Lack Of Accessible Data Research Articles

Understanding health service utilisation patterns for care home residents during the COVID-19 pandemic using routinely collected healthcare data

BackgroundHealthcare in care homes during the COVID-19 pandemic required a balance, providing treatment while minimising exposure risk. Policy for how residents should receive care changed rapidly throughout the pandemic. A lack of accessible data on care home residents over this time meant policy decisions were difficult to make and verify. This study investigates common patterns of healthcare utilisation for care home residents in relation to COVID-19 testing events, and associations between utilisation patterns and resident characteristics.MethodsDatasets from County Durham and Darlington NHS Foundation Trust including secondary care, community care and a care home telehealth app are linked by NHS number used to define daily healthcare utilisation sequences for care home residents. We derive four 10-day sets of sequences related to Pillar 1 COVID-19 testing; before [1] and after [2] a resident’s first positive test and before [3] and after [4] a resident’s first test. These sequences are clustered, grouping residents with similar healthcare patterns in each set. Association of individual characteristics (e.g. health conditions such as diabetes and dementia) with healthcare patterns are investigated.ResultsWe demonstrate how routinely collected health data can be used to produce longitudinal descriptions of patient care. Clustered sequences [1,2,3,4] are produced for 3,471 care home residents tested between 01/03/2020–01/09/2021. Clusters characterised by higher levels of utilisation were significantly associated with higher prevalence of diabetes. Dementia is associated with higher levels of care after a testing event and appears to be correlated with a hospital discharge after a first test. Residents discharged from inpatient care within 10 days of their first test had the same mortality rate as those who stayed in hospital.ConclusionWe provide longitudinal, resident-level data on care home resident healthcare during the COVID-19 pandemic. We find that vulnerable residents were associated with higher levels of healthcare usage despite the additional risks. Implications of findings are limited by the challenges of routinely collected data. However, this study demonstrates the potential for further research into healthcare pathways using linked, routinely collected datasets.

3DSC - a dataset of superconductors including crystal structures

Data-driven methods, in particular machine learning, can help to speed up the discovery of new materials by finding hidden patterns in existing data and using them to identify promising candidate materials. In the case of superconductors, the use of data science tools is to date slowed down by a lack of accessible data. In this work, we present a new and publicly available superconductivity dataset (‘3DSC’), featuring the critical temperature TC of superconducting materials additionally to tested non-superconductors. In contrast to existing databases such as the SuperCon database which contains information on the chemical composition, the 3DSC is augmented by approximate three-dimensional crystal structures. We perform a statistical analysis and machine learning experiments to show that access to this structural information improves the prediction of the critical temperature TC of materials. Furthermore, we provide ideas and directions for further research to improve the 3DSC. We are confident that this database will be useful in applying state-of-the-art machine learning methods to eventually find new superconductors.

Navigating Design, Data, and Decision in an Age of Uncertainty

The Future of Design Education working group on technical systems argues that the approach to handling data—the methods used, and the expectations for outcomes—can transform design practice. In contrast to design’s past defined by a lack of accessible data, today’s rapidly evolving age of data abundance informs the choices available—the decision space—with far-reaching consequences for organizational, social, and environmental well-being. The shifting design landscape requires new tools and techniques to navigate this data age effectively. This paper proposes a new curricular approach that intersects data, technology, and design to create an environment where students can evaluate their roles and impact, and interact effectively through data with humans and computational collaborators. This data-oriented curriculum includes foundational technical skills proficiency, data analytical skills, rhetorical skills for arguing with data, interdisciplinary design studies, and a focus on designing for society. It embraces the complexities and opportunities of the data age, and acknowledges the inherent uncertainty in this new landscape. The aim is to prepare the next generation of designers to create data-informed, human-centered, ethical, and sustainable designs, thereby fostering an inclusive, equitable, and sustainable future.

Bibliometric trends of scientific publications on antimicrobial resistance in Escherichia coli in Peru from 2009-2019.

Antibiotic resistance is a growing problem worldwide. While the most visible aspect is the direct effect of antimicrobial resistance in clinical settings, this problem affects all microorganisms in all environments. Nevertheless, most of the data available on antibiotic resistance outside clinical settings are from high-income countries, with the current situation in most low- and middle-income countries remaining under-reported. Escherichia coli is considered a good marker of “antibiotic pressure” due to its cosmopolitan distribution. In this scenario, we selected E. coli as an appropriate microorganism to perform an initial bibliometric analysis of publications focused on antimicrobial resistance outside disease-causing microorganisms. Thus, the present manuscript performs an analysis of the studies reporting measurable levels of antibiotic resistance of E. coli not causing human disease in Peru published during the period from 2009-2019. Following a search in PubMed and Google, 35 documents were selected as reporting measurable data of antibiotic resistance. Of these, 8 (22.8%) were reported in thesis format. The mean delay between sampling and publishing in article format was 3.7 years, with one study having been published 18 years after sampling. The number of publications developed in Peru describing antimicrobial resistance levels in E. coli out of hospital settings in the period 2009-2019 is scarce. Of these, a relevant number is of unpublished thesis. These findings showing the gap of knowledge and lack of accessible data about antimicrobial resistance out of hospital environments. Efforts and strategies must be developed to fill this knowledge gap.

