Analysis of rubella antibody distribution from newborn dried blood spots using finite mixture models

Estimating dengue transmission intensity from serological data: A comparative analysis using mixture and catalytic models

Background

Dengue virus (DENV) infection is a global health concern of increasing magnitude. To target intervention strategies, accurate estimates of the force of infection (FOI) are necessary. Catalytic models have been widely used to estimate DENV FOI and rely on a binary classification of serostatus as seropositive or seronegative, according to pre-defined antibody thresholds. Previous work has demonstrated the use of thresholds can cause serostatus misclassification and biased estimates. In contrast, mixture models do not rely on thresholds and use the full distribution of antibody titres. To date, there has been limited application of mixture models to estimate DENV FOI.

Methods

We compare the application of mixture models and time-constant and time-varying catalytic models to simulated data and to serological data collected in Vietnam from 2004 to 2009 (N ≥ 2178) and Indonesia in 2014 (N = 3194).

Results

The simulation study showed larger mean FOI estimate bias from the time-constant and time-varying catalytic models (-0.007 (95% Confidence Interval (CI): -0.069, 0.029) and -0.006 (95% CI -0.095, 0.043)) than from the mixture model (0.001 (95% CI -0.036, 0.065)). Coverage of the true FOI was > 95% for estimates from both the time-varying catalytic and mixture model, however the latter had reduced uncertainty. When applied to real data from Vietnam, the mixture model frequently produced higher FOI and seroprevalence estimates than the catalytic models.

Conclusions

Our results suggest mixture models represent valid, potentially less biased, alternatives to catalytic models, which could be particularly useful when estimating FOI from data with largely overlapping antibody titre distributions.

Characterising the transmission intensity of dengue virus is essential to inform the implementation of interventions, such as vector control and vaccination, and to better understand the environmental drivers of transmission locally and globally. It is therefore important to understand how methodological differences and model choice may influence the accuracy of estimates of transmission intensity. Using a simulation study, we assessed the performance of catalytic and mixture models to reconstruct the force of infection (FOI) from simulated antibody titre data. Furthermore, we estimated the FOI of dengue virus from antibody titre data collected in Vietnam and Indonesia. The models produced consistent estimates of FOI when they were applied to data with clear separation between the distributions of seronegative and seropositive antibody titres. We observed greater bias in FOI estimates obtained from catalytic models than from mixture models when they were applied to data with high overlap in the bimodal distribution of antibody titres. Our results indicate that mixture models could be preferential to estimate dengue virus FOI when the antibody titre distributions of the seronegative and seropositive components largely overlap.

Serological surveys to estimate cumulative incidence of SARS-CoV-2 infection in adults (Sero-MAss study), Massachusetts, July-August 2020: a mail-based cross-sectional study

OBJECTIVES

To estimate the seroprevalence of anti-SARS-CoV-2 IgG and IgM among Massachusetts residents and to better understand asymptomatic SARS-CoV-2 transmission during the summer of 2020.

DESIGN

Mail-based cross-sectional survey.

SETTING

Massachusetts, USA.

PARTICIPANTS

Primary sampling group: sample of undergraduate students at the University of Massachusetts, Amherst (n=548) and a member of their household (n=231).Secondary sampling group: sample of graduate students, faculty, librarians and staff (n=214) and one member of their household (n=78). All participants were residents of Massachusetts without prior COVID-19 diagnosis.

PRIMARY AND SECONDARY OUTCOME MEASURES

Prevalence of SARS-CoV-2 seropositivity. Association of seroprevalence with variables including age, gender, race, geographic region, occupation and symptoms.

RESULTS

Approximately 27 000 persons were invited via email to assess eligibility. 1001 households were mailed dried blood spot sample kits, 762 returned blood samples for analysis. In the primary sample group, 36 individuals (4.6%) had IgG antibodies detected for an estimated weighted prevalence in this population of 5.3% (95% CI: 3.5 to 8.0). In the secondary sampling group, 10 participants (3.4%) had IgG antibodies detected for an estimated adjusted prevalence of 4.0% (95% CI: 2.2 to 7.4). No samples were IgM positive. No association was found in either group between seropositivity and self-reported work duties or customer-facing hours. In the primary sampling group, self-reported febrile illness since February 2020, male sex and minority race (Black or American Indian/Alaskan Native) were associated with seropositivity. No factors except geographic regions within the state were associated with evidence of prior SARS-CoV-2 infection in the secondary sampling group.

