Out-of-hospital cardiac arrest remains a major cause of preventable death. Rapid defibrillation is essential, yet access to automated external defibrillators is often delayed due to poor visibility and limited availability. Real-time geolocation platforms have emerged to improve access and shorten response times. A scoping review was conducted following Joanna Briggs Institute and PRISMA-ScR guidelines. Four databases: PubMed, Scopus, Embase, and Web of Science were searched from inception to April 2025. Studies reporting on real-time geolocation platforms designed to support early defibrillation in community or prehospital settings were included. Fourteen studies from seven countries were included. Most systems were smartphone-based or web applications integrated with emergency medical services. These platforms demonstrated potential to optimize early response: citizen responders arrived before emergency teams in 13-42% of cases, performed cardiopulmonary resuscitation in up to 69%, and delivered defibrillation in nearly 50%. Reported survival ranged from 8% to 88%, and restoration of spontaneous circulation occurred in approximately 30-39% of cases. However, inadequate device visibility (67%), restricted access (36%), and limited geographic coverage remained major operational barriers. Geolocation-based systems may improve community response to cardiac arrest and enhance survival through earlier defibrillation. Rigorous prospective studies are required to evaluate their long-term impact and determine their applicability across different emergency care systems.
Pollen is a robust and widespread substance that captures a historical snapshot of a specific time and place, and it can be used to track movements through space by examining the pollen deposited on various objects. Palynology, the study of pollen, is used across fields such as conservation, natural history, and forensics, where it is particularly useful for tracing the origin and movement of objects. However, pollen has remained underutilized due to the difficulty of distinguishing many pollen taxa beyond the family level and limited pollen reference material to support location predictions. With recent developments in pollen DNA metabarcoding these issues have been rectified, but much of the available pollen data are primarily from wind-pollinated species, which are widespread and less informative of specific sample locations. Bee-collected pollen presents an untapped resource in training predictive models to geolocate sample origin. Here we compiled bee-collected pollen DNA sequence relative abundance data from three projects in the western U.S. and assessed the accuracy of supervised machine learning models to predict the location of sample origin based solely on pollen assemblage, without the need of incorporating additional data. Random Forest and k-Nearest Neighbors models yielded high accuracy across all projects. We also found that models trained on taxonomically clustered pollen assigned sequence variants (ASVs) performed slightly better than those trained on raw sequence data, but the difference was minor, indicating that models trained on raw sequence data can reliably predict location and avoid the time-consuming taxonomic assignment process. Our results demonstrate the utility of repurposing bee-collected pollen for geolocation and provide a framework for employing supervised machine learning in future geolocation efforts. Bee-collected pollen metabarcoding data was used to accurately predict sample originRandom Forest and k-Nearest Neighbors algorithms were most accurate with lowest errorTaxonomically-classified and raw DNA sequence data training sets performed comparably.
High-frequency (HF) skywave propagation exploits ionospheric reflection for beyond-line-of-sight transmission, making time-difference-of-arrival (TDOA)-based geolocation a primary technique for localizing non-cooperative HF emitters. However, reliable TDOA estimation remains challenging due to time-varying ionospheric conditions, wideband multipath dispersion, and low signal-to-noise ratio (SNR). This paper proposes an integrated framework coupling realistic channel synthesis, deep learning-based TDOA estimation, and convex optimization-based localization. Three contributions are made. First, an improved wideband ionospheric channel model is constructed by integrating the International Reference Ionosphere (IRI) with region-specific calibration and a stochastic perturbation module, yielding time-varying multipath responses for physics-consistent waveform generation. Second, a convolutional neural network (CNN)-based TDOA estimator is designed to jointly exploit time-domain complex-baseband in-phase/quadrature (I/Q) waveforms, multi-weight generalized cross-correlation (GCC) feature maps, and channel-state information (CSI) within a unified regression network, achieving robust delay estimation under severe noise and multipath conditions. Third, the geolocation problem is formulated as a bias-regularized constrained least-squares problem with unknown ionospheric excess-delay surrogates, and a semidefinite programming (SDP) relaxation is derived to yield a tractable solution without prescribing a fixed virtual reflection height. Simulations show that the proposed estimator consistently outperforms competing algorithms across a wide SNR range and narrows the gap to the Cramér-Rao lower bound (CRLB) at high SNR. On field-recorded signals, the estimator reduces the mean absolute TDOA deviation by 51% relative to GCC with phase transform (GCC-PHAT), and the end-to-end pipeline achieves a mean geolocation error of 19.67 km across 100 field segments, outperforming all compared baselines.
