Rapid Assessment Research Articles

Development and validation of a predictive risk tool for VTE in women with breast cancer under chemotherapy: a cohort study in China.

The incidence of venous thromboembolism (VTE) is significantly elevated in breast cancer patients, with a three-to-fourfold increase, and further escalates to sixfold in those undergoing chemotherapy. This study aims to identify the risk factors for VTE and develop a Nomogram risk prediction model distinct from the traditional Khorana score. Univariate Cox regression analysis assessed the impact of each variable on the occurrence of VTE, while stepwise multivariate Cox regression analysis identified independent predictors. Based on these results, we constructed a Nomogram model. The model's performance was validated using the C-index, receiver-operating characteristic curve (ROC), calibration curves, and decision curve analysis (DCA). Comparisons were made with the Khorana score to evaluate the practical application value. Out of the 903 patients, 108 (11.96%) developed VTE. Cox regression analysis identified that Stage, undergoing surgery, age, white blood cells (WBC), D-dimer, and carcinoembryonic antigen (CEA) were significant risk factors for VTE (P < 0.05). The Nomogram model's C-index was 0.77 (95% CI 0.72-0.83) in the training set and 0.76 (95% CI 0.69-0.84) in the validation set. The model demonstrated excellent predictive accuracy and generalizability on the receiver-operating characteristic (ROC) curves and calibration curves. Compared to the traditional Khorana score, the Nomogram model showed superior predictive accuracy and greater clinical benefit. This study established a VTE risk prediction model for breast cancer patients undergoing chemotherapy. The model is characterized by its intuitive and straightforward application, making it highly suitable for rapid VTE risk assessment in clinical practice.

Rapid and objective assessment of auditory temporal processing using dynamic amplitude-modulated stimuli.

Current tests of hearing fail to diagnose pathologies in ~10% of patients seeking help for hearing difficulties. Neural ensemble responses to perceptually relevant cues in the amplitude envelope, termed envelope following responses (EFR), hold promise as an objective diagnostic tool to probe these 'hidden' hearing difficulties. But clinical translation is impeded by current measurement approaches involving static amplitude modulated (AM) tones, which are time-consuming and lack optimal spectrotemporal resolution. Here we develop a framework to rapidly measure EFRs using dynamically varying AMs combined with spectrally specific analyses. These analyses offer 5x improvement in time and 30x improvement in spectrotemporal resolution, and more generally, are optimal for analyzing time-varying signals with known spectral trajectories of interest. We validate this approach across several mammalian species, including humans, and demonstrate robust responses that are highly correlated with traditional static EFRs. Our analytic technique facilitates rapid and objective neural assessment of temporal processing throughout the brain that can be applied to track auditory neurodegeneration using EFRs, as well as tracking recovery after therapeutic interventions.

Comparative evaluation of machine learning models for assessment of seabed liquefaction using finite element data

Predicting wave-induced liquefaction around submarine pipelines is crucial for marine engineering safety. However, the complex of interactions between ocean dynamics and seabed sediments makes rapid and accurate assessments challenging with traditional numerical methods. Although machine learning approaches are increasingly applied to wave-induced liquefaction problems, the comparative accuracy of different models remains under-explored. We evaluate the predictive accuracy of four classical machine learning models: Gradient Boosting (GB), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and Random Forest (RF). The results indicate that the GB model exhibits high stability and accuracy in predicting wave-induced liquefaction, due to its strong ability to handle complex nonlinear geological data. Prediction accuracy varies across output parameters, with higher accuracy for seabed predictions than for pipeline surroundings. The combination of different input parameters significantly influences model predictive accuracy. Compared to traditional finite element numerical methods, employing machine learning models significantly reduces computation time, offering an effective tool for rapid disaster assessment and early warning in marine engineering. This research contributes to the safety of marine pipeline protections and provides new insights into the intersection of marine geological engineering and artificial intelligence.

Canada needs a national COVID-19 inquiry now.

