The role of residential locationin apparent helminth and malariaassociationsMark Booth

Department of Pathology, Tennis Court Road, Cambridge, CB5 8QU, UK

Conflicting opinions on the nature of malaria and

helminth coinfections in humans have highlighted the

need for a rational approach to study the effects of

coinfections on morbidity. Here, it is argued that a

variety of factors have led to this confusion but that

many problems might be helped by more deliberate

consideration of residential location and spatial aspects

of exposure in parasitological surveys.

Apparent interactions

The past few years have seen a considerable increase inthe number of publications reporting the effects ofconcurrent infections by multiple species of pathogens,with the bulk of papers concerning either animal models[1,2] or studies of opportunistic infections, includingprotozoans, in HIV patients [3]. There have also beenstudies on the immunoepidemiology of concurrentparasitic infections (independent of HIV) and, in particu-lar, the relationship between two distinct groups – malaria(primarily Plasmodium falciparum) and the helminths(a mixture of nematode and trematode species).

From a malariologist’s perspective, it is clear tosee where the interest in the effect of co-infections onP. falciparum-attributable morbidity would arise. The riskfactors for the entire spectrum of malaria-related disease,from acute fevers to cerebral malaria, are not yet fullyunderstood. Although there have been huge developmentsin understanding the pathogenesis of malaria andimmune responses to infection [4,5], there are stillimportant epidemiological gaps, particularly in terms ofwhy only a fraction of individuals exposed to malariadevelop severe disease and what causes heterogeneity inthe frequency of malaria fevers within a given age groupliving in the same community. The search for moreexplanatory factors is therefore valuable and necessary.

Reasons for suspecting helminths as modifiers of eitherthe incidence of malaria infection or the risk of diseasegiven malaria infection are clear. Helminth infections ofdifferent species are often endemic in the same commu-nities as malaria. Dramatic interactions have beenobserved in animal models between these two phylogen-etically distinct types of organism in various combinations[6,7], and children bear the brunt of morbidity associated

Corresponding author: Booth, M. (mb350@cam.ac.uk).Available online 22 June 2006

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with either type of infection (although age–incidenceprofiles generally only partially overlap).

In the past few years, there has been a small surge ofreports demonstrating apparently biologically plausibleand meaningful interactions. There is, however, consider-able disagreement among authors with respect to con-clusions about the strength and direction of anyinteraction between the parasites. Some of the con-clusions, from malariologists, are that: (i) Ascaris lum-bricoides protects against severe malaria [8], (ii) bothSchistosoma mansoni and Schistosoma haematobiuminfections increase the incidence of malarial fevers[9,10], (iii) S. haematobium infections reduce malarialparasite densities [11], (iv) helminth infections slow downthe development of antimalarial immunity [12] and (v)helminth infections reduce jaundice, renal failure andorganomegaly during acute malaria fever [13].

Researchers whose main focus is on helminth infectionshave unsurprisingly taken a different viewpoint – withmore emphasis on morbidity known to be associated withtheir helminth species of interest. Thus, anaemia has beeninvestigated with respect to coinfections of P. falciparumand hookworm but with no significant association yetreported [14,15]. Elsewhere, it has been observed thatsplenomegaly associated with S. mansoni infection isexacerbated by chronic malaria infection in children [16],and that continued exposure to malaria infection aftertreatment for schistosome infections leaves a burden ofchronic hepatosplenic morbidity in those with highestlevels of exposure [17].

Some of the conflicting inferences and conclusions fromthese studies can be explained by the measurement ofdifferent outcomes in different populations, using differentstudy designs. Some studies are retrospective, others arecross-sectional and some are hospital based. Some reportsdo not separate the effects of different species of parasitebecause different helminth species are assumed to havethe same effect on the host. Each approach might have itsown advantages and disadvantages in terms of logistics,power, precision and selection bias, but only those thatproperly control for confounders or effect modifiers willyield results that lead to correct inferences.

