Anopheles salivary antigens as serological biomarkers of vector exposure and malaria transmission: A systematic review with multilevel modelling

Data are given as study, year of publication, country, malarial endemicity class, malarial DVS, study design (‡ indicates that study was performed solely in children), number of participants and number of samples, number of study-specific salivary antibody outcome observations (study-specific n), entomological and malariometric parameters, and salivary antibody outcomes assessed. Malarial endemicity class (categorical) is derived from P. falciparum prevalence rate in 2–10 year olds (PfPR) extracted from MAP using site geolocations and year of study, and applying established cut-offs reported in Bhatt et al., 2015. If PfPR data were not available (e.g. surveys prior to 2000; or unable to determine study site geolocation and year), endemicity class is given as stated in the study (indicated by *). DVS is as stated in the study or extracted from MAP (indicated by †). Of note, An. gambiae sensu lato (s.l.) includes both An. gambiae sensu stricto and An. arabiensis. Entomological and malariometric parameters include HBR, EIR, prevalence estimates of Plasmodium spp. (Plas+): detected by LM, or PCR, with § indicating prevalence of P. falciparum only and ¶ indicating prevalence of P. vivax only (no footnote indicates P. falciparum and P. vivax co-endemic), as well as PfPR extracted from MAP (The Malaria Atlas, 2017). Salivary antibody outcomes are indicated as either seroprevalence [%] or levels [L], or both [%; L], with ^|| indicating that studies reported results stratified by malarial infection status. Salivary antigens include recombinant full-length proteins, synthetic peptides, and whole SGE. Italicised prefix of salivary antigen indicates species: An. gambiae (g), An. funestus (f), An. darlingi (d), An. albimanus (a), An. dirus (dir).

DVS: dominant vector species; MAP: Malaria Atlas Project; HBR: human biting rate; EIR: entomological inoculation rate; LM: light microscopy; PCR: polymerase chain reaction; SGE: salivary gland extracts.

*Generalised linear multilevel modelling (mixed effects, logistic) estimating the association between anti-gSG6 IgG seropositivity and endemicity class with random effects for study-specific heterogeneity in gSG6 IgG. Model fitted to n = 297 study-specific observations from 22 studies. Of note, nine studies that measured Plasmodium spp. prevalence and IgG antibodies to gSG6 were excluded from this analysis as eight only reported gSG6 IgG levels and one was a case–control study. Endemicity class membership is derived from PfPR from The Malaria Atlas, 2017 (MAP) using cut-offs taken from Bhatt et al., 2015, or where MAP data were unavailable, endemicity was included as indicated in the study.

^†Predicted gSG6 IgG seroprevalence (predicted probability in the average study) is estimated from generalised linear multilevel modelling (mixed effects, logistic).

HBR: human biting rate; EIR: entomological inoculation rate.

HBR association: log odds ratio and standard error (SE), 95% confidence interval (95% CI), p-value, random-effect components (RE): variances (ψ), conditional intraclass correlation coefficient ICC (ρ),* and model log likelihood (ℓ) from generalised linear multilevel modelling (mixed effects, logistic).^† This analysis is based upon n = 132 study-specific observations from 12 studies. Of note, five studies that measured HBR and IgG antibodies to gSG6 were excluded from this analysis as they only reported gSG6 IgG levels.

*ρ = $\frac{{\psi }_k+...+{\psi }_{nk}}{{\psi }_k+...+{\psi }_{nk}+{\pi }^2/3}$ , where ${\psi }_k$ through ${\psi }_{nk}$ are random-effect variance estimates pertaining to each of the respective variance components (see table notes ^‡–¶) from the generalised linear multilevel modelling (mixed effects, logistic) for a specific ICC estimate.

†Generalised linear multilevel modelling (mixed effects, logistic) estimating the association between log transformed HBR and anti-gSG6 IgG seropositivity with random effects for country-specific and study-specific heterogeneity in gSG6 IgG seroprevalence and study-specific heterogeneity in effect of HBR.

‡${\psi }_1$ , ${\psi }_2$, and ${\psi }_3$ represent variances of the random effects for country, study, and effect of HBR, respectively.

§${\rho }_1$ represents conditional ICC for salivary antibody observations from the same country but different study.

¶${\rho }_2$ represents conditional ICC for salivary antibody observations from the same country and study with the median HBR.

