Simplifying the estimation of diagnostic testing accuracy over time for high specificity tests in the absence of a gold standard

Many different methods for evaluating diagnostic test results in the absence of a gold standard have been proposed. In this paper, we discuss how one common method, a maximum likelihood estimate for a latent class model found via the Expectation-Maximization (EM) algorithm can be applied to longitudinal data where test sensitivity changes over time. We also propose two simplified and nonparametric methods which use data-based indicator variables for disease status and compare their accuracy to the maximum likelihood estimation (MLE) results. We find that with high specificity tests, the performance of simpler approximations may be just as high as the MLE.     

A cautionary note on the robustness of latent class models for estimating diagnostic error without a gold standard

Modeling diagnostic error without a gold standard has been an active area of biostatistical research. In a majority of the approaches, model-based estimates of sensitivity, specificity, and prevalence are derived from a latent class model in which the latent variable represents an individual's true unobserved disease status. For simplicity, initial approaches assumed that the diagnostic test results on the same subject were independent given the true disease status (i.e., the conditional independence assumption). More recently, various authors have proposed approaches for modeling the dependence structure between test results given true disease status. This note discusses a potential problem with these approaches. Namely, we show that when the conditional dependence between tests is misspecified, estimators of sensitivity, specificity, and prevalence can be biased. Importantly, we demonstrate that with small numbers of tests, likelihood comparisons and other model diagnostics may not be able to distinguish between models with different dependence structures. We present asymptotic results that show the generality of the problem. Further, data analysis and simulations demonstrate the practical implications of model misspecification. Finally, we present some guidelines about the use of these models for practitioners.     

Insights into latent class analysis of diagnostic test performance

Latent class analysis is used to assess diagnostic test accuracy when a gold standard assessment of disease is not available but results of multiple imperfect tests are. We consider the simplest setting, where 3 tests are observed and conditional independence (CI) is assumed. Closed-form expressions for maximum likelihood parameter estimates are derived. They show explicitly how observed 2- and 3-way associations between test results are used to infer disease prevalence and test true- and false-positive rates. Although interesting and reasonable under CI, the estimators clearly have no basis when it fails. Intuition for bias induced by conditional dependence follows from the analytic expressions. Further intuition derives from an Expectation Maximization (EM) approach to calculating the estimates. We discuss implications of our results and related work for settings where more than 3 tests are available. We conclude that careful justification of assumptions about the dependence between tests in diseased and nondiseased subjects is necessary in order to ensure unbiased estimates of prevalence and test operating characteristics and to provide these estimates clinical interpretations. Such justification must be based in part on a clear clinical definition of disease and biological knowledge about mechanisms giving rise to test results.     

Comparing the likelihood ratios of two binary diagnostic tests in the presence of partial verification

The comparison of the efficiency of two binary diagnostic tests requires one to know the disease status for all patients in the sample, by applying a gold standard. In two-phase studies the gold standard is not applied to all patients in a sample, and the problem of partial verification of the disease arises. At present, one of the approaches most used for comparing two binary diagnostic tests are the likelihood ratios. In this study, the maximum likelihood estimators of likelihood ratios are obtained. The tests of hypothesis to compare the likelihood ratios of two binary diagnostic tests when both are applied to the same random sample in the presence of verification bias are deduced, and simulation experiments are performed in order to investigate the asymptotic behaviour of the tests of hypothesis. The results obtained have been applied to the study of Alzheimer's disease.     

On ROC analysis with nonbinary reference standard

Statistical methods for the evaluation of the accuracy of diagnostic tests usually assume a binary true disease status. However, this assumption may not be realistic in practical settings in which “disease” is defined by dichotomizing continuous or ordinal categorical measures using a pre‐specified threshold value. In this paper, we focus on the analysis of studies in which both the diagnostic test and the reference standard are reported as continuous measures. We propose a semiparametric model for estimating the sensitivity, specificity, and the ROC curve as functions of reference standard thresholds. Under suitable order restrictions on the mean of the test result variable, fitting is done via two alternative approaches: isotonic regression and monotone smoothing splines. The model provides the basis to assess the effect of varying reference standard threshold on the performance of a diagnostic test. An example to evaluate the ability of the maximal SUV‐lean (standardized uptake value normalized to lean body mass) in predicting axillary node involvement in women diagnosed with breast cancer is presented.     

