Public Data Sources Research Articles

A Cost-Effectiveness Analysis for Post-Endoscopic Retrograde Cholangiopancreatography Pancreatitis Prophylaxis in the United States

Post-endoscopic retrograde cholangiopancreatography pancreatitis (PEP) is the most common adverse event after endoscopic retrograde cholangiopancreatography, and is responsible for substantial morbidity and health care expenditures of at least $200 million. Therapies for PEP prevention include pancreatic stent placement (PSP), rectal indomethacin, sublingual nitrates, and aggressive lactated Ringer's hydration. Our objective was to determine which PEP prophylactic strategies are cost effective. We developed 2 separate decision trees to evaluate PEP prophylactic strategies. The first, in high-risk patients, compared rectal indomethacin, PSP, PSP with indomethacin, sublingual nitrates, aggressive hydration with lactated Ringer's, and no prophylaxis. The second, in average-risk patients, compared rectal indomethacin, sublingual nitrates, aggressive hydration, and no prophylaxis. We used incidence rates, transition probabilities, and costs from publications and public data sources. Outcome measures were reported as incremental cost-effectiveness ratios, with a willingness-to-pay (WTP) threshold of $100,000/quality-adjusted life years (QALYs). Compared with no prophylaxis, all strategies were cost effective at a WTP of $100,000 in preventing PEP in high-risk patients. When directly compared with each other, rectal indomethacin was the cost-effective strategy in high-risk patients ($31,589/QALYs). In average-risk patients, indomethacin and sublingual nitrates were cost effective at a WTP of $100,000/QALYs compared with no prophylaxis. When directly compared with each other, rectal indomethacin was the cost-effective strategy ($53,016/QALYs). Rectal indomethacin was the cost-effective strategy for preventing PEP in both average-risk and high-risk patients undergoing endoscopic retrograde cholangiopancreatography. All strategies were cost effective when compared with no prophylaxis in high-risk patients, whereas all strategies except for aggressive hydration with lactated Ringer's were cost effective in average-risk patients. Further studies are needed to improve the utilization of PEP prevention strategies.

Analysis of the Effects of COVID-19 Mask Mandates on Hospital Resource Consumption and Mortality at the County Level.

Coronavirus disease 2019 (COVID-19) threatens vulnerable patient populations, resulting in immense pressures at the local, regional, national, and international levels to contain the virus. Laboratory-based studies demonstrate that masks may offer benefit in reducing the spread of droplet-based illnesses, but few data are available to assess mask effects via executive order on a population basis. We assess the effects of a county-wide mask order on per-population mortality, intensive care unit (ICU) utilization, and ventilator utilization in Bexar County, Texas. We used publicly reported county-level data to perform a mixed-methods before-and-after analysis along with other sources of public data for analyses of covariance. We used a least-squares regression analysis to adjust for confounders. A Texas state-level mask order was issued on July 3, 2020, followed by a Bexar County-level order on July 15, 2020. We defined the control period as June 2 to July 2 and the postmask order period as July 8, 2020-August 12, 2020, with a 5-day gap to account for the median incubation period for cases; longer periods of 7 and 10 days were used for hospitalization and ICU admission/death, respectively. Data are reported on a per-100,000 population basis using respective US Census Bureau-reported populations. From June 2, 2020 through August 12, 2020, there were 40,771 reported cases of COVID-19 within Bexar County, with 470 total deaths. The average number of new cases per day within the county was 565.4 (95% confidence interval [CI] 394.6-736.2). The average number of positive hospitalized patients was 754.1 (95% CI 657.2-851.0), in the ICU was 273.1 (95% CI 238.2-308.0), and on a ventilator was 170.5 (95% CI 146.4-194.6). The average deaths per day was 6.5 (95% CI 4.4-8.6). All of the measured outcomes were higher on average in the postmask period as were covariables included in the adjusted model. When adjusting for traffic activity, total statewide caseload, public health complaints, and mean temperature, the daily caseload, hospital bed occupancy, ICU bed occupancy, ventilator occupancy, and daily mortality remained higher in the postmask period. There was no reduction in per-population daily mortality, hospital bed, ICU bed, or ventilator occupancy of COVID-19-positive patients attributable to the implementation of a mask-wearing mandate.

