Annual Plasmodium falciparum entomological inoculation rates (EIR) across Africa: literature survey, internet access and review

Simon I Hay; David J Rogers; Jonathan F Toomer; Robert W Snow

doi:10.1016/s0035-9203(00)90246-3

. Author manuscript; available in PMC: 2011 Oct 31.

Published in final edited form as: Trans R Soc Trop Med Hyg. 2000 Mar-Apr;94(2):113–127. doi: 10.1016/s0035-9203(00)90246-3

Annual Plasmodium falciparum entomological inoculation rates (EIR) across Africa: literature survey, internet access and review

Simon I Hay ^1,^*, David J Rogers ¹, Jonathan F Toomer ¹, Robert W Snow ^2,³

PMCID: PMC3204456 EMSID: UKMS36405 PMID: 10897348

Abstract

This paper presents the results of an extensive search of the formal and informal literature on annual Plasmodium falciparum entomological inoculation rates (EIR) across Africa from 1980 onwards. It first describes how the annual EIR data were collated, summarized, neo-referenced and staged for public access on the internet. Problems of data standardization, reporting accuracy and the subsequent publishing of information on the internet follow. The review was conducted primarily to investigate the spatial heterogeneity of malaria exposure in Africa and supports the idea of highly heterogeneous risk at the continental, regional and country levels. The implications for malaria control of the significant spatial (and seasonal) variation in exposure to infected mosquito bites are discussed.

Keywords: malaria, Plasmodium fulciparum, entomological inoculation rate (EIR), biting rate, sporozoite index, transmission, disease control, Africa

Introduction

Malaria continues to pose a major public-health threat to many countries in Africa (Snow et al., 1999a). The launch of Roll Back Malaria (RBM) (Nabarro & Tayler, 1998; WHO, 1998) therefore was regarded as timely by many. International public health initiatives such as RBM, aimed at reducing continental burdens of malaria, require an understanding of contemporary malaria distribution, risk and burden (Snow et al., 1996; Le Sueur et al., 1997, Snow et al., 1998a). Several attempts have been made to explore continental malaria distribution (Sutherst, 1993; Lindsay & Martens, 1998; Lindsay et al., 1998; Craig et al., 1999) and disease burden (Snow et al., 1999a, 1999b) using climate models and malariometric data gathered from the literature. There is increasing evidence, however, that the relationship between the frequency of infection and disease outcome is complex and control options should be selected accordingly (Snow et al., 1997, 1998b; Snow & Marsh, 1998: Gupta et al., 1999a, 1999b).

In the absence of a comprehensive reference on malaria exposure in Africa, and owing to its importance in international efforts in malaria control, an overview of the available entomological evidence describing the average annual risk across Africa of receiving a Plasmodium falciparum infected bite from the local vector population [the annual entomological inoculation rate (EIR)] was initiated. This review focuses particularly on the spatial heterogeneity of annual EIR within Africa and its implications for the rationalization of malaria control. A discussion of some of the methodological difficulties involved in comparing annual EIR information is also provided. There is further consideration of the limitations and benefits of data-sharing over the internet. The data described were predicted for non-surveyed areas using remotely sensed imagery from meteorological satellites (D. J. Rogers et al., paper in preparation), to provide the first annual EIR surfaces for the African continent.

Materials and Methods

Annual EIR definition

The activity of the anopheline vector of malaria provides the basis for calculating the EIR, h’, the daily number of infective mosquito bites received per person (MacDonald, 1957). Algebraically h’ = mas; where m is the anopheline density in relation to humans; a is the average number of persons bitten by 1 mosquito in a day, and s is the proportion of mosquitoes with sporozoites in their salivary glands. It is obvious that if the EIR value is to be representative of the year, the estimates of the biting rate and the sporozoite index must be repeated at a monthly (or higher) frequency, for at least a year or complete transmission season. The annual EIR is a favoured measure for assessing malaria endemicity (Burkot & Graves, 1995) and thus the suitability of vector control (Coosemans et al., 1992), as well as the risk of epidemic development (Onori & Grab, 1980). Measuring the annual EIR presents several major difficulties, however, since the entomological methods used in its estimation have not been standardized (Githeko et al., 1996).

Human biting rate

The most direct way to measure the human biting rate (the product of ma) is the human bait catch (WHO, 1975). This involves a team waiting in a given location, usually throughout the night, collecting all the mosquitoes that attempt to feed on exposed individuals. Despite being expensive, technically difficult to replicate and unethical in areas of drug-resistant malaria, this method is unique in that it directly samples human-biting mosquitoes (Le Goff et al., 1997). Other sampling methods such as pyrethrum spray collections and light and exit traps depend on mosquito behaviours that are less directly associated with feeding on humans (Garret-Jones, 1970; Service, 1993). Fortunately, the sampling biases between the most commonly used techniques, human bait and light traps, have been investigated (Lines et al., 1991; Faye et al., 1992; Mbogo et al., 1993a; Davis et al., 1995; Smith, 1995). Correction factors have also been suggested to account for children experiencing lower biting rates than adults in the same location (Port et al., 1980). Details of the protocol used for the standardization of data are presented below.

Sporozoite index

Measurements of the sporozoite index (s) require the number of infective mosquitoes (those with sporozoites in their salivary glands) in the local population to be determined (WHO, 1975). Ideally, but not always, the sporozoite index is derived from the biting rate sample. The traditional method was to dissect all sampled mosquitoes for their salivary glands and subject them to procedures designed to help reveal potential sporozoites under the microscope. More recently, the enzyme-linked immunosorbent assay (ELISA) techniques, which detect Plasmodium-specific circumsporozoite antigens from mosquito head and/or thorax samples, are being increasingly used owing to their greater sensitivity and species specificity (Burkot et al., 1984). No attempt was made to standardize the sporozoite index in this study because sensitivity and specificity of microscopy (Kilian et al., 2000) and ELISA techniques will vary between studies.

Identification of sources of annual EIR data

Online abstracting databases (Biological Abstracts®, BIOSIS, Philadelphia, Pennsylvania, USA, MEDLINE®, US National Library of Medicine, Bethesda, Maryland, USA; CAB HEALTH, CAB International Inc., Wallingford, Oxfordshire, UK; and the Zoological Record Online®, BIOSIS, Philadelphia, Pennsylvania, USA) were searched with the following keywords (entomological inoculation rate, EIR, h’, biting rate, ma, sporozoite index, s, Plasmodium falciparum Anopheles gambiae, Anopheles funestus, Anopheles, vectorial capacity, malaria transmission, malaria control, bednets, ITBN, human bait, pyrethroid spray, light trap, exit trap, bionomics). This search resulted in a large number of references which were scrutinized for annual EIR data. From this subset of publications a ‘key author list’ was compiled and these names were re-entered into the abstracting databases and further relevant manuscripts were retrieved. The bibliographies of all recovered manuscripts were then checked for potential additional references. These references were collected and the searching strategies repeated until no new information was forthcoming. The list of papers found for each author was then compiled and a letter sent to each individual requesting they check that the bibliography was complete and the data were abstracted correctly. Forty-three letters were sent, to which there were 21 replies, mostly with further information from the ‘grey’ literature.

Recording and standardization of the annual EIR values

The values recorded in Table 1 are P. falciparum-infected bites per adult, per night indoors, using human biting rates averaged over a year. In most studies the author(s) had expressed their data in the above format. When adjustment was necessary, this commonly involved converting light-trap catches to their human-bait equivalent by multiplying by 1·5 (Lines et al., 1991) and more rarely converting child EIR values to those of adults by multiplying by 3·57 (Port et al., 1980). In addition to the annual EIR, the average annual biting rate and the sporozoite index are also presented. It should be noted, however, that owing to the approximation inherent in rounding, the product of the biting and sporozoite indices may not exactly match the recorded annual EIR. For each location an index of the length of the transmission season was expressed as the number of months in which 75% of the annual EIR was transmitted. When studies did not provide enough information for this to be calculated the length of the transmission season only was recorded. The percentage of the total annual EIR transmitted by Anopheles gambiae s.l. freshwater species, An. funestus and all other species was also noted. Finally, the land-use in which the study site was located was classified as dominantly rural, irrigated rice or urban.

