Periodic Reporting for period 3 - CRISP (Cognitive Aging: From Educational Opportunities to Individual Risk Profiles)
Periodo di rendicontazione: 2022-01-01 al 2023-06-30
The five-year project CRISP (2019-2023) investigates cognitive ageing and dementia from a life course and social perspective, with a particular focus on inequalities related to education, socioeconomic status, and gender. By understanding better how to create environments that enable the development of cognitive reserve over the life course, we will be able to make recommendations to policymakers in the domains of education and work. By improving current knowledge on who is at particular risk of cognitive decline, and who will benefit from lifestyle interventions, we will be able to identify vulnerable individuals and ways to support the delaying of cognitive decline. Ideally, we will be able to make recommendations for behavior changes in order to decrease risk of cognitive decline. This project receives funding from the European Research Council (grant agreement no. 803239).
Inequalities by education and socio-economic background, gender, and cognitive functioning later in life
Do societal conditions determine to which extent individuals are able to build up cognitive reserve? Since there is no medical cure available to delay cognitive ageing, we need to understand how to create the best possible environments to build up cognitive reserve. We investigate the different opportunities of men and women in terms of education, work and pay, and how they relate to cognitive performance in later life. We also investigate how inequalities in educational opportunities – schooling systems that favor children from higher socio-economic backgrounds – play out their influence on cognitive functioning over the life course.
Improving long-term dementia risk prediction and lifestyle interventions with new methods
We have some understanding about the high risk groups to develop dementia, and the Lancet Commission on dementia estimates that 35 to 40% of all dementia cases could be prevented by eliminating modifiable risk factors. There is also evidence on benefits of multidomain lifestyle interventions to delay cognitive decline. However, we have very limited generalized knowledge of what intervention works for whom and when, and over longer time periods. That is why we need to understand more clearly the long-term effects of risk factors, and the potential and limits of lifestyle changes. How do we do this? We use new causal inference frameworks to analyse observational data in order to identify target groups and promising components of lifestyle interventions. Additionally, we implement recently developed machine learning methods to improve estimation accuracy.
Inequalities by education and socio-economic background, gender, and cognitive functioning later in life
Do societal conditions determine to which extent individuals are able to build up cognitive reserve? Since there is no medical cure available to delay cognitive ageing, we need to understand how to create the best possible environments to build up cognitive reserve. We investigate the different opportunities of men and women in terms of education, work and pay, and how they relate to cognitive performance in later life. We also investigate how inequalities in educational opportunities – schooling systems that favor children from higher socio-economic backgrounds – play out their influence on cognitive functioning over the life course.
Improving long-term dementia risk prediction and lifestyle interventions with new methods
We have some understanding about the high risk groups to develop dementia, and the Lancet Commission on dementia estimates that 35 to 40% of all dementia cases could be prevented by eliminating modifiable risk factors. There is also evidence on benefits of multidomain lifestyle interventions to delay cognitive decline. However, we have very limited generalized knowledge of what intervention works for whom and when, and over longer time periods. That is why we need to understand more clearly the long-term effects of risk factors, and the potential and limits of lifestyle changes. How do we do this? We use new causal inference frameworks to analyse observational data in order to identify target groups and promising components of lifestyle interventions. Additionally, we implement recently developed machine learning methods to improve estimation accuracy.
The first research question is to better understand how contextual determinants influence cognitive functioning and ageing (WP1). We investigated to which extent inequality in educational opportunity at time of schooling is linked to cognitive ageing in later mid-adulthood and early old age:
Leist, A. K., Bar-Haim, E., & Chauvel, L. (2021). Inequality of educational opportunity at time of schooling predicts cognitive functioning in later adulthood. SSM-Population Health, 100837.
We investigated the mediators of changes in neighborhood concentrated disadvantage and changes in cognitive functioning in later life:
Settels, J., & Leist, A. K. (2021). Changes in neighborhood-level socioeconomic disadvantage and older Americans’ cognitive functioning. Health & Place, 68, 102510.
WP2 focuses on how gender inequalities produce gender differences in cognitive functioning and ageing. We used data from Sao Paulo, Brazil, to better understand gender differences in later-life risk of memory impairment in a quite gender-unequal society:
Ribeiro, F. S., Duarte, Y. A. d. O., Santos, J. L., F., & Leist, A. K. Changes in the prevalence of cognitive impairment and associated risk factors 2000-2015 in São Paulo, Brazil. BMC Geriatrics (R&R)
We have extended this work to understand more deeply the differences by sex/gender, age and education, as well as the temporal trends in in mild cognitive impairment and dementia in Latin America and the Caribbean by conducting two systematic reviews and meta-analyses. We find that the associations and secular trends of improved brain health observed in the high-income countries (Europe and the United States) don't hold in this world region.
The second major research question is to better understand which risk factors, risk profiles, and risk changes are linked most to cognitive ageing and dementia (WPs 3-5). We are approaching this research question from different angles. First, we improved the estimation of effects of risk factors through the use of advanced statistical methods:
Ford, K. J., & Leist, A. K. (2021). Returns to educational and occupational attainment in cognitive performance for middle-aged South Korean men and women. Gerontology and Geriatric Medicine, 7, 23337214211004366.
We also investigated the impact of changes in risk factors and behaviors, as only the changes give us insights into potential causal mechanisms at work:
Bertogg, A., & Leist, A. K. (2021). Partnership and Cognitive Aging in Europe: Mediating Factors and Social Stratification. The Journals of Gerontology: Series B, 76(6), 1173-1185.
