ScitoVation, Research Triangle Park, North Carolina, United States of America.
Independent Consultant, Chapel Hill, North Carolina, United States of America.
PLoS One. 2019 Jul 18;14(7):e0219769. doi: 10.1371/journal.pone.0219769. eCollection 2019.
Mathematical models of the natural history of disease can predict incidence rates based on prevalence data and support simulations of populations where thyroid function affects other aspects of physiology. We developed a Markov chain model of functional thyroid disease status over the lifetime. Subjects were in one of seven thyroid disease states at any given point in their lives [normal, subclinical hypothyroidism, overt hypothyroidism, treated thyroid disease (ever), subclinical hyperthyroidism, overt hyperthyroidism, and reverted to normal thyroid status]. We used a Bayesian approach to fitting model parameters. A priori probabilities of changing from each disease state to another per unit time were based on published data and summarized using meta-analysis, when possible. The probabilities of changing state were fitted to observed prevalence data based on the National Health and Nutrition Examination Survey 2007-2012. The fitted model provided a satisfactory fit to the observed prevalence data for each disease state, by sex and decade of age. For example, for males 50-59 years old, the observed prevalence of ever having treated thyroid disease was 4.4% and the predicted value was 4.6%. Comparing the incidence rates of treated disease predicted from our model with published values revealed that 82% were within a 4-fold difference. The differences seemed to be systematic and were consistent with expectation based on national iodine intakes. The model provided new and comprehensive estimates of functional thyroid disease incidence rates for the U.S. Because the model provides a reasonable fit to national prevalence data and predicts thyroid disease status over the lifetime, it is suitable for simulating populations, thereby making possible quantitative bias analyses of selected epidemiologic data reporting an association of thyroid disease with serum concentrations of environmental contaminants.
疾病自然史的数学模型可以根据流行率数据预测发病率，并支持对甲状腺功能影响其他生理方面的人群进行模拟。我们开发了一种终身功能性甲状腺疾病状态的马尔可夫链模型。在生命的任何特定时刻，受试者都处于七种甲状腺疾病状态之一[正常、亚临床甲状腺功能减退、显性甲状腺功能减退、治疗性甲状腺疾病（既往）、亚临床甲状腺功能亢进、显性甲状腺功能亢进和恢复正常甲状腺功能]。我们使用贝叶斯方法拟合模型参数。每个疾病状态在单位时间内转变为另一种疾病状态的先验概率基于已发表的数据，并尽可能使用荟萃分析进行总结。状态变化的概率根据 2007-2012 年全国健康和营养调查的观察流行率数据进行拟合。拟合模型对每个疾病状态的观察流行率数据提供了令人满意的拟合，按性别和年龄十年进行划分。例如，对于 50-59 岁的男性，既往治疗性甲状腺疾病的观察流行率为 4.4%，预测值为 4.6%。将我们的模型预测的治疗性疾病发生率与已发表的值进行比较，发现 82%的差异在 4 倍以内。这些差异似乎是系统性的，与基于全国碘摄入量的预期一致。该模型为美国提供了新的、全面的功能性甲状腺疾病发病率估计值。由于该模型对全国流行率数据提供了合理的拟合，并预测了一生中的甲状腺疾病状态，因此它适用于模拟人群，从而有可能对选择报告甲状腺疾病与环境污染物血清浓度之间关联的特定流行病学数据进行定量偏倚分析。