Putting pesticides on the map for pollinator research and conservation

Wild and managed pollinators are essential to food production and the function of natural ecosystems; however, their populations are threatened by multiple stressors including pesticide use. Because pollinator species can travel hundreds to thousands of meters to forage, recent research has stressed the importance of evaluating pollinator decline at the landscape scale. However, scientists’ and conservationists’ ability to do this has been limited by a lack of accessible data on pesticide use at relevant spatial scales and in toxicological units meaningful to pollinators. Here, we synthesize information from several large, publicly available datasets on pesticide use patterns, land use, and toxicity to generate novel datasets describing pesticide use by active ingredient (kg, 1997–2017) and aggregate insecticide load (kg and honey bee lethal doses, 1997–2014) for state-crop combinations in the contiguous U.S. Furthermore, by linking pesticide datasets with land-use data, we describe a method to map pesticide indicators at spatial scales relevant to pollinator research and conservation.

A review of the known distribution of Halophila spinulosa in Indonesia with herbarium from Laikang in South Sulawesi

Seagrasses are true plants living in the marine realm. The Coral Triangle is also the centre of seagrass diversity with 14 species recorded in Indonesian waters. There is a growing awareness of the ecological and socio-economic importance of seagrass meadows as well as the growing threats to these valuable coastal ecosystems. However, there is a lack of accessible data on seagrass distribution at ecosystem and species scales. The most speciose seagrass genus is Halophila, with at least 7 species (Halophila beccarii, H. decipiens, H. major, H. minor, H. ovalis, H. sulawesii and H. spinulosa) known to occur in Indonesia. Reported from Malaysia to eastern Australia, Halophila spinulosa is a data deficient tropical seagrass. We review the known distribution of H. spinulosa based on a search of Indonesian and international literature. We also present an annotated herbarium of H. spinulosa samples collected from Laikang in South Sulawesi, Indonesia. Samples were collected in September 2020 at low tide at depths of around 0.75 to 1.25 m on a silty sand substrate along with samples of other seagrass species present. Herbarium specimens were cleaned of epiphytes and sand. The samples were laid out carefully on A4 paper, arranged so that all parts were clearly visible, covered with gauze, and placed on folded newspaper. The prepared samples were then sandwiched between piles of newspaper and placed in a wooden press. The herbarium specimens will be preserved in the Marine Biology Laboratory collection, Marine Science Department, Universitas Hasanuddin. We consider the distribution data for H spinulosa are far from complete; this review and herbarium should inform and spur further research on this species and it’s distribution at macro (site) and micro (within site) scales.

Protective effect of predator species richness on human hantavirus infection incidence

Are predators of rodents beneficial for public health? This question focuses on whether predators regulate the spillover transmission of rodent-borne diseases. No clear answer has emerged because of the complex linkages across multiple trophic levels and the lack of accessible data. Although previous empirical findings have suggested ecological mechanisms, such as resource partitioning, which implies protective effects from predator species richness, epidemiological evidence is needed to bolster these arguments. Thus, we investigated the association between predator species richness and incidence of rodent-borne haemorrhagic fever with renal syndrome in the human population using district-level longitudinal data of 13 years for South Korea. With the exception of districts with low species richness, we found a significant negative association between the incidence of haemorrhagic fever with renal syndrome and the species richness of both avian and mammalian predators; the trends for both predator types were similar. Thus, biodiversity conservation may benefit public health.

Deep echocardiography: data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease

Deep learning and computer vision algorithms can deliver highly accurate and automated interpretation of medical imaging to augment and assist clinicians. However, medical imaging presents uniquely pertinent obstacles such as a lack of accessible data or a high-cost of annotation. To address this, we developed data-efficient deep learning classifiers for prediction tasks in cardiology. Using pipeline supervised models to focus relevant structures, we achieve an accuracy of 94.4% for 15-view still-image echocardiographic view classification and 91.2% accuracy for binary left ventricular hypertrophy classification. We then develop semi-supervised generative adversarial network models that can learn from both labeled and unlabeled data in a generalizable fashion. We achieve greater than 80% accuracy in view classification with only 4% of labeled data used in solely supervised techniques and achieve 92.3% accuracy for left ventricular hypertrophy classification. In exploring trade-offs between model type, resolution, data resources, and performance, we present a comprehensive analysis and improvements of efficient deep learning solutions for medical imaging assessment especially in cardiology.

A comprehensive database of thermal developmental plasticity in reptiles

How temperature influences development has direct relevance to ascertaining the impact of climate change on natural populations. Reptiles have served as empirical models for understanding how the environment experienced by embryos can influence phenotypic variation, including sex ratio, phenology and survival. Such an understanding has important implications for basic eco-evolutionary theory and conservation efforts worldwide. While there is a burgeoning empirical literature of experimental manipulations of embryonic thermal environments, addressing widespread patterns at a comparative level has been hampered by the lack of accessible data in a format that is amendable to updates as new studies emerge. Here, we describe a database with nearly 10, 000 phenotypic estimates from 155 species of reptile, collected from 300 studies manipulating incubation temperature (published between 1974–2016). The data encompass various morphological, physiological, behavioural and performance traits along with growth rates, developmental timing, sex ratio and survival (e.g., hatching success). This resource will serve as an important data repository for addressing overarching questions about thermal plasticity of reptile embryos.