Semiparametric estimation of the covariate-specific ROC curve in presence of ignorable verification bias

Covariate-specific receiver operating characteristic (ROC) curves are often used to evaluate the classification accuracy of a medical diagnostic test or a biomarker, when the accuracy of the test is associated with certain covariates. In many large-scale screening tests, the gold standard is subject to missingness due to high cost or harmfulness to the patient. In this article, we propose a semiparametric estimation of the covariate-specific ROC curves with a partial missing gold standard. A location-scale model is constructed for the test result to model the covariates' effect, but the residual distributions are left unspecified. Thus the baseline and link functions of the ROC curve both have flexible shapes. With the gold standard missing at random (MAR) assumption, we consider weighted estimating equations for the location-scale parameters, and weighted kernel estimating equations for the residual distributions. Three ROC curve estimators are proposed and compared, namely, imputation-based, inverse probability weighted, and doubly robust estimators. We derive the asymptotic normality of the estimated ROC curve, as well as the analytical form of the standard error estimator. The proposed method is motivated and applied to the data in an Alzheimer's disease research.     

Algorithm for diagnosis of early Schistosoma haematobium using prodromal signs and symptoms in pre-school age children in an endemic district in Zimbabwe

IntroductionPrompt diagnosis of acute schistosomiasis benefits the individual and provides opportunities for early public health intervention. In endemic areas schistosomiasis is usually contracted during the first 5 years of life, thus it is critical to look at how the infection manifests in this age group. The aim of this study was to describe the prodromal signs and symptoms of early schistosomiasis infection, correlate these with early disease progression and risk score to develop an easy to use clinical algorithm to identify early Schistosoma haematobium infection cases in resource limited settings.MethodologyTwo hundred and four, preschool age children who were lifelong residence of a schistosomiasis endemic district and at high risk of acquiring schistosomiasis were followed up from July 2019 to December 2019, during high transmission season. The children received interval and standard full clinical evaluations and laboratory investigations for schistosomiasis by clinicians blinded from their schistosomiasis infection status. Diagnosis of S. haematobium was by urine filtration collected over three consecutive days. Signs and symptoms of schistosomiasis at first examination visit were compared to follow-up visits. Signs and symptoms common on the last schistosomiasis negative visit (before a subsequent positive) were assigned as early schistosomiasis infection (ESI), after possible alternative causes were ruled out. Logistic regression identified clinical predictors. A model based score was assigned to each predictor to create a risk for every child. An algorithm was created based on the predictor risk scores and validated on a separate cohort of 537 preschool age children.ResultsTwenty-one percent (42) of the participants were negative for S. haematobium infection at baseline but turned positive at follow-up. The ESI participants at the preceding S. haematobium negative visit had the following prodromal signs and symptoms in comparison to non-ESI participants; pruritic rash adjusted odds ratio (AOR) = 21.52 (95% CI 6.38–72.66), fever AOR = 82 (95% CI 10.98–612), abdominal pain AOR = 2.6 (95% CI 1.25–5.43), pallor AOR = 4 (95% CI 1.44–11.12) and a history of facial/body swelling within the previous month AOR = 7.31 (95% CI 3.49–15.33). Furthermore 16% of the ESI group had mild normocytic anaemia, whilst 2% had moderate normocytic anaemia. A risk score model was created using a rounded integer from the relative risks ratios. The diagnostic algorithm created had a sensitivity of 81% and a specificity of 96.9%, Positive predictive value = 87.2% and NPV was 95.2%. The area under the curve for the algorithm was 0.93 (0.90–0.97) in comparison with the urine dipstick AUC = 0.58 (0.48–0.69). There was a similar appearance in the validation cohort as in the derivative cohort.ConclusionThis study demonstrates for the first time prodromal signs and symptoms associated with early S. haematobium infection in pre-school age children. These prodromal signs and symptoms pave way for early intervention and management, thus decreasing the harm of late diagnosis. Our algorithm has the potential to assist in risk-stratifying pre-school age children for early S. haematobium infection. Independent validation of the algorithm on another cohort is needed to assess the utility further.     

