Quality Of Primary Data Research Articles

Incidence of liver cancer in young adults according to the Global Burden of Disease database 2019.

The worldwide burden of cancer is increasing in younger populations. However, the epidemiology of primary liver cancer remains understudied in young adults compared to other cancer forms. This study analyzed data from the Global Burden of Disease study between 2010 and 2019 to assess the age-standardized incidence, mortality, and disability-adjusted life years associated with primary liver cancer in the young (15-49y), stratified by region, nation, sociodemographic index, and sex. The study found a global estimate of 78,299 primary liver cancer cases, 60,602 deaths, and 2.90 million disability-adjusted life years in the young population. The Western Pacific region exhibited the highest burden in 2019, showing the most significant increase compared to other regions between 2010 and 2019. More than half of the countries worldwide have undergone an increase in primary liver cancer incidence rates in young adults. Around 12.51% of deaths due to primary liver cancer occur in young individuals. Throughout the study period, there was a significant decline in primary liver cancer mortality due to most etiologies, except for metabolic dysfunction-associated steatotic liver disease-attributable primary liver cancer (annual percentage change + 0.87%, 95% CI: 0.70%-1.05%) and alcohol-attributable primary liver cancer (annual percentage change + 0.21%, 95% CI: 0.01%-0.42%). The limitations of the Global Burden of Disease database include reliance on the quality of primary data and possible underestimation of alcohol consumption. Over the past decade, there has been a marked increase in the burden of primary liver cancer, especially that originating from steatotic liver disease. This trend calls for the development of urgent and comprehensive strategies to mitigate this rising burden globally.

Impacts of the data quality of remote sensing vegetation index on gross primary productivity estimation

ABSTRACT As the most commonly used driven data for gross primary productivity (GPP) estimation, satellite remote sensing vegetation indexes (VI), such as the leaf area index (LAI), often seriously suffer from data quality problems induced by cloud contamination and noise. Although various filtering methods are applied to reconstruct the missing data and eliminate noises in the VI time series, the impacts of these data quality problems on GPP estimation are still not clear. In this study, the accuracy differences of the GPP estimations driven by different VI series are comprehensively analyzed based on two light use efficiency (LUE) models (the big-leaf MOD17 and the two-leaf RTL-LUE). Four VI filtering methods are applied for comparison, and GPP data across 169 eddy covariance (EC) sites are used for validation. The results demonstrate that all the filtering methods can improve the GPP simulation accuracy, and the SeasonL1 filtering method exhibits the best performance both for the MOD17 model (∆R2 = 0.06) and the RTL-LUE model (∆R2 = 0.07). The reconstruction of the key change points in the temporally continuous gaps may be the primary reason for the different performance of the four methods. Moreover, the effects of filtering processes on GPP estimation vary with latitudes and seasons due to the differences in the primary data quality. More significant improvements can be observed during the growing season and in the regions near the equator, where the data quality is relatively poor with lower primary GPP estimation accuracy. This study can guide the preprocessing of the VI data before GPP estimation.

Biodiversity and elevation gradients: Insights on sampling biases across worldwide mountains

AbstractAimIdentifying macroecological patterns and biases in species distribution is a challenging but essential task in biodiversity‐oriented studies. Despite extensive attempts to find consistent species richness elevation (SRE) patterns, the topic remains controversial owing to widespread conflicting, idiosyncratic and non‐generalizable underlying mechanisms. We used a meta‐analytical review to answer why patterns of species‐richness in elevation gradients remain elusive, a long‐standing, central but contentious macroecological and biogeographical question.LocationGlobal elevation gradients.TaxonMajor terrestrial taxa (invertebrates, vertebrates and plants).MethodsWe tested the effect of elevation on species richness using multilevel mixed‐effects meta‐analytical models. Data from 127 studies spawning almost one century of research were integrated to test the effect of elevation across distinct (1) SRE models, (2) quality of primary data (e.g. mountain sampling coverage), (3) biogeographic realms, (4) studied taxa and (5) organism mobility.ResultsThe linear negative pattern showed the strongest model fit followed by the hump‐shaped and the linear positive models. Studies with higher sampling sizes showed a consistent decrease in the strength of SRE patterns. Further, the larger the mountain coverage and sampled range, the stronger the detection of some SRE patterns. Overall, the elevational effect on species richness was consistent across biogeographical realms, taxonomic groups and organism mobility.Main ConclusionsThis study indicates a bias in the detection of SRE patterns, driven mostly by mountain comprehensiveness, namely the number of sampling units, sampled range and mountain sampling coverage. These results call attention to the evidence that undersampled elevation gradients may bias our understanding on the complex relationships between elevation and biodiversity, thus impairing a broad understanding on the ecology, evolution, biogeography and conservation of mountain biota.

