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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Epidemiological Data Challenges: Planning for a More Robust Future Through Data Standards

Accessible epidemiological data are of great value for emergency preparedness and response, understanding disease progression through a population, and building statistical and mechanistic disease models that enable forecasting. The status quo, however, renders acquiring and using such data difficult in practice. In many cases, a primary way of obtaining epidemiological data is through the internet, but the methods by which the data are presented to the public often differ drastically among institutions. As a result, there is a strong need for better data sharing practices. This paper identifies, in detail and with examples, the three key challenges one encounters when attempting to acquire and use epidemiological data: (1) interfaces, (2) data formatting, and (3) reporting. These challenges are used to provide suggestions and guidance for improvement as these systems evolve in the future. If these suggested data and interface recommendations were adhered to, epidemiological and public health analysis, modeling, and informatics work would be significantly streamlined, which can in turn yield better public health decision-making capabilities.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

Muddy Water? Variation in Reporting Receipt of Breast Cancer Radiation Therapy by Population-Based Tumor Registries

Purpose: To evaluate, in the setting of breast cancer, the accuracy of registry radiation therapy (RT) coding compared with the gold standard of Medicare claims. Methods and Materials: Using Surveillance, Epidemiology, and End Results (SEER)–Medicare data, we identified 73,077 patients aged ≥66 years diagnosed with breast cancer in the period 2001-2007. Underascertainment (1 - sensitivity), sensitivity, specificity, κ, and χ{sup 2} were calculated for RT receipt determined by registry data versus claims. Multivariate logistic regression characterized patient, treatment, and geographic factors associated with underascertainment of RT. Findings in the SEER–Medicare registries were compared with three non-SEER registries (Florida, New York, and Texas). Results: In the SEER–Medicare registries, 41.6% (n=30,386) of patients received RT according to registry coding, versus 49.3% (n=36,047) according to Medicare claims (P<.001). Underascertainment of RT was more likely if patients resided in a newer SEER registry (odds ratio [OR] 1.70, 95% confidence interval [CI] 1.60-1.80; P<.001), rural county (OR 1.34, 95% CI 1.21-1.48; P<.001), or if RT was delayed (OR 1.006/day, 95% CI 1.006-1.007; P<.001). Underascertainment of RT receipt in SEER registries was 18.7% (95% CI 18.6-18.8%), compared with 44.3% (95% CI 44.0-44.5%) in non-SEER registries. Conclusions: Population-based tumor registries are highly variable in ascertainment of RT receipt and should be augmented with other data sources when evaluating quality of breast cancer care. Future work should identify opportunities for the radiation oncology community to partner with registries to improve accuracy of treatment data.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Creating a Tools Ecosystem for Cross-Discipline Environmental Data Reuse

Reusing data is difficult even within well-defined science communities and only gets worse when combining data from multiple communities and disciplines. Through the lens of current work on constructing an environmental epidemiological data set from multiple disciplinary sources, we demonstrate the need for a new tool ecosystem to support heterogeneous Big Data science. Extending existing community standards for schemas and/or data formats through human auditing and wrangling of the data is not feasible at scale. This work therefore suggests new approaches for the multi-disciplinary communities to build a shared tool ecosystem for big data. We discuss both the larger context of data wrangling of epidemiological data sets for novel artificial intelligence algorithms and the specific lessons from working with these multi-disciplinary data sets. Adopting a more model-driven, automatable approach promises not only better efficiency but also removes key sources of human-generated errors and promotes reuse and reproducibility of science data.

Logan, Jeremy↗
DOE OSTI2024

AMIA KDDM Working Group Collaborative Workshop: Enriching Electronic Health Records with Social Determinants of Health to Improve Outcomes and Health Equity

