A UK framework for the assessment and integration of different scientific evidence streams in chemical risk assessment.

BACKGROUND: Few methods are available for transparently combining different evidence streams for chemical risk assessment to reach an integrated conclusion on the probability of causation. Hence, the UK Committees on Toxicity (COT) and on Carcinogenicity (COC) have reviewed current practice and developed guidance on how to achieve this in a transparent manner, using graphical visualisation. METHODS/APPROACH: All lines of evidence, including toxicological, epidemiological, new approach methodologies, and mode of action should be considered, taking account of their strengths/weaknesses in their relative weighting towards a conclusion on the probability of causation. A qualitative estimate of the probability of causation is plotted for each line of evidence and a combined estimate provided. DISCUSSION/CONCLUSIONS: Guidance is provided on integration of multiple lines of evidence for causation, based on current best practice. Qualitative estimates of probability for each line of evidence are plotted graphically. This ensures a deliberative, consensus conclusion on likelihood of causation is reached. It also ensures clear communication of the influence of the different lines of evidence on the overall conclusion on causality. Issues on which advice from the respective Committees is sought varies considerably, hence the guidance is designed to be sufficiently flexible to meet this need.

Leveraging data to support health equity in an integrated delivery and finance system.

Introduction: To accelerate healthcare transformation and advance health equity, scientists in learning health systems (LHSs) require ready access to integrated, comprehensive data that includes information on social determinants of health (SDOH). Methods: We describe how an integrated delivery and finance system leveraged its learning ecosystem to advance health equity through (a) a cross-sector initiative to integrate healthcare and human services data for better meeting clients' holistic needs and (b) a system-level initiative to collect and use patient-reported SDOH data for connecting patients to needed resources. Results: Through these initiatives, we strengthened our health system's capacity to meet diverse patient needs, address health disparities, and improve health outcomes. By sharing and integrating healthcare and human services data, we identified 281 000 Shared Services Clients and enhanced care management for 100 adult Medicaid/Special Needs Plan members. Over a 1-year period, we screened 9173 (37%) patients across UPMC's Women's Health Services Line and connected over 700 individuals to social services and supports. Conclusions: Opportunities exist for LHSs to improve, expand, and sustain their innovative data practices. As learnings continue to emerge, LHSs will be well positioned to accelerate healthcare transformation and advance health equity.

Mediation analysis using incomplete information from publicly available data sources.

Our work was motivated by the question whether, and to what extent, well-established risk factors mediate the racial disparity observed for colorectal cancer (CRC) incidence in the United States. Mediation analysis examines the relationships between an exposure, a mediator and an outcome. All available methods require access to a single complete data set with these three variables. However, because population-based studies usually include few non-White participants, these approaches have limited utility in answering our motivating question. Recently, we developed novel methods to integrate several data sets with incomplete information for mediation analysis. These methods have two limitations: (i) they only consider a single mediator and (ii) they require a data set containing individual-level data on the mediator and exposure (and possibly confounders) obtained by independent and identically distributed sampling from the target population. Here, we propose a new method for mediation analysis with several different data sets that accommodates complex survey and registry data, and allows for multiple mediators. The proposed approach yields unbiased causal effects estimates and confidence intervals with nominal coverage in simulations. We apply our method to data from U.S. cancer registries, a U.S.-population-representative survey and summary level odds-ratio estimates, to rigorously evaluate what proportion of the difference in CRC risk between non-Hispanic Whites and Blacks is mediated by three potentially modifiable risk factors (CRC screening history, body mass index, and regular aspirin use).

Detecting management gaps for biodiversity conservation: An integrated assessment.

