Sensitive and accurate tools to measure and monitor changes in malaria transmission are essential to track progress towards malaria control and elimination goals. Currently, the gold standard measurement of malaria transmission intensity is the entomological inoculation rate (EIR), a population measure defined as the number of infective Anopheles mosquito bites a person receives per unit of time. EIR is calculated as the human biting rate (HBR; measured at the population level by entomological vector-sampling methodologies [gold standard: human landing catch]) multiplied by the sporozoite index (proportion of captured Anopheles with sporozoites present in their salivary glands). However, estimation of EIR and HBR via entomological investigations is inherently labour and resource intensive, requiring trained collectors, specialised laboratories, and skilled entomologists. Furthermore, these approaches provide a crude population-level estimate of total vector exposure at a particular time and location, precluding investigation of heterogeneity and natural transmission dynamics of individual-level vector–human interactions (Monroe et al., 2020). For example, indoor human landing catches provide poor estimates of outdoor biting and thus total vector exposure (Mathenge et al., 2005). The sensitivity of EIR is further compromised in low transmission settings where the number of Plasmodium-infected specimens detected is low and often zero.

Evaluation of the human antibody response to Anopheles spp. salivary proteins has the potential to be a logistically practical approach to estimate levels of exposure to vector bites at an individual level. Several Anopheles salivary proteins have been shown to be immunogenic in individuals naturally exposed to the bites of Anopheles vectors and have been investigated as serological biomarkers to measure Anopheles exposure (Badu et al., 2012b; Drame et al., 2013a; Drame et al., 2010a; Drame et al., 2010b; Drame et al., 2015; Rizzo et al., 2011a; Rizzo et al., 2011b; Stone et al., 2012; Drame et al., 2012), malaria transmission (Londono-Renteria et al., 2015a; Ya-Umphan et al., 2017; Noukpo et al., 2016), and as an outcome for vector control intervention studies (Drame et al., 2013a; Drame et al., 2010a; Drame et al., 2010b; Noukpo et al., 2016; Idris et al., 2017). However, a major shortcoming of the literature is that studies are largely descriptive and do not quantify the association between entomological and malariometric measures and anti-Anopheles salivary antibody responses. We undertook a systematic review with multilevel modelling to quantify the association between HBR, EIR, and other markers of malaria transmission, with anti-Anopheles salivary antibody responses, and to understand how these associations vary according to transmission setting and dominant Anopheles vectors which can exhibit different biting behaviours. In particular, we were interested in comparing the African context (where Anopheles gambiae and Plasmodium falciparum predominates) to non-African settings (where An. gambiae is absent and where both P. falciparum and Plasmodium vivax are prevalent). This knowledge is pertinent to advance the use of salivary antibody biomarkers as a vector and malaria transmission serosurveillance tool.

Literature searches identified 158 potentially relevant studies, of which 42 studies were included in the systematic review (Figure 1) and are described in Table 1. From these studies, we extracted n = 393 study-specific observations of anti-Anopheles salivary antibodies determined from antibody measurements in a total of 42,764 sera samples. These studies were performed in 16 countries mostly in hypo- or mesoendemic areas of Africa (32 studies), with a minority performed in South America (four studies), Asia (four studies), and the Pacific (two studies). Studies were classified according to their DVS which reflected the region where the study was conducted. An. gambiae s.l. was a DVS in all African study sites (n = 151 study-specific observations from 23 studies where An. gambiae s.l. was the only DVS and n = 68 from 16 studies where An. gambiae s.l. was present with additional DVS [i.e. Anopheles funestus, Anopheles pharoensis]), with the exception of one study, which together with the 10 non-African studies contributed n = 174 study-specific estimates where An. gambiae s.l. was absent. Most observations came from cross-sectional (n = 191 from 16 studies) or repeated cross-sectional studies (n = 137 from 18 studies), with n = 60 from cohort studies (six studies) and n = 5 from case–control studies (two studies).