Large earthquakes commonly generate surface rupture accompanied by both localized on-fault slip and spatially distributed off-fault deformation. Capturing both components is essential for understanding rupture processes and improving earthquake hazard assessment, yet field mapping alone often fails to fully document diffuse deformation. Here we evaluate the applicability of high-resolution Korea Multi-Purpose Satellite (KOMPSAT)-3 and -3A optical imagery for mapping near-field co-seismic deformation using sub-pixel optical image correlation (OIC), through two case-study areas affected by the 6 February 2023 Kahramanmaraş, Türkiye, earthquake sequence. We processed pre- and post-event stereo-mode KOMPSAT imagery using a MicMac-based workflow to generate orthorectified products and displacement fields, and compared the results with published Sentinel-2 OIC products and independent airborne Light Detection and Ranging (LiDAR) measurements. In the Hatay Airport area, KOMPSAT-3/3A OIC recovered a displacement pattern consistent with Sentinel-2, indicating ~5 m of relative motion across the fault, while the ~1 m effective spatial resolution enabled identification of localized infrastructure offsets (runway displacement) that were not detectable in 10 m Sentinel-2 imagery. In the Elbistan near-epicenter area, KOMPSAT-3/3A OIC resolved block motions of ~6 m and ~2 m in opposing directions. Swath profile analysis indicates an average on-fault slip of 6.8 m, whereas the total slip including distributed deformation reaches 9.3 m, implying that approximately 27% of the deformation is accommodated off-fault. Airborne LiDAR mapping provides an independent benchmark, with on-fault net slip of ~6.13 m and horizontal slip of 5.57 ± 1.40 m, consistent with the KOMPSAT-derived on-fault estimates and supporting the quantitative validity of the OIC results. However, the rupture geometry inferred from OIC is simpler than LiDAR-derived mapping, and absolute geolocation uncertainty remains a limiting factor with a post-correction Root Mean Square Error (RMSE) of 10.25 m and Circular Error with 90% Confidence (CE90) of 11.34 m, requiring cautious interpretation of absolute displacement magnitudes. Overall, our results demonstrate that KOMPSAT-3/3A imagery can serve as an effective resource for rapid rupture mapping and quantifying both on-fault and distributed deformation, while highlighting key requirements for improving geolocation control and integrating complementary datasets for robust three-dimensional deformation assessment.
Suicide represents a major public health issue influenced by a complex interplay of individual, social, and environmental factors. While suicidological research traditionally focuses on psychological and clinical aspects, spatial dimensions of suicidal behavior remain less explored, particularly in Central Europe. This study presents a geospatial analysis of completed suicides in the Olomouc and Zlín Regions of the Czech Republic between 2018 and 2022, using a unique dataset derived from forensic autopsy records. The primary goal was to demonstrate the potential of spatial processing and interdisciplinary collaboration between forensic medicine and geoinformatics in identifying contextual patterns of suicidal behavior. A dataset of 585 completed suicides was compiled from forensic autopsy reports, including detailed individual-level characteristics and geolocation data. Spatial analyses were conducted using GIS software, integrating additional layers such as land use, demographic and socioeconomic indicators, and quality of life indices. Both point-level and aggregated data (municipality and administrative district levels) were used to explore correlations and spatial variability. The study confirmed known trends - such as male predominance and the role of alcohol - and identified new spatial relationships, including a negative correlation between blood alcohol concentration and latitude, and between age and distance from residence to suicide location. Spatial analyses were conducted at multiple levels of aggregation and combined with selected socioeconomic and environmental indicators, including quality of life, urban-rural context, and foreclosure (enforcement) rates. The results demonstrate that spatial patterns and correlations between suicide rates and area-based characteristics vary depending on the spatial scale of analysis. The findings illustrate the potential of interdisciplinary collaboration between forensic medicine and geoinformatics and provide an exploratory basis for further research and more context-sensitive suicide prevention approaches.
