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      The power of knowledge about dementia in Latin America across health professionals working on aging

      research-article
      1 , 2 , 3 , 4 , 5 , , 6 , 2 , 3 , 2 , 3 , 2 , 7 , 8 , 9 , 10 , 11 , 8 , 12 , 13 , 14 , 15 , 15 , 15 , 7 , 7 , 16 , 17 , 17 , 17 , 1 , 1
      Alzheimer's & Dementia : Diagnosis, Assessment & Disease Monitoring
      John Wiley and Sons Inc.
      behavioral insights, data‐sharing platforms, diagnosis manuals, expert knowledge, Latin American and Caribbean countries, public policy, stigma

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          Abstract

          Introduction

          Expert knowledge is critical to fight dementia in inequitable regions like Latin American and Caribbean countries (LACs). However, the opinions of aging experts on public policies’ accessibility and transmission, stigma, diagnostic manuals, data‐sharing platforms, and use of behavioral insights (BIs) are not well known.

          Methods

          We investigated opinions among health professionals working on aging in LACs (N = 3365) with regression models including expertise‐related information (public policies, BI), individual differences (work, age, academic degree), and location.

          Results

          Experts specified low public policy knowledge ( X 2  = 41.27, P < .001), high levels of stigma ( X 2  = 2636.37, P < .001), almost absent BI knowledge ( X 2  = 56.58, P < .001), and needs for regional diagnostic manuals ( X 2  = 2893.63, df = 3, P < .001) and data‐sharing platforms (X 2 = 1267.5, df = 3, P < .001). Lack of dementia knowledge was modulated by different factors. An implemented BI‐based treatment for a proposed prevention program improved perception across experts.

          Discussion

          Our findings help to prioritize future potential actions of governmental agencies and non‐governmental organizations (NGOs) to improve LACs’ dementia knowledge.

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          Most cited references46

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          Version 3 of the National Alzheimer’s Coordinating Center’s Uniform Data Set

          Supplemental Digital Content is available in the text.
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            2014 Update of the Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception.

            The Alzheimer's Disease Neuroimaging Initiative (ADNI) is an ongoing, longitudinal, multicenter study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer's disease (AD). The initial study, ADNI-1, enrolled 400 subjects with early mild cognitive impairment (MCI), 200 with early AD, and 200 cognitively normal elderly controls. ADNI-1 was extended by a 2-year Grand Opportunities grant in 2009 and by a competitive renewal, ADNI-2, which enrolled an additional 550 participants and will run until 2015. This article reviews all papers published since the inception of the initiative and summarizes the results to the end of 2013. The major accomplishments of ADNI have been as follows: (1) the development of standardized methods for clinical tests, magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers in a multicenter setting; (2) elucidation of the patterns and rates of change of imaging and CSF biomarker measurements in control subjects, MCI patients, and AD patients. CSF biomarkers are largely consistent with disease trajectories predicted by β-amyloid cascade (Hardy, J Alzheimer's Dis 2006;9(Suppl 3):151-3) and tau-mediated neurodegeneration hypotheses for AD, whereas brain atrophy and hypometabolism levels show predicted patterns but exhibit differing rates of change depending on region and disease severity; (3) the assessment of alternative methods of diagnostic categorization. Currently, the best classifiers select and combine optimum features from multiple modalities, including MRI, [(18)F]-fluorodeoxyglucose-PET, amyloid PET, CSF biomarkers, and clinical tests; (4) the development of blood biomarkers for AD as potentially noninvasive and low-cost alternatives to CSF biomarkers for AD diagnosis and the assessment of α-syn as an additional biomarker; (5) the development of methods for the early detection of AD. CSF biomarkers, β-amyloid 42 and tau, as well as amyloid PET may reflect the earliest steps in AD pathology in mildly symptomatic or even nonsymptomatic subjects and are leading candidates for the detection of AD in its preclinical stages; (6) the improvement of clinical trial efficiency through the identification of subjects most likely to undergo imminent future clinical decline and the use of more sensitive outcome measures to reduce sample sizes. Multimodal methods incorporating APOE status and longitudinal MRI proved most highly predictive of future decline. Refinements of clinical tests used as outcome measures such as clinical dementia rating-sum of boxes further reduced sample sizes; (7) the pioneering of genome-wide association studies that leverage quantitative imaging and biomarker phenotypes, including longitudinal data, to confirm recently identified loci, CR1, CLU, and PICALM and to identify novel AD risk loci; (8) worldwide impact through the establishment of ADNI-like programs in Japan, Australia, Argentina, Taiwan, China, Korea, Europe, and Italy; (9) understanding the biology and pathobiology of normal aging, MCI, and AD through integration of ADNI biomarker and clinical data to stimulate research that will resolve controversies about competing hypotheses on the etiopathogenesis of AD, thereby advancing efforts to find disease-modifying drugs for AD; and (10) the establishment of infrastructure to allow sharing of all raw and processed data without embargo to interested scientific investigators throughout the world.
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              Common pitfalls in statistical analysis: Measures of agreement