The latest report on the state of nature in Europe (2013-2018) shows that biodiversity is declining at an alarming rate, with most protected species and habitats in poor condition. Despite an increasing volume of collected biodiversity information, urgent action is needed to integrate biodiversity data and knowledge to improve conservation efforts. We conducted a study in Catalonia (NE Spain), where we collected management measures implemented between 2013 and 2018, including allocation, budget, pressures aimed, and habitats/species potentially benefiting. We integrated information on pressures and habitats/species with the measures to identify non-spatial management gaps. Then, we integrated the spatially explicit information to determine the spatial management gap, identifying geographical areas where species/habitats are under pressure without registered measures. We demonstrated the importance of integrating existing information. Our findings revealed that resources were often not distributed adequately across species/habitats, with biases towards certain taxa being a common issue. The non-spatial management gap analysis identified taxonomic groups, especially plants and mollusks with the wider management gaps. We also identified threatened areas, especially in the northeast of the region with the larger spatial management gaps. These results could guide priority objectives to optimize conservation efforts. Integrating different information sources provided a broader view of the challenges that conservation science is facing nowadays. Our study offers a path toward bending the curve of biodiversity loss by providing an integrative framework that could optimize the use of the available information and help narrow the knowing-doing gap. In the context of the EU, this example demonstrates how information can be used to promote some environmental policy instruments, such as the Prioritized Action Frameworks (PAFs). Additionally, our findings highlight the importance of supporting decision-making with systematic assessments to identify deficiencies in the conservation process, reduce the loss of critical ecosystems and species, and avoid biases among taxa.

Standardizing digital biobanks: integrating imaging, genomic, and clinical data for precision medicine.

Advancements in data acquisition and computational methods are generating a large amount of heterogeneous biomedical data from diagnostic domains such as clinical imaging, pathology, and next-generation sequencing (NGS), which help characterize individual differences in patients. However, this information needs to be available and suitable to promote and support scientific research and technological development, supporting the effective adoption of the precision medicine approach in clinical practice. Digital biobanks can catalyze this process, facilitating the sharing of curated and standardized imaging data, clinical, pathological and molecular data, crucial to enable the development of a comprehensive and personalized data-driven diagnostic approach in disease management and fostering the development of computational predictive models. This work aims to frame this perspective, first by evaluating the state of standardization of individual diagnostic domains and then by identifying challenges and proposing a possible solution towards an integrative approach that can guarantee the suitability of information that can be shared through a digital biobank. Our analysis of the state of the art shows the presence and use of reference standards in biobanks and, generally, digital repositories for each specific domain. Despite this, standardization to guarantee the integration and reproducibility of the numerical descriptors generated by each domain, e.g. radiomic, pathomic and -omic features, is still an open challenge. Based on specific use cases and scenarios, an integration model, based on the JSON format, is proposed that can help address this problem. Ultimately, this work shows how, with specific standardization and promotion efforts, the digital biobank model can become an enabling technology for the comprehensive study of diseases and the effective development of data-driven technologies at the service of precision medicine.

The Status of Data Management Practices Across German Medical Data Integration Centers: Mixed Methods Study.

BACKGROUND: In the context of the Medical Informatics Initiative, medical data integration centers (DICs) have implemented complex data flows to transfer routine health care data into research data repositories for secondary use. Data management practices are of importance throughout these processes, and special attention should be given to provenance aspects. Insufficient knowledge can lead to validity risks and reduce the confidence and quality of the processed data. The need to implement maintainable data management practices is undisputed, but there is a great lack of clarity on the status. OBJECTIVE: Our study examines the current data management practices throughout the data life cycle within the Medical Informatics in Research and Care in University Medicine (MIRACUM) consortium. We present a framework for the maturity status of data management practices and present recommendations to enable a trustful dissemination and reuse of routine health care data. METHODS: In this mixed methods study, we conducted semistructured interviews with stakeholders from 10 DICs between July and September 2021. We used a self-designed questionnaire that we tailored to the MIRACUM DICs, to collect qualitative and quantitative data. Our study method is compliant with the Good Reporting of a Mixed Methods Study (GRAMMS) checklist. RESULTS: Our study provides insights into the data management practices at the MIRACUM DICs. We identify several traceability issues that can be partially explained with a lack of contextual information within nonharmonized workflow steps, unclear responsibilities, missing or incomplete data elements, and incomplete information about the computational environment information. Based on the identified shortcomings, we suggest a data management maturity framework to reach more clarity and to help define enhanced data management strategies. CONCLUSIONS: The data management maturity framework supports the production and dissemination of accurate and provenance-enriched data for secondary use. Our work serves as a catalyst for the derivation of an overarching data management strategy, abiding data integrity and provenance characteristics as key factors. We envision that this work will lead to the generation of fairer and maintained health research data of high quality.