CONCLUSIONS

This study fills a critical gap in estimating the levels of subclinical and asymptomatic infection. Estimates can be used to calibrate models estimating levels of population immunity over time, and these data are critical for informing public health interventions and policy.

Determining seropositivity-A review of approaches to define population seroprevalence when using multiplex bead assays to assess burden of tropical diseases

Background

Serological surveys with multiplex bead assays can be used to assess seroprevalence to multiple pathogens simultaneously. However, multiple methods have been used to generate cut-off values for seropositivity and these may lead to inconsistent interpretation of results. A literature review was conducted to describe the methods used to determine cut-off values for data generated by multiplex bead assays.

Methodology/Principal findings

A search was conducted in PubMed that included articles published from January 2010 to January 2020, and 308 relevant articles were identified that included the terms “serology”, “cut-offs”, and “multiplex bead assays”. After application of exclusion of articles not relevant to neglected tropical diseases (NTD), vaccine preventable diseases (VPD), or malaria, 55 articles were examined based on their relevance to NTD or VPD. The most frequently applied approaches to determine seropositivity included the use of presumed unexposed populations, mixture models, receiver operating curves (ROC), and international standards. Other methods included the use of quantiles, pre-exposed endemic cohorts, and visual inflection points.

Conclusions/Significance

For disease control programmes, seropositivity is a practical and easily interpretable health metric but determining appropriate cut-offs for positivity can be challenging. Considerations for optimal cut-off approaches should include factors such as methods recommended by previous research, transmission dynamics, and the immunological backgrounds of the population. In the absence of international standards for estimating seropositivity in a population, the use of consistent methods that align with individual disease epidemiological data will improve comparability between settings and enable the assessment of changes over time.

Serological surveys can provide information regarding population-level disease exposure by assessing immune responses created during infection. Multiplex bead assays (MBAs) allow for an integrated serological platform to monitor antibody responses to multiple pathogens concurrently. As programs adopt integrated disease control strategies, MBAs are especially advantageous since many of these diseases may be present in the same population and antibodies against all pathogens of interest can be detected simultaneously from a single blood sample. Interpreting serological data in a programmatic context typically involves classifying individuals as seronegative or seropositive using a ‘cut-off’, whereby anyone with a response above the defined threshold is considered to be seropositive. Although studies increasingly test blood samples with MBAs, published studies have applied different methods of determining seropositivity cut-offs, making results difficult to compare across settings and over time. The lack of harmonized methods for defining seropositivity is due to the absence of international standards, pathogen biology, or assay-specific methods that may impact resulting data. This review highlights the need for a standardized approach for which cut-off methods to use per pathogen when applied to integrated disease surveillance using platforms such as MBAs.

Serological surveys to estimate cumulative incidence of SARS-CoV-2 infection in adults (Sero-MAss study), Massachusetts, July-August 2020: a mail-based cross-sectional study

BACKGROUND

The SARS-CoV-2 pandemic is an unprecedented global health crisis. The state of Massachusetts was especially impacted during the initial stages; however, the extent of asymptomatic transmission remains poorly understood due to limited asymptomatic testing in the "first wave." To address this gap, a geographically representative and contact-free seroprevalence survey was conducted in July-August 2020, to estimate prior undetected SARS-CoV-2 infections.