Prior research has demonstrated that social determinants of health (SDoH) are major drivers of health outcomes and contributors to widespread health inequities. It was estimated that, in the United States, SDoH could be responsible for up to 40% of all preventable deaths, significantly higher than the 10-15% for which better medical care is responsible. Public health interventions that target SDoH are instrumental for improving health outcomes and reducing long-standing health inequities. Currently, most mainstream EHR vendors have implemented SDoH screeners in their EHR systems. However, the utility of the screeners is low, rendering patient-level SDoH still widely unavailable in the structured fields. SDoH are sometimes mentioned in free-text clinical notes (e.g., social context section) where natural language processing (NLP) can be applied to extract relevant information. Contextual-level SDoH can be identified from multiple data sources, many of which are publicly available and spatiotemporally linked to EHR data. As such, there is an opportunity for the KDDM research community to create innovative solutions to draw meaningful insights by creating and using rich data with SDoH to improve health outcomes while reducing disparities. In this workshop organized by AMIA Knowledge Discovery and Data Mining Working Group (AMIA KDDM WG), we will invite world-leading experts from academia, national laboratories, and life science industry with varied backgrounds in biomedical informatics, epidemiology, data science, machine learning, natural language processing, and pediatric cardiology to discuss the best practice of capturing, standardizing, and using SDoH information in various applications aiming at improving outcomes and health equity.

He, Zhe↗
DOE OSTI2025

Recent evolution of risk analyses in atomic bomb survivor studies: new methods and applications

Abstract Several decades ago a dramatic leap forward occurred in the development and application of statistical methods for modeling radiation risk at the Radiation Effects Research Foundation (RERF). Poisson regression analysis for grouped person-year cohort data and the linear excess relative risk model were introduced, and subsequently a devoted software system, Epicure® (https://www.hirosoft.com), was developed by researchers at RERF and at the U.S. National Cancer Institute. Numerous advancements in understanding radiation effects on humans were made possible with these methods, which are still the state-of-the-art for risk assessment at RERF and have remained part of the standard toolbox for radiation—and other environmental—epidemiological studies worldwide. Nevertheless, as our understanding of radiation risk has increased, so have the breadth and depth of questions that require answers based on emerging data that are not amenable to these conventional methods. This overview briefly recounts the conventional methods and then describes our recent diversification into the use or development of new statistical approaches to meet the challenges of burgeoning biological data and emerging mechanistic information. We briefly discuss the development and application of new methods, current and planned, that are part of the RERF Statistics Department’s role in supporting institution-wide research, especially in our collaborations involving the Life Span Study, Adult Health Study, and First-generation Offspring Clinical Study. Some approaches to modeling and assessing radiation risk with newer methods mentioned herein have already been published, while some are still in development or are only beginning at the proposal stage.

Oncology↗
DOE OSTI2025

Reevaluation of Radiation-Protection Standards for Workers and the Public Based on Current Scientific Evidence

President Trump’s recent executive orders to “Usher in a Nuclear Renaissance,” coupled with the global pledge to triple nuclear energy capacity by 2050, underscore nuclear energy’s importance to national security and economic prosperity. This renewed interest has prompted efforts to spur nuclear-energy deployment, including assessing factors impeding it. One issue that has previously been identified as adding to the cost of nuclear energy is excessively conservative requirements, including those related to radiation protection. This technical review, therefore, examines current radiation-protection standards that were established decades ago when more limited data were available and nuclear-energy expansion was not a national priority. The review focuses on scientific evidence regarding the health effects of ionizing radiation at annual doses of 10,000 mrem or less. The review evaluates epidemiological studies, radiobiological research, and positions of relevant professional organizations to assess whether such doses result in discernable or observable increases in negative health outcomes. The review also surveys the literature related to economic and practical implications of current radiation-protection standards and practices. Based on this assessment, we propose maintaining an annual occupational whole-body dose limit of 5,000 mrem/yr and eliminating all “as low as reasonably achievable” requirements and limits below this threshold. This change could potentially reduce radiation-protection costs by millions of dollars annually for each reactor, as well as decrease the overall costs and correct misconceptions about the risks associated with all nuclear technologies. The evidence further supports future consideration of a 10,000 mrem/yr limit that would maintain appropriate safety margins while further reducing protection costs. Similarly, given the data and that the average annual radiation dose per person in the U.S. is 620 mrem, we believe the public dose limits of 100 mrem/yr are unnecessarily restrictive; increasing to 500 mrem/yr would maintain substantial safety margins—a factor of 10 below the occupational limit—while reducing regulatory burdens and associated bureaucracy. While we acknowledge ongoing scientific debate and encourage continued research on the health effects of ionizing radiation, our review indicates current frameworks are overly conservative. These overly stringent limits not only impose unnecessary economic burdens without corresponding health benefits but also divert safety focus and resources from more important considerations. Although this study was motivated by nuclear-power considerations, reforms to radiation-protection requirements have significant positive implications for other areas, such as nuclear medical applications, environmental remediation, nuclear-waste management and disposal, and industrial applications of nuclear technologies.

