Akushevich I, Yashkin A P, Kravchenko J, Fang F, Arbeev K, Sloan F, Yashin A I
Biodemography of Aging Research Unit, Center for Population Health and Aging, Duke University, Durham, NC, United States.
Biodemography of Aging Research Unit, Center for Population Health and Aging, Duke University, Durham, NC, United States.
Theor Popul Biol. 2017 Apr;114:117-127. doi: 10.1016/j.tpb.2017.01.003. Epub 2017 Jan 24.
In this study, we present a new theory of partitioning of disease prevalence and incidence-based mortality and demonstrate how this theory practically works for analyses of Medicare data. In the theory, the prevalence of a disease and incidence-based mortality are modeled in terms of disease incidence and survival after diagnosis supplemented by information on disease prevalence at the initial age and year available in a dataset. Partitioning of the trends of prevalence and mortality is calculated with minimal assumptions. The resulting expressions for the components of the trends are given by continuous functions of data. The estimator is consistent and stable. The developed methodology is applied for data on type 2 diabetes using individual records from a nationally representative 5% sample of Medicare beneficiaries age 65+. Numerical estimates show excellent concordance between empirical estimates and theoretical predictions. Evaluated partitioning model showed that both prevalence and mortality increase with time. The primary driving factors of the observed prevalence increase are improved survival and increased prevalence at age 65. The increase in diabetes-related mortality is driven by increased prevalence and unobserved trends in time-periods and age-groups outside of the range of the data used in the study. Finally, the properties of the new estimator, possible statistical and systematical uncertainties, and future practical applications of this methodology in epidemiology, demography, public health and health forecasting are discussed.
在本研究中，我们提出了一种基于疾病患病率和发病率的死亡率划分新理论，并展示了该理论如何实际应用于医疗保险数据的分析。在该理论中，疾病患病率和基于发病率的死亡率是根据疾病发病率和诊断后的生存率进行建模的，并辅以数据集中初始年龄和年份的疾病患病率信息。患病率和死亡率趋势的划分是在最少假设的情况下计算得出的。趋势组成部分的最终表达式由数据的连续函数给出。该估计量是一致且稳定的。所开发的方法应用于2型糖尿病的数据，使用了来自全国代表性的65岁及以上医疗保险受益人的5%样本的个人记录。数值估计显示经验估计与理论预测之间具有极好的一致性。评估的划分模型表明患病率和死亡率均随时间增加。观察到的患病率增加的主要驱动因素是生存率的提高和65岁时患病率的增加。糖尿病相关死亡率的增加是由患病率的增加以及研究中使用的数据范围之外的时间段和年龄组中未观察到的趋势驱动的。最后，讨论了新估计量的性质、可能的统计和系统不确定性，以及该方法在流行病学、人口统计学、公共卫生和健康预测中的未来实际应用。
