In one paragraphArticle in Neurology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 99% of its field
1 · What the graph read from itWhat it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registryThe trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
3 · Its place in the literatureWho cites it
0 citing papers in PubMed, 0 citations in OpenAlex.
No citing paper in PubMed yet.
4 · The recordCorrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what moneyAuthors and funding
9 authors at 1 institution in 2 countries.
Tanisha G Hill-JarrettFrom the Department of Neurology (T.G.H.-.J), Memory and Aging Center; Department of Epidemiology and Biostatistics (M.C., P.T.B.); Bakar Computational Health Sciences Institute (S.M.); Department of Psychiatry and Behavioral Sciences (M.D.T.), Weill Institute for Neurosciences; Department of Epidemiology and Biostatistics (Y.Y., K.D.S., M.M.G.); and Institute for Health Policy Studies (M.H.K.), University of California San Francisco.ORCID 0000-0002-6924-0702 Minhyuk ChoiFrom the Department of Neurology (T.G.H.-.J), Memory and Aging Center; Department of Epidemiology and Biostatistics (M.C., P.T.B.); Bakar Computational Health Sciences Institute (S.M.); Department of Psychiatry and Behavioral Sciences (M.D.T.), Weill Institute for Neurosciences; Department of Epidemiology and Biostatistics (Y.Y., K.D.S., M.M.G.); and Institute for Health Policy Studies (M.H.K.), University of California San Francisco.ORCID 0009-0000-6658-5054 Peter T ButoFrom the Department of Neurology (T.G.H.-.J), Memory and Aging Center; Department of Epidemiology and Biostatistics (M.C., P.T.B.); Bakar Computational Health Sciences Institute (S.M.); Department of Psychiatry and Behavioral Sciences (M.D.T.), Weill Institute for Neurosciences; Department of Epidemiology and Biostatistics (Y.Y., K.D.S., M.M.G.); and Institute for Health Policy Studies (M.H.K.), University of California San Francisco.ORCID 0000-0001-5052-6009 Silvia MiramontesFrom the Department of Neurology (T.G.H.-.J), Memory and Aging Center; Department of Epidemiology and Biostatistics (M.C., P.T.B.); Bakar Computational Health Sciences Institute (S.M.); Department of Psychiatry and Behavioral Sciences (M.D.T.), Weill Institute for Neurosciences; Department of Epidemiology and Biostatistics (Y.Y., K.D.S., M.M.G.); and Institute for Health Policy Studies (M.H.K.), University of California San Francisco.ORCID 0000-0001-5805-0500 Marilyn D ThomasFrom the Department of Neurology (T.G.H.-.J), Memory and Aging Center; Department of Epidemiology and Biostatistics (M.C., P.T.B.); Bakar Computational Health Sciences Institute (S.M.); Department of Psychiatry and Behavioral Sciences (M.D.T.), Weill Institute for Neurosciences; Department of Epidemiology and Biostatistics (Y.Y., K.D.S., M.M.G.); and Institute for Health Policy Studies (M.H.K.), University of California San Francisco.ORCID 0000-0003-3245-6363 Yulin YangFrom the Department of Neurology (T.G.H.-.J), Memory and Aging Center; Department of Epidemiology and Biostatistics (M.C., P.T.B.); Bakar Computational Health Sciences Institute (S.M.); Department of Psychiatry and Behavioral Sciences (M.D.T.), Weill Institute for Neurosciences; Department of Epidemiology and Biostatistics (Y.Y., K.D.S., M.M.G.); and Institute for Health Policy Studies (M.H.K.), University of California San Francisco.ORCID 0000-0002-6662-1198 Min Hee KimFrom the Department of Neurology (T.G.H.-.J), Memory and Aging Center; Department of Epidemiology and Biostatistics (M.C., P.T.B.); Bakar Computational Health Sciences Institute (S.M.); Department of Psychiatry and Behavioral Sciences (M.D.T.), Weill Institute for Neurosciences; Department of Epidemiology and Biostatistics (Y.Y., K.D.S., M.M.G.); and Institute for Health Policy Studies (M.H.K.), University of California San Francisco.ORCID 0000-0003-0829-1968 Kendra D SimsFrom the Department of Neurology (T.G.H.-.J), Memory and Aging Center; Department of Epidemiology and Biostatistics (M.C., P.T.B.); Bakar Computational Health Sciences Institute (S.M.); Department of Psychiatry and Behavioral Sciences (M.D.T.), Weill Institute for Neurosciences; Department of Epidemiology and Biostatistics (Y.Y., K.D.S., M.M.G.); and Institute for Health Policy Studies (M.H.K.), University of California San Francisco.ORCID 0000-0002-6740-3002 M Maria GlymourFrom the Department of Neurology (T.G.H.-.J), Memory and Aging Center; Department of Epidemiology and Biostatistics (M.C., P.T.B.); Bakar Computational Health Sciences Institute (S.M.); Department of Psychiatry and Behavioral Sciences (M.D.T.), Weill Institute for Neurosciences; Department of Epidemiology and Biostatistics (Y.Y., K.D.S., M.M.G.); and Institute for Health Policy Studies (M.H.K.), University of California San Francisco.ORCID 0000-0001-9644-3081 University Memory and Aging Center · US