Figure 1

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The salivary antigen most commonly assessed was An. gambiae salivary gland 6 (gSG6), as a full-length protein (n = 67 from 8 studies) and synthetic peptide (An. gambiae salivary gland 6 peptide 1 [gSG6-P1]; n = 270 from 24 studies). Additional salivary antigens assessed included An. gambiae gSG6-P2 (n = 119 from three studies), recombinant cE5 (n = 15 from two studies), g-5’nuc (n = 3 from one study), and recombinant An. funestus fSG6 (n = 6 from two studies) and f-5’nuc (n = 3 from one study). Seven studies measured antibodies to whole salivary gland extracts (SGE) from An. gambiae (n = 24 from four studies), Anopheles darlingi (n = 5 from two studies), Anopheles albimanus (n = 2 from one study), and Anopheles dirus (n = 3 from one study), while one study assessed antibodies against synthetic peptides of An. albimanus (n = 2) (Table 1). All studies investigated total IgG and only five determined an additional isotype or subclass (Drame et al., 2015; Lawaly et al., 2012; Rizzo et al., 2014a; Rizzo et al., 2014b; Waitayakul et al., 2006). The paucity of studies investigating these latter-mentioned antibody types and Anopheles salivary biomarkers precluded extensive multilevel analyses; instead, we present their associations in narrative terms in Appendix 10. Analyses reported below focus on quantifying the relationships between HBR, EIR, and markers of malaria transmission with total IgG to An. gambiae gSG6. The distributions of exposure observations were: HBR (n = 197 from 24 studies, median: 3.0 bites per person per night, IQR: 0.9–12.1; range: 0–121.4), EIR (n = 60 from 8 studies, median: 7.3 infectious bites received per person per year, IQR: 0–36.4; range: 0–585.6), and Plasmodium spp. prevalence (n = 266 from 22 studies, median: 9.1%; IQR: 4–22%; range: 0–94.6%).

Generalised linear multilevel modelling (mixed effects, logistic) of n = 132 study-specific observations from 12 studies estimated a positive association between Anopheles spp.-HBR (log transformed) and seroprevalence of IgG to An. gambiae gSG6 salivary antigen (Drame et al., 2010a; Drame et al., 2015; Rizzo et al., 2011a; Stone et al., 2012; Drame et al., 2012; Ya-Umphan et al., 2017; Soma et al., 2018; Traoré et al., 2019; Sagna et al., 2013b; Sarr et al., 2012; Ali et al., 2012; Pollard et al., 2019; Figure 2, Appendix 4—table 1). As we have log transformed HBR to account for the non-linear relationship between HBR and log odds of gSG6 IgG seropositivity, we have presented estimated odds ratios for different incremental percent increases in HBR (Figure 2—figure supplement 1). For example, the magnitude of the association was such that a twofold (100% relative) increase in HBR was associated with a 23% increase (OR: 1.23; 95% CI: 1.10–1.37; p<0.001) in the odds of anti-gSG6 IgG seropositivity (Figure 2). Heterogeneity in the effect of HBR on gSG6 across studies was observed (likelihood ratio χ²(1) = 109.25, p<0.001); the 95% reference range of study-specific effects for a twofold increase in HBR ranged from a 12% reduction to a 70% increase in odds (OR: 0.88–1.70). There was no evidence that the association between HBR and gSG6 IgG varied according to vector collection method (human landing catch or other indirect methods; p=0.443) or study design (longitudinal cohort or cross-sectional/repeated cross-sectional; p=0.138). Given the global heterogeneity in the distribution of Anopheles species, we sought to quantify the extent to which the association between An. gambiae gSG6 IgG seropositivity and HBR is moderated by DVS. We observed that the magnitude of the association between An. gambiae gSG6 IgG seropositivity and HBR was greatest in African studies where An. gambiae s.l. was the only dominant vector (p<0.001, Appendix 5); a twofold increase in HBR was associated with a 37% increase (OR: 1.37; 95% CI: 1.19–1.58; p<0.001) in the odds of gSG6 IgG seropositivity compared to an attenuated association for African studies where An. gambiae s.l. was not the only DVS (OR: 1.14 per twofold increase in HBR; 95% CI: 0.98–1.33; p=0.079) and non-African studies where An. gambiae s.l. was absent (OR: 1.05 per twofold increase in HBR; 95% CI: 1.03–1.08; p<0.001). In order to quantify the relationship between gSG6 IgG seroprevalence and HBR, for given HBR values we estimated gSG6 IgG seroprevalence by producing model-based predicted probabilities overall and by DVS (Figure 3). In African studies where An. gambiae s.l. is the only DVS, predicted seroprevalence of An. gambiae gSG6 ranged from 21% (95% CI: 0–45%) to 86% (95% CI: 67–100%) for an HBR of 0.1–100 bites per person per night, respectively (Figure 3, Figure 3—figure supplement 1).

