The impacts of social determinants of health and cardiometabolic factors on cognitive and functional aging in Colombian underserved populations.
The impacts of social determinants of health and cardiometabolic factors on cognitive and functional aging in Colombian underserved populations.
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DOI：
10.1007/s11357-023-00755-z
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发表时间：
2023-08
期刊：
影响因子：
5.6
通讯作者：
Ibanez, Agustin
中科院分区：
文献类型：
--
作者：
Santamaria-Garcia, Hernando;Moguilner, Sebastian;Rodriguez-Villagra, Odir Antonio;Botero-Rodriguez, Felipe;Pina-Escudero, Stefanie Danielle;O'Donovan, Gary;Albala, Cecilia;Matallana, Diana;Schulte, Michael;Slachevsky, Andrea;Yokoyama, Jennifer S.;Possin, Katherine;Ndhlovu, Lishomwa C.;Al-Rousan, Tala;Corley, Michael J.;Kosik, Kenneth S.;Muniz-Terrera, Graciela;Miranda, J. Jaime;Ibanez, Agustin
关键词：
全球倡议呼吁进一步了解不平等对服务不足人口老龄化的影响。先前在低收入和中等收入国家（LMIC）的研究在评估不平等和结果（即认知和功能）的综合来源方面存在局限性。在这项研究中，我们评估了健康社会决定因素 (SDH)、心脏代谢因素 (CMF) 和其他医学/社会因素如何预测哥伦比亚老龄人口的认知和功能。我们进行了一项横断面研究，在一项基于人群的研究中结合了理论（结构方程模型）和数据驱动（机器学习）方法（N = 23,694；M = 69.8年），以评估认知和功能的最佳预测因素。我们发现 SDH 和 CMF 的组合可以准确地预测认知和功能，尽管 SDH 是更强的预测因子。 SDH 对认知的预测准确率最高，其次是人口统计、CMF 和其他因素。 SDH、年龄、CMF 和其他身体/心理因素的组合是功能状态的最佳预测因素。结果强调了不平等在预测大脑健康和推进解决方案以减少中低收入国家认知和功能下降方面的作用。在线版本包含可在 10.1007/s11357-023-00755-z 获取的补充材料。
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. The online version contains supplementary material available at 10.1007/s11357-023-00755-z.
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