Filaria Monitoring Visualization System: A Geographical Information System–Based Application to Manage Lymphatic Filariasis in Andhra Pradesh,India

Abstract

Among various public health diseases, filariasis constitutes a major public health problem in India, wherein an estimated 553.7 million people are at risk of infection. The aim of this article is to present a spatial mapping and analysis of filariasis data over a 3-year period (2004–2007) from Karimnagar, Chittoor, East and West Godavari districts of Andhra Pradesh, India. The data include epidemiological and entomological studies (i.e., infection rate, infectivity rate, mosquito per man hour, and microfilaria rate). These parameters were customized on Geographical Information System (GIS) platform and developed filaria monitoring visualization system (FMVS) for identifying the endemic/risk areas of filariasis among these four districts. GIS map for filariasis transmission from the study areas was created and stratified into different spatial entities like low, medium, and high risk zones. On the basis of the data and FMVS maps, it was demonstrated that filariasis remained unevenly distributed within the districts. Balancing the intervention coverage in different villages with overall mass drug administration and continued promotion of the proper use of control measures are necessary for further reduction of filarial cases in these districts.

Introduction

L ymphatic filariasis (LF) has been identified as a major public health problem and is endemic in >80 countries. Currently, 120 million people are infected with Wuchereria bancrofti in about 83 endemic countries (Michael et al. 1996), and it is estimated that 40 million people have evidence of chronic manifestations such as hydrocele and lymphoedema/elephantiasis. Besides this, affected individuals suffer repeated episodes of adenolymphangitis (acute attacks), which result in less man hours of work, depleting their income (Ravindran 2003). World Health Assembly passed a resolution (WHA: 50.29) on elimination of LF as a public health problem by the year 2020. The Global Programme to Eliminate Lymphatic Filariasis was launched during 1999 to help endemic countries to initiate national programs (Molyneux and Zagaria 2002, WHO 2004) by interrupting transmission of LF (once-yearly, single-dose, two-drug regimen, i.e., albendazole with either diethylcarbamazine [DEC] or ivermectin) (Molyneux and Taylor 2001, Gyapong et al. 2005). LF is an important public health problem in India, where 553.7 million people are at risk of infection in 243 districts from 22 states/union territories and 221 million with chronic filarial cases, accounting for 40% of the global burden (WHO 1994, 2006, Michael et al. 1996, Reddy et al. 2000).

Out of 22 states, Andhra Pradesh is one of the severely affected states in which 16 districts have been reported as endemic zones for filariasis (Reddy et al. 2000) and the affected villages in all the districts were under Mass Drug Administration (MDA) program. The Government of India has initiated this program during 2004, with annual single dose of DEC tablets to all the population living in the risk zones of filariasis, but to the contrary, a high number of filariasis cases have been reported from Karimnagar, Chittoor, East Godavari, and West Godavari districts of Andhra Pradesh (Biswas et al. 1996, Mukhopadhyay et al. 2008). There is no report available on the quantification of disease load in these four districts. Hence, a pilot scale study has been carried out on epidemiological and entomological aspects of LF in these districts, which will provide valuable information to the health officials to suppress the disease load in future control programs.

The powerful tools of the spatial technology have revolutionized the way the epidemiological research on vector-borne diseases is now being carried out. Remote sensing data in Geographical Information System (GIS) have been widely used for identification, characterization, monitoring, surveillance of breeding habitats, and mapping of diseases risk zones (Clarke et al. 1996, Tanser and Le Sueur 2002, Srivastava et al. 2009). GIS maps on community-based data and output of spatial and temporal information could be enormously valuable as an operational tool for early detection and timely response to disease management. This strategy has been used in the current study to explore the impact of intervention coverage and people's adherence to the intervention on filariasis in the targeted villages in various geographic locations among four districts of Andhra Pradesh.

The prime objective of this study was to estimate the prevalence of LF in these four districts of Andhra Pradesh. The secondary objective was to develop spatial distribution maps of LF in endemic areas and to determine the parasite load at the village level. The endemic areas are classified as high, medium, and low according to their geographical location, persistence, and disease risk. Making the most from GIS technological advancement for filariasis detection, several authors have been developing GIS methodologies and models for control of various public health diseases through integrated approach. This work also aimed to contribute further to this theme, integrating system resources and geoprocessing technologies readily available, to establish disease alert levels for effective logic that identifies the real degree of danger. Thus, proper disease control actions can be established faster and further spreading of the disease can be prevented.