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A positive association was also found between seroprevalence of anti-gSG6 IgG antibodies and EIR in analysis of n = 38 study-specific observations from eight studies (Figure 4, Appendix 6) [Rizzo et al., 2011b; Ya-Umphan et al., 2017; Soma et al., 2018; Ali et al., 2012; Ambrosino et al., 2010; Perraut et al., 2017; Pollard et al., 2019; Badu et al., 2012b]. For a twofold increase in EIR, the odds of anti-gSG6 IgG seropositivity increased by 11% (OR: 1.11; 95% CI: 1.05–1.17; p<0.001), with heterogeneity in the study-specific effects (95% reference range: 1.00–1.24; likelihood ratio χ²(1) = 15.02, p<0.001). There was no evidence of effect modification by either vector collection method (p=0.095) or DVS (p=0.080) on the association between seroprevalence of anti-gSG6 IgG and EIR.

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Similar positive associations were also found between anti-gSG6 IgG levels, HBR, and EIR in 11 studies [Drame et al., 2015; Drame et al., 2012; Stone et al., 2012; Soma et al., 2018; Ali et al., 2012; Rizzo et al., 2011a; Rizzo et al., 2011b; Poinsignon et al., 2010b; Poinsignon et al., 2008a; Charlwood et al., 2017; Sagna et al., 2013b] and 3 studies [Rizzo et al., 2011b; Ya-Umphan et al., 2017; Ali et al., 2012], respectively, but 7 studies showed no association between HBR and levels of IgG to gSG6 [Drame et al., 2010a; Ya-Umphan et al., 2017; Traoré et al., 2018; Traoré et al., 2019; Sarr et al., 2012; Poinsignon et al., 2009; Pollard et al., 2019].

The association between anti-gSG6 IgG seroprevalence and population-level prevalence of Plasmodium spp. infection was investigated. Generalised linear multilevel modelling (mixed effects, logistic) of n = 212 from 14 studies that measured Plasmodium spp. prevalence contemporaneously in their study [Badu et al., 2012b; Rizzo et al., 2011b; Ya-Umphan et al., 2017; Soma et al., 2018; Koffi et al., 2015; Traoré et al., 2019; Badu et al., 2015; Sagna et al., 2013b; Sarr et al., 2012; Perraut et al., 2017; Drame et al., 2010a; Idris et al., 2017; Proietti et al., 2013; Kerkhof et al., 2016] showed that for a twofold increase in the prevalence of Plasmodium spp. infection the odds of gSG6 IgG seropositivity increased by 38%, although the confidence intervals were wide (OR: 1.38; 95% CI: 0.89–2.12; p=0.148) and heterogeneity in the study-specific effects was observed (95% reference range: 0.30–6.37; likelihood ratio χ²(1) = 235.5, p<0.001) (Figure 5 and Appendix 7). In the association between gSG6 IgG seropositivity and Plasmodium spp. infection, there was no evidence for a moderating effect of Plasmodium spp. detection method (light microscopy or PCR, p=0.968), or species (African studies with P. falciparum versus non-African studies where P. falciparum and P. vivax are co-prevalent, p=0.538).

