Cost-effective pooling of DNA from nasopharyngeal swab samples for large-scale detection of bacteria by real-time PCR

Nested Group Testing Procedure

We investigated the false-negative, true-negative, false-positive, and true-positive predictive values from a general group testing procedure for a heterogeneous population. We show that its false (true)-negative predictive value of a specimen is larger (smaller), and the false (true)-positive predictive value is smaller (larger) than that from individual testing procedure, where the former is in aversion. Then we propose a nested group testing procedure, and show that it can keep the sterling characteristics and also improve the false-negative predictive values for a specimen, not larger than that from individual testing. These characteristics are studied from both theoretical and numerical points of view. The nested group testing procedure is better than individual testing on both false-positive and false-negative predictive values, while retains the efficiency as a basic characteristic of a group testing procedure. Applications to Dorfman's, Halving and Sterrett procedures are discussed. Results from extensive simulation studies and an application to malaria infection in microscopy-negative Malawian women exemplify the findings.

Evaluation of the influenza and respiratory syncytial virus (RSV) targets in the AusDiagnostics SARS-CoV-2, Influenza and RSV 8-well assay: sample pooling increases testing throughput

During the COVID-19 pandemic, sample pooling has proven an effective strategy to overcome the limitations of reagent shortages and expand laboratory testing capacity. The inclusion of influenza and respiratory syncytial virus (RSV) in a multiplex tandem PCR platform with SARS-CoV-2 provides useful diagnostic and infection control information. This study aimed to evaluate the performance of the influenza and RSV targets in the AusDiagnostics SARS-CoV-2, Influenza and RSV 8-well assay, including the effect of pooling samples on target detection. RSV target detection in clinical samples was compared to the Cepheid Xpert Xpress Flu/RSV assay as a reference standard. Samples were then tested in pools of four and detection rates were compared. Owing to the unavailability of clinical samples for influenza, only the effect of sample pooling on simulated samples was evaluated for these targets. RSV was detected in neat clinical samples with a positive percent agreement (PPA) of 100% and negative percent agreement (NPA) of 99.5% compared to the reference standard, demonstrating 99.7% agreement. This study demonstrates that sample pooling by four increases the average Ct value by 2.24, 2.29, 2.20 and 1.91 cycles for the target's influenza A, influenza A typing, influenza B and RSV, respectively. The commercial AusDiagnostics SARS-CoV-2, Influenza and RSV 8-well assay was able to detect influenza and RSV at an intermediate concentration within the limit of detection of the assay. Further studies to explore the applicability of sample pooling at the lower limit of detection of the assay is needed. Nevertheless, sample pooling has shown to be a viable strategy to increase testing throughput and reduce reagent usage. In addition, the multiplexed platform targeting various respiratory viruses assists with public health and infection control responses, clinical care, and patient management.

Pooled Testing Strategies for SARS-CoV-2 diagnosis: A comprehensive review

SARS-CoV-2 has surged across the globe causing the ongoing COVID-19 pandemic. Systematic testing to facilitate index case isolation and contact tracing is needed for efficient containment of viral spread. The major bottleneck in leveraging testing capacity has been the lack of diagnostic resources. Pooled testing is a potential approach that could reduce cost and usage of test kits. This method involves pooling individual samples and testing them 'en bloc'. Only if the pool tests positive, retesting of individual samples is performed. Upon reviewing recent articles on this strategy employed in various SARS-CoV-2 testing scenarios, we found substantial diversity emphasizing the requirement of a common protocol. In this article, we review various theoretically simulated and clinically validated pooled testing models and propose practical guidelines on applying this strategy for large scale screening. If implemented properly, the proposed approach could contribute to proper utilization of testing resources and flattening of infection curve.

Array testing for multiplex assays

Group testing involves pooling individual specimens (e.g., blood, urine, swabs, etc.) and testing the pools for the presence of disease. When the proportion of diseased individuals is small, group testing can greatly reduce the number of tests needed to screen a population. Statistical research in group testing has traditionally focused on applications for a single disease. However, blood service organizations and large-scale disease surveillance programs are increasingly moving towards the use of multiplex assays, which measure multiple disease biomarkers at once. Tebbs and others (2013, Two-stage hierarchical group testing for multiple infections with application to the Infertility Prevention Project. Biometrics69, 1064-1073) and Hou and others (2017, Hierarchical group testing for multiple infections. Biometrics73, 656-665) were the first to examine hierarchical group testing case identification procedures for multiple diseases. In this article, we propose new non-hierarchical procedures which utilize two-dimensional arrays. We derive closed-form expressions for the expected number of tests per individual and classification accuracy probabilities and show that array testing can be more efficient than hierarchical procedures when screening individuals for multiple diseases at once. We illustrate the potential of using array testing in the detection of chlamydia and gonorrhea for a statewide screening program in Iowa. Finally, we describe an R/Shiny application that will help practitioners identify the best multiple-disease case identification algorithm.

