The geographical distribution of lymphatic filariasis infection in Malawi

  • Bagrey MM Ngwira1, 2Email author,

    Affiliated with

    • Phillimon Tambala3,

      Affiliated with

      • A Maria Perez2,

        Affiliated with

        • Cameron Bowie2 and

          Affiliated with

          • David H Molyneux1

            Affiliated with

            Filaria Journal20076:12

            DOI: 10.1186/1475-2883-6-12

            Received: 17 April 2007

            Accepted: 29 November 2007

            Published: 29 November 2007

            Abstract

            Mapping distribution of lymphatic filariasis (LF) is a prerequisite for planning national elimination programmes. Results from a nation wide mapping survey for lymphatic filariasis (LF) in Malawi are presented. Thirty-five villages were sampled from 23 districts excluding three districts (Karonga, Chikwawa and Nsanje) that had already been mapped and Likoma, an Island, where access was not possible in the time frame of the survey. Antigenaemia prevalence [based on immunochromatographic card tests (ICT)] ranged from 0% to 35.9%. Villages from the western side of the country and distant from the lake tended to be of lower prevalence. The exception was a village in Mchinji district on the Malawi-Zambia border where a prevalence of 18.2% was found. In contrast villages from lake shore districts [Salima, Mangochi, Balaka and Ntcheu (Bwanje valley)] and Phalombe had prevalences of over 20%.

            A national map is developed which incorporates data from surveys in Karonga, Chikwawa and Nsanje districts, carried out in 2000. There is a marked decline in prevalence with increasing altitude. Further analysis revealed a strong negative correlation (R2 = 0.7 p < 0.001) between altitude and prevalence. These results suggest that the lake shore, Phalombe plain and the lower Shire valley will be priority areas for the Malawi LF elimination programme. Implications of these findings as regards implementing a national LF elimination programme in Malawi are discussed.

            Background

            Lymphatic filariasis (LF) has been identified as a major public health problem and is endemic in over 80 countries. It is currently estimated that up to 120 million people are infected with Wuchereria bancrofti in about 83 endemic countries [1]. Of these, it is estimated 40 million people have evidence of chronic manifestations such as hydrocele and lymphoedema/elephantiasis. In addition the affected individuals suffer repeated episodes of adenolymphangitis ('acute attacks') which result in marked loss in their economic productivity [2]. Improved therapies and diagnostic methods have led to the realisation that it should be possible to interrupt transmission and eliminate LF by repeated, annual cycles of mass drug administration (MDA), with single dose combination regimens [3]. Thus, in 1997 the World Health Assembly passed a resolution calling for strengthening of activities leading to the elimination of LF as a "public health problem [4]." This resulted in the initiation of the now well-established Global Program to Eliminate Lymphatic Filariasis (GPELF) in 2000.

            Malawi has two previously known LF foci: one in the southern part (Shire valley) and the other in the northern region along the Songwe river which forms its border with Tanzania [5, 6]. However there had been no detailed community based surveys for LF in Malawi apart from one in the northern focus which was conducted in 1960. This survey, based on microscopic examination (for microfilarae) of thick bloodsmears which were made from samples collected at night, showed a high prevalence of microfilaraemia amongst adults (40%) and suggested that human infection with W. bancrofti was confined to communities in close proximity to the Songwe River [7].

            More recently, surveys in these two foci have reported high antigenaemia prevalence based on immunochromatographic (ICT) card tests that approached 80% in some of the sampled villages [8, 9]. There was also a higher than expected prevalence of LF associated disease in both areas (4% lymphoedema and up to 18% hydrocele). In addition, the survey in Karonga established that W. bancrofti infection is more wide spread than previously recognised, whereas in the lower Shire valley a markedly higher antigenaemia prevalence (55%) was found amongst children (aged 1–9 years) than what has been reported in any of the published literature.