Environmental and social risk factors in association with spatial clustering of childhood cancer incidence

BACKGROUND AND AIM: Childhood cancer incidence is known to vary by age, sex, and race/ethnicity, but evidence is limited regarding external risk factors. We aim to identify harmful combinations of air pollutants and other environmental and social risk factors in association with the incidence of childhood cancer based on 2003-2017 data from the Georgia Cancer Registry. METHODS: We calculated the standardized incidence ratios (SIR) of Central Nervous System (CNS) tumors, leukemia and lymphomas based on age, gender and ethnic composition in each of the 159 counties in Georgia, USA. County-level information on eight carcinogenic air pollutants (benzene, trichloroethylene, ethylene-oxide, formaldehyde, pentachlorophenol, styrene, tetrachloroethylene, 1,3-butadiene), socioeconomic status, tobacco smoking, alcohol drinking and obesity were derived from US EPA and other public data sources. We applied an unsupervised learning tool (self-organizing map, SOM) to identify pertinent types of multi-exposure combinations. Spatial Bayesian Poisson models (Leroux-CAR) were fit with indicators for each multi-exposure category as exposure and SIR of childhood cancers as outcomes. RESULTS:Based on county-level information of external risk factors, the SOM algorithm divided the state of Georgia into 5 distinct clusters (clusters 4-5 comprising metropolitan/urban areas). Mean SIR ranged from 0.8 (rural-cluster-1) to 1.1 (urban-cluster-5) for leukemia, 1.0 (rural-cluster-3) to 1.4 (rural-cluster-1) for lymphomas and 1.5 (rural-cluster-1) to 1.8 (urban-cluster-4) for CNS tumors. Children living in urban counties characterized by higher air pollution concentrations and proportions of alcohol drinking (urban-cluster-4) had higher SIR of childhood leukemia and lymphomas than children living in rural-cluster-1 (risk ratio [95%-credible intervals]: 1.3 [1.0,1.6]) and rural-cluster-3 (1.2 [1.0, 1.4]), respectively. We found no significant associations for spatial variation of CNS tumors. CONCLUSIONS:We found spatial clustering of childhood cancer in counties with higher air pollution concentrations and larger proportions of alcohol drinking. More research is needed to identify the causal risk factors for these associations. KEYWORDS: Air pollution, Cancer and cancer precursors, Children's environmental health, Socio-economic factors, Multi-pollutant, Environmental epidemiology

Estimating the Memory Consumption of a Hardware IP Defragmentation Block

IP fragmentation is still prevalent on the Internet. Defragmented traffic is a prerequisite for many network processing algorithms. This work focuses on the size and organization of a flow table, which is an essential ingredient of the hardware IP defragmentation block. Previous research suggests that fragmented IP traffic is highly local, and a relatively small flow table (on the order of a thousand entries) can process most of the traffic. Samples of IP traffic were obtained from public data sources and used for a statistical analysis, revealing the key factors in achieving design goals. The findings were backed by an extensive design space exploration of the software defragmentation model, which resulted in the efficiency estimates. To provide a robust score of the simulation model, a new validation technique is employed that helps to overcome the limitations of the samples.

Easy-Prime: a machine learning\u2013based prime editor design tool

Prime editing is a revolutionary genome-editing technology that can make a wide range of precise edits in DNA. However, designing highly efficient prime editors (PEs) remains challenging. We develop Easy-Prime, a machine learning–based program trained with multiple published data sources. Easy-Prime captures both known and novel features, such as RNA folding structure, and optimizes feature combinations to improve editing efficiency. We provide optimized PE design for installation of 89.5% of 152,351 GWAS variants. Easy-Prime is available both as a command line tool and an interactive PE design server at: http://easy-prime.cc/.

Public data sources for regulatory genomic features.