Exploiting recombinant antibodies in point-of-care (POC) diagnostics: the combinatorial advantage

Antibodies are ubiquitously deployed on in vitro diagnostic (IVD) platforms for detecting a panoply of analytes indicative of environmental and food contamination, residue adulteration and both veterinary and medical diagnostics. In the clinical realm, rapid and accurate determination of disease status is paramount. The significance of immunodiagnostic performance cannot be overemphasized and in many cases reliable diagnosis informs medical intervention which can mean the difference between patient recovery and demise. Cardiovascular disease (CVD) is the single biggest cause of adult mortality in the western world and principal burden on the healthcare services. Although the troponin (Tn) family, in particular troponin I (TnI), are regarded as the gold standard for diagnosis of myocardial damage, over the last decade much research has focused on the identification of alternative cardiac biomarker molecules that can either supplant or complement TnI metrics to add value to cardiac risk stratification criteria. In particular, markers that appear earlier than TnI in the pathophyisiology of cardiac disease are highly sought after. The subject?of this addendum represents part of a broader challenge to deliver novel rapid point-of-care (POC) diagnostics to provide a chip-based multi-plexed platform for more comprehensive profiling of cardiac status with additive diagnostic and prognostic value.?Specifically, it outlines proof-of-concept direct myeloperoxidase (MPO) detection, demonstrates the benefits of using recombinant antibodies in POC diagnostics and describes optimized strategies for generation of superior candidate antibody panels.     

A validated algorithm for register-based identification of patients with recurrence of breast cancer—Based on Danish Breast Cancer Group (DBCG) data

Background: Cancer recurrence is not routinely and completely registered in Danish national health registers, which challenges register-based research. The aim of this study was to develop and validate a register-based algorithm to identify patients with recurrence of breast cancer (BC).Methods: We conducted a cohort study based on data from Danish national health registers and used the Danish National Patient Register and the Danish National Pathology Register as sources to identify BC recurrence. We used data from the Danish Breast Cancer Group (DBCG) validated against medical records on recurrence status and recurrence date for 471 women with early stage unilateral BC as the gold standard of BC recurrence to assess the accuracy of the algorithm to identify BC recurrence.Results: The algorithm displayed a sensitivity of 97.3% (95% confidence interval (CI): 93.2–99.3), a specificity of 97.2% (95% CI: 94.8–98.7) and a positive predictive value of 94.4% (95% CI: 89.2–97.3). The concordance correlation coefficient for the agreement between recurrence dates generated by the algorithm and the gold standard was 0.97 (95% CI: 0.96-0.98), and the date was estimated within +/−30 days of the gold standard in 66% of the patients and within +/−60 days in 76% of the patients.Conclusion: The developed algorithm almost perfectly identified BC recurrence and with reasonable timing compared to the gold standard.     

Sensitivity of test for overdispersion in Poisson regression

Overdispersion or extra-Poisson variation is very common for count data. This phenomenon arises when the variability of the counts greatly exceeds the mean under the Poisson assumption, resulting in substantial bias for the parameter estimates. To detect whether count data are overdispersed in the Poisson regression setting, various tests have been proposed and among them, the score tests derived by Dean (1992) are popular and easy to implement. However, such tests can be sensitive to anomalous or extreme observations. In this paper, diagnostic measures are proposed for assessing the sensitivity of Dean's score test for overdispersion in Poisson regression. Applications to the well-known fabric faults and Ames salmonella assay data sets illustrate the usefulness of the diagnostics in analyzing overdispersed count data.     

Oligonucleotide and polymer functionalized nanoparticles for amplification-free detection of DNA

Sensitive and quantitative nucleic acid testing from complex biological samples is now an important component of clinical diagnostics. Whereas nucleic acid amplification represents the gold standard, its utility in resource-limited and point-of-care settings can be problematic due to assay interferants, assay time, engineering constraints, and costs associated with both wetware and hardware. In contrast, amplification-free nucleic acid testing can circumvent these limitations by enabling direct target hybridization within complex sample matrices. In this work, we grew random copolymer brushes from the surface of silica-coated magnetic nanoparticles using azide-modified and hydroxyl oligo ethylene glycol methacrylate (OEGMA) monomers. The azide-functionalized polymer brush was first conjugated, via copper-catalyzed azide/alkyne cycloaddition (CuAAC), with herpes simplex virus (HSV)-specific oligonucleotides and then with alkyne-substituted polyethylene glycol to eliminate all residual azide groups. Our methodology enabled control over brush thickness and probe density and enabled multiple consecutive coupling reactions on the particle grafted brush. Brush- and probe-modified particles were then combined in a 20 min hybridization with fluorescent polystyrene nanoparticles modified with HSV-specific reporter probes. Following magnetic capture and washing, the particles were analyzed with an aggregate fluorescence measurement, which yielded a limit of detection of 6 pM in buffer and 60 pM in 50% fetal bovine serum. Adoption of brush- and probe-modified particles into a particle counting assay will result in the development of diagnostic assays with significant improvements in sensitivity.     