Methods

Study site

This study was undertaken covering 120 villages from four districts (Table 1) of Andhra Pradesh (30 villages from Karimnagar and Chittoor district, 45 villages from East Godavari district, and 15 villages from West Godavari district) during 2004 to 2007. The villages in these districts are populated predominantly by paddy farmers. The climate of these four districts is characterized by summer (46°C–20°C), winter (32°C–11°C), and monsoon (June–December). The South West monsoon and North East monsoon are responsible for the total rainfall in Andhra Pradesh.

Table 1.

The Geographical Position of Study Areas in Andhra Pradesh, India

District	Geographical position
Karimnagar	18°25′48″ N, 79°9′0″ E
Chittoor	12°37′ to 14°8′ N, 78°3′ to 79°55′ E
East Godavari	16°30′ to 18°20′ N, 81°30′ to 82°30′ E
West Godavari	16°15′ to 17°30′ N, 80°55′ to 81°55′ E

Study design

For this study, 120 villages were selected from the four districts in consultation with the Department of Health, Government of Andhra Pradesh. Before investigations, the local authorities and the residents of the study villages were informed about the proposed study for their authorization. Individuals from all households involved were offered filarial treatment during the period of the study. Before collecting blood samples, all individuals who participated were interviewed, using a structured questionnaire, and data regarding the socioeconomic status and their knowledge, attitude, and behavior in relation to filariasis were collected. The households for survey were selected by the stratified random sampling methodology. During the study period, a large volume of data (epidemiology, entomology, and socioeconomic data) has been collected, and in the current study, the summarized data pertaining to only four parameters (i.e., infection rate, infectivity rate, mosquito per man hour [PMH], and microfilaria [MF] rate) have been focused for spatial mapping.

Blood sample collection

A total of 23,624 blood smears (5794 from Karimnagar, 5830 from Chittoor, 9000 from East Godavari, and 3000 from West Godavari) were collected from 120 villages of four districts during the study period (2004–2007) from different age groups and genders. From each village nearly 200 blood smears (from 45 households) have been collected to assess the MF rate. The blood sample details are summarized in Table 2. Each selected household was considered a sampling unit, and all individuals present at the time of survey were registered for screening of microfilaraemia of W. bancrofti species, and the disease was assessed through house-to-house visit. From each respondent, about 20 μL of blood was collected between 20:00 and 23.00 h by finger prick method, smears were prepared on clean prenumbered glass slides, and the presence of MF was examined and counted under light microscopy (Sasa 1976).

Table 2.

Prevalence of Microfilaria (Positive) by Gender Wise and District Wise

	Total blood samples collected		Microfilaria-positive carriers
District	Male	Female	Male ^a	Female ^a	Microfilaria positive (%)	Total samples	Odds ratio (95% confidence interval
Karimnagar	2873	2921	71	53	21.40	124/5794	Reference
Chittoor	2962	2868	28	24	8.91	52/5830	2.4 (1.75–3.37)
East Godavari	4367	4633	208	200	45.33	408/9000	0.46 (0.37–0.56)
West Godavari	1569	1431	85	75	53.33	160/3000	0.38 (0.30–0.49)
Total	11, 771	11, 853	392	352	31.49	744/23,624

p>0.05.

Entomological survey

Indoor-resting mosquitoes were collected with the help of mechanical aspirators (Hausherr's machine Works) during 06:00 to 09:00 h from the study areas during the study period (2004–2007). Only female Culex quinquefasciatus mosquitoes, the principal vectors of bancroftian filariasis were identified by using the key developed by Reuben et al. (1994) and subjected for dissection. The vector abundance was expressed as the number of female C. quinquefasciatus mosquitoes collected per man per hour. To assess the transmission levels of the disease, C. quinquefasciatus mosquitoes were dissected to identify the stage of the MF, using the key developed by Nelson (1959) and Yen et al. (1982), and the infection rate was calculated by the presence of any stage (L₁, L₂, and L₃) of MF. Infectivity rates were recorded based on the presence of third stage (L₃) of MF only.

