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DOE OSTI2025

Comparison of Expert Vocabulary Usage Patterns Between Mental Health and Nonmental Health Clinicians When Diagnosing Pediatric Anxiety Disorders

Objective: To compare the utilization patterns of expert vocabulary (EVo) in diagnosing pediatric anxiety between mental health and non-mental health clinical notes from electronic health records to understand the role of Evo in informing classification and decision-making in anxiety diagnoses. Study design: We conducted a retrospective study using a cohort less than age 25 from Cincinnati Children's Hospital including 897 685 patients with 61 586 446 notes. We analyzed EVo, collected from mental health clinicians, in both mental and nonmental health notes. We compared classification accuracy using EVo-based patient-level embedding from all clinical notes, mental-health notes, and nonmental health notes for 2 tasks: 1) pre-vs postdiagnosis anxiety patients, and 2) prediagnosis anxiety vs nonanxiety patients. Results: EVo usage was highest in prediagnosis anxiety, lower in nonanxiety, and lowest in post-diagnosis. Classification models using EVo features from all, mental-health, and non-mental health notes showed similar F1 scores for prediagnosis anxiety (0.70 ± 0.2 for 2 categories). For anxiety vs nonanxiety classification, all clinical and nonmental health notes had better F1 scores than mental-health notes (above 0.90 for 3 categories). There was a notable difference in class-wise performance across both tasks. Conclusions: There are significant differences in anxiety EVo use between mental health and nonmental health clinicians. Despite less anxiety-specific terminology, non-mental health notes still captured key aspects of patient presentations, emphasizing the importance of including all clinicians' notes in analysis. EVo's utility for anxiety classification is most effective in prediagnostic phases, suggesting the need for a dedicated diagnostic lexicon and further study before incorporating EVo into classification models.

feature engineering↗

DOE OSTI2024

Generalizability of heat-related health risk associations observed in a large healthcare claims database of patients with commercial health insurance

Extreme ambient heat is unambiguously associated with higher risk of illness and death. The Optum Labs Data Warehouse (OLDW), a database of medical claims from US-based patients with commercial or Medicare Advantage health insurance, has been used to quantify heat-related health impacts. Whether results for the insured sub-population are generalizable to the broader population has to our knowledge not been documented. We sought to address this question, for the US population in California from 2012 to 2019. We examined changes in daily rates of emergency department (ED) encounters and in-patient hospitalization encounters for all-causes, heat-related outcomes, renal disease, mental/behavioral disorders, cardiovascular disease, and respiratory disease. OLDW was the source for health data for insured individuals in California, and health data for the broader population were gathered from the California Department of Health Care Access and Information (HCAI). We defined extreme heat exposure as any day in a group of 2 or more days with maximum temperatures exceeding the county-specific 97.5 th percentile and used a space-time-stratified case–crossover design to assess and compare the impacts of heat on health. Average incidence rates of medical encounters differed by dataset. However, rate ratios for ED encounters were similar across datasets for all causes (ratio of incidence rate ratios (rIRR) = 0.989; 95% confidence interval (CI) = 0.973, 1.011), heat-related causes (rIRR = 1.080; 95% CI = 0.999, 1.168), renal disease (rIRR = 0.963; 95% CI = 0.718, 1.292), and mental health disorders (rIRR = 1.098; 95% CI = 1.004, 1.201). Rate ratios for inpatient encounters were also similar. This work presents evidence that OLDW can continue to be a resource for estimating the health impacts of extreme heat.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Developing Scenario‐Based Strategies for Health, Climate, and Environmental Preparedness: The One Health, One Earth Approach