Bringing Micro to the Macro: How Citizen Science Data Enrich Geospatial Visualizations to Advance Health Equity.

Social and spatial contexts affect health, and understanding nuances of context is key to informing successful interventions for health equity. Layering mixed methods and mixed scale data sources to visualize patterns of health outcomes facilitates analysis of both broad trends and person-level experiences across time and space. We used micro-scale citizen scientist-collected data from four Bay Area communities along with aggregate epidemiologic and population-level data sets to illustrate barriers to, and facilitators of, physical activity in low-income aging adults. These data integrations highlight the synergistic value added by combining data sources, and what might be missed by relying on either a micro- or macro-level data source alone. Mixed methods and granularity data integration can generate a deeper understanding of environmental context, which in turn can inform more relevant and attainable community, advocacy, and policy improvements.

An integrated data management and informatics framework for continuous drug product manufacturing processes: A case study on two pilot plants.

The pharmaceutical industry continuously looks for ways to improve its development and manufacturing efficiency. In recent years, such efforts have been driven by the transition from batch to continuous manufacturing and digitalization in process development. To facilitate this transition, integrated data management and informatics tools need to be developed and implemented within the framework of Industry 4.0 technology. In this regard, the work aims to guide the data integration development of continuous pharmaceutical manufacturing processes under the Industry 4.0 framework, improving digital maturity and enabling the development of digital twins. This paper demonstrates two instances where a data integration framework has been successfully employed in academic continuous pharmaceutical manufacturing pilot plants. Details of the integration structure and information flows are comprehensively showcased. Approaches to mitigate concerns in incorporating complex data streams, including integrating multiple process analytical technology tools and legacy equipment, connecting cloud data and simulation models, and safeguarding cyber-physical security, are discussed. Critical challenges and opportunities for practical considerations are highlighted.

Research and Evaluation in a Child-Focused Place-Based Initiative: West Philly Promise Neighborhood.

Place-based initiatives attempt to reduce persistent health inequities through multisectoral, cross-system collaborations incorporating multiple interventions targeted at varying levels from individuals to systems. Evaluations of these initiatives may be thought of as part of the community change process itself with a focus on real-time learning and accountability. We described the design, implementation, challenges, and initial results of an evaluation of the West Philly Promise Neighborhood, which is a comprehensive, child-focused place-based initiative in Philadelphia, Pennsylvania. Priorities for the evaluation were to build processes for and a culture of ongoing data collection, monitoring, and communication, with a focus on transparency, accountability, and data democratization; establish systems to collect data at multiple levels, with a focus on multiple uses of the data and future sustainability; and adhere to grant requirements on data collection and reporting. Data collection activities included the compilation of neighborhood-level indicators; the implementation of a program-tracking system; administrative data linkage; and neighborhood, school, and organizational surveys. Baseline results pointed to existing strengths in the neighborhood, such as the overwhelming majority of caregivers reporting that they read to their young children (86.9%), while other indicators showed areas of need for additional supports and were programmatic focuses for the initiative (e.g., about one-quarter of young children were not engaged in an early childhood education setting). Results were communicated in multiple formats. Challenges included aligning timelines, the measurement of relationship-building and other process-focused outcomes, data and technology limitations, and administrative and legal barriers. Evaluation approaches and funding models that acknowledge the importance of capacity-building processes and allow the development and measurement of population-level outcomes in a realistic timeframe are critical for measuring the success of place-based approaches.

Improving the representativeness of the tribal behavioral risk factor surveillance system through data integration.

BACKGROUND: Previous literature showed significant health disparities between Native American population and other populations such as Non-Hispanic White. Most existing studies for Native American Health were based on non-probability samples which suffer with selection bias. In this paper, we are the first to evaluate the effectiveness of data integration methods, including calibration and sequential mass imputation, to improve the representativeness of the Tribal Behavioral Risk Factor Surveillance System (TBRFSS) in terms of reducing the biases of the raw estimates. METHODS: We evaluated the benefits of our proposed data integration methods, including calibration and sequential mass imputation, by using the 2019 TBRFSS and the 2018 and 2019 Behavioral Risk Factor Surveillance System (BRFSS). We combined the data from the 2018 and 2019 BRFSS by composite weighting. Demographic variables and general health variables were used as predictors for data integration. The following health-related variables were used for evaluation in terms of biases: Smoking status, Arthritis status, Cardiovascular Disease status, Chronic Obstructive Pulmonary Disease status, Asthma status, Cancer status, Stroke status, Diabetes status, and Health Coverage status. RESULTS: For most health-related variables, data integration methods showed smaller biases compared with unadjusted TBRFSS estimates. After calibration, the demographic and general health variables benchmarked with those for the BRFSS. CONCLUSION: Data integration procedures, including calibration and sequential mass imputation methods, hold promise for improving the representativeness of the TBRFSS.