We are now in the fifth year of an ongoing pandemic, and Canada continues to experience significant surges of COVID-19 infections. In addition to the acute impacts of deaths and hospitalizations, there is growing awareness of an accumulation of organ damage and disability which is building a "health debt" that will affect Canadians for decades to come. Calls in 2023 for an inquiry into the handling of the COVID-19 pandemic went unheeded, despite relevant precedent. Canada urgently needs a comprehensive review of its successes and failures to chart a better response in the near- and long-term. While Canada fared better than many comparators in the early years of the COVID-19 pandemic, it is clearly still in a public health crisis. Infections are not only affecting Canadians' daily lives but also eroding healthcare capacity. Post-COVID condition is having accumulating and profound individual, social, and economic consequences. An inquiry is needed to understand the current evidence underlying policy choices, identify a better course of action on various fronts, and build resilience. More must be done to reduce transmission, including a serious public education campaign to better inform Canadians about COVID and effective mitigations, especially the benefits of respirator masks. We need a national standard for indoor air quality to make indoor public spaces safer, particularly schools. Data collection must be more robust, especially to understand and mitigate the disproportionate impacts on under-served communities and high-risk populations. General confidence in public health must be rebuilt, with a focus on communication and transparency. In particular, the wide variation in provincial policies has sown mistrust: evidence-based policy should be consistent. Finally, Canada's early success in vaccination has collapsed, and this development needs a careful post-mortem. A complete investigation of Canada's response to the pandemic is not yet possible because that response is still ongoing and, while we have learned much, there remain areas of dispute and uncertainty. However, an inquiry is needed to conduct a rapid assessment of the current evidence and policies and provide recommendations on how to improve in 2025 and beyond as well as guidance for future pandemics.

Unfolding Electrolyzer Characteristics to Reveal Solar-to-Chemical Efficiency Potential: Rapid Analysis Method Bridging Electrochemistry and Photovoltaics.

Development of photovoltaic-electrochemical (PV-EC) systems for energy storage and industry decarbonization requires multidisciplinary collaborative efforts of different research groups from both photovoltaic and electrochemical research communities. Consequently, the evaluation of the solar-to-chemical or solar-to-fuel efficiency of a new electrolyzer (EC) as a part of a PV-EC system is a time-consuming task that is challenging in a routine optimization loop. To address this issue, a new rapid assessment method is proposed. This method employs power balance requirements to unfold the input EC characteristics into the parameter space of PV-EC systems. The system parameters, composed with the EC output characteristics, yield the solar-to-chemical efficiency attainable by the electrolyzer in combination with any PV device under any irradiance at any relative PV-to-EC scaling and any mode of power coupling. This comprehensive overview is achieved via a mathematically simple conversion of the EC characteristics in any spreadsheet software. The method, designed to streamline the development and minimize the efforts of both the photovoltaic and electrochemical communities, is demonstrated via the analysis of CO2-reduction electrolyzer characteristics and verified with dedicated PV-EC experiments.

Field Trials of an Autonomous eDNA Sampler in Lotic Waters.

Environmental DNA (eDNA) analysis has become a transformative technology, but sample collection methods lack standardization and sampling at effective frequencies requires considerable field effort. Autonomous eDNA samplers that can sample water at high frequencies offer potential solutions to these problems. We present results from four case studies using a prototype autonomous eDNA sampler as part of the U.S. Geological Survey's Rapid Environmental eDNA Assessment and Deployment Initiative & Network (READI-Net) project. These case studies involved short-term deployments of an eDNA autosampler (Smith-Root) across a range of riverine habitats with the objectives of (a) identifying what insights could be gained from high-frequency autosampling and (b) benchmarking these autosamples against manually collected samples. The high frequency autosampling revealed high temporal variability of eDNA concentrations and provided valuable insights about eDNA associations with environmental covariates, such as discharge and turbidity. Benchmarking assessments indicated autosamples had similar detection rates to manual samples and obtained similar or greater eDNA quantities. We did find minimal carryover contamination in autosampler field controls. We conclude that eDNA autosamplers have potential to improve freshwater biosurveillance by reducing logistical sampling barriers, standardizing collection methods, and clarifying the influence of environmental covariates on eDNA results.

Coverage Estimation of Droplets Sprayed on Water-Sensitive Papers Based on Domain-Adaptive Segmentation

Unmanned aerial spraying systems (UASSs) are widely used today for the effective control of pests affecting crops, and more advanced UASS techniques are now being developed. To evaluate such systems, artificial targets are typically used to assess droplet coverage through image processing. To evaluate performance accurately, high-quality binary image processing is necessary; however, this involves labor for sample collection, transportation, and storage, as well as the risk of potential contamination during the process. Therefore, rapid assessment in the field is essential. In the present study, we evaluated droplet coverage on water-sensitive papers (WSPs) under field conditions. A dataset was constructed consisting of paired training examples, each comprising source and target data. The source data were high-quality labeled images obtained from WSP samples through image processing, while the target data were aligned RoIs within field images captured in situ. Droplet coverage estimation was performed using an encoder–decoder model, trained on the labeled images, with features adapted to field images via self-supervised learning. The results indicate that the proposed method detected droplet coverage in field images with an error of less than 5%, demonstrating a strong correlation between measured and estimated values (R2 = 0.99). The method proposed in this paper enables immediate and accurate evaluation of the performance of UASSs in situ.