Climate change amplifies many threats to human health. Despite advances in understanding climate change dynamics and impacts, there remains a critical gap in translating scientific knowledge into equitable, and community-driven health interventions. The inaugural One Earth, One Health workshop sought to explore this gap through human-centered design exercises involving interdisciplinary researchers from climate and Earth sciences, engineering, epidemiology, microbiology, and environmental health. Although participants did not co-develop solutions with affected communities, they used stakeholder role-playing to guide ideation and lay groundwork for actionable plans. Through these methods, participants identified community needs and proposed prototype solutions to alleviate health threats exacerbated by global environmental change. Prototypes were organized around infectious diseases, extreme weather, and air quality, as illustrative themes rather than an exhaustive set of risks. Key solutions included strategies for anticipatory systems and early warning (e.g., integrating environmental signals with health data), inclusive communication and infrastructure needs for responding to extreme weather events, and integrated platforms visualizing air quality trends to support tailored, context-aware guidance beyond one-size-fits-all alerts. The workshop highlighted opportunities such as leveraging machine learning, Earth observation, and real-time surveillance to protect communities, but also noted barriers including data quality, technological redundancy, privacy, and governance challenges. Additionally, participants emphasized the need for interdisciplinary teams capable of collaborating across sectors, breaking down silos and addressing gaps in training and education. Overall, the workshop illustrates how process-driven, human-centered approaches can help surface user needs and generate testable prototype concepts, while underscoring the importance of direct community partnership for implementation.

Abadi, Azar M. [University of Alabama, Birmingham,↗

DOE OSTI2025

Machine Learning Analysis of Temperature-Strain Relationships for Structural Health Monitoring of Pipes: Self-powered wireless sensor system for health monitoring of liquid-sodium cooled fast reactors

This report presents machine learning (ML) analysis of temperature-strain relationships for structural health monitoring of nuclear reactor stainless steel (SS) pipes with the strain gauge sensor directly printed on the pipe with a 3D conformal aerosol jet printer. We investigate correlations for two sensor pairs installed on the same SS304 pipe: commercial K-type thermocouple with a printed gold strain gauge (TC3-SG3), and commercial K-type thermocouple with commercial Kyowa strain gauge (TC0-SG0). The temperature ranges for the sensor pairs TC0-SG0 and TC3-SG3 are 20.00°C to 266.37°C and 39.95°C to 219.28°C respectively. ML algorithms in this study include Linear Regression (baseline method), Ridge Regression, Lasso Regression, and Gradient Boosting. Performance evaluation metrics include Root Mean Square Error (RMSE), Mean Square Error (MSE), Mean Absolute Error (MAE), R 2 Score, and Explained Variance. Using advanced feature engineering techniques, we extracted 27 temperature-based features and 30 strategic inclusion features. The best performance was obtained with the Gradient Boosting method, which achieves prediction accuracy of R 2 = 0.9999 and RMSE = 7.69 μStrain for TC0-SG0, and R 2 = 0.9998 and RMSE = 18.03 μStrain for TC3-SG3. While the temperature-strain correlations are weaker for the gauge directly printed on the pipe than for the commercial strain gauge, deployment-ready performance exceeding industry standards is achieved for both sensor pairs.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

The overlooked interaction of emerging contaminants and microbial communities: a threat to ecosystems and public health

Abstract Context and aims Emerging contaminants (ECs) and microbial communities should not be viewed in isolation, but through the One Health perspective. Both ECs and microorganisms lie at the core of this interconnected framework, as they directly influence the health of humans, animals, and the environment. The interactions between ECs and microbial communities can have profound implications for public health, affecting all three domains. However, these ECs-microorganism interactions remain underexplored, potentially leaving significant public health and ecological risks unrecognized. Therefore, this article seeks to alert the scientific community to the overlooked interactions between ECs and microbial communities, emphasizing the pivotal role these interactions may play in the management of ‘One Health.’ Results The most extensively studied interaction between ECs and microbial communities is biodegradation. However, other more complex and concerning interactions demand attention, such as the impact of ECs on microbial ecology (disruptions in ecosystem balance affecting nutrient and energy cycles) and the rise and spread of antimicrobial resistance (a growing global health crisis). Although these ECs-microbial interactions had not been extensively studied, there are scientific evidence that ECs impact on microbial communities may be concerning for public health and ecosystem balance. Conclusions So, this perspective summarizes the impact of ECs through a One Health lens and underscores the urgent need to understand their influence on microbial communities, while highlighting the key challenges researchers must overcome. Tackling these challenges is vital to mitigate potential long-term consequences for both ecosystems and public health.

