Bayesian Model Averaging with Change Points to Assess the Impact of Vaccination and Public Health Interventions.
Bayesian Model Averaging with Change Points to Assess the Impact of Vaccination and Public Health Interventions.
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DOI：
10.1097/ede.0000000000000719
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发表时间：
2017-11
期刊：
影响因子：
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通讯作者：
Weinberger DM
中科院分区：
文献类型：
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作者：
Kürüm E;Warren JL;Schuck-Paim C;Lustig R;Lewnard JA;Fuentes R;Bruhn CAW;Taylor RJ;Simonsen L;Weinberger DM
补充数字内容可在文本中找到。肺炎球菌结合疫苗（PCV）可预防侵袭性肺炎球菌疾病和肺炎。然而，一些低收入和中等收入国家尚未将PCV引入其免疫规划，部分原因是缺乏对潜在影响的确定性。评估PCV的益处是具有挑战性的，因为通常缺乏关于肺炎球菌疾病的具体数据，并且很难区分疫苗以外的因素的影响，这些因素也可能影响肺炎球菌疾病的发病率。我们通过将贝叶斯模型平均与变点模型相结合来评估PCV的影响，以估计疫苗相关变化的时间和幅度，同时控制季节性和其他协变量。我们将我们的方法应用于美国，巴西和智利5岁以下儿童肺炎球菌感染相关的年龄分层住院的月度时间序列。我们的方法准确地检测到数据的变化，我们知道其中发生了真实且值得注意的变化，即，在模拟数据和侵袭性肺炎球菌疾病中。此外，在疫苗引入后24个月，我们发现美国、巴西和智利0至<1岁年龄组的全因肺炎（ACP）住院率分别下降了14%、9%和9%。我们的方法提供了一种灵活而敏感的方法来检测引入疫苗或其他干预措施后发生的疾病发病率变化，同时避免了当前时间趋势分析方法中存在的偏差。
Supplemental Digital Content is available in the text. Pneumococcal conjugate vaccines (PCVs) prevent invasive pneumococcal disease and pneumonia. However, some low-and middle-income countries have yet to introduce PCV into their immunization programs due, in part, to lack of certainty about the potential impact. Assessing PCV benefits is challenging because specific data on pneumococcal disease are often lacking, and it can be difficult to separate the effects of factors other than the vaccine that could also affect pneumococcal disease rates. We assess PCV impact by combining Bayesian model averaging with change-point models to estimate the timing and magnitude of vaccine-associated changes, while controlling for seasonality and other covariates. We applied our approach to monthly time series of age-stratified hospitalizations related to pneumococcal infection in children younger 5 years of age in the United States, Brazil, and Chile. Our method accurately detected changes in data in which we knew true and noteworthy changes occurred, i.e., in simulated data and for invasive pneumococcal disease. Moreover, 24 months after the vaccine introduction, we detected reductions of 14%, 9%, and 9% in the United States, Brazil, and Chile, respectively, in all-cause pneumonia (ACP) hospitalizations for age group 0 to <1 years of age. Our approach provides a flexible and sensitive method to detect changes in disease incidence that occur after the introduction of a vaccine or other intervention, while avoiding biases that exist in current approaches to time-trend analyses.