Multiple Data Sources Research Articles

Exploring Journey Maps as Products From Qualitative Research: Application Through Food Insecure Veterans' Experiences.

Journey maps are graphic representations of participant, user, customer, or patient experiences or "journeys" with a particular phenomenon, product, business, or organization. Journey maps help visualize complex pathways and phases in accessible, digestible ways. They also capture emotions, reactions, and values associated with the processes participants undergo, complemented by images or quotes from participants. Here, we outline the foundations of journey maps in research and in practice settings. Our goal is to describe journey maps to researchers new to the product and emphasize the novelty and utility of journey maps as visual products from qualitative research particularly in a health setting. To explore journey maps-including their benefits, drawbacks, and relevance-we discuss examples including our own process for designing a journey map of food insecure Veterans' experiences using qualitative, in-depth interviews and supported by member checking. Our journey map depicts food insecurity as a repetitive process, a unique contribution given that many journey maps are designed with discrete starting and stopping points. We conclude by discussing the novelty of journey maps as innovative products that researchers can use to identify opportunities for process improvements and innovation using multiple data sources or methods.

B-110 Descriptive Analysis of Multiple Myeloma Participants in the NIH All of Us Research Program Cohort: Insights into Demographics Disparities

Abstract Background Multiple Myeloma (MM) is a complex hematological malignancy characterized by significant heterogeneity in participant characteristics and treatment responses. The All of Us Research Program seeks to enroll 1 million diverse participants who share multiple sources of data, providing unique opportunities for research. A comprehensive descriptive analysis of MM participants within Electronic Health Records (EHR) databases can provide valuable insights into the disease's epidemiology, participant demographics, comorbidities, and treatment outcomes. Objective Using AI/ML predictive analytics, this study conducted a detailed descriptive analysis of MM participants’ EHR data in the All of Us Research Program cohort, which is enrolling 1 million diverse participants who share multiple sources of data. Methods We extracted data for participants diagnosed with MM from an extensive EHR database. The analysis included participant demographics (age, gender, race, ethnicity), diagnostic laboratory tests (SPEP, UPEP, CBC, renal function), comorbidities (renal impairment, bone disease, anemia, cardiovascular diseases), treatment details (chemotherapy, stem cell transplantation), and genetic markers (cytogenetic abnormalities). Results The analysis revealed diverse demographic patterns and a wide range of comorbidities associated with MM (Table 1). Conclusions The analysis demonstrated marked disparities in MM prevalence among African Americans and “More than one race,” revealing social and medical disparities. Implications: By understanding the diverse profiles of MM participants, stakeholders can more effectively address disparities in healthcare outcomes (1). Reference: 1. Zeng, C., Schlueter, D. J., Tran, T. C., Babbar, A., Cassini, T., Bastarache, L. A., & Denny, J. C. (2024). Comparison of phenomic profiles in the All of Us Research Program against the US general population and the UK Biobank. Journal of the American Medical Informatics Association. Retrieved from, https://doi.org/10.1093/jamia/ocad260

B-111 Advancing Precision Medicine in Multiple Myeloma: Addressing Demographic Variabilities and Imbalanced Data in the NIH All of Us Research Program Cohort

