TY - JOUR
T1 - The impacts of social determinants of health and cardiometabolic factors on cognitive and functional aging in Colombian underserved populations
AU - Santamaria-Garcia, Hernando
AU - Moguilner, Sebastian
AU - Rodriguez-Villagra, Odir Antonio
AU - Botero-Rodriguez, Felipe
AU - Pina-Escudero, Stefanie Danielle
AU - O’Donovan, Gary
AU - Albala, Cecilia
AU - Matallana, Diana
AU - Schulte, Michael
AU - Slachevsky, Andrea
AU - Yokoyama, Jennifer S.
AU - Possin, Katherine
AU - Ndhlovu, Lishomwa C.
AU - Al-Rousan, Tala
AU - Corley, Michael J.
AU - Kosik, Kenneth S.
AU - Muniz-Terrera, Graciela
AU - Miranda, J. Jaime
AU - Ibanez, Agustin
N1 - Publisher Copyright:
© 2023, The Author(s).
PY - 2023/8
Y1 - 2023/8
N2 - Global initiatives call for further understanding of the impact of inequity on aging across underserved populations. Previous research in low- and middle-income countries (LMICs) presents limitations in assessing combined sources of inequity and outcomes (i.e., cognition and functionality). In this study, we assessed how social determinants of health (SDH), cardiometabolic factors (CMFs), and other medical/social factors predict cognition and functionality in an aging Colombian population. We ran a cross-sectional study that combined theory- (structural equation models) and data-driven (machine learning) approaches in a population-based study (N = 23,694; M = 69.8 years) to assess the best predictors of cognition and functionality. We found that a combination of SDH and CMF accurately predicted cognition and functionality, although SDH was the stronger predictor. Cognition was predicted with the highest accuracy by SDH, followed by demographics, CMF, and other factors. A combination of SDH, age, CMF, and additional physical/psychological factors were the best predictors of functional status. Results highlight the role of inequity in predicting brain health and advancing solutions to reduce the cognitive and functional decline in LMICs.
AB - Global initiatives call for further understanding of the impact of inequity on aging across underserved populations. Previous research in low- and middle-income countries (LMICs) presents limitations in assessing combined sources of inequity and outcomes (i.e., cognition and functionality). In this study, we assessed how social determinants of health (SDH), cardiometabolic factors (CMFs), and other medical/social factors predict cognition and functionality in an aging Colombian population. We ran a cross-sectional study that combined theory- (structural equation models) and data-driven (machine learning) approaches in a population-based study (N = 23,694; M = 69.8 years) to assess the best predictors of cognition and functionality. We found that a combination of SDH and CMF accurately predicted cognition and functionality, although SDH was the stronger predictor. Cognition was predicted with the highest accuracy by SDH, followed by demographics, CMF, and other factors. A combination of SDH, age, CMF, and additional physical/psychological factors were the best predictors of functional status. Results highlight the role of inequity in predicting brain health and advancing solutions to reduce the cognitive and functional decline in LMICs.
KW - Cardiometabolic factors
KW - Cognition
KW - Functionality
KW - National Aging Population Survey
KW - Social determinants of Health
UR - https://www.scopus.com/pages/publications/85149029365
U2 - 10.1007/s11357-023-00755-z
DO - 10.1007/s11357-023-00755-z
M3 - Artículo
C2 - 36849677
AN - SCOPUS:85149029365
SN - 2509-2715
VL - 45
SP - 2405
EP - 2423
JO - GeroScience
JF - GeroScience
IS - 4
ER -