Data Collection Events Research Articles

Forging Community Partnerships to Examine Pesticide Exposure and Risk Perceptions among Latinx Farmworkers

Background: Research with structurally marginalized populations has a history of being extractive and providing a benefit primarily to the researchers rather than the participants. To address these concerns, there is an increasing emphasis in the field of environmental epidemiology to conduct community-based work and return exposure results to study participants. The aim of this work is to forge partnerships with trusted community organizations to support and help facilitate a mixed-methods investigation of pesticide exposure and risk perceptions among Latinx farmworkers, a population that faces many barriers to health protection. We further aim to create multi-media science communication products to effectively translate individual- and group-level study findings and to increase engagement and empowerment among participants. Methods: We focused six months of the one-year study period prior to data collection on community engagement and gaining trust, first with community partners and then study participants. We used various strategies, including social media engagement, holding teleconference and in-person meetings, and snowball sampling to establish and build upon existing partnerships. Focusing our study on the benefit to participants and research communication plan was key in obtaining buy-in from community partners. Results: We partnered with County Health Coalitions; migrant head start programs; non-profit organizations; housing providers; social media groups; and mobile health clinics to recruit participants, establish trusting relationships, and develop strategies to maximize the benefits of participating in the study. Examples include developing tailored research communication products; providing food boxes with organic, culturally appropriate food items; and providing childcare during data collection events. Conclusion: Forging collaborations with diverse individuals and community groups is critical in establishing trust with structurally marginalized populations. Our work provides an example of how to develop these partnerships, center community priorities, and maximize the benefit of participation both for study participants and partner organizations. Key Words: pesticides, farmworker, community-engaged research

All that glitters is not gold: Relational events models with spurious events

AbstractAs relational event models are an increasingly popular model for studying relational structures, the reliability of large-scale event data collection becomes more and more important. Automated or human-coded events often suffer from non-negligible false-discovery rates in event identification. And most sensor data are primarily based on actors’ spatial proximity for predefined time windows; hence, the observed events could relate either to a social relationship or random co-location. Both examples imply spurious events that may bias estimates and inference. We propose the Relational Event Model for Spurious Events (REMSE), an extension to existing approaches for interaction data. The model provides a flexible solution for modeling data while controlling for spurious events. Estimation of our model is carried out in an empirical Bayesian approach via data augmentation. Based on a simulation study, we investigate the properties of the estimation procedure. To demonstrate its usefulness in two distinct applications, we employ this model to combat events from the Syrian civil war and student co-location data. Results from the simulation and the applications identify the REMSE as a suitable approach to modeling relational event data in the presence of spurious events.

Integrating Process Mining with Discrete-Event Simulation for Dynamic Productivity Estimation in Heavy Civil Construction Operations

Construction companies are increasingly utilizing sensing technologies to automatically record different steps of the construction process in detail for effective monitoring and control. This generates a significant amount of event data that can be used to learn the underlying behavior of agents in a construction site using process mining. While process mining can be used to discover the real process and identify and analyze deviations and bottlenecks in operations, it is a backward-looking approach. On the other hand, discrete event simulation (DES) provides a means to forecast future performance from historical data to enable proactive decision-making by operation managers relating to their projects. However, this method is largely unused by the industry due to the specialized knowledge required to create the DES models. This paper thus proposes a framework that extends the utility of collecting event data and their process models, by transforming them into DES models for forecasting future performance. This framework also addresses another challenge of using DES relating to its inability to update itself as the project progresses. This challenge is addressed by using the Bayesian updating technique to continuously update the input parameters of the simulation model for the most up-to-date estimation based on data collected from the field. The proposed framework was validated on a real-world case study of an earthmoving operation. The results show that the process mining techniques could accurately discover the process model from the event data collected from the field. Furthermore, it was noted that continuous updating of DES model input parameters can provide accurate and reliable productivity estimates based on the actual data generated from the field. The proposed framework can help stakeholders to discover the underlying sequence of their operations, and enable timely, data-driven decisions regarding operations control.

