonal geometry of each of these regions was also explored, through raster analysis (Forman and Godron, 1986; McGarigal and Marks, 1994).

(days/year)

categories [brown hill, terai, alluvial/fluvisols (Indo–Gangetic), solanchaks/solonetz, yellow podzol, latosols, reddish–brown laterite, mixed red and black, vertisols, fluvisol (highly calcareous), histosols or ‘other laterite’]. A GIS was then used to overlay the locations of VL-endemic districts on this map, so that the risk of endemic VL on each soil type could be determined.

(ha)


39

40

BHUNIA ET AL.

FIG. 2. A land-cover/land-use map of the Gangetic plain, plotted from satellite data and a resolution of 1 km.

TABLE 2. Monthly numbers of Phlebotomus argentipes caught, in 2007, in light traps on the Gangetic plain and the corresponding mean temperatures and relative humidities No. of sandflies Month January February March April May June July August September October November December

Temperature

Relative humidity

No. of collection sites

Total

Per trap-night

(uC)

(%)

45 50 50 52 54 50 52 51 49 51 52 50

14 5 211 427 83 257 0 15 71 214 338 10

0.45 1.25 6.90 8.62 5.475 8.11 0.00 1.54 10.25 12.65 7.80 1.25

18.0 19.7 25.9 33.9 30.7 31.0 29.0 36.0 30.5 27.5 23.6 22.5

74.0 80.0 83.5 72.4 85.0 93.5 91.7 72.0 90.0 82.0 86.0 79.5

41


FIG. 3. A map of the Gangetic plain, plotted using a geographical information system, showing the locations of the districts that had endemic visceral leishmaniasis in 2007 (m) and mean annual rainfall.

TABLE 3. Areas of the Gangetic plain falling under various land-cover/land-use categories

Land-use category Waterbody/river Evergreen forest Dense forest Woodland/grassland Closed shrubland Open shrubland Grassland Crops/agricultural land Urban/built-up All

Description/notes

No. of pixels

Area (ha)

% of study area

Natural inland water bodies, rivers and irrigation land Mostly tall, broad-leaved, hardwood trees More than 50% tree cover Less than 10% tree cover Shrubs (1–2 m in height) covering .70% of area Shrubs covering 10%–30% Grasses and herbaceous plants

10,048

1,205,736

2.92

230 11,917 58,190 36,372

26,478 1,429,796 6,725,337 4,315,866

0.06 3.47 16.30 10.46

8052 62,327 182,004 2167

873,764 6,632,665 19,805,323 238,299

2.12 16.08 48.01 0.58

41,253,263

100

Residential land as well as land used for mining and transportation

East Champaran

West Champaran

Supal

Saran

Patna

Munger

Khagaria

Darbhanga

Buxar

Bhagalpur

Begusarai

Araria

Ara

District

2.29 VH 2.73 VH 0.43 LM 0.24 L 20.66 VL 1.95 VH 1.37 H 1.31 H 1.89 VH 0.88 M 1.00 M 0.93

M 1.07

H

I Risk I Risk I Risk I Risk I Risk I Risk I Risk I Risk I Risk I Risk I Risk I

Risk I

Risk

Waterbody

H

1.38

2.08 VH –

0.74 M 2.53 VH –

–

1.23 H –

1.68 VH 2.29 VH –

Woodland

L

LM

H 0.49

0.62 M 0.72 M 20.02 VL 0.00 L 0.43 LM 0.06 L 0.31 L 0.70 M 0.31 LM 0.74 M 0.72 M 1.03

20.06 VL 0.98 M 0.68 M 0.61 M 20.28 VL 0.98 M 1.23 H 0.23 L 20.09 VL 1.06 H 0.85 M 0.23 L 0.11

Closed

Woodland/ grassland

H

VH 1.35

1.98 VH 1.09 H 20.01 VL 20.03 VL 1.49 H 0.32 LM 0.22 L 0.57 M 0.81 M 0.68 M 1.16 H 1.96

Open

Shrubland

VL

L 20.09

0.67 M 20.36 VL 20.45 VL 20.42 VL 0.55 M 20.4 VL 20.56 VL 0.09 L 0.06 L 20.42 VL 20.35 VL 0.24

Grassland

Value for land categorized as:

VL

VL 20.30

20.13 VL 20.26 VL 20.54 VL 20.54 VL 20.26 VL 0.16 L 20.62 VL 20.37 VL 20.23 VL 20.09 VL 20.24 VL 20.08

Crops

VH

VH 2.07

1.76 VH 2.18 VH 1.29 H 1.25 H 1.78 VH 2.08 VH 2.08

2.18 VH 1.81 VH 1.53 VH 1.17 H –

Builtup

VH –

2.05

–

–

–

–

–

–

–

–

–

–

–

Evergreen forest

0.68

0.71

0.81

0.78

0.73

0.74

0.46

0.54

0.28

0.13

0.18

1.00

1.03

Mean

0.82

0.84

0.90

0.91

0.98

0.75

0.94

0.83

0.72

0.55

0.63

1.11

1.01

S.D.

