Robustness of group testing in the estimation of proportions

In binomial group testing, unlike one-at-a-time testing, the test unit consists of a group of individuals, and each group is declared to be defective or nondefective. A defective group is one that is presumed to include one or more defective (e.g., infected, positive) individuals and a nondefective group to contain only nondefective individuals. The usual binomial model considers the individuals being grouped as independent and identically distributed Bernoulli random variables. Under the binomial model and presuming that groups are tested and classified without error, it has been shown that, when the proportion of defective individuals is low, group testing is often preferable to individual testing for identifying infected individuals and for estimating proportions of defectives. We discuss the robustness of group testing for estimating proportions when the underlying assumptions of (i) no testing errors and (ii) independent individuals are violated. To evaluate the effect of these model violations, two dilution-effect models and a serial correlation model are considered. Group testing proved to be quite robust to serial correlation. In the presence of a dilution effect, smaller group sizes should be used, but most of the benefits of group testing can still be realized.     

Detection of Mycobacterium tuberculosis infection in chacma baboons (Papio ursinus) using the QuantiFERON-TB Gold (In-Tube) assay

Background  Early diagnosis of simian tuberculosis (TB) is vital to prevent transmission of this disease. We evaluated the ability of the QuantiFERON-TB Gold (In-Tube Method) assay (QFG-IT) to detect TB in chacma baboons ( Papio ursinus ).
Methods  Fifty-one baboons were tested using the Tuberculin Skin Test (TST) and the QFG-IT. Baboons testing positive, and animals exposed to infected individuals, were euthanised and subjected to necropsy. Selected tissues were processed for histopathology, mycobacterial culture and genetic speciation.
Results  Tuberculosis was confirmed in one TST positive/QFG-IT positive animal and one TST negative/QFG-IT positive animal. One TST positive/QFG-IT negative animal and five TST negative/QFG-IT negative animals were confirmed uninfected following necropsy.
Conclusion  The QFG-IT correctly detected TB in two baboons, including one TST negative individual and correctly identified six baboons as uninfected, including one TST positive individual. The QFG-IT shows promise as a sensitive, specific test for TB in chacma baboons.     

More Powerful Likelihood Ratio Tests for Isotonic Binomial Proportions

Binomial group testing involves pooling individuals into groups and observing a binary response on each group. Results from the group tests can then be used to draw inference about population proportions. Its use as an experimental design has received much attention in recent years, especially in public‐health screening experiments and vector‐transfer designs in plant pathology. We investigate the benefits of group testing in situations wherein one desires to test whether or not probabilities are increasingly ordered across the levels of an observed qualitative covariate, i.e., across strata of a population or among treatment levels. We use a known likelihood ratio test for individual testing, but extend its use to group‐testing situations to show the increases in power conferred by using group testing when operating in this constrained parameter space. We apply our methods to data from an HIV study involving male subjects classified as intraveneous drug users.     

Group testing regression model estimation when case identification is a goal

Group testing is frequently used to reduce the costs of screening a large number of individuals for infectious diseases or other binary characteristics in small prevalence situations. In many applications, the goals include both identifying individuals as positive or negative and estimating the probability of positivity. The identification aspect leads to additional tests being performed, known as “retests”, beyond those performed for initial groups of individuals. In this paper, we investigate how regression models can be fit to estimate the probability of positivity while also incorporating the extra information from these retests. We present simulation evidence showing that significant gains in efficiency occur by incorporating retesting information, and we further examine which testing protocols are the most efficient to use. Our investigations also demonstrate that some group testing protocols can actually lead to more efficient estimates than individual testing when diagnostic tests are imperfect. The proposed methods are applied retrospectively to chlamydia screening data from the Infertility Prevention Project. We demonstrate that significant cost savings could occur through the use of particular group testing protocols.     

