Objective To evaluate coronary CT angiography (CCTA) combined with Coronary Artery Disease Reporting and Data System (CAD-RADS) grading and with high-risk plaque characteristics for predicting 30 day major adverse cardiovascular events (MACE) in patients with acute coronary syndrome (ACS).Material and methods A prospective, multicenter cohort study was conducted by enrolling 300 ACS patients admitted to four tertiary hospitals from January 2023 to June 2024. All patients underwent CCTA examination within 24 h of admission. Coronary artery stenosis severity was assessed using CAD-RADS 2.0 criteria, and high-risk plaque characteristics, including low-density plaque, positive remodeling, spotty calcification, and napkin-ring sign, were analyzed. Baseline clinical data were collected, Global Registry of Acute Coronary Events (GRACE) scores were calculated, and the 30 day MACE incidence was evaluated. Logistic regression analysis was used to evaluate risk factors, and receiver operating characteristic (ROC) curves were used to assess diagnostic performance.Results The incidence of 30 day MACE was 22.7 % (68 / 300 cases). Spearman's rank correlation analysis demonstrated that MACE incidence showed a significant positive correlation with the CAD-RADS grade (ρ=0.658, p<0.05), increasing from 0 % in CAD-RADS grade 0 to 100 % in CAD-RADS grade 5. Patients in the MACE group were older, had higher prevalence of diabetes and higher GRACE scores (all p<0.05). High-risk plaque characteristics, i.e., low-density plaque, positive remodeling, and napkin-ring sign, were detected more frequently in the MACE group (all p<0.05). Multivariate analysis showed that the GRACE score and positive remodeling were independent predictors of 30 day MACE (both p<0.05). The comprehensive prediction model combining GRACE score, CAD-RADS grading, and high-risk plaque characteristics achieved an area under the ROC curve (AUC) of 0.789, significantly superior to the GRACE score model alone (AUC=0.723, p=0.018), representing a 9.1 % improvement in discriminative ability.Conclusion A non-invasive imaging examination, CCTA, combined with CAD-RADS grading and high-risk plaque assessment can improve the prediction of 30 day MACE risk in ACS patients beyond traditional risk scores, providing important reference for clinical risk stratification and precision treatment decision-making.

Electronic health record (EHR) systems were designed to enhance clinical decision-making, yet the way data is organized and displayed can create significant cognitive demands for physicians. This study examines how EHR data usability (data quality, data completeness, and data-driven use) and system usability jointly shape physicians’ cognitive load. Using survey responses from 564 physicians across 32 specialties, we tested a mediated model with covariance-based structural equation modeling. Reliability and validity were assessed through standard psychometric criteria. Findings show that stronger data usability increases germane cognitive load, promoting deeper engagement with clinically meaningful information. In contrast, higher system usability reduces extraneous cognitive load by aligning interface design with clinical workflow and minimizing navigation-related effort. Information overload partially mediated these effects, suggesting that better data usability helps physicians better filter irrelevant data and stay focused on diagnostically relevant cues. Overall, the results highlight two levers for improving cognitive performance: enhancing system usability lowers unnecessary cognitive effort and documentation-related errors, while improving data usability supports reasoning-intensive diagnostic work. Optimizing both fosters balanced cognitive load and more sustainable, error-resilient clinical decision-making.

