Traditional Surveillance Research Articles

Monitoring Drug Safety in Pregnancy with Scan Statistics: A Comparison of Two Study Designs.

Traditional surveillance of adverse infant outcomes following maternal medication exposures relies on pregnancy exposure registries, which are often underpowered. We characterize the statistical power of TreeScan, a data mining tool, to identify potential signals in the setting of perinatal medication exposures and infant outcomes. We used empirical data to inform background incidence of major congenital malformations and other birth conditions. Statistical power was calculated using two probability models compatible with TreeScan, Bernoulli and Poisson, while varying the sample size, magnitude of the risk increase, and incidence of a specified outcome. We also simulated larger referent to exposure matching ratios when using the Bernoulli model in the setting of fixed N:1 propensity score matching. Finally, we assessed the impact of outcome misclassification on power. The Poisson model demonstrated greater power to detect signals than the Bernoulli model across all scenarios and suggested a sample size of 4,000 exposed pregnancies is needed to detect a twofold increase in risk of a common outcome (approximately 8 per 1,000) with 85% power. Increasing the fixed matching ratio with the Bernoulli model did not reliably increase power. An outcome definition with high sensitivity is expected to have somewhat greater power to detect signals than an outcome definition with high positive predictive value. Use of the Poisson model with an outcome definition that prioritizes sensitivity may be optimal for signal detection. TreeScan is a viable method for surveillance of adverse infant outcomes following maternal medication use.

Wikipedia searches and the epidemiology of infectious diseases: A systematic review

This review aims to collect, analyse and synthesize the available evidence that can be provided by Wikipedia for epidemiologic surveillance purposes. PRISMA guidelines were followed. PubMed/Medline and Scopus were consulted. Out of 238 retrieved articles, 16 articles were included in the systematic review. The most frequently assessed infectious disease was Influenza, followed by arboviruses and measles. Influenza studies show that Wikipedia could be considered a scientifically valid surveillance system that fills the main gaps in existing traditional surveillance systems. As regards arboviruses, searches on the Web have positively mediated the relationship between epidemiological data and the number of Wikipedia page visualization. Regarding measles, studies showed a strong/moderate temporal correlation between infectious disease notification bulletins and Wikipedia search trends. Despite the type of infectious agents, three main aims can be detected: (i) understand the public’s interest, (ii) explore the use of Wikipedia by organizations, and (iii) assess the accuracy of Wikipedia content. These new strategies for surveillance of infectious diseases should be implemented, to date they could be useful in supporting traditional surveillance.

The benefit of augmenting open data with clinical data-warehouse EHR for forecasting SARS-CoV-2 hospitalizations in Bordeaux area, France.

The aim of this study was to develop an accurate regional forecast algorithm to predict the number of hospitalized patients and to assess the benefit of the Electronic Health Records (EHR) information to perform those predictions. Aggregated data from SARS-CoV-2 and weather public database and data warehouse of the Bordeaux hospital were extracted from May 16, 2020 to January 17, 2022. The outcomes were the number of hospitalized patients in the Bordeaux Hospital at 7 and 14days. We compared the performance of different data sources, feature engineering, and machine learning models. During the period of 88 weeks, 2561 hospitalizations due to COVID-19 were recorded at the Bordeaux Hospital. The model achieving the best performance was an elastic-net penalized linear regression using all available data with a median relative error at 7 and 14days of 0.136 [0.063; 0.223] and 0.198 [0.105; 0.302] hospitalizations, respectively. Electronic health records (EHRs) from the hospital data warehouse improved median relative error at 7 and 14days by 10.9% and 19.8%, respectively. Graphical evaluation showed remaining forecast error was mainly due to delay in slope shift detection. Forecast model showed overall good performance both at 7 and 14days which were improved by the addition of the data from Bordeaux Hospital data warehouse. The development of hospital data warehouse might help to get more specific and faster information than traditional surveillance system, which in turn will help to improve epidemic forecasting at a larger and finer scale.

Worldwide Evolution of Vaccinable and Nonvaccinable Viral Skin Infections: Google Trends Analysis.