METHODS

Students, faculty, librarians and staff members at the University of Massachusetts, Amherst without a previous COVID-19 diagnosis were invited to participate in this study along with one member of their household in June 2020. Two separate sampling frames were generated from administrative lists: all undergraduates and their household members (primary sampling group) were randomly selected with probability proportional to population size. All staff, faculty, graduate students and librarians (secondary sampling group) were selected as a simple random sample. After informed consent and a socio-behavioral survey, participants were mailed test kits and asked to return self-collected dried blood spot (DBS) samples. Samples were analyzed via ELISA for anti-SARS-CoV-2 IgG antibodies, and then IgM antibodies if IgG-positive. Seroprevalence estimates were adjusted for survey non-response. Binomial models were used to assess factors associated with seropositivity in both sample groups separately.

RESULTS

Approximately 27,000 persons were invited via email to assess eligibility. Of the 1,001 individuals invited to participate in the study, 762 (76%) returned blood samples for analysis. In the primary sampling group 548 returned samples, of which 230 enrolled a household member. Within the secondary sampling group of 214 individuals, 79 enrolled a household member. In the primary sample group, 36 (4.6%) had IgG antibodies detected for an estimated weighed prevalence for this population of 5.3% (95% CI: 3.5 to 8.0). In the secondary sampling group, 10 (3.4%) of 292 individuals had IgG antibodies detected for an estimated adjusted prevalence of 4.0% (95% CI: 2.2 to 7.4). No samples were IgM positive. No association was found in either sample group between seropositivity and self-reported work duties or customer-facing hours. In the primary sampling group, self-reported febrile illness since Feb 2020, male sex, and minority race (Black or American Indian/Alaskan Native) were associated with seropositivity. No factors except geographic regions within the state were associated with evidence of prior SARS-CoV-2 infection in the secondary sampling group.

INTERPRETATION

This study provides insight into the seroprevalence of university-related populations and their household members across the state of Massachusetts during the summer of 2020 of the pandemic and helps to fill a critical gap in estimating the levels of sub-clinical and asymptomatic infection. Estimates like these can be used to calibrate models that estimate levels of population immunity over time to inform public health interventions and policy.

Respiratory syncytial virus in young children: community cohort study integrating serological surveys, questionnaire and electronic health records, Born in Bradford cohort, England, 2008 to 2013

Background

Bronchiolitis caused by respiratory syncytial virus (RSV) is a major cause of mortality and morbidity in infants.

Aim

To describe RSV epidemiology in children in the community in a high-income setting.

Methods

We used stored blood samples from the United Kingdom Born in Bradford cohort study that had been collected at birth, age 1 and 2 years old, tested for IgG RSV postfusion F antibody and linked to questionnaires and primary and hospital care records. We used finite mixture models to classify children as RSV infected/not infected according to their antibody concentrations at age 1 and 2 years. We assessed risk factors for primary RSV infection at each age using Poisson regression models.

Results

The study cohort included 700 children with cord blood samples; 490 had additional blood samples taken at both ages 1 and 2 years old. Of these 490 children, 258 (53%; 95% confidence interval (CI): 48–57%) were first infected with RSV at age 1, 99 of whom (38%; 95% CI: 33–43%) had been in contact with healthcare during peak RSV season (November–January). Having older siblings, birth in October–June and attending formal childcare were associated with risk of RSV infection in infancy. By age 2, a further 164 of 490 children (33%; 95% CI: 29–38%) had been infected.

Conclusion

Over half of children experienced RSV infection in infancy, a further one third had evidence of primary RSV infection by age 2, and one in seven remained seronegative by their second birthday. These findings will inform future analyses to assess the cost-effectiveness of RSV vaccination programmes in high-income settings.

The seroprevalence of cytomegalovirus infection in Belgium anno 2002 and 2006: a comparative analysis with hepatitis A virus seroprevalence

Cytomegalovirus (CMV) infection is endemic worldwide but its seroprevalence varies widely. The goal of this study was to estimate the age-specific seroprevalence of CMV infection in Belgium based on two cross-sectional serological datasets from 2002 and 2006. The seroprevalence was estimated relying on diagnostic test results based on cut-off values pre-specified by the manufacturers of the tests as well as relying on mixture models applied to continuous pathogen-specific immunoglobulin G antibody titre concentrations. The age-specific seroprevalence of hepatitis A virus (HAV), based on three Belgian cross-sectional serological datasets from 1993, 2002 and 2006, was used as a comparator since individuals acquire lifelong immunity upon recovery, implying an increasing seroprevalence with age. The age group weighted overall CMV seroprevalence derived from the mixture model was 32% (95% confidence interval (CI) 31-34%) in 2002 and 31% (95% CI 30-32%) in 2006. We demonstrated that CMV epidemiology differs from the immunizing infection HAV. This was the first large-scale study of CMV and HAV serial datasets in Belgium, estimating seroprevalence specified by age and birth cohort.

