The 10/66 Dementia Research Group's fully operationalised DSM-IV dementia computerized diagnostic algorithm, compared with the 10/66 dementia algorithm and a clinician diagnosis: a population validation study.
BACKGROUND
The criterion for dementia implicit in DSM-IV is widely used in research but not fully operationalised. The 10/66 Dementia Research Group sought to do this using assessments from their one phase dementia diagnostic research interview, and to validate the resulting algorithm in a population-based study in Cuba.


METHODS
The criterion was operationalised as a computerised algorithm, applying clinical principles, based upon the 10/66 cognitive tests, clinical interview and informant reports; the Community Screening Instrument for Dementia, the CERAD 10 word list learning and animal naming tests, the Geriatric Mental State, and the History and Aetiology Schedule - Dementia Diagnosis and Subtype. This was validated in Cuba against a local clinician DSM-IV diagnosis and the 10/66 dementia diagnosis (originally calibrated probabilistically against clinician DSM-IV diagnoses in the 10/66 pilot study).


RESULTS
The DSM-IV sub-criteria were plausibly distributed among clinically diagnosed dementia cases and controls. The clinician diagnoses agreed better with 10/66 dementia diagnosis than with the more conservative computerized DSM-IV algorithm. The DSM-IV algorithm was particularly likely to miss less severe dementia cases. Those with a 10/66 dementia diagnosis who did not meet the DSM-IV criterion were less cognitively and functionally impaired compared with the DSMIV confirmed cases, but still grossly impaired compared with those free of dementia.


CONCLUSIONS
The DSM-IV criterion, strictly applied, defines a narrow category of unambiguous dementia characterized by marked impairment. It may be specific but incompletely sensitive to clinically relevant cases. The 10/66 dementia diagnosis defines a broader category that may be more sensitive, identifying genuine cases beyond those defined by our DSM-IV algorithm, with relevance to the estimation of the population burden of this disorder.
Prince
Martin J
MJ
Section of Epidemiology, Health Services Research, King's College London, De Crespigny Park, London SE5 8AF, UK. m.prince@iop.kcl.ac.uk
de Rodriguez
Juan Llibre
JL
Noriega
L
L
Lopez
A
A
Acosta
Daisy
D
Albanese
Emiliano
E
Arizaga
Raul
R
Copeland
John R M
JR
Dewey
Michael
M
Ferri
Cleusa P
CP
Guerra
Mariella
M
Huang
Yueqin
Y
Jacob
K S
KS
Krishnamoorthy
E S
ES
McKeigue
Paul
P
Sousa
Renata
R
Stewart
Robert J
RJ
Salas
Aquiles
A
Sosa
Ana Luisa
AL
Uwakwa
Richard
R
D000368:Aged; D000465:Algorithms; D016022:Case-Control Studies; D002391:Catchment Area, Health; D003071:Cognition; D003156:Community Mental Health Services; D003462:Cuba; D003704:Dementia / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted; D039721:Diagnostic and Statistical Manual of Mental Disorders; D005260:Female; D015577:Geriatric Assessment; D006801:Humans; D007407:Interviews as Topic; D008297:Male; D011581:Psychological Tests