Beach litter remains a persistent threat to coastal ecosystems, with far-reaching ecological, social, and economic consequences. Although official statistics provide essential information, they frequently overlook the numerous voluntary beach clean-up initiatives led by local associations, schools, companies, and informal groups. This study evaluates the potential of social media as a complementary data source for documenting such activities. Spain was selected as a case study due to its extensive coastline and strong reliance on tourism. Through keyword-based searches and geolocation techniques applied to posts on X and Instagram, we identified 487 beach-cleaning events in 2024, of which 458 (94%) were absent from official registries. These data reveal spatial and temporal patterns not captured by traditional monitoring systems, demonstrating the added value of user-generated content for coastal stewardship assessment. We also estimated the economic contribution of these initiatives from €1.13 to €2.86 million, depending on the approach, representing approximately 0.71-1.80% of Spain's annual public expenditure on beach litter removal. Beyond their measurable economic and ecological value, beach clean-ups promote social participation, environmental awareness, and engagement in marine conservation. Overall, our findings show that social media monitoring can effectively complement official statistics by providing a more comprehensive understanding of the scope and contribution of citizen-led efforts in coastal management and marine-litter mitigation.
The relocation of Indonesia's capital to Ibu Kota Nusantara (IKN) in East Kalimantan, a malaria and dengue hotspot, presents new risks of infectious disease transmission due to land-use changes and population movements. Current knowledge on the impact of these changes on vector-borne diseases, especially Plasmodium knowlesi malaria and other arboviruses, is limited. Serological surveillance offers a robust method for assessing population exposure. A community-based cross-sectional study will be conducted in IKN and its surrounding area, in East Kalimantan. Approximately 2,000 individuals aged >1 year will be enrolled. Finger-prick blood samples will be collected for serological analysis (multiplex bead-based assays for malaria species, and dengue virus serotypes) and malaria RDTs. Demographic, clinical, environmental, and geolocation data will also be collected. Statistical and geostatistical models will be used to assess seroprevalence, spatial patterns, and risk factors of exposure to malaria and dengue. Our study aims to understand the historical transmission risk of malaria and dengue among communities living in and around the new capital city. We will recruit around 2,000 people, aged ≥1 year old from randomly selected households in a development area of new capital city, Indonesia. From each consenting participant, we will take a finger-prick blood sample. This sample will be used for two purposes: first, a malaria diagnostic test (RDT), so we can refer to anyone who is ill for immediate treatment. Second, we will use laboratory tests to look for antibodies in the blood. Antibodies are a sign that a person’s immune system has been exposed to infections (or vaccinations) in the past. In our study, we aim to estimate the proportion of the population who have been exposed to malaria or dengue infections in the past, even if they never felt ill. By integrating health data with satellite-derived environmental data, we will identify hotspots where the risk of infection is highest and understand the factors that drive disease transmission. This information will be important evidence for the Indonesian Ministry of Health in planning effective disease control programmes and protecting the health of people living in these areas.
