Doctoral Dissertation Research: Application of Markov Chain Theory to Identify Health Disparities

博士论文研究：应用马尔可夫链理论识别健康差异

• 批准号: 1059573
• 负责人: Emilia Bagiella
• 金额: $ 1万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-05-01 至 2012-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1059573&HistoricalAwards=false
• 关键词: Doctoral; Dissertation; Research; Application; Markov

Chronic diseases pose major health and economic burdens for Americans. Not only are chronic diseases the leading causes of disability and death, but they also account for approximately 75% of total health care costs in the United States. Building upon existing life table-based methodologies, this project will use models of chronic disease progression that incorporate current biological understanding of chronic diseases and Markov Chain theory, a statistical theory used to describe discrete stochastic processes, to model individuals' chance of transitioning across different disease progression-related health states. This project has two objectives. First, the project will design biologically plausible models of chronic disease progression for diabetes and chronic obstructive pulmonary disease (COPD) complemented by surveillance data to inform disease-specific multistate life tables. Second, the project will use Markov Chain Theory to estimate the likelihood of transitioning across the health states of these biologically plausible models conditional on age and sociodemographic factors and calculate health-state specific life expectancies. In collaboration with South Carolina's Office of Research and Statistics, the project also will apply this methodology to statewide data on State Health Plan recipients from 1999 to 2008, complemented by information on mortality from the state death registry and the National Death Index - Plus.The intellectual merit of this research is its interdisciplinary construct, which combines biological models of chronic disease progression, statistical theories of stochastic processes, social theories of the fundamental causes of health disparities, and epidemiologic knowledge of population health. Broader impacts of this research include the potential benefits to public health professionals of a methodology for identifying health disparities in the lifetime burden of specific chronic diseases. In addition, it is likely that this methodology will be generalizable to other chronic diseases and to other populations. As a Doctoral Dissertation Research Improvement award, support is provided to enable a promising student to establish a strong, independent research career.

慢性病给美国人带来了重大的健康和经济负担。 慢性病不仅是导致残疾和死亡的主要原因，而且还占美国医疗保健总费用的约75%。 在现有的基于生命表的方法的基础上，该项目将使用慢性疾病进展模型，该模型结合了当前对慢性疾病的生物学理解和马尔可夫链理论，这是一种用于描述离散随机过程的统计理论，以模拟个体在不同疾病进展相关健康状态之间过渡的机会。 该项目有两个目标。 首先，该项目将设计糖尿病和慢性阻塞性肺疾病（COPD）慢性疾病进展的生物学合理模型，并辅以监测数据，为疾病特异性多状态生命表提供信息。 其次，该项目将使用马尔可夫链理论来估计这些生物学上合理的模型在年龄和社会人口因素的条件下过渡到健康状态的可能性，并计算健康状态特定的预期寿命。 在与南卡罗来纳州研究和统计办公室的合作中，该项目还将把这种方法应用于1999年至2008年州健康计划接受者的全州数据，并辅之以来自州死亡登记处和国家死亡指数的死亡率信息。随机过程的统计理论，健康差距的根本原因的社会理论，以及人口健康的流行病学知识。 这项研究的更广泛的影响包括公共卫生专业人员的潜在利益的方法，用于确定特定慢性疾病的终身负担的健康差异。 此外，这种方法很可能推广到其他慢性病和其他人群。 作为博士论文研究改进奖，提供支持，使有前途的学生建立一个强大的，独立的研究生涯。