Table 1.

Geo-referenced locations in Africa for which annual P. falciparum EIR data were published

Country and site	Location¹ (Long., Lat.)	Land-use²	Study date	Sporozoite index³ (%)	Biting rate⁴ (ma annual)	EIR⁵ (annual)	Seasonality⁶ (months)	Relative EIR transmission by species⁷ (%)	Citation
Benin
Cotonou, Gbégamé quarter	2·45, 6·36^a	U	Jan 1987–Dec 1987	2·80^e	1179^g	33·0	1	100:0:0	Akogbeto et al., 1992
Cotonou, Ladji quarter	2·43, 6·38^a	R	Jan 1987–Dec 1987	1·60^e	3666^g	58·0	4	100:0:0	Akogbeto et al., 1992
Cotonou, Sainte-Rita Norde quarter	2·42, 6·37^a	R	Jan 1987–Dec 1987	1·40^e	3363^g	47.0	4	100:0:0	Akogbeto et al., 1992
Ganvié	2·42, 6·47^b	R	Jan 1993–Dec 1993	0·43^e	2555^g	11·0	6	P:A:P	Akogbeto, 1995
Ganvié, near lake Nokoué	2·42, 6·42^a	R	Jan 1993–Dec 1995	0·37 ^e	2917^g	10·8	6	100:0:0	Akogbeto & Nahum, 1996
Burkina Faso
Barna	−4·42, 11·38^b	R	Jan 1981–Dec 1981	NA^NA	NA^NA	175·2	3	57:35:8	Carnevale & Robert, 1987
Baré	−4·10, 11·08^b	R	Jan 1981–Dec 1981	NA^NA	NA^NA	91·3	3	57:35:8	Carnevale & Robert, 1987
Barkournbilen and Barkoundouba	−1·23, 12·67^b	R	Aug 1994–Jun 1995	NA^f	NA^h	140·0	*4–5*	P:P:A	Modiano et al., 1996
Bobo-Dioulasso	−4·30, 11·20^b	U	Nov 1991–Jan 1993	0·10^e	2150^g	2·2	NA	100:0:0	Lochouarn & Gazin, 1993
Bobo-Dioulasso, Colrna- Nord quarter	−4·30, 11·21^a	U/R	Jan 1985–Dec 1985	0·19^e	2421 ^g	4·6	NA	100:0:0	Robert et al., 1986
Bobo-Dioulasso, Diaradougou quarter	−4·29, 11·18^a	U	Jan 1985–Dec 1985	0·19^e	74 ^g	0·1	NA	100:0:0	Robert et al., 1986
Bobo-Dioulasso, Dioulassoba quarter	−4·30, 11·19^a	U	Jan 1985–Dec 1985	0·19^e	289 ^g	0·6	NA	100:0:0	Robert et al., 1986
Bobo-Dioulasso, Sarfalao quarter	−4·30, 11·20^b	U	Jun 1993–Sep 1993	0·50^e	800 ^g	4·0	NA	100:0:0	Gazin et al., 1996
Dandé and Tago	−4·55, 11·59^a	R	Jan 1983–Dec 1984	^3·99 ^e	1380^g	55·0	3	69:31:0	Robert et al., 1985
Desso	−4·28, 11·35^b	R	Jan 1981–Dec 1981	NA^NA	NA^NA	208·1	3	57:35:8	Carnevale & Robert, 1987
Gounghin Nord	−1·55, 12·37^a	U	Jul 1984–Dec 1984	NA^e	NA^h	0·0	0	0:0:0	Rossi et al., 1986
Karangasso	−4·63, 11·22^b	R	Jan 1985–Feb 1986	4·13 ^f	6360^g	262·8	4	57:41:2*	Boudin et al., 1991
Karangasso, Koko suburb	−4·65, 11·22^a	R	Feb 1985–Feb 1986	NA^e	NA^g	116.0	4	P:P:P	Robert et al., 1988
Karangasso, Koko suburb	−4·65, 11·22^a	R	May 1985–Apr 1986	2·60 ^e	4548^g	116·1	5	51:49:0	Carnevale et al., 1988
Karangasso, Koko suburb	−4·65, 11·22^a	R	May 1986–Apr 1987	4·56 ^e	4913^g	223·8	4	59:41:0	Carnevale et al., 1988
Karangasso, Massasso suburb	−4·64, 11·21^a	R	Feb 1985–Feb 1986	NA^e	NA^g	370·0	4	P:P:P	Robert et al., 1988
Karangasso, Massasso suburb	−4.64, 11.21^a	R	May 1985–Apr 1986	5·75 ^e	7012^g	403·3	4	60:37:3	Carnevale et al., 1988
Kongodjan	−4·45, 11·58^a	R	Jan 1983–Dec 1984	1·78 ^e	7480^g	133·0	4	52:48:0	Robert et al., 1985
Kongodjan	−4·45, 11·58^a	R	Jan 1983–Dec 1983	NA^e	NA^g	133·0	3	P:P:A	Gazin et al., 1988
Koro	−4·20, 11·15^b	R	Jan 1981–Dec 1981	NA^NA	NA^NA	171·6	3	57:35:8	Carnevale & Robert, 1987
Koubri	−1·38, 12·15^a	R	Jul 1984–Dec 1984	NA^e	NA^h	441·6	3	P:P:A	Rossi et al., 1986
Ouagadougou, Kologh Naba suburb	−1·54, 12·39^a	U	Jul 1984–Dec 1984	NA^e	NA^h	1·5	3	P:P:A	Rossi et al., 1986
Ouagadougou, Nongremassm suburb	−1·51, 12·40^a	U	Jul 1984–Dec 1984	NA^c	NA^h	7·7	3	P:P:A	Rossi et al., 1986
Ouagadougou, Saint Camille suburb	−1·52, 12·36^a	U	Jul 1984–Dec 1984	NA^e	NA^h	5·6	1	P:P:A	Rossi et al., 1986
Ouagadougou, Saint Léon suburb	−1·52, 12·37^a	U	Jul 1984–Dec 1984	NA^e	NA^h	0·0	0	0:0:0	Rossi et al., 1986
Pabré	−1·58, 12·50^a	R	Jul 1984–Dec 1984	NA^e	NA^h	113·0	3	P:P:A	Rossi et al., 1986
Soumosso	−4·05, 11·02^a	R	Jan 1981–Dec 1981	NA^NA	NA^NA	200·8	3	57:35:8	Carnevale & Robert, 1987
Tago	−4·38, 11·67^a	R	Jan 1983–Dec 1983	NA^e	NA^g	82·0	3	P:P:A	Gazin et al., 1988
Toukoro	−4·25, 11·43^b	R	Jan 1981–Dec 1981	NA^NA	NA^NA	76·7	3	57:35:8	Carnevale & Robert, 1987
VK1	−4·41, 11·35^a	I	Jan 1981–Dec 1981	NA^NA	NA^NA	0	3	72:25:3	Carnevale & Robert, 1987
VK2	−4·41, 11·37^a	I	Jan 1981–Dec 1981	NA^NA	NA^NA	21·9	3	72:25:3	Carnevale & Robert, 1987
VK3	−4·41, 11·38^a	I	Jan 1981–Dec 1981	NA^NA	NA^NA	62·1	3	72:25:3	Carnevale & Robert, 1987
VK4	−4·42, 11·37^a	I	Jan 1981–Dec 1981	NA^NA	NA^NA	20·1	3	72:25:3	Carnevale & Robert, 1987
VK4	−4·42, 11·37^a	I	Jan 1983–Dec 1984	0.36 ^e	14000^g	50·0	4	100:0:0	Robert et al., 1985
VK4	−4·42, 11·37^a	I	May 1985–Apr 1986	0–21 ^e	25857^g	54·9	4	90:10:0	Robert & Carnevale, 1991
VK5	−4·43, 11·38^a	I	Jan 1981–Dec 1981	NA^NA	NA^NA	36·5	3	72:25:3	Carnevale & Robert, 1987
VK6	−4·44, 11·37^a	I	Jan 1981–Dec 1981	NA^NA	NA^NA	54·8	3	72:25:3	Carnevale & Robert, 1987
VK6	−4·44, 11·37^a	I	Jan 1983–Dec 1984	0–43 ^e	13900^g	60·0	2	90:10:0	Robert et al., 1985
Zagtouli	−1·63, 12·33^a	R/I	Jul 1984–Dec1984	NA^e	NA^h	82	2	P:P:A	Rossi et al., 1986
Burundi
Gasange	29·60, −4, 32^a	NA	Nov 1992–0ct 1993	3·14 ^e	8503 ^g	267·0	6	P:P:A	Van Bortel et al., 1996