HBR × dominant vector species (DVS) association: log odds ratio and standard error (SE), 95% confidence interval (95% CI), p-value, random-effect components (RE): variances (ψ), conditional intraclass correlation coefficient (ICC) (ρ),* and model log likelihood (ℓ) from generalised linear multilevel modelling (mixed effects, logistic).^† This analysis is based upon n = 132 study-specific observations from 12 studies. Of note, five studies that measured HBR and IgG antibodies to gSG6 were excluded from this analysis as they only reported gSG6 IgG levels.

^* ρ = $\frac{{\psi }_k+...+{\psi }_{nk}}{{\psi }_k+...+{\psi }_{nk}+{\pi }^2/3}$ , where ${\psi }_k$ through ${\psi }_{nk}$ are random-effect variance estimates pertaining to each of the respective variance components (see table notes ^‡–¶) from the generalised linear multilevel modelling (mixed effects, logistic) for a specific ICC estimate.

^† Generalised linear multilevel modelling (mixed effects, logistic) estimating the association between log transformed HBR and anti-gSG6 IgG seropositivity including an interaction term between DVS and log HBR with random effects for country-specific and study-specific heterogeneity in gSG6 IgG seroprevalence and study-specific heterogeneity in effect of HBR.

^‡ ${\psi }_1$ , ${\psi }_2$, and ${\psi }_3$ represent variances of the random effects for country, study, and effect of HBR, respectively.

^§ ${\rho }_1$ represents the conditional ICC for salivary antibody observations from the same country but different study.

^¶ ${\rho }_2$ represents the conditional ICC for salivary antibody observations from the same country and study with the median HBR.

**indicates p-value from joint Wald test for polytomous variables.

Entomological inoculation rate (EIR) association: log odds ratio and standard error (SE), 95% confidence interval (95% CI), p-value, random-effect components (RE): variances (ψ), conditional intraclass correlation coefficient (ICC) (ρ)* and model log likelihood (ℓ) from generalised linear multilevel modelling (mixed effects, logistic).^† This analysis is based upon n = 38 study-specific observations from eight studies.

^* ρ = $\frac{{\psi }_k+...+{\psi }_{nk}}{{\psi }_k+...+{\psi }_{nk}+{\pi }^2/3}$ , where ${\psi }_k$ through ${\psi }_{nk}$ are random-effect variance estimates pertaining to each of the respective variance components (see table notes ^‡–¶) from the generalised linear multilevel (mixed effects, logistic) modelling for a specific ICC estimate.

^† Generalised linear multilevel modelling (mixed effects, logistic) estimating the association between log transformed EIR and anti-gSG6 IgG seropositivity with random effects for country-specific and study-specific heterogeneity in gSG6 IgG seroprevalence and study-specific heterogeneity in effect of EIR.

^‡ ${\psi }_1$ , ${\psi }_2$, and ${\psi }_3$ represent variances of the random effects for country, study, and effect of EIR, respectively.

^§ ${\rho }_1$ represents the conditional ICC for salivary antibody observations from the same country but different study.

^¶ ${\rho }_2$ represents the conditional ICC for salivary antibody observations from the same country and study with the median EIR

Any Plasmodium species infections (including prevalence estimates of P. falciparum only, P. vivax only, both P. falciparum and P. vivax and untyped infections): log odds ratio and standard error (SE), 95% confidence interval (95% CI), p-value, random-effect components (RE): variances (ψ), conditional intraclass correlation coefficient (ICC) (ρ),* and model log likelihood (ℓ) from generalised linear multilevel modelling (mixed effects, logistic).^† This analysis is based upon n = 212 study-specific observations from 14 studies. Of note, six studies that measured Plasmodium spp. prevalence and IgG antibodies to gSG6 were excluded from this analysis as five only reported gSG6 IgG levels and one was a case–control study.

^* ρ = $\frac{{\psi }_k+...+{\psi }_{nk}}{{\psi }_k+...+{\psi }_{nk}+{\pi }^2/3}$ , where ${\psi }_k$ through ${\psi }_{nk}$ are random-effect variance estimates pertaining to each of the respective variance components (see table notes ^{‡ and §}) from the generalised linear multilevel modelling (mixed effects, logistic) for a specific ICC estimate.

^† Generalised linear multilevel modelling (mixed effects, logistic) estimating the association between the log prevalence of any Plasmodium spp. infection and anti-gSG6 IgG seropositivity with random effects for study-specific heterogeneity in gSG6 IgG seroprevalence and study-specific heterogeneity in effect of Plasmodium spp. prevalence.