Mitigation of Greenhouse Gas Emissions from Agricultural Fields through Bioresource Management

Efficient bioresource management can alter soil biochemistry and soil physical properties, leading to reduced greenhouse gas (GHG) emissions from agricultural fields. The objective of this study was to evaluate the role of organic amendments including biodigestate (BD), biochar (BC), and their combinations with inorganic fertilizer (IF) in increasing carbon sequestration potential and mitigation of GHG emissions from potato (Solanum tuberosum) fields. Six soil amendments including BD, BC, IF, and their combinations BDIF and BCIF, and control (C) were replicated four times under a completely randomized block design during the 2021 growing season of potatoes in Prince Edward Island, Canada. An LI-COR gas analyzer was used to monitor emissions of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) from treatment plots. Analysis of variance (ANOVA) results depicted higher soil moisture-holding capacities in plots at relatively lower elevations and comparatively lesser volumetric moisture content in plots at higher elevations. Soil moisture was also impacted by soil temperature and rainfall events. There was a significant effect of events of data collection, i.e., the length of the growing season (p-value ≤ 0.05) on soil surface temperature, leading to increased GHG emissions during the summer months. ANOVA results also revealed that BD, BC, and BCIF significantly (p-value ≤ 0.05) sequestered more soil organic carbon than other treatments. The six experimental treatments and twelve data collection events had significant effects (p-value ≤ 0.05) on the emission of CO2. However, the BD plots had the least emissions of CO2 followed by BC plots, and the emissions increased with an increase in atmospheric/soil temperature. Results concluded that organic fertilizers and their combinations with inorganic fertilizers help to reduce the emissions from the agricultural soils and enhance environmental sustainability.

Systems approach to improving traumatic brain injury care in Myanmar: a mixed-methods study from lived experience to discrete event simulation

ObjectivesTraumatic brain injury (TBI) is a global health problem, whose management in low-resource settings is hampered by fragile health systems and lack of access to specialist services. Improvement is complex,...

Respiration Monitoring using a Motion Tape Chest Band and Portable Wireless Sensing Node

The measurement of vital signs (such as respiration rate, body temperature, pulse, and blood pressure), especially during strenuous activities, is essential for physical performance and health monitoring. A variety of wearable chest band sensors have been developed, commercialized, and widely used in consumer and healthcare settings. The plethora of technology choices also means that each unique chest band sensor may require different data acquisition hardware and software systems, and data may not be transferable between platforms. Therefore, the objective of this work was to develop a low-cost, disposable, respiration sensor that could be attached onto any elastic chest band. The approach was to spray-coat graphene nanosheet (GNS)-based thin films onto unidirectionally stretchable elastic fabric to form a piezoresistive material. Snap buttons were incorporated at the ends of the fabric so that they could be attached onto any chest band, removed at any time, and replaced for a new data collection event. The resistive nature of the nanocomposite sensor means that they can be easily interfaced (e.g., using a voltage divider) with any existing data acquisition (DAQ) module while adding respiration monitoring capabilities. To facilitate testing of these nanocomposite respiration sensors, a miniature DAQ module with four sensing channels was also prototyped. Then, tests were performed with human subjects wearing a nanocomposite chest band and a reference commercial respiration monitoring chest band. Simultaneous measurements of subject respiration verified the respiration monitoring performance of these low-cost, disposable, nanocomposite fabric sensors.

