Comparison of Methods for Algorithmic Classification of Dementia Status in the Health and Retirement Study.
Background: Dementia ascertainment is time-consuming and costly. Several algorithms use existing data from the US-representative Health and Retirement Study (HRS) to algorithmically identify dementia. However, relative performance of these algorithms remains unknown.Methods: We compared performance...
| Publicado en: | Epidemiology Vol. 30; no. 2; pp. 291 - 303 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
Lippincott Williams & Wilkins
Mar2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=134667828&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 134667828 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10443983 N07 jtl: Epidemiology issn: 10443983 maglogo: N pubinfo: dt: Mar2019 vid: 30 iid: 2 pid: 433 pub: Lippincott Williams & Wilkins place: Baltimore, Maryland artinfo: ui: 134667828 134667828 NLM30461528 134667828 10.1097/EDE.0000000000000945 NLM30461528 134667828 ppf: 291 ppct: 12 formats: tig: atl: Comparison of Methods for Algorithmic Classification of Dementia Status in the Health and Retirement Study. aug: au: Gianattasio, Kan Z. Wu, Qiong Glymour, M. Maria Power, Melinda C. affil: From the Department of Epidemiology and Biostatistics, Milken Institute School of Public Health, George Washington University, Washington, DC sug: subj: Dementia Diagnosis Algorithms Dementia Classification Aged, 80 and Over Female Black Persons White Persons Male Hispanic Americans Human Prospective Studies Aged Neuropsychological Tests Validation Studies Comparative Studies Evaluation Research Multicenter Studies Aged, 80 & over Aged: 65+ years Female Male ab: Background: Dementia ascertainment is time-consuming and costly. Several algorithms use existing data from the US-representative Health and Retirement Study (HRS) to algorithmically identify dementia. However, relative performance of these algorithms remains unknown.Methods: We compared performance across five algorithms (Herzog-Wallace, Langa-Kabeto-Weir, Crimmins, Hurd, Wu) overall and within sociodemographic subgroups in participants in HRS and Wave A of the Aging, Demographics, and Memory Study (ADAMS, 2000-2002), an HRS substudy including in-person dementia ascertainment. We then compared algorithmic performance in an internal (time-split) validation dataset including participants of HRS and ADAMS Waves B, C, and/or D (2002-2009).Results: In the unweighted training data, sensitivity ranged from 53% to 90%, specificity ranged from 79% to 97%, and overall accuracy ranged from 81% to 87%. Though sensitivity was lower in the unweighted validation data (range: 18%-62%), overall accuracy was similar (range: 79%-88%) due to higher specificities (range: 82%-98%). In analyses weighted to represent the age-eligible US population, accuracy ranged from 91% to 94% in the training data and 87% to 94% in the validation data. Using a 0.5 probability cutoff, Crimmins maximized sensitivity, Herzog-Wallace maximized specificity, and Wu and Hurd maximized accuracy. Accuracy was higher among younger, highly-educated, and non-Hispanic white participants versus their complements in both weighted and unweighted analyses.Conclusion: Algorithmic diagnoses provide a cost-effective way to conduct dementia research. However, naïve use of existing algorithms in disparities or risk factor research may induce nonconservative bias. Algorithms with more comparable performance across relevant subgroups are needed. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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