Gihanga Mulira	29·29, −3·19^b	I	Jan 1983–Dec 1983	0·48^e	21499^g	103·2	12	96:4:0	Coosemans, 1985
Gisenga	29·67, −4, 44^a	NA	Nov 1992–May 1994	3·85 ^e	6534 ^g	251·7	5	P:P:A	Van Bortel et al., 1996
Kazirabageni	29·63, −4·23^a	R	Nov 1992–May 1994	2·02 ^e	1753 ^g	35·4	5	P:P:A	Van Bortel et al., 1996
Mugere	29·66, −4, 37^a	I	Nov 1992–0ct 1993	1–02 ^e	59021 ^g	600·9	7	P:P:A	Van Bortel et al., 1996
Cameroon
Ebogo	11·47, 3·40^b	R	Apr 1991–Mar 1992	0·93 ^e	38189^g	355·0	6	14:0:86	Njan Nloga et al., 1993
Edea, Bililang suburb	10·13, 3·80^a	U	Jan 1990–Dec 1990	1·20^e	319^g	3·8	NA	100:0:0	Robert et al., 1993
Edea, Pongo suburb	10·13, 3·80^a	U	Jan 1990–Dec 1990	8·21 ^e	368^g	30·2	NA	100:0:0	Manga et al., 1993
Etoa	11·48, 3·77^b	R	Feb 1996–May 1996	4·10 ^e	11571^g	474·5	NA	P:55:45	Manga et al., 1997b
Mbébé	11·00, 4·15^a	R	Apr 1989–Mar 1990	1–93 ^NA	10330^g	200·0	5	36:9:55	Le Goff et al., 1992
Nditam	11·26, 5·36^a	R	May 1995–Mar 1996	9·40^e	876^g	82·1	1	100:0:0	Manga et al., 1997a
Nsimalen, Ekoko	12·12, 3·82^a	R	Apr 1991–Mar 1992	1·22 ^e	8724^g	106·0	*5–6*	13:0:87	Manga et al., 1995
Nsimalen, Nkol Mefou	11·57, 3·70^a	R	Apr 1991–Mar 1993	2·33 ^e	2920^g	68·0	6	100:0:0	Manga et al., 1995
Sanaga river villages	11·00, 4·15^a	R	Apr 1989–Mar 1990	1·77 ^e	10303^g	182·1	6	43:0:57	Carnevale et al., 1992
Yaoundé, Essos	11·00, 3·00^a	U	Mar 1989–Feb 1990	20·3 ^e	64^g	13·0	NA	100:0:0	Manga et al., 1993
Yaoundé, Nkol Bikok quarter	11·52, 3·87^a	U	Mar 1989–Mar 1990	5·00^e	284·7^g	14·2	1	100:0:0	Fondjo et al., 1992
Congo, Democratic Republic of
Kinkolé	15·51, −4·36^a	R	Feb 1990–Dec 1991	3·24 ^e	1241^g	40·2	12	100:0:0	Karch et al., 1993
Kinshasa, Kimbangu 3 district	15·31, −4, 35·^c	U	Sep 1988–Dec 1989	1·86^f	1862 ^g	29·2	*5–6*	100:0:0	Coene, 1993
Kinshasa, Kwamuthu district	15·27, −4, 46^c	R	Sep 1988–Dec 1989	7·41 ^f	6132 ^g	454·4	*5–6*	86:2:12	Coene, 1993
Kinshasa, rural area	15·30, −4·44^a	R	Apr 1989–0ct 1990	NA^e	NA^g	612·0	NA	100:0:0	Karch et al., 1992
Kinshasa, semi–rural area	15·35, −4·36^a	R/U	Apr 1989–0ct 1990	NA^e	NA^g	198.7	NA	100:0:0	Karch et al., 1992
Kinshasa, urban area	15·31, −4·31^a	U	Apr 1989–Oct 1990	0·3^e	953^g	2·8	NA	100:0:0	Karch et al., 1992
Congo, Republic of
Brazzaville	15·28, −4·26^b	U	Oct 1982–May 1984	3·47^e	650^g	22·5	NA	100:0:0	Trape & Zoulani, 1987b
Kulila	12·43, −4·17^a	R	Oct 1981–Oct 1982	4·91 ^e	8103 ^g	397·9	5	97:3:0	Richard et al., 1988
Linzolo	15·11, −4·41 ^b	R	Oct 1981–Jan 1984	2·11 ^e	11673g	246·0	9	NA	Trape & Zoulani, 1987a
Linzolo	15·11, −4·41 ^b	R	Oct 1983–Sep 1984	NA^NA	NN^g	234·6	9	96:4:0	Trape et al., 1987
Makaba	12·38, −4·14^b	R	Oct 1981–0ct 1982	6·85 ^e	1168 ^g	80·0	6	89:11:0	Richard et al., 1988
Côte d’Ivoire
Alloukoukro	−5·08, 7·80^a	R	Jan 1991–Dec 1991	^3·13 ^e	8507 ^g	266·5	6	77:23:0	Dossou-yovo et al., 1995
Alloukoukro	−5·08, 7·80^a	R	Jan 1992–Dec 1992	3·39 ^e	5792 ^g	196·5	5	81:19:0	Dossou-yovo et al., 1995
Egypt
El Zawya	30·85, 29·42^a	R	Feb 1983–Feb 1984	0·00^e	128^g	0·0	0	0:0:0	El Said et al., 1986
Gambia, The
Bakau	−16·68, 13·48^b	U	Jun 1988–May 1989	0·98^f	128^g	1·0 ^k	NA	100:0:0	Lindsay et al., 1990
Barokunda	−15·32, 13·65^b	R	NA 1988–NA 1988	2·97 ^f	3701ⁱ	110·0	5	100:0:0	Lindsay et al., 1993
Biran Giddo Ya	−15·85, 13·59^a	R	Jun 1986–Dec 1986	2·67 ^e	412^j	11·0	3	100:0:0	Lindsay et al., 1989
Bwiam	−16·09, 13·23^c	R	Jan 1991–Dec 1991	2.47^f	37^h	0·9	5	P:A:P	Thomson et al., 1994
Dasilami^a	−14·27, 13·41 ^c	R	Jan 1991–Dec 1991	2·23^f	54^h	1·2	5	P:A:P	Thomson et al., 1994
Dasilami^b	−15·23, 13·48^b	R	NA 1988–NA 1988	1·29 ^f	1160ⁱ	15·0	5	100:0:0	Lindsay et al., 1993
Dongoro Ba	−15·28, 13·38^b	R	NA 1988–NA 1988	17·86 ^f	448ⁱ	80·0	5	100:0:0	Lindsay et al., 1993
Jahally	−14·97, 13·55^c	R	Jan 1991–Dec 1991	0·95^f	443^h	4·2	5	P:A:P	Thomson et al., 1995
Jalangbereh	−15·40, 13·38^b	R	NA 1988–NA 1988	9·10 ^f	769ⁱ	70·0	5	100:0:0	Lindsay et al., 1993
Jessadi	−15·30, 13·63^b	R	NA 1988–NA 1988	2·69 ^f	2380ⁱ	64·0	5	100:0:0	Lindsay et al., 1993
Katamina	−15·28, 13·55^b	R	NA 1988–NA 1988	0·37 ^f	1076ⁱ	4·0	5	100:0:0	Lindsay et al., 1993
Kerewan	− 16·09, 13·49^c	R	Jan 1991–Dec 1991	0·23^f	211^h	0·4	5	P:A:P	Thomson et al., 1995
Kulari	−14·08, 13·40^c	R	Jan 1991–Dec 1991	7·65^f	0·28^h	7·8	5	P:A:P	Thomson et al., 1995
Madina	−15·25, 13·52^a	R	NA 1988–NA 1988	7·45 ^f	2376ⁱ	177·0	5	100:0:0	Lindsay et al., 1993
Male Kunda	−15·30, 13·55^b	R	NA 1988–NA 1988	2·60 ^f	2692ⁱ	70·0	5	100:0:0	Lindsay et al., 1993
Niawodurulung	−15·22, 13·46^a	R	NA 1988–NA 1988	0·00^f	1481ⁱ	0·0	5	100:0:0	Lindsay et al., 1993
Pakali Ba	−15·25, 13·50^b	R	NA 1988–NA 1988	3·04 ^f	3253ⁱ	99·0	5	100:0:0	Lindsay et al., 1993
Salikene	−15·97, 13·48^c	R	Jan 1991–Dec 1991	0·55^f	360^h	1·9	5	P:A:P	Thomson et al., 1995
Sare Alpha	−13·98, 13·37^b	R	Jan 1991–Dec 1991	6·07^f	187^h	11·2	5	P:A:P	Thomson et al., 1995
Saruja	−14·90, 13·55^c	I	Jan 1991–Dec 1991	2·17^f	231^h	5·0	5	P:A:P	Thomson et al., 1995