^‡ ${\psi }_1$ and ${\psi }_2$ represent variances of the random effects for study and effect of Plasmodium spp. prevalence, respectively.

^§ ${\rho }_1$ represents the conditional ICC for salivary antibody observations from the same study and with the median Plasmodium spp. prevalence.

Effects for each exposure represent separate generalised linear multilevel modelling (mixed effects, logistic) analyses estimating the association between the log of the seroprevalence of antimalarial antibodies and the seroprevalence of anti-gSG6 IgG, with the inclusion of a random intercept for study-specific heterogeneity and a random coefficient to allow the effect of the antimalarial antigen to vary across studies. Table shows log odds ratio and standard error (SE), 95% confidence interval (95% CI), and p-value, number of study-specific salivary antibody observations (Study-specific n) and studies, with associated references. Random effects not shown. Of note, one study that measured antimalarial antibody seroprevalence and IgG antibodies to gSG6 could not be included in analyses as it only reported gSG6 IgG levels.

^* Studies did not include a random coefficient (i.e. slope); as empirical support was not shown.

Association between HBR and fSG6 IgG: log odds ratio and standard error (SE), 95% confidence interval (95% CI), p-value, random-effect components (RE): variances (ψ), conditional intraclass correlation coefficient (ICC) (ψ)* and model log likelihood (ℓ) from generalised linear multilevel modelling (mixed effects, logistic).^† This analysis is based upon n = 6 study-specific observations.

^* ρ = $\frac{{\psi }_k+...+{\psi }_{nk}}{{\psi }_k+...+{\psi }_{nk}+{\pi }^2/3}$ , where ${\psi }_k$ through ${\psi }_{nk}$ are random-effect variance estimates pertaining to each of the respective variance components (see table notes ^{‡ and §}) from generalised linear multilevel model (mixed-effects, logistic) for a specific ICC estimate.

^† Generalised linear multilevel modelling (mixed effects, logistic) estimating the association between anti-An. funestus fSG6 IgG seropositivity and log transformed HBR with random effects for study-specific heterogeneity in fSG6 IgG seropositivity.

^‡ ${\psi }_1$ represents variance of the random effect for study.

^§ ${\rho }_1$ represents conditional ICC for salivary antibody observations from the same study.

Association between log PfCSP seroprevalence and gSG6-P2 IgG: log odds ratio and standard error (SE), 95% confidence interval (95% CI), p-value, random-effect variances (ψ), conditional intraclass correlation coefficient (ICC) (ρ)* and model log likelihood (ℓ) from logistic mixed-effects modelling.^† This analysis is based upon n = 115 study-specific observations.

^* ρ = $\frac{{\psi }_k+...+{\psi }_{nk}}{{\psi }_k+...+{\psi }_{nk}+{\pi }^2/3}$ , where ${\psi }_k$ through ${\psi }_{nk}$ are random-effect (RE) variance estimates pertaining to each of the respective variance components (see table notes ^{‡ and §}) from generalised linear multilevel model (mixed effects, logistic) for a specific ICC estimate.

^† Generalised linear multilevel modelling (mixed effects, logistic) estimating the association between log PfCSP seroprevalence and anti-gSG6-P2 IgG seropositivity with random effects for study-specific heterogeneity in gSG6-P2 IgG seropositivity.

^‡ ${\psi }_1$ represents variance of the random effect for study.

^§ ${\rho }_1$ represents conditional ICC for salivary antibody observations from the same study.

Association between log PfGLURP seroprevalence and gSG6-P2 IgG: log odds ratio and standard error (SE), 95% confidence interval (95% CI), p-value, random-effect variances (ψ), conditional intraclass correlation coefficient (ICC) (ρ)* and model log likelihood (ℓ) from logistic mixed-effects modelling.^† This analysis is based upon n = 116 study-specific observations.

*ρ = , where ψ_k through ψ_nk are random-effect (RE) variance estimates pertaining to each of the respective variance components (see table notes ^{‡ and §}) from generalised linear multilevel model (mixed effects, logistic) for a specific ICC estimate.

^†Generalised linear multilevel modelling (mixed effects, logistic) estimating the association between log PfGLURP seroprevalence and anti-gSG6-P2 IgG seropositivity with random effects for study-specific heterogeneity in gSG6-P2 IgG seropositivity.

^‡ψ₁ represents variance of the random effect for study.

^§ρ₁ represents conditional ICC for salivary antibody observations from the same study.