            Towards the end of 2003 we completed a nation-wide mapping exercise using ICT cards. The objective was to obtain data on the geographical distribution of LF in the remaining districts in Malawi as a prerequisite to initiating national LF elimination activities. This paper presents findings from a 2003 survey and incorporates data from recent surveys in the two known foci that have already appeared in the scientific literature to produce, for the first time, a complete map of the distribution of LF infection (based on adult worm antigenaemia) in Malawi. The implications of this distribution for LF control programme planning and eventual implementation are discussed.

            Methods

            Malawi is administratively divided into northern, central and southern regions. These are further divided into 28 districts. Two new districts (Neno – parent district – Mwanza and Likoma Island – parent district – Nkhata-Bay) were formed after this survey had already been planned and thus were mapped within there parent districts. In addition, access to Likoma, an Island District, was not possible in the time frame of this survey. LF prevalence data were available for three districts; Karonga District in the northern region, Chikwawa and Nsanje Districts in the southern region. The latest survey did not cover these districts. In the remaining districts we aimed to sample a random selection of villages for antigen testing. A database of villages by district was made available via the WHO's HealthMapper software. A programme incorporated in the software was used to provide a random sample of villages to be surveyed. The selected villages had a 50 km buffer zone as recommended by the WHO's rapid assessment for the geographical distribution of lymphatic filariasis (RAGFIL) method [10]. Three additional villages were chosen in the field from inhabited areas from where the database did not contain any villages. The testing protocol adopted followed recommendations of the RAGFIL method that is based on Lot Quality Sampling (LQAS) scheme [11]. Briefly, if at least 10 (20%) of the first 50 individuals (aged >15 years) tested were positive testing could be stopped; otherwise up to 100 individuals were to be tested per sampling point [11]. However since many villages are sparsely populated an adjacent village to the randomly selected one were also invited to participate in order to achieve the required sample size. Hence random selection of subjects was not feasible in most villages. Before testing could be carried out a meeting with village members was held and the objectives of the survey were explained in the local language. Each consenting individual provided demographic data (age and sex) and a finger prick blood sample. The whole blood obtained was immediately applied onto the ICT (Binax Inc., Portland, ME) card and read within ten minutes according to the manufacturer's instructions. If two lines appeared in the viewing window that particular individual was regarded as positive for LF [12]. Individuals found positive were treated on the spot with albendazole (400 mg) and ivermectin (200 μg/kg body weight). All sampled villages had geo-coordinates determined by a portable Geographical Positioning System (GPS-Garmin eTrex®) machine.

            Ethics

            The survey received ethical clearance from the Malawi Ministry of Health Sciences Research Committee (HSRC) and from the Liverpool School of Tropical Medicine Ethics Committee. Individual consent was obtained from each participant or (if they were aged <16) from one of their parents or a guardian.

            Data Management

            Data were entered into the computer using EPINFO 2000 (CDC, Atlanta) software. The data were subsequently exported into STATA version 7 (Stata Corporation, College Station, TX) for descriptive statistical analyses. In order to investigate the relationship between prevalence and altitude, log transformation of the prevalence data was carried out using the formula log10 (x + 1). Village geographical coordinate data were used to produce a map showing the spatial distribution of LF infection using the WHO's HealthMapper software.

            Results

            A total of 35 data points were sampled. Of these three were chosen in the field in inhabited areas where there were no villages on the Healthmapper database. A total of 2913 individuals were examined. The age and sex distribution of the survey participants is shown in Figure 1. There was a female excess (64%) amongst the study participants (more marked in the 20–24 age bracket). Overall there were 269 (9.2%) individuals positive for circulating filarial antigen (CFA) based on ICT results. Significantly more males than females tested positive (11.0% vs 8.2% p = 0.01). Figure 2 shows the proportion of those positive for CFA by age and sex. Amongst the males, those positive, tended to be older (student t test p = 0.08). This relationship was not observed in their female counterparts.
            http://static-content.springer.com/image/art%3A10.1186%2F1475-2883-6-12/MediaObjects/13564_2007_Article_73_Fig1_HTML.jpg
            Figure 1

            The age and sex distribution of survey participants.

            http://static-content.springer.com/image/art%3A10.1186%2F1475-2883-6-12/MediaObjects/13564_2007_Article_73_Fig2_HTML.jpg
            Figure 2

            The proportion of males and females positive for CFA by age.

