Common Exposures Research Articles

Ai-enabled automated common vulnerability scoring from common vulnerabilities and exposures descriptions

Ai-enabled automated common vulnerability scoring from common vulnerabilities and exposures descriptions

Statewide Outbreak of Neisseria meningitidis Serogroup Y, Sequence Type 1466 - Virginia, 2022-2024.

Invasive meningococcal disease (IMD) is a severe illness that can have devastating effects; outbreaks are uncommon in the United States. Vaccination is the preferred control measure for IMD outbreaks when a defined population at risk (e.g., college students or persons experiencing homelessness) can be identified. In August 2022, the Virginia Department of Health (VDH) began investigating an IMD outbreak in Virginia's Eastern Health Planning Region, prompted by the detection of four confirmed cases within 8 weeks. Clinical isolates available from three cases were characterized as Neisseria meningitidis serogroup Y, sequence type 1466. A subsequent statewide investigation identified 36 genetically related cases, including seven deaths (case fatality rate=19.4%) as of March 1, 2024. A majority of patients (63.9%) were in an age group (30-60 years) not generally considered at increased risk for IMD; 78.0% were non-Hispanic Black or African American. No common exposures, affiliations, or risk factors were identified, and a defined population could not be identified for vaccination. VDH recommended quadrivalent (serogroups A, C, W, and Y) meningococcal conjugate vaccination of a subset of close contacts of patients based on IMD risk factors and age range similar to that of patients with identified cases. IMD outbreaks might affect populations without established IMD risk factors. Lack of a well-defined population at risk might prompt exploration of novel control strategies, such as selective vaccination of close contacts.

Uncovering Multi-step Attacks with Threat Knowledge Graph Reasoning

The rapid advancement of information technologies has significantly intensified the focus on cyberspace security across various sectors. In this evolving landscape, attackers deploy many of techniques- including exploits, weakness identification, and complex multi-step attacks- to gain unauthorized access to systems. Conversely, defenders harness insights from a variety of sources to pinpoint potential threats. Prominent public cybersecurity databases such as the Adversarial Tactics, Techniques, and Common Knowledge (ATT&CK), Common Attack Pattern Enumeration and Classification (CAPEC), Common Vulnerabilities and Exposures (CVE), Common Weakness Enumeration (CWE), and Common Platform Enumeration (CPE) provide extensive data on security entities and their interrelations, playing a pivotal role in enriching the understanding of cybersecurity challenges and assisting in comprehensive defensive analyses. However, the semantic cross-analysis of these databases, crucial for identifying obscure threat patterns, remains underexploited. In this study, we amalgamate data from these disparate sources into a cohesive threat knowledge graph and introduce a novel knowledge representation learning approach, A4CKGE (ATT&CK-CAPEC-CWE-CVE-CPE Knowledge Graph Embedding). This method utilizes advanced structural and textual analytics to predict interactions among security entities such as products, vulnerabilities, weaknesses, and multi-step attack sequences, employing complex attack templates generated through a Large Language Model (LLM). Our extensive experiments demonstrate that this approach significantly outperforms existing state-of-the-art methods in effectively predicting these relationships. The findings validate the efficacy of our threat knowledge graph in unveiling hidden connections, thereby highlighting its potential to strengthen cybersecurity defenses substantially.

Can endogenous ethylene glycol production occur in humans? A detailed investigation of adult monozygotic twin sisters