0.27

0.28

0.30

0.30

0.33

0.25

0.31

0.28

0.24

0.19

0.21

0.37

0.34

S.E.

0.19 0.85

21.1 20.8

0.45

0.55

20.1

21.2

0.94

0.73

20.4

20.3

0.98

20.5

0.47

0.65

20.6

0.2

0.87

0.7

1.25

0.28

21.2 1.9

0.11

Skewness 22.1

Kurtosis

TABLE 4. Information values (I) calculated as a measure of the risk of visceral leishmaniasis, in each of the districts on the Gangetic Plain, and used to categorize areas as being at very high (VH), high (H), moderate (M), low–moderate (LM), low (L) or very low (VL) risk of the endemic disease

42 BHUNIA ET AL.

Sitamarhi

Sheohar

Samastipur

Saharsa

Purnea

Nalanda

Muzaffarpur

Madhubani

Madhepura

Lakhisarai

Kishanganj

Kathiar

Jahanabad

Gopalganj

District

I Risk I Risk I Risk I Risk I Risk I Risk I Risk I Risk I Risk I Risk I Risk I Risk I Risk I Risk

TABLE 4. Continued

1.15 H 0.67 M 1.10 H 1.18 H –

0.45 LM 0.76 M 0.47 LM 1.61 VH 1.17 H 1.55 VH 1.09 H 1.55 VH –

Waterbody

–

–

–

1.22 H 1.19 H 1.15 H 1.34 H 0.95 M 1.82 VH 1.24 H 1.02 H 1.18 H 1.18 H –

Woodland

20.65 VL 20.65 VL 1.06 H 1.15 H 0.61 M 0.69 M

0.06 L 20.70 VL 0.30 L 0.73 M 20.46 VL 0.55 M 0.42 LM –

Woodland/ grassland 0.29 L 0.16 L 0.20 L 0.70 M 20.14 VL 0.65 M 20.29 VL 0.26 L 0.14 L 0.14 L 0.73 M 20.02 VL 20.23 VL 20.03 VL

Closed 1.73 VH 2.27 VH 20.50 VL 20.61 VL 0.58 M 0.78 M 1.10 H 0.28 L 1.54 VH 1.54 VH 1.55 VH 20.09 VL 1.04 H 0.74 M

Open

Shrubland

20.68 VL 20.43 VL –

20.38 VL 0.49 LM 20.63 VL 20.65 VL 20.26 VL 20.56 VL 20.62 VL 20.32 VL 20.09 VL 20.08 VL 20.33 VL 20.19 VL 20.44 VL 20.64 VL 20.39 VL 20.66 VL 20.35 VL 20.08 VL 20.40 VL 20.39 VL 20.26 VL 0.30 L 20.61 VL 20.50 VL

–

Crops

Grassland


1.68 VH

1.82 VH 1.60 VH 1.65 VH 1.82 VH 1.66 VH 1.41 H 1.78 VH 1.78 VH 1.95 VH 2.12 VH –

–

–

Builtup

–

–

–

–

–

–

–

–

–

–

–

–

–

–

Evergreen forest

0.22

0.17

0.49

0.60

0.52

0.39

0.46

0.47

0.66

0.35

0.52

0.31

0.41

0.30

Mean

0.72

0.63

0.80

0.81

0.90

0.88

0.69

0.83

0.95

0.77

0.89

0.77

0.9

0.76

S.D.

0.24

0.21

0.27

0.27

0.30

0.29

0.23

0.28

0.32

0.26

0.30

0.26

0.30

0.25

S.E.