61 RADIATION PROTECTION AND DOSIMETRY↗
NASA NTRS2019

Developing an Air Quality Index for Space Vehicles and Habitats

The development of an adequate tool to help the layperson understand pollution levels in their environment is of high importance. This tool must be able to inform about the levels of pollution in a simple and understandable way but also can be used for decision-making and mitigation activities to protect the health of the exposed population. One of the most useful and up-to-date approaches for characterizing air pollution is the Air Quality Index (AQI). It is an easily-calculated, powerful, data-driven tool that summarizes a complex phenomenon, such as air pollution, in straightforward indicators. The AQI system has been developed in different countries around the world, mainly for outdoor environments, based on the results of risk assessments, epidemiological studies, and current local air pollution regulations and standards. There is a need for such a system in low gravity indoor environments where air quality is of fundamental importance to astronaut health, with concerns encompassing both gaseous contaminants and particulate matter. Earth-based AQIs cannot be extrapolated to microgravity indoor environments due to different aerosol transport characteristics and altered lung deposition in low and partial gravity. The objectives of this work are to explore what areas of expertise, types of research, and data will be required to formulate a spacecraft-specific AQI. An initial dataset is available for this effort, combined from two aerosol sampling experiments, which have characterized airborne particulate matter on the International Space Station (ISS). We outline future research needs for formulating a narrowly focused version of a widely-used metric, namely, an indoor AQI for future space missions.

Sorek-Hamer, Meytar↗
DOE OSTI2014

Retrospective Reconstructions of Active Bone Marrow Dose-Volume Histograms

Purpose: To present a method for calculating dose-volume histograms (DVH's) to the active bone marrow (ABM) of patients who had undergone radiation therapy (RT) and subsequently developed leukemia. Methods and Materials: The study focuses on 15 patients treated between 1961 and 1996. Whole-body RT planning computed tomographic (CT) data were not available. We therefore generated representative whole-body CTs similar to patient anatomy. In addition, we developed a method enabling us to obtain information on the density distribution of ABM all over the skeleton. Dose could then be calculated in a series of points distributed all over the skeleton in such a way that their local density reflected age-specific data for ABM distribution. Dose to particular regions and dose-volume histograms of the entire ABM were estimated for all patients. Results: Depending on patient age, the total number of dose calculation points generated ranged from 1,190,970 to 4,108,524. The average dose to ABM ranged from 0.3 to 16.4 Gy. Dose-volume histograms analysis showed that the median doses (D{sub 50%}) ranged from 0.06 to 12.8 Gy. We also evaluated the inhomogeneity of individual patient ABM dose distribution according to clinical situation. It was evident that the coefficient of variation of the dose for the whole ABM ranged from 1.0 to 5.7, which means that the standard deviation could be more than 5 times higher than the mean. Conclusions: For patients with available long-term follow-up data, our method provides reconstruction of dose-volume data comparable to detailed dose calculations, which have become standard in modern CT-based 3-dimensional RT planning. Our strategy of using dose-volume histograms offers new perspectives to retrospective epidemiological studies.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

Contribution to the risk characterization of ciguatoxins: LOAEL estimated from eight ciguatera fish poisoning events in Guadeloupe (French West Indies)