Most common viral skin infections are not reportable conditions. Studying the population dynamics of these viral epidemics using traditional field methods is costly and time-consuming, especially over wide geographical areas. This study aimed to explore the evolution, seasonality, and distribution of vaccinable and nonvaccinable viral skin infections through an analysis of Google Trends. Worldwide search trends from January 2004 through May 2021 for viral skin infections were extracted from Google Trends, quantified, and analyzed. Time series decomposition showed that the total search term volume for warts; zoster; roseola; measles; hand, foot, and mouth disease (HFMD); varicella; and rubella increased worldwide over the study period, whereas the interest for Pityriasis rosea and herpes simplex decreased. Internet searches for HFMD, varicella, and measles exhibited the highest seasonal patterns. The interest for measles and rubella was more pronounced in African countries, whereas the interest for HFMD and roseola was more pronounced in East Asia. Harnessing data generated by web searches may increase the efficacy of traditional surveillance systems and strengthens the suspicion that the incidence of some vaccinable viral skin infections such as varicella, measles, and rubella may be globally increasing, whereas the incidence of common nonvaccinable skin infections remains stable.

The Future of Infodemic Surveillance as Public Health Surveillance.

Public health systems need to be able to detect and respond to infodemics (outbreaks of misinformation, disinformation, information overload, or information voids). Drawing from our experience at the US Centers for Disease Control and Prevention, the COVID-19 State of Vaccine Confidence Insight Reporting System has been created as one of the first public health infodemic surveillance systems. Key functions of infodemic surveillance systems include monitoring the information environment by person, place, and time; identifying infodemic events with digital analytics; conducting offline community-based assessments; and generating timely routine reports. Although specific considerations of several system attributes of infodemic surveillance system must be considered, infodemic surveillance systems share several similarities with traditional public health surveillance systems. Because both information and pathogens are spread more readily in an increasingly hyperconnected world, sustainable and routine systems must be created to ensure that timely interventions can be deployed for both epidemic and infodemic response.

Influence of Surveillance Methods in the Detection of Sports Injuries and Illnesses.

BackgroundEpidemiological data on sports injuries and illnesses depend on the surveillance methodology and the definition of the health problems. The effect of different surveillance methods on the data collection has been investigated for overuse injuries, but not for other health problems such as traumatic injuries and illnesses.PurposeThe purpose of this study was to investigate the new surveillance method developed by the Oslo Sports Trauma Research Center (OSTRC), which is based on any complaint definition (new method), to identify health problems compared with the traditional surveillance method, which is based on time loss definition.Study designDescriptive epidemiology studyMethodsA total of 62 Japanese athletes were prospectively followed-up for 18 weeks to assess differences in health problems identified by both new and traditional methods. Every week, the athletes completed the Japanese version of the OSTRC questionnaire (OSTRC-H2.​JP), whereas the teams’ athletic trainers registered health problems with a time loss definition. The numbers of health problems identified via each surveillance method were calculated and compared with each other to assess any differences between their results.ResultsThe average weekly response rate to the OSTRC-H2.​JP was 82.1% (95% CI, 79.8–84.3). This new method recorded 3.1 times more health problems (3.1 times more injuries and 2.8 times more illnesses) than the traditional method. The difference between both surveillance methods’ counts was greater for overuse injuries (5.3 times) than for traumatic injuries (2.5 times).ConclusionsThis study found that the new method captured more than three times as many health problems as the traditional method. In particular, the difference between both methods’ counts was greater for overuse injuries than for traumatic injuries.Level of evidence2b

Research on Architecture Design of Mobile Video Surveillance System in Information Room Based on Intelligent Robot Technology

With the gradual development of information technology, the idea of combining mobile robot and wireless remote control technology is adopted to build a wireless network platform between a computer and robot. At present, there are conditions to realize this idea. In this idea, the wireless data transmission can be realized on the platform. Users can remotely control and access the monitoring system of the computer room and set up alarm systems and detectors, so as to monitor the working conditions of the robot at the computer terminal, truly realize the man-machine coordination, and ensure that the robot can complete the specified tasks well in various complex environments. This research first deeply analyzes the current development status and existing problems of the information room monitoring technology and then puts forward the mobile video monitoring system architecture of the information room based on the intelligent robot monitoring technology. The system architecture integrates computer technology, network technology, and control technology. With the continuous updating of technology, video surveillance has also entered the mobile era. Combined with the timely monitoring video acquisition system of mobile robots, it solves the current working environment for many industries and enterprises. Using robot chassis technology, it designs the corresponding bottom hardware control and upper equipment to achieve data acquisition with related equipment. The mobile video surveillance system solves the problems encountered by the traditional video surveillance system and can monitor video images through remote operation and centrally monitors the operation status of the equipment in the room and the environmental status of the room, which is more conducive to improving the management efficiency. It provides a new idea to ensure the safe and stable operation of various equipment and systems in various places.