The impact of a qualified medical documentation assistant on trauma room management

PurposeTo improve quality of trauma room management, intra- and inter-hospital benchmarking are important tools. However, primary data quality is crucial for benchmarking reliability. In this study, we analyzed the effect of a medical documentation assistant on documentation completeness in trauma room management in comparison to documentation by physicians involved in direct patient treatment.MethodsWe included all patients treated in the trauma room from 2016/01/01 to 2016/12/31 that were documented with the trauma module of the German Emergency Department Medical Record V2015.1. We divided the data into documentation by medical documentation assistant (DA, 07:00 to 17:00), physician in daytime (PD, 07:00 to 17:00), and physician at night (PN, 17:00 to 07:00). Data were analyzed for completeness (primary outcome parameter) as well as diagnostic intervals.ResultsThere was a significant increase in complete recorded data for DA (74.5%; IQR 14.5%) compared to PD (26.9%; IQR 18.7%; p < 0.001) and PN (30.8%; IQR 18.9; p < 0.001). The time to whole-body computed tomography (WBCT) significantly decreased for DA (19 min; IQR 8.3) compared to PD (24 min; IQR 12.8; p = 0.007) or PN (24.5 min; IQR 10.0; p = 0.001).ConclusionIn presence of a qualified medical documentation assistant, data completeness and time to WBCT improved significantly. Therefore, utilizing a professional DA in the trauma room appears beneficial for data quality and time management.

The Quality of Official Statistical Information: Approaches and International Practices of Evaluation

The article describes the role of statistics in the economic and social policy setting, the development of public and private sector, and international relations. It is noted that the results of a global assessment of the domestic statistical system indicate that European standards for the quality of statistical information are used in official statistics bodies. At the same time, it is pointed out that the domestic statistical activities face unresolved problems associated with a significant burden on respondents, poor quality of primary data, etc. The methodological foundations and practical issues of monitoring the statistical potential of national statistical systems through the prism of the quality of statistical information production processes proposed by the Partnership in Statistics for Development in the 21st Century are investigated. There are 14 tools for assessing the statistical potential used in the international statistical practice for measuring and evaluating various aspects of national statistical data, including the performance of statistical institutions and the quality of their results.&#x0D; A multidimensional statistical potential indicator (SCI) of the World Bank is considered, which diagnostic framework evaluates the statistical methodology, data source, frequency and timeliness of the publication of the indicators reflecting the production of statistical information in a country, which are harmonized with individual global indicators of the SDG. The dynamics of the statistical potential of the national statistical system of Ukraine among 140 developing countries that borrow funds from the International Bank for Reconstruction and Development are analyzed. It is shown that the decreasing level of the statistical potential in Ukraine is associated with the following components: data sources, frequency and timeliness.&#x0D; To enhance the performance and strengthen the institutional capacity of the existing statistical system, it is proposed to take account of the indicators used for the assessment and monitoring of statistical potential in the methodological and practical activities of official statistics bodies. The need to improve quality, accessibility and comparability of the official statistical information is justified, and respective measures are proposed.

The role of spatial data and geomatic approaches in treeline mapping: a review of methods and limitations

In the debate over global warming, treeline position is considered an important ecological indicator of climate change. Currently, analysis of upward treeline shift is often based on various spatial data processed by geomatic techniques. In this work, considering a selection of 31 reference papers, we assessed how the scientific community is using different methods to map treeline position and/or shifts using spatial datasets. We found that a significant number of published studies suffer from a low degree of awareness of processed data, which outcomes in potentially unreliable results that may compromise the validity of inference from the studies. Moreover, we propose an operational approach for easily incorporating consideration of spatial data quality, so as to improve reliability of results and better support ecological conclusions. Finally, we present a simulation of potential treeline vertical error for the Alpine region of Northern Italy, as driven by primary data quality.

Respondent Screening and Revealed Preference Axioms: Testing Quarantining Methods for Enhanced Data Quality in Web Panel Surveys

Online survey methods and online access panels are commonly used in both academic and private industry research. However, many industry reviews indicate the presence of fraudulent and inattentive online panel participants who pose a threat to data validity, with estimates of 8–25 percent of the sample being impacted. Although these challenges are not unique to online surveying, the nature of online panels may offer possibilities to improve data quality through real-time filters, rather than in data analysis. In this study, we retain all panel respondents passing and failing common web-survey quarantining methods to directly analyze filtering results on data quality. Through a choice experiment, we build a mathematical model to measure respondent irrationality through the classic strong axiom of revealed preference (SARP), which serves as the primary data-quality metric to test the efficacy of quarantining methods. We show that inattentive respondents failing “trap” or “red herring” questions, as well as fraudulent respondents heavily selecting low-probability screening questions, have significantly lower data quality compared to passing respondents. Removing failing respondents significantly improves data quality when filters are strategically executed. We also simulate the data-quality benefits of real-time filtering techniques during web-data collection. We conclude by advocating the use of low-probability filters as qualifiers, as well as strategic placement of trap questions just before the most crucial sections of a survey

ChemInform Abstract: Human Protein Atlas of Redox Systems — What Can Be Learnt?

AbstractReview: 89 refs.

Human Protein Atlas of redox systems — What can be learnt?