Giant cell arteritis (GCA) is a chronic large-vessel vasculitis affecting older adults, associated with significant cardiovascular (CV) complications. Understanding CV risk drivers among GCA, including classical CV risk factors and inflammation, is essential for improved patient management. We (i) assessed the association between GCA and risks of cardiovascular events, including aortic events, major adverse cardiovascular events (MACE), peripheral artery disease (PAD) events, and visceral artery events (ii) assessed the impact of traditional CV risk factors and GCA disease activity on these outcomes. Using the French National Health Data System (SNDS), we included 23,193 patients aged ≥50 years diagnosed with incident GCA from 2012-2022. Patients were matched with a general population cohort (92,772 individuals) and a hospitalized cohort (92,772 individuals) using propensity score matching based on CV comorbidities. Primary outcomes were first incidence of aortic events, MACE, PAD events, and visceral artery events. Patients with GCA faced an increased risk of all CV outcomes compared with the general population cohort: aortic events (HR: 5.33; 95%CI, 4.50-6.32), MACE (HR: 2.15; 95% CI, 2.04-2.26), PAD events (HR: 2.72; 95%CI, 2.47-2.99), and visceral artery events (HR: 3.04; 95%CI, 2.33-3.97).When compared with the hospitalized cohort, GCA patients had increased risk of all outcomes except for MACE risk which did not significantly differ (HR: 1.01; 95%CI, 0.96-1.06). GCA disease activity was associated with increased MACE (HR: 1.98; 95%CI, 1.63-2.42). GCA significantly increases risks of vascular complications, highlighting the importance of cardiovascular risk management and inflammation control strategies.

In the context of the digitalization of public relations in the law enforcement practice of forest management digital transformation, organizational and legal problems arise: a lack of up-to-date information about forest plots, which complicates the process of registration and use of forest lands; the problem of ensuring the transparency, relevance, and accessibility of information about forest plots, and users’ difficulties in issuing forest inventory documentation; the absence of complete information about the possibility of constructing residential buildings for farmers on agricultural land; and the problem of restrictions on the use of forest plots for recreational purposes and domestic tourism in forests. The purpose of the research is to identify methods for increasing the efficiency of forest inventory documentation and optimizing the provision of public services related to forest management. Methods: the formal legal method makes it possible to study legal norms governing the process of obtaining and using forest inventory documentation in order to further apply information in the National Spatial Data System; the comparative law method allows to compare the use of digital technologies in various national systems of state forest management. Result: The article concludes that the regulatory framework for maintaining the state forest registry and conducting state forest inventory is inefficient and fragmented. It highlights the necessity of creating integrated databases for forest management and utilization, which should be synchronized with databases of other digital registries, including the National Spatial Data System. The authors propose to develop more detailed mechanisms and procedures for the formation and registration of newly formed forest plots.

Magnetic resonance imaging (MRI) has become an important tool for recommending prostate biopsy (PB) in prostate cancer (PCa) detection. However, the routine use of MRI in patients with previous negative PB remains debatable. This study aimed to evaluate the utility of prostate-specific antigen (PSA) derivatives to guide MRI use and reduce unnecessary scans in such cases. Receiver operating characteristic analysis identified a Prostate Imaging Reporting and Data System score ≥ 4 as the optimal threshold for predicting clinically significant PCa (Gleason score ≥ 7). A cohort of 251 patients with at least one prior negative PB who underwent serum PSA testing, free PSA, and MRI between October 2015 and June 2024 were analyzed. The optimal cutoff values for PSA, PSA density (PSAD), and free-to-total PSA ratio (%fPSA) were 11.87ng/mL, 0.19ng/mL2, and 18.76%, respectively (all p < 0.001). Restricting MRI to patients with PSA < 11.87ng/mL, PSAD < 0.19ng/mL2, or %fPSA > 18.76% could induce MRI use by 22.7% while missing only 9.1% of significant PCa cases on MRI-targeted PB.

Because ICD-10-CM omits multiple dermatology-relevant subcodes, several well-defined rare skin diseases cannot be identified in widely used U.S. real-world data systems. This Perspective discusses how these omissions distort the evidence base as real-world data increasingly inform dermatologic research and guidance, and contrasts this with approaches used in the U.K. and other health systems. Practical pathways to improve phenotypic resolution for rare diseases are outlined.

