Bayesian Analysis of Postoperative Complication Risk Associated With Preoperative Exposure to Fine Particulate Matter: A Single‐Center Cohort Study

Acta Anaesthesiologica Scandinavica · 2026-04-26

ABSTRACT Background Air pollution, especially particle pollution, is increasingly recognized as a potential perioperative risk factor, yet modeling environmental exposures in surgical cohorts remains methodologically underdeveloped. We demonstrate a Bayesian hierarchical framework to quantify probabilistic associations between preoperative fine particulate matter (PM 2.5 ) exposure and postoperative complications, highlighting its interpretability and flexibility for clinical environmental epidemiology. Methods We conducted a single center, retrospective cohort study using data from 49,615 surgical patients in Utah who underwent elective surgical procedures from 2016 to 2018. Patients' addresses were geocoded and linked to daily Census‐tract level PM 2.5 estimates. The exposure variable was defined as the maximum PM 2.5 concentrations in the 7 days prior to surgery. The binary outcome was a composite of postoperative complications: pneumonia, surgical site infection, urinary tract infection, sepsis, stroke, myocardial infarction, or thromboembolic event. A hierarchical Bayesians regression model with weakly informative priors was used adjusting for age, sex, season, neighborhood disadvantage, and the Elixhauser index of comorbidities with census tract as a group (random) effect. We present posterior estimates with credible intervals, highlight model transparency and sensitivity, and discuss contrasts with standard frequentist methods. Results Postoperative complications were associated in a dose‐dependent manner with higher concentrations of PM 2.5 exposure. We found a relative increase of 8.2% in the odds of complications (OR = 1.082) for every 10.ug/m 3 increase in the highest single‐day 24‐h PM 2.5 exposure during the 7 days prior to surgery. For an increase in PM 2.5 from 1 to 30 ug/m 3 , the odds of complication rose to over 27% (95% CI: 4%–55%). The results were robust across prior choices and model specifications. We report full posterior distributions and highlight advantages of Bayesian modeling for uncertainty quantification and clinical interpretability. Conclusions This case study demonstrates the application of hierarchical Bayesian modeling to quantify the probabilistic associations between preoperative PM 2.5 exposure and postoperative complications, highlighting transparent risk estimation and uncertainty characterization that may inform the design of future multicenter perioperative environmental studies. Editorial Comment Using Bayesian statistical analysis, the authors demonstrate a dose‐dependent risk for postoperative complications in patients exposed to air polluted with fine particulate matter with a size of less than 2.5 μm.

Role of obesity in mediating the association between long-term geospatial food access and breast cancer incidence in Metropolitan Chicago

Epidemiology · 2026-04-01

Mobile Intervention for Increasing COVID-19 Testing in K-12 Schools Serving Disadvantaged Communities: Randomized Controlled Trial of SCALE-UP Counts (Preprint)

2025-06-30

<sec> <title>BACKGROUND</title> A key challenge for schools throughout the COVID-19 pandemic was finding ways to monitor and prevent COVID-19 cases. While diagnostic testing and connecting students and their families to appropriate resources to mitigate the spread of COVID-19 were recommended, few schools had scalable infrastructure, including information technology systems, to implement these types of measures. </sec> <sec> <title>OBJECTIVE</title> This study tested a new approach to COVID-19 testing (SCALE-UP Counts) in school settings that used automated bidirectional text messages provided to the school community that alerted parents of students to COVID-19 testing options and guidance on when to test. </sec> <sec> <title>METHODS</title> The SCALE-UP Counts trial was designed as a Sequential Multiple Assignment Randomized Trial and final analyses compared results from parents who received intensive, fully automated, bidirectional text messaging about COVID-19 testing or usual care (control; fully automated unidirectional text messaging about COVID-19 testing), unblinded interventions. From the 16 selected schools, we enrolled all eligible participants who did not opt out of the study. The study provided schools from both arms of the trial with free at-home COVID-19 test kits. The primary outcome was the proportion of parents whose households tested for COVID-19, and the secondary outcome was the number of missed school days. The study asked parents to respond to self-report measures on testing outcomes and missed school days through web-based questionnaires. </sec> <sec> <title>RESULTS</title> The study included 7122 parents of students from 16 schools, half of which were title 1 schools; 2588 were randomized to usual care or control and 4534 to bidirectional text messaging. The SCALE-UP Counts intervention led to increased self-reported testing when compared with the control condition (22.8% vs 13.5%, relative testing rate=1.64, 95% CI 1.31-2.02; &lt;i&gt;P&lt;/i&gt;&amp;lt;.001). There was no observed difference in missed school days between the study arms (0.43 per month vs 0.28 in usual care, relative missed days rate=1.55, 95% CI 0.98-2.45; &lt;i&gt;P&lt;/i&gt;=.06). </sec> <sec> <title>CONCLUSIONS</title> SCALE-UP Counts worked closely with schools and the state’s public health system to implement and test a scalable health information technology approach that delivered automated text messages to students’ parents around COVID-19 testing and provided access to free at-home test kits. Such an approach can help facilitate COVID-19 testing among school communities, including those that provide education and resources to students and their families from racial or ethnic minorities and with low socioeconomic status. Similar health information technology approaches could be used to increase ease of access to testing, reduce testing burden, and provide tailored information on health measures in school communities for a variety of illnesses or public health concerns. </sec> <sec> <title>CLINICALTRIAL</title> ClinicalTrials.gov NCT05112900; http://clinicaltrials.gov/ct2/show/NCT05112900 </sec>

