KLEE, Matthias

Langa, Kenneth M.

LEIST, Anja

Open Repository and Bibliography - Luxembourg

Adapted Langa-Weir probable dementiaclassification outperforms other algorithms in 26 European countries without clinical dementia assessments.Non-Cognitive Predictors of Student Success:A Predictive Validity Comparison Between Domestic and International StudentsINTRO• Classification algorithms for ‘probable dementia’ help to compensate for lacking validated cognitive assessments• Algorithms, such as the Langa-Weir1,2 classification (LW) from the Health and Retirement Study (HRS), have not been applied yet to the Survey of Health, Ageing and Retirement in Europe (SHARE, table 1)3We sought to investigate the potential of the LW classification to detect ‘probable dementia’ in SHARE using a minimal predictor set, with the aim of compensating for underdiagnosis of dementia. TESTED ALGORITHMS• LW (Recall)(P): based on recall (Table 2)• LW (Recall & IADL)(P): based on recall and outlying IADL (Instrumental Activities of Daily Living) sum (Table 2)• Logistic Regression, Random Forest and XGBoost4 based on recall, IADLs, interviewer & sociodemographic variables_______________________________________P recall cutoffs used for classification based on 2.5th percentile / percentile reflecting OECD (Organisation for Economic Co-operation and Development) projected dementia prevalence in 20185,6RESULTS• LW (Recall & IADL)P with best performance (Sensitivity = .43, Specificity = .97)• AUC better in machine learning based classifiers (AUC = .87-.89) compared with LW adaptations (AUC = .63-.73)DISCUSSION• LW (Recall & IADL)P identified ‘probable dementia’ and reduced underdiagnosis in SHARE (figure 1)• Participants classified ‘probable dementia’ have similar cognitive and health profiles as participants with self-reported physician-diagnosis of dementia (figure 2)• Performance is in line with findings in HRS7 but varies by countryDementia Classification in the European Context – Incorporating OECD Data to Adjust for Country-level Variation in Underdiagnosis Matthias Klee, Kenneth M. Langa, Anja K. LeistScan me for more detailed informationxMatthias KleeInstitute for Research on Socio-Economic Inequality@matthias_kleecognitiveageing.uni.luREFERENCES1 Crimmins EM, Kim JK, Langa KM, Weir DR. Assessment of cognition using surveys and neuropsychological assessment: the Health and Retirement Study and the Aging,Demographics, and Memory Study. J Gerontol B Psychol Sci Soc Sci. 2011;66 Suppl 1:i162-171.2 Alzheimer’s Association. 2010 Alzheimer’s disease facts and figures. Alzheimers Dement J Alzheimers Assoc. 2010;6(2):158-194.3 Börsch-Supan A, Brandt M, Hunkler C, et al. Data Resource Profile: The Survey of Health, Ageing and Retirement in Europe (SHARE). Int J Epidemiol. 2013;42(4):992-1001.4 Chen T, Guestrin C. XGBoost: A Scalable Tree Boosting System. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and DataMining. KDD ’16. Association for Computing Machinery; 2016:785-794.5 OECD. Health at a Glance: Europe 2018: State of Health in the EU Cycle. Organisation for Economic Co-operation and Development; 2018. Accessed May 5, 2022.https://www.oecd-ilibrary.org/social-issues-migration-health/health-at-a-glance-europe-2018_health_glance_eur-2018-en.6 Kodesh A. Prevalence and comorbidities of dementia in Israel: A nationally representative cohort study. Int Psychogeriatr. 2019;31(7):1059-1063.7 Gianattasio KZ, Wu Q, Glymour MM, Power MC. Comparison of Methods for Algorithmic Classification of Dementia Status in the Health and Retirement Study. Epidemiol CambMass. 2019;30(2):291-302.FINANCIAL DISCLOSUREThis work was supported by the European Research Council under the European Union’s Horizon 2020 research and innovation programme [803239, AKL]. Figure 1. Dementia prevalence across countries. Y axis illustrates prevalence based on population-weighted SHARE data with self-reported physician-diagnosis of dementia (red rectangles) or Langa-Weir (LW [Recall & IADL]P) ‘probable dementia’ (blue triangles), x axis illustrates prevalence basedon OECD projections for 2018.5,6Table 1. Descriptive Characteristics.Note. Participants in SHARE wave 7 age 60 and older with complete dataon self-reported dementia status, recall, IADLs, age, sex and education.Test Set (N = 28,312) Training Set (N = 28,310)Age M (SD) 71.7 (8.05) 71.7 (8.08)GenderFemale 15,937 (56.3%) 15,931 (56.3%)Male 12,375 (43.7%) 12,379 (43.7%)EducationLower 2nd 11,418 (40.3%) 11,376 (40.2%)Upper 2nd 9,563 (33.8%) 9,512 (33.6%)Tertiary 7,331 (25.9%) 7,422 (26.2%)DementiaYes 591 (2.1%) 585 (2.1%)No 27,721 (97.9%) 27,725 (97.9%)Characteristics HRS (Recall) (Recall & IADL)Cognitive FunctionImmediate Recall SR SR SRDelayed Recall SR SR SRSerial 7’s SR - -Backward Counting SR - -IADLPreparing Meals Proxy - SRShopping Groceries Proxy - SRMaking Phone Calls Proxy - SRTaking Medication Proxy - SRManaging Money Proxy - SRUsing a Map - - SRDoing Housework - - SRIndependent Mobility - - SRDoing Laundry - - SRTable 2. Comparison of LW Adaptations.Note. Proxy = Proxy respondent. SR = Self-reported.Figure 2. Further domains of health and cognitive function. Green fillindicates M, 95%CI for participants with self-reported physician-diagnosis.

Adapted Langa-Weir probable dementia classification outperforms other algorithms in 26 European countries without clinical dementia assessments.

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Adapted Langa-Weir probable dementia classification outperforms other algorithms in 26 European countries without clinical dementia assessments.

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