Saruja	−14·90, 13·55^c	I	Mar 1987–Jun 1988	NA^f	NAⁱ	3·0 ^k	NA	100:0:0	Lindsay et al., 1991
Sibanor	−16·20, 13·21^b	R	Jan 1991–Dec 1991	2·88^f	113^h	3·2	5	P:A:P	Thomson et al., 1994
Sitahuma	−15·40, 13·43^a	R	NA 1988–NA 1988	2·23 ^f	3366ⁱ	75·0	5	100:0:0	Lindsay et al., 1993
Sutukoba	−14·02, 13·50^c	R	Jan 1991–Dec 1991	2·94^f	34^h	1·0	5	P:A:P	Thomson et al., 1994
Turan	−15·72, 13·58^a	R	Jun 1986–Dec 1986	2·70^e	890ⁱ	23·8	3	100:0:0	Lindsay et al., 1989
Wellingara Ba	−15·26, 13·41^a	R	NA 1988–NA 1988	2·19 ^f	1553ⁱ	34·0	5	100:0:0	Lindsay et al., 1993
Kenya
Ahero	34·92, −0·17^c	I	May 1989–Jun 1990	NA^f	NA^g	416·0	7	15:85:0	Githeko et al., 1993
Fumbini	39·84, −3·61^c	NA	Jan 1992–May 1993	2·30 ^f	131^g	3·0	1	95:5:0	Mbogo et al., 1995
Kambi ya Wari	39·84, −3·52^c	U/R	Jan 1992–May 1993	10·54 ^f	37^g	3·6	2	95:5:0	Mbogo et al., 1995
Kaoyeni	39·90, −3·46^c	NA	Jan 1992–May 1993	4·08 ^f	61^g	2·5	1	95:5:0	Mbogo et al., 1995
Kanyawegi	34·67, −0·92^c	R	Jun 1991–May 1992	5·29 ^f	4917^g ^h	259·9	NA	64:36:0	Oloo et al., 1996
Karima	37·32, −0·69^c	NA	Oct 1986–Jun 1987	2·65 ^f	657 ^g	17·4	NA	80:10:10	Ijumba et al., 1990
Kibarani	39·85, −3·58^c	NA	Jan 1992–May 1993	11·31 ^f	159^g	18·0	4	95:5:0	Mbogo et al., 1995
Kilifi Town	39·85, −3·63^b	U	Dec 1990–Nov 1991	2·17 ^f	69^g ^h	1·5	2	100:0:0	Mbogo et al., 1993b
Kisian	34·67, −0·07b	R	Oct 1985–Jun 1988	5·98 ^e ^f	5001^g	299·3	*5–6*	76:24:0	Beier et al., 1990
Loboi swamp	36·06, 0·36^a	R	NA	2·55 ^e	915 ^h	23·4	2	84:16:0	Aniedu, 1997
Mikingirini	39·87, −3·56^c	NA	Jun 1992–May 1993	0·00 ^f	46^g	0·0	0	0:0:0	Mbogo et al., 1995
Miwani	35·01, −0·59^c	R	May 1989–Jun 1990	NA^f	NA^g	91·0	3	80:20:0	Githeko et al., 1993
Mtondia	39·90, −3·57^c	NA	Jun 1992–May 1993	2·09 ^f	2844^g	59·6	2	95:5:0	Mbogo et al., 1995
Mukombe	39·86, −3·52^c	NA	Jun 1992–May 1993	7·25 ^f	52^g	3·8	2	95:5:0	Mbogo et al., 1995
Mumias	34·49, 0·18^c	R	May 1995–Mar 1996	7·22 ^f	647 ^h	46·7	7	63:37:0	Shililu et al., 1998
Perkerra irrigation scheme	35·97, 0·47^a	I	NA	3·08 ^e	533^h	16·4	2	100:0:0	Aniedu, 1997
Saradidi	34·38, −0·12^a	R	Feb 1986–0ct 1987	NA^e ^f	NA^g	273·8	5	90:10:0	Beier et al., 1994
Saradidi	34·38, −0·12^a	R	Oct 1985–Jun 1988	17·1 ^e ^f	1387^g	237·3	5	90:10:0	Beier et al., 1990
Sokoke	39·82, −3·53^b	NA	Dec 1990–Nov 1991	4·23 ^f	189^g ^h	8·0	4	100:0:0	Mbogo et al., 1993b
Ufuoni	39·93, −3·46^c	NA	Jun 1992–May 1993	0·01 ^f	19^g	0·0	1	95:5:0	Mbogo et al., 1995
Zowerani	39·92, −3·51^c	NA	Jun 1992–May 1993	2·35 ^f	68^g	1·6	1	95:5:0	Mbogo et al., 1995
Madagascar
Ambodifotatra, St Joseph, Maromandia	49·90, −17·00^b	R	Nov 1988–0ct 1989	0·59 ^f	13761 ^g	82·0	12	P:P:P	Fontenille et al., 1992
Manarintsoa	47·42, −18·92^c	R	Oct 1987–Jul 1988	0·71^f	212 ^g	1·5	*4–5*	97:3:0	Lepers et al., 1991
Manarintsoa	47·42, −18·92^c	R	Oct 1988–Feb 1990	0·14 ^f	657 ^g	0·9	NA	NA	Fontenille et al., 1990
Mozambique
Matola	32·93, −25·94^a	U/R	Jan 1994–Jun 1995	NA^f	NA^g	12	NA	P:P:A	Thompson et al., 1997
Senegal
Aéré Lao	−14·30, 16·40^a	R	May 1982–Aug 1983	0·24 ^e	1600^g	3·8	*3–4*	100:0:0	Vercruysse, 1985
Affiniam, Diagobel, Tendimane	−16·30, 12·70^a	R	Jan 1985–Nov 1985	0·03 ^e	22490 ^g	7·0	5	100:0:0	Faye et al., 1994
Barkedji	−14·87, 15·28^d	R	Jul 1994–Mar 1996	NA^f	NA^g ^h	111·1	*2–3*	100:0:0	Le Masson et al., 1997
Boké Diallobé	−14·00, 16·10^a	R	May 1982–Aug 1983	1·2 ^e	200^g	2·4	*3–4*	100:0:0	Vercruysse, 1985
Dakar, around Grande Niaye marsh	−17·42, 14·75^d	NA	Jan 1988–Dec 1988	NA^e	NA^g	0·1	NA	100:0:0	Trape et al., 1992
Dielmo	−16·42, 13·72^d	R	Apr 1990–Mar 1992	1·29^f	14790^g	191·5	5	69:31:0	Konaté et al., 1994
Dielmo	−16·42, 13·72^b	R	Apr 1992–Mar 1993	NA^f	NA^g ^h	222·8	6	23:77:0	Fontenille et al., 1997b
Dielmo	−16·42, 13·72^b	R	Apr 1993–Mar 1994	NA^f	NA^g ^h	78·5	*4–5*	74:26:0	Fontenille et al., 1997b
Dielmo	−16·42, 13·72^b	R	Apr 1994–Mar 1995	NA^f	NA^g ^h	135·2	4	66:34:0	Fontenille et al., 1997b
Dielmo	−16·42, 13·72^b	R	Jan 1990–Dec 1990	NA^NA?	NA^NA?	115·0	4	100:0:0	Rogier & Trape, 1993
Dielmo	−16·42, 13·72^b	R	Jun 1990–May 1991	1·04^e	9731 ^g	101·2	4	P:P:A	Trape et al., 1994
Dielmo	−16·42, 13·72^b	R	Jun 1991–May 1992	1.57^e	17357 ^g	272·5	6	P:P:A	Trape et al., 1994
Diohine	−16·52, 14·50^b	R	Jan 1995–Dec 1995	1·95 ^f	680^g	13·3	5	100:0:0	Robert et al., 1998
Diomandou Dieri	−14·44, 16·50^a	I	Jun 1990–Nov 1991	0·00 ^e	2665^g	0·0	0	0:0:0	Faye et al., 1993
Diomandou Walo	−14·43, 16·51^a	I	Jun 1990–Nov 1991	0·09 ^e	5913^g	5·2	1	100:0:0	Faye et al., 1993
Kassack-Nord	−16·03, 16·40^a	R	Sep 1992–Nov 1994	0·00^f	NA^g	0·0	0	0:0:0	Faye et al., 1995b
Kotiokh	−16·58, 14·48^a	R	Jan 1995–Dec 1995	1·75 ^f	1558^g	27·3	8	100:0:0	Robert et al., 1998
Maka-Diama	−16·40, 16·20^a	R	Sep 1992–Nov 1994	0·00^f	NA^g	0·0	0	0:0:0	Faye et al., 1995b
Ndiop	−16·42, 13·75^b	R	Jan 1993–Dec 1993	NA^f	NA^g	63·0	2	100:0:0	Fontenille et al., 1997a
Ndiop	−16·42, 13·75^b	R	Jan 1994–Dec 1994	NA^f	NA^g	17·0	2	100:0:0	Fontenille et al., 1997a