[Characteristics and Risk Assessment of Heavy Metals in the Soil Around Copper Smelting Sites].

Copper smelting can cause heavy metal pollution in surrounding soil and threaten human health. This study examined the characteristics, distribution, and health risk of heavy metals in soil with different land uses around 40 copper smelting sites at home and abroad by collecting published literature data. The results showed that the mean values of ω(As), ω(Cd), ω(Cu), ω(Pb), and ω(Zn) in the soil around the copper smelting sites were 196, 10.5, 1948, 604, and 853 mg·kg-1, respectively. The order of Igeo was Cd(5.63)>Cu(3.88)>As(2.96)>Pb(2.30)>Zn(1.27), and the accumulation of Cd and Cu was the most serious. High Nemero index (NIPI) values were found in the soil around smelting sites with a long history of smelting, outdated process, and insufficient environmental protection measures. Significant correlations were found between the concentrations of heavy metals in the soil, which decreased with the sampling distance. The heavy metals mainly accumulated within 2-3 km from the smelting sites. Compared with the smelting history, scale, and process, land use type had a lower effect on soil heavy metal concentrations. The heavy metals in the soil around copper smelters may pose carcinogenic and non-carcinogenic risks on residents. The high health risks were mainly caused by As and Pb in smelting production areas, and Pb in woodland. These results may guide the risk prevention of heavy metal pollution in the soil around smelting sites.

Harmonizing bifactor models of psychopathology between distinct assessment instruments: Reliability, measurement invariance, and authenticity.

OBJECTIVES: Model configuration is important for mental health data harmonization. We provide a method to investigate the performance of different bifactor model configurations to harmonize different instruments. METHODS: We used data from six samples from the Reproducible Brain Charts initiative (N = 8,606, ages 5-22 years, 41.0% females). We harmonized items from two psychopathology instruments, Child Behavior Checklist (CBCL) and GOASSESS, based on semantic content. We estimated bifactor models using confirmatory factor analysis, and calculated their model fit, factor reliability, between-instrument invariance, and authenticity (i.e., the correlation and factor score difference between the harmonized and original models). RESULTS: Five out of 12 model configurations presented acceptable fit and were instrument-invariant. Correlations between the harmonized factor scores and the original full-item models were high for the p-factor (>0.89) and small to moderate (0.12-0.81) for the specific factors. 6.3%-50.9% of participants presented factor score differences between harmonized and original models higher than 0.5 z-score. CONCLUSIONS: The CBCL-GOASSESS harmonization indicates that few models provide reliable specific factors and are instrument-invariant. Moreover, authenticity was high for the p-factor and moderate for specific factors. Future studies can use this framework to examine the impact of harmonizing instruments in psychiatric research.

A Privacy-Preserving Distributed Medical Data Integration Security System for Accuracy Assessment of Cancer Screening: Development Study of Novel Data Integration System.