            Survey prevalence data by district and village are presented in Table 1. This ranged from 0% to 35.9%. The spatial distribution of the sampled villages with their prevalence category are shown in Figure 3. In general villages in the western side of the country registered a CFA prevalence of less than 10%. This is with the exception of Mzenga Village in Mchinji District along the Malawi-Zambia border where a prevalence of 18.2% was found. Prevalence of over 20% was observed from villages in Salima and Mangochi Districts along the southern shore of Lake Malawi. Also in Ntcheu district (Bwanje Valley), Balaka district near Lake Malombe and finally in Phalombe district along the shores of Lake Chilwa. The highest prevalence (35.9%) was recorded at Kalembo village in Balaka district in southern Malawi.
            Table 1

            ICT antigen prevalence data from the nation wide survey conducted in 2003

            District

            Village

            Number tested

            Number positive

            Prevalence

            Latitude

            Longitude

            Balaka

            Kalembo

            53

            19

            35.8

            14.84500

            35.16900

            Blantyre

            Masanjala Lilangwe

            77

            5

            6.5

            15.54490

            35.02184

            Chiradzulu

            Mbalame

            81

            6

            7.4

            15.70000

            35.10000

            Chitipa

            Chisenga

            85

            0

            0

            9.97500

            33.38977

            Chitipa

            Siyombwe

            77

            0

            0

            9.68441

            33.24764

            Dedza

            Kamenyagwaza

            64

            5

            7.8

            14.40750

            34.98750

            Dowa

            Chimangamsasa

            72

            4

            5.6

            13.70964

            33.99795

            Kasungu

            Kadyaka

            65

            0

            0

            13.07633

            33.48360

            Kasungu

            Kaluluma

            105

            3

            2.9

            12.58077

            33.51870

            Lilongwe

            Mwenda 1 T/A Chadza

            84

            6

            7.1

            14.14074

            33.78825

            Machinga

            Phuteya

            70

            3

            4.3

            15.19000

            35.09887

            Mangochi

            Chilawe

            92

            9

            9.8

            13.80000

            35.10300

            Mangochi

            Chiponde

            90

            12

            13.3

            14.38300

            35.10000

            Mangochi

            Mtuwa

            82

            21

            25.6

            14.68400

            35.55100

            Mchinji

            Chalaswa

            98

            4

            4.1

            14.11689

            33.32919

            Mchinji

            Mzenga

            99

            18

            18.2

            13.60427

            32.73460

            Mulanje

            Gawani

            78

            6

            7.7

            15.98100

            35.78300

            Mulanje

            Mbewa

            69

            13

            18.8

            15.99970

            35.48611

            Mwanza

            Chapita A

            64

            3

            4.7

            15.63022

            34.59139

            Mzimba

            Milingo-Jere

            101

            0

            0

            12.20374

            33.33340

            Mzimba

            Kambombo

            102

            2

            1.9

            11.17551

            33.52649

            Nkhata-Bay

            Kalumpha

            104

            7

            6.7

            12.08733

            34.05695

            Nkhata-Bay

            Mizimu

            103

            8

            7.8

            11.55820

            34.18150

            Nkhotakota

            Mowe

            122

            11

            9

            12.55496

            34.13366

            Nkhotakota

            Tandwe

            81

            3

            3.7

            13.02981

            34.26246

            Ntcheu

            Gwaza

            92

            26

            28.3

            14.52800

            34.68000

            Ntcheu

            Nkonde-1

            66

            6

            9.1

            14.98570

            34.82825

            Ntchisi

            Kalulu

            99

            3

            3

            13.33129

            33.74804

            Phalombe

            Maguda

            78

            19

            24.4

            15.51774

            35.78996

            Rumphi

            Bongololo

            72

            1

            1.4

            10.81276

            33.52233

            Rumphi

            Mhango

            82

            8

            9.8

            10.81000

            33.52379

            Salima

            Chipoka-Nkwizi

            73

            16

            21.9

            14.03676

            34.50614

            Salima

            Kasonda

            78

            13

            16.7

            13.59828

            34.29268

            Thyolo

            Nkaombe

            95

            6

            6.3

            15.99271

            35.04998

            Zomba

            Kapenda

            57

            2

            3.5

            15.35885

            35.40305

            http://static-content.springer.com/image/art%3A10.1186%2F1475-2883-6-12/MediaObjects/13564_2007_Article_73_Fig3_HTML.jpg
            Figure 3

            Map of Malawi showing the prevalence levels recorded in the 2003 survey.