PMH=No. of female mosquitoes collected/the time spent

Infection rate (%)=No. positive for L₁, L₂, L₃/no. of mosquitoes dissected×100

Infectivity rate (%)=No. of mosquitoes positive for infective stage (L₃)/no. of mosquitoes dissected×100

MF rate (%)=The number MF positive cases/total number of persons examined×100

Ethics statement

The study received ethical clearance from the ethics committee that was constituted in our institute (Indian Institute of Chemical Technology) affiliated to Ministry of Science and Technology, Government of India. This ethics committee has approved to carry out the research work. The subjects who provided the blood sample were interviewed individually before the commencement of epidemiological survey. The consent was verbally conveyed, and oral approval was obtained from the respondents of the study area. The verbal consent was obtained because most of the respondents of the study area are illiterate and do not know to read or write. During the study, from each respondent, socioeconomic details were obtained using a structured questionnaire. During the entomological survey, mosquitoes were collected from the private residences after obtaining oral consent in consultation with the concerned family members. In addition to this, the consent of the village head and the concerned health officials pertaining to the village was obtained well in advance to carry out the study. To document verbal consent, the name of each individual who provided verbal consent was recorded, along with the test results for their samples. The verbal consent obtained from the subjects of the study was approved by the ethics committee of our institute.

Spatial data preparation

Global Positioning System (GPS) is a system of 24 satellites that allows the co-ordinates of any point on or near the earth's surface to be measured with extremely high precision (Boulos et al. 2001). GPS 12, a product of Garmin (www.garmin.co.in), a handheld receiver, was used in this study. GPS locations (latitude, longitude, and altitude) of the study villages registered as waypoints were recorded throughout the field survey from the four districts of Andhra Pradesh. The collected GPS data were used to develop geo-referenced files of the study areas with village boundaries. The prevalence of MF rate, vector density, and mosquito infection and infectivity rates was then incorporated into the digital map.

Development of GIS application

GPS data were downloaded from the GPS handheld receiver for GIS mapping and analysis; MapSource software was used to upload and download maps data, points of interest, waypoints, tracks, and routes to and from the GPS unit. Customization of GIS application is the process of leveraging the available functionalities in a desired manner from software development kits. ArcGIS Engine-9.2 is one such kit of GIS functionalities that can be accessed through programming language such as C++, VB, .Net, and Java. For the current application, .Net was used to incorporate various functionalities such as data accessing, data visualization, and thematic representation based on attributes.

In this application the data are stored in Environmental Systems Research Institute personal Geodatabase format, which contains spatial layers such as village boundaries and GPS locations of each of four districts. The data-accessing module is called application and is invoked to display in map window(s) the selected district data, and, subsequently, the user can choose the data through the combo box provided in the interface.

Statistical analysis

SPSS version 15.0 was used for statistical analysis. Risk estimates (odds ratio [OR]) were calculated using bivariate logistic regression. Level of significance was considered as 0.05.

Results

Prevalence of W. bancrofti infection

The overall MF was present in 744 (31.49%) of the 23,624 samples across the 120 villages of the four districts ranging 8.91% (Chittoor), 21.40% (Karimnagar), 45.33% (East Godavari), and 53.33% (West Godavari; Table 2). In our study it was noticed that there was not much difference observed between male and female (χ ²=2.516, p=0.113) filarial cases. In Karimnagar district, out of 5794 blood smears, 124 (71 males and 53 females) were found to be positive; in Chittoor, out of 5830 respondents, 52 (28 males and 24 females) were positive for MF. In East Godavari, 408 samples (male 208 and female 200) were found to be positive from 9000 samples; similarly, in West Godavari, out of 3000 smears collected, 160 (male 85 and female 75) were recorded positive for MF (Table 2). In the study, it was also observed that the MF infection has steadily increased along with the age groups (p=0.001), and the maximum prevalence was found in 20–40-year and >60-year age groups. In bi-variate analysis, Karimnagar district was considered as a reference, and the analysis reveals that Chittoor (OR=2.4, 95% confidence interval [CI]=1.75–3.37), East Godavari (OR=0.46, 95% CI=0.37–0.56), and West Godavari (OR=0.38, 95% CI=0.30–0.49) had significantly lower risk for filariasis. Data collected on various entomological and epidemiological parameters from all the 120 villages of four districts are presented in Tables 3 –6. These tables infer that the intensity of disease rates largely varied among the villages from all these four districts of Andhra Pradesh.

