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Linked administrative data offer a rich source of information that can be harnessed to describe patterns of disease, understand their causes and evaluate interventions. However, administrative data are primarily collected for operational reasons such as recording vital events for legal purposes, and planning, provision and monitoring of services. The processes involved in generating and linking administrative datasets may generate sources of bias that are often not adequately considered by researchers. We provide a framework describing these biases, drawing on our experiences of using the 100 Million Brazilian Cohort (100MCohort) which contains records of more than 131 million people whose families applied for social assistance between 2001 and 2018. Datasets for epidemiological research were derived by linking the 100MCohort to health-related databases such as the Mortality Information System and the Hospital Information System. Using the framework, we demonstrate how selection and misclassification biases may be introduced in three different stages: registering and recording of people’s life events and use of services, linkage across administrative databases, and cleaning and coding of variables from derived datasets. Finally, we suggest eight recommendations which may reduce biases when analysing data from administrative sources.

Increased blood lipid levels are heritable risk factors of cardiovascular disease with varied prevalence worldwide owing to different dietary patterns and medication use 1 . Despite advances in prevention and treatment, in particular through reducing low-density lipoprotein cholesterol levels 2 , heart disease remains the leading cause of death worldwide 3 . Genome-wideassociation studies (GWAS) of blood lipid levels have led to important biological and clinical insights, as well as new drug targets, for cardiovascular disease. However, most previous GWAS 4 – 23 have been conducted in European ancestry populations and may have missed genetic variants that contribute to lipid-level variation in other ancestry groups. These include differences in allele frequencies, effect sizes and linkage-disequilibrium patterns 24 . Here we conduct a multi-ancestry, genome-wide genetic discovery meta-analysis of lipid levels in approximately 1.65 million individuals, including 350,000 of non-European ancestries. We quantify the gain in studying non-European ancestries and provide evidence to support the expansion of recruitment of additional ancestries, even with relatively small sample sizes. We find that increasing diversity rather than studying additional individuals of European ancestry results in substantial improvements in fine-mapping functional variants and portability of polygenic prediction (evaluated in approximately 295,000 individuals from 7 ancestry groupings). Modest gains in the number of discovered loci and ancestry-specific variants were also achieved. As GWAS expand emphasis beyond the identification of genes and fundamental biology towards the use of genetic variants for preventive and precision medicine 25 , we anticipate that increased diversity of participants will lead to more accurate and equitable 26 application of polygenic scores in clinical practice. A genome-wide association meta-analysis study of blood lipid levels in roughly 1.6 million individuals demonstrates the gain of power attained when diverse ancestries are included to improve fine-mapping and polygenic score generation, with gains in locus discovery related to sample size.

Urbanization and concomitant challenges pose a great threat to sustainable development. Urban and rural development interacts through the flows of people, materials, energy, goods, capital, and information. Without building sound urban–rural linkages, achieving development in one area could compromise it in another area. Achieving sustainable development needs customized policy prioritization and implementation in both urban and rural areas. Much literature exists in the research field of urban–rural linkages, but little has been done via a comprehensive analysis from an interlinkage perspective in the context of the Sustainable Development Goals (SDGs). Sustainable Development Goal 11 on sustainable cities and several targets embedded under other Goals provides a good framework for analyzing the urban–rural linkages. This paper contributes to this novel research perspective using Ghana as a case. The study applied an integrated approach by combining the results from a solution-scanning exercise with an SDG interlinkage analysis to identify the challenges and priority solutions and assess the synergies and trade-offs of the identified solutions. It extends the conventional solution-scanning approach by further assessing the synergies and trade-offs of the solutions from an SDG interlinkage perspective. It also enables a more practical SDG interlinkage analysis through the contributions from the multi-stakeholder consultations conducted in Ghana. The analyses show that prioritizing gender inclusion (Goal 5) will positively affect many social and well-being outcomes, including poverty elimination (Goal 1), hunger reduction (Goal 2), health improvement (Goal 3) and access to quality education (Goal 4) and basic services, such as water (Goal 6). However, gender inclusion could have potential trade-offs in the agricultural sector (Goal 2) in the case that women who dominate agricultural value chains could move to work in other sectors. Lack of proper infrastructure (Goal 9), such as transport, will hinder wide gender inclusion. An integrated approach that considers both the synergies and trade-offs of relevant solutions is critical for effective policymaking, specifically in developing countries.