Pooled saliva CMV DNA detection: A viable laboratory technique for universal CMV screening of healthy newborns

BACKGROUND

Most infants with congenital cytomegalovirus (cCMV) have no overt manifestations at birth, yet may later develop CMV-related sensorineural hearing loss (SNHL). With targeted screening, many asymptomatic neonates are missed and lose the opportunity for timely anti-viral treatment to ameliorate SNHL. Saliva is the preferred screening specimen given its ease of collection.

OBJECTIVES

Assess a pooled saliva CMV DNA detection technique for cCMV screening of healthy full-term neonates.

STUDY DESIGN

We conducted a prospective laboratory CMV PCR screening study in a secondary hospital from March-June 2019. Saliva specimens were obtained from 1000 newborns two-four hours after birth. Specimens were analyzed for CMV DNA with a real-time PCR platform (Altona) in pools of 10 and individually (40 μL and 400 μL, respectively). Neonates with positive saliva CMV DNA detection required urine CMV PCR testing to confirm cCMV diagnosis.

RESULTS

From the 1000 saliva samples, there were 6 urine-confirmed cCMV cases, yielding a prevalence rate of 0.6 %. The specificity was high for both pooled and individual saliva sampling (99.9 % and 98.1 %, respectively). The positive predictive value of the pooled sample was 85.7 %, compared to 24.0 % for a single saliva sample.

CONCLUSIONS

Pooling saliva of healthy newborns appears to be a reliable method to identify asymptomatic cCMV infection when positive results are confirmed by urine CMV DNA. Pooling in sizes appropriate to the cCMV prevalence rate may improve the laboratory workflow and decrease costs. Further studies should evaluate the clinical implications of this widespread cCMV pooled screening technique.

Considerations for Pooled Testing of Employees for SARS-CoV-2

OBJECTIVES

To identify important background information on pooled tested of employees that employers workers, and health authorities should consider.

METHODS

This paper is a commentary based on the review by the authors of pertinent literature generally from preprints in medrixiv.org prior to August 2020.

RESULTS/CONCLUSIONS

Pooled testing may be particularly useful to employers in communities with low prevalence of COVID-19. It can be used to reduce the number of tests and associated financial costs. For effective and efficient pooled testing employers should consider it as part of a broader, more comprehensive workplace COVID-19 prevention and control program. Pooled testing of asymptomatic employees can prevent transmission of SARS-CoV-2 and help assure employers and customers that employees are not infectious.

Acquisition of multidrug-resistant bacteria and colistin resistance genes in French medical students on internships abroad

BACKGROUND

Acquisition of multidrug resistant bacteria (MDR) and colistin resistance genes by international travellers has been demonstrated. Studies conducted in medical students during internships abroad are scant.

METHODS

Nasopharyngeal, rectal, and vaginal swabs samples were collected from 382 French medical students before and after travel to investigate the acquisition of MDR bacteria. The bacterial diversity in the samples was assessed by culture on selective media. We also genetically characterised the isolates of MDR bacteria including Extended-spectrum beta-lactamase-producing Enterobacteriaceae (ESBL-E), methicillin-resistant Staphylococcus aureus (MRSA), and Carbapenemase-producing Enterobacteriacae (CPE) using the real-time polymerase chain reaction method. The samples were collected from 293 students and were investigated for mcr colistin-resistance genes using RT-PCR directly on the samples, followed by conventional PCR and sequencing.

RESULTS

A proportion of 29.3% (112/382) of the participants had acquired ESBL-E and 2.6% (10/382) had acquired CPE. The most common species and ESBL-E encoding gene were Escherichia coli (125/127 isolates, 98.4%) and bla_CTX-M-A (121/127, 95.3%), respectively. A proportion of 6.8% (20/293) of the participants had acquired mcr-1 genes, followed by mcr-3 (1/293, 0.3%) and mcr-8 (1/293, 0.3%). We found that taking part in humanitarian missions to orphanages (aRR = 2.01, p < 0.0001), being in contact with children during travel (aRR = 1.78, p = 0.006), the primary destination of travel being Vietnam (aRR = 2.15, p < 0.0001) and north India (aRR = 2.41, p = 0.001), using antibiotics during travel (aRR = 1.77, p = 0.01), and studying in 2018 (aRR = 1.55, p = 0.03) were associated with the acquisition of ESBL-E. When the primary destination of travel was Vietnam (aRR = 2.74, p < 0.0001) and the year of study was 2018 (aRR = 1.93, p < 0.002), this was associated with acquisition of colistin resistance genes.

