Correction: Comparison of Four Active SARS-CoV-2 Surveillance Strategies in Representative Population Sample Points: Two-Factor Factorial Randomized Controlled Trial.

[This corrects the article DOI: 10.2196/44204.].

Diagnostic performance of host protein signatures as a triage test for active pulmonary TB.

The current four-symptom screen recommended by the World Health Organization (WHO) is widely used as screen to initiate diagnostic testing for active pulmonary tuberculosis (TB), yet the performance is poor especially when TB prevalence is low. In contrast, more sensitive molecular tests are less suitable for placement at primary care level in low-resource settings. In order to meet the WHO End TB targets, new diagnostic approaches are urgently needed to find the missing undiagnosed cases. Proteomics-derived blood host biomarkers have been explored because protein detection technologies are suitable for the point-of-care setting and could meet cost targets. This study aimed to find a biomarker signature that fulfills WHO's target product profile (TPP) for a TB screening. Twelve blood-based protein biomarkers from three sample populations (Vietnam, Peru, and South Africa) were analyzed individually and in combinations via advanced statistical methods and machine learning algorithms. The combination of I-309, SYWC and kallistatin showed the most promising results to discern active TB throughout the data sets meeting the TPP for a triage test in adults from two countries (Peru and South Africa). The top-performing individual markers identified at the global level (I-309 and SYWC) were also among the best-performing markers at country level in South Africa and Vietnam. This analysis clearly shows that a host protein biomarker assay is feasible in adults for certain geographical regions based on one or two biomarkers with a performance that meets minimal WHO TPP criteria.

Diagnostic yield of urine lipoarabinomannan and sputum tuberculosis tests in people living with HIV: a systematic review and meta-analysis of individual participant data.

BACKGROUND: Sputum is the most widely used sample to diagnose active tuberculosis, but many people living with HIV are unable to produce sputum. Urine, in contrast, is readily available. We hypothesised that sample availability influences the diagnostic yield of various tuberculosis tests. METHODS: In this systematic review and meta-analysis of individual participant data, we compared the diagnostic yield of point-of-care urine-based lipoarabinomannan tests with that of sputum-based nucleic acid amplification tests (NAATs) and sputum smear microscopy (SSM). We used microbiologically confirmed tuberculosis based on positive culture or NAAT from any body site as the denominator and accounted for sample provision. We searched PubMed, Web of Science, Embase, African Journals Online, and clinicaltrials.gov from database inception to Feb 24, 2022 for randomised controlled trials, cross-sectional studies, and cohort studies that assessed urine lipoarabinomannan point-of-care tests and sputum NAATs for active tuberculosis detection in participants irrespective of tuberculosis symptoms, HIV status, CD4 cell count, or study setting. We excluded studies in which recruitment was not consecutive, systematic, or random; provision of sputum or urine was an inclusion criterion; less than 30 participants were diagnosed with tuberculosis; early research assays without clearly defined cutoffs were tested; and humans were not studied. We extracted study-level data, and authors of eligible studies were invited to contribute deidentified individual participant data. The main outcomes were the tuberculosis diagnostic yields of urine lipoarabinomannan tests, sputum NAATs, and SSM. Diagnostic yields were predicted using Bayesian random-effects and mixed-effects meta-analyses. This study is registered with PROSPERO, CRD42021230337. FINDINGS: We identified 844 records, from which 20 datasets and 10 202 participants (4561 [45%] male participants and 5641 [55%] female participants) were included in the meta-analysis. All studies assessed sputum Xpert (MTB/RIF or Ultra, Cepheid, Sunnyvale, CA, USA) and urine Alere Determine TB LAM (AlereLAM, Abbott, Chicago, IL, USA) in people living with HIV aged 15 years or older. Nearly all (9957 [98%] of 10 202) participants provided urine, and 82% (8360 of 10 202) provided sputum within 2 days. In studies that enrolled unselected inpatients irrespective of tuberculosis symptoms, only 54% (1084 of 1993) of participants provided sputum, whereas 99% (1966 of 1993) provided urine. Diagnostic yield was 41% (95% credible interval [CrI] 15-66) for AlereLAM, 61% (95% Crl 25-88) for Xpert, and 32% (95% Crl 10-55) for SSM. Heterogeneity existed across studies in the diagnostic yield, influenced by CD4 cell count, tuberculosis symptoms, and clinical setting. In predefined subgroup analyses, all tests had higher yields in symptomatic participants, and AlereLAM yield was higher in those with low CD4 counts and inpatients. AlereLAM and Xpert yields were similar among inpatients in studies enrolling unselected participants who were not assessed for tuberculosis symptoms (51% vs 47%). AlereLAM and Xpert together had a yield of 71% in unselected inpatients, supporting the implementation of combined testing strategies. INTERPRETATION: AlereLAM, with its rapid turnaround time and simplicity, should be prioritised to inform tuberculosis therapy among inpatients who are HIV-positive, regardless of symptoms or CD4 cell count. The yield of sputum-based tuberculosis tests is undermined by people living with HIV who cannot produce sputum, whereas nearly all participants are able to provide urine. The strengths of this meta-analysis are its large size, the carefully harmonised denominator, and the use of Bayesian random-effects and mixed-effects models to predict yields; however, data were geographically restricted, clinically diagnosed tuberculosis was not considered in the denominator, and little information exists on strategies for obtaining sputum samples. FUNDING: FIND, the Global Alliance for Diagnostics.