1.04

0.61

20.9 1.1

1.29

0.47

21.0 0.8

0.19

0.74

21.2

21.8

0.06

21.7

0.68

20.11

21.3

21.2

0.61

20.07

21.6 21.2

0.90

1.03

0.89

Skewness

0.7

1.2

0.4

Kurtosis


43

Murshidabad

Maldah

Hooghly

Birbhum

Varanasi

Gonda

Deoria

Ballia

Sahebganj

Pakur

Godda

Dumka

Vaishali

1.49 H 0.93 M 1.94 VH 1.48 H 2.42 VH 2.17 VH 0.53 M 0.94 M 0.82 M 0.65 M 1.40 H 0.80 M 0.47 LM 0.47 LM

Siwan

I Risk I Risk I Risk I Risk I Risk I Risk I Risk I Risk I Risk I Risk I Risk I Risk I Risk I Risk

Waterbody

District

TABLE 4. Continued

–

0.38 LM –

1.88 VH 1.46 H 0.41 LM 0.47 LM 1.01 H –

–

2.43 VH –

2.71 VH –

Woodland 0.97 M 0.68 M 20.18 VL 20.09 VL 20.30 VL 0.45 LM 0.42 LM 0.05 L 0.00 L 0.07 L 0.49 LM 0.65 M 0.76 M 0.57 M

Woodland/ grassland 0.72 M 0.01 L 0.00 VL 0.40 LM 20.77 VL 0.31 LM 0.33 LM 0.78 M 0.48 LM 0.62 M 0.67 M 1.06 H 0.39 LM 0.32 LM

Closed 0.86 M 20.25 VL 1.33 H 1.03 H 0.19 L 0.54 M 0.52 M 0.76 M 0.54 M 0.90 M 1.23 H 20.05 VL 0.58 M 0.50 M

Open

Shrubland

Crops 20.24 VL 20.04 VL 0.62 M 20.34 VL 0.94 M 20.44 VL 20.12 VL 20.23 VL 20.44 VL 20.26 VL 0.12 L 0.42 LM 20.06 VL 20.19 VL

Grassland 20.36 VL 20.68 VL 0.74 M 0.26 L 0.40 LM 0.23 L 0.08 L 0.10 L 0.33 LM 0.31 LM 0.33 L 20.22 VL 20.06 VL 20.10 VL


1.11 H 1.07 H 0.71 M 1.41 H 20.33 VL 1.29 H 20.33 LM

–

–

–

–

2.23 VH 1.62 VH –

Builtup

–

–

–

–

–

–

–

–

–

–

–

–

–

–

Evergreen forest

0.22

0.37

0.30

0.63

0.44

0.36

0.44

0.50

0.57

0.32

0.30

0.77

0.25

0.93

Mean

0.39

0.46

0.48

0.58

0.44

0.46

0.48

0.55

0.88

0.91

0.59

0.95

0.70

1.07

S.D.

0.13

0.15

0.16

0.19

0.15

0.15

0.16

0.18

0.29

0.30

0.20

0.32

0.23

0.36

S.E.

20.13

21.6

0.21

21.2

0.93

0.41

21.7

0.4

20.37

21.2

0.05

21.7

20.26

0.91

20.2

0.0

1.16

1.65 0.3

3.5

1.25

0.80

20.7 0.8

0.91

0.45

Skewness

0.6

20.8

Kurtosis

44 BHUNIA ET AL.

Nadia

Darjeeling

South Dinajpur

Burdwan

North Dinajpur

S24PRG

0.77 M

0.51 M 0.34 LM 0.80 M 1.55 VH 0.80 M –

N24PRG

I Risk I Risk I Risk I Risk I Risk I Risk I Risk

Waterbody

District

TABLE 4. Continued

0.09 L –

1.52 VH –

–

–

–

Woodland

0.73 M

0.06 L 0.27 L 0.79 M –

0.68 M –

Woodland/ grassland

0.55 M 1.08 H 0.16 L 0.25 L

–

0.77 M –

Closed

1.02 H 1.07 H 20.02 VL 0.88 M

–

0.95 M –

Open

Shrubland

0.02 L 20.11 VL 20.22 VL 20.14 VL 20.30 VL 20.39 VL 20.09 VL

20.11 VL –

0.09 L 0.16 L 0.10 L 20.20 VL

–

Crops

Grassland


0.28 L 20.02 VL 20.33 VL 0.82 M 1.08 H 0.29 L 0.67 M

Builtup

–

–

–

–

–

–

–

Evergreen forest

0.33

0.03

0.52

0.63

0.03

0.02

0.34

Mean

0.43

0.19

0.55

0.64

0.31

0.12

0.39

S.D.

