Public Data Sources Research Articles

Ex-intrusion corporate cyber risk: evidence from internet protocol networks

Previous event studies of corporate cyber-risk have been limited to successful attacks on public firms but are biased samples constructed based on the economic magnitude of equity losses. To address this selection bias, we construct a larger and more representative sample of cyber intrusions only to find diminished negative equity (and insignificant corporate bond) market reactions compared to these prior studies. To identify cyber-risk irrespective of observing successful attacks, we match public firms to Internet protocol (IP) network data from the American Registry for Internet Numbers (ARIN) from 1991 to 2017. We find that both stockholders and creditors incorporate external IP network size into firm value. Further, debt and equity market reactions to cyberattacks are mitigated for firms with registered IP networks and that have larger network deployments. Overall, our study demonstrates an important public data source that can help institutions proxy for and more accurately price firm cybersecurity risk.

OpenRank – a novel approach to rank universities using objective and publicly verifiable data sources

PurposeAcademic rankings are facing various issues, including the use of data sources that are not publicly verifiable, subjective parameters, a narrow focus on research productivity and regional biases and so forth. This research work is intended to enhance creditability of the ranking process by using the objective indicators based on publicly verifiable data sources.Design/methodology/approachThe proposed ranking methodology – OpenRank – drives the objective indicators from two well-known publicly verifiable data repositories: the ArnetMiner and DBpedia.FindingsThe resultant academic ranking reflects common tendencies of the international academic rankings published by the Shanghai Ranking Consultancy (SRC), Quacquarelli Symonds (QS) and Times Higher Education (THE). Evaluation of the proposed methodology advocates its effectiveness and quick reproducibility with low cost of data collection.Research limitations/implicationsImplementation of the OpenRank methodology faced the issue of availability of the quality data. In future, accuracy of the academic rankings can be improved further by employing more relevant public data sources like the Microsoft Academic Graph, millions of graduate's profiles available in the LinkedIn repositories and the bibliographic data maintained by Association for Computing Machinery and Scopus and so forth.Practical implicationsThe suggested use of open data sources would offer new dimensions to evaluate academic performance of the higher education institutions (HEIs) and having comprehensive understanding of the catalyst factors in the higher education.Social implicationsThe research work highlighted the need of a purposely built, publicly verifiable electronic data source for performance evaluation of the global HEIs. Availability of such a global database would help in better academic planning, monitoring and analysis. Definitely, more transparent, reliable and less controversial academic rankings can be generated by employing the aspired data source.Originality/valueWe suggested a satisfying solution for improvement of the HEIs' ranking process by making the following contributions: (1) enhancing creditability of the ranking results by merely employing the objective performance indicators extracted from the publicly verifiable data sources, (2) developing an academic ranking methodology based on the objective indicators using two well-known data repositories, the DBpedia and ArnetMiner and (3) demonstrating effectiveness of the proposed ranking methodology on the real data sources.