Abstract Background The diagnosis of multiple myeloma (MM), a complex hematologic malignancy, presents unique challenges in prognostication and treatment planning, exacerbated by disparities across demographic groups and imbalanced datasets. The heterogeneity observed in MM's clinical manifestations and outcomes necessitates a nuanced approach to patient care, emphasizing the need for models that accurately predict long-term health outcomes across the diverse spectrum of MM participants. Given the variable response to treatment observed among different demographic cohorts, achieving precision in treatment strategies and ensuring equitable healthcare provision is paramount. Objective The objective of this study was to develop and assess predictive models tailored for various demographic groups of participants diagnosed with MM in the All of Us Research Program cohort, which is enrolling 1 million diverse participants who share multiple sources of data. Methods The Synthetic Minority Over-Sampling Technique (SMOTE) was used to rectify the prevalent imbalances within datasets, thereby significantly enhancing the predictive accuracy of patient outcomes over a 1-year post-diagnosis period. By addressing these imbalances, we provide a cornerstone for the development of more personalized and effective treatment paradigms. Employing a comprehensive dataset extracted from electronic health records (EHRs), we segmented participants based on key demographic variables such as age, gender, race, and socioeconomic status. This segmentation included detailed analyses of MM diagnosis, treatment regimens, and subsequent patient responses. To tackle the issue of dataset imbalance, two sets of predictive models were meticulously crafted for each demographic segment: one utilized the original dataset distributions and the other leveraged datasets balanced with SMOTE. A suite of performance metrics, including accuracy, precision, recall, Area Under the Curve (AUC), and F1 score, were deployed to evaluate and compare the efficacy of these models. Models trained on SMOTE-balanced datasets demonstrated marked improvements in prediction accuracy, from a baseline of 75% to over 90%. Precision rates climbed to 88%, and recall rates reached 90%, with an AUC of 0.95 and an F1 score of 0.89, highlighting the models' enhanced predictive capabilities. Results The application of SMOTE to balance datasets resulted in significant predictive accuracy improvements for minority groups within the MM patient population. This was especially evident in demographic cohorts historically underrepresented in clinical research, showcasing the potential of SMOTE in creating more equitable treatment outcomes. Conclusions Our findings underscore the potential of employing tailored predictive models in refining prognosis and treatment planning for multiple myeloma, particularly by addressing data imbalances. The integration of SMOTE into our methodology validates its efficacy in enhancing prediction accuracy, setting a new standard for oncological research. Implications Advocating for a more sophisticated and inclusive approach to data handling, our research contributes to a paradigm shift in oncological research, especially within hematological malignancies like MM. This study is poised to revolutionize predictive modeling in oncology, fostering more informed clinical decision-making and advancing the goals of precision medicine.

FOUNTAIN: a modular research platform for integrated real-world evidence generation

BackgroundReal-world evidence (RWE) plays a key role in regulatory and healthcare decision-making, but the potentially fragmentated nature of generated evidence may limit its utility for clinical decision-making. Heterogeneity and a lack of reproducibility in RWE resulting from inconsistent application of methodologies across data sources should be minimized through harmonization.MethodsThis paper’s aim is to describe and reflect upon a multidisciplinary research platform (FOUNTAIN; FinerenOne mUlti-database NeTwork for evidence generAtIoN) with coordinated studies using diverse RWE generation approaches and explore the platform’s strengths and limitations. With guidance from an executive advisory committee of multidisciplinary experts and patient representatives, the goal of the FOUNTAIN platform is to harmonize RWE generation across a portfolio of research projects, including research partner collaborations and a common data model (CDM)–based program. FOUNTAIN’s overarching objectives as a research platform are to establish long-term collaborations among pharmacoepidemiology research partners and experts and to integrate diverse approaches for RWE generation, including global protocol execution by research partners in local data sources and common protocol execution in multiple data sources through federated data networks, while ensuring harmonization of medical definitions, methodology, and reproducible artifacts across all studies. Specifically, the aim of the multiple studies run within the frame of FOUNTAIN is to provide insight into the real-world utilization, effectiveness, and safety of finerenone across its life-cycle.ResultsCurrently, the FOUNTAIN platform includes 9 research partner collaborations and 8 CDM-mapped data sources from 7 countries (United States, United Kingdom, China, Japan, The Netherlands, Spain, and Denmark). These databases and research partners were selected after a feasibility fit-for-purpose evaluation. Six multicountry, multidatabase, cohort studies are ongoing to describe patient populations, current standard of care, comorbidity profiles, healthcare resource use, and treatment effectiveness and safety in different patient populations with chronic kidney disease and type 2 diabetes. Strengths and potential limitations of FOUNTAIN are described in the context of valid RWE generation.ConclusionThe establishment of the FOUNTAIN platform has allowed harmonized execution of multiple studies, promoting consistency both within individual studies that employ multiple data sources and across all studies run within the platform’s framework. FOUNTAIN presents a proposal to efficiently improve the consistency and generalizability of RWE on finerenone.