Re-Imagining the Data Collection and Analysis Research Process by Proposing a Rapid Qualitative Data Collection and Analytic Roadmap Applied to the Dynamic Context of Precision Medicine

Our implementation science study focuses on implementing a new way of practice and offers methodological specificity about how to rapidly investigate an individually tailored precision medicine intervention. A qualitative study advancing a new methodology for speedily identifying barriers and enablers to implementation in the context of childhood cancer. Data were collected through rapid ethnography, coded using the Consolidated Framework for Implementation Research, and analysed by Sentiment Analysis. Thirty-eight data collection events occurred during 14 multidisciplinary tumour board meetings, 14 curation meetings, and 10 informal conversations. Sentiment Analysis distilled Consolidated Framework for Implementation Research codes to reveal key barriers and enablers to implementation. A traffic light labelling system has been used to present levels of positivity and negativity (green for strong enablers and red for strong barriers), highlighting levels of concern regarding implementation. Within the intervention design characteristics, “Adaptability” was the strongest enabler and “Design quality and safety” the strongest barrier. Among the contextual factors: “Networks and communication” were the strongest enabler, and “Available resources” were the strongest barrier. Overall, there was a higher percentage of negative sentiment towards intervention design characteristics and contextual factors than positive sentiment, while more concerns were raised about intervention design factors than contextual factors. This study offers a rapid qualitative data collection and analytic methodological roadmap for establishing barriers and enablers to a paediatric precision medicine intervention.

A mixed-methods longitudinal study of Marshallese infant feeding beliefs and experiences in the United States: a study protocol

BackgroundArkansas has the largest population of Marshallese Pacific Islanders residing in the continental United States. Marshallese are disproportionately burdened by poorer maternal and infant health outcomes. Exclusive breastfeeding can prevent or help mitigate maternal and infant health disparities. However, exclusive breastfeeding among United States Marshallese communities remains disproportionately low, and the reasons are not well documented. This paper describes the protocol of a mixed-methods concurrent triangulation longitudinal study designed to explore the beliefs and experiences that serve as barriers and/or facilitators to exclusive breastfeeding intention, initiation, and duration among Marshallese mothers in northwest Arkansas.MethodsThe mixed-methods design collects qualitative and quantitative data during simultaneous data collection events, at third trimester, six weeks postpartum, and six months postpartum. Quantitative and qualitative data will be analyzed separately and then synthesized during the interpretation phase. The research team will disseminate results to study participants, research stakeholders, the broader Marshallese community, and fellow researchers.DiscussionFindings and results will be presented in subsequent manuscripts upon completion of the study. This study will be an important first step to better understand beliefs and experiences to exclusive breastfeeding intention, initiation, and duration in this community and will inform tools and interventions to help improve health outcomes. The study will also aid in filling the gap in research and providing essential information on the infant feeding beliefs and barriers among a Marshallese community in Arkansas.

Multispectral Biometrics System Framework: Application to Presentation Attack Detection

In this work, we present a general framework for building a biometrics system capable of capturing multispectral data from a series of sensors synchronized with active illumination sources. The framework unifies the system design for different biometric modalities and its realization on face, finger and iris data is described in detail. To the best of our knowledge, the presented design is the first to employ such a diverse set of electromagnetic spectrum bands, ranging from visible to long-wave-infrared wavelengths, and is capable of acquiring large volumes of data in seconds, which enabled us to successfully conduct a series of data collection events. We also present a comprehensive analysis on the captured data using a deep-learning classifier for presentation attack detection. Our analysis follows a data-centric approach attempting to highlight the strengths and weaknesses of each spectral band at distinguishing live from fake samples.

Efficient and lightweight group rekeying protocol for communicating things

Recent advances in emerging technologies have given birth to a new type of networks called Internet of Things (IoT). The emergence of IoT has led practitioners to envision networking a large number of sensors for event monitoring, data collection and filtering. Due to the use of wireless technologies, a secure communication is strongly needed to protect valuable information. Secure group communication is one of the most significant requirements for IoT applications. This employs a group rekeying mechanism for a secure and efficient delivery of data. In this paper, we present a new efficient and scalable rekeying protocol for wireless communicating things. This protocol uses O(log2n3) memory complexity, improving significantly the literature works, where n is the network size. Simulation results show that our protocol has better performances than existing works in several criteria.