Gomes, Inês B. (ORCID:0000000207313662)↗

DOE OSTI2026

VA Community Determinants of Health Data Curation Documentation FY26-Q2

The U.S. Department of Veterans Affairs (VA) places the health and well-being of our nation’s veterans as its top priority. VA is dedicated to offering timely access to high-quality, evidence-based mental health care that meets the needs of veterans and supports their reintegration into society. One of our core missions is to prevent suicide among veterans through innovative approaches and resources. With funding from the VA Office of Mental Health and Suicide Prevention (OMHSP), the Community Determinants of Health (EDH) Data project has developed innovative datasets associated with specific health outcomes, a methodology for transforming spatiotemporal data from one spatial reference (e.g., a 1 km grid) to another (e.g., U.S. Census Tracts), and capabilities for modeling health outcomes. These datasets represent an enhancement of the Agency for Healthcare Research and Quality (AHRQ), addressing key gaps by introducing finer spatial resolution (Census Tract) and additional geographical covariates into existing data. The curation and standardization of these datasets is a complex task since they often originate from various sources and are measured at different spatial and temporal resolutions. For example, U.S. Census data products typically use census blocks, block groups, or counties, while data like weather data are available on 1 km grids. Some economic data may only be available at the ZIP code level. In this context, ‘standardized’ means that all datasets share the same spatial extent (e.g., U.S. Census Tract and/or County), and ‘curated’ implies a repeatable process with data provenance and the use of appropriate methodologies for covariate conversion. The Community Determinants of Health datasets draw from multiple sources, resulting in variables with varying degrees of availability, patterns of missing data, and methodological considerations across different sources, geographies, and years.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2024

The health, climate, and equity benefits of freight truck electrification in the United States

Abstract Long-haul freight shipment in the United States relies on diesel trucks and constitutes ∼3% of U.S. greenhouse gas emissions and a significant share of local air pollution. Here, we compare the climate and air pollution-related health damages from electric versus diesel long-haul truck fleets. We use truck commodity flows to estimate tailpipe emissions from diesel trucks and regional grid emissions intensities to estimate charging emissions from electric trucks under various grid scenarios. We use a reduced complexity air quality model combined with valuation of air pollution-related premature deaths (using two hazard ratios (HRs)) and quantify the distributional health impacts in different scenarios. We find that annual health and climate costs of the current diesel fleet are $195–$249/capita compared to $174–$205/capita for a new diesel fleet, and $156–$177/capita for an electric fleet, depending on the HR. We find that freight electrification could avoid $6.2–8.5 billion in health and climate damages annually when compared to a fleet of new diesel vehicles (with even higher benefits when compared to the current diesel fleet). However, the Midwest and parts of the Gulf Coast would experience an increase in health damages due to vehicles charging using electricity from coal power plants. If old coal power plants (operating in 1980 or earlier) are replaced with zero-emission generation, electrification of all U.S. freight would result in $32.3–39.2 billion in avoided damages annually and health benefits throughout the U.S. Electrifying transport of consumer manufacturing goods (including electronics, transport equipment, and precision instruments) and food, beverage, and tobacco products would provide the largest absolute health and climate benefits, whereas mixed freight and manufacturing goods would result in the largest benefits per tonne-km. We find small variations in health damages across race and income. These results will help policymakers prioritize electrification and charging investment strategies for the freight transportation sub-sector.