            Prevalence data from the 2000 surveys are summarised in Table 2. The geographical distribution of data points sampled (ICT) in Malawi (except two villages in Nsanje District where it was not possible to obtain geographical coordinates) showing prevalence in relation to altitude is presented in Figure 4. Figure 5(a) shows a scatter plot of antigen prevalence by altitude. There is notable decline in prevalence with increasing altitude and further statistical analyses on log transformed prevalence data [Figure 5(b)] have shown a significant negative correlation between altitude and prevalence (R2 = 0.7 p < 0.001).
            Table 2

            ICT antigen prevalence data from surveys conducted in 2000

            District

            Village

            Number tested

            Number positive

            Prevalence

            Latitude

            Longitude

            Karonga

            Mwenitete

            42

            20

            47.6

            9.71257

            33.92973

            Karonga

            Mwakyusa

            91

            44

            48.7

            9.69795

            33.89313

            Karonga

            Mwenepela

            102

            59

            57.8

            9.67193

            33.8252

            Karonga

            Kashata

            50

            22

            44

            9.73315

            33.88652

            Karonga

            Mwamsaku

            50

            22

            44

            9.8092

            33.86483

            Karonga

            Mwambetania

            50

            29

            58

            9.86747

            33.86892

            Karonga

            Kafikisila

            51

            23

            45.1

            9.91213

            33.93105

            Karonga

            Mwenitete-mpata

            50

            24

            48

            9.94957

            33.82237

            Karonga

            Ngosi

            50

            15

            30

            10.01228

            33.94907

            Karonga

            Mwakabanga

            50

            15

            30

            10.14422

            34.01782

            Karonga

            Kanyuka

            51

            14

            27.5

            10.30768

            34.12692

            Karonga

            Bonje

            50

            28

            56

            10.49027

            34.17098

            Nsanje

            Chazuka

            148

            60

            40.5

            16.84261

            35.25259

            Nsanje

            Nchacha18

            148

            86

            58.1

            16.63617

            35.17126

            Nsanje

            Gamba

            84

            56

            66.7

            16.5811

            35.14076

            Chikwawa

            Nchingula

            128

            76

            59.4

            15.99828

            34.48297

            Chikwawa

            Zilipaine

            129

            96

            74.4

            16.07998

            34.88262

            Chikwawa

            Mbande

            108

            76

            70.4

            16.16167

            34.79332

            Chikwawa

            Pende

            116

            79

            68.1

            16.04362

            34.72428

            Chikwawa

            Belo

            196

            155

            79.1

            16.02093

            34.8162

            Chikwawa

            Mfunde

            87

            29

            33.3

            16.19929

            35.01652

            Chikwawa

            Kasokeza

            60

            34

            56.7

            16.11213

            34.92532

            Chikwawa

            Khumbulani

            59

            9

            15.3

            15.99232

            34.8791

            Chikwawa

            Muyaya

            78

            21

            26.9

            16.04667

            34.90783

            http://static-content.springer.com/image/art%3A10.1186%2F1475-2883-6-12/MediaObjects/13564_2007_Article_73_Fig4_HTML.jpg
            Figure 4

            Map of Malawi showing prevalence of all sampled villages (except 2 villages in Nsanje District) in relation to altitude (metres).

            http://static-content.springer.com/image/art%3A10.1186%2F1475-2883-6-12/MediaObjects/13564_2007_Article_73_Fig5_HTML.jpg
            Figure 5

            Prevalence plotted against altitude (metres) (a) and log transformed (prevalence + 1) plotted against altitude (metres) (b).

