The implications of data aggregation on propagating racial and ethnic disparities within the health care landscape: Actionable recommendations and considerations for pharmacists.

As the U.S. population becomes more racially and ethnically diverse, it is increasingly important to characterize health inequities for targeted intervention. As it stands, demographic data regarding race and ethnicity for patients and pharmacy trainees alike are aggregated into heterogenous population groups, resulting in findings that may inaccurately reflect the experiences of smaller subgroups. Disaggregation of patient outcomes data can serve to better inform public health interventions for the most vulnerable populations. In pharmacy, disaggregation can allow for better identification of racial and ethnic subgroups who have been traditionally excluded from funding support among other opportunities. In this commentary, we provide historical context and actionable recommendations to better describe our patient and pharmacy trainee populations, with the objectives of improving pharmacist representation and health equity.

Improving Graduate Medical Education by Aggregating Data Across the Medical Education Continuum.

ABSTRACT: Meaningful improvements to graduate medical education (GME) have been achieved in recent decades, yet many GME improvement pilots have been small trials without rigorous outcome measures and with limited generalizability. Thus, lack of access to large-scale data is a key barrier to generating empiric evidence to improve GME. In this article, the authors examine the potential of a national GME data infrastructure to improve GME, review the output of 2 national workshops on this topic, and propose a path toward achieving this goal.The authors envision a future where medical education is shaped by evidence from rigorous research powered by comprehensive, multi-institutional data. To achieve this goal, premedical education, undergraduate medical education, GME, and practicing physician data must be collected using a common data dictionary and standards and longitudinally linked using unique individual identifiers. The envisioned data infrastructure could provide a foundation for evidence-based decisions across all aspects of GME and help optimize the education of individual residents.Two workshops hosted by the National Academies of Sciences, Engineering, and Medicine Board on Health Care Services explored the prospect of better using GME data to improve education and its outcomes. There was broad consensus about the potential value of a longitudinal data infrastructure to improve GME. Significant obstacles were also noted.Suggested next steps outlined by the authors include producing a more complete inventory of data already being collected and managed by key medical education leadership organizations, pursuing a grass-roots data sharing pilot among GME-sponsoring institutions, and formulating the technical and governance frameworks needed to aggregate data across organizations.The power and potential of big data is evident across many disciplines, and the authors believe that harnessing the power of big data in GME is the best next step toward advancing evidence-based physician education.

Addressing Data Aggregation and Data Inequity in Race and Ethnicity Reporting and the Impact on Breast Cancer Disparities.

Collecting and reporting data on race and ethnicity is vital to understanding and addressing health disparities in the United States. These health disparities can include increased prevalence and severity of disease, poorer health outcomes, decreased access to healthcare, etc., in disadvantaged populations compared with advantaged groups. Without these data, researchers, administrators, public health practitioners, and policymakers are unable to identify the need for targeted interventions and assistance. When researching or reporting on race and ethnicity, typically broad racial categories are used. These include White or Caucasian, Black or African American, Asian American, Native Hawaiian or Other Pacific Islander, or American Indian and Alaska Native, as well as categories for ethnicity such as Latino or Hispanic or not Latino or Hispanic. These categories, defined by the Office of Management and Budget, are the minimum standards for collecting and reporting race and ethnicity data across federal agencies. Of note, these categories have not been updated since 1997. The lack of accurate and comprehensive data on marginalized racial and ethnic groups limits our understanding of and ability to address health disparities. This has implications for breast cancer outcomes in various populations in this country. In this paper, we examine the impact data inequity and the lack of data equity centered processes have in providing appropriate prevention and intervention efforts and resource allocations.

Secure Data Aggregation Based on End-to-End Homomorphic Encryption in IoT-Based Wireless Sensor Networks.

