Detecting temporal and spatial malaria patterns from first antenatal care visits

doi:10.21203/rs.3.rs-2592126/v1

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Detecting temporal and spatial malaria patterns from first antenatal care visits

https://doi.org/10.21203/rs.3.rs-2592126/v1

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published 06 Jul, 2023

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Pregnant women attending first antenatal care (ANC) visits represent a promising malaria surveillance target in Sub-Saharan Africa. Here we assessed the spatio-temporal relationship between malaria at ANC (n=6,471), in children at the community(n=9,362) and at health facilities (n=15,467) in southern Mozambique (2016-2019). ANC P. falciparum rates detected by quantitative polymerase chain reaction mirrored rates in children, regardless of gravidity and HIV status (Pearson correlation coefficient [PCC]>0.8, χ²<1.1), with a 2-3 months lag. Only at rapid diagnostic test detection limits at moderate-to-high transmission, multigravidae showed lower rates than children (PCC=0.61, 95%CI[-0.12-0.94]). Seroprevalence against the pregnancy-specific antigen VAR2CSA reflected declining malaria trends (PCC=0.74, 95%CI[0.24-0.77]). 80% (12/15) of hotspots detected from health facility data using a novel hotspot detector, EpiFRIenDs, were also identified with ANC data. The results show that ANC-based malaria surveillance offers contemporary information on temporal trends and the geographic distribution of malaria burden in the community.

Health sciences/Diseases/Infectious diseases/Malaria

Health sciences/Health care/Public health/Epidemiology

Surveillance is key to inform optimal and equitable resource allocation for malaria control and elimination¹. Estimating malaria trends from clinical cases at health facilities remains challenging due to differences in care-seeking behaviour, unknown denominator populations, and asymptomatic infections². These biases are minimised in nationally-representative cross-sectional surveys, but due to their high costs and complex logistics, they are typically only conducted every 2–3 years³. Pregnant women attending a first antenatal care (ANC) visit have been proposed as a potential convenience group for surveillance of malaria and other infectious diseases^3–5.

In sub-Saharan Africa, 79% of pregnant women attend at least one ANC visit⁶, offering a good representation of the population. Since visits are unrelated to illness, malaria testing is not biased by care-seeking behaviour or testing decisions, and captures asymptomatic infections⁷. A meta-analysis of pooled prevalence data from Sub-Saharan Africa found a strong correlation between malaria burden in pregnant women and children, with lower rates in the later but with large heterogeneity between studies⁸. Less heterogeneity was found for low-prevalence settings (prevalence < 5%), and less difference between women and children was found when restricting the analysis to primigravidae. However, only one study recruited women from an antenatal clinic, and small-scale trends could not be assessed due to pooling data obtained from different administrative levels. Studies using routine ANC data in Tanzania did not provide information on gravidity and were unable to reproduce a similar linear effect as the meta-analysis when comparing young women to children^9–11. Another study analysed data from health centres in conflict settings in the Democratic Republic of Congo and found a strong but non-linear relationship between ANC prevalence and incidence in children¹². However, all the studies used low-sensitivity microscopy or rapid diagnostic tests (RDT), missing the significant proportion of P. falciparum infections with parasite densities below the detection threshold of conventional field diagnostic tools²³. Moreover, the effect of gravidity and other factors such as HIV was not consistently assessed. Also, spatial patterns in malaria burden have not been compared between both groups due to lack of geospatial data. Finally, none of the studies quantified correlation in low-transmission settings, where ANC-based surveillance might be particularly attractive due to local clustering of malaria cases which are difficult to monitor with traditional strategies². Therefore, a better understanding of the validity of ANC prevalence data for monitoring transmission in the community and the factors that affect this relationship remains to be developed.

New surveillance tools, such as antibodies against the pregnancy-specific antigen VAR2CSA that mediates parasite sequestration in the placenta^13–15, can potentially increase sensitivity to detect recent exposure in low transmission settings where detecting active infections is difficult^15,16. Combined with novel clustering approaches, this ANC data can increase the resolution to detect spatial patterns³, therefore allowing a cost-effective approach for targeting interventions to the most affected areas. In this study, we estimate and compare malaria burden at first ANC visits with data from cross-sectional surveys and clinical cases in three settings from southern Mozambique with different transmission levels. We correlate temporal and spatial trends at both RDT- and qPCR-detection levels, and characterise the effect of HIV and gravidity on the correlation. Finally, we assess the added value of antibody data obtained from a bead-based multiplex immunoassay against VAR2CSA and general malaria antigens, and a newly developed hotspot detection algorithm, as novel tools to improve surveillance in malaria endemic areas.