Hennessy, Eleanor M. (ORCID:0000000294715765)↗

DOE OSTI2025

VA Community Determinants of Health Data Curation Documentation FY26-Q1

The U.S. Department of Veterans Affairs (VA) places the health and well-being of our nation’s veterans as its top priority. VA is dedicated to offering timely access to high-quality, evidence-based mental health care that meets the needs of veterans and supports their reintegration into society. One of our core missions is to prevent suicide among veterans through innovative approaches and resources. With funding from the VA Office of Mental Health and Suicide Prevention (OMHSP), the Community Determinants of Health (EDH) Data project has developed innovative datasets associated with specific health outcomes, a methodology for transforming spatiotemporal data from one spatial reference (e.g., a 1km grid) to another (e.g., US Census Tracts), and capabilities for modeling health outcomes. These datasets represent an enhancement of the Agency for Healthcare Research and Quality (AHRQ), addressing key gaps by introducing finer spatial resolution (Census Tract) and additional geographical covariates into existing data. The curation and standardization of these datasets is a complex task since they often originate from various sources and are measured at different spatial and temporal resolutions. For example, US Census data products typically use census blocks, block groups, or counties, while data like weather data are available on 1km grids. Some economic data may only be available at the zip code level. In this context, ‘standardized’ means that all datasets share the same spatial extent (e.g., US Census Tract and/or County), and ‘curated’ implies a repeatable process with data provenance and the use of appropriate methodologies for covariate conversion. The Community Determinants of Health datasets draw from multiple sources, resulting in variables with varying degrees of availability, patterns of missing data, and methodological considerations across different sources, geographies, and years.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2024

Health Care Usage During the COVID-19 Pandemic and the Adoption of Telemedicine: Retrospective Study of Chronic Disease Cohorts

Background: The COVID-19 pandemic accelerated telehealth adoption across disease cohorts of patients. For many patients, routine medical care was no longer an option, and others chose not to visit medical offices in order to minimize COVID-19 exposure. In this study, we take a comprehensive multidisease approach in studying the impact of the COVID-19 pandemic on health care usage and the adoption of telemedicine through the first 12 months of the COVID-19 pandemic. Objective: We studied the impact of the COVID-19 pandemic on in-person health care usage and telehealth adoption across chronic diseases to understand differences in telehealth adoption across disease cohorts and patient demographics (such as the Social Vulnerability Index [SVI]). Methods: We conducted a retrospective cohort study of 6 different disease cohorts (anxiety: n=67,578; depression: n=45,570; diabetes: n=81,885; kidney failure: n=29,284; heart failure: n=21,152; and cancer: n=35,460). We used summary statistics to characterize changes in usage and regression analysis to study how patient characteristics relate to in-person health care and telehealth adoption and usage during the first 12 months of the pandemic. Results: We observed a reduction in in-person health care usage across disease cohorts (ranging from 10% to 24%). For most diseases we study, telehealth appointments offset the reduction in in-person visits. Furthermore, for anxiety and depression, the increase in telehealth usage exceeds the reduction in in-person visits (by up to 5%). We observed that younger patients and men have higher telehealth usage after accounting for other covariates. Patients from higher SVI areas are less likely to use telehealth; however, if they do, they have a higher number of telehealth visits, after accounting for other covariates. Conclusions: The COVID-19 pandemic affected health care usage across diseases, and the role of telehealth in replacing in-person visits varies by disease cohort. Understanding these differences can inform current practices and provides opportunities to further guide modalities of in-person and telehealth visits. Critically, further study is needed to understand barriers to telehealth service usage for patients in higher SVI areas. A better understanding of the role of social determinants of health may lead to more support for patients and help individual health care providers improve access to care for patients with chronic conditions.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