Comparison of Four Active SARS-CoV-2 Surveillance Strategies in Representative Population Sample Points: Two-Factor Factorial Randomized Controlled Trial.

BACKGROUND: The COVID-19 pandemic is characterized by rapid increases in infection burden owing to the emergence of new variants with higher transmissibility and immune escape. To date, monitoring the COVID-19 pandemic has mainly relied on passive surveillance, yielding biased epidemiological measures owing to the disproportionate number of undetected asymptomatic cases. Active surveillance could provide accurate estimates of the true prevalence to forecast the evolution of the pandemic, enabling evidence-based decision-making. OBJECTIVE: This study compared 4 different approaches of active SARS-CoV-2 surveillance focusing on feasibility and epidemiological outcomes. METHODS: A 2-factor factorial randomized controlled trial was conducted in 2020 in a German district with 700,000 inhabitants. The epidemiological outcome comprised SARS-CoV-2 prevalence and its precision. The 4 study arms combined 2 factors: individuals versus households and direct testing versus testing conditioned on symptom prescreening. Individuals aged ≥7 years were eligible. Altogether, 27,908 addresses from 51 municipalities were randomly allocated to the arms and 15 consecutive recruitment weekdays. Data collection and logistics were highly digitized, and a website in 5 languages enabled low-barrier registration and tracking of results. Gargle sample collection kits were sent by post. Participants collected a gargle sample at home and mailed it to the laboratory. Samples were analyzed with reverse transcription loop-mediated isothermal amplification (RT-LAMP); positive and weak results were confirmed with real-time reverse transcription-polymerase chain reaction (RT-PCR). RESULTS: Recruitment was conducted between November 18 and December 11, 2020. The response rates in the 4 arms varied between 34.31% (2340/6821) and 41.17% (2043/4962). The prescreening classified 16.61% (1207/7266) of the patients as COVID-19 symptomatic. Altogether, 4232 persons without prescreening and 7623 participating in the prescreening provided 5351 gargle samples, of which 5319 (99.4%) could be analyzed. This yielded 17 confirmed SARS-CoV-2 infections and a combined prevalence of 0.36% (95% CI 0.14%-0.59%) in the arms without prescreening and 0.05% (95% CI 0.00%-0.108%) in the arms with prescreening (initial contacts only). Specifically, we found a prevalence of 0.31% (95% CI 0.06%-0.58%) for individuals and 0.35% (95% CI 0.09%-0.61%) for households, and lower estimates with prescreening (0.07%, 95% CI 0.0%-0.15% for individuals and 0.02%, 95% CI 0.0%-0.06% for households). Asymptomatic infections occurred in 27% (3/11) of the positive cases with symptom data. The 2 arms without prescreening performed the best regarding effectiveness and accuracy. CONCLUSIONS: This study showed that postal mailing of gargle sample kits and returning home-based self-collected liquid gargle samples followed by high-sensitivity RT-LAMP analysis is a feasible way to conduct active SARS-CoV-2 population surveillance without burdening routine diagnostic testing. Efforts to improve participation rates and integration into the public health system may increase the potential to monitor the course of the pandemic. TRIAL REGISTRATION: Deutsches Register Klinischer Studien (DRKS) DRKS00023271; https://tinyurl.com/3xenz68a. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s13063-021-05619-5.