Influenza surveillance systems using traditional and alternative sources of data: A scoping review

While the World Health Organization's recommendation of syndromic sentinel surveillance for influenza is an efficient method to collect high-quality data, limitations exist. Aligned with the Research Recommendation 1.1.2 of the WHO Public Health Research Agenda for Influenza-to identify reliable complementary influenza surveillance systems which provide real-time estimates of influenza activity-we performed a scoping review to map the extent and nature of published literature on the use of non-traditional sources of syndromic surveillance data for influenza. We searched three electronic databases (PubMed, Web of Science, and Scopus) for articles in English, French, and Spanish, published between January 1 2007 and January 28 2022. Studies were included if they directly compared at least one non-traditional with a traditional influenza surveillance system in terms of correlation in activity or timeliness. We retrieved 823 articles of which 57 were included for analysis. Fifteen articles considered electronic health records (EHR), 11 participatory surveillance, 10 online searches and webpage traffic, seven Twitter, five absenteeism, four telephone health lines, three medication sales, two media reporting, and five looked at other miscellaneous sources of data. Several articles considered more than one non-traditional surveillance method. We identified eight categories and a miscellaneous group of non-traditional influenza surveillance systems with varying levels of evidence on timeliness and correlation to traditional surveillance systems. Analyses of EHR and participatory surveillance systems appeared to have the most agreement on timeliness and correlation to traditional systems. Studies suggested non-traditional surveillance systems as complements rather than replacements to traditional systems.

Exploring the Cystic Fibrosis Lung Microbiome: Making the Most of a Sticky Situation.

Chronic lower respiratory tract infections are a leading contributor to morbidity and mortality in persons with cystic fibrosis (pwCF). Traditional respiratory tract surveillance culturing has focused on a limited range of classic pathogens; however, comprehensive culture and culture-independent molecular approaches have demonstrated complex communities highly unique to each individual. Microbial community structure evolves through the lifetime of pwCF and is associated with baseline disease state and rates of disease progression including occurrence of pulmonary exacerbations. While molecular analysis of the airway microbiome has provided insight into these dynamics, challenges remain including discerning not only “who is there” but “what they are doing” in relation to disease progression. Moreover, the microbiome can be leveraged as a multi-modal biomarker for both disease activity and prognostication. In this article, we review our evolving understanding of the role these communities play in pwCF and identify challenges in translating microbiome data to clinical practice.

Urban monitoring of antimicrobial resistance during a COVID-19 surge through wastewater surveillance

During the early phase of the COVID-19 pandemic, infected patients presented with symptoms similar to bacterial pneumonias and were treated with antibiotics before confirmation of a bacterial or fungal co-infection. We reasoned that wastewater surveillance could reveal potential relationships between reduced antimicrobial stewardship, specifically misprescribing antibiotics to treat viral infections, and the occurrence of antimicrobial resistance (AMR) in an urban community. Here, we analyzed microbial communities and AMR profiles in sewage samples from a wastewater treatment plant (WWTP) and a community shelter in Las Vegas, Nevada during a COVID-19 surge in December 2020. Using a respiratory pathogen and AMR enrichment next-generation sequencing panel, we identified four major phyla in the wastewater, including Actinobacteria, Firmicutes, Bacteroidetes and Proteobacteria. Consistent with antibiotics that were reportedly used to treat COVID-19 infections (e.g., fluoroquinolones and beta-lactams), we also measured a significant spike in corresponding AMR genes in the wastewater samples. AMR genes associated with colistin resistance (mcr genes) were also identified exclusively at the WWTP, suggesting that multidrug resistant bacterial infections were being treated during this time. We next compared the Las Vegas sewage data to local 2018–2019 antibiograms, which are antimicrobial susceptibility profile reports about common clinical pathogens. Similar to the discovery of higher levels of beta-lactamase resistance genes in sewage during 2020, beta-lactam antibiotics accounted for 51 ± 3 % of reported antibiotics used in antimicrobial susceptibility tests of 2018–2019 clinical isolates. Our data highlight how wastewater-based epidemiology (WBE) can be leveraged to complement more traditional surveillance efforts by providing community-level data to help identify current and emerging AMR threats.