CONCLUSION

Medical students are at a potential risk of acquiring ESBL-E, CPE and colistin resistance genes. A number of risk factors have been identified, which may be used to develop targeted preventive measures.

Simple Questionnaires to Improve Pooling Strategies for SARS-CoV-2 Laboratory Testing

Background:

Liberal PCR testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is key to contain the coronavirus disease 2019 (COVID-19) pandemic. Combined multi-sample testing in pools instead of single tests might enhance laboratory capacity and reduce costs, especially in low- and middle-income countries.

Objective:

The purpose of our study was to assess the value of a simple questionnaire to guide and further improve pooling strategies for SARS-CoV-2 laboratory testing.

Methods:

Pharyngeal swabs for SARS-CoV-2 testing were obtained from healthcare and police staff, hospital inpatients, and nursing home residents in the southwestern part of Germany. We designed a simple questionnaire, which included questions pertaining to a suggestive clinical symptomatology, recent travel history, and contact with confirmed cases to stratify an individual’s pre-test probability of having contracted COVID-19. The questionnaire was adapted repeatedly in face of the unfolding pandemic in response to the evolving epidemiology and observed clinical symptomatology. Based on the response patterns, samples were either tested individually or in multi-sample pools. We compared the pool positivity rate and the number of total PCR tests required to obtain individual results between this questionnaire-based pooling strategy and randomly assembled pools.

Findings:

Between March 11 and July 5, 2020, we processed 25,978 samples using random pooling (n = 6,012; 23.1%) or questionnaire-based pooling (n = 19,966; 76.9%). The overall prevalence of SARS-CoV-2 was 0.9% (n = 238). Pool positivity (14.6% vs. 1.2%) and individual SARS-CoV-2 prevalence (3.4% vs. 0.1%) were higher in the random pooling group than in the questionnaire group. The average number of PCR tests needed to obtain the individual result for one participant was 0.27 tests in the random pooling group, as compared to 0.09 in the questionnaire-based pooling group, leading to a laboratory capacity increase of 73% and 91%, respectively, as compared to single PCR testing.

Conclusions:

Strategies that combine pool testing with a questionnaire-based risk stratification can increase laboratory testing capacities for COVID-19 and might be important tools, particularly in resource-constrained settings.

Sample pooling methods for efficient pathogen screening: Practical implications

Due to the large number of negative tests, individually screening large populations for rare pathogens can be wasteful and expensive. Sample pooling methods improve the efficiency of large-scale pathogen screening campaigns by reducing the number of tests and reagents required to accurately categorize positive and negative individuals. Such methods rely on group testing theory which mainly focuses on minimizing the total number of tests; however, many other practical concerns and tradeoffs must be considered when choosing an appropriate method for a given set of circumstances. Here we use computational simulations to determine how several theoretical approaches compare in terms of (a) the number of tests, to minimize costs and save reagents, (b) the number of sequential steps, to reduce the time it takes to complete the assay, (c) the number of samples per pool, to avoid the limits of detection, (d) simplicity, to reduce the risk of human error, and (e) robustness, to poor estimates of the number of positive samples. We found that established methods often perform very well in one area but very poorly in others. Therefore, we introduce and validate a new method which performs fairly well across each of the above criteria making it a good general use approach.

Pooling of Upper Respiratory Specimens Using a SARS-CoV-2 Real-time RT-PCR Assay Authorized for Emergency Use in Low-Prevalence Populations for High-Throughput Testing

Background

Nucleic acid amplification testing is a critical tool for addressing the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. Specimen pooling can increase throughput and conserve testing resources but requires validation to ensure that reduced sensitivity does not increase the false-negative rate. We evaluated the performance of a real-time reverse transcription polymerase chain reaction (RT-PCR) test authorized by the US Food and Drug Administration (FDA) for emergency use for pooled testing of upper respiratory specimens.

Methods

Positive specimens were selected from 3 prevalence groups, 1%–3%, >3%–6%, and >6%–10%. Positive percent agreement (PPA) was assessed by pooling single-positive specimens with 3 negative specimens; performance was assessed using Passing-Bablok regression. Additionally, we assessed the distributions of RT-PCR cycle threshold (Ct) values for 3091 positive specimens.