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Additionally, 14 studies reported observations of anti-gSG6 IgG levels and the prevalence of Plasmodium spp. infections measured contemporaneously in their study. The median anti-gSG6 IgG antibody levels increased with increasing Plasmodium spp. prevalence in six of these studies (Drame et al., 2010a; Ya-Umphan et al., 2017; Idris et al., 2017; Poinsignon et al., 2010b; Sarr et al., 2012; Kerkhof et al., 2016), or in Plasmodium spp.-infected compared to non-infected individuals (Londono-Renteria et al., 2015a; Montiel et al., 2020), but showed no association in eight studies (Rizzo et al., 2011b; Soma et al., 2018; Koffi et al., 2015; Traoré et al., 2018; Traoré et al., 2019; Badu et al., 2015; Sagna et al., 2013b; Poinsignon et al., 2009). Furthermore, we also investigated associations with serological measures of malaria exposure and found that for a twofold increase in pre-erythrocytic and blood stage antigen seroprevalence there was a 2.19-fold (OR: 2.19; 95% CI: 1.18–4.04; p=0.013) and 41% to 5.69-fold (OR range: 1.41–5.69; p range: <0.001 to 0.523) increase in the odds of anti-gSG6 IgG seropositivity, respectively (Appendix 8).

To give epidemiological context, we estimated anti-gSG6 seroprevalence by producing model-based predicted probabilities by malarial endemicity class (a categorical variable derived by applying established cut-off values for the PfPR_2-10 extracted from MAP). Generalised linear multilevel modelling (mixed effects, logistic) on 297 study-specific salivary antibody observations from 22 studies shows that the estimated anti-gSG6 IgG seroprevalence is higher for the higher endemicity classes (eliminating malaria: 20% [95% CI: 8–31%]; hypoendemic: 34% [95% CI: 19–49%]; mesoendemic: 52% [95 CI: 35–68%]; hyperendemic settings: 47% [95% CI: 27–64%]; holoendemic: 78% [95% CI: 67–90%]; p<0.001; Table 2). Interactions with DVS or region (Africa/non-Africa) could not be explored due to collinearity with malaria endemicity class. Therefore, in addition using Bayes best linear unbiased predictions (BLUPs) we estimated country-specific gSG6 IgG seroprevalence from an intercept-only multilevel model fitted to 301 study-specific salivary antibody observations from 22 studies. It showed that IgG seroprevalence to An. gambiae gSG6 was lowest in countries in the Pacific region where An. gambiae is absent (Vanuatu [31%] and Solomon Islands [32%]) and highest in countries where An. gambiae is a DVS (Benin [72%] and Burkina Faso [65%]; Appendix 9).

Assessments of internal and external study validity revealed there was a moderate risk of selection bias (Appendix 2) due to the study-specific inclusion criteria of populations at higher risk of malaria which contributed gSG6 seroprevalence observations. Sensitivity analyses exploring potential study-level outlier influence on the estimated associations between anti-gSG6 IgG seroprevalence, HBR, and EIR showed no evidence of bias (effect estimates for each sensitivity analysis were consistent with model estimates overall) for studies identified as exhibiting potential influence (HBR: n = 6; EIR: n = 6).

This systematic review and multilevel modelling analysis provides the first quantification of a positive non-linear association between seroprevalence of An. gambiae gSG6 IgG antibodies and HBR and demonstrated that its magnitude varied with respect to the DVS present in the area. Importantly, this review identified a paucity of studies conducted outside of Africa, as well as investigating salivary antigens representing different Anopheles spp. and antigenic targets. gSG6 antibodies were positively associated with the prevalence of Plasmodium spp. infection as well as established epidemiological measures of malaria transmission: malaria endemicity class and EIR. Overall, our results demonstrate that antibody seroprevalence specific for Anopheles spp. salivary antigens has the potential to be an effective measure of vector exposure and malaria transmission at the population and, potentially, individual level.