Study area and population

The study was conducted between November 2016 and November 2019 in Manhiça and Magude districts in Maputo Province, southern Mozambique. Malaria transmission is low in Manhiça district¹⁷, with some moderate-to-high transmission areas, such as Ilha Josina¹⁸. Magude district is a low-transmission area resulting from elimination interventions since 2015¹⁹. Data was obtained from 6,471 pregnant women (Supplementary figure S1), residing in the study area, who attended their first ANC visit at Manhiça District Hospital, Ilha Josina Health Centre, or Magude Health Centre, as previously described²⁰. Weekly numbers of RDT-positive clinical malaria cases among children < 5 years old attending the three health facilities (n = 15,467) were obtained from the District Health Information System 2 (DHIS2). In Manhiça district, 37,131 RDT and microscopy results from children < 5 years attending health facilities were available from the paediatric outpatient morbidity surveillance system (OPMSS). Data from 9,362 children aged 2–10 years was collected in age-stratified cross-sectional surveys conducted every May from 2015 to 2019. Geo-localization of pregnant women and children was obtained from a local health and demographic surveillance system using their permanent or family identification number^21,22, from their household identification number or by registering the geo-localization of the households (Supplementary methods).

Parasitological And Immunological Determinations

Finger prick blood drops were collected onto Whatman 903 filter paper (dried blood spots [DBS]) from pregnant women and from children in the cross-sectional surveys. Children were also tested by RDT (HRP2-based SD Bioline Ag Pf, Standard Diagnostics, South Korea). P. falciparum infection was detected and quantified in duplicate from DBS with a qPCR targeting the 18S rRNA gene on an ABI PRISM 7500 HT Real-Time System (Applied Biosystems)²³. Immunoglobulin Gs (IgG) were detected and quantified in a multiplexed bead array using Luminex xMAP© technology (Luminex Corp., Austin TX), as described previously²⁰. In brief, magnetic beads were coupled to our panel (Supplementary table S1) including VAR2CSA antigens (Duffy binding-like recombinant domains DBL3-4, peptide P1 targeting the NTS region, peptides P8 and PD targeting ID1, and P39 targeting DBL5ε), general malaria antigens (19-kDa fragment of the merozoite surface protein-1 [MSP1₁₉], region II/F2 of erythrocyte-binding antigen-175 [EBA175], full-length P. falciparum reticulocyte binding-like homologue protein 2 and 5 [RH2 and RH5]), thrombospondin-related apical merozoite protein [PfTRAMP], and biomarkers of recent P. falciparum exposure (gametocyte exported protein 18 [GEXP18], acyl-CoA synthetase 5 [ACS5] ag3, early transcribed membrane protein 5 [ETRAMP5] ag1, and heat shock protein 40 [HSP40] ag1). Information about antigens, procedures for reconstitution of DBS and quality control, bead-based immunoassay, and data normalisation are described in Supplementary methods.

Data analysis

Study years 1 to 3 were defined from November to October in 2016–2017, 2017–2018 and 2018–2019, respectively. Infections with densities above 100 parasites/µL were defined as RDT-detectable²⁴. Primigravidity was defined as a first pregnancy, and multigravidity as having had one or more previous pregnancies. HIV status of pregnant women was determined from the maternal health card, or if not available, with an HIV serological rapid test²⁰. The threshold of seropositivity against P. falciparum antigens was defined as the geometric mean plus 2 standard deviations of the first component from two-component normal mixture distributions of mean fluorescent intensity values (R package mixtools).

The relationship between P. falciparum parasite rates using RDT (PfPR_RDT) or qPCR (PfPR_qPCR) detection limits in pregnant women and children was analysed using linear regressions, Pearson correlation coefficients (PCC), and \(\chi ²\) statistics (Supplementary methods). Similarly, anti-P. falciparum seroprevalence in pregnant women was compared with PfPR_RDT and PfPR_qPCR in children. Consistency and correlation between temporal variations in both populations were quantified using \(\chi ²\) statistics and PCC, and time lags between the data sources were defined by maximising PCC (Supplementary methods). 2-point correlation functions (2PCF) was used for clustering analysis, which describe the excess of pairs of P. falciparum positive samples with respect to random infections as a function of the geographical distance \(r\) between them:

\(\xi \left(r\right)=\frac{{P}_{1}{P}_{2}\left(r\right)}{{B}_{1}{B}_{2}\left(r\right)}\) − 1,

where \(\xi \left(r\right)\) is the 2PCF at distance \(r\), \({P}_{1}{P}_{2}\left(r\right)\) is the number of pairs of positive cases between populations 1 and 2 separated a distance \(r\) between them, and \({B}_{1}{B}_{2}\left(r\right)\) is the number of background pairs (positive or negative) between both populations at distance \(r\). \({P}_{1}{P}_{2}\left(r\right)\) and \({B}_{1}{B}_{2}\left(r\right)\) were normalised by \({nP}_{1}{nP}_{2}\) and \({nB}_{1}{nB}_{2}\) respectively, where \({nP}_{\text{1,2}}\) and \({nB}_{\text{1,2}}\) are the number of positive samples and the total number of samples respectively for population 1,2. The 2PCF measurements were done using 10 bins in distance in a range from 0 to 60 km. The choosing of these bins was based on the spatial range of pairwise distances and finding the balance between spatial granularity and the statistical power (sample size) of the measurements. The agreement between different 2PCFs was quantified with \(\chi ²\) statistics.

