Disparities in the burden and progression of multi-morbidity across adulthood

项目摘要 相关性：大约四分之一的美国成年人患有多发性硬化症，或两种或多种并发症 慢性病患有多发性硬化症的人经历更多的残疾和更高的死亡风险， 比患有1种慢性病的人的医疗费用更高。我们对疾病的发生和发展的理解 多变量是有限的建议：为了制定临床实践指南和干预措施， 需要有关于多发性硬化症进展、发病年龄和时间的准确信息， 以及不同人群之间的差异。目标：1）统一和衔接国家纵向数据 追踪美国从30岁开始的慢性病和多发性硬化症的发病率。我们将联合收割机7 成人健康的首要国家队列数据集（Add Health，NLSY 79，NLSY 97，PSID，REGARDS，H-EPESE， HRS）创建一个合成的全国代表性成年人队列，从30岁开始，汇编超过1.7 随访的人数为100万人年，包括相当多的少数种族/族裔。2)估计 从30岁开始发展为多发病。我们将确定年龄特异性进展率， 常见的慢性疾病集群和与进展为 其他疾病。3)为美国成年人群制定多发病年龄特异性风险的措施， 计算发生多发性骨髓瘤的终生风险和多发性骨髓瘤的年数。4)重大 种族-民族、性别、经济和地理群体，确定前哨条件，年龄特异性进展， 和终身多死亡的风险。我们将估计各组之间的差异，并量化 通过调整偏差的额外创新， 因为死亡率和收容机构的不同设计：桥接和重新称重程序将 应用于开发具有全国代表性的纵向出生队列的新数据基础设施， 合成队列。我们将使用Poisson回归来模拟多种发病率差异的年龄 出现。为了捕获多Mortocell的序列和起搏，我们将估计 对于具有k个先前慢性病症的那些人，经历额外的（k+1）病症。我们估计 基于Kaplan-Meier生存概率的累积概率。差异将使用 Greenwood估计器影响：该项目将促进对自然的理解，发病， 多发性硬化症的进展。为了告知临床实践和医疗保健指南，我们将确定 鉴于发病年龄和哨点疾病，有条件的风险转变为其他疾病。特别 与政策相关，我们将模拟无病预期寿命的年数， 采取干预措施，延迟或避免哨点疾病的发生。由此产生的数据基础设施 该项目将提供给研究界，并将有助于各种与老龄化有关的 研究问题。

PROJECT SUMMARY RELEVANCE: Approximately 1 in 4 U.S. adults suffers from multimorbidity, or two or more concurrent chronic diseases. People with multimorbidity experience more disability and higher mortality risk and have higher medical costs than people with 1 chronic condition. Our understanding of the onset and progression of multimorbidity is limited. OBJECTIVES: To develop clinical practice guidelines and interventions, it is necessary to have accurate information about the progression of multimorbidity, the age and timing of onset, and differences in these across population groups. AIMS: 1) Harmonize and bridge national longitudinal data to track the incidence of chronic diseases and multimorbidity in the U.S. starting at age 30. We will combine 7 premier national cohort datasets of adult health (Add Health, NLSY79, NLSY97, PSID, REGARDS, H-EPESE, HRS) to create a synthetic nationally representative cohort of adults starting at age 30 years, compiling over 1.7 million person-years of follow-up and include sizeable number of racial/ethnic minorities. 2) Estimate the progression to multimorbidity starting at age 30 years. We will identify the age-specific progression rate of common chronic disease clusters and the sentinel conditions associated with higher risks of progression to additional diseases. 3) Develop measures of age-specific risk of multimorbidity for the US adult population and calculate the lifetime risk of developing multimorbidity and years spent with multimorbidity. 4) For major race-ethnic, sex, economic, and geographic groups, identify the sentinel conditions, age-specific progression, and lifetime risk of multimorbidity. We will estimate differences across groups and quantify the implications of reducing these disparities on disease-free life expectancy, with the additional innovation of adjusting for biases due to differential mortality and institutionalization. DESIGN: Bridging and reweighting procedures will be applied to develop a new data infrastructure with nationally representative longitudinal birth cohorts and synthetic cohorts. We will use Poisson regression to model the ages at which disparities in multi-morbidity emerge. To capture the sequencing and pacing of multimorbidity, we will estimate the probability of experiencing an additional (k+1th) condition for those with k prior chronic conditions. We will estimate cumulative probabilities based on Kaplan-Meier survival probabilities. Disparities will be quantified using Greenwood’s estimator. IMPACT: This project will advance understanding of the nature, onset, and progression of multimorbidity. To inform clinical practice and healthcare guidelines, we will identify conditional risks of transitioning to additional diseases given age of onset and sentinel disease. With particular relevance to policy, we will model the years of disease-free life expectancy that would be gained by interventions that delay or avert the onset of sentinel diseases. The data infrastructure generated from this project will be made available to the research community and will be useful for a variety of aging-related research questions.