From 2010 to 2012, 35 ciguatera fish poisoning (CFP) events involving 87 individuals who consumed locally-caught fish were reported in Guadeloupe (French West Indies). For 12 of these events, the presence of ciguatoxins (CTXs) was indicated in meal remnants and in uncooked fish by the mouse bioassay (MBA). Caribbean ciguatoxins (C-CTXs) were confirmed by liquid chromatography–tandem mass spectrometry (LC–MS/MS) analysis. Using a cell-based assay (CBA), and the only available standard Pacific ciguatoxin-1 (P-CTX-1), the lowest toxins level detected in fish samples causing CFP was 0.022 µg P-CTX-1 equivalent (eq.)·kg{sup −1} fish. Epidemiological and consumption data were compiled for most of the individuals afflicted, and complete data for establishing the lowest observable adverse effects level (LOAEL) were obtained from 8 CFP events involving 21 individuals. Based on toxin intakes, the LOAEL was estimated at 4.2 ng P-CTX-1 eq./individual corresponding to 48.4 pg P-CTX-1 eq. kg{sup −1} body weight (bw). Although based on limited data, these results are consistent with the conclusions of the European Food Safety Authority (EFSA) opinion which indicates that a level of 0.01 µg P-CTX-1 eq. kg{sup −1} fish, regardless of source, should not exert effects in sensitive individuals when consuming a single meal. The calculated LOAEL is also consistent with the U.S. Food and Drug Administration guidance levels for CTXs (0.1 µg C-CTX-1 eq. kg{sup −1} and 0.01 µg P-CTX-1 eq. kg{sup −1} fish). - Highlights: • We report on an epidemiological study on Ciguatera events in the French West Indies. • The collection of consumption data allows for the first time the LOAEL determination. • The LOAEL for ciguatoxins was established at 48.4 pg P-CTX-1 eq. kg{sup −1} bw. • LC–MS/MS provided structural confirmation of C-CTX1 in two suspected samples • Neuro-2A CBA is suitable for assessing composite toxicity levels in fish samples.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Model-Form Epistemic Uncertainty Quantification for Modeling with Differential Equations: Application to Epidemiology

Modeling real-world phenomena to any degree of accuracy is a challenge that the scientific research community has navigated since its foundation. Lack of information and limited computational and observational resources necessitate modeling assumptions which, when invalid, lead to model-form error (MFE). The work reported herein explored a novel method to represent model-form uncertainty (MFU) that combines Bayesian statistics with the emerging field of universal differential equations (UDEs). The fundamental principle behind UDEs is simple: use known equational forms that govern a dynamical system when you have them; then incorporate data-driven approaches – in this case neural networks (NNs) – embedded within the governing equations to learn the interacting terms that were underrepresented. Utilizing epidemiology as our motivating exemplar, this report will highlight the challenges of modeling novel infectious diseases while introducing ways to incorporate NN approximations to MFE. Prior to embarking on a Bayesian calibration, we first explored methods to augment the standard (non-Bayesian) UDE training procedure to account for uncertainty and increase robustness of training. In addition, it is often the case that uncertainty in observations is significant; this may be due to randomness or lack of precision in the measurement process. This uncertainty typically manifests as “noisy” observations which deviate from a true underlying signal. To account for such variability, the NN approximation to MFE is endowed with a probabilistic representation and is updated using available observational data in a Bayesian framework. By representing the MFU explicitly and deploying an embedded, data-driven model, this approach enables an agile, expressive, and interpretable method for representing MFU. In this report we will provide evidence that Bayesian UDEs show promise as a novel framework for any science-based, data-driven MFU representation; while emphasizing that significant advances must be made in the calibration of Bayesian NNs to ensure a robust calibration procedure.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Google Health Trends performance reflecting dengue incidence for the Brazilian states

