Abstract :
[en] Feasibility constraints limit availability of validated cognitive assessments in observational studies. Algorithm-based identification of 'probable dementia' is thus needed, but no algorithm developed so far has been applied in the European context. The present study sought to explore the usefulness of the Langa-Weir (LW) algorithm to detect 'probable dementia' while accounting for country-level variation in prevalence and potential underreporting of dementia. Data from 56 622 respondents of the Survey of Health, Ageing and Retirement in Europe (SHARE, 2017) aged 60 years and older with non-missing data were analyzed. Performance of LW was compared to a logistic regression, random forest and XGBoost classifier. Population-level 'probable dementia' prevalence was compared to estimates based on data from the Organisation for Economic Co-operation and Development. As such, application of the prevalence-specific LW algorithm, based on recall and limitations in instrumental activities of daily living, reduced underreporting from 61.0 (95% CI, 53.3-68.7%) to 30.4% (95% CI, 19.3-41.4%), outperforming tested machine learning algorithms. Performance in other domains of health and cognitive function was similar for participants classified 'probable dementia' and those self-reporting physician-diagnosis of dementia. Dementia classification algorithms can be adapted to cross-national cohort surveys such as SHARE and help reduce underreporting of dementia with a minimal predictor set.
Funding text :
This work was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme [grant number 803239, to AKL]. Mr Klee and Dr Leist are funded by the European Research Council. Dr Langa is funded by the National Institute on Aging and the US Alzheimer’s Association.This study was conducted by Mr Klee and Dr Leist, University of Luxembourg, Institute for Research on Socio-Economic Inequality, Department of Social Sciences, Esch-sur-Alzette, Luxembourg and Dr Langa, University of Michigan, General Medicine, Department of Internal Medicine, Ann Arbor, MI, USA. Conclusions and views that are included in the present study are those of the authors and not necessarily shared by the University of Luxembourg, the University of Michigan or the Veterans Affairs Center for Clinical Management Research. This research has been conducted using SHARE data. The SHARE data collection has been funded by the European Commission, DG RTD through FP5 (QLK6-CT-2001-00360), FP6 (SHARE-I3: RII-CT-2006-062193, COMPARE: CIT5-CT-2005-028857, SHARELIFE: CIT4-CT-2006-028812), FP7 (SHARE-PREP: GA N°211909, SHARE-LEAP: GA N°227822, SHARE M4: GA N°261982, DASISH: GA N°283646) and Horizon 2020 (SHARE-DEV3: GA N°676536, SHARE-COHESION: GA N°870628, SERISS: GA N°654221, SSHOC: GA N°823782, SHARE-COVID19: GA N°101015924) and by DG Employment, Social Affairs & Inclusion through VS 2015/0195, VS 2016/0135, VS 2018/0285, VS 2019/0332, and VS 2020/0313. Additional funding from the German Ministry of Education and Research, the Max Planck Society for the Advancement of Science, the U.S. National Institute on Aging (U01_AG09740-13S2, P01_AG005842, P01_AG08291, P30_AG12815, R21_AG025169, Y1-AG-4553-01, IAG_BSR06-11, OGHA_04-064, HHSN271201300071C, RAG052527A) and from various national funding sources is gratefully acknowledged (see www.share-project). The funders did not have any influence on the design of the study, data collection, analysis, interpretation of data, or writing of the manuscript.
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