BackgroundCoronary computed tomography angiography (CCTA) is a non-invasive diagnostic modality for assessing coronary artery disease (CAD), with a growing global trend favouring a "CT-first" approach. However, this strategy strains healthcare systems as demand for CCTA increases. Pre-test probability (PTP) models for obstructive CAD are used to optimize the usage of CCTA. However, these models perform inconsistently across different subpopulations and offer limited discriminatory accuracy, with low positive predictive values (PPV). This study aims to enhance the PTP models with explainable artificial intelligence (XAI) on electrocardiogram (ECG) data.MethodsPatients who underwent CCTA for suspected CAD with both CAD-Reporting and Data System (CAD-RADS) classification and Coronary Artery Calcium Score (CACS) were included. Obstructive CAD was defined as CAD-RADS 3 or above. Missing values were imputed. The Risk Factor Weighted Clinical Likelihood (RF-CL) model, based on clinical variables (age, sex, symptom type, smoking, hypertension, dyslipidaemia, diabetes, and family history) was computed. Two gradient descent decision tree models, "AI-ECG" and "AI-CAC," were developed for the pilot study. AI-ECG used clinical risk factors and ECG classification as abnormal, borderline or normal, while AI-CAC further incorporated CACS. We used a cutoff value of 15%, reflecting current guidelines. Comparative analysis was performed using area under the curve (AUC) analysis, the DeLong test for statistical comparison, and feature importance using SHAP values. We intend to develop and present a model based on ECG signal data during the conference.Pilot resultsWe identified 6 178 patients who underwent CCTA between 2022 and 2024 at the Cardiology Centers Netherlands (CCN), of which 2 083 were included (mean age 57.4 ± 10.2 years; 48.1% male). Obstructive CAD was found in 413 (19.8%) patients. The RF-CL model was outperformed by both AI-ECG (AUC: 0.69 vs. 0.80, p<0.001) and AI-CACS (AUC: 0.69 vs. 0.90, p<0.001). Sensitivity increased in both AI models, from 0.46 to 0.87 for AI-ECG and to 0.83 for AI-CACS, while PPV improved only in the AI-CACS model (from 0.37 to 0.61). Key predictive features were CACS, age, sex, ECG classification, and family history.Conclusion and DiscussionOur pilot study shows that integrating XAI with clinical risk factors and ECG classification enhances PTP estimation for obstructive CAD. Implementing this model can better stratify patients at risk of obstructive CAD, support more focused CCTA use and reduce unnecessary testing. Nonetheless, further external validation is needed to assess model performance in real-world, prospective clinical settings. Additionally, models based on raw ECG signals have the potential to extract more detailed information.Reciever Operating Characteristic CurvesFeature Importance

Modern data-management frameworks promise a flexible and efficient management of data and metadata across storage backends. However, such claims need to be put to a meaningful test in daily practice. We conjecture that such frameworks should be fit to construct a data backend for workflows which use geographically distributed high-performance and cloud computing systems. Cross-site data transfers within such a backend should largely saturate network bandwidth, in particular when parameters such as buffer sizes are optimized. To explore this further, we evaluate the “integrated Rule-Oriented Data System” iRODS with EUDAT’s B2SAFE module as data backend for the “Distributed Data Infrastructure” within the LEXIS Platform for complex computing workflow orchestration and distributed data management. The focus of our study is on testing our conjectures—i.e., on construction and assessment of the data infrastructure and on measurements of data-transfer performance over the wide-area network between two selected supercomputing sites connected to LEXIS. We analyze limitations and identify optimization opportunities. Efficient utilization of the available network bandwidth is possible and depends on suitable client configuration and file size. Our work shows that systems such as iRODS nowadays fit the requirements for integration in federated computing infrastructures involving web-based authentication flows with OpenID Connect and rich on-line services. We are continuing to exploit these properties in the EXA4MIND project, where we aim at optimizing data-heavy workflows, integrating various systems for managing structured and unstructured data.