Prevalence Distribution and Risk Factors for Schistosoma hematobium Infection among School Children in Blantyre,Malawi

Background

Schistosomiasis is a public health problem in Malawi but estimates of its prevalence vary widely. There is need for updated information on the extent of disease burden, communities at risk and factors associated with infection at the district and sub-district level to facilitate effective prioritization and monitoring while ensuring ownership and sustainability of prevention and control programs at the local level.

Methods and Findings

We conducted a cross-sectional study between May and July 2006 among pupils in Blantyre district from a stratified random sample of 23 primary schools. Information on socio-demographic factors, schistosomiasis symptoms and other risk factors was obtained using questionnaires. Urine samples were examined for Schistosoma hematobium ova using filtration method. Bivariate and multiple logistic regressions with robust estimates were used to assess risk factors for S. hematobium. One thousand one hundred and fifty (1,150) pupils were enrolled with a mean age of 10.5 years and 51.5% of them were boys. One thousand one hundred and thirty-nine (1,139) pupils submitted urine and S. hematobium ova were detected in 10.4% (95%CI 5.43–15.41%). Male gender (OR 1.81; 95% CI 1.06–3.07), child''s knowledge of an existing open water source (includes river, dam, springs, lake, etc.) in the area (OR 1.90; 95% CI 1.14–3.46), history of urinary schistosomiasis in the past month (OR 3.65; 95% CI 2.22–6.00), distance of less than 1 km from school to the nearest open water source (OR 5.39; 95% CI 1.67–17.42) and age 8–10 years (OR 4.55; 95% CI 1.53–13.50) compared to those 14 years or older were associated with infection. Using urine microscopy as a gold standard, the sensitivity and specificity of self-reported hematuria was 68.3% and 73.6%, respectively. However, the positive predictive value was low at 23.9% and was associated with age.

Conclusion

The study provides an important update on the status of infection in this part of sub-Saharan Africa and exemplifies the success of deliberate national efforts to advance active participation in schistosomiasis prevention and control activities at the sub-national or sub-district levels. In this population, children who attend schools close to open water sources are at an increased risk of infection and self-reported hematuria may still be useful in older children in this region.     

Accounting for nonignorable verification bias in assessment of diagnostic tests

A "gold" standard test, providing definitive verification of disease status, may be quite invasive or expensive. Current technological advances provide less invasive, or less expensive, diagnostic tests. Ideally, a diagnostic test is evaluated by comparing it with a definitive gold standard test. However, the decision to perform the gold standard test to establish the presence or absence of disease is often influenced by the results of the diagnostic test, along with other measured, or not measured, risk factors. If only data from patients who received the gold standard test were used to assess the test performance, the commonly used measures of diagnostic test performance--sensitivity and specificity--are likely to be biased. Sensitivity would often be higher, and specificity would be lower, than the true values. This bias is called verification bias. Without adjustment for verification bias, one may possibly introduce into the medical practice a diagnostic test with apparent, but not truly, high sensitivity. In this article, verification bias is treated as a missing covariate problem. We propose a flexible modeling and computational framework for evaluating the performance of a diagnostic test, with adjustment for nonignorable verification bias. The presented computational method can be utilized with any software that can repetitively use a logistic regression module. The approach is likelihood-based, and allows use of categorical or continuous covariates. An explicit formula for the observed information matrix is presented, so that one can easily compute standard errors of estimated parameters. The methodology is illustrated with a cardiology data example. We perform a sensitivity analysis of the dependency of verification selection process on disease.     

Analyzing time-to-event data in a clinical trial when an unknown proportion of subjects has experienced the event at entry

In some clinical trials, where the outcome is the time until development of a silent event, an unknown proportion of subjects who have already experienced the event will be unknowingly enrolled due to the imperfect nature of the diagnostic tests used to screen potential subjects. For example, commonly used diagnostic tests for evaluating HIV infection status in infants, such as DNA PCR and HIV Culture, have low sensitivity when given soon after infection. This can lead to the inclusion of an unknown proportion of HIV-infected infants into clinical trials aimed at the prevention of transmission from HIV-positive mothers to their infants through breastfeeding. The infection status of infants at the end of the trial, when they are more than a year of age, can be determined with certainty. For those infants found to be infected with HIV at the end of the trial, it cannot be determined whether this occurred during the study or whether they were already infected when they were enrolled. In these settings, estimates of the cumulative risk of the event by the end of the study will overestimate the true probability of event during the study period and hypothesis tests comparing two or more intervention strategies can also be biased. We present inference methods for the distribution of time until the event of interest in these settings, and investigate issues in the design of such trials when there is a choice of using both imperfect and perfect diagnostic tests.     