Identifying and preserving biological diversity is fundamental for the conservation of wild populations. The Atlantic bluefin tuna (Thunnus thynnus, ABT) is an apex predator and vital species to the pelagic ecosystems of the North Atlantic Ocean, with populations now rebounding from decades of overfishing due to strict enforcement of conservation measures. Here, we combine high-resolution whole-genome sequencing data with spatial data from electronic tagging to improve our understanding of population structure in ABT. We analyzed 82 whole-genome sequences obtained from mature fish tracked to geographically distinct spawning grounds, as well as larvae representing the two recognized stocks (western and eastern) of ABT. We obtained 11,181,223 single-nucleotide polymorphisms (SNPs) and integrated these genomic data with 12,974 total geolocation days of adult ABT (mean individual deployment length: 271 ± 110.4 days). This extensive dataset of electronic tracks enables spatial assignment of individuals to their respective spawning grounds and the first whole-genome comparison of migratory phenotypes. Both neutral and adaptive SNP markers reflect the same genomic population structure as the spatial movement patterns, likely maintained by natal philopatry, and we highlight candidate genes with potentially adaptive roles. Our analyses show that the two populations diverged ∼27,000 years ago, overlapping with the Last Glacial Maximum, and we suggest that oceanographic variation of the spawning grounds has contributed to shaping present-day bluefin tuna genomic diversity. Overall, these results improve our understanding of adaptive variation in bluefin tuna, which will be important for management decisions.
Trace elements occur naturally in the environment, but anthropogenic activities can amplify their release, increasing exposure and bioaccumulation in marine predators such as seabirds. Mercury (Hg) in liver, blood, and eggs of Leach's storm-petrels (Hydrobates leucorhous), has been investigated as a short-term exposure indicator of hatching and fledging success. However, chronic Hg intake and exposure to other trace elements during the non-breeding period remain poorly understood. This study assessed geographic variation in trace element exposure and relative trophic position in Leach's storm-petrels from five overwintering locations in the Atlantic Ocean, using secondary feathers (S4s) and geolocation data. Hg concentrations and trophic position (δ15N) varied significantly among overwintering locations, whereas other trace elements varied without clear spatial patterns. Sea surface temperature was positively correlated with Hg concentrations, whereas dietary origin (δ13C) and year were associated with relative trophic position. Colony of origin had no effect on Hg or trophic position, likely reflecting high intra-colony variability in wintering locations. Additionally, Hg concentrations were highest in birds overwintering in oligotrophic regions where δ15N values were low, suggesting lower nitrogen baselines in areas dominated by diazotrophic organisms and enhanced methylmercury availability via sulfate-reducing bacteria. In contrast, storm-petrels overwintering in productive upwelling regions such as the Benguela system exhibited lower Hg concentrations, consistent with Hg biodilution driven by rapid decaying phytoplankton export to deep sediments. These findings provide baseline information on trace element exposure during the non-breeding period of Leach's storm-petrels and inform future studies on migratory carry-over effects influencing adult survival and reproductive success.
To determine the physiological demands and their associations with body core temperature (Tc) of tropical recreational runners during mass participation distance running in a warm-humid environment (Dry bulb Temperature: 27.2±0.4°C, relative humidity: 87±2%). 162 individuals participated in a 21km (n=84) or 10km (n=78) race. Participants demographics were recorded in a pre-race questionnaire, and in-race measurements of environmental conditions, heart rate (HR, n=115), Tc (n=102), four-site skin temperature (Tsk, n=34) and fluid balance (n=36) were assessed. Real-time monitoring of HR, Tc and geolocation was conducted via a multi-user dashboard. Race split and finishing times were extracted from official results. Correlation and multiple linear regression analyses were performed between various parameters and peak Tc. Participants achieved peak HR (21km: 183±9(154-209)bpm, 10km: 180±10(156-204)bpm), Tc (21km: 39.4±0.6(38.3-40.8)°C, 10km: 39.3±0.6(38.2-41.1)°C) and Tsk (21km: 34.0±0.6(32.8-35.4) °C, 10km: 33.7±0.8(31.9-35.3) °C). Mean Tc was higher (p<0.05) in 21km (38.7±0.5(37.9-39.8)°C) than in 10km (38.5±0.4(37.2-39.9)°C). Tsk exhibited an 'inverted-U' profile in the 21km but plateaued in the 10km race. Body mass loss was -2.5±1.1(-5.5 to -0.7)% and -1.3±0.7(-2.4 to +0.5)% for the 21km and 10km participants, respectively. Starting Tc (18%), mean HR (13%), Body Surface Area (11%), and average speed (9%), but not age, estimated maximal aerobic capacity nor finishing time, to peak Tc. Recreational runners experienced high cardiovascular and thermal demands. We observed an 'inverted-U' Tsk profile in the 21km race in contrast to a plateau commonly described in laboratory-based findings. Starting Tc, mean HR, Body Surface Area, and average speed were independently associated with inter-individual differences in peak Tc. Real-time monitoring and contributors of peak Tc may inform future development of targeted strategies to optimise safety of recreational populations competing in the heat.