434. Tracking COVID-19 in Real Time: Leveraging Public Data Sources to Inform Infection Prevention Practices

BackgroundGlobal spread of SARS-CoV-2 led to an urgent need for data on national and regional prevalence to inform public health policy. Healthcare systems were also in need of data to develop best practices around defining patient risk. We describe a data analytics tool developed at our institution which uses public data sources to track county-level prevalence of COVID-19 so as to delineate risk for individual patients.MethodsWe investigated a number of data sources tracking COVID-19 case counts, assessing for (1) frequency of updates, (2) granularity of geographic detail (optimally to zip-code or county) and (3) completeness of the data. We chose the Johns Hopkins University CSSE COVID-19 data set. This contains counts of new diagnoses per day by county using Federal Information Processing System (FIPS) codes. The dataset is updated daily with adjustments made for backdated corrections.We developed a data analytics tool which allowed for direct comparison of county period prevalence. We developed a metric of 10-day rolling period prevalence calculated as a total case count from the preceding 10 days, divided by county population from 2018 American Community Survey (ACS) estimates.ResultsBenchmarking against local (peak of 3.12 cases per 1,000 persons) and regional prevalence, we set 6 cases/1,000 persons as the threshold for a Geographic Region with Widespread Community Transmission (GReWCoT). Counties have to reach this threshold for at least 4 out of 7 days within the period 3 to 10 days prior to the evaluation, to adjust for bulking of test results and delayed reporting.We used the analytics tool to support a semimonthly review of geographic regions, and made specific recommendations for patients from qualifying regions including use of modified enhanced precautions (including surgical mask and eye protection), as well as restricted visitation of caregivers.Figure 1. Epidemic curves for 10-day rolling period prevalence of COVID-19 in the Mid-Atlantic Region: Philadelphia County, PA Figure 2. Epidemic curves for 10-day rolling period prevalence of COVID-19 in the Mid-Atlantic Region: Westchester County, NY Figure 3. Epidemic curves for 10-day rolling period prevalence of COVID-19 in the Mid-Atlantic Region: Bergen County, NJ ConclusionThis approach allowed for a nuanced investigation of COVID-19 prevalence in real-time, and provided support for risk stratification of patients throughout our large catchment area. The dashboard was shared on an inward-facing site to support staff messaging about regions of increased risk. Next steps include leveraging international data to inform a similar approach to international travel for our patients and staff.Disclosures All Authors: No reported disclosures

Value-Based Purchasing Financial Outcomes Among Minority-Serving Skilled Nursing Facilities

Skilled Nursing Facility (SNF) Value-Based Purchasing (VBP) ties post-acute payments to readmissions performance. Minority-serving SNFs tend to be poorly resourced and understanding the financial implications of SNF-VBP is important. Our study examined VBP payments and penalties among minority-serving SNFs. We conducted cross-sectional analysis using public data sources. We defined minority-serving as SNFs with >50% of residents who are African American (AA)/Black (n=764) or with >50% of residents who are Hispanic/Latino (n=164). Majority-White SNFs (>50% residents who are White) were the reference group (n=11,002). Outcomes examined were: being in top 20% of performance rankings; receiving a bonus; receiving a bonus that was above the median bonus dollar amount; being in bottom 20% of performance rankings; receiving a penalty; receiving a penalty that was below the median penalty dollar amount. Logistic models estimated the likelihood of experiencing each performance outcome for AA/Black-serving and Hispanic/Latino-serving SNFs in reference to majority-White SNFs. Results show that minority-serving SNFs not only perform worse but also experience greater negative financial impacts. Among those penalized, Hispanic/Latino-serving SNFs had the largest average penalty amounts: $32,575 compared to approximately $26,000 for both AA/Black- and White-serving SNFs. Hispanic/Latino-serving SNFs had 1.69 times the odds of receiving larger than median penalties and AA/Black-serving SNFs had 27% lower odds of receiving higher than median bonus payments. Average penalties approximate the average salary for a certified nursing assistant, the primary direct care worker in this setting. Results from this study raise concerns over long-term impacts. Alternative approaches to encouraging quality should be considered.

Involving the Public in Data Linkage Research

Introduction“The Consensus Statement on Public Involvement and Engagement with Data-Intensive Health Research”, recent data breaches, and growing public awareness and controversy associated with secondary use of health data all highlight the need to understand what data sharing the public will support, under what circumstances, for what purposes and with whom.
 Objectives and ApproachThis symposium explores methods and findings from public engagement at all stages of data linkage research, beginning with short presentations (~6-8 minutes) on recent work:
 
 Mhairi Aitken: Consensus Statement - principles and an application using deliberative workshops to explore public expectations of public benefits from data-intensive health research
 Annette Braunack-Mayer/Felicity Flack: Surveys and citizens’ juries: Sharing government data with private industry
 Kim McGrail/Mike Burgess: Public deliberations on cross-sector data linkage, and combining public and private sources of data
 Alison Paprica: Plain language communication informed by Health Data Research Network Canada’s Public Advisory Council.
 