Patients' and health professionals' perspectives regarding shared decision making in the psychiatric inpatient setting – A multiple qualitative case study

ObjectiveShared decision-making is one promising approach to promoting recovery and person-centred care but seems challenging for implementation in clinical practice. This study aimed to explore how patients and health professionals experience shared decision-making and its facilitators and barriers. MethodsA multiple qualitative case study design was chosen, using a constant comparative method. Multiple data sources were used, including individual interviews, observation, document analysis and a focus group. ResultsThrough first a within-case analysis and then second a cross-case analysis, four patient profiles and their potential for shared decision-making were constructed. The results indicate that in the daily routine of the psychiatric inpatient setting different forms of decision making are used, even though health professionals advocate shared decision-making as the favored approach. Patients also have varying expectations and perceptions regarding shared decision-making, which is reflected in the degree of their involvement. ConclusionShared decision-making could be enhanced in the future by a more proactive communication style and the proactive provision of information on the part of health professionals, in order to enhance patient participation in decision-making. InnovationThe study identified different forms of decision-making within the acute psychiatric inpatient setting, highlighting the gap between the advocated SDM approach and its practical implementation. This divergence is a key aspect, as it underlines the complexity of implementing SDM in real clinical settings.

FLAV: Federated Learning for Autonomous Vehicle privacy protection

Autonomous Vehicle Systems are committed to safer, more efficient and more convenient transportation on the roads of the future. However, concerns about vehicle data privacy and security remain significant. Federated Learning, as a decentralized machine learning approach, allows multiple devices or data sources to collaboratively train models without sharing raw data, providing essential privacy protection. In this paper, we propose a privacy-preserving framework for autonomous vehicles, named FLAV. First, we use a multi-chain parallel aggregation strategy to transmit model parameters and design a model parameter filtering mechanism, which reduces communication overhead by filtering out the local model parameters of certain vehicles, thereby alleviating bandwidth pressure. Second, we introduce a dynamic adjustment mechanism that automatically adjusts the regularization strength in the loss function by comparing the differences between each vehicle’s local model parameters and the cumulative parameters from the preceding vehicles in the chain. This mechanism effectively balances local model training with global model consistency, ensuring the model’s adaptability to local data while enhancing the coordination of models between vehicles in the chain. Experimental results demonstrate that our proposed method reduces communication costs while improving model accuracy and privacy protection, effectively ensuring the security of autonomous driving data.

Preliminary Guidelines for Combining Data Integration and Visual Data Analysis.

Data integration is often performed to consolidate information from multiple disparate data sources during visual data analysis. However, integration operations are usually separate from visual analytics operations such as encode and filter in both interface design and empirical research. We conducted a preliminary user study to investigate whether and how data integration should be incorporated directly into the visual analytics process. We used two interface alternatives featuring contrasting approaches to the data preparation and analysis workflow: manual file-based ex-situ integration as a separate step from visual analytics operations; and automatic UI-based in-situ integration merged with visual analytics operations. Participants were asked to complete specific and free-form tasks with each interface, browsing for patterns, generating insights, and summarizing relationships between attributes distributed across multiple files. Analyzing participants' interactions and feedback, we found both task completion time and total interactions to be similar across interfaces and tasks, as well as unique integration strategies between interfaces and emergent behaviors related to satisficing and cognitive bias. Participants' time spent and interactions revealed that in-situ integration enabled users to spend more time on analysis tasks compared with ex-situ integration. Participants' integration strategies and analytical behaviors revealed differences in interface usage for generating and tracking hypotheses and insights. With these results, we synthesized preliminary guidelines for designing future visual analytics interfaces that can support integrating attributes throughout an active analysis process.

Reexamining exposure from truck emissions considering daily movement of individuals

With the growing demand for urban freight transportation, trucks emit a large amount of pollutants such as particulate matters and nitrogen oxides, increasingly affecting public health. This study establishes a modelized air dispersion structure to simulate pollutant concentration distribution. By integrating multiple data sources including mobile phone signals and satellite images, we reconstruct the daily trajectories of individuals and further incorporate simulated pollution concentrations in calculating dynamic and static exposure of individuals to truck emissions. Econometric models considering spatial dependence are developed to evaluate the influencing factors and elucidate the mechanisms of pollutant exposure. Results show factors including freight demand, road network, residential and employment locations, personal commuting distance, and population age structure matter in assessing truck emission exposure. As a result, a mixture of vehicular emission standards, urban traffic control, land planning, and industrial policies is proposed to reduce truck pollutant exposure and safeguard public health.