A novel malaria hotspot detector, Epidemiological Foci Relating Infections by Distance (EpiFRIenDs), was developed to detect areas with higher levels of P. falciparum infections (hotspots) and seropositivity against P. falciparum antigens (seroclusters) than statistically expected in a stable period of time². EpiFRIenDs was designed to detect structures of arbitrary shapes and sizes that account for the background population distribution, a difference with the most commonly used scan statistics based on the SaTScan software ^25,26,27 that detect structures of predefined shapes. EpiFRIenDs detects hotspots and seroclusters by linking positive cases when they are closer than a given pre-defined distance and indirectly link them to all the positive cases that are close to their connections. The negative cases are then included in the hotspots from their close positive cases. The EpiFRIenDs software is publicly available in Python²⁸ and R²⁹ and it is described in detail in the section EpiFRIenDs of Supplementary material. The hotspot detection using EpiFRIenDs and SaTScan (scan statistics) was first compared in simulated data with a prevalence of 20% reproducing three different scenarios: first, a random spatial distribution of positive and negative cases; second, four circular clusters of positive cases on top of a background random distribution of negative cases; and third, a sinusoidal distribution of positive cases on top of a background random distribution of negative cases. EpiFRIenDs was then applied to identify hotspots and serological clusters from ANC, OPMSS and antibody data (Supplementary methods). Hotspots were compared between ANC and clinical data from the Manhiça District Hospital and the Ilha Josina Health Centre, for which geolocation information was available. Since EpiFRIenDs is a density-based clustering algorithm, clinical data was randomly sub-sampled to obtain the same sample size of positive cases than for ANC data, with five hundred different random sub-samples to obtain statistical significance. Linking distance (1km), temporal windows (one month or one year) and hotspot size thresholds (three or five positive cases per hotspot) were defined based on sample density and minimum false detections estimations from 500 realisations with random infections (Supplementary methods). A hotspot from ANC data was considered to be matched by a hotspot from clinical data (or vice versa) if at least one member of an ANC hotspot was found closer than 2km to a member of a hotspot from clinical data.

All analyses were stratified by gravidity and HIV status, the main factors shown to affect PfPR_qPCR in our study population (Supplementary table S2), with primigravid HIV-negative women considered separately to take potential correlations between gravidity and HIV into account³². Statistical significance was set at p < 0\(.\)05. \(\chi ²\) values were interpreted as good consistency from thresholds corresponding to p < 0\(.\)05 in a null-hypothesis statistical test (Supplementary methods). Error bars and 95% confidence intervals were obtained from bootstrap resampling with replacement. The analysis was conducted using Python 3.8.12, Jupyter Lab 3.1.14, R 4.2.1, and EpiFRIenDs 1.0.

Ethics

All study protocols were approved by the institutional ethics committees at CISM and Barcelona Hospital Clínic, and the Mozambican Ministry of Health National Bioethics Committee. All data and biological samples were collected only if research participants (or representatives in the case of minors) gave full written informed consent.

P. falciparum burden

PfPR_qPCR in pregnant women and in children 2–10 years old were highly correlated (PCC = 0.94 [95% CI 0.70–0.99]) and showed a consistent linear relationship (slope = 0.97 [95% CI 0.53–2.14], origin = 0.03 [95% CI -0.01-0.08]). Similar high correlations (PCC > 0.85), linear relationships (slope ~ 1 and origin ~ 0) and consistencies (χ²<1.1) were found regardless of gravidity and HIV status (Fig. 1, Supplementary figure S2, Supplementary table S3). At RDT-detection levels, parasite rates in pregnant women and children showed lower correlations and weaker linear relationships (Fig. 1D-F, Supplementary table S3). Only primigravidae showed a 1-to-1 linear relationship with children (Fig. 1E), whereas multigravidae showed lower rates that were not correlated (PCC = 0.61 [95% CI -0.12-0.94]), with a linear regression slope not consistent with equality (0.17 [95% CI -0.035-0.49]; Fig. 1K). However, in low-transmission Magude and Manhiça, good consistency of both PfPR_qPCR and PfPR_RDT between pregnant women and children was observed (χ²<1.10), regardless of gravidity or HIV (Fig. 1).

P. falciparum temporal trends

Overall, PfPR_qPCR in pregnant women declined from 10.7–4.1% during the study, resembling an overall decline of 62% (65%, 58%, and 62% in Magude, Ilha Josina, Manhiça, respectively, p < 0.001; Fig. 2A, Supplementary table S4). A similar decline of 60% (from 4.4–1.8%) was observed for PfPR_RDT (60% in both Ilha Josina and Manhiça, p = 0.001; 52% in Magude, p = 0.034). Clinical cases showed an overall decline of 47% (73%, 38% and 52% in Magude, Ilha Josina and Manhiça, respectively, p < 0.001). Statistically significant declines in parasite rates among children 2–10 years old from cross-sectional surveys were only observed in Ilha Josina for PfPR_qPCR (90%, p = 0.004). The PfPR_qPCR temporal patterns in pregnant women and clinical cases were consistent (χ²<1) and correlated (PCC = 0.87 [95%CI 0.69–0.91]) with a delay of ~ 90 days, regardless of gravidity (Fig. 2, Table 1). Similar results were found at RDT-detection levels (PCC > 0.78 [95%CI 0.27–0.85]; Fig. 2), but with shorter time lags (~ 40 days) when including multigravida women. HIV did not significantly impact estimates (Supplementary figure S3).