An. gambiae gSG6 IgG seropositivity increased with increasing HBR, although these increases had diminishing impact on An. gambiae gSG6 IgG seropositivity at higher levels of HBR (approximately greater than two bites per person per night). In our study, 17 studies performed across Africa (Angola, Benin, Burkina Faso, Cote d’Ivoire, and Senegal) and the Asia Pacific (Cambodia, Myanmar, and the Solomon Islands) reported an HBR < 2, demonstrating the applicability of gSG6 as a biomarker of HBR across a broad range of malaria-endemic regions. We also observed that the association was strongest in areas where An. gambiae s.l. was the only DVS (i.e. concordant An. gambiae species-specific HBR with An. gambiae gSG6 antibodies). Associations, albeit weaker, were also observed between discordant species-specific HBR and gSG6, most likely because the An. gambiae SG6 gene shares moderate sequence identity with vector species that are dominant in other regions (Africa: 80% An. funestus; Asia: 79% Anopheles stephensi and Anopheles maculatus; 54% An. dirus; Pacific: 52.5% Anopheles farauti), and is absent from the DVS of the Americas (An. albimanus and An. darlingi) (Arcà et al., 2017). The generalisability of An. gambiae gSG6 IgG as a biomarker of exposure to other Anopheles spp. may therefore be limited. However, our review also identified a paucity of studies investigating additional salivary antigenic targets and Anopheles species not present in Africa. The identification of novel salivary antigens that are species-specific will be valuable in quantifying exposure to the other Anopheles vectors that share limited identity with An. gambiae SG6 (such as An. farauti and An. dirus), as well as Anopheles spp. which lack SG6 (as done for An. albimanus and An. darlingi; Londono-Renteria et al., 2020a; Londono-Renteria et al., 2020b). An Anopheles species-specific serological platform could advance vector surveillance by more accurately capturing exposure to DVS in the South American and Asia Pacific regions which exhibit diverse biting behaviours and vector competence (DVS typically bite outdoors during the night and day, respectively; The Malaria Atlas, 2017; Sinka et al., 2012; Sinka et al., 2010; Trung et al., 2005; Herrera et al., 2015; Chaumeau et al., 2018), as well as the increasing threat of urban malaria from An. stephensi in Africa (Takken and Lindsay, 2019; Sinka et al., 2020).

This review demonstrated that the prevalence of Anopheles salivary antibodies increased with increasing prevalence of Plasmodium spp. infection (although confidence intervals were wide and we observed heterogeneity in the effect between studies) as well as established epidemiological measures of malaria transmission: malaria endemicity class and EIR. Anti-salivary antibodies, such as SG6 IgG, may therefore have the potential to serve as a proxy measure for receptivity of a population to sustain malaria transmission. Their application could be particularly relevant in pre-elimination areas, or non-endemic areas under threat of imported malaria, where Anopheles salivary antibodies are more readily detectable than parasites; salivary antibodies were predicted to be prevalent (20%) in areas defined as eliminating malaria (<1% PfPR_2-10). Furthermore, if SG6 IgG seroprevalence can be effectively combined with a measurement of the sporozoite index, salivary antibodies as a marker of HBR could help overcome sensitivity limitations of EIR in low transmission areas. Additional measures could include estimates of malaria prevalence or serological biomarkers that are species- or life stage-specific (e.g. Plasmodium spp. pre-erythrocytic antigens as biomarkers for recent parasite inoculation). Indeed, positive associations between antibodies specific for Plasmodium spp. pre-erythrocytic and blood stage antigens with gSG6 were demonstrated in analyses of data from diverse malaria-endemic areas. Serological tools combining salivary antigens with antigens specific for the different Plasmodium spp. could be easy to employ and complement malaria surveillance programmes. These tools may be particularly useful in the Asia Pacific, a region of relatively low malaria transmission with goals of elimination, but the highest burden of P. vivax malaria where blood stage infection can be caused by relapses from dormant liver stages. In these areas, parasite prevalence may therefore overestimate ongoing malaria transmission, making vector surveillance tools essential to informing elimination strategies in the Asia Pacific and other regions where P. vivax is endemic.

