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首页> 外文期刊>Neuroepidemiology >Post hoc Parkinson's disease: identifying an uncommon disease in the cardiovascular health study.
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Post hoc Parkinson's disease: identifying an uncommon disease in the cardiovascular health study.

机译:帕金森病后:在心血管健康研究中发现一种罕见的疾病。

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BACKGROUND: Although ongoing cohort studies offer a unique opportunity to apply existing information collected prospectively to further the scientific understanding of Parkinson's disease (PD), they typically have limited information for clinical diagnosis. METHODS: We used combinations of self-report, International Classification of Diseases - 9th edition codes and antiparkinsonian medications to identify PD in the Cardiovascular Health Study. To determine whether the expected inverse association between smoking and PD is evident using our outcome definitions, we assessed baseline smoking characteristics for various definitions of PD. RESULTS: We identified 60 cases with prevalent PD (1.0%; 95% confidence interval, CI = 0.8-1.3%) and 154 with incident PD by year 14. Clear associations were observed for current smokers (odds ratio, OR = 0.50; 95% CI = 0.26-0.95) and for those who smoked >/=50 pack-years (OR = 0.53; 95% CI = 0.29-0.96). Estimates for smoking were similar when >/=2 data sources were required. Estimates for self-report alone were attenuated towards null. CONCLUSIONS: Using multiple data sources to identify PD represents an alternative method of outcome identification in a cohort that would otherwise not be possible for PD research. Ongoing cohort studies can provide settings in which rapid replication and explorations of new hypotheses for PD are possible.
机译:背景:尽管正在进行的队列研究提供了一个独特的机会,可以应用前瞻性收集的现有信息来进一步理解帕金森氏病(PD),但对于临床诊断,它们通常具有有限的信息。方法:在心血管健康研究中,我们结合使用了自我报告,国际疾病分类-第9版规范和抗帕金森病药物的组合来识别PD。为了使用我们的结果定义来确定吸烟与PD之间的预期反向关联是否明显,我们针对PD的各种定义评估了基线吸烟特征。结果:到第14年,我们确定了60例PD患病率高(1.0%; 95%置信区间,CI = 0.8-1.3%)和154例PD患病率。 %CI = 0.26-0.95)和那些吸烟> / = 50包年的烟民(OR = 0.53; 95%CI = 0.29-0.96)。当需要> / = 2个数据源时,吸烟的估计值相似。仅自我报告的估计就减弱为零。结论:使用多个数据源识别PD是队列研究中结果鉴定的另一种方法,否则PD研究将无法进行。正在进行的队列研究可以提供可能快速复制和探索PD新假设的环境。

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