Cost analysis in choosing group size when group testing for Potato virus Y in the presence of classification errors

In many areas of the world, Potato virus Y (PVY) is one of the most economically important disease problems in seed potatoes. In Taiwan, generation 2 (G2) class certified seed potatoes are required by law to be free of detectable levels of PVY. To meet this standard, it is necessary to perform accurate tests at a reasonable cost. We used a two‐stage testing design involving group testing which was performed in Taiwan's Seed Improvement and Propagation Station to identify plants infected with PVY. At the first stage of this two‐stage testing design, plants are tested in groups. The second stage involves no retesting for negative test groups and exhaustive testing of all constituent individual samples from positive test groups. In order to minimise costs while meeting government standards, it is imperative to estimate optimal group size. However, because of limited test accuracy, classification errors for diagnostic tests are inevitable; to get a more accurate estimate, it is necessary to adjust for these errors. Therefore, this paper describes an analysis of diagnostic test data in which specimens are grouped for batched testing to offset costs. The optimal batch size is determined by various cost parameters as well as test sensitivity, specificity and disease prevalence. Here, the Bayesian method is employed to deal with uncertainty in these parameters. Moreover, we developed a computer program to determine optimal group size for PVY tests such that the expected cost is minimised even when using imperfect diagnostic tests of pooled samples. Results from this research show that, compared with error free testing, when the presence of diagnostic testing errors is taken into account, the optimal group size becomes smaller. Higher diagnostic testing costs, lower costs of false negatives or smaller prevalence can all lead to a larger optimal group size. Regarding the effects of sensitivity and specificity, optimal group size increases as sensitivity increases; however, specificity has little effect on determining optimal group size. From our simulated study, it is apparent that the Bayesian method can truly update the prior information to more closely approximate the intrinsic characteristics of the parameters of interest. We believe that the results of this study will be useful in the implementation of seed potato certification programmes, particularly those which require zero tolerance for quarantine diseases in certified tubers.     

Estimating infection prevalence: Best practices and their theoretical underpinnings

Accurately estimating infection prevalence is fundamental to the study of population health, disease dynamics, and infection risk factors. Prevalence is estimated as the proportion of infected individuals (“individual‐based estimation”), but is also estimated as the proportion of samples in which evidence of infection is detected (“anonymous estimation”). The latter method is often used when researchers lack information on individual host identity, which can occur during noninvasive sampling of wild populations or when the individual that produced a fecal sample is unknown. The goal of this study was to investigate biases in individual‐based versus anonymous prevalence estimation theoretically and to test whether mathematically derived predictions are evident in a comparative dataset of gastrointestinal helminth infections in nonhuman primates. Using a mathematical model, we predict that anonymous estimates of prevalence will be lower than individual‐based estimates when (a) samples from infected individuals do not always contain evidence of infection and/or (b) when false negatives occur. The mathematical model further predicts that no difference in bias should exist between anonymous estimation and individual‐based estimation when one sample is collected from each individual. Using data on helminth parasites of primates, we find that anonymous estimates of prevalence are significantly and substantially (12.17%) lower than individual‐based estimates of prevalence. We also observed that individual‐based estimates of prevalence from studies employing single sampling are on average 6.4% higher than anonymous estimates, suggesting a bias toward sampling infected individuals. We recommend that researchers use individual‐based study designs with repeated sampling of individuals to obtain the most accurate estimate of infection prevalence. Moreover, to ensure accurate interpretation of their results and to allow for prevalence estimates to be compared among studies, it is essential that authors explicitly describe their sampling designs and prevalence calculations in publications.     