Beyond rankings: Learning (more) from algorithm validation.

Challenges have become the state-of-the-art approach to benchmark image analysis algorithms in a comparative manner. While the validation on identical data sets was a great step forward, results analysis is often restricted to pure ranking tables, leaving relevant questions unanswered. Specifically, little effort has been put into the systematic investigation on what characterizes images in which state-of-the-art algorithms fail. To address this gap in the literature, we (1) present a statistical framework for learning from challenges and (2) instantiate it for the specific task of instrument instance segmentation in laparoscopic videos. Our framework relies on the semantic meta data annotation of images, which serves as foundation for a General Linear Mixed Models (GLMM) analysis. Based on 51,542 meta data annotations performed on 2,728 images, we applied our approach to the results of the Robust Medical Instrument Segmentation Challenge (ROBUST-MIS) challenge 2019 and revealed underexposure, motion and occlusion of instruments as well as the presence of smoke or other objects in the background as major sources of algorithm failure. Our subsequent method development, tailored to the specific remaining issues, yielded a deep learning model with state-of-the-art overall performance and specific strengths in the processing of images in which previous methods tended to fail. Due to the objectivity and generic applicability of our approach, it could become a valuable tool for validation in the field of medical image analysis and beyond.

Cost and cost-effectiveness of four different SARS-CoV-2 active surveillance strategies: evidence from a randomised control trial in Germany.

INTRODUCTION: The COVID-19 pandemic has entered its third year and continues to affect most countries worldwide. Active surveillance, i.e. testing individuals irrespective of symptoms, presents a promising strategy to accurately measure the prevalence of SARS-CoV-2. We aimed to identify the most cost-effective active surveillance strategy for COVID-19 among the four strategies tested in a randomised control trial between 18th November 2020 and 23rd December 2020 in Germany. The four strategies included: (A1) direct testing of individuals; (A2) direct testing of households; (B1) testing conditioned on upstream COVID-19 symptom pre-screening of individuals; and (B2) testing conditioned on upstream COVID-19 symptom pre-screening of households. METHODS: We adopted a health system perspective and followed an activity-based approach to costing. Resource consumption data were collected prospectively from a digital individual database, daily time records, key informant interviews and direct observations. Our cost-effectiveness analysis compared each strategy with the status quo and calculated the average cost-effective ratios (ACERs) for one primary outcome (sample tested) and three secondary outcomes (responder recruited, case detected and asymptomatic case detected). RESULTS: Our results showed that A2, with cost per sample tested at 52,89 EURO, had the lowest ACER for the primary outcome, closely followed by A1 (63,33 EURO). This estimate was much higher for both B1 (243,84 EURO) and B2 (181,06 EURO). CONCLUSION: A2 (direct testing at household level) proved to be the most cost-effective of the four evaluated strategies and should be considered as an option to strengthen the routine surveillance system in Germany and similar settings.

Addressing the diagnostic gap in hypertension through possible interventions and scale-up: A microsimulation study.