Funding
BMI Bioinformatics Training GrantsT32GM067547 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI HERNANDEZ, RYAN D. · 2003 to 2022
$6.6MTranslational Epidemiology - Training for Research on Aging and Chronic diseaseT32AG049663 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Elizabeth Rose Mayeda, Mark J Pletcher · 2016 to 2026
$5.8MClosing the gap between observational research and randomized trials for prevention of Alzheimer's Disease and dementiaR01AG057869 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI GRAFF, REBECCA ELIZABETH, POWER, MELINDA C · 2018 to 2022
$3.9MResearch Fellowship for Equity in Alzheimer's Disease and Brain HealthT32AG078115 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI BRUCE L MILLER, VICTOR VALCOUR · 2022 to 2026
$1.7MImpact of Historically Black Colleges and Universities on Late-life Cognition in Black AdultsR00AG076973 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Marilyn D Thomas · 2024 to 2026
$741kImpact of Historically Black Colleges and Universities on Late-life Cognition in Black AdultsK99AG076973 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI THOMAS, MARILYN D · 2022 to 2023
$250kRacial Disparities in Delayed Dementia Diagnosis: Structural Determinants and Validation of Timeliness MeasuresK99AG078405 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI KIM, MIN HEE · 2022 to 2023
$248kNIA NIH HHS K99 AG076973NIA NIH HHS K99 AG078405NIA NIH HHS R00 AG076973NIA NIH HHS R01 AG057869NIA NIH HHS T32 AG049663NIA NIH HHS T32 AG078115NIGMS NIH HHS T32 GM067547
6 · The paper itselfAbstract
BACKGROUND AND
objectivesRapid developments in Alzheimer disease (AD) biomarker research suggest that predictive testing may become widely available. To ensure equal access to AD predictive testing, it is important to understand factors that affect testing interest. Discrimination may influence attitudes toward AD testing, particularly among racially and ethnically minoritized populations, because of structural racism in health care systems. This study examined whether everyday or lifetime discrimination experiences shape interest in AD predictive testing.
methodsIn the 2010 and 2012 biennial Health and Retirement Study waves, respondents were randomly selected to complete questions on interest in receiving free testing that could determine whether they would develop AD in the future. The exposures were everyday discrimination (6 items) and lifetime discrimination (7 items); both were transformed into a binary variable. Logistic regression models predicting interest in AD testing were controlled for deciles of propensity scores for each discrimination measure. Odds ratios were re-expressed as risk differences (RDs).
resultsOur analytic sample included 1,499 respondents. The mean age was 67 (SD = 10.2) years, 57.4% were women, 65.7% were White, and 80% endorsed interest in AD predictive testing. Most of the participants (54.7%) experienced everyday discrimination in at least one domain; 24.1% experienced major lifetime discrimination in at least one domain. Those interested in predictive testing were younger (66 vs 70 years) and more likely to be Black (20% vs 15%) or Latinx (14% vs 8%) than participants uninterested in testing. The probability of wanting an AD test was not associated with discrimination for Black (RD everyday discrimination = -0.026; 95% CI [-0.081 to 0.029]; RD lifetime discrimination = -0.012; 95% CI [-0.085 to 0.063]) or Latinx (RD everyday discrimination = -0.023, 95% CI [-0.082 to 0.039]; RD lifetime discrimination = -0.011; 95% CI [-0.087 to 0.064]) participants. DISCUSSION: Despite historical and contemporary experiences of discrimination, Black and Latinx individuals express interest in AD testing. However, Black and Latinx individuals remain underrepresented in AD research, including research on AD testing. Interest in personalized information about dementia risk may be a pathway to enhance their inclusion in research and clinical trials.
Indexed as
Alzheimer DiseaseAgedFemaleHumansLogistic ModelsMaleOdds RatioPropensity ScoreRetirement
Identifiers
PMID38266219
PMCPMC11383877
OpenAlexW4391204406
What OpenQuestion holds
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