              Agreement between measurements refers to the degree of concordance between two (or more) sets of measurements. Statistical methods to test agreement are used to assess inter-rater variability or to decide whether one technique for measuring a variable can substitute another. In this article, we look at statistical measures of agreement for different types of data and discuss the differences between these and those for assessing correlation.
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                Author and article information

                Contributors
                agustin.ibanez@gbhi.org
                Journal
                Alzheimers Dement (Amst)
                Alzheimers Dement (Amst)
                10.1002/(ISSN)2352-8729
                DAD2
                Alzheimer's & Dementia : Diagnosis, Assessment & Disease Monitoring
                John Wiley and Sons Inc. (Hoboken )
                2352-8729
                14 October 2020
                2020
                : 12
                : 1 ( doiID: 10.1002/dad2.v12.1 )
                : e12117
                Affiliations
                [ 1 ] Global Brain Health Institute and the Memory and Aging Center, Weill Institute for Neurosciences, Department of Neurology University of California, San Francisco (UCSF) San Francisco California USA
                [ 2 ] Universidad de San Andrés Buenos Aires Argentina
                [ 3 ] National Scientific and Technical Research Council (CONICET) Buenos Aires Argentina
                [ 4 ] Center for Social and Cognitive Neuroscience (CSCN), School of Psychology Universidad Adolfo Ibáñez Santiago de Chile Chile
                [ 5 ] Universidad Autónoma del Caribe Barranquilla Colombia
                [ 6 ] Intramed Buenos Aires Argentina
                [ 7 ] Memory and Neuropsychiatric Clinic (CMYN), Neurology Department Del Salvador Hospital and University of Chile Faculty of Medicine Santiago Chile
                [ 8 ] Geroscience Center for Brain Health and Metabolism (GERO), Faculty of Medicine University of Chile Santiago Chile
                [ 9 ] Neuropsychology and Clinical Neuroscience Laboratory (LANNEC), Physiopathology Department ‐ ICBM, Neuroscience and East Neuroscience Departments, Faculty of Medicine University of Chile Santiago Chile
                [ 10 ] Department of Neurology and Psychiatry Clínica Alemana‐Universidad del Desarrollo Santiago Chile
                [ 11 ] Cognitive Neurology, Neurology Department Dr César Milstein Hospital Buenos Aires Argentina
                [ 12 ] Unit Cognitive Impairment and Dementia Prevention, Cognitive Neurology Center Peruvian Institute of Neurosciences Lima Perú
                [ 13 ] Faculty of Nursing Universidad Andres Bello Santiago Chile
                [ 14 ] Millennium Institute for Research in Depression and Personality Santiago Chile
                [ 15 ] Institute of Translational and Cognitive Neuroscience (INCYT), INECO Foundation, Favaloro University National Scientific and Technical Research Council (CONICET) Buenos Aires Argentina
                [ 16 ] Department of Speech and Language Pathology, Federal University of Health Sciences of Porto Alegre (UFCSPA) Atlantic Fellow for Equity in Brain Health Porto Alegre Brazil
                [ 17 ] Faculdade de Medicina Universidade de São Paulo São Paulo Brazil
                Author notes
                [*] [* ] Correspondence

                Agustin Ibanez, Global Brain Health Institute and the Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco (UCSF), San Francisco, CA, USA.

                Email: agustin.ibanez@ 123456gbhi.org

                Author information
                https://orcid.org/0000-0001-6758-5101
                Article
                DAD212117
                10.1002/dad2.12117
                7560513
                33088898
                e5820daa-9235-4b3d-aa0a-9d62e16a5ea2
                © 2020 The Authors. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring published by Wiley Periodicals LLC on behalf of Alzheimer's Association

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

                History
                : 30 June 2020
                : 01 September 2020
                : 16 September 2020
                Page count
                Figures: 3, Tables: 2, Pages: 12, Words: 7497
                Funding
                Funded by: National Institutes of Health , open-funder-registry 10.13039/100000001;
                Funded by: National Institutes of Aging
                Award ID: R01 AG057234
                Funded by: Alzheimer's Association , open-funder-registry 10.13039/100000957;
                Award ID: SG‐20‐725707
                Funded by: Alzheimer's Association GBHI GBHI ALZ
                Award ID: UK‐20‐639295
                Categories
                Research Article
                Diagnostic Assessment & Prognosis
                Custom metadata
                2.0
                2020
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.9.2 mode:remove_FC converted:15.10.2020

                behavioral insights,data‐sharing platforms,diagnosis manuals,expert knowledge,latin american and caribbean countries,public policy,stigma

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