High-throughput technologies have led to a continuously growing amount of information about regulatory features in the genome. A wealth of data generated by large international research consortia is available from online databases. Disease-driven studies provide details on specific DNA elements or epigenetic modifications regulating gene expression in specific cellular and developmental contexts, but these results are usually only published in scientific articles. All this information can be helpful in interpreting variants in the regulatory genome. This review describes a selection of high-profile data sources providing information on the non-coding genome, as well as pitfalls and techniques to search and capture information from the literature.

Multi_Scale_Tools: A Python Library to Exploit Multi-Scale Whole Slide Images

Algorithms proposed in computational pathology can allow to automatically analyze digitized tissue samples of histopathological images to help diagnosing diseases. Tissue samples are scanned at a high-resolution and usually saved as images with several magnification levels, namely whole slide images (WSIs). Convolutional neural networks (CNNs) represent the state-of-the-art computer vision methods targeting the analysis of histopathology images, aiming for detection, classification and segmentation. However, the development of CNNs that work with multi-scale images such as WSIs is still an open challenge. The image characteristics and the CNN properties impose architecture designs that are not trivial. Therefore, single scale CNN architectures are still often used. This paper presents Multi_Scale_Tools, a library aiming to facilitate exploiting the multi-scale structure of WSIs. Multi_Scale_Tools currently include four components: a pre-processing component, a scale detector, a multi-scale CNN for classification and a multi-scale CNN for segmentation of the images. The pre-processing component includes methods to extract patches at several magnification levels. The scale detector allows to identify the magnification level of images that do not contain this information, such as images from the scientific literature. The multi-scale CNNs are trained combining features and predictions that originate from different magnification levels. The components are developed using private datasets, including colon and breast cancer tissue samples. They are tested on private and public external data sources, such as The Cancer Genome Atlas (TCGA). The results of the library demonstrate its effectiveness and applicability. The scale detector accurately predicts multiple levels of image magnification and generalizes well to independent external data. The multi-scale CNNs outperform the single-magnification CNN for both classification and segmentation tasks. The code is developed in Python and it will be made publicly available upon publication. It aims to be easy to use and easy to be improved with additional functions.

Investigating associations between COVID-19 mortality and population-level health and socioeconomic indicators in the United States: A modeling study.

BackgroundWith the availability of multiple Coronavirus Disease 2019 (COVID-19) vaccines and the predicted shortages in supply for the near future, it is necessary to allocate vaccines in a manner that minimizes severe outcomes, particularly deaths. To date, vaccination strategies in the United States have focused on individual characteristics such as age and occupation. Here, we assess the utility of population-level health and socioeconomic indicators as additional criteria for geographical allocation of vaccines.Methods and findingsCounty-level estimates of 14 indicators associated with COVID-19 mortality were extracted from public data sources. Effect estimates of the individual indicators were calculated with univariate models. Presence of spatial autocorrelation was established using Moran’s I statistic. Spatial simultaneous autoregressive (SAR) models that account for spatial autocorrelation in response and predictors were used to assess (i) the proportion of variance in county-level COVID-19 mortality that can explained by identified health/socioeconomic indicators (R2); and (ii) effect estimates of each predictor.Adjusting for case rates, the selected indicators individually explain 24%–29% of the variability in mortality. Prevalence of chronic kidney disease and proportion of population residing in nursing homes have the highest R2. Mortality is estimated to increase by 43 per thousand residents (95% CI: 37–49; p < 0.001) with a 1% increase in the prevalence of chronic kidney disease and by 39 deaths per thousand (95% CI: 34–44; p < 0.001) with 1% increase in population living in nursing homes. SAR models using multiple health/socioeconomic indicators explain 43% of the variability in COVID-19 mortality in US counties, adjusting for case rates. R2 was found to be not sensitive to the choice of SAR model form. Study limitations include the use of mortality rates that are not age standardized, a spatial adjacency matrix that does not capture human flows among counties, and insufficient accounting for interaction among predictors.ConclusionsSignificant spatial autocorrelation exists in COVID-19 mortality in the US, and population health/socioeconomic indicators account for a considerable variability in county-level mortality. In the context of vaccine rollout in the US and globally, national and subnational estimates of burden of disease could inform optimal geographical allocation of vaccines.