By definition, the aggregating methodology ensures that transmitted data remain visible in clear text in the aggregated units or nodes. Data transmission without encryption is vulnerable to security issues such as data confidentiality, integrity, authentication and attacks by adversaries. On the other hand, encryption at each hop requires extra computation for decrypting, aggregating, and then re-encrypting the data, which results in increased complexity, not only in terms of computation but also due to the required sharing of keys. Sharing the same key across various nodes makes the security more vulnerable. An alternative solution to secure the aggregation process is to provide an end-to-end security protocol, wherein intermediary nodes combine the data without decoding the acquired data. As a consequence, the intermediary aggregating nodes do not have to maintain confidential key values, enabling end-to-end security across sensor devices and base stations. This research presents End-to-End Homomorphic Encryption (EEHE)-based safe and secure data gathering in IoT-based Wireless Sensor Networks (WSNs), whereby it protects end-to-end security and enables the use of aggregator functions such as COUNT, SUM and AVERAGE upon encrypted messages. Such an approach could also employ message authentication codes (MAC) to validate data integrity throughout data aggregation and transmission activities, allowing fraudulent content to also be identified as soon as feasible. Additionally, if data are communicated across a WSN, then there is a higher likelihood of a wormhole attack within the data aggregation process. The proposed solution also ensures the early detection of wormhole attacks during data aggregation.

Wearable Sensor Data Classification for Identifying Missing Transmission Sequence Using Tree Learning.

Wearable Sensor (WS) data accumulation and transmission are vital in analyzing the health status of patients and elderly people remotely. Through specific time intervals, the continuous observation sequences provide a precise diagnosis result. This sequence is however interrupted due to abnormal events or sensor or communicating device failures or even overlapping sensing intervals. Therefore, considering the significance of continuous data gathering and transmission sequence for WS, this article introduces a Concerted Sensor Data Transmission Scheme (CSDTS). This scheme endorses aggregation and transmission that aims at generating continuous data sequences. The aggregation is performed considering the overlapping and non-overlapping intervals from the WS sensing process. Such concerted data aggregation generates fewer chances of missing data. In the transmission process, allocated first-come-first-serve-based sequential communication is pursued. In the transmission scheme, a pre-verification of continuous or discrete (missing) transmission sequences is performed using classification tree learning. In the learning process, the accumulation and transmission interval synchronization and sensor data density are matched for preventing pre-transmission losses. The discrete classified sequences are thwarted from the communication sequence and are transmitted post the alternate WS data accumulation. This transmission type prevents sensor data loss and reduces prolonged wait times.

Dataset development of pre-formulation tests on fast disintegrating tablets (FDT): data aggregation.

OBJECTIVES: Tablet manufacturing development is costly, laborious, and time-consuming. Technologies related to artificial intelligence like ,predictive model ,can be used in the control process to facilitate and accelerate the tablet manufacturing process. predictive models have become popular recently. However, predictive models need a comprehensive dataset of related data in the field, due to the lack of a dataset of tablet formulations, the aim of this study is to aggregate and integrate fast disintegration tablet's formulation into a comprehensive dataset. DATA DESCRIPTION: The search strategy has been prepared between the years of 2010 to 2020, consisting of the keyword's 'formulation' ,'disintegrating' and 'Tablet', as well as their synonyms. By searching four databases, 1503 articles were retrieved, from these articles only 232 articles met all of the study's criteria. By reviewing 232 articles, 1982 formulations have been extracted, afterward pre-processing and cleaning data, contain steps of unifying the name and units, removing inappropriate formulations by an expert, and finally, data tidying was done on data. The developed dataset contains valuable information from various FDT's formulations, which can be used in pharmaceutical studies that are critical to the discovery and development of new drugs. this method can be applied to aggregate datasets from the other dosage forms.

Innovative platforms for data aggregation, linkage and analysis in the context of pandemic and epidemic intelligence.