BACKGROUND: Cardiovascular diseases (CVDs) are the leading cause of mortality globally with almost a third of all annual deaths worldwide. Low- and middle-income countries (LMICs) are disproportionately highly affected covering 80% of these deaths. For CVD, hypertension (HTN) is the leading modifiable risk factor. The comparative impact of diagnostic interventions that improve either the accuracy, the reach, or the completion of HTN screening in comparison to the current standard of care has not been estimated. METHODS AND FINDINGS: This microsimulation study estimated the impact on HTN-induced morbidity and mortality in LMICs for four different scenarios: (S1) lower HTN diagnostic accuracy; (S2) improved HTN diagnostic accuracy; (S3) better implementation strategies to reach more persons with existing tools; and, lastly, (S4) the wider use of easy-to-use tools, such as validated, automated digital blood pressure measurement devices to enhance screening completion, in comparison to the current standard of care (S0). Our hypothetical population was parametrized using nationally representative, individual-level HPACC data and the global burden of disease data. The prevalence of HTN in the population was 31% out of which 60% remained undiagnosed. We investigated how the alteration of a yearly blood pressure screening event impacts morbidity and mortality in the population over a period of 10 years. The study showed that while improving test accuracy avoids 0.6% of HTN-induced deaths over 10 years (13,856,507 [9,382,742; 17,395,833]), almost 40 million (39,650,363 [31,34,233, 49,298,921], i.e., 12.7% [9.9, 15.8]) of the HTN-induced deaths could be prevented by increasing coverage and completion of a screening event in the same time frame. Doubling the coverage only would still prevent 3,304,212 million ([2,274,664; 4,164,180], 12.1% [8.3, 15.2]) CVD events 10 years after the rollout of the program. Our study is limited by the scarce data available on HTN and CVD from LMICs. We had to pool some parameters across stratification groups, and additional information, such as dietary habits, lifestyle choice, or the blood pressure evolution, could not be considered. Nevertheless, the microsimulation enabled us to include substantial heterogeneity and stochasticity toward the different income groups and personal CVD risk scores in the model. CONCLUSIONS: While it is important to consider investing in newer diagnostics for blood pressure testing to continuously improve ease of use and accuracy, more emphasis should be placed on screening completion.

Effectiveness and cost-effectiveness of four different strategies for SARS-CoV-2 surveillance in the general population (CoV-Surv Study): study protocol for a two-factorial randomized controlled multi-arm trial with cluster sampling.

BACKGROUND: To achieve higher effectiveness in population-based SARS-CoV-2 surveillance and to reliably predict the course of an outbreak, screening, and monitoring of infected individuals without major symptoms (about 40% of the population) will be necessary. While current testing capacities are also used to identify such asymptomatic cases, this rather passive approach is not suitable in generating reliable population-based estimates of the prevalence of asymptomatic carriers to allow any dependable predictions on the course of the pandemic. METHODS: This trial implements a two-factorial, randomized, controlled, multi-arm, prospective, interventional, single-blinded design with cluster sampling and four study arms, each representing a different SARS-CoV-2 testing and surveillance strategy based on individuals' self-collection of saliva samples which are then sent to and analyzed by a laboratory. The targeted sample size for the trial is 10,000 saliva samples equally allocated to the four study arms (2500 participants per arm). Strategies differ with respect to tested population groups (individuals vs. all household members) and testing approach (without vs. with pre-screening survey). The trial is complemented by an economic evaluation and qualitative assessment of user experiences. Primary outcomes include costs per completely screened person, costs per positive case, positive detection rate, and precision of positive detection rate. DISCUSSION: Systems for active surveillance of the general population will gain more importance in the context of pandemics and related disease prevention efforts. The pandemic parameters derived from such active surveillance with routine population monitoring therefore not only enable a prospective assessment of the short-term course of a pandemic, but also a more targeted and thus more effective use of local and short-term countermeasures. TRIAL REGISTRATION: ClinicalTrials.gov DRKS00023271 . Registered November 30, 2020, with the German Clinical Trials Register (Deutsches Register Klinischer Studien).

Effectiveness and cost-effectiveness of four different strategies for SARS-CoV-2 surveillance in the general population (CoV-Surv Study): a structured summary of a study protocol for a cluster-randomised, two-factorial controlled trial.