Ndiop	−16·42, 13·75^b	R	Jan 1995–Dec 1995	NA^f	NA^g	37·0	2	93:7:0*	Fontenille et al., 1997a
Ndiop	−16·42, 13·75^b	R	Jan 1996–Dec 1996	NA^f	NA^g	7·0	1	100:0:0	Fontenille et al., 1997a
Ndiop	−16·42, 13·75^b	R	May 1993–Dec 1996	NA^f	NA^g	31·0	2	98:2:0	Fontenille et al., 1997a
Ngayokheme	−16·43, 14·53^b	R	Jan 1995–Dec 1995	1·80f	512^g	9·2	NA	100:0:0	Robert et al., 1998
Pout region	−17·07, 14·77^a	NA	Aug 1988–Jul 1989	0·00^e	²³⁹⁰⁸ ^g	0·0	0	0:0:0	Faye, 1992
Pikine	−17·40, 14·75^b	U	Oct 1979–Dec 1980	0·55^e	7818 ^g	43·0	4	100:0:0	Vercruysse & Jancloes, 1981
Thiaye, Ngadiaga, Diamballo	−17·05, 14·95^a	R	Jul 1991–Jun 1992	0·48^f	2305^g	11·0	4	100:0:0	Faye et al0, 1995a
Takème and Ousseuk	−16·20, 12·80^a	NA	Jan 1985–Nov 1985	0·36 ^e	15123 ^g	55·0	5	100:0:0	Faye et al., 1994
Toulde Galle	−14·60, 16·53^a	I	Jun 1990–Nov 1991	0·00 ^e	438^g	0·0	0	0:0:0	Faye et al., 1993
Sierra Leone
Bayama	−11·77, 8·00^a	R	Nov 1990–Oct 1991	4·36 ^f	20294 ^g	884·2	7	100:0:0	Bockarie et al., 1995
Bayama	−11·77, 8·00^a	R	Nov 1990–Oct 1991	3·90^f	20732^g	808·5	12	100:0:0	Bockarie et al., 1993
Bayama	−11·77, 8·00^a	R	Aug 1993–Nov 1994	6·82^f	6125^g	417·6	NA	NA	E. B. Magbity, 1999, pers. com.; Magbity et al., 1999
Baoma	−11·73, 8·04^a	R	Aug 1993–Nov 1994	2·16^f	3924^g	84·8	NA	NA	E. B. Magbity, 1999, pers. com.; Magbity et al., 1999
Dandabu	−11·64, 8·15^a	R	Aug 1993–Nov 1994	5·38^f	1507^g	81·1	NA	NA	E. B. Magbity, 1999, pers, com.; Magbity et al., 1999
Gumahun	−11·50, 8·18^a	R	Jun 1992–Jul 1993	6·94^f	175^g ⁱ	12·1	NA	100:0:0	Magbity et al., 1997; E. B. Magbity, 1999, pers. com.
Jaiama	−11·69, 8·16^a	R	Aug 1993–Nov 1994	8·72^f	2602^g	226·9	NA	NA	E. B.Magbity, 1999, pers. com.; Magbity et al., 1999
Konjodorma	−11·50, 8·15^a	R	Jun 1992–Jul 1993	3·96^f	818^g ^j	32·4	NA	100:0:0	Magbity et al., 1997; E. B. Magbity, 1999, pers. com.
Kpetema	−11·50, 8·13^a	R	Jun 1992–Jul 1993	8·81^f	1551^g ^j	136·7	NA	100:0:0	Magbity et al., 1997; E. B. Magbity, 1999, pers. com.
Manjama	−11·74, 8·02^a	R	Aug 1993–Nov 1994	12·13^f	2340^g	283·7	NA	NA	E. B. Magbity, 1999, pers. com.; Magbity et al., 1999
Mendewa	−11·48, 8·16^a	R	Jun 1992–Jul 1993	4·74^f	128^g ^j	6·1	NA	100:0:0	Magbity et al., 1997; E. B. Magbity, 1999, pers. com.
Mendewa	−11·48, 8·16^b	R	Jan 1990–Apr 1991	NA^f	NA^NA	21·9	*4–5*	58:42:0	Bockarie et al., 1994
Nengbema	−11·68, 8·13^a	R	Jun 1992–Jul 1993	4·42^f	471^g ^j	20·8	NA	100:0:0	Magbity et al., 1997; E. B. Magbity, 1999, pers. com.
Nengbema	−11·68, 8·13^a	R	Jan 1990–Apr 1991	NN	NA^NA	21·5	*4–5*	97:3:0	Bockarie et al., 1994
Ngalu	−11·56, 8·11^a	R	Jun 1992–Jul 1993	6·08^f	712^g ^j	43·3	NA	100:0:0	Magbity et al., 1997; E. B. Magbity, 1999, pers. com.
Njala-Komboya	−11·54, 8·20^a	R	Jan 1990–Apr 1991	NA^f	NA^NA	26·7	*4–5*	96:4:0	Bockarie et al., 1994
Nyandeyama	−11·66, 8·12^a	R	Jun 1992–Jul 1993	4·05^f	993^g ^j	40·2	NA	100:0:0	Magbity et al., 1997; E. B. Magbity, 1999, pers. com.
Nyandeyama	−11·66, 8·12^a	R	Jan 1990–Apr 1991	NA^f	NA^NA	35·5	*4–5*	97:3:0	Bockarie et al., 1994
Tondoya	−11·64, 8·13^a	R	Jun 1992–Jul 1993	7·30^f	1424^g ^j	102.0	NA	100:0:0	Magbity et al., 1997; E. B. Magbity, 1999, pers. com.
Tanzania
Chasimba	38·82, −6·58^c	R	Jan 1992–Dec 1992	NA^f	NAⁱ	217·7	9	87:13:0	Shiff et al., 1995
Idete	36·48, −8·10^b	R	Jan 1992–Dec 1994	NA^f	NAⁱ	584·0	12	P:P:A	Charlwood et al., 1998
Kongo	38·83, −6·53^b	R	Jan 1992–Dec 1992	NA^f	NAⁱ	576·7	12	81:19:0	Shiff et al., 1995
Kaole	38·93, −6·45^c	R	Jan 1996–Dec 1996	NA^f	NAⁱ	124·3	12	66:34:0	Temu et al., 1998
Kerege	39·03, −6·57^b	R	Jan 1992–Dec 1992	NA^f	NAⁱ	271·6	11	88:12:0	Shiff et al., 1995
Kikwawila	36·75, −8·08^a	R	Dec 1983–Aug 1984	2·49 ^e	11427 ^NA	284·0	3	69:31:0	Biro, 1987
Kikwazu	38·82, −5·40^a	R	Apr 1988–Dec 1989	NA^e	NAⁱ	667·0	3	100:0:0	Mnzava, 1991
Kongo	38·83, −6·53^c	R	Jan 1996–Dec 1996	NA^f	NAⁱ	306·3	12	83:17:0	Temu et al., 1998
Kumbamtoni	38·76, −5·10^a	R	Jan 1987–Dec 1988	7·14 ^f	6004ⁱ	428·8	NA	P:P:P	Magesa et al., 1991
Kumbamtoni	38·82, −5·31^a	R	Apr 1987–Mar 1989	NA^e	NAⁱ	420·0	6	100:0:0	Mnzava, 1991
Mapinga	39·07, −6·60^b	R	Jan 1992–Dec 1992	NA^f	NAⁱ	235·6	9	83:17:0	Shiff et al., 1995
Matimbwa	38·87, −6·50^b	R	Jan 1992–Dec 1992	NA^f	NAⁱ	702·6	12	90:10:0	Shiff et al., 1995
Matimbwa	38·87, −6·50^c	R	Jan 1996–Dec 1996	NA^f	NAⁱ	122·1	12	51:49:0	Temu et al., 1998
Michenga	36·63, −8·12^a	R	Jan 1990–Dec 1990	NA^e	NAⁱ	547·5	4	100:NA:NA	Babiker et al., 1997
Michenga	36·63, −8·12^a	R	Jan 1990–Dec 1991	NA^f	NAⁱ	548·0	4	100:NA:NA	Lyimo, 1993
Namawala	36·40, −8·15^e	R	Mar 1990–Jul 1991	1.37 ^f	24090ⁱ	329·0	6	P:P:A	Smith et al., 1993
Temgini and Enzi	38·76, −5·18^c	NA	NA 1995–NA 1996	NA^f	NA^g ⁱ	405·0	NA	NA	C. F. Curtis, 1998, pers. com.
Umba	38·87, −5,17^a	R	Jan 1987–Dec 1988	4.68 ^f	11899ⁱ	556·7	NA	P:P:P	Magesa et al., 1991
Yombo	38·85, −6·57^b	R	Jan 1992–Dec 1992	NA^f	NAⁱ	220·6	10	79:21:0	Shiff et al., 1995
Zinga	38·98, −6·52^b	R	Jan 1992–Dec 1992	NA^f	NAⁱ	93·7	7	88:12:0	Shiff et al., 1995

