Background: The latent variable δ has been proposed as a proxy for dementia. Previous validation studies have been conducted using convenience samples. It is currently unknown how δ performs in population-wide data.
Objective: To validate δ in Singapore using population-wide epidemiological study data on persons aged 60 and above.
Methods: δ was constructed using items from the Community Screening Instrument for Dementia (CSI’D) and World Health Organization Disability Assessment Schedule (WHODAS II). Confirmatory factor analysis (CFA) was conducted to examine δ model fit. Convergent validity was examined with the Clinical Dementia Rating scale (CDR) and GMS-AGECAT dementia. Divergent validity was examined with GMS-AGECAT depression.
Results: The δ model demonstrated fit to the data, χ2(df) = 249.71(55), p < 0.001, CFI = 0.990, TLI = 0.997, RMSEA = 0.037. Latent variable δ was significantly associated with CDR and GMS-AGECAT dementia (range: β= 0.32 to 0.63), and was not associated with GMS-AGECAT depression. Compared to unadjusted models, δ model fit was poor when adjusted for age, gender, ethnicity, and education.
Conclusion: The study found some support for δ as a proxy for dementia in Singapore based on population data. Both convergent and divergent validity were established. In addition, the δ model structure appeared to be influenced by age, gender, ethnicity, and education covariates.
Alzheimer’s Disease International (2009) World Alzheimer Report 2009.
2.
PrinceM, BryceR, AlbaneseE, WimoA, RibeiroW, FerriCP (2013) The global prevalence of dementia: A systematic review and metaanalysis. Alzheimers Dement9, 63–75.e2.
3.
Alzheimer’s Disease International (2015) World Alzheimer Report 2015: The Global Impact of Dementia - An analysis of prevalence, incidence, cost and trends.
4.
RoyallDR, PalmerRF (2012) Getting past g: Testing a new model of dementing processes in dementia. J Neuropsychiatry Clin Neurosci24, 37–46.
5.
SpearmanC (1904) General Intelligence, objectively determined and measured. Am J Psychol15, 201–292.
6.
ShenJ, CraneP, GaoS (2003) A latent variable model approach for assembling and scoring screening tests for dementia. J Alzheimers Dis5, 399–407.
7.
RoyallDR, PalmerRF, O’BryantSE (2012) Validation of a latent variable representing the dementing process. J Alzheimers Dis30, 639–649.
8.
RoyallDR, PalmerRF (2013) Validation of a latent construct for dementia case-finding in Mexican-Americans. J Alzheimers Dis37, 89–97.
9.
GavettBE, VudyV, JeffreyM, JohnSE, GurnaniAS, AdamsJW (2015) The δ latent dementia phenotype in the uniform data set: Cross-validation and extension. Neuropsychology29, 344–352.
10.
RoyallDR, PalmerRF, MatsuokaT, KatoY, TaniguchiS, OgawaM, FujimotoH, OkamuraA, ShibataK, NakamuraK, NakaakiS, KoumiH, MimuraM, FukuiK, NarumotoJ (2015) δ scores are exportable across cultural and linguistic boundaries. J Alzheimers Dis49, 561–570.
11.
RoyallDR1, PalmerRF, VidoniED, HoneaRA, BurnsJM (2012) The default mode network and related right hemisphere structures may be the key substrates of dementia. J Alzheimers Dis32, 467–478.
12.
RoyallDR, PalmerRF (2014) Executive functions cannot be distinguished from general intelligence: Two variations on a single theme within a symphony of latent variance. Front Behav Neurosci8, 369.
13.
KopparaA, WolfsgruberS, KleineidamL, SchmidtkeK, FrolichL, KurzA, SchulzS, HampelH, HeuserI, PetersO, ReischiesFM, JahnH, LuckhausC, HullM, GertzHJ, SchroderJ, PantelJ, RienhoffO, RutherE, HennF, WiltfangJ, MaierW, JessenF, KornhuberJ, WagnerM (2015) The latent dementia phenotype δ is associated with cerebrospinal fluid biomarkers of Alzheimer’s disease and predicts conversion to dementia in subjects with mild cognitive impairment. J Alzheimers Dis49, 547–560.
14.
RoyallDR, PalmerRF (2014) Does ethnicity moderate dementia’s biomarkers?Neurobiol Aging35, 336–344.
PalmerRF, RoyallDR (2015) Future dementia severity is almost entirely explained by the latent variable δ’s intercept and slope. J Alzheimers Dis49, 521–529.
17.
Department of Statistics Singapore (2015) Population Trends, 2015.
18.