Introduction To the best of our knowledge, clinically significant endogenous ethylene glycol production has never been reported in humans, very seldom reported in other animals or microorganisms, and then only under rare and specific conditions. We describe the detailed investigations we undertook in two adult monozygotic twin sisters to ascertain whether they were producing endogenous ethylene glycol. Methods Two previously healthy monozygotic adult twin sisters presented with recurrent episodes of apparent ethylene glycol poisoning beginning at age 35, requiring chronic hemodialysis to remove ethylene glycol and its metabolites as well as to restore metabolic homeostasis. The sisters denied ingestion or exposure to ethylene glycol. At their request, they were admitted to hospital under strict supervision to exclude surreptitious ingestion of ethylene glycol and to evaluate the need for treatment. Hemodialysis was withheld during this prospective study. Twin A was admitted for 14 days and twin B for 11 days. Serial biochemical analyses were performed in blood and urine. Clinical exome sequencing and mitochondrial deoxyribonucleic acid sequencing were also completed. Results In both twins, ethylene glycol was detected in urine, along with intermittent increases in concentrations of lactate, glycolate, and glycine in blood and/or urine. Blood ethylene glycol concentrations, however, remained <62 mg/L (<1 mmol/L) but became positive soon after discharge. The oxalate concentration remained normal in blood and urine. Plasma and urine amino acid profiles showed intermittent small increases in glycine, serine, taurine, proline, and/or alanine concentrations. Exome sequencing and mitochondrial deoxyribonucleic acid sequencing were non-diagnostic. Neither twin has been admitted with metabolic acidosis nor ethylene glycol poisoning since chronic hemodialysis was started. Twin A developed a calcium oxalate dihydrate lithiasis. Discussion Mitochondrial disease, methylmalonic/propionic/isovaleric aciduria, primary hyperoxaluria, and analyte error were all excluded in these twins, as were obvious common environmental exposures. Conclusion Detailed investigations were performed in adult monozygotic twin sisters to ascertain whether they were producing endogenous ethylene glycol. Alternative explanations were excluded to the very best of our efforts and knowledge. Global metabolomics, gut microbiome analyses, and whole genome sequencing are pending.

Assessment of Web Security Vulnerabilities for Common Open Source Virtualization Software

Open-source hypervisors have emerged as an integral technology for virtualizing server resources in cloud and data center computing. Hypervisor security efficiency is determined by virtual machine isolation, which is a de facto adoption factor in the selection process, as well as its ability to respond to web attacks. This paper assesses the security performance of Proxmox VE and XenServer for type 1 hypervisors, and Kernel Virtual Machine and Oracle Virtual Box for type 2 hypervisors. Security analysis was conducted using common exposures extracted from vulnerability databases and mapped against the OWASP 2013 and 2017 projects. For clarity, experiments were carried out on a testbed with prebuilt virtual machines, each hosting one hypervisor installed as an attack target. Kali Linux was configured in one virtual machine to run recursive penetration testing for information gathering, vulnerability detection, penetration attempts, and exploitation of weak spots. The infrastructure was set in both homogeneous and heterogeneous execution environments, with a series of tests nested with each other. All four hypervisors are vulnerable to physical kernel isolation, as unprivileged users can gain root access and launch guest-to-guest and host-to-guest attacks. Among the two, guest-to-guest attacks were found to be more common than host-to-guest attacks, indicating that virtual machine isolation is weaker than the underlying host. Type 1 hypervisors have a lower rate of host-to-guest attacks than guest to-guest attacks, implying that XenServer and Proxmox VE provide better isolation than KVM and OVB due to the near-native speed, security, and efficiency of their virtual machines. All four hypervisors were found to be vulnerable to buffer overflow exploits and error-triggering sensitive information leaks, which were primarily caused by adopter default misconfigurations in the deployment process rather than software design flaws. This implies that greater efforts are required by open-source adopters when shifting from physical to virtual computing.