0.14

0.06

0.18

0.21

0.1

0.04

0.13

S.E.

0.08

22.1

20.31

21.9

21.27

0.39

21.4

3.4

1.99

2.46

0.35

Skewness

5.5

7.2

21.6

Kurtosis


45

46

BHUNIA ET AL.

FIG. 4. A map of the Gangetic plain, plotted using a geographical information system, showing the locations of the districts that had endemic visceral leishmaniasis in 2007 (m) and mean annual temperature.

land-cover category, either individually or in combination, are known to influence the incidence of VL (Kitron, 1998; Ghosh et al., 1999; Thomson et al., 1999; Kishore et al., 2000a, b; Bernardi, 2001; Al-Jaser, 2006; Dahal, 2008; Singh et al., 2008). To try to explore these relationships in the present study area, each district was given a weighted score for its annual mean temperature, annual mean relative humidity, annual mean precipitation, soil type and agro–ecological region and then the five scores for each district were summed, as an index of VL risk. Scores and weightings were set according to the incidence of VL associated with each category of the five variables considered, and the overall relative

risk linked to each variable (see Table 5). To allow levels of risk to be compared, each risk index was expressed as a percentage of the highest value possible.

RESULTS AND DISCUSSION In the present study, an attempt was made to assess the associations between various environmental factors and VL incidence, using a combination of remote sensing, data collected on the ground and GIS-based technology. The population of adult P. argentipes in the study region started building up from the month of March (i.e. before the mon-


47

FIG. 5. A map of the Gangetic plain, plotted using a geographical information system, showing the locations of the districts that had endemic visceral leishmaniasis in 2007 (m) and mean annual relative humidity.

soon season), peaking, at 10.25/trap-night, at the end of the monsoon, in September (Table 2). The largest numbers of these potential vectors were seen between June and September, when mean temperatures (27.5–31.0uC) and relative humidities (73%–93%) were higher than at other times of the study year (Table 2). This is consistent with the observations of Sharma and Singh (2008), who found that adult P. argentipes in India ‘preferred’ relatively high temperatures and humidities. The general abundance of P. argentipes in a district was positively correlated with the

mean annual rainfall in that district (P,0.0001), perhaps indicating that the rainfall on most of the Gangetic plain is generally not heavy enough to flood sandfly breeding sites and kill the immature stages. According to Lysenko (1971), the breeding success of sandflies is more dependent on the duration of rainfall than the intensity. By creating a map of the study area showing mean annual rainfall (for 2000–2005) divided into seven categories and then overlaying it with the locations of the VL cases recorded in 2007, a statistically significant association (P,0.01) between an

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FIG. 6. A map of the Gangetic plain, plotted using a geographical information system, showing the locations of the districts that had endemic visceral leishmaniasis in 2007 (m) and soil type.

annual rainfall of 100–,160 cm and endemic VL was illustrated (Fig. 3). The classification of the study area by land-use/land-cover indicated that most (80%) of the total area was covered by crops and agricultural land, a mix of woodland and grassland or pure grassland, with the remaining 20% — where almost all of the new cases of VL recorded in the study area in 2007 occurred — covered by waterbody/river, evergreen forest, dense forest, shrubland or urban/built-up areas (Figures 1 and 2 and Table 3). Because the conditions under which the vectors can thrive are limited, human visceral leishmaniasis appears to be a disease that is very sensitive to environmental characteristics (Victora et al., 1997;

Bucheton et al., 2002; Kishore et al., 2006; Sharma and Singh, 2008). The main goal of the present study was to determine if those environmental conditions observable via AVHRR imagery were correlated with the local abundance of P. argentipes and thus with the level of transmission of L. donovani and, consequently, the incidence of VL. Due to the 1-km limitation in the spatial resolution of the AVHRR imagery, neither sandflies nor their micro-environments can be seen directly from satellites. Calculation of Ij values indicated wide variation in the risk of VL across the study area (Table 4), confirming that the risk of the disease was particularly high in areas with waterbodies, woodland and urban development.


49

FIG. 7. A map of the Gangetic plain, plotted using a geographical information system, showing the locations of the districts that had endemic visceral leishmaniasis in 2007 (m) and agro–ecological region.