During the COVID-19 pandemic, open-access platforms that aggregate, link and analyse data were transformative for global public health surveillance. This perspective explores the work of three of these platforms: Our World In Data (OWID), Johns Hopkins University (JHU) COVID-19 Dashboard (later complemented by the Coronavirus Resource Center), and Global.Health, which were presented in the second World Health Organization (WHO) Pandemic and Epidemic Intelligence Innovation Forum. These platforms, operating mostly within academic institutions, added value to public health data that are collected by government agencies by providing additional real-time public health intelligence about the spread of the virus and the evolution of the public health emergency. Information from these platforms was used by health professionals, political decision-makers and members of the public alike. Further engagement between government and non-governmental surveillance efforts can accelerate the improvements needed in public health surveillance overall. Increasing the diversity of public health surveillance initiatives beyond the government sector comes with several benefits: technology innovation in data science, engagement of additional highly skilled professionals, greater transparency and accountability for government agencies, and new opportunities to engage with members of society.

SDAA: Secure Data Aggregation and Authentication Using Multiple Sinks in Cluster-Based Underwater Vehicular Wireless Sensor Network.

Security is one of the major concerns while designing robust protocols for underwater sensor networks (UWSNs). The underwater sensor node (USN) is an example of medium access control (MAC) that should control underwater UWSN, and underwater vehicles (UV) combined. Therefore, our proposed method, in this research, investigates UWSN combined with UV optimized as an underwater vehicular wireless network (UVWSN) that can completely detect malicious node attacks (MNA) from the network. Thus, MNA that engages the USN channel and launches MNA is resolved by our proposed protocol through SDAA (secure data aggregation and authentication) protocol deployed in UVWSN. SDAA protocol plays a significant role in secure data communication, as the cluster-based network design (CBND) network organization creates a concise, stable, and energy-efficient network. This paper introduces SDAA optimized network known as UVWSN. In this proposed SDAA protocol, the cluster head (CH) is authenticated through the gateway (GW) and the base station (BS) to guarantee that a legitimate USN oversees all clusters deployed in the UVWSN are securely established for providing trustworthiness/privacy. Furthermore, the communicated data in the UVWSN network guarantee that data transmission is secure due to the optimized SDAA models in the network. Thus, the USNs deployed in the UVWSN are securely confirmed to maintain secure data communication in CBND for energy efficiency. The proposed method is implemented and validated on the UVWSN for measuring reliability, delay, and energy efficiency in the network. The proposed method is utilized for monitoring scenarios for inspecting vehicles or ship structures in the ocean. Based on the testing results, the proposed SDAA protocol methods improve energy efficiency and reduce network delay compared to other standard secure MAC methods.

An Energy Efficient Load Balancing Tree-Based Data Aggregation Scheme for Grid-Based Wireless Sensor Networks.

A wireless sensor network (WSN) consists of a very large number of sensors which are deployed in the specific area of interest. A sensor is an electronic device equipped with a small processor and has a small-capacity memory. The WSN has the functions of low cost, easy deployment, and random reconfiguration. In this paper, an energy-efficient load balancing tree-based data aggregation scheme (LB-TBDAS) for grid-based WSNs is proposed. In this scheme, the sensing area is partitioned into many cells of a grid and then the sensor node with the maximum residual energy is elected to be the cell head in each cell. Then, the tree-like path is established by using the minimum spanning tree algorithm. In the tree construction, it must meet the three constraints, which are the minimum energy consumption spanning tree, the network depth, and the maximum number of child nodes. In the data transmission process, the cell head is responsible for collecting the sensing data in each cell, and the collected data are transmitted along the tree-like path to the base station (BS). Simulation results show that the total energy consumption of LB-TBDAS is significantly less than that of GB-PEDAP and PEDAP. Compared to GB-PEDAP and PEDAP, the proposed LB-TBDAS extends the network lifetime by more than 100%. The proposed LB-TBDAS can avoid excessive energy consumption of sensor nodes during multi-hop data transmission and can also avoid the hotspot problem of WSNs.

Disaggregation of Race and Ethnicity Group Data: Research-to-Practice Issues in Clinical Environments.

This Viewpoint discusses the importance of accurately categorizing and collecting race and ethnicity data, matching self-identity with race and ethnicity labels, in an effort to quantify the extent of health disparities.

Bayesian network meta-analysis methods for combining individual participant data and aggregate data from single arm trials and randomised controlled trials.