Machine learning algorithms to predict the 1 year unfavourable prognosis for advanced schistosomiasis

Short-term prognosis of advanced schistosomiasis has not been well studied. We aimed to construct prognostic models using machine learning algorithms and to identify the most important predictors by utilising routinely available data under the government medical assistance programme. An established database of advanced schistosomiasis in Hunan, China was utilised for analysis. A total of 9541 patients for the period from January 2008 to December 2018 were enrolled in this study. Candidate predictors were selected from demographics, clinical features, medical examinations and test results. We applied five machine learning algorithms to construct 1 year prognostic models: logistic regression (LR), decision tree (DT), random forest (RF), artificial neural network (ANN) and extreme gradient boosting (XGBoost). An area under the receiver operating characteristic curve (AUC) was used to evaluate the model performance. The important predictors of the optimal model for unfavourable prognosis within 1 year were identified and ranked. There were 1249 (13.1%) cases having unfavourable prognoses within 1 year of discharge. The mean age of all participants was 61.94 years, of whom 70.9% were male. In general, XGBoost showed the best predictive performance with the highest AUC (0.846; 95% confidence interval (CI): 0.821, 0.871), compared with LR (0.798; 95% CI: 0.770, 0.827), DT (0.766; 95% CI: 0.733, 0.800), RF (0.823; 95% CI: 0.796, 0.851), and ANN (0.806; 95% CI: 0.778, 0.835). Five most important predictors identified by XGBoost were ascitic fluid volume, haemoglobin (HB), total bilirubin (TB), albumin (ALB), and platelets (PT). We proposed XGBoost as the best algorithm for the evaluation of a 1 year prognosis of advanced schistosomiasis. It is considered to be a simple and useful tool for the short-term prediction of an unfavourable prognosis for advanced schistosomiasis in clinical settings.     

Diagnostic test accuracy for detecting Schistosoma japonicum and S. mekongi in humans: A systematic review and meta-analysis

BackgroundMost of national schistosomiasis elimination programmes in Asia are relying on stool examination, particularly Kato Katz stool examination technique for regular transmission monitoring. However, the Kato-Katz technique has shown low sensitivity for the detection of light-intensity infections, and therefore highly sensitive diagnostic tools are urgently required to monitor prevalence of infection in low transmission settings. The objective of this systematic review was to evaluate and synthesize the performance of diagnostic tests for detecting Schistosoma japonicum and S. mekongi infection in people living in endemic areas.Methodology/Principal findingsWe comprehensively searched these nine electronic databases and other resources until July 2019, with no language or publication limits: PubMed, EMBASE, MEDLINE, Web of Science, BIOSIS Citation Index, HTA, CINAHL PLUS, The Cochrane Library, and PsycINFO. We included original studies that assessed diagnostic performance using antibody, antigen, and molecular tests with stool examination test as a reference standard. Two reviewers independently extracted a standard set of data and assessed study quality. We estimated the pooled estimates of sensitivity and specificity for each index test. We used diagnostic odds ratio to determine the overall accuracy and hierarchical summary receiver operating characteristics (HSROC) curve to assess the index tests performance.Fifteen studies (S. japonicum [n = 13] and S. mekongi [n = 2]) testing 15,303 participants were included in the review. Five studies reported performance of enzyme-linked immunosorbent assay (ELISA), seven studies reported indirect hemagglutination assay (IHA), and four studies reported polymerase chain reaction (PCR) for detecting S. japonicum. The pooled sensitivity and specificity were 0.93 (95% CI: 0.84–0.98) and 0.40 (95% CI: 0.29–0.53) for ELISA, 0.97 (95% CI: 0.90–0.99) and 0.66 (95% CI: 0.58–0.73) for IHA, and 0.89 (95% CI: 0.71–0.96) and 0.49 (95% CI: 0.29–0.69) for PCR respectively. A global summary indicated the best performance for IHA, closely followed by ELISA. We were unable to perform meta-analysis for S. mekongi due to insufficient number of studies.Conclusions/SignificanceIHA showed the highest detection accuracy for S. japonicum. Further studies are needed to determine the suitable diagnostic methods to verify the absence of transmission of S. mekongi and also to compare detection accuracy against more sensitive reference standards such as PCR.     