From convergence to congruence: European integration and citizen–elite congruence

Recent research argues that European integration has led to an ideological convergence of member state party systems, which is purported to have significant consequences for democratic representation. We argue that convergence of party positions is less problematic if congruence between governed and governing is maintained. We therefore turn to test whether integration has had an effect on congruence between the public and their governing elites. Using five measures of integration, two sources of public opinion data, and expert surveys on political parties, we find little evidence that integration into the European Union reduces congruence between the public and the national party system, government or legislature either ideologically or across five issue areas. These results should assuage concerns about integration’s effect on domestic political representation.

Use of hypnotic drugs among children, adolescents, and young adults in Scandinavia.

Hypnotic use in children and adolescents is controversial. To describe the use of hypnotic drugs (melatonin, z-drugs, and sedating antihistamines) among 5- to 24-year-old Scandinavians during 2012 to 2018. Aggregate-level data were obtained from public data sources in Sweden, Norway, and Denmark. We calculated annual prevalence (users/1000 inhabitants) stratified by age group, sex, and country. Quantity of use (Defined Daily Dose (DDD)/user/day) was estimated for Norway and Denmark. Melatonin was the most commonly used hypnotic, and its use increased markedly from 2012 to 2018, particularly among females and 15- to 24-year-old individuals. Sweden had the highest increase in use (6.5 to 25/1000) compared with Norway (10-20/1000) and Denmark (5.7-12/1000). The annual prevalence of sedating antihistamine use was also highest in Sweden, reaching 13/1000 in 2018 in comparison to 7.5/1000 in Norway and 2.5/1000 in Denmark. Z-drug use decreased in all countries toward 2018, dropping to 3.5/1000 in Sweden, 4.4/1000 in Norway, and 1.7/1000 in Denmark. The quantity of hypnotic use in Norway and Denmark was 0.8-1.0 DDD/user/day for melatonin in 2018, as compared to 0.1-0.3 for z-drugs and antihistamines. The use of melatonin and sedating antihistamines increased among young Scandinavians during 2012-2018, and the increase was twice as high in Sweden compared with Norway and Denmark. In addition, Sweden had the highest use of sedating antihistamines. The Scandinavian variation of hypnotic use could reflect differences in frequency of sleep problems between populations or variation of healthcare access or clinical practice between countries.

Association of Primary and Behavioral Health Integrated Care Upon Pediatric Mental Disorder Treatment.

The objective of this study was to examine whether linkage with mental health (MH) treatment differed across 3 different integrated care arrangements (ICAs), following incident attention deficit hyperactive disorder (ADHD) and major depressive disorder (MDD) diagnoses given by primary care providers (PCPs) in the pediatric setting. Using claims linking with multiple public data sources, we examined the treatment initiation among children receiving an incident diagnosis of ADHD or MDD from PCPs working in practices with various ICAs. ICAs were categorized as PCP practiced alone (non-co-located), PCP practiced with specialist outside the practice but co-located at the practice site (co-located), and employed specialists who were co-located (co-located and co-affiliated). A total of 4203 incident ADHD and 298 incident MDD cases diagnosed by PCPs were identified, of which 3123 (74%) with ADHD and 200 (67%) with MDD received treatment within 90 days since the diagnosis. Children diagnosed with ADHD by co-located and co-affiliated PCPs were twice as likely to receive treatment as those diagnosed by non-co-located PCPs (odds ratio [OR]=1.93; 95% confidence interval [CI], 1.24-2.78). Of those treated, children diagnosed by co-located and co-affiliated PCPs were 2 times more likely to receive guideline recommended psychotherapy (OR=2.15; 95% CI, 1.35-3.44). These patients were also more likely to be treated at the diagnosing site versus elsewhere. Similar beneficial effects were not observed in those first diagnosed by co-located but non-affiliated PCPs. Service co-location between co-affiliated PCPs and MH specialists was associated with significant higher ADHD treatment rate and the receipt of guideline-recommended psychotherapy.