Phylogeny and antimicrobial resistance of extended-spectrum beta-lactamaseproducing Escherichia coli from hospitalized oncology patients in Perú

ResumenIntroducción. Las infecciones asociadas con la atención en salud constituyen un problema de salud pública porque aumentan la morbimortalidad de los pacientes, sobre todo de aquellos con factores de riesgo, como la inmunosupresión debida a enfermedades oncológicas. Es importante conocer la diversidad genética de los principales microorganimos causantes de infecciones hospitalarias mediante la vigilancia epidemiológica tradicional y la epidemiología molecular, para hacer un mejor seguimiento y detectar brotes tempranamente.Objetivo. Determinar el grupo filogenético y la resistencia a antibióticos de las cepas de Escherichia coli aisladas de pacientes con cáncer hospitalizados.Materiales y métodos. Se hizo un estudio de tipo transversal que incluyó 67 cepas de Escherichia coli productoras de betalactamasas de espectro extendido (BLEE). Se determinó el grupo filogenético, el perfil de resistencia a los antibióticos, los genes de resistencia a betalactámicos, el tipo de las muestras y los servicios de hospitalización de donde fueron recuperadas.Resultados. El grupo filogenético más frecuente fue el B2 (36 %). El 57 % de las cepas B2 fueron aisladas de muestras de orina y el 33 % provenía del servicio de urología. La resistencia a ciprofloxacino y gentamicina fue de 91 y 53 %, respectivamente, y el 79 % de las cepas tenía el gen bla CTX-M. Se encontró una relación significativa (p<0,05) entre los grupos filogenéticos y la resistencia a ciprofloxacina, así como a la edad del paciente.Conclusión. El filogrupo de E. coli predominante fue el B2. Se evidenció una gran resistencia a ciprofloxacina y gentamicina, una proporción elevada de cepas BLEE con el bla CTX-M, y una relación entre el grupo filogenético y la resistencia a ciprofloxacino.

Leveraging Google Search Data and Artificial Intelligence Methods for Provincial-level Influenza Forecasting: A South African Case Study

This paper investigates the usefulness of Google search patterns with Artificial Intelligence (AI) techniques for timely influenza-like illness (ILI) forecasting for each of the nine South African provinces. Traditional surveillance methods are limited by delays in reporting. Existing digital disease surveillance studies that employ alternative online data have scarcely explored sub-Saharan African countries. In South Africa, Google search data has only been recently studied for ILI surveillance at the national level. Meanwhile, the differences in socio-economic and technological conditions across provinces call for a finer spatial investigation. We perform correlation analysis between Google trends (GT) data for 21 ILI-related terms and real-life ILI surveillance data for each province. Next, we develop models to assess the predictive performance of these GT data for forecasting ILI rates, using time series, machine learning, and deep learning methods. We observe sufficient correlation for only two of the nine provinces: Gauteng and Western Cape. Thus, GT data could only be used to forecast ILI in these two provinces. Interestingly, these two provinces are regarded as the most economically developed. In the other seven provinces, LSTM, a deep learning technique, gives more accurate predictions than a baseline autoregressive model when only past ILI data are used for forecasting future ILI trends. The results reveal that, for provinces for which GT data is sufficiently available, it is not only free and fast, but is an effective predictor on its own as well as when added to past ILI data for forecasting future ILI infection rates. The correlation analysis suggests an association between provincial socio-economic development and the use of digital platforms for disease surveillance. Overall, the study established the need for finer scale ILI forecasting which will inform targeted planning for disease surveillance and interventions.

The Landscape of Participatory Surveillance Systems Across the One Health Spectrum: Systematic Review.