Gas chromatography mass spectrometry-based metabolite profiling in plants

The concept of metabolite profiling has been around for decades, but technical innovations are now enabling it to be carried out on a large scale with respect to the number of both metabolites measured and experiments carried out. Here we provide a detailed protocol for gas chromatography mass spectrometry (GC-MS)-based metabolite profiling that offers a good balance of sensitivity and reliability, being considerably more sensitive than NMR and more robust than liquid chromatography-linked mass spectrometry. We summarize all steps from collecting plant material and sample handling to derivatization procedures, instrumentation settings and evaluating the resultant chromatograms. We also define the contribution of GC-MS-based metabolite profiling to the fields of diagnostics, gene annotation and systems biology. Using the protocol described here facilitates routine determination of the relative levels of 300-500 analytes of polar and nonpolar extracts in approximately 400 experimental samples per week per machine.     

Emerging technologies for salivaomics in cancer detection

Salivary diagnostics has great potential to be used in the early detection and prevention of many cancerous diseases. If implemented with rigour and efficiency, it can result in improving patient survival times and achieving earlier diagnosis of disease. Recently, extraordinary efforts have been taken to develop non‐invasive technologies that can be applied without complicated and expensive procedures. Saliva is a biofluid that has demonstrated excellent properties and can be used as a diagnostic fluid, since many of the biomarkers suggested for cancers can also be found in whole saliva, apart from blood or other body fluids. The currently accepted gold standard methods for biomarker development include chromatography, mass spectometry, gel electrophoresis, microarrays and polymerase chain reaction‐based quantification. However, salivary diagnostics is a flourishing field with the rapid development of novel technologies associated with point‐of‐care diagnostics, RNA sequencing, electrochemical detection and liquid biopsy. Those technologies will help introduce population‐based screening programs, thus enabling early detection, prognosis assessment and disease monitoring. The purpose of this review is to give a comprehensive update on the emerging diagnostic technologies and tools for the early detection of cancerous diseases based on saliva.     

Methods for Identifying Neisseria meningitidis Carriers: A Multi-Center Study in the African Meningitis Belt

Objective

Detection of meningococcal carriers is key to understanding the epidemiology of Neisseria meningitidis, yet no gold standard has been established. Here, we directly compare two methods for collecting pharyngeal swabs to identify meningococcal carriers.

Methods

We conducted cross-sectional surveys of schoolchildren at multiple sites in Africa to compare swabbing the posterior pharynx behind the uvula (U) to swabbing the posterior pharynx behind the uvula plus one tonsil (T). Swabs were cultured immediately and analyzed using molecular methods.

Results

One thousand and six paired swab samples collected from schoolchildren in four countries were analyzed. Prevalence of meningococcal carriage was 6.9% (95% CI: 5.4-8.6%) based on the results from both swabs, but the observed prevalence was lower based on one swab type alone. Prevalence based on the T swab or the U swab alone was similar (5.2% (95% CI: 3.8-6.7%) versus 4.9% (95% CI: 3.6-6.4%) respectively (p=0.6)). The concordance between the two methods was 96.3% and the kappa was 0.61 (95% CI: 0.50-0.73), indicating good agreement.

Conclusions

These two commonly used methods for collecting pharyngeal swabs provide consistent estimates of the prevalence of carriage, but both methods misclassified carriers to some degree, leading to underestimates of the prevalence.     

Reduction in the prevalence and intensity of hookworm infections after praziquantel treatment for schistosomiasis infection

Praziquantel exhibits activity against all major human schistosome parasites and has become the cornerstone for treatment and morbidity control of schistosomiasis. Praziquantel is also active against a wide range of trematodes, human and veterinary cestodes and displays cysticidal effects. To the best of our knowledge anthelminthic properties have never been documented. Here, we report a study among 96 schoolchildren from an area highly endemic for Schistosoma mansoni and hookworm infection, and place particular emphasis on the effect of praziquantel on the prevalence and intensity of hookworm infections. Stool specimens were screened over several consecutive days prior and 4 weeks after systematic administration of praziquantel. We found a significant reduction in the prevalence of hookworm infection from 75.0 to 40.6% (odds ratio (OR)=0.21; 95% confidence interval (CI): 0.11-0.40). Infection intensities, expressed by geometric mean egg counts of all children, were also reduced significantly from 10.7 to 2.0 eggs per gram stool (paired t-test=7.78, P<0.001). If these findings are confirmed in other epidemiological settings - following a similarly sensitive diagnostic approach - they might become of considerable relevance. In areas where both schistosome and hookworm coexist, and praziquantel is being recommended for schistosomiasis control, large-scale application of this drug might also reduce the burden of hookworms.