BACKGROUND: Increasingly in network meta-analysis (NMA), there is a need to incorporate non-randomised evidence to estimate relative treatment effects, and in particular in cases with limited randomised evidence, sometimes resulting in disconnected networks of treatments. When combining different sources of data, complex NMA methods are required to address issues associated with participant selection bias, incorporating single-arm trials (SATs), and synthesising a mixture of individual participant data (IPD) and aggregate data (AD). We develop NMA methods which synthesise data from SATs and randomised controlled trials (RCTs), using a mixture of IPD and AD, for a dichotomous outcome. METHODS: We propose methods under both contrast-based (CB) and arm-based (AB) parametrisations, and extend the methods to allow for both within- and across-trial adjustments for covariate effects. To illustrate the methods, we use an applied example investigating the effectiveness of biologic disease-modifying anti-rheumatic drugs for rheumatoid arthritis (RA). We applied the methods to a dataset obtained from a literature review consisting of 14 RCTs and an artificial dataset consisting of IPD from two SATs and AD from 12 RCTs, where the artificial dataset was created by removing the control arms from the only two trials assessing tocilizumab in the original dataset. RESULTS: Without adjustment for covariates, the CB method with independent baseline response parameters (CBunadjInd) underestimated the effectiveness of tocilizumab when applied to the artificial dataset compared to the original dataset, albeit with significant overlap in posterior distributions for treatment effect parameters. The CB method with exchangeable baseline response parameters produced effectiveness estimates in agreement with CBunadjInd, when the predicted baseline response estimates were similar to the observed baseline response. After adjustment for RA duration, there was a reduction in across-trial heterogeneity in baseline response but little change in treatment effect estimates. CONCLUSIONS: Our findings suggest incorporating SATs in NMA may be useful in some situations where a treatment is disconnected from a network of comparator treatments, due to a lack of comparative evidence, to estimate relative treatment effects. The reliability of effect estimates based on data from SATs may depend on adjustment for covariate effects, although further research is required to understand this in more detail.

phylogatR: Phylogeographic data aggregation and repurposing.

Patterns of genetic diversity within species contain information the history of that species, including how they have responded to historical climate change and how easily the organism is able to disperse across its habitat. More than 40,000 phylogeographic and population genetic investigations have been published to date, each collecting genetic data from hundreds of samples. Despite these millions of data points, meta-analyses are challenging because the synthesis of results across hundreds of studies, each using different methods and forms of analysis, is a daunting and time-consuming task. It is more efficient to proceed by repurposing existing data and using automated data analysis. To facilitate data repurposing, we created a database (phylogatR) that aggregates data from different sources and conducts automated multiple sequence alignments and data curation to provide users with nearly ready-to-analyse sets of data for thousands of species. Two types of scientific research will be made easier by phylogatR: large meta-analyses of thousands of species that can address classic questions in evolutionary biology and ecology, and student- or citizen- science based investigations that will introduce a broad range of people to the analysis of genetic data. phylogatR enhances the value of existing data via the creation of software and web-based tools that enable these data to be recycled and reanalysed and increase accessibility to big data for research laboratories and classroom instructors with limited computational expertise and resources.

The Model Minority Myth, Data Aggregation, and the Role of Medical Schools in Combating Anti-Asian Sentiment.

The COVID-19 pandemic has resulted in an alarming increase in hate incidents directed toward Asian Americans and Pacific Islanders (AAPIs), including verbal harassment and physical assault, spurring the nationwide #StopAsianHate movement. This rise in anti-Asian sentiment is occurring at a critical time of racial reckoning across the United States, galvanized by the Black Lives Matter movement, and of medical student calls for the implementation of antiracist medical curricula. AAPIs are stereotyped by the model minority myth, which posits that AAPIs are educated, hardworking, and therefore able to achieve high levels of success. This myth acts as a racial wedge between minorities and perpetuates harm that is pervasive throughout the field of medicine. Critically, the frequent aggregation of all AAPI subgroups as one monolithic community obfuscates socioeconomic and cultural differences across the AAPI diaspora while reinforcing the model minority myth. Here, the authors illustrate how the model minority myth and data aggregation have negatively affected the recruitment and advancement of diverse AAPI medical students, physicians, and faculty. Additionally, the authors discuss how data aggregation obscures health disparities across the AAPI diaspora and how the model minority myth influences the illness experiences of AAPI patients. Importantly, the authors outline specific actionable policies and reforms that medical schools can implement to combat anti-Asian sentiment and support the AAPI community.