Background High-throughput screening projects are popular approaches to yield a vast amount of information amenable for database mining and “hypothesis generation”. The keys to success for these approaches depend upon the quality of primary data, choice of algorithms for data analyses, solidity in data annotations and the general usefulness of the results. A large initiative aimed at mapping the expression of all human proteins is the Human Protein Atlas ( www.proteinatlas.org), encompassing immunohistochemical analyses of human tissues utilizing antibodies raised against a large number of human proteins. Here, we wished to probe what could be learnt from this atlas using a manual in-depth analysis of the results regarding the expression of key proteins in the human glutathione and thioredoxin systems. Methods The freely available on-line data of immunohistochemical analyses for selected human redox proteins within the Human Protein Atlas were here analyzed, provided that reasonably solid data existed for the antibodies that were employed. This included tissue expression data for thioredoxin 1 (Trx1), Trx2, thioredoxin reductase 1 (TrxR1), TrxR2, glutathione reductase (GR), glucose 6-phosphate dehydrogenase (G6PD), γ-glutamyl cysteinyl synthase (gGCS) and the six peroxiredoxins Prx1 to Prx6. The data were further complemented with a screen using a polyclonal peptide antibody raised against the unique glutaredoxin domain of TXNRD1_v3 (“v3”). The results from fifteen major tissues and organs are presented (lung, kidney, liver, lymph node, testis, prostate, ovary, breast, pancreas, cerebellum, hippocampus, cerebral cortex, skin, skeletal muscle and heart muscle) and discussed considering earlier findings described in the literature. Results Staining patterns proved to be highly variable and often unexpected both in terms of tissues analyzed and the individual target proteins. Among the analyzed tissues, only macrophages of the lung, tubular cells of the kidney, lymphoid cells of lymph nodes, Leydig cells in the testis, glandular cells of the prostate and exocrine glandular cells of the pancreas, showed positive staining with all of the fourteen antibodies that were analyzed. Among these antibodies, those against Trx1, TrxR2 and G6PD showed the most restricted staining across different tissues, while others including the antibodies against Trx2, TrxR1, GR, Prx3, Prx4 and Prx6 gave strong staining in most tissues. Staining for v3 was strong in many cells and tissues, which was unexpected considering previous results mapping transcripts for this protein. No obvious co-variation in staining across tissues could be noted when comparing any two of the analyzed antibodies. Staining for G6PD was weak in most tissues, except for cells of the seminiferous ducts in testis and follicular cells of the ovary, where G6PD staining was strong. Conclusions Results from high-throughput screening projects such as the Human Protein Atlas must be taken with caution and need to be duly confirmed by thorough in-depth follow-up studies. The varying staining intensities comparing tissues as seen here for most of the analyzed antibodies nonetheless suggest that the overall profile of the human redox systems may vary significantly between different cell types and between different tissues. General significance The Human Protein Atlas data suggest that the individual proteins of the human thioredoxin and glutathione systems may be strikingly tissue- and cell type-specific in terms of expression levels, but we also conclude that these type of high-throughput results should be taken with significant caution and must be duly verified using subsequent focused and detailed hypothesis-guided follow-up studies. This article is part of a Special Issue entitled Human and Murine Redox Protein Atlases.

Applications of biotope mapping for spatial environmental planning and policy: case studies in urban ecosystems in Korea

The purpose of this paper is to investigate the current status and method of biotope mapping for practical use for landscape planning and environmental policy in urban ecosystem in Korea. We examine current ecological mapping of Seoul, Seongnam, Daegu, and Yongin. Ecological mapping is examined closely in terms of investigation methodology, investigation subject, classification of urban landscape, and the present condition of application. Biotope mapping in Seoul and Seongnam were carried out by the city governments concerned with the pre-set budgets earmarked for mapping. In order to promote the utilization of biotope maps for city planning in Korea, the following actions should be considered. First, the survey method should be standardized by introducing a uniform standard with respect to the scope of survey, the quality of primary data used, the survey method, and the level of the survey. Second, it is necessary to identify a basic category of biotope for each area by consolidating the outcome of the previous surveys. Third, it is highly desirable to minimize the differences between the evaluation criteria and the assessment factors. Fourth, it is ideal to apply the results of the biotope evaluation to city planning in an indirect manner through reflecting the results first in the landscape plans. In order to facilitate this alternative utilization, it is necessary to strengthen the control provisions contained in the ordinances of the city concerned or to enact a set of new provisions in the ordinances so that biotope mapping could be used more widely as a criterion for the spatial environmental impact assessment.

Establishing sensitivity requirements for environmental analyses from project data quality objectives.

This article proposes a simple strategy for establishing sensitivity requirements (quantitation limits) for environmental chemical analyses when the primary data quality objective is to determine if a contaminant of concern is greater or less than an action level (e.g., an environmental "cleanup goal," regulatory limit, or risk-based decision limit). The approach assumes that the contaminant concentrations are normally distributed with constant variance (i.e., the variance is not significantly dependent upon concentration near the action level). When the total or "field" portion of the measurement uncertainty can be estimated, the relative uncertainty at the laboratory's quantitation limit can be used to determine requirements for analytical sensitivity. If only the laboratory component of the total uncertainty is known, the approach can be used to identify analytical methods or laboratories that will not satisfy objectives for sensitivity (e.g., when selecting methodology during project planning).