Re-emergence of Oropouche virus between 2023 and 2024 in Brazil: an observational epidemiological study

Oropouche virus is an arthropod-borne virus that has caused outbreaks of Oropouche fever in central and South America since the 1950s. This study investigates virological factors contributing to the re-emergence of Oropouche fever in Brazil between 2023 and 2024. In this observational epidemiological study, we combined multiple data sources for Oropouche virus infections in Brazil and conducted in-vitro and in-vivo characterisation. We collected serum samples obtained in Manaus City, Amazonas state, Brazil, from patients with acute febrile illnesses aged 18 years or older who tested negative for malaria and samples from people with previous Oropouche virus infection from Coari municipality, Amazonas state, Brazil. Basic clinical and demographic data were collected from the Brazilian Laboratory Environment Management System. We calculated the incidence of Oropouche fever cases with data from the Brazilian Ministry of Health and the 2022 Brazilian population census and conducted age-sex analyses. We used reverse transcription quantitative PCR to test for Oropouche virus RNA in samples and subsequently performed sequencing and phylogenetic analysis of viral isolates. We compared the phenotype of the 2023-24 epidemic isolate (AM0088) with the historical prototype strain BeAn19991 through assessment of titre, plaque number, and plaque size. We used a plaque reduction neutralisation test (PRNT50) to assess the susceptibility of the novel isolate and BeAn19991 isolate to antibody neutralisation, both in serum samples from people previously infected with Oropouche virus and in blood collected from mice that were inoculated with either of the strains. 8639 (81·8%) of 10 557 laboratory-confirmed Oropouche fever cases from Jan 4, 2015, to Aug 10, 2024, occurred in 2024, which is 58·8 times the annual median of 147 cases (IQR 73-325). Oropouche virus infections were reported in all 27 federal units, with 8182 (77·5%) of 10 557 infections occurring in North Brazil. We detected Oropouche virus RNA in ten (11%) of 93 patients with acute febrile illness between Jan 1 and Feb 4, 2024, in Amazonas state. AM0088 had a significantly higher replication at 12 h and 24 h after infection in mammalian cells than the prototype strain. AM0088 had a more virulent phenotype than the prototype in mammalian cells, characterised by earlier plaque formation, between 27% and 65% increase in plaque number, and plaques between 2·4-times and 2·6-times larger. Furthermore, serum collected on May 2 and May 20, 2016, from individuals previously infected with Oropouche virus showed at least a 32-fold reduction in neutralising capacity (ie, median PRNT50 titre of 640 [IQR 320-640] for BeAn19991 vs <20 [ie, below the limit of detection] for AM0088) against the reassortant strain compared with the prototype. These findings provide a comprehensive assessment of Oropouche fever in Brazil and contribute to an improved understanding of the 2023-24 Oropouche virus re-emergence. Our exploratory in-vitro data suggest that the increased incidence might be related to a higher replication efficiency of a new Oropouche virus reassortant for which previous immunity shows lower neutralising capacity. São Paulo Research Foundation, Burroughs Wellcome Fund, Wellcome Trust, US National Institutes of Health, and Brazilian National Council for Scientific and Technological Development. For the Portuguese translation of the abstract see Supplementary Materials section.

Inferring Covariance Structure from Multiple Data Sources via Subspace Factor Analysis

Factor analysis provides a canonical framework for imposing lower-dimensional structure such as sparse covariance in high-dimensional data. High-dimensional data on the same set of variables are often collected under different conditions, for instance in reproducing studies across research groups. In such cases, it is natural to seek to learn the shared versus condition-specific structure. Existing hierarchical extensions of factor analysis have been proposed, but face practical issues including identifiability problems. To address these shortcomings, we propose a class of SUbspace Factor Analysis (SUFA) models, which characterize variation across groups at the level of a lower-dimensional subspace. We prove that the proposed class of SUFA models lead to identifiability of the shared versus group-specific components of the covariance, and study their posterior contraction properties. Taking a Bayesian approach, these contributions are developed alongside efficient posterior computation algorithms. Our sampler fully integrates out latent variables, is easily parallelizable and has complexity that does not depend on sample size. We illustrate the methods through application to integration of multiple gene expression datasets relevant to immunology.

A landscape analysis of investors in the global breast milk substitute industry to target for advocacy.