Exposure to noise at home, emotional behaviour, and attention deficit hyperactivity disorder in 9-year-old children

The purpose of this study was to assess the relationship between noise disturbance at home, sleep disturbance, and neurodevelopmental problems in 9-year-old children. Noise exposure (frequency) perceived by 430 9-year-old children from the INMA cohort in Valencia, Spain, were reported by their mothers. The risk of developing attention deficit hyperactivity disorder, as well as internalizing and externalizing problems, were assessed using the Child Behaviour Checklist. The risk of internalizing (18%) and externalizing problems (11.7%) was higher compared to the risk of developing attention deficit hyperactivity disorder (1.4%) and were more prevalent in boys than in girls. The most common and bothersome noise exposures were generated at home (50.8-55.3%) and by neighbours (24.5%). The risk of neurodevelopmental problems was associated with sleep disturbances, particularly in relation with attention deficit hyperactivity disorder (16.1 vs 4%; p<0.001), with no differences observed between sex. Sleep disturbances were significantly more common in children exposed to noise from household or neighbours. High levels of noise exposure from street traffic and neighbours were linked to an increased risk of attention deficit hyperactivity disorder, while noise from other children at home was associated with a higher risk of internalizing and externalizing problems. These effects remained consistent even after adjusting for sleep disturbances. High levels of noise annoyance from various sources perceived at home are differently associated with the risks of different neurodevelopmental problems in 9-year-old boys and girls, with sleep disturbances not influencing this relationship.

NG_MDERANK: A software vulnerability feature knowledge extraction method based on N‐gram similarity

AbstractAs software grows in size and complexity, software vulnerabilities are increasing, leading to a range of serious insecurity issues. Open‐source software vulnerability reports and documentation can provide researchers with great convenience for analysis and detection. However, the quality of different data sources varies, the data are duplicated and lack of correlation, which often requires a lot of manual management and analysis. In order to solve the problems of scattered and heterogeneous data and lack of correlation in traditional vulnerability repositories, this paper proposes a software vulnerability feature knowledge extraction method that combines the N‐gram model and mask similarity. The method generates mask text data based on the extraction of N‐gram candidate keywords and extracts vulnerability feature knowledge by calculating the similarity of mask text. This method analyzes the samples efficiently and stably in the environment of large sample size and complex samples and can obtain high‐value semi‐structured data. Then, the final node, relationship, and attribute information are obtained by secondary knowledge cleaning and extraction of the extracted semi‐structured data results. And based on the extraction results, the corresponding software vulnerability domain knowledge graph is constructed to deeply explore the semantic information features and entity relationships of vulnerabilities, which can help to efficiently study software security problems and solve vulnerability problems. The effectiveness and superiority of the proposed method is verified by comparing it with several traditional keyword extraction algorithms on Common Weakness Enumeration (CWE) and Common Vulnerabilities and Exposures (CVE) vulnerability data.

Refining CVE-to-CWE mapping with enhanced attention in BERT-based models

In this study, we introduce CySecBERT-ARD, an advanced approach for classifying software vulnerabilities that maps Common Vulnerabilities and Exposures (CVE) to Common Weakness Enumerations (CWE). Our approach is to use a pretrained transformer-based model CySecBERT tailored for cybersecurity contexts, the model is enhanced with additive attention and relative position encoding which allow for a deeper understanding of the vulnerability descriptions of CVE by capturing the contextual relationships. Our approach achieves an impressive accuracy of 91.34% and F1-score of 91.32% during the evaluation and testing phase compared to the base models. The results demonstrate the potential of CySecBERT-ARD in enhancing the efficiency and effectiveness of vulnerability classification.

Prediction of vulnerability severity using vulnerability description with natural language processing and deep learning

One of the most critical aspects of a software piece is its vulnerabilities. Regardless of the years of experience, type of project, or the size of the team, it is impossible to avoid introducing vulnerabilities while developing or maintaining software. This aspect becomes crucial when the software is deployed in production or released to the final users. At that point finding vulnerabilities becomes a race between the developers and malicious intruders, whoever finds it first can either exploit it or fix it. Acknowledging this situation and using the tools and standards that we have available in the field, such as common vulnerability exposures and common vulnerability scoring systems, and based on modern researches, in this study, we propose to have an approach different from the common practices of manual classification, using a 2-layer convolutional neuronal network (CNN) to automatize the classification of vulnerabilities, speeding up this process and enabling developers to have a faster response towards vulnerabilities, producing safer software. The experimental results obtained in this study suggest that pre-trained word embeddings contributed to an increase in accuracy of approximately 2% and the overall accuracy become 0.816%.