 Half the session will be spent interacting with the audience through live polling. The moderator will post a series of poll question such as “What is the most important thing for meaningful public engagement?” to prompt audience thinking on the topic. After the audience responses are revealed, panelists will share their own views about what they think is the best answer, and the main reason(s) behind their choice. The last 10-15 minutes of the session will be reserved for Q&A and dialogue with the audience.
 ResultsWe anticipate that this approach will surface emerging and tacit knowledge from presenters and the audience, and augment that through generative discussion.
 Conclusion / ImplicationsSession attendees will leave with a better understanding of the current state of knowledge and ways to talk about that understanding with other researchers, policy makers and the public.

PSY34 What Do Public DATA Sources Tell US about Systemic LUPUS Erythematosus in Brazil?

Systemic lupus erythematosus (SLE), a severe autoimmune disease, substantially impacts patients’ lives due to disease activity and treatment side effects. This research aims to describe the impact of SLE in Brazil. Open data were obtained from the Brazilian Hospital Information System (2009–2019) and Mortality Information System (2009–2018). Robotic process automation was used to convert data from both information systems into an SQLite-compatible format. Data extraction was performed for ICD-10 M32 and its subtypes using a Structured Query Language. Analyses were performed for mortality, hospitalization frequency and hospitalization costs in Brazilian reais (BRL). The exchange rate varied from 1 USD = 2.37 BRL (2009) to 3.73 BRL (2019). In 2018, 1,008 SLE-related deaths occurred in Brazil (3/day), representing a 19% increase compared with 2009 (844 deaths). Furthermore, the number of in-hospital SLE-related deaths increased 11-fold from 5 in 2009 to 56 in 2019. The mean annual growth in the total number of hospitalizations was 14.8%, and for admission to an intensive care unit (ICU) was 52.9%. A mean of 19% of hospitalizations were paediatric (0–17 years) patients. The number and total cost of SLE-related hospitalizations increased 4.5- and 6-fold, respectively (2009 vs 2019: 573 vs 2,611; USD 82,362.97 vs 501,368.11). The number and cost of ICU admissions increased from 2009 to 2019, with 3 versus 111 admissions (37-fold increase) and USD 8,003.44 versus 118,499.27 (14-fold increase), respectively. In 2019, ICU costs represented 24% of the total hospitalization costs. The mean total length of hospital stay was 17 days (9 days inpatient treatment, 8 ICU). In contrast to other countries, the annual number of SLE-related hospitalizations and deaths in Brazil has grown over the last 10 years. These data highlight an unmet need to improve SLE management in Brazil.

PMU43 Biosimilar in China: Will Indication-Based Approach Help Improve Patient Access?

Since the introduction of biosimilars in 2004, China has become a market with enormous potential given the demand for more affordable therapeutic alternatives. We explored the approaches taken by biosimilar manufacturers, especially around their choices of initial indications at launch. We used rituximab as an example for this analysis. We conducted a non-systematic review of public data sources to identify formal guidance for biosimilar development. For rituximab, we reviewed ongoing and proposed clinical trials for rituximab and its biosimilars in China. Biosimilars offer a more affordable alternative than biologics in several indications and may improve patient access, as drug cost can be a challenge for Chinese patients on public health insurance programs. Rituximab is currently approved for three indications - Non-Hodgkin’s Lymphoma (NHL), Diffuse Large B-cell NHL (DLBCL), and Chronic Lymphocytic Leukemia (CLL). The first rituximab biosimilar, HLX01 was approved in February 2019 by the Chinese National Medicinal Products Administration (NMPA) for two of those indications - NHL and DLBCL. Domestic biotechnology company Innovent in collaboration with Eli Lilly followed shortly with an application for IBI301 in CD20-Positive Lymphoma which was accepted for review in June 2019. As of July 2nd, 2020, there are two active biosimilar clinical trials for HLX01; one open-label Phase II (NCT04361279) in patients with indolent B-cell Lymphoma (iBCL), and a Phase III (NCT03522415) in patients with Rheumatoid Arthritis (RA). Rituximab does not currently have a clinical trial in development for RA nor iBCL in China. Biosimilar development is typically focused on originator on-label indications. However, with an active trial in iBCL, HLX01 may be seeking to demonstrate clinical benefit in targeted patient subgroups that are more difficult-to-treat in the on-label indications as compared to rituximab. This would potentially improve physician perceptions of biosimilars and their willingness to prescribe and use in clinical practice.