BackgroundParticipatory surveillance systems augment traditional surveillance systems through bidirectional community engagement. The digital platform evolution has enabled the expansion of participatory surveillance systems, globally, for the detection of health events impacting people, animals, plants, and the environment, in other words, across the entire One Health spectrum.ObjectiveThe aim of this landscape was to identify and provide descriptive information regarding system focus, geography, users, technology, information shared, and perceived impact of ongoing participatory surveillance systems across the One Health spectrum.MethodsThis landscape began with a systematic literature review to identify potential ongoing participatory surveillance systems. A survey was sent to collect standardized data from the contacts of systems identified in the literature review and through direct outreach to stakeholders, experts, and professional organizations. Descriptive analyses of survey and literature review results were conducted across the programs.ResultsThe landscape identified 60 ongoing single-sector and multisector participatory surveillance systems spanning five continents. Of these, 29 (48%) include data on human health, 26 (43%) include data on environmental health, and 24 (40%) include data on animal health. In total, 16 (27%) systems are multisectoral; of these, 9 (56%) collect animal and environmental health data; 3 (19%) collect human, animal, and environmental health data; 2 (13%) collect human and environmental health data; and 2 (13%) collect human and animal health data. Out of 60 systems, 31 (52%) are designed to cover a national scale, compared to those with a subnational (n=19, 32%) or multinational (n=10, 17%) focus. All systems use some form of digital technology. Email communication or websites (n=40, 67%) and smartphones (n=29, 48%) are the most common technologies used, with some using both. Systems have capabilities to download geolocation data (n=31, 52%), photographs (n=29, 48%), and videos (n=6, 10%), and can incorporate lab data or sample collection (n=15, 25%). In sharing information back with users, most use visualization, such as maps (n=43, 72%); training and educational materials (n=37, 62%); newsletters, blogs, and emails (n=34, 57%); and disease prevention information (n=32, 53%). Out of the 46 systems responding to the survey regarding perceived impacts of their systems, 36 (78%) noted “improved community knowledge and understanding” and 31 (67%) noted “earlier detection.”ConclusionsThe landscape demonstrated the breadth of applicability of participatory surveillance around the world to collect data from community members and trained volunteers in order to inform the detection of events, from invasive plant pests to weekly influenza symptoms. Acknowledging the importance of bidirectionality of information, these systems simultaneously share findings back with the users. Such directly engaged community detection systems capture events early and provide opportunities to stop outbreaks quickly.

Design and Implementation of Satellite ADS-B Receiver

With the rapid development of Chinese aviation industry, the amount of air flights has increased rapidly, and the traditional ground radar surveillance system has a limited geographic range, while the satellite ADS-B surveillance system has the advantages of all-time, all-weather, large-scale, and long-distance. It is an effective supplement to the aircraft ground monitoring system. This paper conducts research from the satellite ADS-B receiver system functions, technical indicators, hardware design, software design, structural design, etc., and establishes a test environment to prove that the satellite ADS_B receiver performance index design meets the technical requirements and has high application value.

Video Based Human Activity Recognition Surveillance System

It is argued that current state-of-the-art methods of home surveillance such as motion detection technology like CCTV intrusion alert are insufficient, in particular to cater the modern need of whole automation with vulnerabilities such as requiring human involvement. We propose an alternative system, a video-based Human Activity Recognition (HAR) approach using the combination of Convolution Neural Network (CNN) and Long Short-Term Memory (LSTM) algorithm, to dealwith the identified shortcomings. Our proposal does not require any changes to the existing home security protocols and is easily implemented using only low-cost, commercial-off-the-shelf hardware. We can easily use the traditional surveillance camera for computer vision tasks. We evaluate our approach using real-world activity data collected via video-based sensor. We quantify its effectiveness, by plotting Loss and accuracy curves. Our results show that the video-based HAR approach can provide full automation in home surveillancesystem compared to conventional CCTV motion detectors by an accuracy of more than 93%. Further system’s accuracy can be increased and we can achieve significantly better results by implementing LRCN (Long Term Recurrent Convolution Network) approach.

Dengue Vector Surveillance using Acoustic Signals through Sequential Model of Convolutional Neural Networks

Dengue fever is among the most dangerous infectious viral diseases transmitted through the bite of infected Aedes Aegypti mosquitoes. One way to decline the spread of dengue is by raising awareness to the community about mosquito habitats through continuous surveillance. The traditional surveillance techniques of Aedes Aegypti are difficult, time taking, and can lead to severe health risks. This paper presents a possible way of dengue vector surveillance through acoustic signals generated by wingbeat of Aedes Aegypti using the sequential model of convolutional neural network. Mel-frequency spectrum is given as an input feature to the sequential model that significantly improves classification performance up to 93% accuracy. The system generates notification through a specially designed mobile application to alert detected dengue vectors in the region. It is helpful in continuous monitoring of dengue vectors to take early precautionary measures for effective control and prevention.