Structure of general-population antibody titer distributions to influenza A virus

Seroepidemiological studies aim to understand population-level exposure and immunity to infectious diseases. Their results are normally presented as binary outcomes describing the presence or absence of pathogen-specific antibody, despite the fact that many assays measure continuous quantities. A population's natural distribution of antibody titers to an endemic infectious disease may include information on multiple serological states - naiveté, recent infection, non-recent infection, childhood infection - depending on the disease in question and the acquisition and waning patterns of immunity. In this study, we investigate 20,152 general-population serum samples from southern Vietnam collected between 2009 and 2013 from which we report antibody titers to the influenza virus HA1 protein using a continuous titer measurement from a protein microarray assay. We describe the distributions of antibody titers to subtypes 2009 H1N1 and H3N2. Using a model selection approach to fit mixture distributions, we show that 2009 H1N1 antibody titers fall into four titer subgroups and that H3N2 titers fall into three subgroups. For H1N1, our interpretation is that the two highest-titer subgroups correspond to recent and historical infection, which is consistent with 2009 pandemic attack rates. Similar interpretations are available for H3N2, but right-censoring of titers makes these interpretations difficult to validate.

Simulation Models of Misclassification Error for Single Thresholds of High-Sensitivity Cardiac Troponin I Due to Assay Bias and Imprecision

BACKGROUND

Clinical outcome studies for cardiac troponins (cTn) are expensive and difficult to design owing to variation in patients, in the assays, and in the incidence of different types of myocardial infarction (MI). To overcome these difficulties, simulation models were used to estimate the rate of misclassification error for MI and risk prediction resulting from assay bias and imprecision.

METHODS

Finite mixture analysis of Abbott high-sensitivity cTnI (hs-cTnI) results at time 0 h in patients presenting early with acute coronary syndrome (ACS) symptoms to the emergency department (ED) [n = 145, Reducing the Time Interval for Identifying New Guideline (RING) study] allowed derivation of a simulation data set (n = 10000). hs-cTnI concentrations were modified by addition of bias or imprecision error. The percentage of all 10000 modified hs-cTnI results that were misclassified for MI at thresholds of 2, 5, 26.2, and 52 ng/L was determined by Monte Carlo simulation. Analyses were replicated with an all-comer emergency department (ED) population (n = 1137) ROMI (Optimum Troponin Cutoffs for ACS in the ED) study.

RESULTS

In the RING study, simulation at 26.2-ng/L (99th percentile) and 52-ng/L thresholds were affected by both bias ±2 ng/L and imprecision (10%–20%) and had misclassification rates of 0.4% to 0.6%. Simulations at the 2-ng/L and 5-ng/L thresholds were only affected by bias. Misclassification rates at bias of ±1 ng/L were 10% for the 2-ng/L threshold, and 5% for the 5-ng/L threshold.

CONCLUSIONS

Simulation models predicted that hs-cTnI results are seldom misclassified (<1% of patients) when interpretative thresholds are near or exceed the overall 99th percentile. However, simulation models also predicted that low hs-cTnI results, as recommended in guidelines, are prone to misclassification of 5%–10% of patients.