Rapid Air Quality Assessment and Modelling of an Educational Institute Located in a Coastal Region near National Highway

Rapid Air Quality Assessment and Modelling of an Educational Institute Located in a Coastal Region near National Highway

A Rapid Colorimetric and Fluorescent Assay of Aspergillus Fumigatus in Food by Loop-Mediated Isothermal Amplification.

Aspergillus fumigatus is a foodborne mycete that can induce recurrent pneumonia, but the current detection methods have insufficient sensitivity and rapidity. Here, we aim to develop an efficient and sensitive loop-mediated isothermal amplification (LAMP) primer set for A. fumigatus detection. First, we designed a novel set of LAMP primers by targeting the Beta-tubulin (β-tub) gene. The LAMP reaction system was optimized by screening reaction temperature and betaine concentration. And then, the specificity of the proposed primers was verified by using 10 interferent microorganism species. The sensitivity of the designed method was compared with that of polymerase chain reaction (PCR) on pure cultures and complex matrix. The accuracy and response time of the method were examined by simulated samples. Our proposed primer set could accurately detect A. fumigatus from different food matrices with no response to other microorganisms. More intriguingly, this method possessed a low limit of detection (2 copies/reaction, 10-fold less than PCR), a short measuring time (<30 min), and a naked-eye readability. A real sample test demonstrates the good recovery rate and accuracy in apple, corn, milk, and other food matrix. Our proposed β-tub primer set provides great potential for rapid assessment of A. fumigatus contamination in food by integrating portable equipment and microscale reaction system.

HEALTH IN THE WORKPLACE: POSTURE, ERGONOMICS AND MUSCULAR DISORDERS

Objective: The study aimed to map the epidemiological profile of chronic musculoskeletal disorders and the ergonomic risks of body posture in the office environment. Methods: Data were collected using an electronic form consisting of sociodemographic questions and adapted versions of the Nordic Musculoskeletal Symptom and Rapid Office Strain Assessment questionnaires. Results: The mean age of the 73 participants in the extension project was 25.3 years and most had higher education. Of these, 86.3% felt pain, numbness or tingling in the last 12 months and of these, 90.5% had more than one body part affected by the symptoms. Of the thirteen body postures evaluated (sitting, leaning, position of the knees, feet, elbows, forearms and head), all were identified as ergonomically inadequate, especially those related to the chair used. Conclusion: There is a clear need for immediate health care actions for this population, since the office environments were not ergonomically adequate and the workers were already ill. Ergonomic intervention and health education actions are essential to guide the risks of this environment and propose behavioral changes to reduce/prevent musculoskeletal disorders. A limitation of the study may be that the sample was affected by the healthy worker effect, that is, those who were already affected by chronic musculoskeletal disorders were interested in participating in the project.

Redesign of work facilities at the tofu pressing station with a participatory ergonomic approach

Tahu Palda is a small and medium-sized micro business in the food industry that produces tofu. This factory has been producing tofu since 2005, located in North Aceh. The workers in this tofu usually five workers, with an average of 7 working hours per day. Based on the observations at several workstations, initial data indicate that the dominant work posture is standing, bending, and lifting heavy loads such as a 20 kg weight for pressing and lifting a mold holder with a 15 kg load. This is done continuously for 7 working hours each day. Based on the results of the distribution of the Cornell Musculoskeletal Discomfort Questionnaire (CMDQ), it was found that workers at the station experience complaints such as back pain, lower back pain, and upper arm pain due to the high frequency of lifting heavy loads. If this continues to be neglected, it can lead to work-related diseases such as musculoskeletal disorders. The assessment of workers' posture using the Rapid Entire Body Assessment (REBA) method shows that the score obtained for the job of pouring soybean paste is 7, which means a moderate risk, the job of lifting the press weight is 5, which means a moderate risk, and the job of flipping molds has a score of 10, which falls into the high-risk category and needs improvement. Improvements were made using a participatory ergonomics approach, resulting in the decision to redesign the tofu press tool to fit the workers' body anthropometry. The anthropometric calculations for the height range of the press support tool and cutting tool are 141.1 cm, the press support range is 68.78 cm, the width of the press tool is 49.4 cm, the height of the tray is 4.54 cm, and the height of the tray holder is 103.85 cm.