Table 3.

Entomological Details in Karimnagar District of Andhra Pradesh

S. No.	Village name	Latitude (N)	Longitude (E)	Microfilaria rate	Per man hour	Infection rate	Infectivity rate
1.	Alugunoor	79.14584	18.40466	4.5	56	25	7.14
2.	Annaram	79.23633	18.41058	0.5	24	6	0.2
3.	Anthakkapeta	79.21719	18.06593	2	36	12	1.2
4.	Asif Nagar	79.01744	18.44051	0	28	0	0
5.	Baddipalli	79.03292	18.47253	0.5	24	6.42	0
6.	Basavapuram	78.83476	18.32077	6.5	38	26	6.5
7.	Bommakkal	79.34069	18.16202	1.5	32	10	0.8
8.	Bomminapalli	79.23751	18.20765	1	28	8	0.4
9.	C.C.Palli	79.12362	18.14634	0	30	0	0
10.	Choppadandi	79.16851	18.57672	1.5	30	8	0.6
11.	Desiraju Palli	79.07086	18.52431	2.5	40	18.2	1.8
12.	Durshed	79.18740	18.46048	1	28	8.6	0.6
13.	Erukula	79.18698	18.47021	0.5	26	6.2	0.2
14.	Gopala Puram	79.17249	18.46219	0	28.8	0	0
15.	Gunmukula	79.37868	18.37216	0	28.6	0	0
16.	Jillela	78.81113	18.27545	1.5	34	9.8	1
17.	Kandikattkur	78.96189	18.38407	0	30.6	0	0
18.	Kothapalli	79.08868	18.49882	1	28	8	0.6
19.	Kurikyala	79.00315	18.54533	4	38	20.4	2.8
20.	Malkapuram	79.08301	18.46019	0.5	24	6	0
21.	Manakonduru	79.18730	18.39773	1.5	34	9.8	1
22.	MannemPalli	79.20674	18.34207	10.5	78	30.76	14.2
23.	Modi Manikyam	79.13502	18.25074	1.5	32	10	0.8
24.	Nagireddypur	79.02681	18.59405	4.5	52	24	7
25.	Narayanpur	78.65745	18.36161	2	34	11.76	1
26.	Parlapalli	79.22354	18.29060	5	40	27.5	4.5
27.	Peddalingapur	78.87798	18.28230	1	30	8.6	0.6
28.	Raikal	79.31720	18.15837	0	30	0	0
29.	Ramavaram	79.13142	18.01921	5.5	36	22.22	5.5
30.	Tadijerri	78.99156	18.51625	1.5	34	9.2	0.8

Table 4.

Entomological Details in Chittoor District of Andhra Pradesh

S. No	Village name	Latitude (N)	Longitude (E)	Microfilaria rate	Per man hour	Infection rate	Infectivity rate
1.	Anoop Palli	79.10150	13.16228	0.5	24	0	0
2.	Atmakuru	79.21378	13.18928	0	27.48	0	0
3.	Avalikonda	79.26060	13.19530	0	32	0	0
4.	Chinnakam Palli	79.02288	13.28147	1	31.26	3.6	0
5.	Chinnareddy Palle	79.01741	13.15275	0.5	30.2	4.2	0.5
6.	Chittapara	79.09087	13.04147	3	40.06	15	2.6
7.	Doddipalli	79.12072	13.23934	0	30	0	0
8.	Ekambara Kuppam	NA	NA	0.5	28	1.2	0.1
9.	Kanipaka Patnam	79.04812	13.26222	4	46	22.72	5.8
10.	Karvetinagaram	79.44744	13.22073	0	28.84	0	0
11.	Madhavaram	79.05158	13.17483	0	32.6	0	0
12.	Mahasamudram	79.01076	13.18871	1	26.4	3.6	0
13.	Mallam Gunta	79.40637	13.60184	0	26.28	0	0
14.	Mapakshi	79.09490	13.14207	0	32	0	0
15.	Mittapalli	79.01401	13.19509	1	28	4	0.5
16.	Mukaltoor	79.19894	13.21019	0	30	0	0
17.	Mungalipattu	79.25008	13.56007	0	26	0	0
18.	Nangamangalam	79.17763	13.04497	0.5	26.42	2	0
19.	Penumuru	79.19672	13.36900	2	36.4	12.24	1.8
20.	Perumall Palle	79.04166	13.16598	0	30	0	0
21.	Poola Kandiga	NA	NA	0.5	26.4	2	0.2
22.	Pymagam	78.95716	13.28042	1.5	32	8.4	0
23.	Ragi Ram Reddy Palli	NA	NA	0	28	0	0
24.	Ramapuram	79.18370	13.10121	3.5	40.28	16.82	3
25.	T.Puttur	79.06944	13.24353	0	24	0	0
26.	Thumbapalem	78.84329	13.26201	0	26.26	0	0
27.	Thumendula Palem	79.06144	13.08700	7	52	26.08	12.2
28.	Tirumanpalli	79.04064	13.28527	0	26	0	0
29.	Uttara Brahmana Palli	79.02440	13.27185	0	28	0	0
30.	V.P.Agraharam	NA	NA	0	30.46	0	0