Abstract Background Dengue fever is a mosquito-borne infection transmitted by Aedes aegypti and mainly found in tropical and subtropical regions worldwide. Since its re-introduction in 1986, Brazil has become a hotspot for dengue and has experienced yearly epidemics. As a notifiable infectious disease, Brazil uses a passive epidemiological surveillance system to collect and report cases; however, dengue burden is underestimated. Thus, Internet data streams may complement surveillance activities by providing real-time information in the face of reporting lags. Methods We analyzed 19 terms related to dengue using Google Health Trends (GHT), a free-Internet data-source, and compared it with weekly dengue incidence between 2011 to 2016. We correlated GHT data with dengue incidence at the national and state-level for Brazil while using the adjusted R squared statistic as primary outcome measure (0/1). We used survey data on Internet access and variables from the official census of 2010 to identify where GHT could be useful in tracking dengue dynamics. Finally, we used a standardized volatility index on dengue incidence and developed models with different variables with the same objective. Results From the 19 terms explored with GHT, only seven were able to consistently track dengue. From the 27 states, only 12 reported an adjusted R squared higher than 0.8; these states were distributed mainly in the Northeast, Southeast, and South of Brazil. The usefulness of GHT was explained by the logarithm of the number of Internet users in the last 3 months, the total population per state, and the standardized volatility index. Conclusions The potential contribution of GHT in complementing traditional established surveillance strategies should be analyzed in the context of geographical resolutions smaller than countries. For Brazil, GHT implementation should be analyzed in a case-by-case basis. State variables including total population, Internet usage in the last 3 months, and the standardized volatility index could serve as indicators determining when GHT could complement dengue state level surveillance in other countries.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Benchmark datasets for SARS-CoV-2 surveillance bioinformatics

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of coronavirus disease 2019 (COVID-19), has spread globally and is being surveilled with an international genome sequencing effort. Surveillance consists of sample acquisition, library preparation, and whole genome sequencing. This has necessitated a classification scheme detailing Variants of Concern (VOC) and Variants of Interest (VOI), and the rapid expansion of bioinformatics tools for sequence analysis. These bioinformatic tools are means for major actionable results: maintaining quality assurance and checks, defining population structure, performing genomic epidemiology, and inferring lineage to allow reliable and actionable identification and classification. Additionally, the pandemic has required public health laboratories to reach high throughput proficiency in sequencing library preparation and downstream data analysis rapidly. However, both processes can be limited by a lack of a standardized sequence dataset. We identified six SARS-CoV-2 sequence datasets from recent publications, public databases and internal resources. In addition, we created a method to mine public databases to identify representative genomes for these datasets. Using this novel method, we identified several genomes as either VOI/VOC representatives or non-VOI/VOC representatives. To describe each dataset, we utilized a previously published datasets format, which describes accession information and whole dataset information. Additionally, a script from the same publication has been enhanced to download and verify all data from this study.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Cancer epidemiology update, following the 2011 IARC evaluation of radiofrequency electromagnetic fields (Monograph 102)

Highlights: • Increased risk of brain, vestibular nerve and salivary gland tumors are associated with mobile phone use. • Nine studies (2011–2017) report increased risk of brain cancer from mobile phone use. • Four case-control studies (3 in 2013, 1 in 2014) report increased risk of vestibular nerve tumors. • Concern for other cancers: breast (male & female), testis, leukemia, and thyroid. • Based on the evidence reviewed it is our opinion that IARC's current categorization of RFR as a possible human carcinogen (Group 2B) should be upgraded to Carcinogenic to Humans (Group 1). Epidemiology studies (case-control, cohort, time trend and case studies) published since the International Agency for Research on Cancer (IARC) 2011 categorization of radiofrequency radiation (RFR) from mobile phones and other wireless devices as a possible human carcinogen (Group 2B) are reviewed and summarized. Glioma is an important human cancer found to be associated with RFR in 9 case-control studies conducted in Sweden and France, as well as in some other countries. Increasing glioma incidence trends have been reported in the UK and other countries. Non-malignant endpoints linked include acoustic neuroma (vestibular Schwannoma) and meningioma. Because they allow more detailed consideration of exposure, case-control studies can be superior to cohort studies or other methods in evaluating potential risks for brain cancer. When considered with recent animal experimental evidence, the recent epidemiological studies strengthen and support the conclusion that RFR should be categorized as carcinogenic to humans (IARC Group 1). Opportunistic epidemiological studies are proposed that can be carried out through cross-sectional analyses of high, medium, and low mobile phone users with respect to hearing, vision, memory, reaction time, and other indicators that can easily be assessed through standardized computer-based tests. As exposure data are not uniformly available, billing records should be used whenever available to corroborate reported exposures.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2014

MO-D-BRF-01: Pediatric Treatment Planning II: The PENTEC Report On Normal Tissue Complications