VA Determinants of Health Data Curation Documentation FY25-Q2

The U.S. Department of Veterans Affairs (VA) places the health and well-being of our nation’s veterans as its top priority. VA is dedicated to offering timely access to high-quality, evidence-based mental health care that meets the needs of veterans and supports their reintegration into society. One of our core missions is to prevent suicide among veterans through innovative approaches and resources. With funding from the VA Office of Mental Health and Suicide Prevention (OMHSP), the Determinants of Health (EDH) project has developed innovative datasets associated with specific health outcomes, a methodology for transforming spatiotemporal data from one spatial reference (e.g., a 1km grid) to another (e.g., US Census Tracts), and capabilities for modeling health outcomes. These datasets represent an enhancement of the Agency for Healthcare Research and Quality (AHRQ), addressing key gaps by introducing finer spatial resolution (Census Tract) and additional geographical covariates into existing data. The curation and standardization of these datasets is a complex task since they often originate from various sources and are measured at different spatial and temporal resolutions. For example, US Census data products typically use census blocks, block groups, or counties, while data like weather data are available on 1km grids. Some economic data may only be available at the zip code level. In this context, ‘standardized’ means that all datasets share the same spatial extent (e.g., US Census Tract and/or County), and ‘curated’ implies a repeatable process with data provenance and the use of appropriate methodologies for covariate conversion. The Determinants of Health datasets draw from multiple sources, resulting in variables with varying degrees of availability, patterns of missing data, and methodological considerations across different sources, geographies, and years.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

VA Determinants of Health Data Curation Documentation FY25-Q3

The U.S. Department of Veterans Affairs (VA) places the health and well-being of our nation’s veterans as its top priority. VA is dedicated to offering timely access to high-quality, evidence-based mental health care that meets the needs of veterans and supports their reintegration into society. One of our core missions is to prevent suicide among veterans through innovative approaches and resources. With funding from the VA Office of Mental Health and Suicide Prevention (OMHSP), the Determinants of Health (EDH) project has developed innovative datasets associated with specific health outcomes, a methodology for transforming spatiotemporal data from one spatial reference (e.g., a 1km grid) to another (e.g., US Census Tracts), and capabilities for modeling health outcomes. These datasets represent an enhancement of the Agency for Healthcare Research and Quality (AHRQ), addressing key gaps by introducing finer spatial resolution (Census Tract) and additional geographical covariates into existing data. The curation and standardization of these datasets is a complex task since they often originate from various sources and are measured at different spatial and temporal resolutions. For example, US Census data products typically use census blocks, block groups, or counties, while data like weather data are available on 1km grids. Some economic data may only be available at the zip code level. In this context, ‘standardized’ means that all datasets share the same spatial extent (e.g., US Census Tract and/or County), and ‘curated’ implies a repeatable process with data provenance and the use of appropriate methodologies for covariate conversion. The Determinants of Health datasets draw from multiple sources, resulting in variables with varying degrees of availability, patterns of missing data, and methodological considerations across different sources, geographies, and years.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

VA Community Determinants of Health Data Curation Documentation FY25-Q4

The U.S. Department of Veterans Affairs (VA) places the health and well-being of our nation’s veterans as its top priority. VA is dedicated to offering timely access to high-quality, evidence-based mental health care that meets the needs of veterans and supports their reintegration into society. One of our core missions is to prevent suicide among veterans through innovative approaches and resources. With funding from the VA Office of Mental Health and Suicide Prevention (OMHSP), the Determinants of Health (EDH) project has developed innovative datasets associated with specific health outcomes, a methodology for transforming spatiotemporal data from one spatial reference (e.g., a 1km grid) to another (e.g., US Census Tracts), and capabilities for modeling health outcomes. These datasets represent an enhancement of the Agency for Healthcare Research and Quality (AHRQ), addressing key gaps by introducing finer spatial resolution (Census Tract) and additional geographical covariates into existing data. The curation and standardization of these datasets is a complex task since they often originate from various sources and are measured at different spatial and temporal resolutions. For example, US Census data products typically use census blocks, block groups, or counties, while data like weather data are available on 1km grids. Some economic data may only be available at the zip code level. In this context, standardized’ means that all datasets share the same spatial extent (e.g., US Census Tract and/or County), and ‘curated’ implies a repeatable process with data provenance and the use of appropriate methodologies for covariate conversion. The Determinants of Health datasets draw from multiple sources, resulting in variables with varying degrees of availability, patterns of missing data, and methodological considerations across different sources, geographies, and years.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