PAGER-CoV: a comprehensive collection of pathways, annotated gene-lists and gene signatures for coronavirus disease studies.

PAGER-CoV (http://discovery.informatics.uab.edu/PAGER-CoV/) is a new web-based database that can help biomedical researchers interpret coronavirus-related functional genomic study results in the context of curated knowledge of host viral infection, inflammatory response, organ damage, and tissue repair. The new database consists of 11 835 PAGs (Pathways, Annotated gene-lists, or Gene signatures) from 33 public data sources. Through the web user interface, users can search by a query gene or a query term and retrieve significantly matched PAGs with all the curated information. Users can navigate from a PAG of interest to other related PAGs through either shared PAG-to-PAG co-membership relationships or PAG-to-PAG regulatory relationships, totaling 19 996 993. Users can also retrieve enriched PAGs from an input list of COVID-19 functional study result genes, customize the search data sources, and export all results for subsequent offline data analysis. In a case study, we performed a gene set enrichment analysis (GSEA) of a COVID-19 RNA-seq data set from the Gene Expression Omnibus database. Compared with the results using the standard PAGER database, PAGER-CoV allows for more sensitive matching of known immune-related gene signatures. We expect PAGER-CoV to be invaluable for biomedical researchers to find molecular biology mechanisms and tailored therapeutics to treat COVID-19 patients.

Visualization Environment for Federated Knowledge Graphs: Development of an Interactive Biomedical Query Language and Web Application Interface.

BackgroundEfforts are underway to semantically integrate large biomedical knowledge graphs using common upper-level ontologies to federate graph-oriented application programming interfaces (APIs) to the data. However, federation poses several challenges, including query routing to appropriate knowledge sources, generation and evaluation of answer subsets, semantic merger of those answer subsets, and visualization and exploration of results.ObjectiveWe aimed to develop an interactive environment for query, visualization, and deep exploration of federated knowledge graphs.MethodsWe developed a biomedical query language and web application interphase—termed as Translator Query Language (TranQL)—to query semantically federated knowledge graphs and explore query results. TranQL uses the Biolink data model as an upper-level biomedical ontology and an API standard that has been adopted by the Biomedical Data Translator Consortium to specify a protocol for expressing a query as a graph of Biolink data elements compiled from statements in the TranQL query language. Queries are mapped to federated knowledge sources, and answers are merged into a knowledge graph, with mappings between the knowledge graph and specific elements of the query. The TranQL interactive web application includes a user interface to support user exploration of the federated knowledge graph.ResultsWe developed 2 real-world use cases to validate TranQL and address biomedical questions of relevance to translational science. The use cases posed questions that traversed 2 federated Translator API endpoints: Integrated Clinical and Environmental Exposures Service (ICEES) and Reasoning Over Biomedical Objects linked in Knowledge Oriented Pathways (ROBOKOP). ICEES provides open access to observational clinical and environmental data, and ROBOKOP provides access to linked biomedical entities, such as “gene,” “chemical substance,” and “disease,” that are derived largely from curated public data sources. We successfully posed queries to TranQL that traversed these endpoints and retrieved answers that we visualized and evaluated.ConclusionsTranQL can be used to ask questions of relevance to translational science, rapidly obtain answers that require assertions from a federation of knowledge sources, and provide valuable insights for translational research and clinical practice.