Considerations When Aggregating Data to Measure Performance Across Levels of the Health Care System.

BACKGROUND: Measuring quality at varying levels of the health care system requires attribution, a process of determining the patients and services for which each level is responsible. However, it is important to ensure that attribution approaches are equitable; otherwise, individuals may be assigned differentially based upon social determinants of health. METHODS: First, we used Medicaid claims (2010-2018) from Michigan to assess the proportion of children with sickle cell anemia who had less than 12 months enrollment within a single Medicaid health plan and could therefore not be attributed to a specific health plan. Second, we used the Medicaid Analytic eXtract data (2008-2009) from 26 states to simulate adapting the 30-Day Pediatric All-Condition Readmission measure to the Accountable Care Organization (ACO) level and examined the proportion of readmissions that could not be attributed. RESULTS: For the sickle cell measure, an average of 300 children with sickle cell anemia were enrolled in Michigan Medicaid each year. The proportion of children that could not be attributed to a Medicaid health plan ranged from 12.2% to 89.0% across years. For the readmissions measure, of the 1,051,365 index admissions, 22% were excluded in the ACO-level analysis because of being unable to attribute the patient to a health plan for the 30 days post discharge. CONCLUSIONS: When applying attribution models, it is essential to consider the potential to induce health disparities. Differential attribution may have unintentional consequences that deepen health disparities, particularly when considering incentive programs for health plans to improve the quality of care.

A Lightweight and Privacy-Friendly Data Aggregation Scheme against Abnormal Data.

Abnormal electricity data, caused by electricity theft or meter failure, leads to the inaccuracy of aggregation results. These inaccurate results not only harm the interests of users but also affect the decision-making of the power system. However, the existing data aggregation schemes do not consider the impact of abnormal data. How to filter out abnormal data is a challenge. To solve this problem, in this study, we propose a lightweight and privacy-friendly data aggregation scheme against abnormal data, in which the valid data can correctly be aggregated but abnormal data will be filtered out during the aggregation process. This is more suitable for resource-limited smart meters, due to the adoption of lightweight matrix encryption. The automatic filtering of abnormal data without additional processes and the detection of abnormal data sources are where our protocol outperforms other schemes. Finally, a detailed security analysis shows that the proposed scheme can protect the privacy of users' data. In addition, the results of extensive simulations demonstrate that the additional computation cost to filter the abnormal data is within the acceptable range, which shows that our proposed scheme is still very effective.

Mitigating bias in estimating epidemic severity due to heterogeneity of epidemic onset and data aggregation.

Outbreaks of infectious diseases, such as influenza, are a major societal burden. Mitigation policies during an outbreak or pandemic are guided by the analysis of data of ongoing or preceding epidemics. The reproduction number, R0, defined as the expected number of secondary infections arising from a single individual in a population of susceptibles is critical to epidemiology. For typical compartmental models such as the Susceptible-Infected-Recovered (SIR) R0 represents the severity of an epidemic. It is an estimate of the early-stage growth rate of an epidemic and is an important threshold parameter used to gain insights into the spread or decay of an outbreak. Models typically use incidence counts as indicators of cases within a single large population; however, epidemic data are the result of a hierarchical aggregation, where incidence counts from spatially separated monitoring sites (or sub-regions) are pooled and used to infer R0. Is this aggregation approach valid when the epidemic has different dynamics across the regions monitored? We characterize bias in the estimation of R0 from a merged data set when the epidemics of the sub-regions, used in the merger, exhibit delays in onset. We propose a method to mitigate this bias, and study its efficacy on synthetic data as well as real-world influenza and COVID-19 data.

Compressive sensing based secure data aggregation scheme for IoT based WSN applications.

