Zhang Yuan S, Gross Alden L, Dougherty Ryan J, Kobayashi Lindsay C, Schrack Jennifer A, Freedman Vicki A
Department of Sociomedical Sciences, Mailman School of Public Health, Columbia University, New York City, NY, USA.
Robert N. Butler Columbia Aging Center, Columbia University, New York City, NY, USA.
SSM Popul Health. 2025 Apr 3;30:101796. doi: 10.1016/j.ssmph.2025.101796. eCollection 2025 Jun.
Large population-based studies are crucial for dementia research; yet variation in cognitive tests and dementia classification approaches can lead to inconsistent findings. We harmonized cognitive data from two nationally representative US studies of aging to facilitate comparisons.
We examined 2016 data for individuals aged ≥70 years from the National Health and Aging Trends Study (NHATS) (n = 5696) and the Harmonized Cognitive Assessment Protocol (HCAP) of the Health and Retirement Study (HRS) (n = 2731). We derived factor scores for general cognitive performance in the NHATS cognitive test battery that were co-calibrated to the HRS-HCAP battery and identified cutpoints for dementia that returned the expected prevalence in each study. We evaluated diagnostic characteristics of the cutpoints against study-specific dementia algorithms with Area Under the Curve analysis.
Study-specific algorithms yielded comparable dementia prevalence estimates: 10.8 % in NHATS and 11.1 % in HRS-HCAP. Co-calibrated scores showed similar distributions and had acceptable reliability, with similar dementia cutpoints. In both studies, sensitivity was higher among lower-educated (vs. higher educated) and non-White (vs. non-Hispanic White) groups. Co-calibrated populations with dementia in both studies had similar age and gender distributions but differed somewhat in education levels and race/ethnicity profiles.
NHATS and HRS-HCAP both provide reliable cognitive function measures and dementia prevalence estimates for older Americans. Co-calibrated scores based on each study's cognitive test battery provide a valid and feasible approach for comparative US research. Better aligned algorithmic approaches across studies could strengthen opportunities for comparative studies of disparities in the US context using the co-calibration approach.
基于大规模人群的研究对痴呆症研究至关重要；然而，认知测试和痴呆症分类方法的差异可能导致研究结果不一致。我们对来自两项美国全国代表性衰老研究的认知数据进行了协调，以促进比较。
我们检查了来自国家健康与衰老趋势研究（NHATS）（n = 5696）和健康与退休研究（HRS）的统一认知评估协议（HCAP）（n = 2731）中70岁及以上个体的2016年数据。我们在NHATS认知测试组合中得出了一般认知表现的因子得分，这些得分与HRS - HCAP组合进行了共同校准，并确定了痴呆症的切点，以使每项研究中的患病率达到预期。我们使用曲线下面积分析评估了切点相对于特定研究的痴呆症算法的诊断特征。
特定研究的算法得出了可比的痴呆症患病率估计值：NHATS中为10.8%，HRS - HCAP中为11.1%。共同校准的得分显示出相似的分布且具有可接受的可靠性，痴呆症切点相似。在两项研究中，低教育程度（与高教育程度相比）和非白人（与非西班牙裔白人相比）群体的敏感性更高。两项研究中患有痴呆症的共同校准人群年龄和性别分布相似，但在教育水平和种族/族裔特征方面存在一定差异。
NHATS和HRS - HCAP都为美国老年人提供了可靠的认知功能测量和痴呆症患病率估计值。基于每项研究的认知测试组合的共同校准得分提供了一种有效且可行的美国比较研究方法。跨研究更好地对齐算法方法可以加强在美国背景下使用共同校准方法进行差异比较研究的机会。