Open in a new tab

Sources of latitude/longitude:

published maps

GeoName™ digital gazetteer CD-ROM;

correspondence with authors;

cited in the reference.

R, rural; U, urban; I, irrigated rice.

Sporozoite detection method:

dissection;

ELISA.

⁴

ma sampling technique:

human bait;

insecticide spray;

ⁱ

i light traps;

exit traps.

⁵

EIR values:

converted using (Port et al., 1980) average child-to-adult conversion factor, f, of 3·57.

⁶

Values in italics record the number of months in which 75% of transmission occurs. Other values record the length of the entire transmission season.

⁷

For Anopheles garnbiae s.l.: An. funestus: any other locally important vector. Where absolute values are unavailable P indicates presence only and A absence.

NA refers to data that were not available, or not applicable. Values in bold refer to calculated values.

Criteria for data exclusion

Annual EIR data measured before 1980 were excluded because it was not clear whether information collected over 20 years ago would be representative of the conditions today. In addition, the data were also extracted for comparison with contemporaneous meteorological satellite sensor data available from 1981 to date (D. J. Rogers et al., paper in preparation). Moreover, data before 1980 were more difficult to search using electronic abstracting databases, although such information would make a useful addition to those compiled here. Sites were also excluded if malaria control activities, local bednet and/or insecticide usage were reported. The possibility of unreported use of bednets, insecticide and repellents in the studies remains a problem, however, so these data are best interpreted as potential EIR values. Finally, data were not included if the sampling frequency and duration of observation were insufficient to record the EIR throughout the entire year or transmission season.

Methodological information

EIR sampling methods vary considerably and those used in obtaining the biting rate (human bait, pyrethroid spraying, light or exit traps) and the means of measuring the sporozoite index (dissection or ELISA) are noted in Table 1. Calculation of the sporozoite index was often complicated by the subdivision of the index into proportions due to different Plasmodium species. Only the sporozoite indices attributable to P. falciparum were used. Where the relative contribution to total transmission level for each species was not documented, the total was assumed to be due to P. falciparum.

Geo-referencing

Sites were geo-referenced using information from the original references, published maps and/or the GeoName™ digital gazetteer CD-ROM (GDE Systems Inc., San Diego, CA, USA). Sites for which co-ordinates were found are included in Table 1. Those sites which had conflicting latitude and longitude values from different sources were double-checked and the erroneous co-ordinates discarded. The method used to geo-reference each annual EIR value was therefore also recorded.

Data distribution

The information reviewed in this paper is available for downloading from both the Mapping malaria risk in Africa/Atlas du risque de la malaria en Afrique (MARA/ARMA URL; http://www.mara.org.za) and Scientists for Health And REsearch for Development (SHARED URL; http://www.shared.de) web sites as comma separated text files. Mechanisms for correcting existing and adding additional information will be staged in the near future. The Appendix, which provides an example of a completed data sheet used to abstract data in this study, is also available for downloading.

Results

Annual EIR data

Four hundred references were retrieved and searched for annual EIR data of which 91 satisfied the selection criteria. These papers contained 201 temporally distinct annual EIR measurements from 16 countries. Of these, 159 were spatially distinct. Table 1 contains all the 193 geo-registered sites from 15 countries with data collected after 1980. Table 2 contains annual EIR data that could not be geo-registered: 8 sites in 5 countries. The Figure shows the distribution of the study sites detailed in Table 1. The apparent disparity in the number of sites is due to the close proximity of many studies which could not be resolved on a map of Africa at the continental scale.

