From the Division of Epidemiology, Department of Health Research and Policy (L.M.N.), Stanford University School of Medicine, Stanford, CA; Department of Veterans Affairs Multiple Sclerosis Center of Excellence (VA MSCoE) and Georgetown University School of Medicine (M.T.W.), Washington, DC; Department of Internal Medicine (R.A.M.), Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Canada; VA MS Center of Excellence and University of Maryland (W.J.C.), Baltimore; Neurology Department (A.L.-G.), Kaiser Permanente Southern California, Los Angeles; University of Colorado (J.C.), Denver; Brown University (S.B.), Providence, RI; University of British Columbia (H.T.), Vancouver, Canada; University of Alabama at Birmingham (G.C.); McKing Consulting Corporation (W.K., L.W.), Atlanta, GA; and National Multiple Sclerosis Society (N.G.L.), New York, NY.
Neurology. 2019 Mar 5;92(10):469-480. doi: 10.1212/WNL.0000000000007044. Epub 2019 Feb 15.
Considerable gaps exist in knowledge regarding the prevalence of neurologic diseases, such as multiple sclerosis (MS), in the United States. Therefore, the MS Prevalence Working Group sought to review and evaluate alternative methods for obtaining a scientifically valid estimate of national MS prevalence in the current health care era.
We carried out a strengths, weaknesses, opportunities, and threats (SWOT) analysis for 3 approaches to estimate MS prevalence: population-based MS registries, national probability health surveys, and analysis of administrative health claims databases. We reviewed MS prevalence studies conducted in the United States and critically examined possible methods for estimating national MS prevalence.
We developed a new 4-step approach for estimating MS prevalence in the United States. First, identify administrative health claim databases covering publicly and privately insured populations in the United States. Second, develop and validate a highly accurate MS case-finding algorithm that can be standardly applied in all databases. Third, apply a case definition algorithm to estimate MS prevalence in each population. Fourth, combine MS prevalence estimates into a single estimate of US prevalence, weighted according to the number of insured persons in each health insurance segment.
By addressing methodologic challenges and proposing a new approach for measuring the prevalence of MS in the United States, we hope that our work will benefit scientists who study neurologic and other chronic conditions for which national prevalence estimates do not exist.
在美国,有关神经疾病(如多发性硬化症[MS])的流行情况的知识存在相当大的差距。因此,MS 流行工作组试图回顾和评估获得当前医疗保健时代全国 MS 流行率的科学有效估计的替代方法。
我们对以下 3 种方法进行了优势、劣势、机会和威胁(SWOT)分析,以估计 MS 的流行率:基于人群的 MS 登记处、全国概率健康调查和分析行政健康索赔数据库。我们回顾了在美国进行的 MS 流行率研究,并批判性地审查了估计全国 MS 流行率的可能方法。
我们开发了一种新的四步方法来估计美国的 MS 流行率。首先,确定涵盖美国公共和私人保险人群的行政健康索赔数据库。其次,开发并验证一种高度准确的 MS 病例发现算法,可在所有数据库中标准应用。第三,应用病例定义算法来估计每个人群中的 MS 流行率。第四,根据每个医疗保险部分的参保人数,将 MS 流行率估计值合并为一个美国流行率的单一估计值。
通过解决方法学挑战并提出一种新的方法来衡量美国 MS 的流行率,我们希望我们的工作将使研究神经和其他慢性疾病的科学家受益,因为这些疾病目前没有全国流行率估计值。