Compressive Sensing (CS) based data collection schemes are found to be effective in enhancing the data collection performance and lifetime of IoT based WSNs. However, they face major challenges related to key distribution and adversary attacks in hostile and complex network deployments. As a result, such schemes cannot effectively ensure the security of data. Towards the goal of providing high security and efficiency in data collection performance of IoT based WSNs, we propose a new security scheme that amalgamates the advantages of CS and Elliptic Curve Cryptography (ECC). We present an efficient algorithms to enhance the security and efficiency of CS based data collection in IoT-based WSNs. The proposed scheme operates in five main phases, namely Key Generation, CS-Key Exchange, Data Compression with CS Encryption, Data Aggregation and Encryption with ECC algorithm, and CS Key Re-generation. It considers the benefits of ECC as public key algorithm and CS as encryption and compression method to provide security as well as energy efficiency for cluster based WSNs. Also, it solves the CS- Encryption key distribution problem by introducing a new key sharing method that enables secure exchange of pseudo-random key between the BS and the nodes in a simple way. In addition, a new method is introduced to safeguard the CS scheme from potential security attacks. The efficiency of our proposed technique in terms of security, energy consumption and network lifetime is proved through simulation analysis.

A novel fault-tolerant privacy-preserving cloud-based data aggregation scheme for lightweight health data.

Mobile health networks (MHNWs) have facilitated instant medical health care and remote health monitoring for patients. Currently, a vast amount of health data needs to be quickly collected, processed and analyzed. The main barrier to doing so is the limited amount of the computational storage resources that are required for MHNWs. Therefore, health data must be outsourced to the cloud. Although the cloud has the benefits of powerful computation capabilities and intensive storage resources, security and privacy concerns exist. Therefore, our study examines how to collect and aggregate these health data securely and efficiently, with a focus on the theoretical importance and application potential of the aggregated data. In this work, we propose a novel design for a private and fault-tolerant cloud-based data aggregation scheme. Our design is based on a future ciphertext mechanism for improving the fault tolerance capabilities of MHNWs. Our scheme is privatized via differential privacy, which is achieved by encrypting noisy health data and enabling the cloud to obtain the results of only the noisy sum. Our scheme is efficient, reliable and secure and combines different approaches and algorithms to improve the security and efficiency of the system. Our proposed scheme is evaluated with an extensive simulation study, and the simulation results show that it is efficient and reliable. The computational cost of our scheme is significantly less than that of the related scheme. The aggregation error is minimized from ${\rm{O}}\left( {\sqrt {{\bf{w + 1}}} } \right)$ in the related scheme to O(1) in our scheme.

Mathematically aggregating experts' predictions of possible futures.

Structured protocols offer a transparent and systematic way to elicit and combine/aggregate, probabilistic predictions from multiple experts. These judgements can be aggregated behaviourally or mathematically to derive a final group prediction. Mathematical rules (e.g., weighted linear combinations of judgments) provide an objective approach to aggregation. The quality of this aggregation can be defined in terms of accuracy, calibration and informativeness. These measures can be used to compare different aggregation approaches and help decide on which aggregation produces the "best" final prediction. When experts' performance can be scored on similar questions ahead of time, these scores can be translated into performance-based weights, and a performance-based weighted aggregation can then be used. When this is not possible though, several other aggregation methods, informed by measurable proxies for good performance, can be formulated and compared. Here, we develop a suite of aggregation methods, informed by previous experience and the available literature. We differentially weight our experts' estimates by measures of reasoning, engagement, openness to changing their mind, informativeness, prior knowledge, and extremity, asymmetry or granularity of estimates. Next, we investigate the relative performance of these aggregation methods using three datasets. The main goal of this research is to explore how measures of knowledge and behaviour of individuals can be leveraged to produce a better performing combined group judgment. Although the accuracy, calibration, and informativeness of the majority of methods are very similar, a couple of the aggregation methods consistently distinguish themselves as among the best or worst. Moreover, the majority of methods outperform the usual benchmarks provided by the simple average or the median of estimates.