Ford, K. J., Batty, G. D., & Leist, A. K. (2021). Examining gender differentials in the association of low control work with cognitive performance in older workers. European Journal of Public Health, 31(1), 174-180.
Leist, A. K., Terrera, G. M., & Solomon, A. (2020). Using cohort data to emulate lifestyle interventions: Long‐term beneficial effects of initiating physical activity on cognitive decline and dementia. Alzheimer's & Dementia, 16, e044493.
Leist, A. K., Bar-Haim, E., & Chauvel, L. (2021). Inequality of educational opportunity at time of schooling predicts cognitive functioning in later adulthood. SSM-Population Health, 100837.
We investigated the mediators of changes in neighborhood concentrated disadvantage and changes in cognitive functioning in later life:
Settels, J., & Leist, A. K. (2021). Changes in neighborhood-level socioeconomic disadvantage and older Americans’ cognitive functioning. Health & Place, 68, 102510.
WP2 focuses on how gender inequalities produce gender differences in cognitive functioning and ageing. We used data from Sao Paulo, Brazil, to better understand gender differences in later-life risk of memory impairment in a quite gender-unequal society:
Ribeiro, F. S., Duarte, Y. A. d. O., Santos, J. L., F., & Leist, A. K. Changes in the prevalence of cognitive impairment and associated risk factors 2000-2015 in São Paulo, Brazil. BMC Geriatrics (R&R)
We have extended this work to understand more deeply the differences by sex/gender, age and education, as well as the temporal trends in in mild cognitive impairment and dementia in Latin America and the Caribbean by conducting two systematic reviews and meta-analyses. We find that the associations and secular trends of improved brain health observed in the high-income countries (Europe and the United States) don't hold in this world region.
The second major research question is to better understand which risk factors, risk profiles, and risk changes are linked most to cognitive ageing and dementia (WPs 3-5). We are approaching this research question from different angles. First, we improved the estimation of effects of risk factors through the use of advanced statistical methods:
Ford, K. J., & Leist, A. K. (2021). Returns to educational and occupational attainment in cognitive performance for middle-aged South Korean men and women. Gerontology and Geriatric Medicine, 7, 23337214211004366.
We also investigated the impact of changes in risk factors and behaviors, as only the changes give us insights into potential causal mechanisms at work:
Bertogg, A., & Leist, A. K. (2021). Partnership and Cognitive Aging in Europe: Mediating Factors and Social Stratification. The Journals of Gerontology: Series B, 76(6), 1173-1185.
Ford, K. J., Batty, G. D., & Leist, A. K. (2021). Examining gender differentials in the association of low control work with cognitive performance in older workers. European Journal of Public Health, 31(1), 174-180.
Leist, A. K., Terrera, G. M., & Solomon, A. (2020). Using cohort data to emulate lifestyle interventions: Long‐term beneficial effects of initiating physical activity on cognitive decline and dementia. Alzheimer's & Dementia, 16, e044493.
(1) The paper on inequality of educational opportunity (SSM–Population Health) is the first to provide an extensive linkage between sociological theories of mobility and epidemiological and neuropsychological theories of cognitive development. It is also the first to investigate the role of unequal schooling systems (inequality of educational opportunity) on cognitive decline and identify those individual profiles with highest risk to be harmed by unequal schooling conditions.
(2) We improve the understanding between the social and health sciences and machine learning experts by providing an extensive mapping of possible machine learning approaches to research questions of description, prediction, and causal inference in the social and health sciences. This paper is the first of its kind to help researchers from different disciplines understand each other, and advance the uptake of new and improved methods in the social and health sciences:
Leist, A. K., Klee, M., Kim, J. H., Rehkopf, D. H., Bordas, S., Muniz-Terrera, G., & Wade, S. (2021). Machine learning in the social and health sciences. arXiv preprint arXiv:2106.10716.
(3) Continuing the ongoing research in the CRISP project, we expect to make significant contributions to improve our understanding in the following fields:
In the field of dementia prevention, to answer the question, what works for whom and when?, and determine the value of new protective and risk factors in cognitive ageing such as
- Continuing to work up to advanced ages
- Depressive symptom trajectories
- Area- and individual-level socioeconomic status and dementia
- Value of behavior changes to reduce risk of dementia in individuals with depression
- Effects of gender inequalities on later-life gender differences in brain health
(2) We improve the understanding between the social and health sciences and machine learning experts by providing an extensive mapping of possible machine learning approaches to research questions of description, prediction, and causal inference in the social and health sciences. This paper is the first of its kind to help researchers from different disciplines understand each other, and advance the uptake of new and improved methods in the social and health sciences:
Leist, A. K., Klee, M., Kim, J. H., Rehkopf, D. H., Bordas, S., Muniz-Terrera, G., & Wade, S. (2021). Machine learning in the social and health sciences. arXiv preprint arXiv:2106.10716.
(3) Continuing the ongoing research in the CRISP project, we expect to make significant contributions to improve our understanding in the following fields:
In the field of dementia prevention, to answer the question, what works for whom and when?, and determine the value of new protective and risk factors in cognitive ageing such as
- Continuing to work up to advanced ages
- Depressive symptom trajectories
- Area- and individual-level socioeconomic status and dementia
- Value of behavior changes to reduce risk of dementia in individuals with depression
- Effects of gender inequalities on later-life gender differences in brain health