With advances in multimodality therapy, childhood cancer cure rates approach 80%. However, both radiotherapy and chemotherapy may cause debilitating or even fatal ‘late effects’ that are critical to understand, mitigate, or prevent. QUANTEC identified the uncertainties relating to side-effects of adult treatments, but this is more complicated for children in whom a mosaic of tissues develops at different rates and temporal sequences. Childhood cancer survivors have long life expectancy and may develop treatmentinduced secondary cancers and severe organ/tissue injury decades after treatment. Collaborative long-term observational studies and clinical research programs for survivors of pediatric and adolescent cancer provide some dose-response data for follow-up periods exceeding 40 years. Data analysis is challenging due to the influence of both therapeutic and developmental variables. PENTEC is a group of radiation oncologists, pediatric oncologists, subsepcialty physicians, medical physicists, biomathematic modelers/statisticians, and epidemiologists charged with conducting a critical synthesis of existing literature aiming to: critically analyze radiation dose-volume effects on normal tissue tolerances as a function of age/development in pediatric cancer patients in order to inform treatment planning and improve outcomes for survivors; describe relevant physics issues specific to pediatric radiotherapy; propose dose-volumeoutcome reporting standards to improve the knowledge base to inform future treatment guidelines. PENTEC has developed guidelines for systematic literature reviews, data extraction tolls and data analysis. This education session will discuss:1. Special considerations for normal tissue radiation response of children/adolescents, e.g. the interplay between development and radiotherapy effects.2. Epidemiology of organ/tissue injuries and secondary cancers.3. Exploration of dose-response differences between children and adults4. Methodology for literature review, data mining of outcomes databases, and NTCP or longitudinal modeling of doseresponse. 5. PENTEC goals and timetable. Learning Objectives: Understand important differences between normal tissue effects of radiation therapy in pediatric and adult patients. Be able to identify situations where there is ‘interplay’ between organ development and radiation-induced complications. Identify methods to systematically extract quantitative dose-volumeresponse relationships from existing outcomes databases. Provide guidance for the medical physicist to properly understand, implement, guide and control contemporary technology and applications in pediatric radiation oncology.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Phylodynamic Inference across Epidemic Scales

Within-host genetic diversity and large transmission bottlenecks confound phylodynamic inference of epidemiological dynamics. Conventional phylodynamic approaches assume that nodes in a time-scaled pathogen phylogeny correspond closely to the time of transmission between hosts that are ancestral to the sample. However, when hosts harbor diverse pathogen populations, node times can substantially pre-date infection times. Imperfect bottlenecks can cause lineages sampled in different individuals to coalesce in unexpected patterns. To address realistic violations of standard phylodynamic assumptions we developed a new inference approach based on a multi-scale coalescent model, accounting for nonlinear epidemiological dynamics, heterogeneous sampling through time, non-negligible genetic diversity of pathogens within hosts, and imperfect transmission bottlenecks. We apply this method to HIV-1 and Ebola virus (EBOV) outbreak sequence data, illustrating how and when conventional phylodynamic inference may give misleading results. Within-host diversity of HIV-1 causes substantial upwards bias in the number of infected hosts using conventional coalescent models, but estimates using the multi-scale model have greater consistency with reported number of diagnoses through time. In contrast, we find that within-host diversity of EBOV has little influence on estimated numbers of infected hosts or reproduction numbers, and estimates are highly consistent with the reported number of diagnoses through time. The multi-scale coalescent also enables estimation of within-host effective population size using single sequences from a random sample of patients. We find within-host population genetic diversity of HIV-1 p17 to be 2Nμ=0.012 (95% CI 0.0066–0.023), which is lower than estimates based on HIV envelope serial sequencing of individual patients.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Improved Survival With Radiation Therapy in Stage I-II Primary Mediastinal B Cell Lymphoma: A Surveillance, Epidemiology, and End Results Database Analysis

