<HashMap><database>biostudies-literature</database><scores><citationCount>0</citationCount><reanalysisCount>0</reanalysisCount><viewCount>63</viewCount><searchCount>0</searchCount></scores><additional><submitter>Grasset L</submitter><funding>Medical Research Council</funding><funding>Alzheimer's Society</funding><pagination>490-497</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC6178133</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>10</volume><pubmed_abstract>Introduction:The aims of this study are to examine the evolution of clinical dementia diagnosis over 3 decades and to investigate secular trends of dementia. Methods:Four cohorts covering a period from 1988 to 2013 were used: the Personnes Agées Quid and Three-City-Bordeaux studies, and the Cognitive Function and Aging Study (CFAS) I and II. Mini-Mental State Examination scores at clinical diagnosis were evaluated over a 24-year follow-up period in French studies. An algorithmic approach was applied to CFAS I and II to provide dementia prevalence and incidence estimates. Results:A significant increase of the Mini-Mental State Examination score at diagnosis was observed until 2000 and a significant decrease after. We reported a prevalence of 8.8% for CFAS I (1990-1993) compared with a prevalence of 6.5% in CFAS II (2008-2011). The 2-year incidence rate was estimated at 31.2/1000 (95% confidence interval = 28.0-34.8) for CFAS I and 15.0/1000 (95% confidence interval = 13.5-16.7) for CFAS II. Discussion:Applying a stable algorithm to different cohorts across time can provide a robust method for time trends estimation.</pubmed_abstract><journal>Alzheimer's &amp; dementia (Amsterdam, Netherlands)</journal><pubmed_title>Evolution of dementia diagnosis over time (1988-2013): Evidence from French and English cohorts. Implication for secular trends analyses.</pubmed_title><pmcid>PMC6178133</pmcid><funding_grant_id>MC_U105292687</funding_grant_id><funding_grant_id>G9901400</funding_grant_id><funding_grant_id>294</funding_grant_id><pubmed_authors>Dartigues JF</pubmed_authors><pubmed_authors>Peres K</pubmed_authors><pubmed_authors>Brayne C</pubmed_authors><pubmed_authors>Matthews FE</pubmed_authors><pubmed_authors>Foubert-Samier A</pubmed_authors><pubmed_authors>Helmer C</pubmed_authors><pubmed_authors>Grasset L</pubmed_authors><view_count>63</view_count></additional><is_claimable>false</is_claimable><name>Evolution of dementia diagnosis over time (1988-2013): Evidence from French and English cohorts. Implication for secular trends analyses.</name><description>Introduction:The aims of this study are to examine the evolution of clinical dementia diagnosis over 3 decades and to investigate secular trends of dementia. Methods:Four cohorts covering a period from 1988 to 2013 were used: the Personnes Agées Quid and Three-City-Bordeaux studies, and the Cognitive Function and Aging Study (CFAS) I and II. Mini-Mental State Examination scores at clinical diagnosis were evaluated over a 24-year follow-up period in French studies. An algorithmic approach was applied to CFAS I and II to provide dementia prevalence and incidence estimates. Results:A significant increase of the Mini-Mental State Examination score at diagnosis was observed until 2000 and a significant decrease after. We reported a prevalence of 8.8% for CFAS I (1990-1993) compared with a prevalence of 6.5% in CFAS II (2008-2011). The 2-year incidence rate was estimated at 31.2/1000 (95% confidence interval = 28.0-34.8) for CFAS I and 15.0/1000 (95% confidence interval = 13.5-16.7) for CFAS II. Discussion:Applying a stable algorithm to different cohorts across time can provide a robust method for time trends estimation.</description><dates><release>2018-01-01T00:00:00Z</release><publication>2018</publication><modification>2021-02-20T12:09:15Z</modification><creation>2019-03-27T00:14:32Z</creation></dates><accession>S-EPMC6178133</accession><cross_references><pubmed>30310851</pubmed><doi>10.1016/j.dadm.2018.07.005</doi></cross_references></HashMap>