BACKGROUND: Big data useful for epidemiological research can be obtained by integrating data corresponding to individuals between databases managed by different institutions. Privacy information must be protected while performing efficient, high-level data matching. OBJECTIVE: Privacy-preserving distributed data integration (PDDI) enables data matching between multiple databases without moving privacy information; however, its actual implementation requires matching security, accuracy, and performance. Moreover, identifying the optimal data item in the absence of a unique matching key is necessary. We aimed to conduct a basic matching experiment using a model to assess the accuracy of cancer screening. METHODS: To experiment with actual data, we created a data set mimicking the cancer screening and registration data in Japan and conducted a matching experiment using a PDDI system between geographically distant institutions. Errors similar to those found empirically in data sets recorded in Japanese were artificially introduced into the data set. The matching-key error rate of the data common to both data sets was set sufficiently higher than expected in the actual database: 85.0% and 59.0% for the data simulating colorectal and breast cancers, respectively. Various combinations of name, gender, date of birth, and address were used for the matching key. To evaluate the matching accuracy, the matching sensitivity and specificity were calculated based on the number of cancer-screening data points, and the effect of matching accuracy on the sensitivity and specificity of cancer screening was estimated based on the obtained values. To evaluate the performance, we measured central processing unit use, memory use, and network traffic. RESULTS: For combinations with a specificity ≥99% and high sensitivity, the date of birth and first name were used in the data simulating colorectal cancer, and the matching sensitivity and specificity were 55.00% and 99.85%, respectively. In the data simulating breast cancer, the date of birth and family name were used, and the matching sensitivity and specificity were 88.71% and 99.98%, respectively. Assuming the sensitivity and specificity of cancer screening at 90%, the apparent values decreased to 74.90% and 89.93%, respectively. A trial calculation was performed using a combination with the same data set and 100% specificity. When the matching sensitivity was 82.26%, the apparent screening sensitivity was maintained at 90%, and the screening specificity decreased to 89.89%. For 214 data points, the execution time was 82 minutes and 26 seconds without parallelization and 11 minutes and 38 seconds with parallelization; 19.33% of the calculation time was for the data-holding institutions. Memory use was 3.4 GB for the PDDI server and 2.7 GB for the data-holding institutions. CONCLUSIONS: We demonstrated the rudimentary feasibility of introducing a PDDI system for cancer-screening accuracy assessment. We plan to conduct matching experiments based on actual data and compare them with the existing methods.

Letter to the Editor: FHIR RDF - Why the world needs structured electronic health records.

This comment discusses the benefits of representing and reusing the information in Electronic Health Record databases as knowledge graphs in the RDF format based on the FHIR RDF specification. As a structured representation of clinical data, FHIR RDF-based electronic health records allow a simpler and more effective integration of biomedical information using semantic alignment, queries, interoperability, and federation to provide better support for health practice and research.

A numerically stable algorithm for integrating Bayesian models using Markov melding.

When statistical analyses consider multiple data sources, Markov melding provides a method for combining the source-specific Bayesian models. Markov melding joins together submodels that have a common quantity. One challenge is that the prior for this quantity can be implicit, and its prior density must be estimated. We show that error in this density estimate makes the two-stage Markov chain Monte Carlo sampler employed by Markov melding unstable and unreliable. We propose a robust two-stage algorithm that estimates the required prior marginal self-density ratios using weighted samples, dramatically improving accuracy in the tails of the distribution. The stabilised version of the algorithm is pragmatic and provides reliable inference. We demonstrate our approach using an evidence synthesis for inferring HIV prevalence, and an evidence synthesis of A/H1N1 influenza.

[Characteristics and Risk Assessment of Heavy Metals in Urban Soils of Major Cities in China].

The rapid urbanization in China may lead to heavy metal pollution in urban soil, threatening the health of residents. By collecting literature data published in the last 15 years, the characteristics and risks of heavy metals in the urban soils of 52 cities in China were analyzed. The results showed that the average ω(Pb), ω(Cd), ω(Cu) and ω(Zn) in the urban soils of China were 58.5, 0.49, 42.1, and 156.3 mg·kg-1, respectively, and the average Igeo values were ordered as follows Cd(1.10) > Zn(0.36) > Pb(0.28) > Cu(0.13). The high concentrations of heavy metals in the urban soils were mainly found in cities located in coastal economically developed provinces (such as Jiangsu, Zhejiang, etc.) and resource-based provinces (such as Hunan, Henan, Inner Mongolia, etc.). The cities of Kaifeng, Yangzhou, Hohhot, Taiyuan, and Xiangtan had relatively high Igeo values for heavy metals in the soils. The concentrations of heavy metals in soils from industrial areas and roadsides were significantly higher than those from residential areas and parks, suggesting that heavy traffic and developed heavy industry were the main causes of heavy metal accumulation in the urban soils. No significant correlations between the average concentrations of heavy metals in urban soil and urban economic and environmental indicators[such as permanent population, GDP, ρ (PM10), ρ(PM2.5), and SO2 emissions] were found. The concentrations of heavy metals in urban soils showed large spatial heterogeneity, and hence the average concentrations may not reflect the overall accumulation level in a city. The non-carcinogenic risks for children posed by heavy metals in urban soils were generally low, and the main risk contributor was Pb. However, the exposure to heavy metals in soils in cities with developed smelting industries is worthy of attention.