OBJECTIVES: In this cluster-randomised controlled study (CoV-Surv Study), four different "active" SARS-CoV-2 testing strategies for general population surveillance are evaluated for their effectiveness in determining and predicting the prevalence of SARS-CoV-2 infections in a given population. In addition, the costs and cost-effectiveness of the four surveillance strategies will be assessed. Further, this trial is supplemented by a qualitative component to determine the acceptability of each strategy. Findings will inform the choice of the most effective, acceptable and affordable strategy for SARS-CoV-2 surveillance, with the most effective and cost-effective strategy becoming part of the local public health department's current routine health surveillance activities. Investigating its everyday performance will allow us to examine the strategy's applicability to real time prevalence prediction and the usefulness of the resulting information for local policy makers to implement countermeasures that effectively prevent future nationwide lockdowns. The authors would like to emphasize the importance and relevance of this study and its expected findings in the context of population-based disease surveillance, especially in respect to the current SARS-CoV-2 pandemic. In Germany, but also in many other countries, COVID-19 surveillance has so far largely relied on passive surveillance strategies that identify individuals with clinical symptoms, monitor those cases who then tested positive for the virus, followed by tracing of individuals in close contact to those positive cases. To achieve higher effectiveness in population surveillance and to reliably predict the course of an outbreak, screening and monitoring of infected individuals without major symptoms (about 40% of the population) will be necessary. While current testing capacities are also used to identify such asymptomatic cases, this rather passive approach is not suitable in generating reliable population-based estimates of the prevalence of asymptomatic carriers to allow any dependable predictions on the course of the pandemic. To better control and manage the SARS-CoV-2 pandemic, current strategies therefore need to be complemented by an active surveillance of the wider population, i.e. routinely conducted testing and monitoring activities to identify and isolate infected individuals regardless of their clinical symptoms. Such active surveillance strategies will enable more effective prevention of the spread of the virus as they can generate more precise population-based parameters during a pandemic. This essential information will be required in order to determine the best strategic and targeted short-term countermeasures to limit infection spread locally. TRIAL DESIGN: This trial implements a cluster-randomised, two-factorial controlled, prospective, interventional, single-blinded design with four study arms, each representing a different SARS-CoV-2 testing and surveillance strategy. PARTICIPANTS: Eligible are individuals age 7 years or older living in Germany's Rhein-Neckar Region who consent to provide a saliva sample (all four arms) after completion of a brief questionnaire (two arms only). For the qualitative component, different samples of study participants and non-participants (i.e. eligible for study, but refuse to participate) will be identified for additional interviews. For these interviews, only individuals age 18 years or older are eligible. INTERVENTION AND COMPARATOR: Of the four surveillance strategies to be assessed and compared, Strategy A1 is considered the gold standard for prevalence estimation and used to determine bias in other arms. To determine the cost-effectiveness, each strategy is compared to status quo, defined as the currently practiced passive surveillance approach. Strategy A1: Individuals (one per household) receive information and study material by mail with instructions on how to produce a saliva sample and how to return the sample by mail. Once received by the laboratory, the sample is tested for SARS-CoV-2 using Reverse Transcription Loop-mediated Isothermal Amplification (RT-LAMP). Strategy A2: Individuals (one per household) receive information and study material by mail with instructions on how to produce their own as well as saliva samples from each household member and how to return these samples by mail. Once received by the laboratory, the samples are tested for SARS-CoV-2 using RT-LAMP. Strategy B1: Individuals (one per household) receive information by mail on how to complete a brief pre-screening questionnaire which asks about COVID-19 related clinical symptoms and risk exposures. Only individuals whose pre-screening score crosses a defined threshold, will then receive additional study material by mail with instructions on how to produce a saliva sample and how to return the sample by mail. Once received by the laboratory, the saliva sample is tested for SARS-CoV-2 using RT-LAMP. Strategy B2: Individuals (one per household) receive information by mail on how to complete a brief pre-screening questionnaire which asks about COVID-19 related clinical symptoms. Only individuals whose pre-screening score crosses a defined threshold, will then receive additional study material by mail with instructions how to produce their own as well as saliva samples from each household member and how to return these samples by mail. Once received by the laboratory, the samples are tested for SARS-CoV-2 using RT-LAMP. In each strategy, RT-LAMP positive samples are additionally analyzed with qPCR in order to minimize the number of false positives. MAIN OUTCOMES: The identification of the one best strategy will be determined by a set of parameters. Primary outcomes include costs per correctly screened person, costs per positive case, positive detection rate, and precision of positive detection rate. Secondary outcomes include participation rate, costs per asymptomatic case, prevalence estimates, number of asymptomatic cases per study arm, ratio of symptomatic to asymptomatic cases per study arm, participant satisfaction. Additional study components (not part of the trial) include cost effectiveness of each of the four surveillance strategies compared to passive monitoring (i.e. status quo), development of a prognostic model to predict hospital utilization caused by SARS-CoV-2, time from test shipment to test application and time from test shipment to test result, and perception and preferences of the persons to be tested with regard to test strategies. RANDOMISATION: Samples are drawn in three batches of three continuous weeks. Randomisation follows a two-stage process. First, a total of 220 sampling points have been allocated to the three different batches. To obtain an integer solution, the Cox-algorithm for controlled rounding has been used. Afterwards, sample points have been drawn separately per batch, following a probability proportional to size (PPS) random sample. Second, for each cluster the same number of residential addresses is randomly sampled from the municipal registries (self-weighted sample of individuals). The 28,125 addresses drawn per municipality are then randomly allocated to the four study arms A1, A2, B1, and B2 in the ratio 5 to 2.5 to 14 to 7 based on the expected response rates in each arm and the sensitivity and specificity of the pre-screening tool as applied in strategy B1 and B2. Based on the assumptions, this allocation should yield 2500 saliva samples in each strategy. Although a municipality can be sampled by multiple batches and the overall number of addresses per municipality might vary, the number of addresses contacted in each arm is kept constant. BLINDING (MASKING): The design is single-blinded, meaning the staff conducting the SARS-CoV-2 tests are unaware of the study arm assignment of each single participant and test sample. SAMPLE SIZES: Total sample size for the trial is 10,000 saliva samples equally allocated to the four study arms (i.e. 2,500 participants per arm). For the qualitative component, up to 60 in-depth interviews will be conducted with about 30 study participants (up to 15 in each arm A and B) and 30 participation refusers (up to 15 in each arm A and B) purposefully selected from the quantitative study sample to represent a variety of gender and ages to explore experiences with admission or rejection of study participation. Up to 25 asymptomatic SARS-CoV-2 positive study participants will be purposefully selected to explore the way in which asymptomatic men and women diagnosed with SARS-CoV-2 give meaning to their diagnosis and to the dialectic between feeling concurrently healthy and yet also being at risk for transmitting COVID-19. In addition, 100 randomly selected study participants will be included to explore participants' perspective on testing processes and implementation. TRIAL STATUS: Final protocol version is "Surveillance_Studienprotokoll_03Nov2020_v1_2" from November 3, 2020. Recruitment started November 18, 2020 and is expected to end by or before December 31, 2020. TRIAL REGISTRATION: The trial is currently being registered with the German Clinical Trials Register (Deutsches Register Klinischer Studien), DRKS00023271 ( https://www.drks.de/drks_web/navigate.do?navigationId=trial . HTML&TRIAL_ID=DRKS00023271). Retrospectively registered 30 November 2020. FULL PROTOCOL: The full protocol is attached as an additional file, accessible from the Trials website (Additional file 1). In the interest in expediting dissemination of this material, the familiar formatting has been eliminated; this Letter serves as a summary of the key elements of the full protocol.