CBPR: A Cluster-Based Backpressure Routing for the Internet of Things

The concept of the Internet of Things (IoT)-based Wireless Sensor Network (WSN) is rapidly gaining wide-spread recognition and acceptance in our day-to-day lives. Nowadays, the application of the IoT sensor nodes in various domains of endeavors such as health-care, smart homes, industrial and production sectors, control networks, and in many other fields has continued to increase steadily. In IoT-based WSN, sensor nodes dynamically join the internet and collaborate to accomplish a mission by sensing and collecting event data from the application field. The sensor nodes thus forward the collected information to the sink nodes or to the nearest base station for further transmission. However, one of the significant drawbacks of the IoT-based WSN networks is that the battery life of the sensor nodes is often short-lived due to the energy-limited nature of the electronic sensors, resulting in the network’s short lifetime. Thus, prolonging the lifetime duration of the sensor nodes becomes a fundamental task. Whether the battery life of a sensor node is extended for a reasonable length of time or depleted in a moment depends mainly on the energy efficiency of the underlying routing protocol. Therefore, the issue of network lifetime can be fundamentally addressed by implementing an efficient and robust energy-aware routing protocol for sustainable and prolonged network operation time in a WSN based IoT network scenario. In this paper, a cluster-based Backpressure routing (CBPR) scheme has been proposed, which targets to prolong the network lifetime and enhance the data transmission reliability using energy load-balancing mechanism. For every cluster of the sensor node, the CBPR scheme elects a cluster head which has the highest energy level and the shortest distance to the sink node. The proposed CBPR routing scheme further utilizes a very robust data aggregation algorithm to checkmate and prevent the circulation of redundant data packets in the network while also exploiting the Backpressure scheduling machine for data packets queueing and for route selection, which allows it to select the next-hop sensor node based on the queue length value of the sensor nodes. Extensive simulations have been performed to evaluate the efficiency of the proposed CBPR routing scheme, which was compared with that of other well-known routing schemes such as the Information Fusion Based Role Assignment and Data Routing for In-Network Aggregation in terms of throughput, energy consumption, and packet delivery ratio.

A Pathologist-Annotated Dataset for Validating Artificial Intelligence: A Project Description and Pilot Study

Purpose: Validating artificial intelligence algorithms for clinical use in medical images is a challenging endeavor due to a lack of standard reference data (ground truth). This topic typically occupies a small portion of the discussion in research papers since most of the efforts are focused on developing novel algorithms. In this work, we present a collaboration to create a validation dataset of pathologist annotations for algorithms that process whole slide images. We focus on data collection and evaluation of algorithm performance in the context of estimating the density of stromal tumor-infiltrating lymphocytes (sTILs) in breast cancer. Methods: We digitized 64 glass slides of hematoxylin- and eosin-stained invasive ductal carcinoma core biopsies prepared at a single clinical site. A collaborating pathologist selected 10 regions of interest (ROIs) per slide for evaluation. We created training materials and workflows to crowdsource pathologist image annotations on two modes: an optical microscope and two digital platforms. The microscope platform allows the same ROIs to be evaluated in both modes. The workflows collect the ROI type, a decision on whether the ROI is appropriate for estimating the density of sTILs, and if appropriate, the sTIL density value for that ROI. Results: In total, 19 pathologists made 1645 ROI evaluations during a data collection event and the following 2 weeks. The pilot study yielded an abundant number of cases with nominal sTIL infiltration. Furthermore, we found that the sTIL densities are correlated within a case, and there is notable pathologist variability. Consequently, we outline plans to improve our ROI and case sampling methods. We also outline statistical methods to account for ROI correlations within a case and pathologist variability when validating an algorithm. Conclusion: We have built workflows for efficient data collection and tested them in a pilot study. As we prepare for pivotal studies, we will investigate methods to use the dataset as an external validation tool for algorithms. We will also consider what it will take for the dataset to be fit for a regulatory purpose: study size, patient population, and pathologist training and qualifications. To this end, we will elicit feedback from the Food and Drug Administration via the Medical Device Development Tool program and from the broader digital pathology and AI community. Ultimately, we intend to share the dataset, statistical methods, and lessons learned.