Abstract Aim The 2–2½-year review is the final universal mandated contact in England’s Healthy Child Programme, with a child development assessment using the Ages and Stages Questionnaire-3 (AQ®-3). Amid a wider digitisation agenda, the UK government is exploring digital alternatives to the paper-based ASQ®-3 tool. Understanding stakeholder perspectives is critical for informing implementation. Subject and methods 15 focus groups (63 participants), including parents, health visiting professionals, local authority colleagues, and policy officials, analysed using Framework Analysis. Results Stakeholders reported potential benefits of a digital tool: user experience, service efficiency, and alignment with national digital priorities. However, where services were trying to meet the mandate to review every child aged 2–2½-years with limited resources and workforce, our participants saw a risk that a digital tool might replace a full in-person assessment. Parents and professionals agreed that any digital tool must not compromise the holistic, relational nature of the 2–2½-year review or undermine the universal coverage of in-person contacts with families. Participants highlighted the complexities of digital exclusion, incompatibility with local data systems, and staff training. Conclusions Digitisation must be implemented carefully to avoid undermining service equity and the core components of the service (universal in-person assessments), must include system interoperability, professional training.

Background/Objectives: Breast cancer is the most common malignancy among women, and early detection is critical for improving outcomes. The Breast Imaging Reporting and Data System (BI-RADS) standardizes reporting, but the BI-RADS 4 category presents a major challenge, with malignancy risk ranging from 2% to 95%. Consequently, most women in this category undergo biopsies that ultimately prove unnecessary. This study evaluated whether exhaled breath analysis could distinguish malignant from benign findings in BI-RADS 4 patients. Methods: Participants referred to the McGill University Health Centre Breast Center with BI-RADS 3-5 findings provided multiple breath specimens. Breathprints were captured using an electronic nose (eNose) powered breathalyzer, and diagnoses were confirmed by imaging and pathology. An autoencoder-based model fused the breath data with BI-RADS scores to predict malignancy. Model performance was assessed using repeated cross-validation with ensemble voting, prioritizing sensitivity to minimize false negatives. Results: The breath specimens of eighty-five participants, including sixty-eight patients with biopsy-confirmed benign lesions and seventeen patients with biopsy-confirmed breast cancer within the BI-RADS 4 cohort were analyzed. The model achieved a mean sensitivity of 88%, specificity of 75%, and a negative predictive value (NPV) of 97%. Results were consistent across BI-RADS 4 subcategories, with particularly strong sensitivity in higher-risk groups. Conclusions: This proof-of-concept study shows that exhaled breath analysis can reliably differentiate malignant from benign findings in BI-RADS 4 patients. With its high negative predictive value, this approach may serve as a non-invasive rule-out tool to reduce unnecessary biopsies, lessen patient burden, and improve diagnostic decision-making. Larger, multi-center studies are warranted.

ABSTRACT In this paper, we study the influence of a damping term on the dynamics of a coupled Korteweg–de Vries type system, namely the mKdV–KdV system, posed on the real line. The presence of damping plays a significant role in the long-time behavior of solutions and requires refined analytical techniques. By employing suitable energy estimates in Sobolev spaces and analytic function spaces, we first establish the local well-posedness of the damped coupled mKdV–KdV system for analytic initial data. The analysis is carried out in a framework that allows us to control both regularity and analyticity of solutions. Furthermore, by combining the local existence theory with an analytic approximate conservation law adapted to the damped setting, we extend the local solutions globally in time. As an additional qualitative result, we prove that the radius of spatial analyticity of solutions does not degenerate over time, but instead remains uniformly bounded below by a positive constant for all times. This demonstrates the stabilizing effect of the damping mechanism on the analytic structure of the solutions.