Facial Movements Extracted from Video for the Kinematic Classification of Speech

Speech Sound Disorders (SSDs) are prevalent communication problems in children that pose significant barriers to academic success and social participation. Accurate diagnosis is key to mitigating life-long impacts. We are developing a novel software solution—the Speech Movement and Acoustic Analysis Tracking (SMAAT) system to facilitate rapid and objective assessment of motor speech control issues underlying SSD. This study evaluates the feasibility of using automatically extracted three-dimensional (3D) facial measurements from single two-dimensional (2D) front-facing video cameras for classifying speech movements. Videos were recorded of 51 adults and 77 children between 3 and 4 years of age (all typically developed for age) saying 20 words from the mandibular and labial-facial levels of the Motor-Speech Hierarchy Probe Wordlist (MSH-PW). Measurements around the jaw and lips were automatically extracted from the 2D video frames using a state-of-the-art facial mesh detection and tracking algorithm, and each individual measurement was tested in a Leave-One-Out Cross-Validation (LOOCV) framework for its word classification performance. Statistics were evaluated at the α=0.05 significance level and several measurements were found to exhibit significant classification performance in both the adult and child cohorts. Importantly, measurements of depth indirectly inferred from the 2D video frames were among those found to be significant. The significant measurements were shown to match expectations of facial movements across the 20 words, demonstrating their potential applicability in supporting clinical evaluations of speech production.

Redesign of stroller work tools at PT. Ika Bina Agro Wisesa

PT. Ika Bina Agro Wisesa (PT. IBAS) is one of the agro-industries that manages or produces palm oil with a production capacity of 30 tons / hour and a total of 420 tons / day. The raw material of Fresh Fruit Bunches (FFB) of this factory comes from the plantation land of the community around the factory. The activity of harvesting FFB in this oil palm plantation has many manual material handling activities, especially for workers who lift and push wheelbarrows (angkong). The angkong work tool used is relatively old and looks unergonomic which can be seen when pushing the angkong in a very bent position so it is assumed that there is a large amount of force expended by workers. This research begins with a survey of the company and the field, then distributing Nordic Body Map (NBM) questionnaires to workers, using the Rapid Entire Body Assessment (REBA) method used to determine the level of risk of work posture in workers before and after the design of the tool, for the redesign of the stroller, anthropometric measurements are taken then using the Biomechanics method to determine the amount of force generated when pushing. This stroller redesign was carried out using operator anthropometry with the 95th percentile. After using the proposed tool, the work posture assessment is categorized as moderate which means that action is needed. The final results of the redesign are the length of the cart 90 cm, the width of the cart 66 cm, the diameter of the tire 32 cm, the height of the cart from the ground 125 cm, the height of the handle 96 cm, the width of the handle 40 cm, and the diameter of the handle 3 cm and the final results of the biomechanical assessment after the design concluded that the operator was no longer dangerous when pushing.

Abstract 4139198: A Systematic Approach to Prompting Large Language Models for Automated Feature Extraction from Cardiovascular Imaging Reports

Introduction: Cardiovascular radiology reports contain valuable diagnostic information linked to images, but the unstructured text format makes feature extraction difficult on a large scale. Large language models (LLMs) allow for feature extraction where string parsing alone is insufficient, but require careful prompting for accurate results. Hypothesis: We hypothesize that a systematic prompting approach using LLMs can expedite the extraction of features from unstructured text in transesophageal echocardiography (TEE) reports. Methods: The data consisted of 7106 intraoperative TEE reports, 600 of which were manually reviewed to obtain pre- and post-intervention ground truth values for left ventricular ejection fraction (LVEF), right ventricular systolic function (RVSF), and tricuspid regurgitation (TR). Reports are paired with an imaging study consisting of 50-200 clips. For each feature considered, 100 of the 600 labeled reports were used to engineer a prompt in Llama-2 that maximized feature extraction accuracy. Results: We found that using multiple, shorter prompts yielded higher accuracy than did fewer, longer prompts. Additionally, when imposing semantic information onto a numerical scale, prompt engineering in combination with string parsing (Figure 1) gave the best results. When evaluated on the 500 labeled reports withheld for testing, the finalized prompts had accuracies of 94.1%, 94.8%, and 91.3% for LVEF, RVSF, and TR, respectively. Using this strategy, 5000 intraoperative TEE reports were analyzed and used to train and evaluate a regression model for LVEF estimation from TEE clips (Figure 2). Conclusion: We have shown that performing prompt engineering on Llama-2 can be used to extract features from unstructured TEE reports in an accurate manner. As an extension of these methods, automated feature prediction from echocardiograms can be used to create rapid, low-cost, and accessible cardiac assessments.