Community-associated Carbapenem-Resistant Organism Case Investigations in New York City, December 2020-May 2023

Epidemiology of carbapenem-resistant organisms (CRO) has focused on transmission in acute care hospital or long-term care facility (LTCF) settings. Few investigations have examined community-associated (CA)-CRO, with no consensus about common exposures. To explore possible exposures, the New York City (NYC) Department of Health and Mental Hygiene investigated suspected CA-CRO cases through routine surveillance among NYC residents with specimens collected during December 2020-May 2023. CA-CRO cases were defined as urine or skin specimens with bacterial cultures exhibiting carbapenem resistance, among individuals aged ≤70 years with no international travel, hospitalization, or LTCF stays within 12 months before specimen collection. Inclusion was determined by reviewing data from health information exchanges, when available electronic medical records, and telephone screening for those not excluded through record review. We identified 426 suspected cases for review, those not meeting the case definition were excluded; 44 individuals were not reached for screening. A preliminary questionnaire was fielded with 12 individuals and then refined to capture additional potential exposures. Analyses were completed with 23 individuals interviewed with the refined questionnaire. Of the 23, 70% were female; 39% were Hispanic, 17% Black, and 17% White; their median age was 60 years (range: 26-70 years). Further, 83% reported an outpatient appointment, 48% reported an outpatient procedure/surgery, and 9% reported having a hospitalized household member, all within 12 months before specimen collection; 26% had a urinary catheter or indwelling device within 2 days of specimen collection. Additionally, 30% reported taking antibiotics within 3 months of specimen collection, 52% denied taking antibiotics, 9% were unsure about antibiotic use, and 9% did not answer the question. Whole genome sequencing (WGS) was performed on 14 available isolates from CA-CRO cases by the NYC Public Health Laboratory or Wadsworth Center (WC), of which only 7 could be compared with isolates previously sequenced at WC (2017-2023). Six isolates were separated by &gt;50 mutation events, suggesting no close genomic relationship. One isolate from 2021 was 11 mutation events from a 2018 isolate from the same individual, consistent with the expected evolutionary rate. While infrequent, CA-CRO cases occur in NYC. Outpatient healthcare, antibiotic use, and urinary catheters or indwelling devices were common self-reported exposures. Analyses were limited by screening non-response. Increased specimen availability for WGS could enhance investigation of CA-CRO exposure patterns. Health information exchange data were often incomplete and future surveillance could benefit from healthcare and public health partnerships and better documentation for more complete electronic medical histories.

Enterprise-Centric Intelligence: A Prioritization Scheme for Cyberthreat Intelligence and Common Vulnerabilities and Exposures

Enterprise-Centric Intelligence: A Prioritization Scheme for Cyberthreat Intelligence and Common Vulnerabilities and Exposures

Transaction-level transparency and portfolio mimicking

This study examines whether an increase in the transparency of investment transactions facilitates portfolio mimicking. While there are reported benefits of transparency in enhancing regulatory monitoring and discipline, an increase in the transparency of investment transactions can also facilitate mimicking of peer firms’ investment strategies. I exploit an exogenous increase in the broad dissemination of transaction-level investment disclosures of U.S.-based insurers and find a significant increase in portfolio similarity at the individual security level. Increases in portfolio similarity are more pronounced in smaller, less sophisticated insurers mimicking their larger, more sophisticated peers. Shared asset positions and common exposures to risk can exacerbate collective risk across firms. Accordingly, I find that the detectable increases in portfolio similarity are positively associated with measures of systemic risk, especially in those smaller insurers mimicking their peers. This study adds to a nascent literature on portfolio mimicking and highlights a potential negative externality of increased transparency.