POTENTIAL DISGUISING ATTACK VECTORS ON SECURITY OPERATION CENTERS AND SIEM SYSTEMS

In this article we highlight several potential vectors of attacks that can be carried out on a monitoring capacities powered by SOC SIEM using its common features and misconfigurations. Widely spread problems like excessive amounts of false positive alerts or not absolutely accurate configuration of the correlation rules may lead to situation where an attacker is able to trigger an undesired state of the monitoring system. We’ve find three potential vectors for evasion the SIEM powered SOCs monitoring. The first vector grounds on mechanisms used to collect event data – log collectors: the malfunctioning SIEM state can be achieved with generating and submitting the bogus event data to the processing party like SIEM. Fake data flow may cause generation of mistaken alerts which can confuse the analytics stuff. The second vector employs some of the attacker’s knowledge about actual SIEM configuration – exploitation of correlation rule flaws. Taking into account the fact that correlation rules are mostly hand-written, they are prone to some logic flaws – certain detection rules may not be triggered by all of the malicious attack indicators. An attacker with knowledge about that feature may fulfill the unrecorded conditions and trick the SIEM to treat the attack flow as benign activity. The last researched vector is based on redundantly sensitive detection rules which produce a lot of false positive alarms but are not removed. An attacker may trigger the malfunctioning alarm continuously to distract the analytics stuff and perform its actions under the cover of noise. Those discussed vectors are derived from analysis of the actual SIEM installations and SOC processes used as best practices. We have no actual indicators that those attacks are carried out “in wild” at the moment of issuing of this article, but it is highly probable that those tactics may be used in the future. The purpose of this research is to highlight the possible risks for the security operation centers connected with actual processes and practices used in industry and to develop the remediation strategy in perspective.

Distributed and scalable platform architecture for smart cities complex events data collection: Covid19 pandemic use case.

For decades, numerous names have been given to boost an urban city: digital city, green city, smart cities and the list goes on. They are all accompanied with ideas and propositions to enrich citizens life quality, by employing latest information technology to improve environment’s sustainability, through better energy usage, targeting problems affecting infrastructure costs, automation and efficient human resources distribution. Consequently, cities governors provide plans and conceive laws so society including individuals and organizations collaborate in a cycle of providers and consumers to make steps ahead toward smarti-fication of the city in which they all operate. Hence, hundreds of cities around the world are living example of what a smart cities could be resembling in terms of information technology advancement and everyday usage. Each application or to be general system serve and exist for a specific purpose, using mobile applications and small sensors together to cooperate and deliver a value imposing a huge economical and social value and significant source of data. However, most of this applications are tied to specific domains and solely designed to solve predefined problems. Thus, for a decision maker point of view, decisions’ the cost becomes high to correlate multiple data flow in different shapes. As a solution, in this paper we propose a system that is based on abstracting city events of different backgrounds—social, urban and natural, we chose to call them complex space time events. Furthermore, we present its architecture and how it plays with its external actors, and finally, we explain a use case instance made specifically to counter Covid19 pandemic spread and retain public order.