Results

PPA was 100% for the 101 pooled specimens. There was a linear relationship between Ct values for pooled and single-tested specimens (r = 0.96–0.99; slope ≈ 1). The mean pooled Ct shifts at 40 cycles were 2.38 and 1.90, respectively, for the N1 and N3 targets. The median Cts for 3091 positive specimens were 25.9 (N1) and 24.7 (N3). The percentage of positive specimens with Cts between 40 and the shifted Ct was 1.42% (N1) and 0.0% (N3).

Conclusions

Pooled and individual testing of specimens positive for SARS-CoV-2 demonstrated 100% agreement, which demonstrates the viability of pooled specimens for SARS-COV-2 testing using a dual-target RT-PCR system. Pooled specimen testing can help increase testing capacity for SARS-CoV-2 with a low risk of false-negative results.

Simulation of pooled-sample analysis strategies for COVID-19 mass testing

OBJECTIVE

To evaluate two pooled-sample analysis strategies (a routine high-throughput approach and a novel context-sensitive approach) for mass testing during the coronavirus disease 2019 (COVID-19) pandemic, with an emphasis on the number of tests required to screen a population.

METHODS

We used Monte Carlo simulations to compare the two testing strategies for different infection prevalences and pooled group sizes. With the routine high-throughput approach, heterogeneous sample pools are formed randomly for polymerase chain reaction (PCR) analysis. With the novel context-sensitive approach, PCR analysis is performed on pooled samples from homogeneous groups of similar people that have been purposively formed in the field. In both approaches, all samples contributing to pools that tested positive are subsequently analysed individually.

FINDINGS

Both pooled-sample strategies would save substantial resources compared to individual analysis during surge testing and enhanced epidemic surveillance. The context-sensitive approach offers the greatest savings: for instance, 58-89% fewer tests would be required for a pooled group size of 3 to 25 samples in a population of 150 000 with an infection prevalence of 1% or 5%. Correspondingly, the routine high-throughput strategy would require 24-80% fewer tests than individual testing.

CONCLUSION

Pooled-sample PCR screening could save resources during COVID-19 mass testing. In particular, the novel context-sensitive approach, which uses pooled samples from homogeneous population groups, could substantially reduce the number of tests required to screen a population. Pooled-sample approaches could help countries sustain population screening over extended periods of time and thereby help contain foreseeable second-wave outbreaks.

Implementation of Antibody Rapid Diagnostic Testing versus Real-Time Reverse Transcription-PCR Sample Pooling in the Screening of COVID-19: a Case of Different Testing Strategies in Africa

Coronavirus disease 2019 (COVID-19) has wreaked havoc across the globe; although the number of cases in Africa remains lower than in other regions, it is on a gradual upward trajectory. To date, COVID-19 cases have been reported in 54 out of 55 African countries. However, due to limited severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) real-time reverse transcription-PCR (rRT-PCR) testing capacity and scarcity of testing reagents, it is probable that the total number of cases could far exceed published statistics. In this viewpoint, using Ghana, Malawi, South Africa, and Zimbabwe as examples of countries that have implemented different testing strategies, we argue that the implementation of sample pooling for rRT-PCR over antibody rapid diagnostic testing could have a greater impact in assessing disease burden. Sample pooling offers huge advantages compared to single test rRT-PCR, as it reduces diagnostic costs, personnel time, burnout, and analytical run times. Africa is already strained in terms of testing resources for COVID-19; hence, cheaper alternative ways need to be implemented to conserve resources, maximize mass testing, and reduce transmission in the wider population.

Regression analysis and variable selection for two-stage multiple-infection group testing data

Group testing, as a cost-effective strategy, has been widely used to perform large-scale screening for rare infections. Recently, the use of multiplex assays has transformed the goal of group testing from detecting a single disease to diagnosing multiple infections simultaneously. Existing research on multiple-infection group testing data either exclude individual covariate information or ignore possible retests on suspicious individuals. To incorporate both, we propose a new regression model. This new model allows us to perform a regression analysis for each infection using multiple-infection group testing data. Furthermore, we introduce an efficient variable selection method to reveal truly relevant risk factors for each disease. Our methodology also allows for the estimation of the assay sensitivity and specificity when they are unknown. We examine the finite sample performance of our method through extensive simulation studies and apply it to a chlamydia and gonorrhea screening data set to illustrate its practical usefulness.