Background: Primary mediastinal B cell lymphoma (PMBCL) is an uncommon lymphoma for which trials are few with small patient numbers. The role of radiation therapy (RT) after standard immunochemotherapy for early-stage disease has never been studied prospectively. We used the Surveillance, Epidemiology, and End Results (SEER) database to evaluate PMBCL and the impact of RT on outcomes. Methods and Materials: We queried the SEER database for patients with stage I-II PMBCL diagnosed from 2001 to 2011. Retrievable data included age, gender, race (white/nonwhite), stage, extranodal disease, year of diagnosis, and use of RT as a component of definitive therapy. Kaplan-Meier overall survival (OS) estimates, univariate (UVA) log-rank and multivariate (MVA) Cox proportional hazards regression analyses were performed. Results: Two hundred fifty patients with stage I-II disease were identified, with a median follow-up time of 39 months (range, 3-125 months). The median age was 36 years (range, 18-89 years); 61% were female; 76% were white; 45% had stage I disease, 60% had extranodal disease, and 55% were given RT. The 5-year OS for the entire cohort was 86%. On UVA, OS was improved with RT (hazard ratio [HR] 0.446, P=.029) and decreased in association with nonwhite race (HR 2.70, P=.006). The 5-year OS was 79% (no RT) and 90% (RT). On MVA, white race and RT remained significantly associated with improved OS (P=.007 and .018, respectively). The use of RT decreased over time: 61% for the 67 patients whose disease was diagnosed from 2001 to 2005 and 53% in the 138 patients treated from 2006 to 2010. Conclusion: This retrospective population-based analysis is the largest PMBCL dataset to date and demonstrates a significant survival benefit associated with RT. Nearly half of patients treated in the United States do not receive RT, and its use appears to be declining. In the absence of phase 3 data, the use of RT should be strongly considered for its survival benefit in early-stage disease.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

Numerical methods and hypoexponential approximations for gamma distributed delay differential equations

Abstract Gamma distributed delay differential equations (DDEs) arise naturally in many modelling applications. However, appropriate numerical methods for generic gamma distributed DDEs have not previously been implemented. Modellers have therefore resorted to approximating the gamma distribution with an Erlang distribution and using the linear chain technique to derive an equivalent system of ordinary differential equations (ODEs). In this work, we address the lack of appropriate numerical tools for gamma distributed DDEs in two ways. First, we develop a functional continuous Runge–Kutta (FCRK) method to numerically integrate the gamma distributed DDE without resorting to Erlang approximation. We prove the fourth-order convergence of the FCRK method and perform numerical tests to demonstrate the accuracy of the new numerical method. Nevertheless, FCRK methods for infinite delay DDEs are not widely available in existing scientific software packages. As an alternative approach to solving gamma distributed DDEs, we also derive a hypoexponential approximation of the gamma distributed DDE. This hypoexponential approach is a more accurate approximation of the true gamma distributed DDE than the common Erlang approximation but, like the Erlang approximation, can be formulated as a system of ODEs and solved numerically using standard ODE software. Using our FCRK method to provide reference solutions, we show that the common Erlang approximation may produce solutions that are qualitatively different from the underlying gamma distributed DDE. However, the proposed hypoexponential approximations do not have this limitation. Finally, we apply our hypoexponential approximations to perform statistical inference on synthetic epidemiological data to illustrate the utility of the hypoexponential approximation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Inferring the spread of COVID-19: the role of time-varying reporting rate in epidemiological modelling

The role of epidemiological models is crucial for informing public health officials during a public health emergency, such as the COVID-19 pandemic. However, traditional epidemiological models fail to capture the time-varying effects of mitigation strategies and do not account for under-reporting of active cases, thus introducing bias in the estimation of model parameters. To infer more accurate parameter estimates and to reduce the uncertainty of these estimates, we extend the SIR and SEIR epidemiological models with two time-varying parameters that capture the transmission rate and the rate at which active cases are reported to health officials. Using two real data sets of COVID-19 cases, we perform Bayesian inference via our SIR and SEIR models with time-varying transmission and reporting rates and via their standard counterparts with constant rates; our approach provides parameter estimates with more realistic interpretation, and 1-week ahead predictions with reduced uncertainty. Furthermore, we find consistent under-reporting in the number of active cases in the data that we consider, suggesting that the initial phase of the pandemic was more widespread than previously reported.

42 ENGINEERING↗