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首页> 外文期刊>American journal of public health >Detection of Excess Influenza Severity: Associating Respiratory Hospitalization and Mortality Data With Reports of Influenza-Like Illness by Primary Care Physicians
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Detection of Excess Influenza Severity: Associating Respiratory Hospitalization and Mortality Data With Reports of Influenza-Like Illness by Primary Care Physicians

机译：过量流感严重程度的检测：将呼吸住院和死亡率数据与基层医疗医生报告的类似流感的疾病相关联

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Objectives. We explored whether excesses in influenza severity can be detected by combining respiratory syndromic hospital and mortality data with data on influenza-like illness (ILI) cases obtained from general practitioners. Methods. To identify excesses in the severity of influenza infections in the population of the Netherlands between 1999 and 2005, we looked for increases in influenza-associated hospitalizations and mortality that were disproportionate to the number of ILI cases reported by general practitioners. We used generalized estimating equation regression models to associate syndromic hospital and mortality data with ILI surveillance data obtained from general practitioners. Virus isolation and antigenic characterization data were used to interpret the results. Results. Disproportionate increases in hospitalizations and mortality (relative to ILI cases reported by general practitioners) were identified in 2003/04 during the A/Fujian/411/02(H3N2) drift variant epidemic. Conclusions. Combined surveillance of respiratory hospitalizations and mortality and ILI data obtained from general practitioners can capture increases in severe influenza-associated illness that are disproportionate to influenza incidence rates. Therefore, this novel approach should complement traditional seasonal and pandemic influenza surveillance in efforts to detect increases in influenza case fatality rates and percentages of patients hospitalized. Syndromic surveillance is increasingly used to monitor symptoms or clinical diagnoses such as shortness of breath or pneumonia as indicators of infectious disease. The primary objective of many syndromic surveillance systems is the detection of unexpected disease increases such as those that occur as a result of bioterrorism attacks or outbreaks of emerging diseases such as severe acute respiratory syndrome (SARS). However, the signals generated by such syndromic surveillance also reflect influenza activity. 1 – 4 Worldwide, influenza continues to result in serious morbidity and mortality. 5 , 6 The recurrence of influenza epidemics is predominantly caused by both the antigenic drift of influenza viruses and changes in the dominant virus types or subtypes. Antigenic drift occurs during the replication process of influenza viruses when mutations in surface proteins lead to declines in the level of immunity acquired through natural infection or vaccination. 7 In addition, the annual variations in dominant virus types or subtypes, such as A(H1), A(H3), and B, can lead to differences in influenza-related morbidity and mortality. For example, in recent decades levels of morbidity and mortality seem to have been lower in the influenza A(H1) and B epidemic seasons than in the A(H3) seasons. 8 , 9 In the Netherlands, as in many countries, surveillance of influenza is conducted by a network of sentinel general practitioners. Influenza-like illness (ILI) consultations are reported weekly, and antigenic properties of isolated viruses are analyzed to determine their effects on annual ILI fluctuations. 10 , 11 Such sentinel surveillance is considered adequate for monitoring the onset and magnitude of annual influenza epidemics. However, it is not sufficient for monitoring the incidence of severe influenza infections leading to hospitalization or death. Although the relationship between the virulence and transmission capacity of influenza viruses is still incompletely understood, 7 variations in virulence may result in disproportionate increases in severe illness relative to increases in the number of patients with ILI consulting their general practitioners. Such increases might be captured by monitoring temporal changes in the association of ILI data obtained from general practitioners (hereafter GP–ILI data) with hospitalization and mortality surveillance data. Such monitoring is not a part of current global influenza monitoring activities, although in some countries ILI data in addition to hospitalization and mortality data are included in influenza surveillance. 12 , 13 We explored the potential of this monitoring strategy to detect excesses in influenza infection severity by investigating shifts in the annual association of respiratory hospitalizations and mortality with GP–ILI incidence data in the Netherlands between 1999 and 2005. In addition, we evaluated whether such shifts were associated with reported circulation of influenza virus drift variants, mismatches with vaccine strains, or changes in dominant circulating virus types or subtypes.
机译：目标。我们探讨了通过将呼吸系统综合症医院和死亡率数据与从全科医生那里获得的流感样疾病（ILI）病例数据相结合，是否可以检测出流感严重程度超标。方法。为了确定荷兰人口在1999年至2005年之间流感感染严重性的过高情况，我们寻求与流感相关的住院治疗和死亡率增加，这些增加与全科医生报告的ILI病例数不成比例。我们使用广义估计方程回归模型将综合症医院和死亡率数据与从全科医生获得的ILI监测数据相关联。病毒分离和抗原表征数据用于解释结果。结果。在2003/04年A / Fujian / 411/02（H3N2）漂移变异流行病中，发现住院和死亡率的增加不成比例（相对于全科医生报告的ILI病例）。结论。呼吸道住院和死亡率的综合监测以及从全科医生那里获得的ILI数据可以捕获与流感发生率不成比例的严重流感相关疾病的增加。因此，这种新颖的方法应补充传统的季节性和大流行性流感监测，以努力发现流感病例死亡率和住院患者百分比的增加。症状监测越来越多地用于监测症状或临床诊断，例如呼吸急促或肺炎作为传染病的指标。许多症状监测系统的主要目的是发现意外疾病的增加，例如由于生物恐怖袭击或严重急性呼吸道综合症（SARS）等新兴疾病的爆发而引起的疾病。但是，这种症状监测产生的信号也反映出流感的活动。 1-4 在世界范围内，流感继续导致严重的发病率和死亡率。 5，6 流感的复发流行主要由流感病毒的抗原性漂移和主要病毒类型或亚型的变化引起。流感病毒复制过程中会发生抗原漂移，这是由于表面蛋白突变导致通过自然感染或接种获得的免疫力下降。 7 此外，主要病毒类型或亚型的年度变化，例如A（H1），A（H3）和B，可能导致与流感相关的发病率和死亡率差异。例如，在最近的几十年中，甲型（H1）和乙型流感流行季节的发病率和死亡率水平似乎低于甲型（H3）季节。 8，9 在荷兰，与许多国家一样，流感监测是由前哨全科医生组成的网络进行的。每周都会报告类似流感的疾病（ILI）咨询，并分析分离出的病毒的抗原特性，以确定其对ILI年度波动的影响。 10，11 这种前哨监测被认为足以监测发病和发作时间。年度流感流行的严重程度。但是，这不足以监测导致住院或死亡的严重流感感染的发生率。尽管仍未完全了解流感病毒的毒力与传播能力之间的关系，但毒力变化 7 可能导致重症疾病的增加不成比例，而与ILI咨询其全科医生的患者人数增加相比。可以通过监测从全科医生获得的ILI数据（以下简称GP-ILI数据）与住院和死亡率监测数据之间的关联的时间变化来捕获此类增加。尽管在某些国家/地区，除了住院和死亡率数据外，ILI数据还包括在流感监测中，但这种监测并不是当前全球流感监测活动的一部分。 12，13 我们探索了这种监测策略的潜力通过调查1999年至2005年间荷兰的呼吸道住院和死亡率的年度关联变化与GP–ILI发病率数据来检测流感感染严重程度的过剩。此外，我们评估了这种变化是否与报告的流感病毒漂移传播有关变异，与疫苗株的不匹配或主要循环病毒类型或亚型的变化。

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