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Development and initial validation of the Retrospective Indigenous Childhood Enrichment scale (RICE)

Published online by Cambridge University Press:  17 November 2017

Cecilia Minogue*
Affiliation:
School of Psychology, University of Sydney, Sydney, NSW, Australia Neuroscience Research Australia, Sydney, NSW, Australia
Kim Delbaere
Affiliation:
Neuroscience Research Australia, Sydney, NSW, Australia
Kylie Radford
Affiliation:
Neuroscience Research Australia, Sydney, NSW, Australia Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia Australian Research Council Centre of Excellence in Cognition and its Disorders, Sydney, NSW, Australia
Tony Broe
Affiliation:
Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia Australian Research Council Centre of Excellence in Cognition and its Disorders, Sydney, NSW, Australia
Wendy Sue Forder
Affiliation:
NUM, La Perouse Aboriginal Community Health Centre
Suncica Lah
Affiliation:
School of Psychology, University of Sydney, Sydney, NSW, Australia Australian Research Council Centre of Excellence in Cognition and its Disorders, Sydney, NSW, Australia
*
Correspondence should be addressed to: Cecilia Minogue, Neuroscience Research Australia, Barker Street, Randwick, NSW, 2031, Australia. Email: [email protected].

Abstract

Background:

Years of education is the most commonly used proxy measure of cognitive reserve. Other forms of cognitive stimulation in childhood may provide similar protection against cognitive decline, particularly in Indigenous groups, where education may have been lacking in quality or quantity. The Retrospective Indigenous Childhood Enrichment (RICE) scale was developed to measure non-school-based activities and environmental stimulation during childhood that are likely to have enhanced cognitive reserve. The aim of the study was to assess the validity and reliability of the RICE scale with a group of older Aboriginal Australians.

Methods:

294 Aboriginal Australian people (60–92 years), living in urban or regional areas of NSW, completed the RICE scale as part of a longer face-to-face interview. Additional data was collected on their formal education, childhood environment, and childhood trauma (Study 1). Test–retest, inter-method and inter-rater reliability were assessed in a convenience sample of a further 38 participants by re-administration of the RICE scale at two time points, approximately 14 days apart (M = 14.11, SD = 6.78) (Study 2).

Results:

Factor analyses reduced the scale from 21 items to 18 and identified three factors: (1) Traditional, (2) Intellectual, and (3) Community. Higher scores on the RICE scale were related to higher years of formal education and lower scores on a childhood trauma questionnaire. The RICE scale had good internal consistency (Cronbach's α 0.79), and excellent test–retest reliability (ICC = 0.95, 95% CI 0.90–0.97) and inter-rater reliability (0.99, CI 95% 0.997–0.999).

Conclusions:

The RICE is, to our knowledge, the first standardized measure that assesses the level of childhood environmental stimulation in older Aboriginal Australians. This could provide an important supplementary measure, in addition to formal education, to investigate cognitive reserve and dementia risk in this population and enhance understanding of the links between childhood experiences and late-life cognitive decline.

Type
Research Article
Copyright
Copyright © International Psychogeriatric Association 2017 

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References

Anstey, K. J. (2013). Optimizing cognitive development over the life course and preventing cognitive decline: introducing the cognitive health environment life course model (CHELM). International Journal of Behavioural Development, 38, 110.Google Scholar
Bernstein, D. P. and Fink, L. (1997). Childhood Trauma Questionnaire. San Antonio, Texas: Psychological Corporation.Google Scholar
Borenstein, A., Copenhaver, C. and Mortimer, J. (2006). Early-life risk factors for Alzheimer disease. Alzheimer's Disease and Associated Disorders, 20, 6372.Google Scholar
Caamaño-Isorna, F., Corral, M., Montes-Martinez, A. and Takkouche, B. (2006). Education and dementia: a meta-analytic study. Neuroepidemiology, 26, 226232.Google Scholar
Hall, K. and Hendrie, H. (2012). Early childhood environment and dementia. The Lancet, 380, 1112.Google Scholar
Jefferson, A. et al. (2013). A life course model of cognitive activities, socioeconomic status, education, reading ability and cognition. Journal of the American Geriatrics Society, 59, 14031411.Google Scholar
Kramer, A., Bherer, L., Colcombe, S., Dong, W. and Greenough, W. (2004). Environmental influences on cognitive and brain plasticity during aging. Journal of Gerontology. 59, 940957.Google Scholar
Little, R. and Rubin, D. (1987). Statistical Analysis with Missing Data. New York: John Wiley & Sons.Google Scholar
Majer, M., Nater, U., Lin, J. M. S., Capuron, L. and Reeves, W. (2010). Association of childhood trauma with cognitive function in healthy adults: a pilot study. BMC Neurology, 10, 61.Google Scholar
Manly, J. and Mayeux, R. (2004). Ethnic differences in dementia and Alzheimer's disease. In Anderson, N., Bulato, R. and Cohen, B. (eds.), Critical Perspectives on Racial and Ethnic Differences in Health in Late Life. Washington: National Acadamies Press. accessed at ncbi.nim.nih.gov 3 March 2016.Google Scholar
Meng, X. and D'Arcy, C. (2012). Education and dementia in the context of the cognitive reserve hypothesis: a systematic review with meta-analyses and qualitative analyses. PLos ONE, 7, e38268. doi: 10.1371.journal.oine.0038268.Google Scholar
Nucci, M., Mapelli, D. and Mondini, S. (2012). Cognitive reserve index questionnaire (CRIq): a new instrument for measuring cognitive reserve. Aging Clinical and Experimental Research, 24, 218226. Epub 2011/06/22.Google Scholar
Pesonen, A. K. et al. (2013). Cognitive ability and decline after early life stress exposure. Neurobiology of Aging, 34, 16741679.Google Scholar
Radford, K. et al. (2014). The koori growing old well study: investigating aging and dementia in urban Aboriginal Australians. International Psychogeriatrics, 26, 10331043.Google Scholar
Radford, K. et al. (2015). Prevalence of dementia in urban and regional Aboriginal Australians. Alzheimers Dementia, 11, 271279.Google Scholar
Scribner, S. and Cole, M. (1973). Cognitive consequences of formal and informal education. Science, 182, 553559.Google Scholar
Sharp, E. and Gatz, M. (2011). The relationship between education and dementia: an updated Systematic Review. Alzheimer's Disease and Associated Disorders, 25, 289304.Google Scholar
Smith, K. (2008). High prevalence of dementia and cognitive impairment in Indigenous Australians. Neurology, 71, 14701473.Google Scholar
Stern, Y. (2009). Cognitive reserve. Neuropsychologia, 47, 20152028.Google Scholar
Tabachnick, B. and Fidell, L. S. (1996). Using Multivariate Statistics, 3rd edn. New York, NY: HarperCollins College Publishers.Google Scholar
Wilson, R., Barnes, L. and Bennett, D. (2003). Assessment of lifetime participation in cognitively stimulating activities. Journal of Clinical and Experimental Neuropsychology, 25, 634642.Google Scholar