Rapid urbanization and emerging dependence on private motorized transport have significantly increased greenhouse gas (GHG) emissions and air quality decline in Indian metropolitan cities, particularly the tech hub of India Bengaluru. The transport sector has emerged as a presiding contributor to urban emissions due to traffic congestion, rapid vehicle growth, and limited adoption of sustainable mobility alternatives. This paper presents a structured literature-based assessment of urban mobility and transport-related emissions in Bengaluru, with a focused examination of how advanced computing techniques-especially Artificial Intelligence (AI) and data-driven systems-can support emission reduction strategies. By integrating findings from government reports, peer-reviewed studies, and recent urban transport research, the study identifies key emission sources, evaluates current smart-city interventions, and emphasizes the role of AI in traffic management, public transport optimization, and electric vehicle integration. The analysis unveils that targeted, computation-enabled interventions such as intelligent traffic control, predictive mobility analytics, and optimized public transit systems can substantially reduce emissions while improving transport efficiency. The paper also discusses implementation challenges related to data quality, infrastructure, and system interoperability. The findings offer a practical framework for implementing advanced computing technologies to support sustainable urban mobility and climate-aligned transport planning in rapidly developing cities

ABSTRACT Data scientists and users are experts in different domains, with the former possessing expertise in technology and the latter having experience in the problem domain. Current approaches to machine learning (ML) design enable data scientists and technical developers to drive the process. In contrast, this research presents an approach grounded in theory that enables the non-technical user to control ML design, at least initially. We discuss a study that applies a socio-technical systems thinking approach to enable non-technical users to drive machine learning design and ensure their actual needs are met. We extend socio-technical research by introducing a novel type of analysis- a social data system analysis, and provide empirical evidence on the feasibility of non-technical user-driven ML design. We provide practical guidelines for practitioners to enable non-technical user-driven ML design. The study also demonstrates the potential of a socio-technical approach in increasing fairness in training datasets.

Limitations of Large Language Models in Assisting PI-RADS Scoring on Prostate Biparametric MRI Text Reports.

Nodular fasciitis (NF) is a rare, benign myofibroblastic proliferation that is often misinterpreted as a malignancy due to its rapid growth. NF of the breast is an exceptionally uncommon entity, presenting significant diagnostic challenges. This report details the case of a young woman in her early 20s who presented with a rapidly enlarging right breast mass, initially categorised as Breast Imaging Reporting and Data System (BIRADS)-III, which progressed to BIRADS-IVA within 3 months. Core needle biopsy identified a spindle cell neoplasm, with subsequent histopathological evaluation confirming NF through Alpha-Smooth Muscle Actin (ASMA) positivity. The patient underwent an excisional biopsy, leading to complete resolution and alleviation of initial concerns regarding malignancy. This case underscores the necessity of including NF in the differential diagnosis of spindle cell lesions of the breast to prevent misdiagnosis and unnecessary aggressive interventions. A comprehensive understanding of its clinical presentation and pathological characteristics is essential for optimising diagnostic accuracy and ensuring appropriate management.

Outcome monitoring supports quality improvement (QI) by helping organisations track performance, identify gaps, and guide improvements. This is particularly important for the management of costly chronic diseases with high practice variation such as inflammatory bowel disease (IBD). Although the value of outcome data for QI is increasingly recognised, little is known about its use in practice. We explored how Dutch IBD centres implement outcome-based QI. A survey was sent to 67 Dutch IBD centres covering outcome monitoring practices, data infrastructure, involvement of healthcare providers in QI discussions, and the perceived value of using outcomes for QI. Fourteen follow-up interviews explored experiences, barriers, and facilitators. Twenty-eight centres were included (54% non-academic teaching, 25% academic, and 21% general hospitals), of which 79% regularly discussed outcomes within their IBD teams to support QI efforts. Of those, 95% implemented ≥ 1 QI initiatives annually informed by these discussions and 47% assessed their effectiveness regularly. However, consistent use of outcome-based QI was uncommon-only 18% discussed outcomes > 2 times per year. Commonly monitored outcomes were medication use (68%) clinician-reported outcomes (55%), and patient-reported outcomes (55%). Interviews revealed QI efforts were often limited by informal discussions that lacked aggregate data use and clear goals. Data systems were fragmented, and staff responsibilities were unclear. Staff engagement and management support were key enablers. While outcome monitoring is common, it is not consistently used to support QI. Clarifying roles, improving data integration, and support in selecting meaningful outcomes may strengthen sustainable outcome-based QI.

