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Abstract The social space refers to physical or virtual places where people interact with one another. It decisively influences the emergence of human behaviors. However, little is known about the nature and complexity of the social space, nor its relationship to context and spatial scale. Recently, the science of complex systems has bridged between fields of knowledge to provide quantitative responses to fundamental sociological questions. In this paper, we analyze the shifting behavior of social space in terms of human interactions and wealth distribution across multiple scales using fine-grained data collected from both official (US Census Bureau) and unofficial data sources (social media). We use these data to unveil how patterns strongly depend upon the observation scale. Therefore, it is crucial for any analysis to be framed within the appropriate context to avoid biased results and/or misleading conclusions. Biased data analysis may lead to the adoption of fragile and poor decisions. Including context and a proper understanding of the spatial scale are essential nowadays, especially with the pervasive role of data-driven tools in decision-making processes.

Abstract The COVID-19 pandemic was an inevitable outcome of a globalized world in which a highly infective disease is able to reach every country in a matter of weeks. While lockdowns and strong mobility restrictions have proven to be efficient to contain the exponential transmission of the virus, its pervasiveness has made it impossible for economies to maintain this kind of measures in time. Understanding precisely how the spread of the virus occurs from a territorial perspective is crucial not only to prevent further infections but also to help with policy design regarding human mobility. From the large spatial differences in the behavior of the virus spread we can unveil which areas have been more vulnerable to it and why, and with this information try to assess the risk that each community has to suffer a future outbreak of infection. In this work we have analyzed the geographical distribution of the cumulative incidence during the first wave of the pandemic in the region of Galicia (north western part of Spain), and developed a mathematical approach that assigns a risk factor for each of the different municipalities that compose the region. This risk factor is independent of the actual evolution of the pandemic and incorporates geographic and demographic information. The comparison with empirical information from the first pandemic wave demonstrates the validity of the method. Our results can potentially be used to design appropriate preventive policies that help to contain the virus.

Abstract To analyze the epidemiology, clinical features and costs of hospitalized patients with gout during the last decade in Spain. Retrospective observational study based on data from the Minimum Basic Data Set (MBDS) from the Spanish National Health Service database. Patients ≥ 18 years with any gout diagnosis at discharge who had been admitted to public or private hospitals between 2005 and 2015 were included. Patients were divided in two periods: p1 (2005–2010) and p2 (2011–2015) to compare the number of hospitalizations, mean costs and mortality rates. Data from 192,037 patients with gout was analyzed. There was an increase in the number of hospitalized patients with gout (p

Abstract Immunogenicity related to treatment with TNF inhibitors (TNFi) is one of the causes for the decreased attainment of clinical response in patients with rheumatoid arthritis (RA). The B-cell activating factor (BAFF) may be playing a role in the development of immunogenicity. The objective of this study was to analyse the association of baseline concentration of serum B-cell activating factor (BAFF) with immunogenicity after 6 months of TNFi treatment. A total of 127 patients with RA starting a TNFi (infliximab, adalimumab, certolizumab pegol or golimumab) were followed-up for 6 months. Serum samples were obtained at baseline and at 6 months and anti-drug antibody (ADA) and BAFF concentrations were measured. Logistic regression models were employed in order to analyse the association between BAFF concentrations and immunogenicity. Receiver operating characteristic analysis was performed to determine the BAFF concentrations with a greater likelihood of showing immunogenicity association. At 6 months, 31 patients (24%) developed ADA. A significant interaction between the age and baseline BAFF concentration was found for the development of ADA (Wald chi-square value = 5.30; p = 0.02); therefore, subsequent results were stratified according to mean age (≤ / > 55 years). Baseline serum BAFF concentration was independently associated with ADA development only in patients over 55 years (OR = 1.51; 95% CI 1.03–2.21). Baseline serum BAFF ≥ 1034 pg/mL predicted the presence of ADA at 6 months (AUC = 0.81; 95% confidence interval (CI) 0.69–0.93; p = 0.001; positive likelihood ratio = 3.7). In conclusion, our results suggest that the association of BAFF concentration and immunogenicity depends on the patient’s age. Baseline serum BAFF concentration predicts the presence of ADA within 6 months of TNFi therapy in older patients with RA.