Table 2.

Locations in Africa for which annual EIR data were published that could not be geo-referenced

Country and site	Location¹ (Long., Lat.)	Land- use²	Study date	Sporozoite index³ (%)	Biting rate⁴ (ma annual)	EIR⁵ (annual)	Seasonality⁶ (months)	Relative EIR transmission by species⁷ (%)	Citation
Cameroon
Yaoundé, Nikol Bisson	NA, NA	R	Mar 1989–Mar 1990	1·67^e	1814^g	30·3	3	100:0:0	Fondjo et al., 1992
Congo, Democratic Republic of
Mbangu-mbamu	NA, NA	R	Feb 1990–Nov 1990	2·50^e	17·3^g	157·0	12	100:0:0	Karch et al., 1993
Mbansale	NA, NA	R	Feb 1990–Nov 1990	3·50^e	5001^g	175·2	12	100:0:0	Karch et al., 1993
Mbansalé	NA, NA	R	Feb 1991–Nov 1991	4·80^e	6826^g	324·9	12	100:0:0	Karch et al., 1993
Côte d’Ivoire
Bouaké, market garden districts	NA, NA	R	Jan 1992–Dec 1992	2·01 ^NA	4380^g	88	8	100:0:0	Dossou-yovo et al., 1994
Bouaké, rice fields district	NA, NA	I	Jan 1992–Dec 1992	0–68 ^NA	18615^g	126	9	100:0:0	Dossou-yovo et al., 1994
Egypt
Abheet village	NA, NA	R	Feb 1983–Feb 1984	0·59^e	308^g	1·8	1	0:0:100	El Said et al., 1986
Sudan
Asar	NA, NA	R	Oct 1990–Dec 1990	2·06^e	NAⁱ ^h	0·6	NA	100:0:0	Babiker et al., 1997

Open in a new tab

Sources of latitude/longitude:

published maps;

GeoName™digital gazetteer CD–ROM;

correspondence with authors;

cited in the reference.

R, rural; U, urban; I, irrigated rice.

Sporozoite detection method:

dissection;

ELISA.

⁴

ma sampling technique:

human bait;

insecticide spray;

ⁱ

light traps;

exit traps.

⁵

EIR values:

converted using (Port et al., 1980) average child–to–adult conversion factor, f, of 3·57.

⁶

Values in italics record the number of months in which 75% of transmission occurs. Other values record the length of the entire transmission season.

⁷

For Anopheles gambiae s.l.: An. funestus: any other locally important vector. Where absolute values are unavailable P indicates presence only and A absence.

NA refers to data that were not available, or not applicable. Values in bold refer to calculated values

A map showing the geo-referenced locations in Africa for which annual EIR data were published. The top left corner is 40°N, 20°W. Each grid square is 5 × 5 degrees and north is to the top of the page.

These studies collectively demonstrate that Africa has substantial cross-continent variability in annual EIR. The mean annual EIR value for the 159 spatially distinct sites was 121 infected bites per annum, although exposure ranged from a maximum of 884 to a minimum of 0. The local land-use also had a major effect on annual EIR. The ‘rural’ class had an overall mean of 146 (ranae 0–884) while those surrounded by irrigated rice were less exposed with a mean of 99 (range 0–601) and those in urban areas receiving significantly lower exposure with a mean of 14 (range 0–43). The variance in annual EIR expressed as the [(standard error/mean) × 100] is shown by country in Table 3. Spatially distinct rural sites only (n = 133) were used, to help control for major agricultural or demographic impact, and only countries with at least 10 spatially distinct study sites were included. Tanzania showed the least variance at 10·9%, and Senegal the highest at 42%. Finally, of the 133 sites for which seasonality information could be gathered, 30% showed acute seasonal variation in annual EIR (i.e., with 75% of annual transmission occurring in 1–3 months).

Table 3.

Variance in EIR values within countries for all spatially unique rural sites (133 sites total with temporal duplicates averaged)

	Burkina Faso	Kenya	Senegal	Sierra Leone	Tanzania	Gambia, The	All Africa
Mean¹	156·8	64·3	28·7	129·0	367·0	42·1	134·2
Median	133	17·4	9·2	62·2	405	11	76·7
Maximum	441·6	299·3	159·5	703·4	667	177	703·4
Minimum	55	0	0	12·1	93·7	0	0
Range	386·6	299·3	159·5	691·3	573·3	177	703·4
Standard deviation	96·5	102·3	46·8	184·5	164·6	52·3	162·2
Standard error of mean	24·9	24·8	12·1	49·3	39·9	12·7	14·1
Variation	15·9	38·6	42·0	38·2	10·9	30·1	10·5
Number of sites	15	17	15	14	17	17	133

Open in a new tab

The arithmetic mean is used as a measure of central tendency in the data (Dossou-Yovo et al., 1994). Median values are also presented as they are less sensitive to non-normal distributions.

Study methodology

Biting rates were determined primarily by human-bait samples (n = 111), followed by light-traps (n = 31), pyrethrum spray-catches (n = 22) and exit-traps (n = 2) measurements. Combinations of the above techniques were used in 17 studies, whilst 18 more provided no information on how the biting rate estimate was obtained. The methods used to evaluate the sporozoite index were roughly even, with 82 determined by dissection and 99 by ELISA. Three records were calculated using the average of dissection and ELISA and 17 did not record the methodology used.

Geo-referencing

Three of the 201 annual EIR values were published with an accurate latitude and longitude of the study area. Correspondence with authors provided co-ordinate details for a further 33 sites. The largest source of geo-referencing information was obtained from the GeoName™ digital gazetteer CD-ROM which geo-referenced 100 annual EIR values. Finally, published maps were used to uncover the co-ordinates of a further 57 sites. This left the 8 unlocated study sites shown in Table 2.

Discussion

Annual EIR heterogeneity

It has been observed that Africa can support a very wide range of EIRs (Gilles, 1993). More recently, however, variation in infection risk has been linked to very different clinical patterns and public health burdens (Snow et al., 1997, 1998b; Snow & Marsh, 1998; Gupta et al., 1999a, 1999b). Although this study cannot claim to have identified every study of estimated annual EIR in Africa since 1980, the results of the search do support the claim for a diverse transmission pattern for the continent. Of particular interest in this respect is the enormous apparent variation within countries such as Senegal and Kenya. If this variation reflects sub-regional ecological heterogeneity, rather than sampling biases in the distribution of studies, it has important implications for disease management.

The results demonstrate a marked demographic influence on annual EIR values. The relatively small annual EIR in urban versus rural settings was not unexpected and is exemplified in the series of studies around Brazzaville in The Congo (Trape & Zoulani, 1987b) and neighbouring Kinshasa in Zaire (Coene, 1993). The impact of irrigated rice farming on surrounding EIRs is complicated. It has been shown to increase (Coosemans, 1985), have little overall effect upon (Robert et al., 1985; Githeko et al., 1993; Dossou-yovo et al., 1994) and also decrease (Robert et al., 1985; Githeko et al., 1993; Dossou-yovo et al., 1994) malaria transmission depending on the location. This variation can be due to many factors, such as relative effects of irrigation on species abundance and sporozoite rate, the number of rice harvests, surrounding human population numbers and levels of breeding site contamination, the number of cattle in the locality, as well as the degree of local immunity. The data collated here are insufficient to explore any of these mechanisms in detail, but it is interesting to note that when populations near irrigated rice areas were compared to those from the sample of rural Africa as a whole they were, on average, less exposed.

Year-to-year variation in annual EIR is also very important, particularly in naturally seasonal areas. Too few investigations published data over multiple years, however, to make reliable generalizations. The following studies from Senegal should be considered when interpreting single annual EIR estimates from a site, since the annual EIR ranged from 89 to 238 in Dielmo over a 3-year monitoring period (Fontenille et al., 1997b) and from 7 to 63 infected bites per year over a 4-year monitoring period in Ndiop (Fontenille et al., 1997a).