Table 1

Comparison of *Plasmodium falciparum* temporal patterns estimated from pregnant women at first antenatal care visit and clinical cases in children. Pearson correlation coefficients and χ² statistics of the comparison between the temporal trends in PfPR_qPCR (first six rows) and PfPR_RDT (last six rows) in different populations of pregnant women at first ANC visit and the mean weekly number of clinical cases, with their time lag of optimal correlation.
Test	Population	χ2	Pearson CC (95%CI)	Time lag (days)
qPCR
	All prenatal	1	0.87 (0.69, 0.91)	89
	Primigravidae	0.97	0.69 (0.31, 0.81)	81
	Multigravidae	0.97	0.90 (0.66, 0.92)	89
	HIV+	0.56	0.80 (0.26, 0.86)	127
	HIV-	0.83	0.87 (0.64, 0.92)	89
	Primigravid HIV-	0.94	0.72 (0.24, 0.80)	37
RDT
	All prenatal	1.49	0.78 (0.27, 0.85)	39
	Primigravidae	1.23	0.66 (0.14, 0.78)	91
	Multigravidae	0.78	0.82 (0.24, 0.87)	33
	HIV+	0.34	0.79 (0.06, 0.84)	43
	HIV-	1.14	0.71 (0.24, 0.81)	39
	Primigravid HIV-	1.06	0.66 (0.13, 0.80)	37

Spatial Trends

Pregnant women and children 2–10 years old showed consistent 2PCF statistics (χ²<1.50) in all three years, regardless of gravidity and HIV status at both qPCR and RDT-detection levels (Fig. 3A-C, Supplementary figure S4, Supplementary table S5). 2PCFs from ANC data significantly deviated from 0 (Supplementary table S6), being negative at distances between sample pairs within and between Magude and Manhica (< 10 km and ~ 45 km, respectively), and positive at distances between Ilha Josina and north of Manhiça (~ 30 km).

P. falciparum hotspots

The performance of EpiFRIenDs and SaTScan to detect clusters on simulated data was compared for three different scenarios (Supplementary material section EpiFRIenDs). In the first scenario with a random spatial distribution of positive and negative cases, the only difference between the methods was observed on the detection of false small clusters in small-scale parameterisations of EpiFRIenDs, which can be corrected after a parameter calibration (Methods, Supplementary methods). In the second scenario, positive cases were correctly identified as part of the four circular clusters in both methods. And in the third scenario, EpiFriends could correctly assign all positive cases as part of the sinusoidal cluster which could not be detected with SaTScan.

Due to its higher capacity to detect clusters of arbitrary shape, further analysis was conducted using EpiFRIenDs. With this spatial algorithm, 10 hotspots, all in Ilha Josina, were detected with qPCR results from all geolocalized pregnant women (n = 3,616; Fig. 3). Four of them, and the two most persistent ones, occurred during the first year (Fig. 3, Supplementary figure S5), when transmission was highest. Six hotspots persisted for more than 20 days, one of them for more than 90 days. Fifteen hotspots were found using OPMSS clinical data from Manhiça district (from a 18% sub-sampling with P. falciparum-positive cases out of 6,662 visits, see Supplementary methods), 13 (80%) of them in Ilha Josina. Eleven hotspots persisted for more than 20 days, two of them for more than 40 days. All 11 hotspots detected with ANC data were also detected from clinical data (seven with p ≤ 0.054, and two with p ≤ 0.91). Hotspots showed temporal heterogeneity, with 12 (80%) of clinical case hotspots detected in year 1 and 3, while 9 (90%) of ANC-hotspots were detected in between. Three hotspots (20%) detected from clinical data were missed by ANC data. With one-year temporal windows (n = 4,686 ANC), four RDT hotspots (all in Ilha Josina) and 11 qPCR hotspots were found, regardless of whether HIV-positive or multigravid women were included (Supplementary table S7).

Seroprevalence of antibodies against P. falciparum antigens

A total of 6,038 DBS was analysed for the presence of antibodies against 11 P. falciparum antigens using a quantitative multiplexed bead array (Supplementary figure S1). Seroprevalence at first ANC visit ranged from 3.9% (95%CI 3.0-5.1) for VAR2CSA_P8 in Magude during the third year, to 76.8% (95%CI 72.3–80.8) for MSP1 in Ilha Josina during the first year (Supplementary table S8). ANC-seroprevalences were correlated with PfPR_qPCR in children (PCC > 0.7), with antigen-dependent linear regression parameters (Fig. 4A, Supplementary table S9). The highest correlated antibodies were those against GEXP18, RH2, RH5, VAR2CSA_{DBL3 − 4}, and peptides VAR2CSA PD, P39, and P8 (PCC > 0.85; Fig. 4A,B). Correlations remained high across groups of gravidity and HIV status, however, wide confidence intervals increased to include potentially no correlation for some antibodies (Fig. 4A). Significant declines in seroprevalence across all areas were only observed for VAR2CSA_{DBL3 − 4} (Fig. 4B, Supplementary table S8, Supplementary table S10). The highest correlation between seroprevalence and PfPR_RDT in clinical cases was found for VAR2CSA_{DBL3 − 4} (PCC = 0.74 [95%CI 0.24–0.77]) and VAR2CSA_P1 (PCC = 0.74 [95%CI 0.27–0.76]), remaining high when stratifying by gravidity (Supplementary table S11). Seroprevalence trends lagged up to 10 months behind PfPR_RDT from clinical cases, with no clear pattern based on pregnancy-specificity or longevity of antibodies.