Social vulnerability, lower broadband internet access, and rurality associated with lower telemedicine use in U.S. Counties

JAMIA Open · 2025-07-03 · 6 citations

Objective: Our objective was to determine how social vulnerabilities, broadband access, and rurality relate to telemedicine use across the United States through large-scale analysis of real-world telemedicine data. Materials and Methods: We conducted a retrospective, observational study of dyadic U.S. telemedicine sessions that occurred January 1, 2022 to December 31, 2022, linked to the 2020 Centers for Disease Control and Prevention Social Vulnerability Index (SVI) and the National Center for Health Statistics Urban-Rural Classification Scheme for Counties. We examined county-level telemedicine use rates (sessions per 1000 population) in relation to SVI indexes, broadband internet access, and rurality classifications using polynomial regression and data visualization. Results: We found a negative, nonlinear association between overall social and socioeconomic status vulnerabilities and telemedicine use. Telemedicine rates in urban counties exceeded that of rural counties. There was more variability in telemedicine use for the urban counties according to social vulnerability and broadband access. Discussion: Rurality and broadband access demonstrated a greater effect on telemedicine use than social vulnerability, and the relationship between social vulnerability, broadband access, and telemedicine use differed for rural versus urban areas. Conclusion: This observational study of nearly 8 million U.S. telemedicine sessions showed that rurality and broadband access are key drivers of telemedicine use and may be more important than many social vulnerabilities in determining community-level telemedicine use. We also found nuanced differences in the relationship between social vulnerability and telemedicine use between rural and urban counties, and at different levels of broadband access.

Longer-Term Geospatial Food Access and the Incidence of Breast Cancer in Metropolitan Chicago

medRxiv · 2025-09-12

ABSTRACT Diet quality contributes to breast cancer (BC) risk and is shaped in part by the neighborhood food environment. Yet, the long-term impact of residential food environments on BC incidence remains largely underexplored. We linked residential histories of 7,396 BC cases and 21,900 controls in Chicago (1990–2019) to food outlet data from the National Establishment Time Series. Cumulative (time-weighted), inverse distance-weighted (IDW), food access scores were derived along with nearest distance metrics for “ healthy ” and “ less healthy” food access. Associations with incidence were stronger for walking than driving distance-based measures and for nearest distance-based measures over IDW measures. In multivariable logistic regression models, BC incidence decreased monotonically with shorter walking distance to the nearest healthy food outlet, reaching a 60% lower incidence for shorter vs. longer walking distances (OR=0.42, 95% CI=0.38, 0.48). Similarly, incidence increased monotonically with shorter walking distance to the nearest less healthy food outlet, reaching a 150% greater incidence for shorter vs. longer walking distances (OR=2.49, 95% CI=2.19, 2.83). This is the first study to use residential histories to define long-term, time-weighted geospatial food access metrics in BC epidemiology, highlighting how cumulative neighborhood environments have the potential to shape cancer risk and informing targeted interventions.

Designing a Secure and Resilient Distributed Smartphone Participant Data Collection System

ArXiv.org · 2025-10-22

Real-world health studies require continuous and secure data collection from mobile and wearable devices. We introduce MotionPI, a smartphone-based system designed to collect behavioral and health data through sensors and surveys with minimal interaction from participants. The system integrates passive data collection (such as GPS and wristband motion data) with Ecological Momentary Assessment (EMA) surveys, which can be triggered randomly or based on physical activity. MotionPI is designed to work under real-life constraints, including limited battery life, weak or intermittent cellular connection, and minimal user supervision. It stores data both locally and on a secure cloud server, with encrypted transmission and storage. It integrates through Bluetooth Low Energy (BLE) into wristband devices that store raw data and communicate motion summaries and trigger events. MotionPI demonstrates a practical solution for secure and scalable mobile data collection in cyber-physical health studies.