Application of the Electrical Microbial Growth Analyzer Method for Efficiently Quantifying Viable Bacteria in Ready-to-Eat Sea Cucumber Products

Accurate and efficient quantification of viable bacteria in ready-to-eat food products is crucial for food safety and public health. The rapid and accurate assessment of foodborne bacteria in complex food matrices remains a significant challenge. Herein a culture-based approach was established for easily quantifying viable bacteria in ready-to-eat sea cucumber (RSC) products. Samples of the liquid companion within the package were directly transferred into test tubes to determine bacterial growth curves and growth rate curves, utilizing the electrical microbial growth analyzer. Viable bacteria in the samples were then quantified based on the time required to attain the maximum growth rate indicated on the growth rate curve. At a concentration of 5.0 × 103 CFU/mL of viable bacteria in the liquid companion, the recovery rates were 108.85–112.77% for Escherichia coli (E. coli) and 107.01–130.54% for Staphylococcus aureus (S. aureus), with standard deviations of 1.60 and 3.92, respectively. For the solid content in the package, the quantification was performed using the same methodology following an additional homogenization step. At a concentration of 5.0 × 103 CFU/mL of viable bacteria in the sample, the recovery rates were 91.94–102.24% for E. coli and 81.43–104.46% for S. aureus, with standard deviations of 2.34 and 2.38, respectively. In instances where the viable bacterial concentration was 5.0 × 103 CFU/mL in RSC products, the total time required for the quantification did not exceed 10.5 h. This method demonstrated advantages over traditional plate counting and PCR methods regarding simplicity and efficiency, representing a promising alternative for the quantification of viable bacteria in food like RSC products.

Deep mutational scanning of H5 hemagglutinin to inform influenza virus surveillance.

H5 influenza is considered a potential pandemic threat. Recently, H5 viruses belonging to clade 2.3.4.4b have caused large outbreaks in avian and multiple nonhuman mammalian species. Previous studies have identified molecular phenotypes of the viral hemagglutinin (HA) protein that contribute to pandemic potential in humans, including cell entry, receptor preference, HA stability, and reduced neutralization by polyclonal sera. However, prior experimental work has only measured how these phenotypes are affected by a handful of the >10,000 different possible amino-acid mutations to HA. Here, we use pseudovirus deep mutational scanning to measure how all mutations to a 2.3.4.4b H5 HA affect each phenotype. We identify mutations that allow HA to better bind α2-6-linked sialic acids and show that some viruses already carry mutations that stabilize HA. We also measure how all HA mutations affect neutralization by sera from mice and ferrets vaccinated against or infected with 2.3.4.4b H5 viruses. These antigenic maps enable rapid assessment of when new viral strains have acquired mutations that may create mismatches with candidate vaccine virus, and we show that a mutation present in some recent H5 HAs causes a large antigenic change. Overall, the systematic nature of deep mutational scanning combined with the safety of pseudoviruses enables comprehensive measurements of the phenotypic effects of mutations that can inform real-time interpretation of viral variation observed during surveillance of H5 influenza.

Ground Deformation and Damage Assessment using SAR Observations: A Case Study of Hinatuan, Philippines in the 2023 Surigao Del Sur Earthquake

Abstract. Earthquakes are among the major hazards that lead to economic loss every year. Rapid damage assessment is essential for a more efficient disaster response. The 2023 Surigao del Sur earthquake in the Philippines with moment magnitude (Mw) 7.4 occurred on December 2, 2023, with Hinatuan as the nearest municipality located 12 km south of the epicenter. Thus, this study examined the co-seismic impacts in Hinatuan using Synthetic Aperture Radar (SAR) observations. Using phase interferometry and displacement maps to estimate the ground surface deformation, results show that the phase interferograms, Line-of-sight (LOS) displacements, and vertical displacements were consistent, with most of Hinatuan experiencing an uplift of up to 7.2 cm. In contrast, subsidence as low as –9.0 cm was observed in some isolated areas. The damage assessment conducted through change detection analysis revealed 6.15 ha (6.4%) of built area out of the total 84 ha was significantly damaged. The damage mainly occurred in the uplifted regions between 0 to 5 cm. Building codes, land-use planning, and risk management and assessment can be improved and strengthened with these findings for a more earthquake-resilient community. The findings will be a basis for disaster risk reduction and management actions. These may, furthermore, serve as a reference for the development planning of local government units, especially for urban and built-up areas. These can also be used in developing Information, Education, and Communication (IEC) materials and advisory for residents in Hinatuan and the vulnerable adjacent regions.