The Figure shows that entomological studies are preferentially conducted where malaria is known to exist and is often a recognized local health problem. For example, the range of estimates for Burundi (Coosemans, 1985; Van Bortel et al., 1996) suggests a country of intense transmission, whilst most of Burundi is at high altitude and free from malaria (Van der Stuyft et al., 1993). The Figure also shows that studies are more likely to be conducted in locations where malaria research has a strong historical basis. In Kenya, for example, the sites for most data neighbour the Kenyan Medical Research Institute (KEMRI) research centres on the coast and Lake Victoria. The annual EIR statistics presented for each country must therefore be interpreted with this caveat. It would also be of value if an international network could be developed to map EIR using standardized methods in a grid-based system across Africa to record more closely the spatial distribution of malaria transmission on the continent, rather than the distribution of medical research centres and their accessible field sites.

Information technology issues

The data presented in Table 1 have been staged on the internet for public access (see Materials and Methods) along with predicted maps of annual EIR for the African continent (D. J. Rogers et al., paper in preparation). This follows an objective to initiate the provision of web-based information to guide malaria control by the year 2000 (MARA/ARMA, 1998). There is a growing emphasis in many sectors on the potential benefits of the rapid internet-based supply of quality information. The greatest value of this increased information flow should be the possibility for iterative information updates with new, missed or corrected information. The most obvious problems to resolve are those of data-quality control and the provision of information in a manner accessible to a variety of users. The range of issues pertaining to data supply for malaria control planning is far from being resolved and is not appropriately addressed in this article. The authors have simply sought to make information available that might be of use to those collating data on malaria risk. Furthermore, these data are presented in such a way that those who may take issue with the protocol adopted can consult the original sources at the internet sites specified in Materials and Methods.

Data quality issues

It is evident that considerable resources have been expended by researchers, institutes and donors to provide annual EIR information across a range of sites in Africa over the past 2 decades. One of the major problems involved in comparing this information is the absence of data standardization between studies. An attempt has been made in this paper to highlight these issues.

The second major problem relates to the comprehensiveness of the reporting of annual EIR information. Most studies were incomplete in the range of information recorded. Similar problems have been experienced in parallel exercises to collate parasite rate (MARA/ARMA, 1998) and helminth infection data (Brooker et al., 2000) in Africa. The following data are suggested as the minimum requirement for future peer-reviewed reporting of annual EIR. First, all methodological information should be identified including exactly how, when and for what duration biting rate and sporozoite indices were determined. It is also important that accurate names and co-ordinates are given for each of the study sites. Brief details about the nature of the surrounding land-use are useful in the determination of the extent of agricultural and demographic impacts. Where ELISA techniques are used it is helpful to record the proportion of infective bites that can be attributed to each Plasmodium species and the vector species by which malaria is locally transmitted. If future reporting completed the list of information indicated in the Appendix, the foundation for a centralized, high-quality information database of annual EIR estimates across Africa would be assured.

The third problem is that, since accurate measurement of the EIR is labour intensive and thereby costly, estimates are spatially and temporally infrequent and unavailable for many settings. Another part of the study attempted to overcome this paucity of ground data by predicting annual EIR values across Africa using the information reviewed here and environmental data derived from meteorological satellite sensors (D. J. Rogers et al., paper in preparation).

Reviews are becoming increasingly important within the arena of evidence-based planning for disease control and prevention (Bero & Rennie, 1995; Bero, 1996). Furthermore, improved internet access will facilitate the use of central data resources by a wider spectrum of the research and control community. It is hoped the presentation and synthesis of work here, and in the public domain, will expedite future information gathering required for rationalization of malaria control in space and time.

Acknowledgements

The following people provided substantial help in both the search for and assessment of the accuracy of the information presented in this review: John Beier, Pierre Carnevale, José Coene, Chris Curtis, Pierre Fontenille, Pierre Gazin, Andrew Githeko, S. Karch, Steve Lindsay, L. Manga, Louis Molineaux, Jean Mouchet, Eskild Petersen, J. Pull, Vincent Robert, Clive Shiff, Tom Smith, Marcel Tanner, Emanuel Temu, Madeleine Thompson and Peter Trigg. All remaining errors are entirely the responsibility of the authors. We are grateful to Joseph Lines, David Kelly, Bill Snow and Mike Packer for their comments on the manuscript. We thank the Sir Halley Stewart Trust for providing salary support to J.F.T. This publication is also an output from a research project funded by the Department for International Development (DFID) of the UK, project ZC0012. However, the DFID can accept no responsibility for any information provided, or views expressed. S.I.H. is an Advanced Training Fellow with the Wellcome Trust (#056642). R.W.S. is a senior Wellcome Trust Fellow in Basic Biomedical Sciences (#033340).

Appendix

An example of a completed proforma used to abstract information from references on P. falciparum entomological inoculation rates (EIR) in this study, using data from Oloo et al., 1996

Parameter	Unit	Value
Start date	mm-yy	June 1991
End date	mm-yy	May 1992
Sampling frequency	d-w-m-y	Monthly
Country	No units	Kenya
District	No units	Siaya
Town	No units	Kisumu (0° 06′ S, 34° 45′ E)
Site	No units	Kanyawegi (25 km NW of Kisumu)
Paper latitude	dd	Not reported
Paper longitude	dd	Not reported
Retrieved latitude	dd	00·9233 S (Map)
Retrieved longitude	dd	34·67328 E (Map)
Mosquito species	No units	An. gambiae s.l., An. funestus
Mosquito collection method	No units	Human bait and spray
Mean annual biting rate	b/p/night	8·42,5·05^a
Sporozoite rate determination method	No units	ELISA
Mean annual P. falciparum sporozoite infection rate	%	0·05,5·05^a
Mean annual EIR	b_i/p/year	166·1,93·8 (259·9)^a
Mean annual EIR	b_i/p/night	0·455,0·257 (0·712)^a,^b
Transmission season	Months	NA
Ecotype (rural, urban or irrigated rice)	R/U/I	R

Open in a new tab

d, Day; w, week; m, month; y, year; dd, decimal degrees; b_i, infectious bite; p, person; NA, not available or not applicable.

Data are presented for An. gambiae s.l., An. funestus and (total).

Values calculated from (b_i/p/year)/365 (and match ma × s table values product), not the rounded daily values quoted in Table 1.

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PERMALINK

Annual Plasmodium falciparum entomological inoculation rates (EIR) across Africa: literature survey, internet access and review

Simon I Hay

David J Rogers

Jonathan F Toomer

Robert W Snow

Abstract

Introduction

Materials and Methods

Annual EIR definition

Human biting rate

Sporozoite index

Identification of sources of annual EIR data

Recording and standardization of the annual EIR values

Table 1.

Criteria for data exclusion

Methodological information

Geo-referencing

Data distribution

Results

Annual EIR data

Table 2.

Figure.

Table 3.

Study methodology

Geo-referencing

Discussion

Annual EIR heterogeneity

Information technology issues

Data quality issues

Acknowledgements

Appendix

References

ACTIONS

PERMALINK

RESOURCES

Cite

Add to Collections

PERMALINK

Annual Plasmodium falciparum entomological inoculation rates (EIR) across Africa: literature survey, internet access and review

Simon I Hay

David J Rogers

Jonathan F Toomer

Robert W Snow

Abstract

Introduction

Materials and Methods

Annual EIR definition

Human biting rate

Sporozoite index

Identification of sources of annual EIR data

Recording and standardization of the annual EIR values

Table 1.

Criteria for data exclusion

Methodological information

Geo-referencing

Data distribution

Results

Annual EIR data

Table 2.

Figure.

Table 3.

Study methodology

Geo-referencing

Discussion

Annual EIR heterogeneity

Information technology issues

Data quality issues

Acknowledgements

Appendix

References

ACTIONS

PERMALINK

RESOURCES

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