The International Code of Marketing of Breast-milk Substitutes and subsequent resolutions (the Code) was adopted to address increases in mortality and morbidity resulting from the practices of the breast-milk substitute (BMS) industry. The lack of success in ensuring company compliance with the Code has prompted advocates to consider engaging with investors to shape the governance of BMS companies. To support these efforts, this paper aimed to identify prominent investors in the global BMS industry and explore their Code-related policies and practices. Using multiple methods and data sources, we developed a novel approach to identify and rank investors in the world's leading publicly listed BMS companies. We also examined the policies and voting behaviour of a sample of investors using publicly accessible materials from 2020 to 2022. We found that a small number of large investors, led by BlackRock and Vanguard, hold a substantial share in the global BMS industry. Of the top-10 ranked investors, only Norway's Government Pension Fund (NBIM) reported policy information relating specifically to BMS marketing. Most of these large investors also opposed the sample of public health-related shareholder proposals analysed. In addition, we identified several investors that have reported engaging with BMS companies on Code-related issues, including NBIM, Pictet, and UBS, along with several potential investor targets for future advocacy efforts, including some North American public pension funds. The inclusion of Code-related issues as part of broader policies, disclosures and regulations related to environmental, social and governance oriented investment warrants increased attention.

An Optimal Multi-Modal Approach for Stock Market Price Forecasting with Fused Sentiment Analysis for Real Time Data

Abstract: This study presents an innovative fusion-based methodology that integrates real-time stock market technical indicators with news sentiment analysis from financial news feeds to enhance stock selection decisions. The proposed framework employs a Bidirectional Long Short-Term Memory (Bi-LSTM) model for forecasting stock prices and a Deep Neural Network (DNN) used in conjunction with transformer-based model for sentiment classification, both optimized through the incorporation of real-time datasets. To further refine feature selection, Artificial Bee Colony (ABC) and Firefly algorithms are utilized, significantly improving the accuracy of price trend predictions and sentiment analysis. Technical indicators from stock market data are processed using the Bi-LSTM model to predict future stock prices. Concurrently, sentiment data from the Economic Times is pre-processed through Term Frequency-Inverse Document Frequency (TF-IDF) vectorization and pre-trained transformer models to extract key sentiment scores. The ABC algorithm enhances textual feature selection, while the Firefly algorithm reduces skewness in the error distribution of technical indicators, thereby improving the alignment between forecasted and actual stock prices. The fusion mechanism integrates predicted price movements with sentiment analysis to provide actionable insights for stock selection. The decision-making framework classifies stocks based on the consistency between price movements and sentiment: stocks predicted to rise with positive sentiment are labelled as "Sure Selection"; rising prices with negative sentiment are categorized as "Dicey Selection"; declining prices with positive sentiment result in a "Risky Selection"; and negative sentiment with falling prices trigger an "Avoid Selection". The proposed approach achieved a prediction accuracy of 67.00% for the Bi-LSTM model, with a precision of 0.714, recall of 0.695, and an F1 score of 0.677, demonstrating the model's robustness. The mean absolute error (MAE) for the Bi-LSTM model was 4.30, indicating strong predictive performance. The combined use of ABC and Firefly algorithms optimizes both technical and sentiment features, significantly contributing to the overall performance of the prediction model. By fusing multiple data sources and employing advanced optimization techniques, this methodology offers a unique and effective solution for stock market prediction and decision-making

Critical elements of successful partnership in public health-A case study.

Intersectoral action is integral in promoting population health and health equity. In South Australia, public health partnership authorities (PHPAs) are one mechanism to promote intersectoral collaboration for addressing complex public health issues. This case study examined contemporary evidence on the critical elements of successful partnerships, explored the role of formalisation in success, and examined how identified critical elements were operationalised over time in an existing PHPA. In the first phase, a scoping review was conducted. The second phase was a retrospective longitudinal case study of a government-academic PHPA agreement, involving partnership document review and key informant interviews. Data triangulation and iterative thematic analysis were performed. The critical elements of successful partnership, triangulated through multiple data sources, are mutuality, governance, communication, resources, leadership, and interactions and relationships. These elements have a high degree of interdependence. Trust supports and is reinforced by these elements, particularly during times of change or crisis. Formalisation of a partnership supports success in many ways but is insufficient on its own. Operating in dynamic environments, a partnership requires dedicated time and effort to be successful. Attention to the strength and interconnectedness of the critical elements over the course of a partnership is crucial. SO WHAT?: This case study describes how the elements of successful partnership can be applied to progress beyond transactional relationships towards authentic collaboration and overcome cross-border and cross-boundary public health challenges to promote population health and well-being.