NA, not available.

Table 5.

Entomological Details in East Godavari District of Andhra Pradesh

S. No	Village name	Latitude (N)	Longitude (E)	Microfilaria rate	Per man hour	Infection rate	Infectivity rate
1	Anuru	NA	NA	14.5	35	11	2
2	Arikarevula	16.83188	82.04599	25.5	50.9	20	4
3	Bandarulanka	16.58751	81.97125	14.1	47.2	12.8	4.5
4	Bhogapuram	NA	NA	11	24	6	0.5
5	Bhogapuram Prattipadu	NA	NA	16	31	7	1
6	Bobbililanka	17.09011	81.74995	2.5	36.4	18	3
7	Chelluru	16.81976	81.99751	9	66.9	29.2	7.3
8	Chitrada	17.08664	82.24755	16.5	26.5	12	2.4
9	Domada	16.95404	82.14081	8.5	44	12.6	2
10	Dowleishwaram	16.96527	81.78368	7.5	26	12	2
11	Gollaprolu	17.14846	82.28128	10.5	34.8	9	1
12	Gudivada	17.12720	82.14719	4.5	41.4	14.2	1.25
13	Gummileru	16.82812	81.90766	7.5	34	10.8	3.6
14	Jagganna Timmapuram	NA	NA	10	33	10	1.5
15	K.Jaganadhapuram	16.64155	82.04494	20.7	33.5	14.2	2.5
16	Katavaram	17.10631	81.74235	4	24	6	0.5
17	Kattamuru	17.10880	82.11734	16	44	10	1.5
18	Kothuru	17.05125	82.11735	16.5	38	14	2.6
19	Kovvada	16.96503	82.19994	6	30.2	14.2	3
20	Kurmapuram	16.78009	81.99063	21.5	59.3	28.9	6.9
21	Kuttukuluru	16.87389	81.96772	22	67.3	38	7.7
22	Madapuram	17.12403	82.28214	11	24	6	0.5
23	Maredubaka	16.86306	81.94383	4.5	48	11	3.5
24	Melluru	NA	NA	20.2	45	17	2.5
25	Muggalla	17.14801	81.70692	5	48	10	2.15
26	Nagulapalli	17.12213	82.33494	18.5	31.2	8.5	0.5
27	Narandrapuram	16.63287	81.89953	20	34.8	11.8	3.6
28	Navakandavaram	17.10384	82.28043	4	47.2	10.8	4.5
29	Nelaturu	16.84631	81.93896	7.5	34.5	16.2	2.5
30	Panduru	NA	NA	4	30	9	1
31	Penumarti	NA	NA	7	40	9.5	1.5
32	Rayabhupalapatnam	17.04908	82.10722	8.5	54	16	4
33	Sarpavaram	16.99896	82.22527	4	48.4	12	3.5
34	Sirivada	17.12828	82.13875	11.1	35.2	12	3
35	Sitanagaram	17.17748	81.69811	8.5	31.2	8.5	0.15
36	Tapeswaram	16.88788	81.92509	6	41.7	17.5	5.3
37	Thimmapuram	17.03160	82.24947	1.5	30	9	1.5
38	Thoorongi	16.93303	82.22458	7	36.4	18	3
39	Ulapalli	NA	NA	7.5	34.5	16	2
40	Valluru	16.82850	81.94064	4.5	36.4	14.4	4
41	Velampeta	NA	NA	7.5	34	10	3
42	Vemavarapadu	NA	NA	8.5	39.4	16.4	4.6
43	Venturu	NA	NA	19.5	44	16	2
44	Vilasavilli	16.58086	82.05890	13.6	41.7	17.5	5.3
45	Yanamadala	16.84684	82.06974	20.5	57.6	20.3	5.9

Table 6.