The City of Hope POSEIDON enterprise-wide platform for real-world data and evidence in cancer.

e18813 Background: The City of Hope Center for Precision Medicine developed an enterprise-wide platform and precision medicine program to unlock the research potential and clinical value of complex and unique datasets by combining patient data with comprehensive genomic profiling and proprietary analytics. POSEIDON (Precision Oncology Software Environment Interoperable Data Ontologies Network) is a secure, cloud-based Oncology Insights Engine enabling exploration, analysis, visualization, and collaboration on our patient clinico-genomic data along with public data sources. This platform enables investigators to access and visualize data from clinical and multi-omics data and provides an engine that can be utilized for cohort discovery and exploration, preliminary feasibility testing to deriving patient specific insights based on real world data (RWD) and real-world evidence (RWE). Patients are consented through an IRB-approved protocol with active, opt-in participation. Methods: The POSEIDON Common Data Model (PCDM) is a standard, extensible data schema that incorporates patient data to support Precision Medicine. Data are incorporated from disparate data sources and stored in a combined harmonized manner promoting consistency of data and meaning across downstream applications. A multi-step process was created to capture and structure multiple data types into the PCDM. Natural language processing (NLP) tools are deployed to automate and structure valuable data elements from unstructured documents including pathology reports and clinical notes. NLP augmented software tools were developed to assist manual data abstractors to capture more complex terms and disease specific data elements which can include disease progression, progression free survival, and other outcomes. Results: Comprehensive data from 175,000 City of Hope patients are included within this environment for cohort exploration, longitudinal follow-up, outcomes, hypothesis development, and queries for synthetic controls. Data from disease specific-research registries constitute a rich dataset within POSEIDON by disease and tumor type, including lung cancer, colorectal cancer, breast cancer, leukemia, lymphoma and multiple myeloma, among other disease types. Automated genomic workflows were created to gain access to genomic profiling and whole exome sequencing. Genomic data is associated with the clinical data in the PCDM. Automated data flows from the Enterprise Data Warehouse EDW include data that is captured in discrete formats in the EDW and provided for in the PCDM and further enrich the data that flows from the disease registries. Statistically rigorous methods for de-identification are applied for collaborative studies. Conclusions: The City of Hope Center for Precision Medicine and the POSEIDON platform offer an exceptional resource for collaborative RWD &amp; RWE studies.

The Envirome Web Service: Patient context at the point of care

Patient context – the “envirome” – can have a significant impact on patient health. While envirome indicators are available through large scale public data sources, they are not provided in a format that can be easily accessed and interpreted at the point of care by healthcare providers with limited time during a patient encounter. We developed a clinical decision support tool to bring envirome indicators to the point of care in a large pediatric hospital system in the Kansas City region. The Envirome Web Service (EWS) securely geocodes patient addresses in real time to link their records with publicly available context data. End-users guided the design of the EWS, which presents summaries of patient context data in the electronic health record (EHR) without disrupting the provider workflow. Through surveys, focus groups, and a formal review by hospital staff, the EWS was deployed into production use, integrating publicly available data on food access with the hospital EHR. Evaluation of EWS usage during the 2020 calendar year shows that 1,034 providers viewed the EWS, with a total of 29,165 sessions. This suggests that the EWS was successfully integrated with the EHR and is highly visible. The results also indicate that 63 (6.1%) of the providers are regular users that opt to maintain the EWS in their custom workflows, logging more than 100 EWS sessions during the year. The vendor agnostic design of the EWS supports interoperability and makes it accessible to health systems with disparate EHR vendors.