Biomarker-based HIV incidence in a community sample of men who have sex with men in Paris, France

BACKGROUND: Population-based estimates of HIV incidence in France have revealed that men who have sex with men (MSM) are the most affected population and contribute to nearly half of new infections each year. We sought to estimate HIV incidence among sexually active MSM in Paris gay community social venues. METHODOLOGY/ PRINCIPAL FINDINGS: A cross-sectional survey was conducted in 2009 in a sample of commercial venues such as bars, saunas and backrooms. We collected a behavioural questionnaire and blood sample. Specimens were tested for HIV infection and positive specimens then tested for recent infection by the enzyme immunoassay for recent HIV-1 infection (EIA-RI). We assessed the presence of antiretroviral therapy among infected individuals to rule out treated patients in the algorithm that determined recent infection. Biomarker-based cross-sectional incidence estimates were calculated. We enrolled 886 MSM participants among which 157 (18%) tested HIV positive. In positive individuals who knew they were infected, 75% of EIA-RI positive results were due to ART. Of 157 HIV positive specimens, 15 were deemed to be recently infected. The overall HIV incidence was estimated at 3.8% person-years (py) [95%CI: 1.5-6.2]. Although differences were not significant, incidence was estimated to be 3.5% py [0.1-6.1] in men having had a negative HIV test in previous year and 4.8% py [0.1-10.6] in men having had their last HIV test more than one year before the survey, or never tested. Incidence was estimated at 4.1% py [0-8.3] in men under 35 years and 2.5% py [0-5.4] in older men. CONCLUSIONS/ SIGNIFICANCE: This is the first community-based survey to estimate HIV incidence among MSM in France. It includes ART detection and reveals a high level of HIV transmission in sexually active individuals, despite a high uptake of HIV testing. These data call for effective prevention programs targeting MSM engaged in high-risk behaviours.     

Individual variations in infectiousness explain long-term disease persistence in wildlife populations

Viral disease persistence in species without a reservoir host is of importance for public health and disease management. But how can disease persistence be explained? We developed a spatially‐explicit individual‐based model that takes into account both ecological and viral traits as well as variable space to test disease persistence hypotheses under debate. We introduce a novel concept of modeling alternative disease courses at the individual level, causing transient infections or killing infected animals, with the lethally infected having a variable life‐expectancy. We systematically distinguish between disease invasion and persistence. We use classical swine fever (CSF), an economically very important livestock disease in a social host, the wild boar, as a reference system to test and rank the persistence hypotheses under debate. Parameter values for host population demographics and CSF epidemiology reflect current knowledge. Sensitivity analysis of the model parameters revealed that the most important factor for disease persistence is a disease profile with mostly transient, i.e. surviving individuals requiring immunity, and some chronically, long‐term infected animals. Immune individuals can constantly produce susceptible offspring, while some chronically infected individuals act as ‘super spreaders’ in time. Thus, variations in the course of the disease at the individual level are important factors determining persistence, which is usually not taken into account in the prominent measure of epidemiology, i.e. the basic reproductive number R₀, which reflects the ‘reproductive potential’ of the infected sub‐population. We discuss our results with regard to the general issues of modeling epidemics and disease management issues.     

Efficient Identification of HIV Serodiscordant Couples by Existing HIV Testing Programs in South Brazil

Objective

To examine the feasibility of identifying HIV negative at risk individuals in HIV serodiscordant couples, during voluntary HIV testing in South Brazil.

Methods

We surveyed HIV testers at 4 public testing sites in Rio Grande do Sul. We obtained information on risk behaviors and sexual partnerships. HIV testing and testing for recent infection were performed; HIV prevalence and risk behaviors were assessed among subjects who reported having a steady partner who was HIV positive (serodiscordant group) and compared with the general testing population.

Results

Among 3100 patients, 490 (15.8%) reported being in a steady relationship with an HIV positive partner. New HIV infections were diagnosed in 23% of the serodiscordant group (vs. 13% in the general population, p = 0.01); among newly positive subjects, recent HIV infections were more frequent (23/86, 26.7%) among testers with positive partners than among the general testing group (52/334; 15.6%; p = 0.016). Less than half of the serodiscordant testers reported having used a condom during the last sexual intercourse with their HIV-positive partner. Participants with inconsistent condom use with steady partner were four times more likely to test positive for HIV compared to those who reported always using condoms with the steady partner (OR: 4.2; 95% CI: 2.3 to 7.5).

