RESUMO
Dementia in community settings is often diagnosed by computerized algorithms. This study examines the extent to which independent diagnosticians agreed among themselves in diagnosing dementia, severity and type when presented with data obtained during a population-based incidence study of cognitive decline and dementia. Secondly, it examines how judgements, based initially on respondents' self-reports and cognitive performance, were affected first by informants' reports and then by short case-vignettes written by trained lay interviewers. Thirdly, it compares diagnosticians' diagnosis of dementia with the algorithmic diagnosis (AGECAT). The items presented were selected from two screening interviews at wave 1 and wave 2 separated by an interval of 2 years and from wave 2 assessment and informant interviews, and included medical, psychiatric and ADL items and interviewers' own observations. The sample (N = 42) was derived from the first year of the wave 2 assessments, potential dementia cases entering consecutively while presumed normals were selected randomly. Informants were available in 30. Agreement on diagnosis and type of dementia improved with increasing information, particularly from informants, but remained poor regarding severity. The number of cases of dementia, defined operationally, increased from 10 to 12 and uncertain cases fell from eight to six, but no respondent initially diagnosed as a dementia case was rediagnosed as a non-case, or vice versa. Dementia type changed from agreement about Alzheimer's disease to agreement about vascular dementia in one case. Operational and algorithmic diagnoses showed good agreement. Causes of disagreement, the role of vignettes and the relevance of the results for population surveys are discussed.