A Multiple Targets ISAR Imaging Method with Removal of Micro-Motion Connection Based on Joint Constraints

Combining multiple data sources, Digital Earth is an integrated observation platform based on air–space–ground–sea monitoring systems. Among these data sources, the Inverse Synthetic Aperture Radar (ISAR) is a crucial observation method. ISAR is typically utilized to monitor both military and civilian ships due to its all-day and all-weather superiority. However, in complex scenarios, multiple targets may exist within the same radar antenna beam, resulting in severe defocusing due to different motion conditions. Therefore, this paper proposes a multiple-target ISAR imaging method with the removal of micro-motion connections based on the integration of joint constraints. The fully motion-compensated targets exhibit low rank and local similarity in the high-resolution range profile (HRRP) domain, while the micro-motion components possess sparsity. Additionally, targets display sparsity in the image domain. Inspired by this, we formulate a novel optimization by promoting the low-rank, the Laplacian, and the sparsity constraints of targets and the sparsity constraints of the micro-motion components. This optimization problem is solved by the linearized alternative direction method with adaptive penalty (LADMAP). Furthermore, the different motions of various targets degrade their inherent characteristics. Therefore, we integrate motion compensation transformation into the optimization, accordingly achieving the separation of rigid bodies and the micro-motion components of different targets. Experiments based on simulated data demonstrate the effectiveness of the proposed method.

Adolescence in the life course spotlight.

The articles in this special issue were inspired by the late John Schulenberg's long view of adolescence, a perspective that emphasizes the integral role that the teens and twenties play in the life course. Using multiple longitudinal data sources to explore myriad developmental topics, the authors delve into the ways that adolescence connects, disrupts, and stands out from childhood and adulthood as a means of integrating rather than isolating these developmentally dense years. In this commentary, I highlight what this collection of studies does to drive home some basic tenets of the long view of adolescence and point out some other tenets that should garner more attention moving forward. Specifically, I discuss the need to connect multiple periods of life before and after adolescence, voice some caution about not letting the long view of adolescence keep us from continuing our in-depth exploration of adolescence itself, and encourage more macro-level conceptualizations of context to go with more common micro- and meso-level connections in order to better interrogate inequality. John's career has taught us a great deal about how to think about adolescent development, and just because he left us too soon does not mean that we will stop learning.

Comparing Confirmed Sodium Nitrite Suicide Deaths With Poison Center Surveillance Estimates

Sodium nitrite is a curing agent increasingly used for self-harm and suicide, and multiple locales have reported increasing cases. However, approaches to forensic investigation of these cases are not standardized, and current modes of surveillance in the United States may be inadequate. To define a potential standard approach for identifying sodium nitrite deaths based on forensic confirmation, and compare findings based on this standard to poison center surveillance. This retrospective case series of sodium nitrite exposures and deaths was conducted in 2 urban medical examiner jurisdictions in New York State from 2000 to 2022. The population-based sample included individuals from (1) medical examiner reports of decedents where the cause of death was sodium nitrite and (2) poison center reports of intentional exposures to sodium nitrite. Sodium nitrite as either cause of death (medical examiner reports) or intentional exposure (poison center reports). Medical examiner determination of sodium nitrite deaths was considered the criterion standard and relied largely on confirmatory blood nitrite testing. Poison center records were assessed for intentional exposures to sodium nitrite. In this case series of 36 decendents, median (range) age was 28 (20-57) years; 23 (63.8%) were male; 6 (16.7%) were African-American, 5 (13.9%) were Chinese, 13 (36.1%) were White, and 4 (11.1%) had unknown race; and 6 (16.7%) were Hispanic. No deaths were found from 2000 to 2018, and yearly increases in deaths from 2019 to 2022; these deaths were largely missed by local poison center surveillance. Most cases (83.3% [n = 30]) had postmortem blood nitrite concentrations available, and multiple decedents had evidence of suicide kit recommendations from internet sources. In this case series of decedents in 2 New York medical examiner jurisdictions, sodium nitrite deaths increased yearly, and the medical examiners were able to obtain confirmatory nitrite concentrations in most cases. These findings suggest that poison center surveillance underestimates confirmed deaths from sodium nitrite; public health authorities should rely on multiple data sources when analyzing this problem, and forensic analyses should be standardized.