Estimation of Clearance and Bioavailability of Therapeutic Monoclonal Antibodies from Only Subcutaneous Injection Data in Humans Based on Comprehensive Analysis of Clinical Data

IntroductionTheoretically, the separate estimation of clearance (CL) and bioavailability (F) requires both intravenous and extravascular injection data. This study investigated whether CL and subcutaneous F of therapeutic monoclonal antibodies (mAbs) in humans can be separately estimated from subcutaneous injection data only.MethodsFirst, the geometric mean of linear pharmacokinetic parameters (CL, intercompartmental CL [Q], volume of distribution in the central compartment [Vc], and volume of distribution in the peripheral compartment [Vp]) after intravenous injection for mAbs in humans that have been reported in public data sources was estimated from 103 mAbs with linear pharmacokinetics and 44 mAbs with nonlinear pharmacokinetics. Next, we estimated the CL and F of 25 mAbs with linear pharmacokinetics from plasma/serum mAb concentration–time profiles after subcutaneous injection in humans by fixing the geometric mean of Q, Vc, and Vp based on the public data. Moreover, the plasma/serum concentration–time profile of 25 mAbs after intravenous injection was simulated using the estimated CL and the geometric mean of Q, Vc, and Vp.ResultsThere were no significant differences in parameters among subclasses (immunoglobulin [Ig] G1, 2, and 4) or in linearity (derivation from linear and nonlinear pharmacokinetics). Using only subcutaneous injection data, we successfully estimated the CL of 23/25 mAbs (92%) and F of all 25 mAbs (100%) within 1.5-fold of the observed value. Moreover, overall, the simulated concentration–time profiles were largely consistent with observed data (90.8% within 1.5-fold of the observed values).ConclusionsThis approach does not require intravenous injection data to separately estimate CL and F after subcutaneous injection in humans and can therefore accelerate the clinical development of mAbs.Supplementary InformationThe online version contains supplementary material available at 10.1007/s40262-021-01023-z.

English

BACKGROUND Acute undifferentiated febrile illness (AUFI / AFI) is one of the most common reasons for people seeking medical care globally. Investigation into the aetiology and trends in AUFI cases are valuable public health data source. Absence of such evidence-based data may result in unintended public health consequences. Present study was undertaken to address the gap in period specific trends in AUFI cases reported from the region. METHODS A record based descriptive study was done in patients treated with AUFI from January 1st, 2015 to December 31st , 2019 in Government Medical College, Ernakulam. Universal sampling technique was adopted, and the data was entered in Microsoft Excel and analysed using Epi-Info software. Line diagram was constructed to obtain the trends in AUFI cases over the five-year period. Chisquare test was applied to find out the association between age group (paediatric and others) and causative agents of AUFI. RESULTS A total of 7998 AUFI cases were included in this study. Highest number of AUFI cases (1986) was reported in the year 2017. Under five age group was the most common, 1564 (19.6 %). Majority of the AUFI were reported among males, 4947 (61.9 %). Undetermined fever was found to be the most common AUFI, 2188 (27.4 %). Dengue fever, 1993 (24.9 %) was the most diagnosed AUFI. Viral aetiology was found to be the most common, 3895 (48.7 %) in both paediatric and other age groups. No statistically significant association was found between (P-value = 0.22) age group and organisms causing AUFI. There were 121 (1.5 %) deaths reported among the AUFI cases during the five-year period. It was seen that the case fatality due to AUFI was highest (2.41 %) in the year 2019. CONCLUSIONS A lack of consistent pattern in the aetiology of AUFI cases reported over the fiveyear study period, calls for persistent vigil by the public health surveillance system in the region, to identify any impending infectious disease outbreak at the earliest. As this study is based on hospital-based surveillance record it can underestimate the disease incidence. KEYWORDS Acute Undifferentiated Febrile Illness (AUFI), Trends, Dengue, Acute Febrile Illness

Inferring trip purpose by clustering sequences of smart card records

Smart card transactions are known as a rich and continuous source of public transit data, but they miss some important attributes about trips and passengers. One of these missing attributes is the trip purpose attribute. This paper proposes a novel method to infer the trip purpose attribute from the sequences of trips of passengers instead of separate trips. The proposed method infers the trip purpose attribute (a missing attribute in the smart card data) from the temporal attributes (available attributes in the smart card data). First, the relation between the temporal attributes and the trip purpose attribute is learnt by discovering clusters of passengers in the Household Travel Survey dataset while each passenger is represented by one sequence of trips. Then, the discovered clusters are utilized to infer the trip purpose of smart card transactions by allocating each passenger to the closest clusters. The proposed method is implemented on the smart card and HTS datasets from southeast Queensland, Australia. The evaluation results showed a considerable improvement in inferring the trip purpose compared to the results published in the literature. Notably, the effect of considering the trip sequence was more significant than considering land use variables.