2PCF measurements of serostatus agreed well with those from qPCR-detected cases in children from cross-sectional surveys (χ²≤1.6; Fig. 4E-G, Supplementary table S12). Similar numbers and locations of sero-clusters and qPCR hotspots were observed for HSP40, Etramp, EBA175, VAR2CSA_{DBL3 − 4}, and combining all VAR2CSA peptides (Supplementary figure S6, Supplementary table S7). However, in year 2 in Ilha Josina (after the steep decline in PfPR_qPCR), more sero-clusters of HSP40, PfTramp, RH5, VAR2CSA_{DBL3 − 4} and combined VAR2CSA peptides were found than hotspots. In addition, eight sero-clusters were found in year 1 in the south-west of Magude, which did not reflect hotspots of qPCR-positive cases. Finally, in the north of Manhiça we detected one hotspot from qPCR data in year 1. In this same area, two sero-clusters (Etramp, VAR2CSA_P39) were detected in year 1, and 6 others (MSP1, EBA175, ACS5, PfTramp, VAR2CSA_{DBL3 − 4} and combining all the VAR2CSA peptides) were detected in year 3. Sero-clusters were more stable than qPCR hotspots, with nine persisting more than 20 days and one persisting for 197 days (Supplementary figure S7).

This population-based spatio-temporal analysis of parasitological and serological data from southern Mozambique shows that qPCR-positivity rates at first ANC visit reflect rates in children with a time lag of 2–3 months relative to clinical cases, regardless of the women’s gravidity, HIV status, and the transmission intensity in their area. Disparities emerge at RDT-detection levels for multigravid women in moderate-to-high transmission settings, indicating the need to consider gravidity in the analysis when using diagnostic tools of limited sensitivity. However, gravidity did not affect the number of hotspots detected which were similar to those detected using passive surveillance data. Finally, VAR2CSA seroprevalence at first ANC visit were found to be highly correlated with parasite rates in children, sensitive to temporal and spatial trends that were missed by RDT data in children, and unaffected by gravidity, therefore constituting a robust adjunct for ANC surveillance. Overall, this data provides evidence for the potential value of pregnant women for programmatic surveillance in malaria endemic regions in sub-Saharan Africa.

Our study, for the first time, provides evidence that parasite rates in pregnant women are highly correlated and consistent with rates in children from cross-sectional surveys at qPCR-detection levels, across the transmission spectrum, and regardless of HIV status and gravidity. The lower RDT-based rates in multigravid women compared with children in high-to-moderate transmission Ilha Josina, in line with previous reports^8,10,11, is probably explained by immunity acquired during successive pregnancies, which enable the control of parasite densities below the RDT-detection limit^30,31. However, temporal trends in ANC-based parasite rates, both detected by qPCR and RDT, reflected trends in clinical cases observed 2–3 months earlier, similar to the 3-month time lag observed in the Democratic Republic of Congo¹². This lag suggests that infections at ANC are older than those in symptomatic children, in accordance with previous studies showing that infections in pregnancy mainly result from a boosting of infections acquired before pregnancy³². RDT rates in multigravid women showed shorter time lags, possibly due to faster clearance of infections by anti-parasite immunity acquired during previous pregnancies. Despite the time lag, ANC data would still be useful to benchmark passive surveillance estimates and population-based cross-sectional surveys, improving population denominators and informing about the burden of asymptomatic infections.

Similar spatial patterns of P. falciparum cases and hotspots were observed among pregnant women at ANC and children in the community, regardless of the women’s gravidity or HIV status. Spatial clustering decreased from year 1 to year 2, reflecting trends in burden. The novel software EpiFRIenDs revealed finer spatial structures of P. falciparum infections and detected several hotspots from ANC and clinical case data. ANC data detected 80% of the clinical case hotspots. At RDT-detection levels, fewer hotspots were identified from HIV-positive and multigravidae than when including all women, probably due to lower sample size and positivity rates. Differences in the temporal distribution of hotspots were observed between ANC and clinical case data, which might result from the time lag observed, different denominator populations, inclusion of asymptomatic cases in ANC data, and variations in care-seeking behaviour. The sample size of clinical cases was limited in order to compare it with ANC data, although the larger sample size of clinical data would probably improve the precision of hotspot detection. However, passive surveillance systems do not usually record the geolocation of children routinely, precluding spatial analysis. Further studies are required to assess the value of ANC data for identifying pockets of transmission missed by case-based surveillance for supplementing reactive strategies.