Estimating Age-Specific Immunity and Force of Infection of Varicella Zoster Virus in Norway Using Mixture Models

This study applies mixture modelling to examine age-specific immunity to varicella zoster virus (VZV) infection in Norway based on the first large-scale serological study in the general population. We estimated the seropositive proportions at different ages and calculated the underlying force of infection by using a sample of 2103 residual sera obtained from patients seeking primary and hospital care. A rapid increase in the VZV-associated immunity is observed in the first years of life with 63% of children being immune by age 5. The increase in the immunity levels slows down thereafter, with a large proportion of adults still susceptible by age 20 (around 14.5%), thus at risk of serious sequelae of varicella infection. The corresponding force of infection peaks during the preschool period, subsequently declines to a minimum between ages 10 and 20 years, and afterwards moderately increases to reach a plateau lasting throughout the childbearing period. In comparison with the traditional cut-off approach, mixture modelling used the whole data without producing any inconclusive cases, led to an unbiased classification of individuals between susceptible and immune, and provided a smoother immune profile by age. These findings represent an important step towards any decision about the introduction of varicella vaccination in Norway, as they are a primary input for mathematical transmission models aimed at evaluating potential vaccination scenarios.

Is it appropriate to use fixed assay cut-offs for estimating seroprevalence?

Population seroprevalence can be estimated from serosurveys by classifying quantitative measurements into positives (past infection/vaccinated) or negatives (susceptible) according to a fixed assay cut-off. The choice of assay cut-offs has a direct impact on seroprevalence estimates. A time-resolved fluorescence immunoassay (TRFIA) was used to test exposure to human parvovirus 4 (HP4). Seroprevalence estimates were obtained after applying the diagnostic assay cut-off under different scenarios using simulations. Alternative methods for estimating assay cut-offs were proposed based on mixture modelling with component distributions for the past infection/vaccinated and susceptible populations. Seroprevalence estimates were compared to those obtained directly from the data using mixture models. Simulation results showed that when there was good distinction between the underlying populations all methods gave seroprevalence estimates close to the true one. For high overlap between the underlying components, the diagnostic assay cut-off generally gave the most biased estimates. However, the mixture model methods also gave biased estimates which were a result of poor model fit. In conclusion, fixed cut-offs often produce biased estimates but they also have advantages compared to other methods such as mixture models. The bias can be reduced by using assay cut-offs estimated specifically for seroprevalence studies.

Use of dried blood spots to define antibody response to the Strongyloides stercoralis recombinant antigen NIE

An approach to improve the diagnosis of Strongyloides stercoralis infection is the use of serologic assays utilising the NIE antigen from S. stercoralis, with good diagnostic sensitivity and excellent specificity reported. Detection of antibody eluted from dried blood spots (DBS) has shown utility in large-scale seroepidemiological studies for a range of conditions and is appealing for use with children where sample collection is difficult. We adapted an existing NIE-enzyme linked immunosorbent assay (ELISA) for the testing of strongyloides antibody response on DBS, and evaluated it in a population screening and mass drug administration programme (MDA) for strongyloidiasis conducted in an Australian indigenous community. Study participants were treated with 200 μg/kg ivermectin (>15 kg) or 3× 400 mg albendazole (<15kg). The sensitivity of the NIE DBS-ELISA was determined by receiver operator characteristic (ROC) analysis to be 85.7%. A total of 214 DBS were collected from 184 participants across two screening and MDA encounters. A total of 27 of 164 participants (16.5%) tested positive for S. stercoralis NIE-DBS prior to MDA treatment, and 6 of 50 participants (12.0%) tested positive after treatment. These prevalence values are similar to those documented by standard serology in the same community. For 30 participants where a DBS was collected at both MDA 1 and 2, a significant decline in ELISA values was evident post treatment (0.12-0.02, p=0.0012). These results are in agreement with previous studies documenting the high seroprevalence of S. stercoralis in remote Australian Indigenous communities, and suggest that collection of dried blood spots may be a useful approach for field diagnosis of S. stercoralis seroprevalence.