Abstract PO-008: Multi-omic Profiling of primary pancreatic adenocarcinomas obtained from the APACT adjuvant trial of nab-paclitaxel + gemcitabine vs gemcitabine

Abstract Pancreatic ductal adenocarcinoma (PDAC) remains a difficult disease to treat, with few therapies available that target specific patient subgroups. Translational studies in pancreatic cancer can be technically challenging due to biopsy specimen characteristics including low tumor cellularity and dense fibrotic stroma. Resected primary pancreatic tumors obtained during the phase 3 APACT clinical trial (NCT01964430 adjuvant nab-paclitaxel + gemcitabine versus gemcitabine monotherapy n = 866) are a unique resource for characterizing molecular and immune subtypes among PDAC tumors and their association with treatment outcomes in the adjuvant chemotherapy setting. Tumor-infiltrating immune cells were assessed in serial sections of 453 tumors by dual-chromogen immuno-histochemical (IHC) staining (CD4 CD8 CD20 CD163 CMAF CD56 FoxP3 PD-1 PD-L1). Image alignment, segmentation, and spatial localization of stained cells relative to a pan-cytokeratin staining-based tumor epithelial mask was performed using a commercial analysis platform. Expression profiles were obtained for 515 macrodissected tumor biopsy regions by RNA-seq. Transcriptional subtypes were assigned based on schema previously reported by Moffit and Bailey, and molecular pathway correlates were characterized using gene set enrichment analysis. Based on immunochemical staining, higher intratumoral CD8+ or lower CD163+ cell densities were associated with modestly longer disease-free or overall survival in patients treated with nab-paclitaxel plus gemcitabine. The combination of both high CD8+ and low CD163+ cell density was notably associated with longer overall survival compared to other subjects treated with the combination regimen (mOS>55months versus 36 months HR=0.46 p=<0.01) Transcriptional subtyping of tumors, based on schema such as those previously reported by Moffit et al or Bailey et al, identified groups defined by anticipated signatures. Classical/progenitor subtypes differentially expressed genes involved in oxidative respiration and endodermal cell fate, while basal/squamous subtypes showed high expression of signatures for hypoxia, mesenchymal transformation, and TGFb signaling. Longer overall survival in subjects treated with nab-paclitaxel plus gemcitabine as compared to gemcitabine monotherapy was seen principally in subjects with tumors of Moffit classical (HR=0.59 P=0.01) and Bailey progenitor (HR=0.42 P<0.01) subtypes. The observations highlighted provide insight into primary PDAC tumor characteristics that are associated with treatment-specific outcomes to nab-paclitaxel based adjuvant chemotherapy. Future work will combine data described here with mutational and genetic studies in progress as well as with other public, proprietary, or commercial data sources. In combination, we anticipate that these data may be useful in identifying patient subsets and molecular mechanisms that might be targeted by novel combination therapies for improved treatment of pancreatic cancer. Citation Format: Thomas Lila, Andrew Biankin, Andrew Browne, David J. Reiss, Brian Lu, Daniel Pierce, Alexander Ratushny, Kao-tai Tsai, Sneh Lata, Sitharthan Kamalakaran, Tomas Babak, Brian Fox, Sam Danziger, Konstantinos Mavrommatis, Matthew W. B. Trotter, David Chang, Fadi Towfic. Multi-omic Profiling of primary pancreatic adenocarcinomas obtained from the APACT adjuvant trial of nab-paclitaxel + gemcitabine vs gemcitabine [abstract]. In: Proceedings of the AACR Virtual Special Conference on Pancreatic Cancer; 2020 Sep 29-30. Philadelphia (PA): AACR; Cancer Res 2020;80(22 Suppl):Abstract nr PO-008.

Assessing public behavioral health services data: a mixed method analysis

BackgroundMeasuring behavioral health treatment accessibility requires timely, comprehensive and accurate data collection. Existing public sources of data have inconsistent metrics, delayed times to publication and do not measure all factors related to accessibility. This study seeks to capture this additional information and determine its importance for informing accessibility and care coordination.MethodsThe 2018 National Survey for Substance Abuse and Treatment Services (N-SSATS) data were used to identify behavioral health facilities in Indiana and gather baseline information. A telephone survey was administered to facilities with questions parallel to the N-SSATS and additional questions regarding capacity and patient intake. Quantitative analysis includes chi-square tests. A standard qualitative analysis was used for theming answers to open-ended questions.ResultsAbout 20% of behavioral health facilities responded to the study survey, and non-response bias was identified by geographic region. Among respondents, statistically significant differences were found in several questions asked in both the study survey and N-SSATS. Data gathered from the additional questions revealed many facilities to have wait times to intake longer than 2 weeks, inconsistency in intake assessment tools used, limited capacity for walk-ins and numerous requirements for engaging in treatment.ConclusionDespite the low response rate to this study survey, results demonstrate that multiple factors not currently captured in public data sources can influence coordination of care. The questions included in this study survey could serve as a framework for routinely gathering these data and can facilitate efforts for successful coordination of care and clinical decision-making.