Among the 14 antigens evaluated in this study, DBL3-4, derived from the pregnancy-specific antigen VAR2CSA³³, was found to be the most promising marker for ANC-based sero-surveillance. DBL3-4 seroprevalence at first ANC visit showed high correlation with qPCR rates in children, across all gravidity and HIV-status groups. Temporal trends in DBL3-4 seroprevalence also showed the highest correlation with temporal trends in clinical cases. Furthermore, DBL3-4 was the only serological marker that mirrored declines of qPCR parasite rates in all three areas. DBL3-4 was able to detect declines missed by RDT, demonstrating the power of this serological marker in settings with few RDT-detectable cases. Along with several other antibodies, DBL3-4 also showed great potential to detect spatial patterns in transmission. In year 2, more sero-clusters were identified than qPCR hotspots. In particular, sero-clusters were found to persist in a low-transmission area after elimination interventions had been deployed and very few P. falciparum cases were detected. This shows the ability of serological markers to capture recent transmission dynamics, which would be especially useful to demonstrate continuous absence of transmission following elimination. A rapid serological test for antibodies against DBL3-4 at ANC could represent a low-cost surveillance tool with improved ability to detect trends missed by RDT, or to detect recent cases in elimination settings. Importantly, even if VAR2CSA-based vaccines under development are widely administered in the near future, DBL3-4 can still be used for sero-surveillance as it is not a vaccine target³⁴.

This study has several limitations. First, data collection might have been affected by RDT stock outs, changes in reporting practices²¹ and human errors¹⁹, and geolocalisation of clinical cases was only collected in Manhiça district. Second, sample sizes differ substantially between clinical, ANC and cross-sectional data, limiting the power of comparisons, especially for spatial analysis. Third, women not attending ANC tend to be older, live in rural settings, and be of lower socio-economic status than attending women, which are all risk factors for malaria³. However, this selection bias is likely to be low in sub-Saharan Africa due to high ANC attendance⁶. Also, other factors not assessed in this study might affect the relationship between malaria in children and in pregnant women, such as other co-infections or age. Finally, single-time-point measurements may limit the ability to infer true infection and serological status due to the complex dynamics of parasites and antibody responses in the infected host^33,35. Conducting similar studies in different epidemiological settings and varying ANC attendance levels can provide useful information to confirm the generalizability of this novel surveillance approach.

In conclusion, malaria testing of pregnant women at their first ANC visit can provide estimates of temporal and spatial trends in malaria burden that reflect those observed in children. However, the time lag of 2–3 months relative to clinical cases, together with gravidity and diagnostic test sensitivity in high transmission settings, need to be considered when interpreting ANC data. The bias introduced by infections missed by RDT in multigravid women at moderate-to-high transmission levels can be avoided by restricting the analysis to primigravidae, using highly sensitive detection tools, such as qPCR or VAR2CSA serology, or with models accounting for immunity. ANC data can also be used to detect malaria hotspots reflecting those detected with passive surveillance data, potentially providing a cost-efficient approach to tailor interventions to areas most in need. The new spatial algorithm developed in this study, EpiFRIenDs, showed its superiority compared to scan statistics using SaTScan^25,26,27 in detecting irregular hotspots, which better reflect the spatial distribution of human populations. However, a previous calibration of EpiFRIenDs is required to minimize the detection of false small hotspots, which will be automatically performed in future versions of the software. This algorithm constitutes a new tool to link foci detection with appropriate targeted approaches. Finally, antibodies against the pregnancy-specific parasite antigen VAR2CSA could serve as a more resilient marker of spatio-temporal malaria trends measured at ANC. Taken together with other potential benefits of a continuous ANC-based surveillance approach, including more precise denominator populations, and the ability to capture asymptomatic infections, surveilling pregnant women at first ANC visit has great potential to complement existing surveillance systems in Africa.

Acknowledgements

We are grateful to the women, children and their families who participated in the study, the clinical teams at the health centres, the field and lab teams at CISM (Henrique Cossa, Judice Miguel, Manuel Muamede, Helga Guambe) and ISGlobal (Laura Puyol, Diana Barrios, Alfons Jiménez, Marta Vidal, Rebecca Santano, Martina Fort Suñé, Marta Menendez García, Gemma Porras, Haily Chen, Elena Buetas, and Ianthe de Jong). We would like to express our gratitude to the communities of Magude and Manhiça districts and the district authorities for their support of the project. We also thank Patrick Walker and Joseph Hicks for the fruitful discussion on this project, and Gillian Stresman, Maria Tusell, Arantxa Roca-Feltrer, and DÍdac Macià for useful discussions on the design of EpiFRIenDs. This work was supported by the National Institute of Health (1R01AI123050), the Departament d’Universitats i Recerca de la Generalitat de Catalunya (AGAUR; grant 2017 SGR 664), Ministerio de Ciencia e Innovación (PID2020-118328RB-I00), the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 890477 and from the “la Caixa'' Foundation (ID 100010434, fellowship code LCF/BQ/DI20/11780016). ISGlobal is a member of the CERCA Programme, Generalitat de Catalunya (http://cerca.cat/en/suma/). CISM is supported by the Government of Mozambique and the Spanish Agency for International Development (AECID). We acknowledge support from the Spanish Ministry of Science and Innovation and State Research Agency through the “Centro de Excelencia Severo Ochoa 2019-2023” Program (CEX2018-000806-S), and support from the Generalitat de Catalunya through the CERCA Program. This research is part of the ISGlobal’s Program on the Molecular Mechanisms of Malaria which is partially supported by the Fundación Ramón Areces. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Author contributions