The Relationship Between Complementary Feeding and Stunting Eventsin 2 to 5 Years of Age

Background : Lack of complementary feeding for children, from the age of 6 months complementary feeding begins to be necessary. Food that not good enough in both quality and quantity would affect stunted growth. 
 Purpose : This research was to determine the relationship of complementary feeding with the stunting in 2 to 5 years of age
 Method : of this research is a retrospective approach. This research is carrying on March 4 2020. The population of this research is one of the parents and children aged 2-5 years as many as 22 people. Sampling retrieval using total sampling, totaling 22 parents and children. The independent variable of complementary feeding data collection with the questionnaire and the dependent variable stunting event data collection by the child's height measurement. Data analysis using spearman rank with α = 0.05.
 Results show that out of 22 respondents, nearly half of which were 10 respondents (45.5%) provide less complementary feeding in the category of less and children aged 2-5 years the majority of 16 respondents (72.7%) having heights in the short category. Correlation test results show p-value = 0.002 α = 0.05, so Ha is accepted.
 Conclusion : There is a relationship between complementary feeding with stunting in children aged 2-5 years, and r = 0.627 so the level of closeness is strong. In this research the becomes a strong predictor in caused the stunting, but was expected to further research can add various other variables to the stunting event.

Overview of retrospective data harmonisation in the MINDMAP project: process and results

BackgroundThe MINDMAP project implemented a multinational data infrastructure to investigate the direct and interactive effects of urban environments and individual determinants of mental well-being and cognitive function in ageing populations....

Developing the TIC Grade: A Youth Self-Report Measure of Perceptions of Trauma-Informed Care.

Service delivery organizations are advancing the provision of trauma-informed care (TIC) for youth to improve outcomes. However, currently there are no validated, reliable evaluation measures to capture the voices of adolescent clients and how well they perceive TIC implementation. The purpose of this project was to create an evaluation measure with strong content validity for adolescent health and service users to give feedback to organizations about their implementation of TIC. This article outlines Step 1 of our instrument development, by discussing our process creating the measure and affirming content validity. Psychometric testing of this measure (Step 2) is described in a companion paper. We combined deductive theory substruction with an inductive participatory process to create, revise, and finalize the measure. The National Center for Trauma-Informed Care's framework of four practices and six principles was substructed into an 18-item draft measure. A four-member community youth advisory board (CYAB) then worked to inductively modify our draft to provide age-appropriate clarity and ensure a nontriggering respondent experience. Finally, the CYAB members conducted cognitive interviews with 10 other adolescents in local evening data collection events, refining the measure for future psychometric testing. The process resulted in a 20-item form based on CYAB feedback. Refinements included providing an accessible definition of trauma, asking questions about trauma history, and asking whether trauma affected the client's visit on the day of service use. The CYAB involvement enhanced content validity and ensured a trauma-informed instrument development approach.

“Drones are a great idea! What is an AED?” novel insights from a qualitative study on public perception of using drones to deliver automatic external defibrillators

BackgroundThe quickest way to ensure survival in an out-of-hospital cardiac arrest (OHCA) is for a bystander to provide immediate cardiopulmonary resuscitation (CPR) and apply an automated external defibrillator (AED). The urgency of OHCA treatment has led to the proposal of alternative avenues for better access to AEDs, particularly in rural settings. More recently, using unmanned aerial vehicles (or drones) to deliver AEDs to rural OHCA sites has proven promising in improving survival rates. ObjectiveA pilot drone AED delivery program is currently being piloted in the community of Caledon, Ontario. The purpose of this study was to develop an understanding of public perception and acceptance of the use of drones for this purpose and to identify tailored community engagement strategies to ensure successful uptake. MethodsIn-depth qualitative descriptive study using interviews and focus group data collection and inductive thematic analysis. Purposive sampling was used to recruit 67 community members (40 interviews; 2 focus groups of 15) at existing community events in the project area. Interview guides were used to ensure consistency across data collection events. Detailed field notes were recorded when audio-recording was not possible. ResultsThe central message seen throughout the data was quickly identified as the potential impact of low levels of CPR and AED literacy in the community over anything else including concerns about the drone. The impact of the community’s existing relationship with the EMS; the need for bystander CPR & AED promotion prior to the program launch; and the value the community places on transparency and accountability related to the research and the drones were also key findings. In general, the drone concept was found to be acceptable but concerns about providing CPR and using the AED was what created anxieties in the lay public that we underestimated. ConclusionDrone-delivered AEDs may be feasible and effective but successful uptake in smaller communities will require a deep understanding of a community’s cardiac arrest literacy levels, information needs and readiness for innovation. This work will inform a robust community engagement plan that will be scalable to other locations considering a drone AED program.