Entomological Details in West Godavari District of Andhra Pradesh

S.No	Village	Latitude (N)	Longitude (E)	Microfilaria rate	Per man hour	Infection rate	Infectivity rate
1	Achanta	16.60208	81.80743	18.5	32.6	13.7	1.05
2	Baggeswaram	16.52152	81.69562	32	41.2	14.5	1.5
3	Digamarru	16.49022	81.71330	17.5	36.5	20.6	1.5
4	Eletipadu	16.68462	81.73967	34	42.5	17.5	2.3
5	Gummaturu	NA	NA	14.5	33.5	17.4	1
6	Jinnuru	16.54738	81.72497	36	35.8	18.5	3.1
7	Kakala	NA	NA	13.2	33.5	12	1.4
8	Mandapaka	16.87918	81.92948	16.9	36.8	15.4	1.4
9	Palangi	16.77064	81.69648	25	51.4	11.2	3.3
10	Penumadam	16.54675	81.76170	19.5	38.2	18.9	4.6
11	Penumanchili	NA	NA	15.5	30	11.5	1
12	Pondita Villuru	NA	NA	36	35.8	18.5	3.1
13	Pydiparru	16.75357	81.66955	29.4	26.5	12.5	2.4
14	Rapaka	NA	NA	16	30	9	2.2
15	Velpur	16.71626	81.67155	16.7	30	9	2.2

Chronic clinical filariasis

The chronic condition of LF, namely, elephantiasis (lymphoedema), was prevalent in the study population. Chronic conditions of elephantiasis alone were recorded in 89 (1.5%) people from Karimnagar, 27 (0.46%) from Chittoor, 152 (1.69%) from East Godavari, and 69 (2.3%) from West Godavari districts. Most of the infected individuals (males and females) had swelling in the right, left, or both in upper and lower limbs. Although these figures are an indication of a substantial disease burden in the communities examined, they do not accurately reflect the level of burden in the study areas on a random survey basis.

The collected data on disease rates and GPS data of each village were customized to derive the filarial endemicity among the surveyed 120 villages. On the basis of the relative contribution of each parameter (i.e., MF rate, PMH, infection, and infectivity rates), a filariasis-monitoring model has been developed and maps were generated on a GIS platform. This decision support system has been built up with user interface facilities for browsing thematic maps of the surveyed villages of the four districts (Figs. 1 –4). This visualization model allows integration of data from any database software or worksheet. The tool is designed as a model for filariasis, and this can be put into function for country-wide LF control operations, with all the desired data.

FIG. 1.

Spatial map showing the village-level prevalence of filariasis (infectivity, infection, MF rate, and PMH) in Chittoor District of Andhra Pradesh. MF, microfilaria; PMH, per man hour. Color images available online at www.liebertonline.com/vbz

FIG. 2.

Spatial map showing the village-level prevalence of filariasis (infectivity, infection, MF rate, and PMH) in Karimnagar District of Andhra Pradesh. Color images available online at www.liebertonline.com/vbz

FIG. 3.

Spatial map showing the village-level prevalence of filariasis (infectivity, infection, MF rate, and PMH) in East Godavari District of Andhra Pradesh. Color images available online at www.liebertonline.com/vbz

FIG. 4.

Spatial map showing the village-level prevalence of filariasis (infectivity, infection, MF rate, and PMH) in West Godavari District of Andhra Pradesh. Color images available online at www.liebertonline.com/vbz

Filaria monitoring visualization system: a GIS-based application

Filaria monitoring visualization system (FMVS) a GIS-based application was developed and classified all the study villages into three groups based on the intensity of the disease—high (red), medium (yellow), and low risk (blue)—by considering the four parameters (i.e., infection rate, infectivity rate, PMH, and MF rate). To represent these intensities, value rendering functionality was used to represent in different colors by reading the corresponding attribute of each village. A unique feature of this application is having four geometrically synchronized map windows, which are used to present each parameter in different window of the same study area. A location map is also provided to display the study area location in the state map. A dropdown box is provided to choose any one of the district, which will be reflected in both map windows and location map areas. The spatial distribution of the sampled villages/districts with their filariasis prevalence is shown in the Figures 1 to 4 on GIS platform. The present study has shown that the areas with risk of filariasis can be identified at the macro level using the FMVS in four districts of Andhra Pradesh.