Population-weighted exposure to air pollution and COVID-19 incidence in Germany

Many countries have enforced social distancing to stop the spread of COVID-19. Within countries, although the measures taken by governments are similar, the incidence rate varies among areas (e.g., counties, cities). One potential explanation is that people in some areas are more vulnerable to the coronavirus disease because of their worsened health conditions caused by long-term exposure to poor air quality. In this study, we investigate whether long-term exposure to air pollution increases the risk of COVID-19 infection in Germany. The results show that nitrogen dioxide (NO2) is significantly associated with COVID-19 incidence, with a 1 μg m−3 increase in long-term exposure to NO2 increasing the COVID-19 incidence rate by 5.58% (95% credible interval [CI]: 3.35%, 7.86%). This result is consistent across various models. The analyses can be reproduced and updated routinely using public data sources and shared R code.

First-Year Teachers' Preparedness in Using Student Assessment Data

In an attempt to better understand if teachers are trained and prepared to use student assessment data, this study used a nationally representative data set. The public data source for this exploratory study was gathered from Schools and Staff Survey (SASS), specifically, the Public Teachers: 2011–12 database. The multiple linear regression findings revealed that teachers feel they influence the content of in-service professional development programs at their school. Also, school leaders should consider gender and race when creating training because results show that professional development experiences of gender and race/ethnicity are different. The article presents several recommendations for school leaders in public institutions.

Mortalidade materna: perfil de um estado do nordeste brasileiro

O presente estudo tem como objetivo analisar o perfil epidemiológico da mortalidade materna no Estado de Alagoas no período entre 2009 e 2018, por meio de uma abordagem quantitativa, sendo o estudo do tipo descritivo e ecológico, utilizando fonte de dados secundária pública. Foram identificadas 290 mortes maternas de mulheres residentes no estado de Alagoas no período analisado, a maioria (38,3%) na faixa etária de 20 a 29 anos, pardas (72,8%), com 1 a 7 anos de estudo (41,4%), solteiras (46,6%), sendo 74,5% dos óbitos ocorridos por causa obstétrica direta no período perinatal; de todos os óbitos do período analisado 92,1% ocorreram no ambiente hospitalar e a principal causa sendo as síndromes hipertensivas (20,3%). Conclui-se que o perfil epidemiológico da mortalidade materna em Alagoas mostrou-se semelhante ao perfil brasileiro e diferente da realidade mundial em relação à principal causa dos óbitos.

Erroneous Reporting of Deaths Attributed to Pneumonia and Influenza at 2 New York City Teaching Hospitals, 2013-2014.

Cause-of-death information, reported by frontline clinicians after a patient's death, is an irreplaceable source of public health data. However, systematic bias in cause-of-death reporting can lead to over- or underestimation of deaths attributable to different causes. New York City consistently reports higher rates of deaths attributable to pneumonia and influenza than many other US cities and the country. We investigated systematic erroneous reporting as a possible explanation for this phenomenon. We reviewed all deaths from 2 New York City hospitals during 2013-2014 in which pneumonia or influenza was reported as the underlying cause of death (n = 188), and we examined the association between erroneous reporting and multiple extrinsic factors that may influence cause-of-death reporting (patient demographic characteristics and medical comorbidities, time and hospital location of death, type of medical provider reporting the death, and availability of certain diagnostic information). Pneumonia was erroneously reported as the underlying cause of death in 163 (86.7%) reports. We identified heart disease and dementia as the more likely underlying cause of death in 21% and 17% of erroneously reported deaths attributable to pneumonia, respectively. We found no significant association between erroneous reporting and the multiple extrinsic factors examined. Our results underscore how erroneous reporting of 1 condition can lead to underreporting of other causes of death. Misapplication or misunderstanding of procedures by medical providers, rather than extrinsic factors influencing the reporting process, are key drivers of erroneous cause-of-death reporting.

