2009 年 4 月至 7 月美国大流行性 H1N1 流感的严重程度:贝叶斯分析。
The severity of pandemic H1N1 influenza in the United States, from April to July 2009: a Bayesian analysis.
机构信息
Medical Research Council Biostatistics Unit, Cambridge, United Kingdom.
出版信息
PLoS Med. 2009 Dec;6(12):e1000207. doi: 10.1371/journal.pmed.1000207. Epub 2009 Dec 8.
BACKGROUND
Accurate measures of the severity of pandemic (H1N1) 2009 influenza (pH1N1) are needed to assess the likely impact of an anticipated resurgence in the autumn in the Northern Hemisphere. Severity has been difficult to measure because jurisdictions with large numbers of deaths and other severe outcomes have had too many cases to assess the total number with confidence. Also, detection of severe cases may be more likely, resulting in overestimation of the severity of an average case. We sought to estimate the probabilities that symptomatic infection would lead to hospitalization, ICU admission, and death by combining data from multiple sources.
METHODS AND FINDINGS
We used complementary data from two US cities: Milwaukee attempted to identify cases of medically attended infection whether or not they required hospitalization, while New York City focused on the identification of hospitalizations, intensive care admission or mechanical ventilation (hereafter, ICU), and deaths. New York data were used to estimate numerators for ICU and death, and two sources of data--medically attended cases in Milwaukee or self-reported influenza-like illness (ILI) in New York--were used to estimate ratios of symptomatic cases to hospitalizations. Combining these data with estimates of the fraction detected for each level of severity, we estimated the proportion of symptomatic patients who died (symptomatic case-fatality ratio, sCFR), required ICU (sCIR), and required hospitalization (sCHR), overall and by age category. Evidence, prior information, and associated uncertainty were analyzed in a Bayesian evidence synthesis framework. Using medically attended cases and estimates of the proportion of symptomatic cases medically attended, we estimated an sCFR of 0.048% (95% credible interval [CI] 0.026%-0.096%), sCIR of 0.239% (0.134%-0.458%), and sCHR of 1.44% (0.83%-2.64%). Using self-reported ILI, we obtained estimates approximately 7-9 x lower. sCFR and sCIR appear to be highest in persons aged 18 y and older, and lowest in children aged 5-17 y. sCHR appears to be lowest in persons aged 5-17; our data were too sparse to allow us to determine the group in which it was the highest.
CONCLUSIONS
These estimates suggest that an autumn-winter pandemic wave of pH1N1 with comparable severity per case could lead to a number of deaths in the range from considerably below that associated with seasonal influenza to slightly higher, but with the greatest impact in children aged 0-4 and adults 18-64. These estimates of impact depend on assumptions about total incidence of infection and would be larger if incidence of symptomatic infection were higher or shifted toward adults, if viral virulence increased, or if suboptimal treatment resulted from stress on the health care system; numbers would decrease if the total proportion of the population symptomatically infected were lower than assumed.
背景
需要准确衡量大流行流感(pH1N1)的严重程度,以评估北半球秋季可能出现的疫情反弹的影响。由于有大量死亡和其他严重后果的辖区的病例数量太多,难以准确评估总病例数,因此严重程度一直难以衡量。此外,严重病例的检测可能更容易,从而导致对平均病例严重程度的高估。我们试图通过结合多个来源的数据来估计有症状感染导致住院、重症监护病房(ICU)入院和死亡的概率。
方法和发现
我们使用了来自美国两个城市的补充数据:密尔沃基试图识别有医疗护理的感染病例,无论是否需要住院治疗,而纽约市则专注于识别住院、ICU 入院或机械通气(以下简称 ICU)和死亡病例。纽约市的数据用于估计 ICU 和死亡的分子,而两个来源的数据——密尔沃基的有医疗护理的病例或纽约的自我报告的流感样疾病(ILI)——用于估计住院病例与症状病例的比例。结合这些数据和每个严重程度级别的检测比例的估计值,我们估计了症状患者死亡(症状病例病死率,sCFR)、需要 ICU(sCIR)和需要住院治疗(sCHR)的比例,总体上和按年龄类别进行了估计。在贝叶斯证据综合框架中分析了证据、先验信息和相关不确定性。使用有医疗护理的病例和症状病例接受医疗护理的比例估计值,我们估计 sCFR 为 0.048%(95%置信区间 [CI] 0.026%-0.096%),sCIR 为 0.239%(0.134%-0.458%),sCHR 为 1.44%(0.83%-2.64%)。使用自我报告的 ILI,我们得到了大约低 7-9 倍的估计值。sCFR 和 sCIR 似乎在 18 岁及以上人群中最高,在 5-17 岁儿童中最低。sCHR 似乎在 5-17 岁人群中最低;我们的数据过于稀疏,无法确定哪个年龄组的 sCHR 最高。
结论
这些估计表明,如果每例病例的严重程度与季节性流感相当,那么秋季-冬季 pH1N1 大流行可能导致一定数量的死亡,其范围从明显低于季节性流感相关死亡到略高,但对 0-4 岁儿童和 18-64 岁成年人的影响最大。这些影响估计取决于对感染总发病率的假设,如果症状性感染的发病率更高或向成年人转移,如果病毒毒力增加,或者由于医疗保健系统的压力导致治疗不理想,那么这些估计值将会更大;如果感染人群中症状性感染的总比例低于假设值,那么人数将会减少。