Alfredo Mayor and Eusebio Macete designed the study. Sónia Maculuve and Henriques Mbeve recruited study participants at health facilities. Boaventura Cuna, Cardoso Melembe and Nelo Ndimande managed the data and samples. Beatriz Galatas (2015-2017), Humberto Munguambe (2015-2019), Lidia Nhamussua (2015-2018), Wilson Simone (2015-2019) and Júlia Montaña (2018-2019) coordinated the cross-sectional studies recruiting community participants. Arlindo Chidimatembue, Helena Martí-Soler, Beatriz Galatas and Júlia Montaña managed the data from cross-sectional studies. Nanna Brokhattingen, Glória Matambisso, Pau Cisteró, Anna Escoda, Judice Miguel, Elena Buetas, Ianthe de Jong, Gemma Porras, Haily Chen analysed the samples. Kevin Teth, Benoit Gamain, Chetan Chitnis and Virander Chauhan produced recombinant antigens for serological determinations. Arnau Pujol and Nanna Brokhattingen analysed and interpreted the data. Alfredo Mayor and Gloria Matambisso contributed to the interpretation of data. Arnau Pujol and Nanna Brokhattingen drafted the manuscript. All authors critically revised the manuscript, have had full access to the data used in the study, and accepted responsibility to submit it for publication.