On the Gangetic plain, as in many other areas with endemic VL, the survival of the sandfly vector appears to depend largely on climate or, at least, temperature, rainfall and relative humidity (Napier, 1926; Anon., 1993; Ranjan et al., 2005). This would explain why, in areas with particular mean annual temperatures (25.0–27.5uC), the risk of VL is particularly high (Fig. 4), and may partially explain why the seasonal numbers of P. argentipes caught in light traps were closely correlated with mean air temperatures (P,0.000025). It may also explain the

association between the presence of VL and areas with relative humidities of 66%–75% (Fig. 5). When the locations of the VL cases were plotted on a soil map of the Gangetic plain (Fig. 6), it appeared that most cases lived on soils of the fluvisol type, possibly because such soils, which retain water and often have high organic content, are those most likely to provide suitable micro-environments for sandfly breeding and/or survival. Soils of the alluvial/fluvisol type have also been identified as one of the risk factors for VL in

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FIG. 8. A map of the Gangetic plain, plotted using a geographical information system, showing the calculated indices for the risk of endemic visceral leishmaniasis.

several earlier studies in India (Kesari et al., 1992; Sivagnaname and Amalraj, 1997; Kishore et al., 2000b; Ranjan et al., 2005). In terms of the links between agro– ecological region and VL incidence (Table 1), it appears that most cases of the disease occur in the sub-humid Eastern Plain (Fig. 7), a region characterised by relatively hot but wet summers, cool but dry winters, an annual rainfall of 140–180 cm (70% of which is received between July and September), and very gently sloping, alluvium-derived soils. The main crops grown in this region are rice, maize, pigeon, pea and moong in the monsoon (kharif) season and wheat, mustard, lentil and pea in the winter (rabi) season, in addition to some important cash crops (sugar cane, tobacco,

chillies, turmeric and coriander) that are largely restricted to irrigation schemes. The other agro–ecological regions in the study area may be too wet, dry or cool, or have soils that are not so suitable for sandfly breeding, to support such large populations of P. argentipes as those that develop in the Eastern and Northern Plains. Based on the relative contribution (Brooker and Michael, 2000) of each of the eco–environmental characteristics that appear to have an impact on the local abundance of P. argentipes, a model of VL risk was developed and a risk map generated (Figure 8 and Table 5). The values for the calculated VL-risk index varied from 29.5% to 84.0% (of the theoretical maximum) according to locality, with no VL recorded,

REMOTE SENSING AND VISCERAL LEISHMANIASIS TABLE 5. The scores and weighting used to model the risk of endemic human visceral leishmaniasis in each district forming the Gangetic plain

Variable and category

Score

MEAN RELATIVE HUMIDITY

MEAN TEMPERATURE

30

(%)

.75 71–75 66–70 61–65 55–60 ,55

Multiplier for variable*

4 6 5 3 2 2 20

(uC)

25.0–27.5 22.5–,25.0 ANNUAL PRECIPITATION

5 4 20

(cm)

200–400 160–,200 120–,160 100–,120 80–,100 60–,80 40–,60

1 2 5 5 4 2 1

Northern Plain Eastern Plain Northern Plain and Central Highland Western Himalayas Eastern Plateau and Eastern Ghats Bengal and Assam Plain Eastern Plateau

3 5 2 1 3 4 1 15

SOIL TYPE

Brown hill soil Teari soil Fluvisol (Indo–Gangetic) Solanchak and solonetz Yellow podzol Latosol Reddish-brown laterite Mixed red and black Vertisol Fluvisol (highly calcareous) Histosol Other laterite

case detection, and VL control, only on areas where this index is .60%. The present study has some limitations [e.g. it made little allowance for variation in the density of the human population across the study area, or for confounding resulting from the non-independence of some of the variables considered (such as rainfall, humidity and the presence of waterbodies)]. The results do, however, indicate some clear-cut links — such as those between endemic VL and fluvisols and certain ranges for some climatic variables (e.g. mean annual values, for temperature, relative humidity and precipitation, of 25.0– 27.5uC, 66%–75% and 100–,160 cm, respectively) — that should be useful in the rapid identification of areas at high risk for VL, at least in India. The authors thank the Global Land Cover Facility (College Park, MD), for providing free access to its satellite data, and Dr A. K. Tiwari, of the Bihar State Health Society’s Kala-azar Control Programme, for providing data on the incidence of VL in the study area.

ACKNOWLEDGEMENTS.