Decision Support System (DSS) for Improving Production Ergonomics in the Construction Sector

Ergonomics is essential to improving workplace safety and efficiency by reducing the risks associated with physical tasks. This study presents a decision support system (DSS) aimed at enhancing production ergonomics in the construction sector through an analysis of high-risk postures. Using the Ovako Work Posture Analysis System (OWAS), the Revised NIOSH Lifting Equation (NIOSH equation) and Rapid Entire Body Assessment (REBA), the DSS identifies ergonomic risks by assessing body postures across common construction tasks. Three specific postures—X, Y and Z—were selected to represent typical construction activities, including lifting, squatting and repetitive tool use. Posture X, involving a forward-leaning stance with arms above the shoulders and a 25 kg load, was identified as critical, yielding the highest OWAS and NIOSH values, thus indicating an immediate need for corrective action to mitigate risks of musculoskeletal injuries. The DSS provides recommendations for workplace adjustments and posture improvements, demonstrating a robust framework that can be adapted to other postures and industries. Future developments may include application to other postures and sectors, as well as the use of artificial intelligence to support ongoing ergonomic assessments, offering a promising solution to enhance Occupational Safety and Health policies.

Landslide Rapid Impact Assessment at Hydro Dam via THyCAS and UAS

The TNB Hydro Catchment Area Surveillance (THyCAS) is a web-based geographic information system (GIS) used to monitor hydropower dam catchment areas in Peninsular Malaysia. It can also be used for landslide rapid impact assessment by monitoring the surrounding landslide area. However, during the rainy season, satellite images are unable to provide the information required by users due to full cloud cover. Therefore, an Unmanned Aircraft System (UAS) is used to provide the data required by THyCAS by mapping the surrounding area affected by landslides and uploading the processed data in THyCAS. Thus, the user can get the required information regarding the landslide incident. Furthermore, with UAS Full Motion Video (FMV) data, the location of the collapsed power transmission towers can be acquired. In addition, any information on land changes that could jeopardise the surrounding dam area is gathered. Therefore, with the information from the rapid impact assessment provided by THyCAS and UAS data, a preliminary risk assessment plan can be established and implemented by the relevant parties. The results obtained from the rapid assessment can also be used as additional information to find out the actual cause of landslides in the area.

Rapid Assessment Amid an Injection Drug Use-Driven HIV Outbreak in Massachusetts' Merrimack Valley: Highlights from a Case Study.

Illicitly manufactured fentanyl within the drug supply has substantially increased opioid-related overdose deaths and driven infectious disease outbreaks among people who use drugs (PWUD). Local jurisdictions often lack the data and tools necessary to detect and translate such moments into actionable and effective responses. Informed by a risk environment framework, this case study adopted a mixed-methods design spanning two rapid assessment studies with PWUD in Lowell (n = 90) and Lawrence (n = 40), Massachusetts, during an HIV outbreak (2017, Study 1) and following the outbreak (2019, Study 2). Survey responses (n = 130) were summarized by computing descriptive statistics, ethnographic field notes were reviewed, and qualitative interviews (n = 34) were thematically analyzed to contextualize quantitative findings. Study 1 participants reported constrained syringe access, high injecting frequency, syringe reuse, and unsafe syringe disposal practices, and uncovered entrenched social and structural factors exacerbating existing substance use and HIV risks: housing instability, limited harm reduction supplies and services (e.g., MOUD access), and tensions between law enforcement and PWUD. Following the outbreak and substantial local investments in harm reduction and treatment infrastructures, Study 2 participants indicated lower injection risk reporting fewer syringe reuse episodes, improved syringe disposal practices, high frequency of naloxone administration at recent overdoses, frequent testing for HIV and HCV, and greater engagement with syringe services programs. A rapid assessment approach to fentanyl's emergence and a concomitant HIV outbreak provided time-sensitive, critical insights and identified needed and local response strategies. However, unless social and structural risk factors are also addressed, community vulnerability to future HIV outbreaks remains.