Illicit drug source tracing increasingly combines forensic toxicology, analytical chemistry, and genomics, yet connecting packaging or production to specific individuals remains challenging when touch DNA is limited, degraded, or contaminated. Here, we applied an integrated forensic genomics approach to a major transnational maritime cocaine trafficking case. Touch DNA recovered from multilayer packaging underwent 80 Mb SNP capture targeting autosomal, Y-chromosomal, and mitochondrial loci, with whole-genome sequencing used for comparison. Despite low DNA amounts and varying microbial contamination, the combined capture and imputation process produced usable genomic profiles for seven samples, three of which represented unique contributors. Allele-sharing analyses identified two contributors, UN01 and UN02, as closest to American reference populations, while UN03 showed a more complex profile with American, European, and African ancestries. The paternal lineages of UN01 and UN02 belonged to Q1b1a1a1, common in populations with Native American ancestry, whereas UN03 carried E1b1a1a1a2a1a3b1d1c1a, a lineage broadly linked to African ancestry. Maternal lineage inference added context, with UN03 assigned to the Native American-associated D1f and UN01/UN02 linked to deep Eurasian basal lineages. Identity-by-descent and geolocation analyses indicated stronger connections to South American reference groups for all three contributors, although the accuracy of these results was limited by uneven reference sampling and the structure of the training dataset. Overall, these findings demonstrate that combining SNP capture, genotype imputation, biogeographic ancestry inference, and phylogenetic analysis can produce valuable investigative leads from trace and degraded DNA, while also emphasizing the importance of cautious interpretation and awareness of uncertainty in forensic work.
The accurate identification of foraging locations is critical for wildlife conservation. While remote sensing and biologging devices provide much of the necessary data, their deployment is often complicated by factors such as weight, battery life and sensor capacity, limiting their effectiveness for long-term tracking of wide-ranging species. In this study, we evaluate the effectiveness of saltwater immersion data from light-level geolocation loggers (global location sensor; GLS) as a predictor of foraging behaviour in a pursuit-diving seabird, the red-footed booby (Sula sula rubripes). Using co-deployed tri-axial acceleration data as a high-resolution benchmark, we compare the performance of deep learning models for classifying dive and non-dive states. Predictions are cross-validated on withheld individuals for generalizability. Using a small pilot dataset, we find that models trained solely on GLS data only slightly underperform those trained on acceleration data despite the resolution discrepancy, classifying the diving behaviours of unseen birds with 93.65% accuracy. These findings suggest that GLS data alone may be sufficient to reliably infer dive events and, by extension, foraging locations, for pursuit-diving seabirds, providing a minimally invasive, scalable method to enrich year-round GLS migratory tracking studies using models derived from co-deployment of GLS and global positioning system devices.
As digital media become deeply embedded in everyday life, sexual minority men's self-presentation and identity negotiation increasingly unfold through mobile dating applications. This study investigates how Hong Kong sexual minority men navigate self-presentation and well-being on Grindr within intersecting cultural and political frameworks. Based on 16 semi-structured interviews, the analysis integrates the concepts of imagined audience and the minority stress model to examine how users negotiate visibility and safety amid Confucian family ethics, Christian sexual morality inherited from the colonial past, and the globalized politics of LGBTQ+ identity. The findings reveal that users employ blurred photos, coded expressions, and strategic self-presentations to balance familial obligations, religious norms, and global queer expectations, and that these strategies carry significant psychological costs. The analysis is guided by the framework "platform-mediated minority stress," illuminating how platform affordances and governance mechanisms, including geolocation, filtering functions, and content moderation, translate intersecting moral and cultural regimes into individualized forms of psychological tension and identity management. This study extends the minority stress framework into the domain of digital platforms and offers a new lens for understanding queer digital life in postcolonial, hybrid global cities.