Abstract The initial management of rheumatoid arthritis (RA) has a high impact on disease prognosis. Therefore, we need to select the most appropriate treatment as soon as possible. This goal requires biomarkers of disease severity and prognosis. One such biomarker may be the presence of anti-carbamylated protein antibodies (ACarPA) because it is associated with adverse long term outcomes as radiographic damage and mortality. Here, we have assessed the ACarPA as short-term prognostic biomarkers. The study was conducted in 978 prospective early arthritis (EA) patients that were followed for two years. Our results show the association of ACarPA with increased levels of all the disease activity measures in the first visit after arthritis onset. However, the associations were more significant with the high levels in local measures of inflammation and physician assessment than with the increases in systemic inflammation and patient-reported outcomes. More notably, disease activity was persistently increased in the ACarPA positive patients during the two years of follow-up. These differences were significant even after accounting for the presence of other RA autoantibodies. Therefore, the ACarPA could be considered short-term prognostic biomarkers of increased disease activity in the EA patients.

Abstract The present study used 16S rRNA gene amplicon sequencing to assess the impact on salivary microbiome of different grades of dental and periodontal disease and the combination of both (hereinafter referred to as oral disease), in terms of bacterial diversity, co-occurrence network patterns and predictive models. Our scale of overall oral health was used to produce a convenience sample of 81 patients from 270 who were initially recruited. Saliva samples were collected from each participant. Sequencing was performed in Illumina MiSeq with 2 × 300 bp reads, while the raw reads were processed according to the Mothur pipeline. The statistical analysis of the 16S rDNA sequencing data at the species level was conducted using the phyloseq, DESeq2, Microbiome, SpiecEasi, igraph, MixOmics packages. The simultaneous presence of dental and periodontal pathology has a potentiating effect on the richness and diversity of the salivary microbiota. The structure of the bacterial community in oral health differs from that present in dental, periodontal or oral disease, especially in high grades. Supragingival dental parameters influence the microbiota’s abundance more than subgingival periodontal parameters, with the former making a greater contribution to the impact that oral health has on the salivary microbiome. The possible keystone OTUs are different in the oral health and disease, and even these vary between dental and periodontal disease: half of them belongs to the core microbiome and are independent of the abundance parameters. The salivary microbiome, involving a considerable number of OTUs, shows an excellent discriminatory potential for distinguishing different grades of dental, periodontal or oral disease; considering the number of predictive OTUs, the best model is that which predicts the combined dental and periodontal status.

Abstract Classification of patients with rheumatoid arthritis (RA) as quickly as possible improves their prognosis. This reason motivates specially dedicated early arthritis (EA) clinics. Here, we have used 1062 EA patients with two years of follow-up to explore the value of anti-carbamylated protein (anti-CarP) antibodies, a new type of RA specific autoantibodies, for classification. Specifically, we aimed to determine whether the addition of anti-CarP antibodies to IgM rheumatoid factor (RF) and anti-cyclic citrullinated peptide (anti-CCP) antibodies, which are helpful in RA classification, improves it or not. Our analysis showed that incorporation of the anti-CarP antibodies to combinations of the other two antibodies (all joint by the OR Boolean operator) produces a modest increase in sensitivity (2.2% higher), at the cost of decreased specificity (8.1% lower). The cost-benefit ratio was more favorable in the patients lacking the other autoantibodies. However, it did not improve by considering different titer levels of the anti-CarP antibodies, or after exhaustively exploring other antibody combinations. Therefore, the place in RA classification of these antibodies is questionable in the context of current treatments and biomarkers. This conclusion does not exclude their potential value for stratifying patients in joint damage, disease activity, disability, or mortality categories.