Place, Population, and Inequality: A Cross-Sectional National Analysis of Disparities in Neighborhood Physical Activity Environments Across the Urban–Rural Spectrum

medRxiv · 2025-12-17

Objectives: Neighborhood physical activity environments (PAEs)-including walkability, recreational facilities, green space, and civic infrastructure-support active living and population health but are often inequitably distributed. This study examines racial/ethnic and socioeconomic disparities in neighborhood PAEs and assesses variation by urbanicity. Methods: We used population-weighted ordinary least squares regression models with county fixed effects using the latest available 2018 data from 69,889 census tracts in the contiguous United States. Models assessed associations between racial/ethnic and poverty composition and four PAE dimensions-built, physical facilities, natural, and social environments. Sensitivity analyses compared models with and without county fixed effects and population weighting. Urbanicity-stratified models examined disparities across urban, suburban, town, rural, and mixed settings. Results: Non-Hispanic White and low-poverty populations had greater access to PAEs across all domains, except that low-poverty populations lived in areas with lower walkability. Disparities were largest in urban and suburban areas. Rural high-poverty populations had more natural resources but less infrastructure and civic support. County-fixed effects reversed the walkability advantages for non-Hispanic Black observed in unadjusted models. Conclusions: PAE disparities disproportionately affect racial/ethnic minority and high-poverty populations, especially in urban areas. Findings support equity-focused, context-specific interventions for environmental justice in health policy.

Spatial Associations of Anti-Asian Hate on Social Media in the USA During COVID-19

Journal of Racial and Ethnic Health Disparities · 2025-03-18

Construction of Unmanned Aerial Vehicle infrared transmission line inspection system based on optimized flight path algorithm

2025-01-15

This article conducts in-depth research on the construction of UAV (Unmanned Aerial Vehicle) infrared inspection system for power transmission lines. With the increasing complexity of the power system, the maintenance and inspection of transmission lines have become key to ensuring the stable operation of the system. Traditional manual inspection methods have problems such as low efficiency, high cost, and safety hazards. This study mainly explores the application of optimized flight path algorithm in UAV infrared transmission line inspection. To verify the effectiveness of the proposed method, this study conducted experimental verification in a real transmission line environment. The experimental results show that in stability testing, the stability before optimization is poor with significant fluctuations, while the stability after optimization is more stable, maintaining an average flight distance of around 567. It can be seen that the flight path optimization algorithm proposed in this article has significantly improved the infrared inspection efficiency of UAV. In summary, this article provides an efficient and safe new method for UAV infrared inspection of transmission lines, making positive contributions to the modernization and intelligence of the power industry.

Evaluation of Optimal Epoch Lengths for Real-Time Physical Activity Measurement for mHealth Applications: Cross-Sectional Study (Preprint)

2025-10-31

<sec> <title>BACKGROUND</title> Wearable accelerometers have become integral to mobile health (mHealth) research, particularly for delivering real-time physical activity (PA) monitoring and applications in interventions such as Just-in-Time Adaptive Interventions (JITAIs). One critical yet underexplored factor in real-time PA monitoring is epoch length, which is the time interval over which raw accelerometry data are aggregated to classify activity intensities and levels. Shorter epochs (e.g., 1 second) enhance precision but increase computational and battery demands, while longer epochs (e.g., 60 seconds) reduce data burden but may miss brief activity bouts. Although previous studies have examined epoch effects using post-processed data, limited evidence exists regarding their influence on real-time, wrist-based PA estimates, especially for moderate-to-vigorous PA (MVPA). Identifying an optimal epoch length for real-time PA measurement remains a critical gap in supporting scalable and efficient mHealth interventions. </sec> <sec> <title>OBJECTIVE</title> This study determined the impact of varying epoch lengths on real-time MVPA estimates derived from a wrist-worn accelerometer to identify an optimal epoch that balances measurement accuracy with practical feasibility for mHealth applications. </sec> <sec> <title>METHODS</title> Twenty adults (Age: 32.5 ± 15.1 years) completed a series of carefully selected simulated free-living activities in a controlled laboratory setting. Participants wore the MotionSense HRV wristband, which computed real-time Euclidean Norm Minus One (ENMO) values, and a COSMED K5 indirect calorimetry for metabolic reference. ENMO values were aggregated into 5-, 10-, 15-, 30-, and 60-second epochs. MVPA was classified using validated ENMO cut-points. Epoch-level MVPA estimates were compared against the 1-second reference using mean absolute percent error (MAPE), Pearson’s correlations, Bland-Altman (BA) plots, and equivalence testing with a ±10% equivalence zone. </sec> <sec> <title>RESULTS</title> MVPA estimates from all epoch lengths were statistically equivalent to the 1-second standard. The 15-second epoch demonstrated the best trade-off between accuracy and efficiency, with minimal bias (0.05 min), low MAPE (6.3%), and strong correlation (r = 0.97). However, indicators of individual-level error increased with longer epochs; MAPE increased to 9.5% at 60 seconds, and the limits of agreement widened (from ± 2.9 min at 15s to ± 4.9 min at 60s), suggesting greater potential misclassifications in estimating MVPA with longer epochs. </sec> <sec> <title>CONCLUSIONS</title> Although MVPA estimates using the MotionSense HRV wristband were robust across all epoch lengths, findings from this study suggest that a 15-second epoch provides an optimal balance between measurement precision and processing efficiency, making it well-suited for mHealth interventions, such as JITAIs that rely on timely activity detection. </sec>