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Reliability of Self-report Data on Age of Onset in Essential Tremor: Data from a Prospective, Longitudinal Cohort
Neuroepidemiology ( IF 5.7 ) Pub Date : 2022-11-01 , DOI: 10.1159/000527814
Elan D Louis 1 , Diep Nguyen 1 , Ali Ghanem 1
Affiliation  

Background: Essential tremor (ET) is a highly prevalent neurological disease. Age of onset can occur anytime between childhood and advanced age. Tremor generally starts insidiously. Age of onset is a particularly important data item in clinical and epidemiological research. In general, these data are self-reported by ET cases. A fundamental question is whether ET cases reliably report their age of onset. Methods: In this prospective, epidemiological study of 125 ET cases, self-reported age of onset data were collected at regular 18 months intervals over four time points. Results: The correlation between self-reported age of onset was high - intraclass correlation coefficient = 0.972 (95% confidence interval = 0.962 – 0.980, p<0.001). However, agreement was not perfect. Approximately 20% – 25% of participant’s reports at different time intervals differed by as much as 10 years, and approximately 10% of participant’s reports differed by as much as 20 years. Conclusions: There was a robust correlation between self-reports of age of onset. Yet in a not-insignificant number of cases, there were considerable differences, some of which were substantial. These findings have broad implications for development of diagnostic algorithms, data stratification schemes, and analyses that assess correlations between biomarker data and clinical features (e.g., disease duration).


中文翻译:

关于特发性震颤发病年龄的自我报告数据的可靠性:来自前瞻性纵向队列的数据

背景:特发性震颤 (ET) 是一种非常普遍的神经系统疾病。发病年龄可以发生在童年和老年之间的任何时间。震颤通常开始时很隐蔽。发病年龄是临床和流行病学研究中特别重要的数据项。一般来说,这些数据是由 ET 病例自行报告的。一个基本问题是 ET 病例是否可靠地报告了他们的发病年龄。方法:在这项针对 125 例 ET 病例的前瞻性流行病学研究中,在四个时间点每隔 18 个月定期收集自我报告的发病年龄数据。结果:自我报告的发病年龄之间的相关性很高 - 组内相关系数 = 0.972(95% 置信区间 = 0.962 – 0.980,p<0.001)。然而,协议并不完美。大约 20% – 25% 的参与者报告在不同时间间隔内相差多达 10 年,约 10% 的参与者报告相差多达 20 年。结论:自我报告的发病年龄之间存在很强的相关性。然而,在为数不少的情况下,存在相当大的差异,其中一些差异很大。这些发现对于开发诊断算法、数据分层方案以及评估生物标志物数据和临床特征(例如,疾病持续时间)之间相关性的分析具有广泛的意义。差异很大,其中一些差异很大。这些发现对于开发诊断算法、数据分层方案以及评估生物标志物数据和临床特征(例如,疾病持续时间)之间相关性的分析具有广泛的意义。差异很大,其中一些差异很大。这些发现对于开发诊断算法、数据分层方案以及评估生物标志物数据和临床特征(例如,疾病持续时间)之间相关性的分析具有广泛的意义。
更新日期:2022-11-01
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