The gold standard entomological measures HBR and EIR provide crude population-level estimates of vector and malaria exposure that are specific in space and time and preclude investigation of individual-level heterogeneity and natural transmission dynamics. Our study demonstrated that salivary biomarkers measured at the individual level, such as gSG6 IgG, can be used to quantify total vector exposure at the population level, without requiring laborious entomological experiments. However, validating an individual-level serological measure, which demonstrates considerable individual-level variation, against the imperfect population-level gold standards of HBR and EIR is challenging and reflected in the variation in study-specific estimates in the association between gSG6 IgG and HBR in modelling analyses. However, the accuracy of salivary antibodies to measure individual-level exposure to Anopheles bites is yet to be validated; literature searches identified no studies investigating this association at the individual level. Without detailed measurements of individual-level vector exposure, or a detailed knowledge of the half-life of Anopheles salivary antibodies post biting event, the true accuracy of salivary antibodies, such as SG6 IgG, to measure individual-level HBR remains unknown. This knowledge is particularly pertinent where Anopheles salivary biomarkers might be applied to assess the effectiveness of a vector control intervention or used to measure temporal changes in malaria transmission; particularly in areas or populations where there is considerable heterogeneity in individual-level risk of Anopheles exposure (e.g. unmeasured outdoor biting due to occupational exposure for forest workers; Sandfort et al., 2020).

The broad nature of our inclusion and quality criteria was a key strength of our systematic review, which aimed to provide a comprehensive analysis of all Anopheles salivary biomarkers and determine their associations with entomological and malariometric measures of transmission. However, this review has two main limitations. First, despite the inclusive nature, assessment of the external validity of the review revealed a moderate risk of bias; some studies exhibited a high risk of selection bias as they were performed in specific high-risk populations not representative of the overall population (i.e. children only). This is accounted for to some degree by specification of a random effect (i.e. intercept) for study, which accounts for unmeasured study-specific factors that may introduce study-specific measurement error to measurement of the outcome. Second, with respect to internal validity, there may be potential selection bias introduced by the exclusion of studies reporting zero HBR (7 observations from three studies; Rizzo et al., 2011b; Pollard et al., 2019; Sagna et al., 2013b), EIR (22 observations from three studies; Ya-Umphan et al., 2017; Rizzo et al., 2011b; Soma et al., 2018), and malaria prevalence (15 observations from three studies; Idris et al., 2017; Sagna et al., 2013b; Kerkhof et al., 2016) estimates, given we modelled the log of these factors. However, adding a small constant (e.g. 0.001) to a zero value to permit modelling of a log estimate can also introduce considerable bias (i.e. seemingly small differences between values become very large on the log scale). In light of this, we also chose to provide estimates of association and gSG6 IgG seroprevalence according to a selected range of epidemiologically relevant hypothetical HBRs (no widely accepted HBR classification exists in the literature) and according to widely accepted, discrete, endemicity classes according to MAP estimates (which permitted inclusion of all studies) to provide epidemiological context. However, there is the potential for misclassification of malarial endemicity class derived from geospatially extracted MAP predictions of PfPR_2-10 which increase in uncertainty in areas with scarce data. Similarly, we used MAP vector occurrence data to inform DVS categories for 7 (out of 42) studies. Cross-referencing these 7 studies with a 2017 updated database for African vectors (using data for the nearest neighbouring village) identified 10 discrepant datapoints from 3 studies (from a total of 28 datapoints from 7 studies) (Snow, 2017). Any misclassification events may cause us to underestimate the standard error in the effect of malaria endemicity class and DVS on gSG6 IgG.

The current manuscript is a systematic review with multilevel modelling of study level data. The constructed dataset and associated code used for analyses are available at https://github.com/ellenakearney/Anopheles_salivary_biomarker_systematic_review (copy archived at swh:1:rev:eadb7ab1cbf93c730fb463f43e1b13b9ae16ddd3). Data for 'Dominant malaria vector species globally, 2010' (Sinka et al., 2012) and 'Plasmodium falciparum parasite rate in 2-10 year olds globally, 2000-2017' (Weiss et al., 2019) can be accessed from the Malaria Atlas Project Explorer (https://malariaatlas.org/explorer/#/).

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