Conclusion

It is highly feasible to identify large numbers of HIV susceptible individuals who are in HIV serodiscordant relationships in South Brazil testing sites. Condom use within HIV serodiscordant couples is low in this setting, suggesting urgent need for biomedical prevention strategies to reduce HIV transmission.     

An efficient test for comparing sequence diversity between two populations.

We address the problem of comparing interindividual genomic sequence diversity between two populations. Although the methods are general, for concreteness we focus on comparing two human immunodeficiency virus (HIV) infected populations. From a viral isolate(s) taken from each individual in a sample of persons from each population, suppose one or multiple measurements are made on the genetic sequence of a coding region of HIV. Given a definition of genetic distance between sequences, the goal is to test if the distribution of interindividual distances differs between populations. If distances between all pairs of sequences within each group are used, then data-dependencies arising from the use of multiple sequences from individuals invalidates the use of a standard two-sample test such as the t-test. Where this problem has been recognized, a typical solution has been to apply a standard test to a reduced dataset comprised of one sequence or a consensus sequence from each patient. Disadvantages of this procedure are that the conclusion of the test depends on the choice of utilized sequences, often an arbitrary decision, and exclusion of replicate sequences from the analysis may needlessly sacrifice statistical power. We present a new test free of these drawbacks, which is based on a statistic that linearly combines all possible standard test statistics calculated from independent sequence subsamples. We describe statistical power advantages of the test and illustrate its use by application to nucleotide sequence distances measured from HIV-1 infected populations in southern Africa (GenBank accession numbers AF110959--AF110981) and North America/Europe. The test makes minimal assumptions, is maximally efficient and objective, and is broadly applicable.     

Intensive case finding and isoniazid preventative therapy in HIV infected individuals in Africa: economic model and value of information analysis

BACKGROUND: Tuberculosis (TB) accounts of much of the morbidity and mortality associated with HIV. We evaluate the cost-effectiveness of different strategies to actively screen for TB disease in HIV positive individuals, where isoniazid preventative therapy (IPT) is given to those screening negative, and use value of information analysis (VOI) to identify future research priorities. METHODOLOGY/ PRINCIPAL FINDINGS: We built an individual sampling model to investigate the costs (2010 US Dollars) and consequences of screening for TB, and providing TB treatment or IPT in adults testing HIV positive in Sub-Saharan Africa. A systematic review and meta-analysis was conducted to assess performance of the nine different TB screening strategies evaluated. Probabilistic sensitivity analysis was conducted to incorporate decision uncertainty, and expected value of perfect information for the entire model and for groups of parameters was calculated. Screening all HIV infected individuals with sputum microscopy was the least costly strategy, with other strategies not cost-effective at WHO recommended thresholds. Screening those with TB symptoms with sputum microscopy and CXR would be cost-effective at a threshold ICER of $7,800 per quality-adjusted life year (QALY), but associated with significant uncertainty. VOI analysis suggests further information would be of value. CONCLUSIONS/ SIGNIFICANCE: Resource-constrained countries in sub-Saharan Africa wishing to scale up TB preventative services in their HIV infected populations should consider expanding laboratory facilities to enable increased screening for TB with sputum microscopy, whilst improved estimates of the TB prevalence in the population to be screened are needed, as it may influence the optimal strategy.     

Testing the effect of treatment in experiments with correlated binary outcomes.

