Abstract Background Drug–drug interactions (DDIs) that involve antiretrovirals (ARVs) tend to cause harm if unrecognized, especially in the context of comorbidity and polypharmacy. Methods A linkage was established between the drug dispensing registry of Madrid and the Liverpool human immunodeficiency virus (HIV) DDI database (January 2017–June 2017). Polypharmacy was defined as the use of ≥5 non-HIV medications, and DDIs were classified by a traffic-light ranking for severity. Results A total of 22 945 people living with HIV (PLWH) and 6 613 506 individuals without HIV had received medications. ARV regimens were predominantly based on integrase inhibitors (51.96%). Polypharmacy was higher in PLWH (32.94%) than individuals without HIV (22.16%; P < .001); this difference was consistently observed across all age strata except for individuals ≥75 years. Polypharmacy was more common in women than men in both PLWH and individuals without HIV. The prevalence of contraindicated combinations involving ARVs was 3.18%. Comedications containing corticosteroids, quetiapine, or antithrombotic agents were associated with the highest risk for red-flag DDI, and the use of raltegravir- or dolutegravir-based antiretroviral therapy was associated with an adjusted odds ratio of 0.72 (95% confidence interval, .60–.88; P = .001) for red-flag DDI. Conclusions Polypharmacy was more frequent among PLWH across all age groups except those aged ≥75 years and was more common in women. The detection of contraindicated medications in PLWH suggests a likely disconnect between hospital and community prescriptions. Switching to alternative unboosted integrase regimens should be considered for patients with risk of harm from DDIs.

ObjectivesWe assessed the prevalence of potentially inappropriate medication (PIM) among older (≥ 65 years) people living with HIV (O‐PLWH) in the region of Madrid.MethodsWe analysed the dispensation registry of community and hospital pharmacies from the Madrid Regional Health Service (SERMAS) for the period between 1 January and 30 June 2017, looking specifically at PIMs according to the 2019 Beers criteria. Co‐medications were classified according to the Anatomical Therapeutic Chemical (ATC) classification system.ResultsA total of 6 636 451 individuals received medications. Of these individuals, 22 945 received antiretrovirals (ARVs), and of these 1292 were O‐PLWH. Overall, 1135 (87.8%) O‐PLWH were taking at least one co‐medication, and polypharmacy (at least five co‐medications) was observed in 852 individuals (65.9%). A PIM was identified in 482 (37.3%) O‐PLWH. Factors independently associated with PIM were polypharmacy [adjusted odds ratio (aOR) 7.08; 95% confidence interval (CI) 5.16–9.72] and female sex (aOR 1.75; 95% CI 1.30–2.35). The distribution of PIMs according to ATC drug class were nervous system drugs (n = 369; 28.6%), musculoskeletal system drugs (n = 140; 10.8%), gastrointestinal and metabolism drugs (n = 72; 5.6%), cardiovascular drugs (n = 61; 4.7%), respiratory system drugs (n = 13; 1.0%), antineoplastic and immunomodulating drugs (n = 10; 0.8%), and systemic anti‐infectives (n = 2; 0.2%). Five drugs accounted for 84.8% of the 482O PLWH with PIMs: lorazepam (38.2%), ibuprofen (18.0%), diazepam (10.2%), metoclopramide (9.9%), and zolpidem (8.5%).ConclusionsPrescription of PIMs is highly prevalent in O‐PLWH. Consistent with data in uninfected elderly people, the most frequently observed PIMs were benzodiazepines and nonsteroidal anti‐inflammatory drugs . Targeted interventions are warranted to reduce inappropriate prescribing and polypharmacy in this vulnerable population.

Public procurement is a large market affecting almost every organisation and individual. Governments need to ensure efficiency, transparency, and accountability, while creating healthy, competitive and vibrant economies. In this context, we built a platform, consisting of a set of modular APIs and ontologies to publish, curate, integrate, analyse, and visualise an EU-wide, cross-border, and cross-lingual procurement knowledge graph. We developed end-user tools on top of the knowledge graph, for anomaly detection and cross-lingual document search. This paper describes our experiences and challenges faced in creating such a platform and knowledge graph and demonstrates the usefulness of Semantic Web technologies for enhancing public procurement.

Public procurement is a large market affecting almost every organisation and individual; therefore, governments need to ensure its efficiency, transparency, and accountability, while creating healthy, competitive, and vibrant economies. In this context, open data initiatives and integration of data from multiple sources across national borders could transform the procurement market by such as lowering the barriers of entry for smaller suppliers and encouraging healthier competition, in particular by enabling cross-border bids. Increasingly more open data is published in the public sector; however, these are created and maintained in siloes and are not straightforward to reuse or maintain because of technical heterogeneity, lack of quality, insufficient metadata, or missing links to related domains. To this end, we developed an open linked data platform, called TheyBuyForYou, consisting of a set of modular APIs and ontologies to publish, curate, integrate, analyse, and visualise an EU-wide, cross-border, and cross-lingual procurement knowledge graph. We developed advanced tools and services on top of the knowledge graph for anomaly detection, cross-lingual document search, and data storytelling. This article describes the TheyBuyForYou platform and knowledge graph, reports their adoption by different stakeholders and challenges and experiences we went through while creating them, and demonstrates the usefulness of Semantic Web and Linked Data technologies for enhancing public procurement.

Searching for similar documents and exploring major themes covered across groups of documents are common activities when browsing collections of scientific papers. This manual knowledge-intensive task can become less tedious and even lead to unexpected relevant findings if unsupervised algorithms are applied to help researchers. Most text mining algorithms represent documents in a common feature space that abstract them away from the specific sequence of words used in them. Probabilistic Topic Models reduce that feature space by annotating documents with thematic information. Over this low-dimensional latent space some locality-sensitive hashing algorithms have been proposed to perform document similarity search. However, thematic information gets hidden behind hash codes, preventing thematic exploration and limiting the explanatory capability of topics to justify content-based similarities. This paper presents a novel hashing algorithm based on approximate nearest-neighbor techniques that uses hierarchical sets of topics as hash codes. It not only performs efficient similarity searches, but also allows extending those queries with thematic restrictions explaining the similarity score from the most relevant topics. Extensive evaluations on both scientific and industrial text datasets validate the proposed algorithm in terms of accuracy and efficiency.