Short-term heat exposure has been linked with increased risks of acute kidney injury and chronic kidney disease (CKD), but the impact on the incidence or prevalence of CKD is unknown. This study examines the association of high temperatures with CKD prevalence and end-stage kidney disease (ESKD) incidence at county level in the US. County-level diagnosed CKD prevalence data (2005-2019) among Medicare enrollees aged ≥65 years from the US Kidney Disease Surveillance System and ESKD incidence data (2010-2019) from the United States Renal Data System were analyzed. County-specific heat exposure measurements included annual average temperature (AAT) and annual heat wave days (HWD) from nClimGrid-Daily dataset (US National Centers for Environmental Information). We used a linear mixed model to assess associations between heat exposure and diagnosed CKD prevalence as well as ESKD incidence, while geographically weighted regression assessed spatial variations, adjusting for time-trend, county-specific factors and demographics. Stratified analysis compared associations across socioeconomic subgroups. AAT had significantly positive associations with diagnosed CKD prevalence and ESKD incidence. Each 1°C increase in AAT was associated with a 0.23 (95% CI: 0.20-0.27) percentage point increase in the prevalence of diagnosed CKD. Similarly, each 1°C increase in AAT was associated with an additional 1.37 (95% CI: 1.08-1.65) ESKD cases/100,000 population. HWDs were positively associated with both kidney outcomes, and the strength of these associations increased with higher temperature thresholds and longer duration. Stronger associations between heat exposure and both kidney outcomes were observed in high poverty and nonmetropolitan counties (P<0.05). The strength of associations was greater in counties in southern and northwestern regions. The associations between ambient temperature and kidney health, with socioeconomic and regional differences, may have implications for interventions aimed at reducing the potential effects of high temperatures on kidney health, particularly in vulnerable populations.

To develop and validate a model to predict serious infection risk in rheumatoid arthritis (RA) patients initiating biologic or targeted synthetic DMARDs (b/tsDMARDs), and to implement it as an interactive tool (RAISE). We conducted a nationwide cohort study (2010-2023) using the French National Health Data System. Adults with RA initiating a b/tsDMARD were included. The primary outcome was a serious infection (i.e. requiring hospitalization). The dataset was randomly split into a derivation cohort (66%) and a validation cohort (34%) for internal (hold-out) validation. Candidate predictors included demographics, treatment initiated, corticosteroid dose, prior infections and comorbidities. Variable selection used LASSO, followed by a multivariable Cox model to estimate adjusted hazard ratios. In the derivation cohort, 500 bootstrap resamples were used to assess optimism-corrected performance. Model discrimination and calibration (6-24 months) were evaluated in both cohorts. No external validation was performed at this stage. Over median follow-up of 12.5 months (IQR 5.3-33.3), 4,657 and 2,359 serious infections occurred in derivation and validation cohorts, respectively. Predictors included rituximab (aHR 2.20, 95% CI 1.98-2.44), infliximab (aHR 1.75, 1.56-1.97), corticosteroids ≥7.5 mg/day (aHR 1.45, 1.33-1.58), prior infection (aHR 1.62, 1.48-1.77), pulmonary disease (aHR 1.51, 1.40-1.64) and diabetes (aHR 1.34, 1.23-1.46). The model showed moderate discrimination (C-index 0.71) and calibration (mean absolute error ≤0.07). RAISE delivers personalized 6-, 12-, 18- and 24-month infection risk estimates using routinely available data, improving on the scope and relevance of earlier prediction tools. It enables risk-based treatment planning and preventive strategies, with potential for international adoption following external validation.