This paper considers the problem of testing for treatment effect in a randomized experiment with correlated binary outcomes, representing success or failure for different "parts" of a randomized unit. Attention is restricted to tests that are based on a summary score for each individual randomized, and thus are valid regardless of the precise nature of the correlation among parts. The focus is on the efficiency of such tests under various correlation structures, with special emphasis on the case in which the correlation among parts within an individual differs across treatment groups. A class of summary score statistics is defined, and optimal testing is discussed for some simple situations. Three potential general-purpose tests also are described: (1) the ratio estimate test discussed by Henderson et al. (1988, Controlled Clinical Trials 9, 189-205); (2) a modified ratio estimate test with adjusted weighting based on the within-individual correlation between parts; (3) a test defined by applying the Mantel-Haenszel procedure to the proportion of individuals with at least one failure, stratifying by the number of parts. For these general-purpose tests, numerical calculations of asymptotic efficiency are presented under a wide range of designs and correlation structures. On the basis of these results, some practical recommendations for choosing a test are made.     

Screening for 185delAG in the Ashkenazim.

A study was initiated to assess interest, educational effectiveness, and implications of genetic testing for the common BRCA1 mutation, 185delAG, in the Ashkenazim. Of 333 individuals who attended group sessions, 309 (92%) participated in the study. Participants were categorized as having negative family history (67%), positive family history (defined, by a relaxed criterion, as one first-degree relative or two second-degree relatives with breast [premenopausal] or ovarian cancer) (22%), positive personal history (7%), and both positive personal history and positive family history (4%). Group education was effective, as shown by the improvement in participant scores from pre- to posteducation tests. For the 289 individuals (94%) who requested testing, the major reasons included concern for their own risk, concern for the risk of their children, and desire to learn about surveillance options. The most common reason given by participants who declined testing was concern about health insurance. Six participants found to be heterozygous for the 185delAG mutation received results and were offered genetic counseling. Participants had consented for additional testing without receiving results and were screened for the 6174delT mutation in BRCA2, and seven were found to be positive. All identified carriers reported at least one first- or second-degree relative with a history of breast or ovarian cancer, although they did not all meet our study criteria for positive family history. Given these outcomes, we conclude that screening for breast and ovarian cancer susceptibility is most appropriate for individuals with a positive personal or positive family cancer history. We propose a guideline for future studies designed to identify individuals who may benefit from genetic testing for inherited breast and ovarian cancer.     

MHC class II DRB variability and parasite load in the striped mouse (Rhabdomys pumilio) in the Southern Kalahari

Major histocompatibility complex (MHC) variability is believed to be maintained by pathogen-driven selection, mediated either through heterozygous advantage or frequency-dependent selection. However, empirical support for these hypotheses under natural conditions is rare. In this study, we investigated the genetic constitution of the functionally important MHC class II gene (DRB exon 2) and the parasite load in a population of the striped mouse (Rhabdomys pumilio) in the Southern Kalahari. Fifty-eight individuals were genetically examined and the endoparasite load was quantified by counting fecal helminth eggs by using a modified McMaster technique. Thirty-four animals (58.6%) were infected. We identified 20 different MHC alleles with high levels of sequence divergence between alleles. Particularly, the antigen-binding sites revealed a significant higher rate of nonsynonymous substitutions (d(N)) than synonymous substitutions (d(S)), giving strong evidence of balancing selection. Heterozygosity did influence the infection status (being infected or not) and the individual fecal egg count (FEC) value with significantly higher values observed in homozygous individuals. Furthermore, a positive relationship was found between specific alleles and parasite load. The allele Rhpu-DRB*1 significantly occurred more frequently in infected individuals and in individuals with high FEC values (high parasite load). Individuals with the allele Rhpu-DRB*1 had a 1.5-fold higher chance of being infected than individuals without this allele (odds ratio test, P < 0.05). Contrarily, the allele Rhpu-DRB*8 significantly occurred more frequent in individuals with low FEC values. Our results support the hypotheses that MHC polymorphism in R. pumilio is maintained through pathogen-driven selection acting by both heterozygosity advantage and frequency-dependent selection.     