SubramaniamM, ChongSA, VaingankarJA, AbdinE, ChuaBY, ChuaHC, EngGK, HengD, HiaSB, HuangW, JeyagurunathanaA, KuaJ, LeeSP, MahendranR, MagadiH, MalladiS, McCroneP, PangS, PiccoL, SagayadevanV, SambasivamR, SengKH, SeowE, ShafieS, ShahwanS, TanLL, YapM, ZhangY, NgLL, PrinceM (2015) Prevalence of dementia in people aged 60 years and above: Results from the WiSE Study. J Alzheimers Dis45, 1127–1138.
19.
HallKS, HendrieHC, RodgersDD, OsuntokunBO, BrittainHM, NortonJA, PrinceCS, PillayN, BlueAW, KaufertJN, NathA, SheltonP, PostlBD (1993) The development of a dementia screening interview in two distinct languages. Int J Methods Psychiatry3, 1–28.
20.
PrinceM, FerriCP, AcostaD, AlbaneseE, ArizagaR, DeweyM, GavrilovaSI, GuerraM, HuangY, JacobKS, KrishnamoorthyES, McKeigueP, RodriguezJL, SalasA, SosaAL, SousaRMM, StewartR, UwakweR (2007) The protocols for the 10/66 dementia research group population-based research programme. BMC Public Health7, 165.
21.
RoyallDR (2015) Welcome back to your future: The assessment of dementia by the latent variable δ. J Alzheimers Dis49, 515–519.
22.
HallKS, OgunniyiAO, HendrieHC, OsuntokunBO, HuiSL, MusickBS, RodenbergCA, UnverzagtFW, GuerjeO, BaiyewuO (1996) A cross-cultural community based study of dementias: Methods and performance of the survey instrument Indianapolis, USA, and Ibadan. Nigeria Int J Methods Psychiatr Res6, 129–142.
23.
World Health Organization (2000) World Health Organization disability assessment schedule: WHODAS II. Phase 2 field trials. Health services research, World Health Organization, Geneva/.
24.
MorrisJC (1997) Clinical dementia rating: A reliable and valid diagnostic and staging measure for dementia of the Alzheimer type. Int Psychogeriatr9(Suppl 1), 173–178.
25.
CopelandJR, DeweyME, Griffiths-JonesHM (1986) A computerized psychiatric diagnostic system and case nomenclature for elderly subjects: GMS and AGECAT. Psychol Med16, 89–99.
26.
CopelandJR, PrinceM, WilsonKCM, DeweyME, PayneJ, GurlandB (2002) The Geriatric Mental State Examination in the 21st century. Int J Geriatr Psychiatry17, 729–732.
27.
KuaEH (1992) A community study of mental disorders in elderly singaporean chinese using the gms-agecat package. Aust N Z J Psychiatry26, 502–506.
28.
KuaEH, KoSM (1995) Prevalence of dementia among elderly Chinese and Malay residents of Singapore. Int Psychogeriatrics7, 439–446.
29.
SAS Institute Inc (2008) SAS/STAT® 9.2 User’s Guide., SAS Institute Inc., Cary, NC.
30.
MuthenL, MuthenB (2010) Mplus user’s guide, Muthen & Muthen, Los Angeles, CA.
31.
BrownT (2006) Confirmatory factor analysis for applied research. Guildford, New York.
32.
MuthenB (1984) A general structural equation model with dichotomous, ordered categorical, and continuous latent variable indicators. Psychometrika49, 115–132.
33.
BentlerPM (1990) Comparative fit indexes in structural models. Psychol Bull107, 238–246.
34.
BrowneMW, CudeckR (1992) Alternative ways of assessing model fit. Sociol Methods Res21, 230–258.
35.
HuLT, BentlerPM (1999) Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct Equ Model6, 1–55.
36.
RoyallDR, PalmerRF (2016) Thrombopoietin is associated with δ’s intercept, and only in Non-Hispanic Whites. Alzheimers Dement (Amst)3, 35–42.
37.
RoyallDR, PalmerRF, MatsuokaT, KatoY, TaniguchiS, OgawaM, FujimotoH, OkamuraA, ShibataK, NakamuraK, NakaakiS, KoumiH, MimuraM, FukuiK, NarumotoJ (2015) Greater than the sum of its parts: Delta can be constructed from item level data. J Alzheimers Dis49, 571–579.
38.
HallKS, GaoS, EmsleyCL, OgunniyiAO, MorganO, HendrieHC. (2000) Community screening interview for dementia (CSI “D”); performance in five disparate study sites. Int J Geriatr Psychiatry15, 521–531.