Loss of neuronal lysosomal acid lipase drives amyloid pathology in Alzheimer's disease.

Underlying drivers of late-onset Alzheimer's disease (LOAD) pathology remain unknown. However, multiple biologically diverse risk factors share a common pathological progression. To identify convergent molecular abnormalities that drive LOAD pathogenesis we compared two common midlife risk factors for LOAD, heavy alcohol use and obesity. This revealed that disrupted lipophagy is an underlying cause of LOAD pathogenesis. Both exposures reduced lysosomal flux, with a loss of neuronal lysosomal acid lipase (LAL). This resulted in neuronal lysosomal lipid (NLL) accumulation, which opposed Aβ localization to lysosomes. Neuronal LAL loss both preceded (with aging) and promoted (targeted knockdown) Aβ pathology and cognitive deficits in AD mice. The addition of recombinant LAL ex vivo and neuronal LAL overexpression in vivo prevented amyloid increases and improved cognition. In WT mice, neuronal LAL declined with aging and correlated negatively with entorhinal Aβ. In healthy human brain, LAL also declined with age, suggesting this contributes to the age-related vulnerability for AD. In human LOAD LAL was further reduced, correlated negatively with Aβ1-42, and occurred with polymerase pausing at the LAL gene. Together, this finds that the loss of neuronal LAL promotes NLL accumulation to impede degradation of Aβ in neuronal lysosomes to drive AD amyloid pathology.

Gestational Age, Infection, and Suboptimal Maternal Prepregnancy BMI Independently Associate with Placental Histopathology in a Cohort of Pregnancies without Major Maternal Comorbidities.

Background: The placenta undergoes morphological and functional adaptations to adverse exposures during pregnancy. The effects ofsuboptimal maternal body mass index (BMI), preterm birth, and infection on placental histopathological phenotypes are not yet well understood, despite the association between these conditions and poor offspring outcomes. We hypothesized that suboptimal maternal prepregnancy BMI and preterm birth (with and without infection) would associate with altered placental maturity and morphometry, and that altered placental maturity would associate with poor birth outcomes. Methods: Clinical data and human placentae were collected from 96 pregnancies where mothers were underweight, normal weight, overweight, or obese, without other major complications. Placental histopathological characteristics were scored by an anatomical pathologist. Associations between maternal BMI, placental pathology (immaturity and hypermaturity), placental morphometry, and infant outcomes were investigated for term and preterm births with and without infection. Results: Fetal capillary volumetric proportion was decreased, whereas the villous stromal volumetric proportion was increased in placentae from preterm pregnancies with chorioamnionitis compared to preterm placentae without chorioamnionitis. At term and preterm, pregnancies with maternal overweight and obesity had a high percentage increase in proportion of immature placentae compared to normal weight. Placental maturity did not associate with infant birth outcomes. We observed placental hypermaturity and altered placental morphometry among preterm pregnancies with chorioamnionitis, suggestive of altered placental development, which may inform about pregnancies susceptible to preterm birth and infection. Conclusions: Our data increase our understanding of how common metabolic exposures and preterm birth, in the absence of other comorbidities or complications, potentially contribute to poor pregnancy outcomes and developmental programming.

Assessment of vulnerability of Server-side Web Application and prevention of their manifestation.