Among Chinese men with prostate-specific antigen (PSA) 4-20ng/mL scheduled for biopsy,<20% harbor clinically significant prostate cancer (csPCa, International Society of Urological Pathology [ISUP] ≥2); most procedures target non-clinically significant prostate cancer (non-csPCa; ISUP <2) or benign prostatic hyperplasia (BPH). We tested whether prostate-specific membrane antigen (PSMA) positron-emission tomography/computed tomography (PET/CT) combined with multiparametric imaging and laboratory assays can accurately identify non-csPCa/BPH and safely reduce unnecessary biopsies. We retrospectively enrolled patients from two centers. In the discovery cohort, participants underwent PSA testing, mpMRI, and 68Ga-PSMA-PET/CT, and were assessed for Prostate Imaging Reporting and Data System (PI-RADS) and PRIMARY scores. Receiver operating characteristic (ROC) curves, decision curve analysis, and diagnostic tests compared the efficacy of each indicator for non-csPCa/BPH and established the optimal strategy. The validation cohort independently verified this strategy. Discovery cohort (n =243): PRIMARY score area under the curve (AUC) 0.92 (95% confidence interval [CI]: 0.88-0.95); cutoff ≤3 provided 83.5% sensitivity and 90.9% specificity, outperforming PI-RADS (P =0.002), PSA density (PSAD), free/total PSA (f/tPSA), and total PSA (tPSA) (all P < 0.0001). Combined models: PRIMARY score+PI-RADS AUC 0.95 (0.92-0.97); PRIMARY+PSAD or+f/tPSA both 0.94. A strategy of PRIMARY score ≤3 plus PSAD ≤0.2 achieved 100% specificity and positive predictive value (PPV) for csPCa, sparing 54.1% of unnecessary biopsies with zero csPCa missed - superior to European Association of Urology (EAU)-recommended PI-RADS ≤2+PSAD ≤0.2 (sensitivity 19.6%). External cohort (n =149) validated 100% specificity/PPV, avoiding 69.6% of biopsies while maintaining 0% csPCa miss rate. In men with suspected prostate cancer (PSA 4-20ng/mL or abnormal digital rectal examination), the combined criterion of PRIMARY score ≤3 and PSAD ≤0.2ng/mL/cm3 enabled pre-biopsy triage that safely avoided immediate diagnostic biopsy in more than 50% of patients ultimately found to harbor non-csPCa and BPH in our cohorts, while ensuring that no csPCa was missed. These findings require confirmation in larger prospective, multicenter studies before routine clinical implementation.

Background:Residential mobility during pregnancy and between births is common and can introduce exposure misclassification. It may also reflect broader sociodemographic and environmental inequalities that influence maternal and child health. The objective of this study is to evaluate associations between maternal sociodemographic characteristics, PM2.5 exposure, and residential mobility. Among mothers who moved, additional analyses evaluated predictors of relocation to lower-income census tracts.Methods:Data were obtained from 155,270 mothers with matched sibling birth records from the Massachusetts Pregnancy to Early Life Longitudinal data system (2001–2009). We define residential mobility as a change in geocoded birth addresses and identified relocation to a lower-income census tract among movers. PM2.5 exposure estimates at each birth address were assigned using annual averages from a previously validated spatiotemporal model. Logistic regression was used to examine associations between residential mobility and maternal age, race/ethnicity, education, parity, and residential PM2.5 exposure.Results:Among mothers with linked births, 49.3% moved between births, and 19.9% relocated to a census tract with a lower median income. Mothers that moved between births had significantly lower PM2.5 at the subsequent birth address compared with mothers that did not move. Compared with mothers living at low PM2.5 exposure levels (5th percentile, 1 µg/m3), mothers living at high PM2.5 exposure levels (95th percentile, 11 µg/m3) had a nearly three-fold higher odds of moving. Relocation to a lower-income tract was less likely among older mothers, non-Hispanic mothers, and those with more than a high school education.Conclusion:Environmental and sociodemographic factors shape residential mobility patterns between births. It is important to account for residential mobility to reduce exposure misclassification and improve accuracy in perinatal epidemiology.