Regression models for disease prevalence with diagnostic tests on pools of serum samples

Whether the aim is to diagnose individuals or estimate prevalence, many epidemiological studies have demonstrated the successful use of tests on pooled sera. These tests detect whether at least one sample in the pool is positive. Although originally designed to reduce diagnostic costs, testing pools also lowers false positive and negative rates in low prevalence settings and yields more precise prevalence estimates. Current methods are aimed at estimating the average population risk from diagnostic tests on pools. In this article, we extend the original class of risk estimators to adjust for covariates recorded on individual pool members. Maximum likelihood theory provides a flexible estimation method that handles different covariate values in the pool, different pool sizes, and errors in test results. In special cases, software for generalized linear models can be used. Pool design has a strong impact on precision and cost efficiency, with covariate-homogeneous pools carrying the largest amount of information. We perform joint pool and sample size calculations using information from individual contributors to the pool and show that a good design can severely reduce cost and yet increase precision. The methods are illustrated using data from a Kenyan surveillance study of HIV. Compared to individual testing, age-homogeneous, optimal-sized pools of average size seven reduce cost to 44% of the original price with virtually no loss in precision.     

Factors determining dissemination of results and uptake of genetic testing in families with known BRCA1/2 mutations

BACKGROUND: Uptake of genetic testing remains low, even in families with known BRCA1 and BRCA2 (BRCA1/2) mutations, despite effective interventions to reduce risk. We report disclosure and uptake patterns by BRCA1/2-positive individuals to at-risk relatives, in the setting of no-cost genetic counseling and testing. METHODS: Relatives of BRCA1/2-positive individuals were offered cost-free and confidential genetic counseling and testing. If positive for a BRCA1/2 mutation, participants were eligible to complete a survey about their disclosure of mutation status and the subsequent uptake of genetic testing by at-risk family members. RESULTS: One hundred and fifteen of 142 eligible individuals responded to the survey (81%). Eighty-eight (77%) of those surveyed disclosed results to all at-risk relatives. Disclosure to first-degree relatives (FDRs) was higher than to second-degree relatives (SDRs) and third-degree relatives (TDR) (95% vs. 78%; p < 0.01). Disclosure rates to male versus female relatives were similar, but reported completion of genetic testing was higher among female versus male FDRs (73% vs. 49%; p < 0.01) and SDRs (68% vs. 43%; p < 0.01), and among members of maternal versus paternal lineages (63% vs. 0%; p < 0.01). Men were more likely than women to express general difficulty discussing positive BCRA1/2 results with at-risk family members (90% vs. 70%; p = 0.03), while women reported more emotional distress associated with disclosure than men (48% vs. 13%; p < 0.01). DISCUSSION: We report a very high rate of disclosure of genetic testing information to at-risk relatives. However, uptake of genetic testing among at-risk individuals was low despite cost-free testing services, particularly in men, SDRs, and members of paternal lineages. The complete lack of testing among paternally related at-risk individuals and the lower testing uptake among men signify a significant barrier to testing and a challenge for genetic counselors and physicians working with high-risk groups. Further research is necessary to ensure that family members understand their risk and the potential benefits of genetic counseling.     

Viral Genetic Linkage Analysis in the Presence of Missing Data

Analyses of viral genetic linkage can provide insight into HIV transmission dynamics and the impact of prevention interventions. For example, such analyses have the potential to determine whether recently-infected individuals have acquired viruses circulating within or outside a given community. In addition, they have the potential to identify characteristics of chronically infected individuals that make their viruses likely to cluster with others circulating within a community. Such clustering can be related to the potential of such individuals to contribute to the spread of the virus, either directly through transmission to their partners or indirectly through further spread of HIV from those partners. Assessment of the extent to which individual (incident or prevalent) viruses are clustered within a community will be biased if only a subset of subjects are observed, especially if that subset is not representative of the entire HIV infected population. To address this concern, we develop a multiple imputation framework in which missing sequences are imputed based on a model for the diversification of viral genomes. The imputation method decreases the bias in clustering that arises from informative missingness. Data from a household survey conducted in a village in Botswana are used to illustrate these methods. We demonstrate that the multiple imputation approach reduces bias in the overall proportion of clustering due to the presence of missing observations.     