Inferring influenza infection attack rate from seroprevalence data

Seroprevalence survey is the most practical method for accurately estimating infection attack rate (IAR) in an epidemic such as influenza. These studies typically entail selecting an arbitrary titer threshold for seropositivity (e.g. microneutralization [MN] 1∶40) and assuming the probability of seropositivity given infection (infection-seropositivity probability, ISP) is 100% or similar to that among clinical cases. We hypothesize that such conventions are not necessarily robust because different thresholds may result in different IAR estimates and serologic responses of clinical cases may not be representative. To illustrate our hypothesis, we used an age-structured transmission model to fully characterize the transmission dynamics and seroprevalence rises of 2009 influenza pandemic A/H1N1 (pdmH1N1) during its first wave in Hong Kong. We estimated that while 99% of pdmH1N1 infections became MN1∶20 seropositive, only 72%, 62%, 58% and 34% of infections among age 3-12, 13-19, 20-29, 30-59 became MN1∶40 seropositive, which was much lower than the 90%-100% observed among clinical cases. The fitted model was consistent with prevailing consensus on pdmH1N1 transmission characteristics (e.g. initial reproductive number of 1.28 and mean generation time of 2.4 days which were within the consensus range), hence our ISP estimates were consistent with the transmission dynamics and temporal buildup of population-level immunity. IAR estimates in influenza seroprevalence studies are sensitive to seropositivity thresholds and ISP adjustments which in current practice are mostly chosen based on conventions instead of systematic criteria. Our results thus highlighted the need for reexamining conventional practice to develop standards for analyzing influenza serologic data (e.g. real-time assessment of bias in ISP adjustments by evaluating the consistency of IAR across multiple thresholds and with mixture models), especially in the context of pandemics when robustness and comparability of IAR estimates are most needed for informing situational awareness and risk assessment. The same principles are broadly applicable for seroprevalence studies of other infectious disease outbreaks.

Estimating the population prevalence and force of infection directly from antibody titres

The use of threshold values in order to diagnose individual subjects as being ‘susceptible’ or ‘infected or recovered/immune’ for a specific infection is virtually always prone to false positive, false negative or inconclusive classifications. Such misclassifications might lead to biased estimates for epidemiological parameters, such as the prevalence and the force of infection. In this article, we propose to estimate these epidemiological parameters directly from antibody titres, using an underlying mixture model. The method is applied to estimate the Salmonella serological prevalence in pigs and the age-dependent force of infection using serological data on the Varicella-Zoster virus (VZV) in humans. The threshold and direct method are compared through a simulation study.

Evaluation of biological sample preparation for immunosignature-based diagnostics

To address the need for a universal system to assess health status, we previously described a method termed "immunosignaturing" which splays the entire humoral antibody repertoire across a peptide microarray. Two important issues relative to the potential broad use of immunosignatures are sample preparation and stability. In the present study, we compared the immunosignatures developed from serum, plasma, saliva, and antibodies eluted from blood dried onto filter paper. We found that serum and plasma provide identical immunosignatures. Immunosignatures derived from dried blood also correlated well with those from nondried serum from the same individual. Immunosignatures derived from dried blood were capable of distinguishing naïve mice from those infected with influenza virus. Saliva was applied to the arrays, and the IgA immunosignature correlated strongly with that from dried blood. Finally, we demonstrate that dried blood retains immunosignature information even when exposed to high temperature. This work expands the potential diagnostic uses for immunosignatures. These features suggest that different forms of archival samples can be used for diagnosis development and that in prospective studies samples can be easily procured.

Using serological measures to monitor changes in malaria transmission in Vanuatu

Background

With renewed interest in malaria elimination, island environments present unique opportunities to achieve this goal. However, as transmission decreases, monitoring and evaluation programmes need increasingly sensitive tools to assess Plasmodium falciparum and Plasmodium vivax exposure. In 2009, to assess the role of serological markers in evaluating malaria transmission, a cross-sectional seroprevalence study was carried out in Tanna and Aneityum, two of the southernmost islands of the Vanuatu archipelago, areas where malaria transmission has been variably reduced over the past few decades.

Methods

Malaria transmission was assessed using serological markers for exposure to P. falciparum and P. vivax. Filter blood spot papers were collected from 1,249 people from Tanna, and 517 people from Aneityum to assess the prevalence of antibodies to two P. falciparum antigens (MSP-1₁₉ and AMA-1) and two P. vivax antigens (MSP-1₁₉ and AMA-1). Age-specific prevalence was modelled using a simple catalytic conversion model based on maximum likelihood to generate a community seroconversion rate (SCR).