This study describes a dataset of georeferenced unproductive vines (dead and missing) and associated observations on fields and soil. The dataset results from a survey of 14 287 unproductive vines performed in a French Mediterranean vineyard made of 50 blocks with a total area of 28 hectares. The survey was carried out over two successive years, 2022 and 2023. Geolocation-accurate data (<5 cm) was collected via a Real Time Kinematic (RTK) Global Navigation Satellite System (GNSS) and includes detailed information of the 50 vineyard blocks, such as boundaries, area, grape variety, plantation density and location of surveyed unproductive vines. In order to provide information on the soil situation, apparent soil resistivity data at three depths (0-50cm, 0-1m, 0-1.5m) acquired with Multi-depth continuous electrical profiling (MuCep) is also provided. This dataset has already been used to investigate optimised sampling methods aiming at assessing the proportion of unproductive vines at the field level. To our knowledge, a spatialized dataset of this precision and scale concerning unproductive vines is currently unavailable. This dataset is valuable for driving any experimentation aimed to study the spatial distribution of unproductive vines, potential correlations with soil characteristics, variety, and other factors. Furthermore, this data is relevant to consider the feasibility of estimating the proportion of unproductive vines, for example, via remote sensing techniques.
Health research has shifted from a disease-centered approach towards emphasizing functioning and more specific lived health. Lived health, the actual performance of daily activities in one's environment, has nevertheless received limited attention, and its assessment remains methodologically challenging. Ecological Momentary Assessment (EMA), a real-time method capturing behaviors, emotions, and context in natural settings, holds promise in this regard. Although EMA research is increasing, insight into its use for studying daily activities is still limited. To address this gap, this study systematically maps EMA applications across diverse health and disability populations to better understand lived health, defined as actual engagement in daily activities. A bibliometric analysis was conducted using a literature search on Web of Science with keywords related to EMA combined with daily activity terms, yielding 3,692 English-language articles. Publications were classified according to general characteristics, distribution of disability and health populations, actual engagement in daily activities following the person-environment-occupational model (PEO-model), and interaction analyses combining the last two analyses. The results show that mental disorders dominate the EMA research on daily activities, representing 75% of the dataset, which has significantly shaped the overall research landscape. Moreover, while personal factors are frequently highlighted, occupational and environmental dimensions remain underrepresented. These findings suggest that future EMA research should better integrate aspects of person, occupation, and environment, for instance by using tools such as geolocation and passive sensing to capture daily functioning more holistically. Expanding research beyond mental health and increasing secondary analyses will further strengthen the relevance and impact of EMA on health research.
Dementia is increasing in Latin America, creating demand for non-pharmacological support that can be delivered safely at home. Smart environments and related digital tools may help caregivers and people with early-stage dementia by supporting safety, reminders, and communication. This study assessed needs and acceptability in Colombia and produced a methodological guide for technology selection. We conducted a sequential exploratory mixed-methods study. First, a focused evidence synthesis informed a feature catalogue and instrument design. Second, we administered a cross-sectional questionnaire to caregivers and people living with early-stage dementia. Quantitative data were summarised with descriptive statistics and non-parametric group comparisons; open-ended responses were analysed thematically and integrated with the quantitative findings. Fifty-one responses were analysed. Safety-oriented functions (for example, fall detection and geolocation), reminders for activities of daily living, tele-assistance, and cognitive tele-stimulation were the most frequently prioritised. Acceptability was generally higher for low-burden technologies with clear usefulness, and age differences were limited across key comparisons. In this sample, smart-environment-enabled non-pharmacological support was feasible and broadly acceptable for early-stage dementia care. The methodological guide emphasises prioritising safety and reminders, reducing interaction burden, and incorporating privacy-by-design. Further studies should validate these findings with larger and more diverse samples and evaluate implementation outcomes.