Low prevalence of resistance genes in sheltered homeless population in Marseille, France, 2014-2018

Objectives: The present study has explored the prevalence and potential factors contributing to the presence of nasal/pharyngeal resistant genes in homeless people. Methods: During the winters 2014-2018, we enrolled sheltered homeless adults and controls and collected nasal/pharyngeal samples. Sixteen antibiotic resistance genes (ARGs), including genes encoding for beta-lactamases and colistin-resistance genes, were searched by real-time polymerase chain reaction (qPCR) performed directly on respiratory samples and followed by conventional PCR and sequencing. Results: Over a 5-year period, using qPCR, we identified in homeless group (n=715) the presence of bla _TEM (396/710, 54.7%), bla_SHV (27/708, 3.6%), bla _OXA-23 (1/708, 0.1%), while other genes including colistin-resistance genes (mcr-1 to mcr-5) were absent. We found a significantly higher proportion of ARG carriage among controls (74.1%) compared to homeless population (57.1%), p=0.038. Tobacco smoking (OR=4.72, p<0.0001) and respiratory clinical signs (OR=4.03, p=0.002) were most prevalent in homeless people, while vaccination against influenza (OR=0.31, p=0.016) was lower compared to controls. Among homeless people, type of housing (shelter A versus B, OR=1.59, p=0.006) and smoking tobacco (smoker versus non-smoker, OR=0.55, p=0.001) were independent factors associated with ARG carriage. By sequencing, we obtained a high diversity of bla _TEM and bla_SHV in both populations. Conclusion: The lower risk for ARGs in the homeless population could be explained by limited access to health care and subsequently reduced exposure to antibiotics.

Group testing case identification with biomarker information

Screening procedures for infectious diseases, such as HIV, often involve pooling individual specimens together and testing the pools. For diseases with low prevalence, group testing (or pooled testing) can be used to classify individuals as diseased or not while providing considerable cost savings when compared to testing specimens individually. The pooling literature is replete with group testing case identification algorithms including Dorfman testing, higher-stage hierarchical procedures, and array testing. Although these algorithms are usually evaluated on the basis of the expected number of tests and classification accuracy, most evaluations in the literature do not account for the continuous nature of the testing responses and thus invoke potentially restrictive assumptions to characterize an algorithm's performance. Commonly used case identification algorithms in group testing are considered and are evaluated by taking a different approach. Instead of treating testing responses as binary random variables (i.e., diseased/not), evaluations are made by exploiting an assay's underlying continuous biomarker distributions for positive and negative individuals. In doing so, a general framework to describe the operating characteristics of group testing case identification algorithms is provided when these distributions are known. The methodology is illustrated using two HIV testing examples taken from the pooling literature.

Electrical Detection of Pneumococcus through the Nanoparticle Decoration Method

A simple method of nanoparticle decoration can be used in the detection of pneumococcus. After the pneumococcal bacteria were captured by an antibody (pneumococcal C-polysaccharide (PnC) antibody) between the interdigitated electrodes, the gold nanoparticles conjugated with the PnC antibodies were let to bind onto an outer membrane of the bacteria. Upon successfully dense decoration, the bacteria surface will become conductive owing to the metal nanoparticles, and a distinctive conductance change between the electrodes can be observed. Since this success ratio, or the probability of the conductance change, reflects the concentration of the analyte, a number of repeated measurements can be used in the quantification of the bacteria. In this way, we have successfully detected S. pneumoniae in the range of 10–10⁸ CFU/mL. The limit of detection in this work is lower than that in the commercial detection kit. We hope that the nanoparticle decoration method will play a role in the facile detection of various bacteria.

Group testing regression models with dilution submodels

Group testing, where specimens are tested initially in pools, is widely used to screen individuals for sexually transmitted diseases. However, a common problem encountered in practice is that group testing can increase the number of false negative test results. This occurs primarily when positive individual specimens within a pool are diluted by negative ones, resulting in positive pools testing negatively. If the goal is to estimate a population-level regression model relating individual disease status to observed covariates, severe bias can result if an adjustment for dilution is not made. Recognizing this as a critical issue, recent binary regression approaches in group testing have utilized continuous biomarker information to acknowledge the effect of dilution. In this paper, we have the same overall goal but take a different approach. We augment existing group testing regression models (that assume no dilution) with a parametric dilution submodel for pool-level sensitivity and estimate all parameters using maximum likelihood. An advantage of our approach is that it does not rely on external biomarker test data, which may not be available in surveillance studies. Furthermore, unlike previous approaches, our framework allows one to formally test whether dilution is present based on the observed group testing data. We use simulation to illustrate the performance of our estimation and inference methods, and we apply these methods to 2 infectious disease data sets.