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

项目总结 相关性：大约每4名美国成年人中就有1人患有多发病，或同时患有两种或两种以上疾病 慢性病。患有多发病的人有更多的残疾和更高的死亡风险，并 比患有1种慢性病的人更高的医疗费用。我们对心绞痛的发生和发展的理解 多发病是有限的。目标：制定临床实践指南和干预措施，这是 有必要掌握有关多发性疾病进展、年龄和发病时间的准确信息， 以及不同人口群体之间的差异。目标：1)协调和连接国家纵向数据 以跟踪美国从30岁开始的慢性病和多发病的发生率。我们将结合7个 主要的国家成人健康队列数据集(ADD Health，NLSY79，NLSY97，PSID，CORETS，H-EPESE， 小时)创建一个综合的、具有全国代表性的成年人队列，从30岁开始，汇编超过1.7个 100万人年的后续行动，包括相当数量的种族/族裔少数群体。2)估算 从30岁开始发展为多病。我们将确定年龄特定的进展率 常见慢性疾病聚集性和与更高的进展风险相关的哨兵情况 其他疾病。3)为美国成年人口制定年龄特定的多发病风险的衡量标准，并 计算罹患多病的终生风险和患多病的年数。4)主修专业 种族-民族、性别、经济和地理群体，确定哨兵条件，特定年龄的进展， 以及多发病的终生风险。我们将评估不同组之间的差异，并量化以下各项的影响 通过对偏差进行调整的额外创新，缩小这些无疾病预期寿命方面的差距 由于不同的死亡率和制度化。设计：衔接和重新加权程序将是 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和 人工合成的队列。我们将使用泊松回归来模拟多发性发病率差异的年龄 浮出水面。为了捕捉多发性疾病的序列和起搏，我们将估计 对于那些有k个先前慢性病的人来说，经历额外的(k+1)个条件。我们会估计 基于Kaplan-Meier生存概率的累积概率。差距将被量化，使用 格林伍德的估计器。影响：该项目将促进对该疾病的性质、发病和 多发病的进展。为了向临床实践和医疗保健指南提供信息，我们将确定 考虑到发病年龄和哨兵疾病，过渡到其他疾病的条件风险。带着特别的 与政策相关，我们将建立无疾病预期寿命年数的模型， 延缓或避免哨兵疾病发作的干预措施。由此产生的数据基础架构 该项目将提供给研究社区，并将对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.