Results

Overall seropositivity in Tanna was 9.4%, 12.4% and 16.6% to P. falciparum MSP-1₁₉, AMA-1 and Schizont Extract respectively and 12.6% and 15.0% to P. vivax MSP-1₁₉ and AMA-1 respectively. Serological results distinguished between areas of differential dominance of either P. vivax or P. falciparum and analysis of age-stratified results showed a step in seroprevalence occurring approximately 30 years ago on both islands, indicative of a change in transmission intensity at this time. Results from Aneityum suggest that several children may have been exposed to malaria since the 2002 P. vivax epidemic.

Conclusion

Seroepidemiology can provide key information on malaria transmission for control programmes, when parasite rates are low. As Vanuatu moves closer to malaria elimination, monitoring changes in transmission intensity and identification of residual malaria foci is paramount in order to concentrate intervention efforts.

Chapter 5. Potential contribution of sero-epidemiological analysis for monitoring malaria control and elimination: historical and current perspectives

Anti-malarial antibody responses represent an individual's history of exposure to the disease and, as age sero-conversion rates, reflect cumulative malaria exposure in a population. As such these antibody responses are an alternate measure of malaria transmission intensity and have potential in evaluating changes in exposure. This approach was used in the 1970s to evaluate malaria control and eradication attempts in a variety of different ecological settings. These historical studies provided a wealth of information on how serological data might be used to interpret control measures. However they were limited by a lack of standardized antigens and reproducible high-throughput assays. Current techniques using recombinant antigens with a range of immunogenicities, high-throughput enzyme-linked immunosorbent assays (ELISA) and statistical analysis allow a more robust examination of how serological parameters can be used to evaluate factors affecting malaria transmission. Here we present a review of the historical data and use it to assess the serological contribution to monitoring malaria elimination.

Immunophoretic rapid diagnostic tests as a source of immunoglobulins for estimating malaria sero-prevalence and transmission intensity

Background

Sero-epidemiological methods are being developed as a tool for rapid assessment of malaria transmission intensity. Simple blood collection methods for use in field settings will make this more feasible. This paper describes validation of such a method, by analysing immunoglobulins from blood retained within immunophoretic rapid diagnostic tests (RDTs) for Plasmodium falciparum. RDTs are now widely used for the diagnosis of malaria and estimation of parasite rates, and this method represents a further use for these devices in malaria control.

Methods

Immunoglobulins eluted from RDTs, designed to detect parasite histidine rich protein-2 (HRP-2), were analysed by indirect ELISA for IgG recognizing the P. falciparum blood stage antigens merozoite surface protein-1₁₉ (MSP-1₁₉) and apical membrane antigen-1 (AMA-1). Optimal storage conditions for RDTs were evaluated by comparing antibody responses from RDTs stored in dry or humid conditions at 4°C or at ambient temperature (with or without air-conditioning) for 7, 31 or 70 days. Antibody levels estimated using 3,700 RDT samples from attendees at health facilities in North-eastern Tanzania were compared with contemporaneously collected filter paper blood spots (FPBS) and used to estimate seroconversion rates.

Results

Storage of RDTs at 4°C was optimal for immunoglobulin recovery but short-term storage at ambient temperatures did not substantially affect anti-malarial IgG levels. Results from RDTs were comparable with those from FPBSs, for both antigens. RDT-generated titres tended to be slightly higher than those generated from FPBSs, possibly due to greater recovery of immunoglobulins from RDTs compared to filter paper. Importantly, however, RDT-based seroconversion rates, and hence serological estimates of malaria transmission intensity, agreed closely with those from FPBSs.

Conclusion

RDTs represent a practical option for collecting blood for sero-epidemiological surveys, with potential cost and logistical advantages over filter paper and other blood collection methods. RDT-based seroepidemiology can be incorporated into routine monitoring of malaria endemicity, providing information to supplement parasite prevalence rates and generating rapid, robust assessment of malaria transmission intensity at minimal extra cost.