The vulnerability of the system (technological, communication) of the cyber protection object to negative technical impact is always based on the exploitation of its defects. Defect is a property of software, hardware, software-hardware or system components, which under certain conditions can lead to vulnerability. Attack – an attempt to exploit a defect(s) of the system (object of cyber protection) for the implementation of a negative technical effect (unauthorized reading/writing of data, incorrect operation, circumvention of protective mechanisms, abnormal consumption of resources; execution of imposed commands, etc.) under the manifestation of vulnerability using an exploit (exploit). The appearance of defects is inherent in all stages of the system (cyber protection object) life cycle. Thus, defects are distinguished at the design stage, implementation, adjustment during commissioning, maintenance, and completion of work. The existence of at least one defect in the components of the system, which allows a negative technical effect, makes it vulnerable. Timely detection and elimination of defects reduces the probability of system compromise using at least known exploits. The expediency, rationality and reasonableness of solutions for finding defects is based on the application of proven sources of world-class expert experience. Thus, Adversarial Tactics, Techniques &amp; Common Knowledge defines Server-side Web Application class systems as an attractive attack target for hackers with the highest average number of potential defects in at least three components: Web Server, Web Application Server, DBMS Server. To find out the distribution of Server-side Web Application defects by characteristic classes, it is necessary to use the Open Worldwide Application Security Project, for a detailed study of them - Common Weakness Enumeration. The application of the National Vulnerability Database and Common Vulnerabilities and Exposures complements the evaluation of the found defect.

Predicting Attack Paths from Application Security Vulnerabilities Using a Multi-Layer Perceptron

This paper is in the series of continuing research and proposes an approach to predicting possible attack paths from application security vulnerability-based attack trees. The attack trees are formed by stringing together weaknesses discovered in an application code and a group of applications within a domain. The Common Weakness Enumeration (CWE) and Common Vulnerabilities and Exposures (CVE) linked together as a string of vulnerabilities in the attack trees can be visualized as pathways for attacks. These pathways become potential attacks that can spread vertically and horizontally leading to a multi-path attack that can involve multiple software applications. With more data, and huge number of vulnerabilities, it will become impossible to identify all attack paths unless a full-scale implementation of an autonomous processing mechanism is in place. Machine Learning (ML) and Deep Learning (DL) techniques have been adopted in the cybersecurity space for decades, however all the studies have been around networks, endpoints, and device monitoring. This paper focuses on application security and building on earlier work cited, the use of a vulnerability map that uses attack vectors in a Deep Learning (DL) method implementing a Multi-Layer Perceptron (MLP) forms the basis for developing a predictive model that relates a set of linked vulnerabilities to an attack path. The results are encouraging, and this approach will help in identifying successful or failed attack paths involving multiple applications, isolated or grouped, and will help focus on the right applications and the vulnerabilities associated as priority for remediation.

PFAS and their association with the increased risk of cardiovascular disease in postmenopausal women.

Cardiovascular diseases (CVDs) are one of the major causes of death globally. In addition to traditional risk factors such as unhealthy lifestyles (smoking, obesity, sedentary) and genetics, common environmental exposures, including persistent environmental contaminants, may also influence CVD risk. Per- and polyfluoroalkyl substances (PFASs) are a class of highly fluorinated chemicals used in household consumer and industrial products known to persist in our environment for years, causing health concerns that are now linked to endocrine disruptions and related outcomes in women, including interference of the cardiovascular and reproductive systems. In postmenopausal women, higher levels of PFAS are observed than in premenopausal women due to the cessation of menstruation, which is crucial for PFAS excretion. Because of these findings, we explored the association between perfluorooctanoic acid (PFOA), perfluorooctane sulfonate (PFOS), and perfluorobutanesulfonic acid in postmenopausal women from our previously established CVD study. We used liquid chromatography with tandem mass spectrometry, supported by machine learning approaches, and the detection and quantification of serum metabolites and proteins. Here, we show that PFOS can be a good predictor of coronary artery disease, whereas PFOA can be an intermediate predictor of coronary microvascular disease. We also found that the PFAS levels in our study are significantly associated with inflammation-related proteins. Our findings may provide new insight into the potential mechanisms underlying the PFAS-induced risk of CVDs in this population. This study shows that exposure to PFOA and PFOS is associated with an increased risk of cardiovascular disease in postmenopausal women. PFOS and PFOA levels correlate with amino acids and proteins related to inflammation. These circulating biomarkers contribute to the etiology of CVD and potentially implicate a mechanistic relationship between PFAS exposure and increased risk of cardiovascular events in this population.