Discussion

In India, three states (Uttar Pradesh, Bihar, and Andhra Pradesh) alone account for 52% of the endemic population and 62% of the infected population (Das et al. 2001). National Health Policy 2002 aims at elimination of transmission and the prevention of disability due to LF by the year 2015, through MDA program with annual single dose of DEC. Sixteen out of the 23 districts of Andhra Pradesh are under the grip of filariasis, and 54 million people of the state are under MDA program with annual single dose of DEC tablets once a year (Directorate of National Vector Borne Disease Control Programme 2004). Chittoor, Karimnagar, and East and West Godavari are the worst affected districts in Andhra Pradesh. In an attempt to control LF in East Godavari, from 1999 to 2005, a total of six rounds of MDA programs were organized covering five million people (Government of Andhra Pradesh 2005). Our data demonstrate that these four districts of Andhra Pradesh represent filariasis-endemic districts. Among the sampled population, the overall MF prevalence was recorded as 31.49%. It was observed that the lowest MF prevalence recorded among the four districts was 8.91, and the percentage of MF varied in other districts (Table 2). Despite concentrated efforts of the MDA program, it has been found that the disease is still prevalent as it is an endemic area; our study depicts that the endemicity is due to autochthonous transmission occurring in the community.

C. quinquefasciatus is the major vector of filariasis. It is well adapted to both urban and rural areas, especially those with high population densities with low socioeconomic and poor living conditions, etc. These conditions are favorable for proliferation of vectors (Mott et al. 1990, Albuquerque 1993). The mean value of PMH density recorded in Chittoor was 30.84, Karimnagar 34.40, East Godavari 39.43, and West Godavari 35.62. The high vector density (>40) was recorded in most of the villages of East Godavari district and in few villages in rest of the districts where the study was done.

The risk factors for infection and disease due to W. bancrofti have been difficult to characterize because of the complex life cycle of this mosquito-borne helminths and because of the broad range of clinical signs and symptoms attributable to this nematode. The mean prevalence rate of microfilaraemia in Chittoor was 0.883%, Karimnagar 2.067%, East Godavari 10.982%, and West Godavari 22.713%. From the WHO classification, most of the villages from East and West Godavari districts were seen to be hyperendemic (>10%). Similarly, low (<5%) and medium (<10%) endemicity was reported from both Karimnagar and Chittoor districts of Andhra Pradesh. The microfilaraemia rates varied among the different age groups; higher MF was noticed among adults and older persons. The prevalence of microfilaraemia usually increased with age (Rajagopalan et al. 1989).

The FMVS was developed on a GIS platform and has been used to identify filariasis risk areas. The disease transmission is largely influenced by various variables (environmental and socioeconomic factors), which help identify the disease transmission zones at micro- and macro-scale (Galvez Tan 2003, Sherchand et al. 2003). Similarly, the prevalence of filariasis in human population is directly related to the parameters such as vector abundance, vector infection, infectivity, and biting rate of infective vector population.

In summary, the data on epidemiological and entomological aspects were subjected for spatial mapping of filariasis infection zones in the four districts of Andhra Pradesh during 2004–2007. These results demonstrate that microfilaraemia is still prevailing with various levels of intensity even after the implementation of MDA programs. The application of FMVS will help the end user to quickly assess the intensity of the disease based on the input information provided by the visualization system. The observed prevalence (MF rate and vector density) rates in our study are found to be above the threshold level. Hence, this application will certainly be helpful for the health official to implement the integrated control measures to suppress the filarial load in the community.

Footnotes

Acknowledgments

The authors are grateful to Director, IICT, for his encouragement and support. The authors thank Ministry of Communication and Information Technology for sponsoring the project, and the Government of Andhra Pradesh for supporting the Project. The authors also acknowledge the District Medical and Health Services, Hyderabad, for their support during the study.

Disclosure Statement

No competing financial interests exist.

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