Institute for Health, Healthcare Policy and Aging Research, Department of Family Medicine and Community Health, Rutgers University, New Brunswick, New Jersy, USA.
Division of Geriatrics and Palliative Medicine, Weill Cornell Medicine, New York, New York, USA.
J Gerontol B Psychol Sci Soc Sci. 2022 Dec 29;77(12):e234-e246. doi: 10.1093/geronb/gbac128.
As dementia affects a growing number of older adults, it is important to understand its detection and progression. We identified patterns in dementia classification over time using a longitudinal, nationally representative sample of older adults. We examined the relationship between socioeconomic status and race/ethnicity, and patterns in dementia classification.
Data for 7,218 Medicare beneficiaries from the 2011-2017 National Health and Aging Trends Study (NHATS) were classified into five categories: consistently no dementia, consistently cognitive impairment, "typical" dementia progression, "expected" variation, and "unexpected" variation. Multivariable multinomial logistic regression assessed relative risk of dementia classification by sociodemographic and health factors.
Among NHATS respondents, 59.5% consistently were recorded as having no dementia, 7% consistently cognitively impaired, 13% as having typical progression, 15% as having expected variation, and 5.5% as having unexpected variation. In multivariable models, compared with consistent dementia classification, less education, Medicare-Medicaid-dual enrollment, and identifying as non-Hispanic Black were associated with increased likelihood of unexpected variation (e.g., non-Hispanic Black adjusted risk ratio: 2.12, 95% CI: 1.61-2.78, p < .0001).
A significant minority of individuals have unexpected patterns of dementia classification over time, particularly individuals with low socioeconomic status and identifying as non-Hispanic Black. Dementia classification uncertainty may make it challenging to activate resources (e.g., health care, caregiving) for effective disease management, underscoring the need to support persons from at-risk groups and to carefully evaluate cognitive assessment tools to ensure they are equally reliable across groups to avoid magnifying disparities.
随着痴呆症影响越来越多的老年人,了解其检测和进展非常重要。我们使用纵向、全国代表性的老年人样本,确定了随着时间的推移痴呆症分类的模式。我们研究了社会经济地位和种族/民族之间的关系,以及痴呆症分类模式。
我们对来自 2011-2017 年国家健康老龄化趋势研究(NHATS)的 7218 名医疗保险受益人的数据进行分类,分为以下五类:一直没有痴呆症、一直认知障碍、“典型”痴呆症进展、“预期”变化和“意外”变化。多变量多项逻辑回归评估了社会人口统计学和健康因素对痴呆症分类的相对风险。
在 NHATS 受访者中,59.5%的人一直被记录为没有痴呆症,7%的人一直认知障碍,13%的人有典型的进展,15%的人有预期的变化,5.5%的人有意外的变化。在多变量模型中,与一致的痴呆症分类相比,受教育程度较低、医疗保险-医疗补助双重参保和非西班牙裔黑人身份与意外变化的可能性增加相关(例如,非西班牙裔黑人调整风险比:2.12,95%CI:1.61-2.78,p<0.0001)。
相当一部分人随着时间的推移出现了意外的痴呆症分类模式,特别是社会经济地位较低和非西班牙裔黑人身份的人。痴呆症分类的不确定性可能使有效疾病管理的资源(如医疗保健、护理)的激活变得具有挑战性,突出了需要支持处于高危群体的人和仔细评估认知评估工具的必要性,以确保它们在所有群体中同样可靠,避免扩大差距。