Radiation exposure during orthodontic treatment: risk to children and adolescents.

To estimate radiation risk to children and adolescents during orthodontic treatment by retrieving number and type of radiographs from the patient records. Radiographs, along with justifications for radiation exposure, were obtained retrospectively from the patient records of 1,790 children and adolescents referred to two Swedish orthodontic clinics. Data were grouped according to treatment stage: treatment planning, treatment, and follow-up. Estimated risk was calculated using the concept of effective dose. Each patient had received around seven radiographs for orthodontic purposes. The most common exposures during treatment planning were one panoramic, one lateral, and three intraoral periapical radiographs. A small number of patients received a tomographic examination (8.2%). Few justifications for treatment planning and follow-up, but more in the actual treatment stage, had been recorded. The most common examinations were to assess root resorption and the positions of unerupted teeth, or simply carry out an unspecified control. The estimated risk of developing fatal cancer was considered low. The radiation risk from orthodontic treatment was equivalent to about 5-10 days of natural background radiation. Children and adolescents sometimes undergo multiple radiographic examinations, but despite the low radiation burden, accumulated radiation exposure should be considered and justified in young patients.

Impacts of heat and wildfire on preterm birth

BackgroundClimate change continues to increase the frequency, intensity, and duration of heat events and wildfires, both of which are associated with adverse pregnancy outcomes. Few studies simultaneously evaluated exposures to these increasingly common exposures. ObjectivesWe investigated the relationship between exposure to heat and wildfire smoke and preterm birth (PTB). MethodsIn this time-stratified case-crossover study, participants consisted of 85,806 California singleton PTBs (20–36 gestational weeks) from May through October of 2015–2019. Birthing parent ZIP codes were linked to high-resolution daily weather, PM2.5 from wildfire smoke, and ambient air pollution data. Heat day was defined as a day with apparent temperature >98th percentile within each ZIP code and heat wave was defined as ≥2 consecutive heat days. Wildfire-smoke day was defined as a day with any exposure to wildfire-smoke PM2.5. Conditional logistic regression was used to calculate the odds ratio (OR) and 95% confidence intervals (CI) comparing exposures during a hazard period (lags 0–6) compared to control periods. Analyses were adjusted for relative humidity, fine particles, and ozone. ResultsWildfire-smoke days were associated with 3.0% increased odds of PTB (ORlag0: 1.03, CI: 1.00–1.05). Compared with white participants, associations appeared stronger among Black, Hispanic, Asian, and American Indians/Alaskan Native participants. Heatwave days (ORlag2: 1.07, CI: 1.02–1.13) were positively associated with PTB, with stronger associations among those simultaneously exposed to wildfire smoke days (ORlag2: 1.19, CI: 1.11–1.27). Similar findings were observed for heat days and when other temperature metrics (e.g., maximum, minimum) were used. DiscussionHeat and wildfire increased PTB risk with evidence of synergism. As the occurrence and co-occurrence of these events increase, exposure reduction among pregnant people is critical, especially among racial/ethnic minorities.

Environmental Exposures and Risks During Pregnancy.

The Women in Wilderness Medicine Research Committee of the Wilderness Medical Society conducted a narrative review to address considerations for pregnant individuals in wilderness environments. There is limited evidence behind many opinion-based recommendations on the safety of various environmental exposures in pregnancy. The authors reviewed the literature for the best available evidence, including observational studies, case series, limited controlled trials, and extrapolation from physiological data, as well as evaluating expert consensus statements. The benefits of exposure to natural environments include better pregnancy outcomes and improved maternal mental and physical health. Risks are similar to nonpregnant individuals with the added risks associated with maternal-fetal physiology in wilderness environments and difficulties of evacuation. This narrative review discusses pregnancy-specific concerns in extreme environments, including high altitude, hypothermia, hyperthermia, lightning strikes, envenomations, and common outdoor exposures.