Location and exposure

Traditionally, researchers have attempted to control forconfounding and modification in parasitological surveys

Opinion TRENDS in Parasitology Vol.22 No.8 August 2006

. doi:10.1016/j.pt.2006.06.007

TRENDS in Parasitology

M H

M H

Distance from source of exposure to M

Exp

osur

e

MH

M

Distance from source of exposure to M

Exp

osur

e

0

0

Distance from source of exposure to M

Exp

osur

e

0

HMH

M

(a)

(b)

(c)

Figure 1. Diagrammatic explanation of how location of residence affects the

probability of coinfection with two species of parasite (M and H). Three scenarios

are depicted, with point sources of exposure to each species and gradients of

exposure away from that point. Houses are assumed to be located at various

distances from the sources of infection. The x-axis on each plot represents the

distance from the sources. The width of the triangles or rectangles at each point on

the x-axis represents the level of exposure to each combination of parasite species

(M, H, M and H). In scenario (a), the point of exposure to parasite M is shared with

that of (less prevalent) parasite H. In scenario (b), the source of exposure to parasite

H is some distance away from the point of exposure to parasite M and has an

opposing gradient. In scenario (c), there is no correlation between exposure to M

and H. The probability of observing coinfection can be seen to depend strongly on

both the scenario and the distance from the point source over which individuals are

sampled.

Opinion TRENDS in Parasitology Vol.22 No.8 August 2006360

either through matched case control designs or bystratifying on common confounders such as age and sex.The rationale for this approach is obvious – for example,schistosome faecal egg counts are often higher in malesand increase with age until at least puberty, after whichthere is a sharp decline in reinfection that seems to beindependent of either location or transmission intensity[18]. Likewise, malaria infections are generally moreprevalent in children. However, within these broaddemographic strata is often considerable heterogeneityin several parasitological parameters that frequentlyremains unexplained.

One of the most prominent sources of heterogeneitywithin any demographic stratum, such as a particular agegroup, is location of residence. This might have asubstantial impact on exposure to each pathogen ofinterest, particularly among children (because they areunlikely to have shifted residence as much as adults), andespecially if exposure to at least one infection involves avector or intermediate host that has a spatially restrictedpopulation distribution.

The influence of location on exposure to infection withinmalaria and schistosome epidemiology has been known for

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some time from publications focusing on one or other typeof parasite. For example, the age-specific risk of severemalaria in children depends partly on local transmissionintensity [19]. In a study conducted in the Gambia, it wasreported that distance to a known source of larvalbreeding affected the incidence of malarial fevers andthe probability of presenting with signs of chronicinfection such as splenomegaly [20]. Distance to healthcentres profoundly affects attendance [21,22], and cantherefore affect demographic patterns of disease inrelation to place of residence. Variation in bed net usewithin or between villages can cause small-area variationin exposure to malaria [23,24], and it has been known formany years that schistosome infections have a ‘focal’distribution [25].

Less attention has been given to the influence oflocation in studies of coinfections, despite the fact thatexposure to each parasite might strongly depend on wherean individual is resident. If exposure to each type ofinfection is spatially correlated, coinfections are morelikely to be observed than if their relative exposures arespatially uncorrelated or negatively correlated (Figure 1).Simply observing an association between two species ofparasite (or their associated morbidities) in the absence ofcontrolling for location cannot, therefore, be interpreted asevidence of biological interaction. Location of residencemight confound or modify an apparent association,depending on the spatial correlation between exposuresto different species of infections.

Controlling for location

The control of location in coinfection studies has so farbeen addressed in a relatively straightforward mannerusing standard methods adopted for other confounding ormodifying methods – either stratification or matching.The stratification approach was recently demonstrated ina retrospective study of schistosome morbidity in Kenyanschool children [26,27]. A cohort aged 7–16 yearspresenting with chronically enlarged and firm or hardlivers were treated with praziquantel and followed upannually for three years [27]. Many of the children alsopresented with firm splenomegaly before treatment.Reinfection with schistosome infections was kept to aminimum by mollusciciding the local stream but there wasno specific intervention against malaria. Each year, thesurveys were deliberately conducted outside the malariatransmission season to avoid the confounding effects oftransient organomegaly attributable to acute malaria.Retrospectively mapping the local stream and positions ofcohort members’ houses enabled a much more thoroughanalysis of the effects of exposure to both malaria andschistosomiasis than would have otherwise been possible.The results of the spatially stratified analysis indicatedthat children living in areas of relatively high exposure toboth malaria and schistosomiasis presented with exacer-bated splenomegaly [16], and that resolution of thiscondition was usually less than complete among thesechildren [17].