One of the greatest achievements in the global negotiations of 2015 that delivered the 2030 Agenda for Sustainable Development or Sustainable Development Goals (SDGs) and the Paris Agreement on climate change is that, for the first time, the linkages between climate and development were enshrined in each of the documents. This was done in recognition that climate change and development need to be addressed together in order not only to avoid harmful trade-offs and high costs, particularly for poorer countries, but also to exploit the benefits that come from strengthening these linkages. This review presents some of the latest data that argue for stronger linkages as well as the challenges of implementation which are not only politically and economically related but also include issues such as knowledge gaps, finance and governance. Finally, the review also presents a glimpse at the pathways that will be required to reach the ambitious global temperature targets of the Paris Agreement of less than 2°C above pre-industrial levels with efforts to limit temperature rise even further to 1.5°C. This provides the context for some conclusions and recommendations for policy-makers, including on methodologies for assessing linkages and leveraging them for greater benefit. This article is part of the theme issue 'The Paris Agreement: understanding the physical and social challenges for a warming world of 1.5°C above pre-industrial levels'.

A quantitative approach to characterize lignin—carbohydrate complex (LCC) linkages using a combination of quantitative 13C NMR and HSQC 2D NMR techniques has been developed. Crude milled wood lignin (MWLc), LCC extracted from MWLc with acetic acid (LCC-AcOH) and cellulolytic enzyme lignin (CEL) preparations were isolated from loblolly pine (Pinus taeda) and white birch (Betula pendula) woods and characterized using this methodology on a routine 300 MHz NMR spectrometer and on a 950 MHz spectrometer equipped with a cryogenic probe. Structural variations in the pine and birch LCC preparations of different types (MWL, CEL and LCC-AcOH) were elucidated. The use of the high field NMR spectrometer equipped with the cryogenic probe resulted in a remarkable improvement in the resolution of the LCC signals and, therefore, is of primary importance for an accurate quantification of LCC linkages. The preparations investigated showed the presence of different amounts of benzyl ether, γ-ester and phenyl glycoside LCC bonds. Benzyl ester moieties were not detected. Pine LCC-AcOH and birch MWLc preparations were preferable for the analysis of phenyl glycoside and ester LCC linkages in pine and birch, correspondingly, whereas CEL preparations were the best to study benzyl ether LCC structures. The data obtained indicate that pinewood contains higher amounts of benzyl ether LCC linkages, but lower amounts of phenyl glycoside and γ-ester LCC moieties as compared to birch wood.

This paper aims to study the location, political and governmental factors – in Egypt- affecting the construction project decision-making that may lead to project success or failure. Identifying and predicting these factors and having a comprehensive framework helps top managers in leading their projects. These factors add burdens that lead to many risks (positive/negative). Some burdens such as extra costs added to the project, procurement issues related to transportation and accommodation for materials and human resources…etc. Also, the political factors and legal restrictions act as constraints on the project and increase the project duration or have changes to the project plan. The result of this paper is a decision-making framework that helps decision-makers in analysing situations and taking suitably correct decisions. In the framework, factors are categorized, and worked on who will be the decision maker? What is the decision process to be followed? And what is the final recommended decision or outcome? Defining the decision maker in each situation helps in sharing project risks, liabilities, and responsibilities. It also gives more understanding of their impact on the project. And believing that these factors are not linear and have linkages with each other, these linkages are tackled briefly in the paper.