15

AGRO–ECOLOGY

51

1 2 6 1 3 2 1 2 4 3 1 1

*

Scores for each variable were multiplied by the relevant multiplier and then summed to give an overal risk score for the district.

in 2007, in areas where the value of this index was ,60%. In the future, it may therefore be useful to focus efforts at active

REFERENCES Al-Jaser, M. H. (2006). Studies on the epidemiology of malaria and visceral leishmaniasis in Jizan area, Saudi Arabia. Journal of King Saud University. Science, 19, 9–19. Anon. (1993). Proceedings of Workshops on Entomological and Vector Control of Kala-azar. Delhi: National Institute of Communicable Diseases (Directorate General of Health Services). Bernardi, M. (2001). Linkages between FAO agroclimatic data resources and the development of GIS models for control of vector-borne diseases. Acta Tropica, 79, 21–34. Bora, D. (1999). Epidemiology of visceral leishmaniasis in India. National Medical Journal of India, 12, 62–68. Brooker, S. & Michael, E. (2000). The potential of geographical information syatems and remote sensing in the epidemiology and control of human helminth infections. Advances in Parasitology, 47, 246–288. Bucheton, B., Kheir, M. M., El-Safi, S. H., Hammad, A., Mergani, A., Mary, C., Abel, L. & Dessein, A.

52

BHUNIA ET AL.

(2002). The interplay between environmental and host factors during an outbreak of visceral leishmaniasis in eastern Sudan. Microbes and Infection, 4, 1449–1457. Dahal, S. (2008). Climatic determinants of malaria and kala-azar in Nepal. Regional Health Forum, 12, 32–37. Daniel, M. & Kolar, J. (1990). Using satellite data to forecast the occurrence of the common tick Ixodes ricinus (L). Journal of Hygiene, Epidemiology, Microbiology, and Immunology, 34, 357–363. Desjeux, P. (1992). Human leishmaniasis: epidemiology and public health aspects. World Health Statistics Quarterly, 45, 267–275. Dister, S. W., Fish, D., Bros, S. M., Frank, D. H. & Wood, B. L. (1997). Landscape characterization of peridomestic risk for Lyme disease using satellite imagery. American Journal of Tropical Medicine and Hygiene, 57, 687–692. Forman, R. T. T. & Godron, M. (1986). Landscape Ecology. New York, NY: John Wiley. Frank, D. H., Fish, D. & Moy, F. H. (1998). Landscape features associated with Lyme disease risk in a suburban residential environment. Landscape Ecology, 13, 27–36. Ghosh, K., Mukhopadhyay, J., Desai, M. M., Senroy, S. & Bhattacharya, A. (1999). Population ecology of Phlebotomus argentipes (Diptera: Psychodidae) in West Bengal, India. Journal of Medical Entomology, 36, 588–594. Jacquez, G. M. (1998). GIS as an enabling technology. In GIS and Health, eds Gatrell, T. & Loytonen, M. pp. 17–28. London: Taylor & Francis. Kesari, S., Palit, A. & Kishore, K. (1992). Study of breeding habitats of sandflies — preliminary approach. Journal of Communicable Diseases, 24, 62– 63. Kishore, K., Kesari, S., Palit, A., Kumar, V., Roy, M. S., Sivakumar, S. & Kar, S. K. (2000a). An entomological field evaluation of larval biology of sandfly in kala-azar endemic focus of Bihar — exploration of larval control tool. Journal of Communicable Diseases, 32, 284–288. Kishore, K., Palit, A., Kumar, V., Kesari, S., Sivakumar, S. & Kar, S. K. (2000b). An entomological study of Indian kala-azar with reference to the factors influencing the transmission in endemic villages of Bihar. Journal of Parasitic Diseases, 24, 171–174. Kishore, K., Kumar, V., Kesari, S., Dinesh, D., Kumar, A. J., Das, P. & Bhattacharya, S. K. (2006). Vector control in leishmaniasis. Indian Journal of Medical Research, 123, 467–472 Kitron, U. (1998). Landscape ecology and epidemiology of vector-borne diseases: tools for spatial analysis. Journal of Medical Entomology, 35, 435–445. Kitron, U. & Kazmierczak, J. J. (1997). Spatial analysis of the distribution of Lyme disease in Wisconsin. American Journal of Epidemiology, 145, 558–566.