Intelligent dual stream CNN and echo state network for anomaly detection

Traditional video surveillance systems detect abnormal events via human involvement, which is exhausting and erroneous, while computer vision-based automated anomaly detection techniques replace human intervention for secure video surveillance applications. Automated anomaly detection in real-world scenarios is challenging due to diverse nature, complex, and infrequent occurrence of anomalous events. Therefore, in this paper, we propose an intelligent dual stream convolution neural network-based framework for accurate anomalous events detection in real-world surveillance scenarios. The proposed framework comprises two phases: in first phase, we develop a 2D CNN as an autoencoder, followed by a 3D visual features extraction machanism in the second phase. Autoencoder extracts spatial optimal features and forward them to echo state network to acquire a single spatial and temporal information-aware feature vector that is fused with 3D convolutional features for events patterns learning. The fused feature vector is used for anomalous events detection via a trained classifier. The proposed dual stream framework achieves significantly enhanced performance on challenging surveillance and non-surveillance anomaly and violence detection datasets.

COVID-19 forecasts using Internet search information in the United States

As the COVID-19 ravaging through the globe, accurate forecasts of the disease spread are crucial for situational awareness, resource allocation, and public health decision-making. Alternative to the traditional disease surveillance data collected by the United States (US) Centers for Disease Control and Prevention (CDC), big data from Internet such as online search volumes also contain valuable information for tracking infectious disease dynamics such as influenza epidemic. In this study, we develop a statistical model using Internet search volume of relevant queries to track and predict COVID-19 pandemic in the United States. Inspired by the strong association between COVID-19 death trend and symptom-related search queries such as “loss of taste”, we combine search volume information with COVID-19 time series information for US national level forecasts, while leveraging the cross-state cross-resolution spatial temporal framework, pooling information from search volume and COVID-19 reports across regions for state level predictions. Lastly, we aggregate the state-level frameworks in an ensemble fashion to produce the final state-level 4-week forecasts. Our method outperforms the baseline time-series model, while performing reasonably against other publicly available benchmark models for both national and state level forecast.

Wastewater-Based Detection of Two Influenza Outbreaks

Traditional influenza surveillance informs control strategies but can lag behind outbreak onset and undercount cases. Wastewater surveillance is effective for monitoring near real-time dynamics of outbreaks but has not been attempted for influenza. We quantified influenza A virus (IAV) RNA in wastewater during two active outbreaks on university campuses in different parts of the United States and during different times of year using case data from an outbreak investigation and high-quality surveillance data from student athletes. In both cases, the IAV RNA concentrations were strongly associated with reported IAV incidence rates (Kendall’s τ values of 0.58 and 0.67 for the University of Michigan and Stanford University, respectively). Furthermore, the RNA concentrations reflected outbreak patterns and magnitudes. For the University of Michigan outbreak, evidence from sequencing IAV RNA from wastewater indicated the same circulating strain identified in cases during the outbreak. The results demonstrate that wastewater surveillance can effectively detect influenza outbreaks and will therefore be a valuable supplement to traditional forms of influenza surveillance.

A Comprehensive Analysis of Injuries During Army Basic Military Training.

The injury definitions and surveillance methods commonly used in Army basic military training (BMT) research may underestimate the extent of injury. This study therefore aims to obtain a comprehensive understanding of injuries sustained during BMT by employing recording methods to capture all physical complaints. Six hundred and forty-six recruits were assessed over the 12-week Australian Army BMT course. Throughout BMT injury, data were recorded via (1) physiotherapy reports following recruit consultation, (2) a member of the research team (third party) present at physical training sessions, and (3) recruit daily self-reports. Two hundred and thirty-five recruits had ≥1 incident injury recorded by physiotherapists, 365 recruits had ≥1 incident injury recorded by the third party, and 542 recruits reported ≥1 injury-related problems via the self-reported health questionnaire. Six hundred twenty-one, six hundred eighty-seven, and two thousand nine hundred sixty-four incident injuries were recorded from a total of 997 physiotherapy reports, 1,937 third-party reports, and 13,181 self-reported injury-related problems, respectively. The lower extremity was the most commonly injured general body region as indicated by all three recording methods. Overuse accounted for 79% and 76% of documented incident injuries from physiotherapists and the third party, respectively. This study highlights that injury recording methods impact injury reporting during BMT. The present findings suggest that traditional injury surveillance methods, which rely on medical encounters, underestimate the injury profile during BMT. Considering accurate injury surveillance is fundamental in the sequence of injury prevention, implementing additional injury recording methods during BMT may thus improve injury surveillance and better inform training modifications and injury prevention programs.