Cost-effectiveness of maintenance hormonal therapy in patients with advanced low grade serous ovarian cancer

ObjectivesTo assess the cost-effectiveness of using maintenance hormonal therapy in patients with low grade serous ovarian cancer (LGSC). MethodsA simulated decision analysis with a Markov decision model over a lifetime horizon was performed using the base case of a 47-year old patient with stage IIIC, LGSC following first-line treatment with primary cytoreductive surgery and adjuvant chemotherapy. Two treatment strategies were analyzed - maintenance daily letrozole until disease progression and routine observation. The analysis was from the perspective of the healthcare payer. Direct medical costs were estimated using public data sources and previous literature and were reported in adjusted 2018 Canadian dollars. The model estimated lifetime cost, quality-adjusted life years (QALY), life years (LY), median overall survival (OS), and number of recurrences with each strategy. Cost-effectiveness was compared using an incremental cost-effectiveness ratio (ICER). A strategy was considered cost-effective when the ICER was less than the willingness to pay (WTP) threshold of $50,000 CAD per QALY. Deterministic sensitivity analysis was performed to assess the impact of changing key clinical and cost variables. ResultsMaintenance letrozole was the preferred strategy with an associated lifetime cost of $69,985 CAD ($52,620 USD) and an observed improvement of 0.91 QALYs and 1.55 LYs. The ICER for letrozole maintenance therapy was an additional $11,037 CAD ($8298 USD) per QALY. The modeled median OS was 150 months with maintenance letrozole and 126 months in the observation strategy. The maintenance letrozole strategy resulted in 34% and 17% fewer first recurrences at 5-year and 10-year follow-up, respectively. ConclusionMaintenance letrozole is a cost-effective treatment strategy in patients with advanced LGSC resulting in clinically-relevant improvement in QALYs, LYs, and fewer disease recurrences.

Innovative Empirical Model for Predicting National Banks’ Financial Failure with Artificial Intelligence Subset Data Analysis in the United States

The principal objective of this research study was to investigate the impact of the Great Economic Recession of 2008 on national banks’ equity investment valuations and create an empirical model for predicting national banks’ financial failure in the United States. The focal period of the study was from 2009 to 2012, and public data sources used. It is not known to what extent national banks’ stock value investments are based on the return on equity. This causal-comparative study explores the degree to which national banks’ value investment in terms of the price to earnings ratio impacts their return on equity and the extent to which these banks’ stock value investment in terms of dividend yield impacts their return on equity. We used statistical modeling and the machine learning model to find hidden patterns in the input data. The principal finding of this research is that the median earnings per share in 2012 and the dividend yield in 2009 were significantly larger than the median return on equity in 2009 and 2012. Additionally, the dividend yield in 2012 was significantly smaller than the median return on equity in 2012. These findings can contribute to improving our understanding of how banks can predict financial failure using the new machine learning features of artificial intelligence to build an early warning system with the innovative risk measurement tool.

Estimating the government public economic benefits attributed to investing in assisted reproductive technology: a South African case study.