            Discussion

            The present survey, in the remaining unmapped districts in Malawi, has shown that infection with W. bancrofti as determined by antigenaemia prevalence is more widespread than previously appreciated. The female excess observed amongst our survey population probably reflects the fact that males are often out in the field during the day thus not available for testing. The implication of this being that the prevalence we found in some of our sampled villages is likely to be an under-estimate of the true prevalence. This is due to the fact that in most communities significantly more males tend to carry the infection as has been observed in this survey and in other surveys from Malawi and elsewhere in Africa [9, 13].

            In all districts, except Chitipa in the north, there was at least one individual who was positive on ICT. The low prevalence found in villages from the western side of Malawi could be explained by the fact that these areas are dry, of relatively higher altitude and thus not ideal for extensive mosquito breeding. The 18.2% prevalence observed at Mzenga Village in Mchinji along the Zambia border is intriguing. This is particularly so as there have been no anecdotal reports of LF disease from either the Malawi or Zambia side of the border in this area. Of note is that this village is in close proximity to a perennial stream that sustains a reasonable amount of irrigated onion farming. Whether this setting is conducive for supporting extensive mosquito breeding and thus driving W. bancrofti infection as has been observed in Northern Malawi and Ghana will need further investigation [14]. Ideally this should be coupled with human night blood examination for microfilariae.

            It is also interesting to note that some villages from districts (Rumphi, Nkhata-Bay and Nkhotakota) along the lake shore had prevalence of less than 10%. A possible explanation could be due to the fact that these districts are mountainous and thus well drained consequently limiting potential mosquito breeding sites.

            The relatively high prevalence found in Salima, Ntcheu (Bwanje Valley), Balaka, Mangochi and Phalombe was unexpected. However there have been isolated unpublished reports of cases with chronic manifestation of LF (hydrocele and elephantiasis) in these areas. It is worth noting that the ecological conditions in these districts are ideal for supporting large potential LF vector populations. Incorporating data from 2000 surveys clearly shows that the priority areas for LF control activities in Malawi will be the lakeshore districts, Phalombe plain and the Lower Shire Valley.

            The decline in LF prevalence with increasing altitude has also been reported from other settings in Africa [15]. This is believed to be due to the influence of altitude on temperature which is known to be critical for survival of the vector and development of the parasite within the vector [16].

            These findings have important implications for initiating the "Malawi LF Elimination Programme". First, following WHO's recommendation that all implementation units with a prevalence on ICT of over 1% be considered endemic and thus treated, the Malawi programme would involve 27 districts with a target population of over ten million. The population affected is far greater than ever envisaged. Secondly, both the northern (Karonga) and Southern foci (the Lower Shire Valley) share international borders which are largely porous. This calls for innovative approaches in carrying out control activities as they have to be synchronised with those in neighbouring countries. Thirdly, in some districts (Phalombe, Mulanje, Thyolo, Chikwawa and Mwanza) where LF is co-endemic with onchocercisasis the two programmes will need to be merged. Fourthly, the LF programme will need to establish links with other programmes that are delivering community based interventions such as the ministry of education's deworming and feeding programme and the expanded bed net distribution under the malaria control programme.

            Declarations

            Acknowledgements

            This survey received financial support from the Gates Foundation through the WHO's AFRO office (Sticker No. AF/02/P227728) and the Lymphatic Filariasis Support Centre (supported by the UK's Department of International Development) at the Liverpool School of Tropical Medicine. Technical and administrative support was provided by the Malawi Ministry of Health through the National Onchocerciasis Task Force Office and the Malawi College of Medicine. BN received financial support from a WHO/TDR fellowship.

            Authors’ Affiliations

            (1)
            Lymphatic Filariasis Support Centre, Liverpool School of Tropical Medicine
            (2)
            Malawi College of Medicine
            (3)
            Onchocerciasis Control Programme

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            Copyright

            © Ngwira et al; licensee BioMed Central Ltd. 2007

            This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://​creativecommons.​org/​licenses/​by/​2.​0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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