Efficacy and safety-in analysis of short-course radiation followed by mFOLFOX-6 plus avelumab for locally advanced rectal adenocarcinoma

BackgroundNeoadjuvant chemotherapy and short-course radiotherapy followed by resection has been gaining recognition in the treatment of rectal cancer. Avelumab is a fully human immunoglobulin that binds Programmed Death-Ligand 1 (PD-L1) and prevents the suppression of the cytotoxic T cell immune response. This phase II trial evaluates the safety and pathologic response rate of short-course radiation followed by 6 cycles of mFOLFOX6 with avelumab in patients with locally advanced rectal cancer (LARC).MethodsThis study is prospective single-arm, multicenter phase II trial adopting Simon’s two-stage. Short-course radiation is given over 5 fractions to a total dose of 25 Gy. mFOLFOX6 plus avelumab (10 mg/kg) are given every 2 weeks for 6 cycles. Total mesorectal excision is performed 3–4 weeks after the last cycle of avelumab. Follow up after surgery is done every 3 months to a total of 36 months. Adverse event data collection is recorded at every visit.Results13 out of 44 patients with LARC were enrolled in the first stage of the study (30% from total sample size). All patients met the inclusion criteria and received the full short-course radiation course followed by 6 cycles of mFOLFOX6 plus avelumab. 12 out of the 13 patients completed TME while one patient had progression of disease and was dropped out of the study. The sample consisted of 9 (69%) males and 4 (31%) females with median age of 62 (33–73) years. The first interim analysis revealed that 3 (25%) patients achieved pathologic complete response (pCR) (tumor regression grade, TRG 0) out of 12. While 3 (25%) patients had near pCR with TRG 1. In total, 6 out of 12 patients (50%) had a major pathologic response. All patients were found to be MMR proficient. The protocol regimen was well tolerated with no serious adverse events of grade 4 reported.ConclusionIn patients with LARC, neoadjuvant radiation followed by mFOLFOX6 with avelumab is safe with a promising pathologic response rate.Trial Registration Number and Date of Registration ClinicalTrials.gov NCT03503630, April 20, 2018. https://clinicaltrials.gov/ct2/show/NCT03503630?term=NCT03503630&draw=2&rank=1.

Do probiotics in pregnancy reduce the risk of group B streptococcal colonisation?

Rectovaginal group B streptococcal (GBS) colonisation affects approximately 30% of pregnant women, which significantly increases the use of antibiotics in mothers and babies. In this review, we aim to answer two questions: (i) In pregnant women, does the use of probiotics prevent GBS colonisation prior to delivery when compared with placebo; and (ii) In GBS positive pregnant women, does the use of probiotics reduce the risk of GBS colonisation prior to delivery when compared with standard treatment. Pubmed, Cochrane library, EMBASE, OVID, clinical trials.gov, grey literature and conference proceedings were systematically searched to identify eligible trials. References of included trials were also reviewed. A total of four studies (n = 402) were included in the final review. Two studies reported on the prevention of GBS colonisation in healthy pregnant women, and two studies reported on the rate of GBS clearance after probiotic administration. Meta-analysis of the two studies using random effects model indicated that GBS clearance with probiotics was not statistically significant with odds ratio 2.12 (95% confidence interval 0.60-7.50, P = 0.17). Collection of adverse events data was not a primary or secondary aim in any of the studies. We conclude that there is limited evidence to recommend the regular use of probiotics to minimise the risk of GBS colonisation. Results from ongoing studies are likely to add to the current existing evidence.