Ohrt, C. et al. Information systems to support surveillance for malaria elimination. Am J Trop Med Hyg 93, 145–152 (2015). https://doi.org:10.4269/ajtmh.14-0257
WHO. Malaria surveillance, monitoring and evaluation: a reference manual. ix, 196 p. http://iris.paho.org/xmlui/handle/123456789/50648 (World Health Organization, 2018).
Mayor, A., Menendez, C. & Walker, P. G. T. Targeting Pregnant Women for Malaria Surveillance. Trends Parasitol 35, 677–686 (2019). https://doi.org: 10.1016/j.pt.2019.07.005
Kwesigabo, G. et al. Monitoring of HIV-1 infection prevalence and trends in the general population using pregnant women as a sentinel population: 9 years experience from the Kagera region of Tanzania. J Acquir Immune Defic Syndr 23, 410–417 (2000). https://doi.org:10.1097/00126334-200004150-00008
Korenromp, E. L. et al. Syphilis prevalence trends in adult women in 132 countries - estimations using the Spectrum Sexually Transmitted Infections model. Sci Rep 8, 11503 (2018). https://doi.org:10.1038/s41598-018-29805-9
van Eijk, A. M. et al. Coverage of intermittent preventive treatment and insecticide-treated nets for the control of malaria during pregnancy in sub-Saharan Africa: a synthesis and meta-analysis of national survey data, 2009-11. Lancet Infect Dis 13, 1029–1042 (2013). https://doi.org:10.1016/s1473-3099(13)70199-3
Yimam, Y., Nateghpour, M., Mohebali, M. & Abbaszadeh Afshar, M. J. A systematic review and meta-analysis of asymptomatic malaria infection in pregnant women in Sub-Saharan Africa: A challenge for malaria elimination efforts. PLoS One 16, e0248245 (2021). https://doi.org:10.1371/journal.pone. 0248245
van Eijk, A. M., Hill, J., Noor, A. M., Snow, R. W. & ter Kuile, F. O. Prevalence of malaria infection in pregnant women compared with children for tracking malaria transmission in sub-Saharan Africa: a systematic review and meta-analysis. Lancet Glob Health 3, e617-628 (2015). https://doi.org:10.1016/s2214-109x(15)00049-2
Brunner, N. C. et al. The potential of pregnant women as a sentinel population for malaria surveillance. Malar J 18, 370 (2019). https://doi.org:10.1186/s12936-019-2999-0
Willilo, R. A. et al. Pregnant women and infants as sentinel populations to monitor prevalence of malaria: results of pilot study in Lake Zone of Tanzania. Malaria Journal 15, 392 (2016). https://doi.org:10.1186/s12936-016-1441-0
Kitojo, C. et al. Estimating malaria burden among pregnant women using data from antenatal care centres in Tanzania: a population-based study. Lancet Glob Health 7, e1695-e1705 (2019). https://doi.org:10.1016/s2214-109x(19)30405-x
Hellewell, J., Walker, P., Ghani, A., Rao, B. & Churcher, T. S. Using ante-natal clinic prevalence data to monitor temporal changes in malaria incidence in a humanitarian setting in the Democratic Republic of Congo. Malar J 17, 312 (2018). https://doi.org:10.1186/s12936-018-2460-9
Fried, M. & Duffy, P. E. Adherence of Plasmodium falciparum to chondroitin sulfate A in the human placenta. Science 272, 1502–1504 (1996). https://doi.org: 10.1126/science.272.5267.1502
Salanti, A. et al. Selective upregulation of a single distinctly structured var gene in chondroitin sulphate A-adhering Plasmodium falciparum involved in pregnancy-associated malaria. Mol Microbiol 49, 179–191 (2003). https://doi.org: 10.1046/j.1365-2958.2003.03570.x
Greenhouse, B. et al. Priority use cases for antibody-detecting assays of recent malaria exposure as tools to achieve and sustain malaria elimination. Gates Open Res 3, 131 (2019). https://doi.org:10.12688/gatesopenres.12897.1
Drakeley, C. & Cook, J. in Advances in Parasitology Vol. 69 299–352 (Academic Press, 2009).
Galatas, B. et al. Field performance of ultrasensitive and conventional malaria rapid diagnostic tests in southern Mozambique. Malar J 19, 451 (2020). https://doi.org:10.1186/s12936-020-03526-9
Galatas, B. et al. A prospective cohort study to assess the micro-epidemiology of Plasmodium falciparum clinical malaria in Ilha Josina Machel (Manhiça, Mozambique). Malar J 15, 444 (2016). https://doi.org:10.1186/s12936-016-1496-y
Galatas, B. et al. A multiphase program for malaria elimination in southern Mozambique (the Magude project): A before-after study. PLOS Medicine 17, e1003227 (2020). https://doi.org:10.1371/journal.pmed.1003227
Matambisso, G. et al. Gravidity and malaria trends interact to modify P. falciparum densities and detectability in pregnancy: a 3-year prospective multi-site observational study. BMC Med 20, 396 (2022). https://doi.org:10.1186/s12916-022-02597-6
Galatas, B. et al. Demographic and health community-based surveys to inform a malaria elimination project in Magude district, southern Mozambique. BMJ Open 10, e033985 (2020). https://doi.org:10.1136/bmjopen-2019-033985
Alonso PL, S. F., Aponte JJ, Gómez-Olivé FX, Nhacolo A, Thomson R, Macete E, Abacassamo & F, V. P., Menéndez C, and Dgedge M. in Popul Health Surviv Vol. 1 295–308 (INDEPTH Sites, 2002).
Mayor, A. et al. How hidden can malaria be in pregnant women? Diagnosis by microscopy, placental histology, polymerase chain reaction and detection of histidine-rich protein 2 in plasma. Clin Infect Dis 54, 1561–1568 (2012). https://doi.org:10.1093/cid/cis236
Moody, A. Rapid diagnostic tests for malaria parasites. Clin Microbiol Rev 15, 66–78 (2002). https://doi.org:10.1128/cmr.15.1.66-78.2002
SaTScan v7.0: Software for the spatial and space-time scan statistics (2006).
Kulldorff, M. A spatial scan statistic. Communications in Statistics - Theory and Methods 26, 1481–1496 (1997).
Kulldorff, M. & Nagarwalla, N. Spatial disease clusters: detection and inference. Stat Med 14, 799–810 (1995).
Pujol, A. Epidemiological Foci Relating Infections by Distance (EpiFRIenDs), https://github.com/arnaupujol/epifriends
Pujol, A. & Majewski, M. Epidemiological Foci Relating Infections by Distance (EpiFRIenDs) for R, https://github.com/arnaupujol/repifriends
Mayor, A. et al. Changing Trends in P. falciparum Burden, Immunity, and Disease in Pregnancy. N Engl J Med 373, 1607–1617 (2015). : 10.1056/NEJMoa1406459
Fried, M. et al. Antibody levels to recombinant VAR2CSA domains vary with Plasmodium falciparum parasitaemia, gestational age, and gravidity, but do not predict pregnancy outcomes. Malar J 17, 106 (2018). https://doi.org:10.1186/s12936-018-2258-9
Tuikue Ndam, N. et al. Persistent Plasmodium falciparum Infection in Women With an Intent to Become Pregnant as a Risk Factor for Pregnancy-associated Malaria. Clin Infect Dis 67, 1890–1896 (2018). https://doi.org:10.1093/cid/ciy380
Fonseca, A. M. et al. VAR2CSA Serology to Detect Plasmodium falciparum Transmission Patterns in Pregnancy. Emerg Infect Dis 25, 1851–1860 (2019). https://doi.org:10.3201/eid2510.181177
Gamain, B., Chêne, A., Viebig, N. K., Tuikue Ndam, N. & Nielsen, M. A. Progress and Insights Toward an Effective Placental Malaria Vaccine. Front Immunol 12, 634508–634508 (2021). https://doi.org:10.3389/fimmu.2021. 634508
Dharmaratne, A. D. V. T. T. et al. Quantification of the dynamics of antibody response to malaria to inform sero-surveillance in pregnant women. Malaria journal 21, 75–75 (2022). https://doi.org:10.1186/s12936-022-04111-y

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Detecting temporal and spatial malaria patterns from first antenatal care visits

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