Automated credit assessment framework using ETL process and machine learning.

In the current business scenario, real-time analysis of enterprise data through Business Intelligence (BI) is crucial for supporting operational activities and taking any strategic decision. The automated ETL (extraction, transformation, and load) process ensures data ingestion into the data warehouse in near real-time, and insights are generated through the BI process based on real-time data. In this paper, we have concentrated on automated credit risk assessment in the financial domain based on the machine learning approach. The machine learning-based classification techniques can furnish a self-regulating process to categorize data. Establishing an automated credit decision-making system helps the lending institution to manage the risks, increase operational efficiency and comply with regulators. In this paper, an empirical approach is taken for credit risk assessment using logistic regression and neural network classification method in compliance with Basel II standards. Here, Basel II standards are adopted to calculate the expected loss. The required data integration for building machine learning models is done through an automated ETL process. We have concluded this research work by evaluating this new methodology for credit risk assessment.

Combining chains of Bayesian models with Markov melding.

A challenge for practitioners of Bayesian inference is specifying a model that incorporates multiple relevant, heterogeneous data sets. It may be easier to instead specify distinct submodels for each source of data, then join the submodels together. We consider chains of submodels, where submodels directly relate to their neighbours via common quantities which may be parameters or deterministic functions thereof. We propose chained Markov melding, an extension of Markov melding, a generic method to combine chains of submodels into a joint model. One challenge we address is appropriately capturing the prior dependence between common quantities within a submodel, whilst also reconciling differences in priors for the same common quantity between two adjacent submodels. Estimating the posterior of the resulting overall joint model is also challenging, so we describe a sampler that uses the chain structure to incorporate information contained in the submodels in multiple stages, possibly in parallel. We demonstrate our methodology using two examples. The first example considers an ecological integrated population model, where multiple data sets are required to accurately estimate population immigration and reproduction rates. We also consider a joint longitudinal and time-to-event model with uncertain, submodel-derived event times. Chained Markov melding is a conceptually appealing approach to integrating submodels in these settings.

Multiomics and digital monitoring during lifestyle changes reveal independent dimensions of human biology and health.

We explored opportunities for personalized and predictive health care by collecting serial clinical measurements, health surveys, genomics, proteomics, autoantibodies, metabolomics, and gut microbiome data from 96 individuals who participated in a data-driven health coaching program over a 16-month period with continuous digital monitoring of activity and sleep. We generated a resource of >20,000 biological samples from this study and a compendium of >53 million primary data points for 558,032 distinct features. Multiomics factor analysis revealed distinct and independent molecular factors linked to obesity, diabetes, liver function, cardiovascular disease, inflammation, immunity, exercise, diet, and hormonal effects. For example, ethinyl estradiol, a common oral contraceptive, produced characteristic molecular and physiological effects, including increased levels of inflammation and impact on thyroid, cortisol levels, and pulse, that were distinct from other sources of variability observed in our study. In total, this work illustrates the value of combining deep molecular and digital monitoring of human health. A record of this paper's transparent peer review process is included in the supplemental information.

Cost evaluation of integrating local HIV and housing data to facilitate service coordination in four demonstration sites.

As part of the evaluation of a federal initiative to integrate HIV medical and housing data at four local jurisdictions in the U.S., we estimated the financial costs of implementing data integration occurring from June 2016 to August 2018. We collected data on labor, non-labor, and overhead expenses based on invoices and surveys of staff time, staff compensation, and non-labor expenses. Non-labor expenses were directly charged or allocated to the project using the number of full-time equivalents as the allocation basis. Reported indirect cost rates were used to estimate overhead expenses. Demonstration sites spent an average of $273,656 over the full 27-month period, with an average monthly spending of $10,010 in 2018 U.S. dollars. There was sizable variation in the data integration costs across sites, implementation phases, and data integration models. Findings may help policymakers and potential adopters of similar data integration efforts customize parameters for local conditions and estimate resources required.