Prediction of local COVID-19 spread in Heidelberg.

Since the first identified case of COVID-19 in Wuhan, China, the disease has developed into a pandemic, imposing a major challenge for health authorities and hospitals worldwide. Mathematical transmission models can help hospitals to anticipate and prepare for an upcoming wave of patients by forecasting the time and severity of infections. Taking the city of Heidelberg as an example, we predict the ongoing spread of the disease for the next months including hospital and ventilator capacity and consider the possible impact of currently imposed countermeasures.

Automatic classification of farms and traders in the pig production chain.

The trade in live pigs is an essential risk factor in the spread of animal diseases. Traders play a key role in the trade network, as they are logistics hubs and responsible for large animal movements. In order to implement targeted control measures in case of a disease outbreak, it is hence strongly advisable to use information about the holding type in the pig production chain. However, in many datasets the types of the producing farms or the fact whether the agent is a trader are unknown. In this paper we introduce two indices that can be used to identify the position of a producing farm in the pig production chain and more importantly, identify traders. This was realized partially through a novel dynamic programming algorithm. Analyzing the pig trade network in Germany from 2005 to 2007, we demonstrate that our algorithm is very sensitive in detecting traders. Since the methodology can easily be applied to trade networks in other countries with similar infrastructure and legislation, we anticipate its use for augmenting the datasets in further network analyses and targeting control measures. For further usage, we have developed an R package which can be found in the supplementary material to this manuscript.