CareWELL: Multimodal Region Representation Learning with Spatial Contexts for Urban Health

Rapid urbanization affects living environments by intensifying exposure to air pollution, heat, noise, and urban dynamics, which together contribute to uneven health outcomes across neighborhoods. For instance, cardiovascular, respiratory, and mental health conditions are each influenced by distinct exposures such as air pollution, extreme temperatures, or limited access to green space. These heterogeneous patterns require understanding the characteristics of geographic regions in order to explain why urban health risks vary across urban areas. Recent work in self-supervised region representation learning provides a promising way to model such characteristics from multimodal geospatial data. However, existing methods face two major limitations: (i) they often depend on non-public datasets, limiting reproducibility and applicability, and (ii) their generic pretraining objectives overlook health-relevant determinants, including temporal variability in environmental exposures and inequalities in social conditions. To address these gaps, we propose Context-Aware Region rEpresentation with Weather, Environment, and Location Learning (CareWELL). CareWELL leverages large language models to encode seasonal variability in weather, employs contrastive learning to align geo-coordinate and weather representations, and introduces a context-aware objective that integrates socio-demographic factors while preserving spatial correlations. We evaluate CareWELL by predicting six urban health outcomes in Manhattan, New York City, and demonstrate that CareWELL consistently outperforms state-of-the-art baselines as well as a traditional spatial computing method. These results suggest the importance of context-aware pretraining objectives for learning health-relevant region representations.

Namgung, Min [ORNL]↗

DOE OSTI2026

Reducing Deferrals by Integrating Health Homes with Weatherization

The U.S. Department of Energy’s Weatherization Assistance Program (WAP) improves energy efficiency and household health for low-income families, yet a substantial number of otherwise eligible homes are deferred from due to health and safety concerns in their homes, including mold, moisture damage, or structural deficiencies. These deferred homes are often occupied by marginalized or otherwise vulnerable residents who may be exposed to elevated indoor air pollutant concentration. Integrating Healthy Homes (HH) interventions with weatherization has been proposed as a strategy to reduce deferrals, address environmental justice concerns, and improve health outcomes. However, limited data on the health and economic implications of such integration exist. This study aimed to assess the potential health benefits and savings-to-investment ratio (SIR) associated with integrating Healthy Homes interventions into weatherization programs for homes typically deferred from WAP services. We collected indoor air pollutant data from homes undergoing health home renovations, including fine particulate matter (PM 2.5 ), nitrogen dioxide (NO 2 ), carbon monoxide (CO), and formaldehyde, with sampling times up to one month. Except for fomaldehyde, which was measured using passive UMEx badges, the other metrics were measured using the airQ Pro indoor air quality monitor.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Putting the soil health principles to the test in Iowa

One of the most popular soil conservation campaigns is based on the USDA Natural Resource Conservation Service's Soil Health Principles (NRCS-SHPs). The NRCS-SHP program identifies four principles—maximize presence of living roots, minimize disturbance, maximize soil cover, and maximize biodiversity—with the underlying assumption that the more principles one follows, the greater improvements in soil health. Despite the popularity of the NRCS-SHPs, this underlying assumption has not been rigorously tested. To do so, we used nine long-term experiments all located in central Iowa, but with varying degree of NRCS-SHP adoption, to determine if greater adoption increases three slow-changing (maximum water holding capacity, bulk density [BD], and soil organic carbon) and three dynamic (microbial biomass carbon [MBC], potentially mineralizable carbon [PMC], and permanganate oxidizable carbon [POXC]) soil health indicators. We regressed these indicators with a soil health principle score that can scale soil management based on adoption of the NRCS-SHPs. Of the slow-changing soil properties, increased adoption of NRCS-SHPs only decreased soil BD (R2 = 0.22, p = 0.024). On the other hand, increased adoption of NRCS-SHPs strongly predicted increases in both MBC and PMC and across two sampling dates (R2 > 0.23, p < 0.015); POXC, however, did not increase with greater adoption. The consistent increases in MBC and PMC with greater adoption of NRCS-SHPs supports their usefulness as sensitive indicators of positive soil health change. Our study provides scientific evidence to support the NRCS-SHPs concept, improving its usefulness as an extension campaign, and stands as a step toward evidence-based soil conservation.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Diagnostic-free onboard battery health assessment