Abstract A131: A transdisciplinary approach to redefining mammographic quality to address disparities

Abstract Breast cancer mortality rates and stage of diagnosis in Wisconsin continue to improve, but only for some. Using learnings from a statewide mammography project in Illinois, the Mammographic Quality Initiative (MQI) studies the mammographic process in Wisconsin to understand the factors that contribute to quality, create a shared measurement system to monitor and improve quality, and reduce disparities in early-stage diagnoses and breast cancer survival rates across the state. MQI is a project of the Community and Cancer Science Network (CCSN) at the Medical College of Wisconsin Cancer Center that brings together transdisciplinary teams made up of academic medicine and non-academic members to develop new research and action agendas to address disparities. Core to the CCSN transdisciplinary (TD) approach are three values–deep equity, systems thinking, and understanding of the issue from biology to policy. When CCSN TD teams come together and apply these values, new ways of seeing the problems that underpin disparities are surfaced and innovative solutions emerge. While building of shared measurement is not new, applying this approach to defining what gets measured, and applying it to mammography, is novel. MQI is using a multilevel model of transdisciplinary engagement. A statewide leadership team coordinates an initial study of mammographic quality and convenes regional learning collaboratives (RLCs) to examine quality and ways to improve. Its Southeastern WI RLC began meeting two years ago and is triangulating multiple sources of qualitative data, including lived experience, expert perspectives, and research, to help determine which factors should be included in a measurement of quality. MQI gathers data using multiple mediums to encourage sharing of knowledge and experiences, including facilitated meetings, literature reviews, surveys, community and survivor world cafes, and crowdsourcing. Learnings highlight that considering only one sector’s perspective results in an incomplete definition of quality and set of measures. Data shows that providers emphasize quantitative measures of the technical aspects of mammography, while navigators and patients emphasize systems conditions and justice orientations that shape the process. Existing definitions and measures of quality readily assess explicit practices, programs, and policies, but understanding additional factors—interpersonal interactions and power dynamics, and mental models around women in health care—offer opportunity to root deeper into causes and potential quality improvement solutions that have not yet been explored in mammography. The CCSN TD approach helps build empathy across perspectives, spotlights the contradictions inherent in provider and patient experiences, highlights enablers and barriers in the system itself, and importantly—lays bare when we are working at the surface, so that teams can dive deeper into solutions at semi-explicit and transformational levels of change (FSG Six Conditions of Systems Change). Citation Format: Kelly Hackett, Laura Pinsoneault, Tobi Cawthra, Bridgid Chanen, Tommi Thompson, Beth Brunner, Gale Johnson, Kristin Troy, Kara Gavin, Vaia Makris, Purushottam Laud, Melinda Stolley, Joan Neuner. A transdisciplinary approach to redefining mammographic quality to address disparities [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr A131.

Soil moisture content estimation of drip-irrigated citrus orchard based on UAV images and machine learning algorithm in Southwest China

Soil moisture content (SMC), as a pivotal component in the energy and matter exchange processes within the soil-plant-atmosphere continuum, plays a crucial role in surface water dynamics, energy fluxes, and carbon cycling within ecosystems. The development of remote sensing technology has offered new perspectives for monitoring soil moisture at regional scales. Unmanned aerial vehicles (UAV) equip with multispectral have distinct advantages for vegetation monitoring, including rapidity and cost-effectiveness, which has superior applicability and practicality. Therefore, in a 5a "Daya" late-maturing citrus orchard, the vegetation index (VI) and texture feature (TF) information of citrus canopy based on UAV multi-spectral images were extracted, and soil and plant analyzer development (SPAD) of citrus was collected. These different data sources were integrated into the framework of the random forest algorithm (RF) and genetic algorithm-optimized random forest (GA-RF) to evaluate the accuracy of surface SMC (SSMC) estimation in citrus orchard. The Biswas model was utilized to simulate the root zone SMC (RSMC). The spatiotemporal variations of SMC in citrus orchard were analyzed, and the potential of low-cost sensor-equipped drones in rapidly acquiring spatial and temporal distribution information of SMC at a large regional scale was explored. The results indicated that the GA-RF models outperformed the RF models in estimating citrus orchard SMC (with R2 ranging from 0.502 to 0.949 and RMSE ranging from 0.552 % to 3.166 % for GA-RF, compared to R2 ranging from 0.430 to 0.936 and RMSE ranging from 0.587 % to 3.449 % for the RF). The GA-RF models using VI+SPAD as inputs exhibited the best performance for SMC at depths of 5 cm, 10 cm, 20 cm and 40 cm (SMC5, SMC10, SMC20 and SMC40) across citrus growth stages (R2 ranging from 0.793 to 0.949 at 5 cm, R2 ranging from 0.702 to 0.938 at 10 cm, R2 ranging from 0.714 to 0.927 at 20 cm). In bud bust to flowering, young fruit and fruit maturation stages (stage Ⅰ, ⅠⅠ and ⅠⅤ), all models demonstrated good accuracy in estimating SMC at depth of 10 cm (R2 ranging from 0.567 to 0.908 in stage Ⅰ, with R2 ranging from 0.681 to 0.916 in stage ⅠⅠ and R2 ranging from 0.579 to 0.938 in stage ⅠⅤ). In fruit expansion stage (stage III), the models performed best in predicting SMC5 (R2 ranging from 0.698 to 0.861). The Biswas model was constructed to simulate SMC40 by utilizing the inverted SMC10 and SMC20, thereby generating spatiotemporal distribution maps of SMC at different depths in citrus orchard. The SSMC was susceptible to environmental factors, exhibiting significant spatiotemporal heterogeneity. In summary, this study illustrated that the integration of multiple data sources into GA-RF enhanced the estimation performance of SMC at different growth stages of late-maturing citrus orchard in the Southwest China. Additionally, it enabled the rapid and efficient monitoring of spatiotemporal variations in SMC, providing an effective method and practical foundation for precision irrigation and improved water use efficiency.