Whether living environment may influence outcome of stroke survivors remains to be elucidated. This registry-based cohort study aimed to assess the relationship between urban greenness around the residence and one-year death or recurrence after a first-ever ischaemic stroke. Patients with a first-ever ischaemic stroke who directly returned home were identified from the population-based registry of Dijon, France. For each patient, after geolocation of residential building, two greenness indices were calculated: the distance by road and pedestrian networks to the nearest public green space, and the area of green spaces within radii of 100 and 400 metres. Atmospheric NO2 and PM10 outdoor concentrations around the residence and deprivation index were assessed. During the 2005-2008 study period, 360 patients were identified and included (median age: 75 years-old (IQR: 63-83), 56% women). Fifteen died and 17 had recurrent stroke during the one year of follow-up. In adjusted models, the distance between public green spaces and patients' residence was associated with stroke recurrence or death (HR = 1.26, 95% CI: 1.08-1.48, P < 0.01, for each 100 metre section of city network). In age-stratified analysis, this association remained significant only in patients aged 65-79 years (HR: 1.37, 95% CI: 1.10-1.71, P < 0.01). When considering separately stroke recurrence and death, this association remained significant for recurrence (HR = 1.30, 95% CI: 1.07-1.58, P < 0.01) but not for death (HR = 1.17, 95% CI: 0.89-1.52). This study highlighted a beneficial influence of greenness on post-stroke recurrence in an urban area. These results indicate that urban planning policy could impact secondary prevention.
The ongoing drug poisoning crisis continues to cause significant mortality, with a disproportionate number of overdose deaths occurring when individuals use drugs alone. While supervised consumption sites (SCS) have proven effective in reducing overdose fatalities, their impact is limited by geographic, social, and systemic barriers. In response, overdose response technologies have emerged to expand access to life-saving interventions beyond the reach of traditional harm reduction infrastructure. Overdose response technologies (e.g., National Overdose Response Service (NORS)) and applications (e.g., Lifeguard App, UnityPhilly) offer real-time monitoring during solitary substance use. Hotlines provide peer-operated support and activate emergency responses if a caller becomes unresponsive, while apps use timers and geolocation to trigger automatic emergency services dispatch. Despite promising early outcomes, these services operate in a fragmented policy landscape without formalized regulatory guidance or implementation best practices. Preliminary data show that services like NORS have successfully prevented overdose deaths; however, published outcomes for most services remain limited. Key areas of priority for standards include the following: ensuring privacy for service, balancing data usage for quality improvement and research, building capacity to further equity of access to healthcare and harm reduction using the virtual platform, standardizing overdose response, and providing appropriate education around the efficacy of services. To enhance the effectiveness and sustainability of overdose response technologies, a comprehensive policy or standards framework is needed. This includes guidance on data privacy, service equity, public education, capacity-building, and outcome evaluation, laying the groundwork for safer, scalable, and more accessible overdose prevention interventions. RéSUMé: CONTEXTE: La crise actuelle liée à l'intoxication médicamenteuse continue d'entraîner une mortalité importante, avec un nombre disproportionné de décès par surdose chez les personnes solitaires qui consomment des drogues. Si les sites de consommation supervisée (SCS) se sont avérés efficaces pour réduire le nombre de décès par surdose, leur impact est limité par des obstacles géographiques, sociaux et systémiques. En réponse à cela, des technologies d'intervention en cas de surdose ont vu le jour afin d'élargir l'accès à des interventions vitales au-delà de la portée des infrastructures traditionnelles de réduction des risques. INTERVENTION: Les technologies d'intervention en cas de surdose (par exemple, le National Overdose Response Service [NORS]) et les applications (par exemple, Lifeguard App, UnityPhilly) offrent une surveillance en temps réel pendant la consommation solitaire de substances. Les lignes d'assistance téléphonique fournissent un soutien assuré par des pairs et activent les services d'urgence si l'appelant ne répond plus, tandis que les applications