Limited resources and high treatment costs are arguments often used in many public health systems in low- and middle-income countries to justify providing limited treatments for people with infertility. In this analysis, we apply a government public economic perspective to evaluate public subsidy for in-vitro fertilization (IVF) in South Africa. A fiscal model was developed that considered lifetime direct and indirect taxes paid and government transfers received by a child conceived by IVF. The model was constructed from public data sources and was adjusted for mortality, age-specific educational costs, participation in the informal economy, proportions of persons receiving social grants, and health costs. Based on current proportions of individuals receiving social grants and average payments, including education and health costs, we estimate each citizen will receive ZAR513,165 (USD35,587) in transfers over their lifetime. Based on inflated age-specific earnings, we estimate lifetime direct and indirect taxes paid per citizen of ZAR452,869 (USD31,405) and ZAR494,521 (USD34,294), respectively, which also includes adjustments for the proportions of persons participating in the informal economy. The lifetime net tax after deducting transfers was estimated to be ZAR434,225 (USD31,112) per person. Based on the average IVF investment cost needed to achieve one live birth, the fiscal return on investment (ROI) for the South African Government is 5.64. Varying the discount rate from 4% to 7%, the ROI ranged from 9.54 to 1.53, respectively. Positive economic benefits can emanate from public financing of IVF. The fiscal analytic framework described here can be a useful approach for health services to evaluate future public economic benefits.

Correcting TLEs at epoch: Application to the GPS constellation

Two-Line Elements (TLEs) issued by the space-track catalog are still the most extensive public data source for space debris tracking to date. However, TLEs accuracy at epoch is typically larger than 1 km in Low Earth Orbit (LEO) and 3 km in Medium Earth Orbit (MEO). Therefore, TLEs are too coarse to enable collision avoidance maneuvers. The present work aims at correcting TLEs orbits at epoch to enable operational conjunction assessment and help satellite operators better protect their assets at moderate cost.Using only Moon-Earth and Sun-Earth distance time series, as well as 2018 TLE data for 14 GPS satellites and SGP4 propagation over a short period, we were able to correct the TLEs of the 29 operational GPS satellites by 65% on average for year 2019, reducing the 3D RMS error at epoch from 2.2 km to 680 m. In other words, we improved TLEs accuracy from 5 km to 1.5 km with a 95% confidence level.

Participatory quantitative health impact assessment of urban transport planning: A case study from Eastern Africa

BackgroundHigh rates of motorization in urban areas of Africa have adverse effects on public health. Transport-related mortality will increase as a result of inadequate transport infrastructure, air pollution and sedentary lifestyles. Health Impact Assessments (HIAs) have proven to be a successful tool to predict and mitigate negative health impact of urban transport planning policies, programmes or projects. Yet, there is a gap of evidence on transport and health in African countries. The aim of this study is assessing the health impacts of transport scenarios in Port Louis (city of 119,018 inhabitants in Mauritius) using a full chain participatory HIA model. MethodsWe estimated health and economic impacts associated to transport scenarios with qualitative data and quantitative comparative risk assessment methods. The health impact modeling was based on differences between the baseline and three transport scenarios (worse, good, ideal), estimating the averted deaths per year and economic outcomes by assessing health determinants of air pollution (AP), traffic deaths and physical activity (PA). Data on air pollution and traffic fatalities were obtained from public data sources. Data used to construct scenarios, establish baseline travel mode shares and physical activity were collected through (a) open-ended individual interviews (IDIs) with 14 stakeholders (b) closed-ended survey questions to 600 citizens and (c) 2 focus group discussions (FGDs) with the same 14 stakeholders from (a). ResultsIn Port Louis, the worse-case transport scenario (doubling in car trips and a reduction in walking, motorcycle, and public transport), resulted in a total increment of 3.28 premature deaths per year. The good-case scenario (reducing car trips by half and increasing walking, motorcycle, and public transport trips) resulted in a total increment of 0.79 premature deaths per year. The ideal-case scenario (reduction in car and motorcycle trips and an increase in walking and public transport trips) resulted in a total reduction of 13.72 premature deaths per year. We estimated USD 23 millions of economic benefits related to mortality if the ideal-case was achieved. ConclusionParticipatory HIA shows that implementing transport policies aiming for less than an ideal situation may not be adequate or sufficient to avoid negative transport-related mortality in Mauritius. Urban transport planning is an opportunity to encourage physical activity in rapidly urbanizing settings of Africa. Transport policies should aim to restrict all forms of private motorized vehicles and promote active and public transport to support public health. We highly recommend the use of participatory approaches in quantitative HIA to ensure context specificity and policy relevance.