Kumar, V. & Kishore, K. (1991). A preliminary observation on larval diapause of Phlebotomus argentipes (Diptera: Psychodidae). Journal of Communicable Diseases, 23, 165–166. Lindgren, E., Ta¨lleklint, L. & Polfeldt, T. (2000). Impact of climatic change on the northern latitude limit and population density of the disease-transmitting European tick Ixodes ricinus. Environmental Health Perspectives, 108, 119–123. Lysenko, A. J. (1971). Distribution of leishmaniasis in the Old World. Bulletin of the World Health Organization, 44, 515–520. McGarigal, K. & Marks, B. J. (1994). Fragstats: Spatial Pattern Analysis Programme for Quantifying Landscape Structure. Corvallis, OR: Forest Science Department, Oregon State University. Napier, L. E. (1926). An epidemiological consideration of the transmission of kala-azar in India. India Medical Research Memoirs, 4, 219–265. O’Neill, R. V., Riitters, K. H., Wickham, J. D. & Jones, K. B. (1999). Landscape pattern matrics and regional assessment. Ecosystem Health, 5, 225–233. Pavlovsky, E. N. (1966). Natural Nidality of Transmissable Disease, with Special Reference to the Landscape Epidemiology of Zooanthroponoses. Urbana, IL: University of Illinois Press. Prakash, C. (1998). Landslide hazard zonation in Doon Valley using remote sensing and GIS. MSc Thesis, Indian Institute of Technology, Kanpur, India. Randolph, S. E. (1993). Climate, satellite imagery and the seasonal abundance of the tick Rhipicephalus appendiculatus in southern Africa: a new perspective. Medical and Veterinary Entomology, 7, 243–258. Ranjan, A., Sur, D., Singh, V. P., Siddique, N. A., Manna, B., Lal, C. S., Sinha, P. K., Kishore, K. & Bhattacharya, S. K. (2005). Risk factors for Indian kala-azar. American Journal of Tropical Medicine and Hygiene, 73, 74–78. Rogers, D. J. & Williams, B. G. (1993). Monitoring trypanosomiasis in space and time. Parasitology, 106 (Suppl.), S77–S92. Sharma, U. & Singh, S. (2008). Insect vectors of Leishmania: distribution, physiology and their control. Journal of Vector Borne Disease, 45, 255–272. Shukla, U. K. & Raju, N. J. (2008). Migration of the Ganga river and its implication on hydro–geological potential of Varanasi area, U.P., India. Journal of Earth System Science, 117, 489–498. Singh, R., Lal, S. & Saxena, V. K. (2008). Breeding ecology of visceral leishmaniasis vector sandfly in Bihar state of India. Acta Tropica, 107, 117–120. Sivagnaname, N. & Amalraj, D. D. (1997). Breeding habitats of vector sandflies and their control in India. Journal of Communicable Diseases, 29, 153– 159. Suresh, K. G. (1999). Rainfall effect in landslide hazard zonation using remote sensing and GIS. MSc Thesis, Indian Institute of Technology, Kanpur, India.

REMOTE SENSING AND VISCERAL LEISHMANIASIS Tabios, G. Q. & Salas, J. D. (1985). A comparative analysis of techniques for spatial interpolation of precipitation. Water Resource Bulletin, 21, 365–380. Thakur, C. P. (2007). A new strategy for elimination of kala-azar from rural Bihar. Indian Journal of Medical Research, 126, 447–451. Thomson, M. C., Cornor, S. J., Miligan, P. & Flasse, S. P. (1997). Mapping malaria risk in Africa: what can satellite data contribute? Parasitology Today, 13, 313–318. Thomson, M. C., Elnaiem, D. A., Ashford, R. W. & Connor, S. J. (1999). Towards a kala azar risk map for Sudan: mapping the potential distribution of Phlebotomus orientalis using digital data of environmental variables. Tropical Medicine and International Health, 4, 105–113.

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Victora, C. G., Huttly, S. R., Fuchs, S. C. & Olinto, M. T. (1997). The role of conceptual frameworks in epidemiological analysis: a hierarchical approach. International Journal of Epidemiology, 26, 224–227. World Health Organization (1990). Control of the Leishmaniases. Report of a WHO Expert Committee. Technical Report Series No. 793. Geneva: WHO. World Health Organization (1998). Life in the Twentyfirst Century: a Vision for All. Geneva: WHO. Yin, K. L. & Yan, T. Z. (1998). Statistical prediction models for slope instability of metamorphosed rocks. In Proceedings of the 5th International Symposium on Landslides, Lausanne, Vol. 2, ed Bonnard, C. pp. 1269–1272. Rotterdam, The Netherlands: Balkema.