The Continuing Decline of Clinical Research on Serious Mental Illnesses at NIMH.

It has been claimed that the National Institute of Mental Health (NIMH) budget, which traditionally has been evenly balanced between basic and clinical research, has shifted sharply and that 90% of NIMH resources are funding basic research. The authors used public data sources to assess this claim: the Research Condition and Disease Categorization Database, ClinicalTrials.gov, and the NIMH Strategic Plan for Research for 2020-2024. From 2016 to 2019, NIMH expenditures on bipolar disorder research decreased 25%, and those for schizophrenia research decreased 17.5%. From 2003 to 2019, NIMH support for treatment trials for schizophrenia, bipolar disorder, and major depressive disorder decreased 90%. NIMH's Strategic Plan for Research for 2020-2024 suggests that the shift toward basic research will continue. Because NIMH's primary purpose is to develop better treatments for current patients as well as future ones, the authors recommend that the ratio of basic to clinical research be readjusted to approximately 50:50.

Resistance, race, and subjectivity in congregation-based community organizing.

Ideas of resistance have become common in media and political discourse in contemporary times where there is growing awareness of racial violence and xenophobia. Calls to "resist" and awakenings to public life prompt questions about the kind of citizenship being cultivated, the social meanings individuals reproduce and create through participation in "resistance," and the changing sense of their positions and agency as they act in the world. Here I examine the citizen-subject that comes into being through "resistance" to racial injustice, drawing on the case of Faith in Action (formerly PICO) and its development of a theological organizing framework, the Theology of Resistance. This study analyzes the discourses and content of two public data sources-The Prophetic Resistance Podcast series and news media about prophetic resistance within the organizing network. These sources offer a means to examine the negotiated nature of political selves that are created through processes that socialize and subjugate as well as through processes wherein subjects produce and sometimes transform social positions. Findings show that centering a racial analytic and prioritizing racial justice outcomes, shifts that were made within FIA, results in the cultivation of a political subject that is reflexive about internal and external subjugating forces, relational as it discards the armor of racial hierarchy and exclusion, and constructive as it creates conditions or contexts for new political subjects through prophetic action. This study contributes to the conceptual development of organizing as a mechanism to generate social change; specifically, it offers the lens of political subjectivity as a meaningful analytic to enrich understandings of this mechanism.

An Analysis of Blockchain Adoption in Supply Chains Between 2010 and 2020

In this research, the evolution of blockchain applied to supply chains has been mapped from the inception of the technology until June 2020, utilizing primarily public data sources. We have analyzed 271 blockchain projects on parameters such as their inception dates, types of blockchain, status, sectors applied to and type of organization that founded the project. We confirm generally understood trends in the blockchain market with new projects following the industry’s general hype and funding levels. We observe most activity in the Agriculture/Grocery sector and the Freight/Logistics sector. We see the shift of market interest from private companies (startups) to public companies and consortia and the change in blockchain adoption from Ethereum to Hyperledger. Finally, we observe more market-ready solutions and fewer inactive projects for Hyperledger-based projects than Ethereum-based projects.

Estimating determinants of healthcare establishment locations with restricted federal administrative data.

We model the locational determinants of nine categories of healthcare services in the contiguous United States using restricted access federal establishment data. These data enable close examination of rural health services, which are subject to suppression in publicly published data sources. After reviewing differences in public and unsuppressed restricted data and testing underlying data generation processes for each healthcare industry, including the Poisson, negative binomial, and their zero-inflated counterparts, we estimate marginal effects for four categories of independent variables: place-based factors, financial access, characteristics of population, and industry interdependencies. Findings show establishments are less likely to be found with high concentrations of Medicare and Medicaid recipients, while agglomerations are associated with more establishments. Nonemployer establishments serve a broader spectrum of people, but the rural poor still experience less access to health care.