utilisent des minuteries et la géolocalisation pour déclencher l'envoi automatique des services d'urgence. Malgré des résultats prometteurs, ces services fonctionnent dans un contexte politique fragmenté, sans directives réglementaires formelles ni de bonnes pratiques de mise en œuvre. RéSULTATS: Les données préliminaires montrent que des services tels que le NORS ont permis de prévenir avec succès des décès par surdose; toutefois, les résultats publiés pour la plupart des services restent limités. Les principaux domaines prioritaires pour les normes sont les suivants: garantir la confidentialité du service; trouver un équilibre entre l'utilisation des données à des fins d'amélioration de la qualité et de recherche; renforcer les capacités afin de favoriser l'équité dans l'accès aux soins de santé et à la réduction des risques à l'aide de la plateforme virtuelle; standardiser les interventions en cas de surdose; et fournir une éducation appropriée sur l'efficacité des services. IMPLICATIONS: Afin d'améliorer l'efficacité et la durabilité des technologies d'intervention en cas de surdose, un cadre politique ou normatif complet est nécessaire. Celui-ci doit inclure des orientations sur la confidentialité des données, l'équité des services, l'éducation du public, le renforcement des capacités et l'évaluation des résultats, afin de jeter les bases des interventions de prévention des surdoses plus sûres, plus évolutives et plus accessibles.
This study examined the endorsement of electronic screening and brief intervention (e-SBI) features among men who have sex with men (MSM) who use substances and live in rural Southern U.S. counties. Additionally, demographic, care access, and substance use correlates of endorsed features were assessed. Participants (N = 412) completed an online cross-sectional survey. Descriptive statistics and split logistic regression models were employed. Over half of participants endorsed features related to substance misuse screenings, a list of local behavioral health professionals, substance misuse prevention information, and virtual communication with a behavioral health professional. Young adults were less likely, whereas racial minority participants were more likely, to support multiple e-SBI features. Employed participants had lower odds of preferring substance use screenings, while health insured participants had higher odds of endorsing in- and outpatient program listings. Participants who reported alcohol and stimulant use were more likely to select geolocation-based notifications, while participants reporting depressants and dissociative use were more likely to endorse listings of and talking with local behavioral health professionals. Findings highlight the heterogeneity in e-SBI feature preferences across demographic and substance use profiles, suggesting that user-informed design approaches may enhance the acceptability and uptake of e-SBIs among rural MSM.
Forest pest insects cause major socio-economic impacts, global losses of millions of dollars, and ecosystem changes. A key challenge for their management is tracing regional dispersal events critical to outbreak dynamics. We developed an integrated tracing framework for pest insects by combining isotope geolocation, ecological data, and atmospheric modeling, and applied this framework to the eastern spruce budworm moth (Choristoneura fumiferana), the most severe defoliator of the North American boreal forest, to trace outbreak dispersal events. We first generated a North American model of bioavailable sulfur isotope (δ34S) variation in space (isoscape) and then calibrated it to spruce budworm tissues of known origin. We then used an automated trap network with high temporal resolution to collect samples and identify potential immigration events of eastern spruce budworm to Nova Scotia, Canada. Finally, we traced the natal origin of these immigrants by sequentially integrating high-probability regions of origin derived from δ34S values and estimated migration routes derived from biologically constrained atmospheric transport models. We find that this integrated framework allows us to narrow down the region of pest origins, restricting it to a few possible locations and demonstrating long-distance dispersal of spruce budworm across ~400 km over the Gulf of St. Lawrence, Quebec. Our framework demonstrates that combining isotopic data with ecological indicators and atmospheric transport modeling offers improved resolution and understanding of insect dispersal ecology. This approach is transferable to trace other migratory insect species to address conservation, agriculture, and bio-surveillance needs in the context of global environmental change.