Time to address gender inequalities against female physicians.

Although the health care system depends heavily on female physicians, it discriminates against women and tends to concentrate female physicians' work in lower status occupations. Gender discrimination has structural, social, and cultural dimensions. Such discrimination is perceived differently by various stakeholders and the public. In addition, there is reluctance to publicly acknowledge gender discrimination, especially in the culturally conservative Middle East region. Gender discrimination leads to underrepresentation of female physicians in leadership roles and certain specialties and hence leads to less attention and understanding of the working conditions of female physicians and their roles in the health care system. The lack of accessible data in the region regarding gender discrimination among physicians leads to stakeholders failing to recognize the existence and magnitude of this type of discrimination. This article takes up the relatively neglected issue of gender discrimination in the health care workforce among the stakeholders of the Ministry of Health and Prevention of the United Arab Emirates. Future research should explore the extent of gender discrimination among physicians and the gender remuneration gap, together with other sorts of discrimination, perception of equal opportunity, and dominant stereotypes of men and women working in health care in relation to job obligation, promotion, retention, remuneration, and education.

Lack of Accessible Data on Prosthetic Heart Valves

Incomplete information on characteristics of prosthetic heart valves (PHV) may lead to inappropriate choices for PHV implantation (patient-prosthesis-mismatch) or erroneous interpretation of PHV function after implantation. No single and easy accessible source provides all relevant information on PHV. The aim of this study was to provide a comprehensive overview of available data for the majority of PHVs and annuloplasty rings. Information was collected by reviewing articles published on www.pubmed.org up to December 2014 and by written contact to all PHV manufacturers. Four areas of interest were defined: (1) PHV image, (2) in vivo transvalvular gradients, (3) effective orifice area (EOA) calculators and (4) PHV dimensions. Available information was classified as complete (all categories), partial (two or three categories) or minimal (one category). 108 PHV (including homografts) and 34 annuloplasty rings systems were identified. The information on PHV was complete, partial or minimal in 19.5, 61.0 and 19.5% of PHV, respectively. In 91.6% a picture of the valve could be obtained, whereas normative data for transvalvular gradients and EOA calculators were available in 63.0 and 25.0% of all PHV, respectively. The available data was summarized on a new open access webpage ( www.valveguide.ch ). There is a lack of accessible data on PHV dimensions, normal transvalvular gradients and effective orifice area calculators, although such information is of crucial importance for proper PHV assessment.

The short-term association of temperature and rainfall with mortality in Vadu Health and Demographic Surveillance System: a population level time series analysis

Background: Research in mainly developed countries has shown that some changes in weather are associated with increased mortality. However, due to the lack of accessible data, few studies have examined such effects of weather on mortality, particularly in rural regions in developing countries.Objective: In this study, we aimed to investigate the relationship between temperature and rainfall with daily mortality in rural IndiaDesign: Daily mortality data were obtained from the Health and Demographic Surveillance System (HDSS) in Vadu, India. Daily mean temperature and rainfall data were obtained from a regional meteorological center, India Meteorological Department (IMD), Pune. A Poisson regression model was established over the study period (January 2003–May 2010) to assess the short-term relationship between weather variables and total mortality, adjusting for time trends and stratifying by both age and sex.Result: Mortality was found to be significantly associated with daily ambient temperatures and rainfall, after controlling for seasonality and long-term time trends. Children aged 5 years or below appear particularly susceptible to the effects of warm and cold temperatures and heavy rainfall. The population aged 20–59 years appeared to face increased mortality on hot days. Most age groups were found to have increased mortality rates 7–13 days after rainfall events. This association was particularly evident in women.Conclusion: We found the level of mortality in Vadu HDSS in rural India to be highly affected by both high and low temperatures and rainfall events, with time lags of up to 2 weeks. These results suggest that weather-related mortality may be a public health problem in rural India today. Furthermore, as changes in local climate occur, adaptation measures should be considered to mitigate the potentially negative impacts on public health in these rural communities.

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SHOULD THE LIMITS OF LIABILITY BE CHANGED?1

ABSTRACT The U.S. Coast Guard commissioned an evaluation of the appropriateness of adjusting the current limits of liability for oil spills from transportation-related onshore facilities, based on the requirements of the Oil Pollution Act of 1990. Extensive data collection and analysis revealed that the type and quality of the data needed to support recommendations for setting limits of liability for oil spills from specific categories of facilities were either not available or not reliable. Therefore, no recommendation can be made regarding a change in the limits of liability. Further, the lack of accessible data cripples decision making in the entire subject of oil spills. Recommendations are made regarding the data acquisition process and the systems needed to support it.