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Trans R Soc Trop Med Hyg. Author manuscript; available in PMC Oct 31, 2011.
Published in final edited form as:
Trans R Soc Trop Med Hyg. 2000 Mar-Apr; 94(2): 113–127.
PMCID: PMC3204456
EMSID: UKMS36405

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

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

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.

Figure
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.
Table 2
Locations in Africa for which annual EIR data were published that could not be geo-referenced

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)

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

ParameterUnitValue
Start datemm-yyJune 1991
End datemm-yyMay 1992
Sampling frequencyd-w-m-yMonthly
CountryNo unitsKenya
DistrictNo unitsSiaya
TownNo unitsKisumu (0° 06′ S, 34° 45′ E)
SiteNo unitsKanyawegi (25 km NW of Kisumu)
Paper latitudeddNot reported
Paper longitudeddNot reported
Retrieved latitudedd00·9233 S (Map)
Retrieved longitudedd34·67328 E (Map)
Mosquito speciesNo unitsAn. gambiae s.l., An. funestus
Mosquito collection methodNo unitsHuman bait and spray
Mean annual biting rateb/p/night8·42,5·05a
Sporozoite rate determination methodNo unitsELISA
Mean annual P. falciparum sporozoite infection rate%0·05,5·05a
Mean annual EIRbi/p/year166·1,93·8 (259·9)a
Mean annual EIRbi/p/night0·455,0·257 (0·712)a,b
Transmission seasonMonthsNA
Ecotype (rural, urban or irrigated rice)R/U/IR

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

aData are presented for An. gambiae s.l., An. funestus and (total).
bValues calculated from (bi/p/year)/365 (and match ma × s table values product), not the rounded daily values quoted in Table 1.

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