Estimating the prevalence of two or more diseases using outcomes from multiplex group testing

When screening a population for infectious diseases, pooling individual specimens (e.g., blood, swabs, urine, etc.) can provide enormous cost savings when compared to testing specimens individually. In the biostatistics literature, testing pools of specimens is commonly known as group testing or pooled testing. Although estimating a population-level prevalence with group testing data has received a large amount of attention, most of this work has focused on applications involving a single disease, such as human immunodeficiency virus. Modern methods of screening now involve testing pools and individuals for multiple diseases simultaneously through the use of multiplex assays. Hou et al. (2017, Biometrics, 73, 656–665) and Hou et al. (2020, Biostatistics, 21, 417–431) recently proposed group testing protocols for multiplex assays and derived relevant case identification characteristics, including the expected number of tests and those which quantify classification accuracy. In this article, we describe Bayesian methods to estimate population-level disease probabilities from implementing these protocols or any other multiplex group testing protocol which might be carried out in practice. Our estimation methods can be used with multiplex assays for two or more diseases while incorporating the possibility of test misclassification for each disease. We use chlamydia and gonorrhea testing data collected at the State Hygienic Laboratory at the University of Iowa to illustrate our work. We also provide an online R resource practitioners can use to implement the methods in this article.     

Latent infection of epidermal Langerhans cells in HIV-positive individuals

Epidermal cell suspension otained fron 3 symptom-free HIV-positive individuals were cultured and marked with monoclonal antibodies for the HIV proteins p15, p24 and gp120 in the alkaline phosphatase anti-alkaline phosphatase staining technique. For 2 individuals, cells were positive after 3 days in culture, and for the third, after 4 days. Supernatant from one of the cultures infected allogeneic peripheral blood mononuclear cells. We conclude that epidermal Langerhans cells from symptom-free HIV-positive individuals are latent-infected and are able to produce and release HIV.     

Identification of potential serodiagnostic and subunit vaccine antigens by antibody profiling of toxoplasmosis cases in Turkey

Toxoplasmosis, caused by infection of the protozoan parasite Toxoplasma gondii, is associated with mild disease in healthy individuals, whereas individuals with depressed immunity may develop encephalitis, neurologic disorders, and other organ diseases. Women who develop acute toxoplasmosis during pregnancy are at risk of transmitting the infection to the fetus, which may lead to fetal damage. A diagnosis is usually confirmed by measuring IgG, or IgM where it is important to determine the onset of infection. A negative IgM result essentially excludes acute infection, whereas a positive IgM test is largely uninterpretable because IgM can persist for up to 18 months after infection. To identify antigens for improved diagnosis of acute infection, we probed protein microarrays displaying the polypeptide products of 1357 Toxoplasma exons with well-characterized sera from Turkey. The sera were classified according to conventional assays into (1) seronegative individuals with no history of T. gondii infection; (2) acute infections defined by clinical symptoms, high IgM titers, and low avidity IgG; (3) chronic/convalescent cases with high avidity IgG but persisting IgM; (iv) true chronic infections, defined by high avidity IgG and no IgM. We have identified 38 IgG target antigens and 108 IgM target antigens that can discriminate infected patients from healthy controls, one or more of which could form the basis of a 'tier-1' test to determine current or previous exposure. Of these, three IgG antigens and five IgM antigens have the potential to discriminate chronic/IgM persisting or true chronics from recent acutely infected patients (a 'tier-2' test). Our analysis of the antigens revealed several enriched features relative to the whole proteome, which include transmembrane domains, signal peptides, or predicted localization at the outer membrane. This is the first protein microarray survey of the antibody response to T. gondii, and will help in the development of improved serodiagnostics and vaccines.