Diverse usage patterns induce complex and variable aging behaviors in lithiumion batteries, complicating accurate health diagnosis and prognosis. Separate diagnostic cycles are often used to untangle the battery’s current state of health from prior complex aging patterns. However, these same diagnostic cycles alter the battery’s degradation trajectory, are time-intensive, and cannot be practically performed in onboard applications. Here, in this work, we leverage portions of operational measurements in combination with an interpretable machine learning model to enable rapid, onboard battery health diagnostics and prognostics without offline diagnostic testing and the requirement of historical data. We integrate mechanistic constraints within an encoder-decoder architecture to extract electrode states in a physically interpretable latent space and enable improved reconstruction of the degradation path. The health diagnosis model framework can be flexibly applied across diverse application interests with slight fine-tuning.

battery aging reconstruction↗

DOE OSTI2025

Including frameworks of public health ethics in computational modelling of infectious disease interventions

Decisions on public health interventions to control infectious diseases are often informed by computational models. Interpreting the predicted outcomes of a public health decision requires not only high-quality modelling but also an ethical framework for assessing the benefits and harms associated with different options. The design and specification of ethical frameworks matured independently of computational modelling, so many values recognized as important for ethical decision-making are missing from computational models. We demonstrate a proof-of-concept approach to incorporate multiple public health values into the evaluation of a simple computational model for vaccination against a pathogen such as SARS-CoV-2. By examining a bounded space of alternative prioritizations of three values relevant to public health ethics (aggregate clinical burden, equity in clinical burden, equity in adverse effects from vaccination), we identify value trade-offs, where the outcomes of optimal strategies differ depending on the ethical framework. This work demonstrates an approach to incorporating diverse values into decision criteria used to evaluate outcomes of models of infectious disease interventions.

"Mathematical Biology"↗

DOE OSTI2025

The health and indoor environmental quality impacts of residential building envelope retrofits: A literature review

Retrofitting existing buildings to improve energy efficiency is an important strategy to meet increasingly stringent energy efficiency targets. While the primary objective of energy efficiency retrofits is to reduce energy consumption and greenhouse gas emissions, retrofits can also result in non-energy impacts (NEIs), which contribute to decision-making processes and overall value of the retrofit. NEIs have been studied extensively in retrofitted residential buildings; however, these studies have historically grouped passive (i.e., building envelope) and active (i.e., heating, ventilation, and air conditioning (HVAC) and energy system) upgrades, making it difficult to identify the underlying mechanism(s) of action for each NEI and developing effective retrofit strategies, based on occupant need. The purpose of this study was to better account for NEIs, based on a literature review, summarizing the current state of knowledge on NEIs associated with residential building envelope retrofits. We limited our search to health- and indoor environmental quality-related NEIs. The review identified strong evidence that building envelope retrofits improve acoustic comfort, wintertime thermal comfort, and respiratory and cardiovascular health outcomes. IAQ outcomes were mixed, with studies reporting both increases and decreases to indoor contaminant concentrations following retrofits. The strength of the effect was generally governed by pre-retrofit contaminant concentrations and whether indoor concentrations were dominated by indoor or outdoor sources. Most studies evaluating summertime thermal comfort identified increased incidence of summertime overheating; however, none of these studies linked the change in thermal conditions to health outcomes. Recommendations for future work include expanding studies to include more market rate housing and the health impacts of summertime overheating in retrofitted buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