Comparison of Methods for Algorithmic Classification of Dementia Status in the Health and Retirement Study
Comparison of Methods for Algorithmic Classification of Dementia Status in the Health and Retirement Study
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DOI：
10.1097/ede.0000000000000945
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发表时间：
2019-03-01
期刊：
影响因子：
5.4
通讯作者：
Power, Melinda C.
中科院分区：
文献类型：
--
作者：
Gianattasio, Kan Z.;Wu, Qiong;Power, Melinda C.
背景：痴呆症的确定是耗时和昂贵的。几种算法使用来自美国代表性健康和退休研究（HRS）的现有数据来识别痴呆症。然而，这些算法的相对性能仍然unknowed.Methods：我们比较了五种算法（Herzog-Wallace，Langa-Kabeto-Weir，Rummins，Hurd，Wu）的整体性能和社会人口统计学亚组内的HRS和A波的老龄化，人口统计学和记忆研究（亚当斯，2000-2002年），HRS子研究，包括在人痴呆症的确定。然后，我们在内部（时间分割）验证数据集中比较了算法性能，包括HRS和亚当斯Waves B、C和/或D（2002-2009）的参与者。结果：在未加权的训练数据中，灵敏度范围为53%至90%，特异性范围为79%至97%，总体准确度范围为81%至87%。尽管未加权验证数据的敏感性较低（范围：18%-62%），但由于特异性较高（范围：82%-98%），总体准确性相似（范围：79%-88%）。在代表符合年龄条件的美国人群的加权分析中，训练数据的准确率范围为91%至94%，验证数据的准确率范围为87%至94%。使用0.5的概率截止值，Rummins最大化了灵敏度，Herzog-Wallace最大化了特异性，Wu和Hurd最大化了准确性。准确性更高，年轻，受过高等教育，和非西班牙裔白色参与者与他们的补充在两个加权和未加权analysis.Conclusion：痴呆症的诊断提供了一个具有成本效益的方式进行痴呆症的研究。然而，在差异或风险因素研究中天真地使用现有算法可能会引起非保守性偏倚。需要在相关子组中具有更可比性能的算法。
Background: Dementia ascertainment is time-consuming and costly. Several algorithms use existing data from the US-representative Health and Retirement Study (HRS) to algorithmically identify dementia. However, relative performance of these algorithms remains unknown.Methods: We compared performance across five algorithms (Herzog-Wallace, Langa-Kabeto-Weir, Crimmins, Hurd, Wu) overall and within sociodemographic subgroups in participants in HRS and Wave A of the Aging, Demographics, and Memory Study (ADAMS, 2000-2002), an HRS substudy including in-person dementia ascertainment. We then compared algorithmic performance in an internal (time-split) validation dataset including participants of HRS and ADAMS Waves B,C, and/or D (2002-2009).Results: In the unweighted training data, sensitivity ranged from 53% to 90%, specificity ranged from 79% to 97%, and overall accuracy ranged from 81% to 87%. Though sensitivity was lower in the unweighted validation data (range: 18%-62%), overall accuracy was similar (range: 79%-88%) due to higher specificities (range: 82%98%). In analyses weighted to represent the age-eligible US population, accuracy ranged from 91% to 94% in the training data and 87% to 94% in the validation data. Using a 0.5 probability cutoff, Crimmins maximized sensitivity, Herzog-Wallace maximized specificity, and Wu and Hurd maximized accuracy. Accuracy was higher among younger, highly-educated, and non-Hispanic white participants versus their complements in both weighted and unweighted analyses.Conclusion: Algorithmic diagnoses provide a cost-effective way to conduct dementia research. However, naive use of existing algorithms in disparities or risk factor research may induce nonconservative bias. Algorithms with more comparable performance across relevant subgroups are needed.