Automating COVID-19 epidemiological situation reports based on multiple data sources, the Netherlands, 2020 to 2023

BackgroundDuring the COVID-19 pandemic, the National Institute for Public Health and the Environment in the Netherlands developed a pipeline of scripts to automate and streamline the production of epidemiological situation reports (epi‑sitrep). The pipeline was developed for the Automation of Data Import, Summarization, and Communication (hereafter called the A-DISC pipeline). ObjectiveThis paper describes the A-DISC pipeline and provides a customizable scripts template that may be useful for other countries wanting to automate their infectious disease surveillance processes. MethodsThe A-DISC pipeline was developed using the open-source statistical software R. It is organized in four modules: Prepare, Process data, Produce report, and Communicate. The Prepare scripts set the working environment (e.g., load packages). The (data-specific) Process data scripts import, validate, verify, transform, save, analyze, and summarize data as tables and figures and store these data summaries. The Produce report scripts gather summaries from multiple data sources and integrate them into a RMarkdown document – the epi‑sitrep. The Communicate scripts send e-mails to stakeholders with the epi‑sitrep. ResultsAs of March 2023, up to ten data sources were automatically summarized into tables and figures by A-DISC. These data summaries were featured in routine extensive COVID-19 epi‑sitreps, shared as open data, plotted on RIVM's website, sent to stakeholders and submitted to European Centre for Disease Prevention and Control via the European Surveillance System -TESSy [38]. DiscussionIn the face of an unprecedented high number of cases being reported during the COVID-19 pandemic, the A-DISC pipeline was essential to produce frequent and comprehensive epi‑sitreps. A-DISC's modular and intuitive structure allowed for the integration of data sources of varying complexities, encouraged collaboration among people with various R-scripting capabilities, and improved data lineage. The A-DISC pipeline remains under active development and is currently being used in modified form for the automatization and professionalization of various other disease surveillance processes at the RIVM, with high acceptance from the participant epidemiologists. ConclusionThe A-DISC pipeline is an open-source, robust, and customizable tool for automating epi‑sitreps based on multiple data sources.

Exploring the mechanisms of underground economy: A case study of the informal bicycle market in a southern China city

This paper aims to explore the typical mechanisms of the underground economic phenomenon through anthropological and grounded theoretical methodology through an ethnographic study on the site of an informal bicycle market in a city in South China. It investigates and analyzes the formation path and mechanism of the informal bicycle market. Data was collected through unobtrusive observation and in-depth interviews. It follows a grounded theory data analysis procedure based on multiple data sources, such as participant observations, interviews, prior literature, and news reports. The model illustrates how the informal bicycle market is formatted and the mechanisms to maintain its operations. The findings indicated that the development of the informal market affects the efficiency of social resource allocation and, in many cases, may erode the overall system of the national economy. The analysis of the formation path and mechanism of the underground economy represented by the informal market is of great significance for the sustainable development of the social economy.