In this analysis, the mapping information was used tostratify individuals to a relatively small area within avillage. It was observed that the spatial distribution of

S. mansoni e.p.g.

N

0 1 2 km

0–5051–100101–200200–1050

lgG3 O.D.0–0.150.15–0.30.3–0.40.4–0.6

N

0 1 2 km

(a)

(b)

(c)

Figure 2. Maps illustrating small-area variation in Schistosoma mansoni egg

counts [eggs per gram of faeces (e.p.g.)] among school children in a Kenyan

village, averaged by household, before treatment with praziquantel (a); and IgG3

responses (O.D., optical density) to Plasmodium falciparum schizont antigen in

the same children (b). An area of overlapping exposure was identified in the

northwest of the study area, which measured approximately 8!4 km, and was

demarcated along its northern border by a seasonal stream. The photographs

(c) illustrate how surface water availability during the dry season varied along

the length of the stream. Variation in surface water affects vegetation and hence

snail populations, which led to clustering of S. mansoni infections at the

western end of the study area. The main environmental factor for exposure to

malaria was proximity to the stream [16]. Parts (a) and (b) reproduced from Ref.

[16]. q (2004) Booth et al.; licensee BioMed Central Ltd.

Opinion TRENDS in Parasitology Vol.22 No.8 August 2006 361

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exposure to S. mansoni and malaria infections only partlyoverlapped (Figure 2). Whereas exposure to malaria wasassociated with proximity to the local stream, the highestS. mansoni egg counts were observed in children living atthe western end of the study area. This latter observationwas attributed to the year-round presence of surface waterat the western end of the stream (Figure 2c). Although thismight not be a typical scenario, a reasonable assumptionis that exposure to malaria and schistosome infections willalways be partly correlated in communities where bothinfections are endemic, owing to the requirement of waterbodies for the intermediate host snails andAnopheles vectors.

Different approaches to the control of location wererecently employed in two studies of malaria and ‘asympto-matic’ S. haematobium infection in Senegal [11,28]. In onestudy [28], the authors matched S. haematobium-positivechildren with children who were negative for ova but wholived in the same sector of a city, and followed the childrenthrough one transmission season. In the other study [11],children were examined several times for malariaparasitaemia, and a multilevel model was used to adjustfor both repeated observations and shared area ofresidence within the study village. S. haematobiuminfection was apparently associated with a reduction ineither the time to clinical malaria [28] or the averagelevels of parasitaemia [11]. Both studies introduced area ofresidence into their analysis but there were no measuresof individual level exposure to either infection, nor was thespatial distribution of either infection reported. Withoutthese pieces of information, it is difficult to infer whetherany apparent association between any pair of species issimply the result of a low degree of spatial overlap inexposure or the results of a biological interaction.

More sophisticated methods for dealing with the issueof spatially structured exposure to both helminth andmalaria infections, which go beyond simple stratificationor matching, have already been published. Fine-resolutionmaps are now available using a combination of globalpositioning system receivers, satellite-derived vegetationindices and, most recently, satellite photographs of 1!1 mresolution [29,30]. These maps are highly valuable toolsfor geo-referencing environmental factors that might bemissed on the ground. Bayesian analysis is becoming morewidespread in spatial analysis of parasitological data [31],and has been recently used to identify negative spatialcorrelations between filarial worm and malaria distri-butions in West Africa [32]. Complex spatial patterns ofinfection can be identified using decision tree algorithms[33], and combining data from multiple sources canfacilitate the construction of risk maps for infectionswith overlapping niches [34]. These approaches point theway towards a more comprehensive approach to dealingwith location as an important variable in analysis ofco-infection data.

Concluding remarks

Estimating the influence of coinfections on the outcome ofinfection by one particular parasite species is an import-ant component of understanding host–parasite relation-ships among humans. It is also important to guard against

Opinion TRENDS in Parasitology Vol.22 No.8 August 2006362

the possibility that apparent associations between speciesor their associated morbidities are an artefact caused byoverlapping areas of exposure. This can be achieved, inpart, by controlling for residential location in either studydesign or analysis. The majority of publications to datethat have reported apparent associations between malariaand helminth infections have not controlled for location ofresidence, and have thereby yielded questionable con-clusions. A less conflicting body of evidence might emergeif steps are taken in future studies to incorporate spatialinformation as a matter of default intoparasitological surveys.

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