223 research outputs found

    Stagnation of a 'Miracle': Botswana’s Governance Record Revisited

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    Digitally-enabled, patient-centred care in rhinitis and asthma multimorbidity: The ARIA-MASK-air® approach

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    MASK-air®, a validated mHealth app (Medical Device regulation Class IIa) has enabled large observational implementation studies in over 58,000 people with allergic rhinitis and/or asthma. It can help to address unmet patient needs in rhinitis and asthma care. MASK-air® is a Good Practice of DG Santé on digitally-enabled, patient-centred care. It is also a candidate Good Practice of OECD (Organisation for Economic Co-operation and Development). MASK-air® data has enabled novel phenotype discovery and characterisation, as well as novel insights into the management of allergic rhinitis. MASK-air® data show that most rhinitis patients (i) are not adherent and do not follow guidelines, (ii) use as-needed treatment, (iii) do not take medication when they are well, (iv) increase their treatment based on symptoms and (v) do not use the recommended treatment. The data also show that control (symptoms, work productivity, educational performance) is not always improved by medications. A combined symptom-medication score (ARIA-EAACI-CSMS) has been validated for clinical practice and trials. The implications of the novel MASK-air® results should lead to change management in rhinitis and asthma

    Digitally‐Enabled, Patient‐Centred Care in Rhinitis and Asthma Multimorbidity: The ARIA‐MASK‐air ® Approach

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    MASK-air® , a validated mHealth app (Medical Device regulation Class IIa) has enabled large observational implementation studies in over 58,000 people with allergic rhinitis and/or asthma. It can help to address unmet patient needs in rhinitis and asthma care. MASK-air® is a Good Practice of DG Santé on digitally-enabled, patient-centred care. It is also a candidate Good Practice of OECD (Organisation for Economic Co-operation and Development). MASK-air® data has enabled novel phenotype discovery and characterisation, as well as novel insights into the management of allergic rhinitis. MASK-air® data show that most rhinitis patients (i) are not adherent and do not follow guidelines, (ii) use as-needed treatment, (iii) do not take medication when they are well, (iv) increase their treatment based on symptoms and (v) do not use the recommended treatment. The data also show that control (symptoms, work productivity, educational performance) is not always improved by medications. A combined symptom-medication score (ARIA-EAACI-CSMS) has been validated for clinical practice and trials. The implications of the novel MASK-air® results should lead to change management in rhinitis and asthma.info:eu-repo/semantics/publishedVersio

    ARIA 2016:Care pathways implementing emerging technologies for predictive medicine in rhinitis and asthma across the life cycle

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    The Allergic Rhinitis and its Impact on Asthma (ARIA) initiative commenced during a World Health Organization workshop in 1999. The initial goals were (1) to propose a new allergic rhinitis classification, (2) to promote the concept of multi-morbidity in asthma and rhinitis and (3) to develop guidelines with all stakeholders that could be used globally for all countries and populations. ARIA-disseminated and implemented in over 70 countries globally-is now focusing on the implementation of emerging technologies for individualized and predictive medicine. MASK [MACVIA (Contre les Maladies Chroniques pour un Vieillissement Actif)-ARIA Sentinel NetworK] uses mobile technology to develop care pathways for the management of rhinitis and asthma by a multi-disciplinary group and by patients themselves. An app (Android and iOS) is available in 20 countries and 15 languages. It uses a visual analogue scale to assess symptom control and work productivity as well as a clinical decision support system. It is associated with an inter-operable tablet for physicians and other health care professionals. The scaling up strategy uses the recommendations of the European Innovation Partnership on Active and Healthy Ageing. The aim of the novel ARIA approach is to provide an active and healthy life to rhinitis sufferers, whatever their age, sex or socio-economic status, in order to reduce health and social inequalities incurred by the disease

    Geolocation with respect to persona privacy for the Allergy Diary app - a MASK study

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    Background: Collecting data on the localization of users is a key issue for the MASK (Mobile Airways Sentinel network: the Allergy Diary) App. Data anonymization is a method of sanitization for privacy. The European Commission's Article 29 Working Party stated that geolocation information is personal data. To assess geolocation using the MASK method and to compare two anonymization methods in the MASK database to find an optimal privacy method. Methods: Geolocation was studied for all people who used the Allergy Diary App from December 2015 to November 2017 and who reported medical outcomes. Two different anonymization methods have been evaluated: Noise addition (randomization) and k-anonymity (generalization). Results: Ninety-three thousand one hundred and sixteen days of VAS were collected from 8535 users and 54,500 (58. 5%) were geolocalized, corresponding to 5428 users. Noise addition was found to be less accurate than k-anonymity using MASK data to protect the users' life privacy. Discussion: k-anonymity is an acceptable method for the anonymization of MASK data and results can be used for other databases.Peer reviewe

    The ARIA-MASK-air® approach

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    Funding Information: The authors thank Ms Véronique Pretschner for submitting the paper. MASK‐air has been supported by Charité Universitätsmedizin Berlin, EU grants (EU Structural and Development Funds Languedoc Roussillon and Region PACA; POLLAR: EIT Health; Twinning: EIP on AHA; Twinning DHE: H2020; Catalyse: Horizon Europe) and educational grants from Mylan‐Viatris, ALK, GSK, Novartis, Stallergènes‐Greer and Uriach. None for the study. ® Publisher Copyright: © 2023 The Authors. Clinical and Translational Allergy published by John Wiley & Sons Ltd on behalf of European Academy of Allergy and Clinical Immunology.MASK-air®, a validated mHealth app (Medical Device regulation Class IIa) has enabled large observational implementation studies in over 58,000 people with allergic rhinitis and/or asthma. It can help to address unmet patient needs in rhinitis and asthma care. MASK-air® is a Good Practice of DG Santé on digitally-enabled, patient-centred care. It is also a candidate Good Practice of OECD (Organisation for Economic Co-operation and Development). MASK-air® data has enabled novel phenotype discovery and characterisation, as well as novel insights into the management of allergic rhinitis. MASK-air® data show that most rhinitis patients (i) are not adherent and do not follow guidelines, (ii) use as-needed treatment, (iii) do not take medication when they are well, (iv) increase their treatment based on symptoms and (v) do not use the recommended treatment. The data also show that control (symptoms, work productivity, educational performance) is not always improved by medications. A combined symptom-medication score (ARIA-EAACI-CSMS) has been validated for clinical practice and trials. The implications of the novel MASK-air® results should lead to change management in rhinitis and asthma.publishersversionpublishe

    Genetic linkage analysis in the age of whole-genome sequencing

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    For many years, linkage analysis was the primary tool used for the genetic mapping of Mendelian and complex traits with familial aggregation. Linkage analysis was largely supplanted by the wide adoption of genome-wide association studies (GWASs). However, with the recent increased use of whole-genome sequencing (WGS), linkage analysis is again emerging as an important and powerful analysis method for the identification of genes involved in disease aetiology, often in conjunction with WGS filtering approaches. Here, we review the principles of linkage analysis and provide practical guidelines for carrying out linkage studies using WGS data
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