Glycosidic linkage analysis was conducted on the unfractionated polysaccharides in alcohol-insoluble residues (AIRs) prepared from six red seaweeds (Gracilariopsis sp., Prionitis sp., Mastocarpus papillatus, Callophyllis sp., Mazzaella splendens, and Palmaria palmata) using GC-MS/FID analysis of partially methylated alditol acetates (PMAAs). The cell walls of P. palmata primarily contained mixed-linkage xylans and small amounts of sulfated galactans and cellulose. In contrast, the unfractionated polysaccharides of the other five species were rich in galactans displaying diverse 3,6-anhydro-galactose and galactose linkages with varied sulfation patterns. Different levels of cellulose were also observed. This glycosidic linkage method offers advantages for cellulose analysis over traditional monosaccharide analysis that is known for underrepresenting glucose in crystalline cellulose. Relative linkage compositions calculated from GC-MS and GC-FID measurements showed that anhydro sugar linkages generated more responses in the latter detection method. This improved linkage workflow presents a useful tool for studying polysaccharide structural variations across red seaweed species. Furthermore, for the first time, relative linkage compositions from GC-MS and GC-FID measurements, along with normalized FID and total ion current (TIC) chromatograms without peak assignments, were analyzed using principal component analysis (PCA) as a proof-of-concept demonstration of the technique’s potential to differentiate various red seaweed species.

We develop a dynamic trade model with spatially distinct labor markets facing varying exposure to international trade. The model captures the role of labor mobility frictions, goods mobility frictions, geographic factors, and input-output linkages in determining equilibrium allocations. We show how to solve the equilibrium of the model and take the model to the data without assuming that the economy is at a steady state and without estimating productivities, migration frictions, or trade costs, which can be difficult to identify. We calibrate the model to 22 sectors, 38 countries, and 50 U.S. states. We study how the rise in China's trade for the period 2000 to 2007 impacted U.S. households across more than a thousand U.S. labor markets distinguished by sector and state. We find that the China trade shock resulted in a reduction of about 0.55 million U.S. manufacturing jobs, about 16% of the observed decline in manufacturing employment from 2000 to 2007. The U.S. gains in the aggregate, but due to trade and migration frictions, the welfare and employment effects vary across U.S. labor markets. Estimated transition costs to the new long-run equilibrium are also heterogeneous and reflect the importance of accounting for labor dynamics.

Fucoidans are marine sulfated biopolysaccharides that have heterogenous and complicated chemical structures. Various sugar monomers, glycosidic linkages, molecular masses, branching sites, and sulfate ester pattern and content are involved within their backbones. Additionally, sources, downstream processes, and geographical and seasonal factors show potential effects on fucoidan structural characteristics. These characteristics are documented to be highly related to fucoidan potential activities. Therefore, numerous chemical qualitative and quantitative determinations and structural elucidation methods are conducted to characterize fucoidans regarding their physicochemical and chemical features. Characterization of fucoidan polymers is considered a bottleneck for further biological and industrial applications. Consequently, the obtained results may be related to different activities, which could be improved afterward by further functional modifications. The current article highlights the different spectrometric and nonspectrometric methods applied for the characterization of native fucoidans, including degree of purity, sugar monomeric composition, sulfation pattern and content, molecular mass, and glycosidic linkages.

Record linkage is increasingly used to expand the information available for public health research. An understanding of record linkage methods and the relevant strengths and limitations is important for robust analysis and interpretation of linked data. Here, we describe the approach used by Clinical Practice Research Datalink (CPRD) to link primary care data to other patient level datasets, and the potential implications of this approach for CPRD data analysis. General practice electronic health record software providers separately submit de-identified data to CPRD and patient identifiers to NHS Digital, excluding patients who have opted-out from contributing data. Data custodians for external datasets also send patient identifiers to NHS Digital. NHS Digital uses identifiers to link the datasets using an 8-stage deterministic methodology. CPRD subsequently receives a de-identified linked cohort file and provides researchers with anonymised linked data and metadata detailing the linkage process. This methodology has been used to generate routine primary care linked datasets, including data from Hospital Episode Statistics, Office for National Statistics and National Cancer Registration and Analysis Service. 10.6 million (M) patients from 411 English general practices were included in record linkage in June 2018. 9.1M (86 %) patients were of research quality, of which 8.0M (88 %) had a valid NHS number and were eligible for linkage in the CPRD standard linked dataset release. Linking CPRD data to other sources improves the range and validity of research studies. This manuscript, together with metadata generated on match strength and linkage eligibility, can be used to inform study design and explore potential linkage-related selection and misclassification biases.