人禽流感（H5N1）流行病学监测
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摘要
背景和目的人禽流感(Human avian influenza,HAI)是人接触禽流感病毒污染的排泄物或者分泌物而感染,并出现以呼吸道感染、粘膜充血等症状为主要表现的人畜共患疾病,死亡率较高。部分高致病性禽流感毒株(如H5N1)的患者可出现呼吸衰竭和多器官损害。2003年12月～2009年2月,人禽流感疫情相继波及全球15个国家,涵盖亚洲、欧洲、非洲、美洲四大洲,共计408例人禽流感病例,其中254人死亡,病死率高达62.3%。因此,有必要通过分析全球历年来实验室确诊人甲型流感(H5N1)病例的流行病学资料以持续监测其发病模式是否发生改变,探索短期病例数的预测方法和影响病例死亡的可能因素;也为建立我国的监测及预警方法打下基础。
方法收集2003年12月至2009年2月全球15个国家向WHO报告的实验室确诊人甲型流感(H5N1)病例的资料建立数据库;通过对全球人禽流感病例的逐年的年龄和性别分布、发病至住院的中位时间及发病至死亡的中位时间、家庭聚集性变化、季节趋势和病死率等指标进行分析,判断人禽流感发病模式有无变化;用灰色动态模型和时间序列ARIMA模型对人禽流感病例月发病例数和月累计发病例数进行预测;运用多水平logstic模型和单水平logstic回归筛选可能影响病例死亡的因素。
结果
1.人禽流感发病模式监测
各年龄组男女均可发病,男女性别比为0.9:1,各年份间和各年龄段间性别比差异无统计学意义;发病到住院的中位时间为4天(范围0～20天),发病到死亡的中位时间为9天(2～31天),各年份间差异无统计学意义;发病高峰期为11月20日～3月6日,不同年份和不同国家间差异无统计学意义;总病死率为62.3%,且不随时间而变化;历年家庭聚集性也无增加趋势。
2.人禽流感病例数预测效果
对月累计发病例数进行预测,时间序列的中位预测误差为0.94%,灰色模型的中位预测误差为1.5%;对月发病例数进行预测,时间序列的中位预测误差为4.46%,灰色模型的中位预测误差为23.36%。
3.影响人禽流感病例存活的相关因素
病例年龄每减少1岁,存活的可能性将增加1.03倍;病例发病到住院时间间隔每减少1天,病例存活可能性将增加1.20倍;有家庭聚集性的人禽流感病例,其存活的概率是无家庭聚集性病例的2.71倍。
结论
1.人禽流感发病模式监测
全球人禽流感H5N1发病模式过去6年没有重大改变:各年龄段男女均可发病;冬春季节高发的特征比较稳定;历年家庭聚集性无增加趋势。
2.人禽流感病例数的预测效果
时间序列ARIMA模型是对人禽流感H5N1月发病数进行预测的比较理想方法。
3.影响人禽流感病例存活的相关因素
年龄、发病到住院天数和有无家庭聚集性可能对病例的存活有影响。
Background & Objectives Human avian influenza (HAI) is an infectious disease with a higher fatality rate caused by exposure to secretion or excretion polluted by avian influenza virus (H5N1). Respiratory tract infection and mucosal hyperemia are the main symptoms. Some patients infected by highly pathogenic avian influenza virus may appear respiratory failure, multiple organ damage . During Dec 2003 to Feb 2009, HAI (H5N1) epidemic has spread in 15 countries worldwide, covering Asia, Europe, Africa, America, for a total of 408 cases, in which 254 people died, the fatality rate was as high as 62.3%. Therefore, it’s necessary to analyze the data of laboratory-confirmed human cases of H5N1 to monitor changes in the epidemic patterns, and explore potential factors affected survival of HAI cases and methods of forecasting number of cases occurred in short term for guiding the prevention and control work in future.
Methods After collecting data of laboratory-confirmed human cases of H5N1, the HAI database was established. x2 test, Kruskal-Wallis test and Spearman’s correlation analysis were performed to analyze the age and sex distribution, time interval from onset of symptoms until hospitalization or that until death, family aggregation, seasonal trends, fatality rates by year to monitor changes in the epidemic patterns of HAI. Time series (ARIMA) and Grey Model (1,1) were performed to predict number of HAI cases in the next month. Multilevel logistic models and single level logistic model were performed to explore potential factors affected survival of HAI cases.
Result
1. Epidemiological surveillance of HAI
Both men and women in each age group can be infected and the sex ratio of male cases to female cases was 0.90:1, with no significant difference among different calendar years or age groups. The median day of time interval from the onset of symptoms until hospitalization or that until death were was 4 days (range 0 - 20 days) or 9 days (range 2 - 31 days), with no significant difference among different years. The peak period of incidence was from November 20 to March 6, there were also no significant difference among different years or countries. While average fatality rate of HAI was 62.3%, there was no increasing tendency of the rate by calendar years. In addition, family aggregation also showed no increasing tendency by calendar years.
2. Prediction effect of monthly number of HAI case
On prediction of monthly accumulative cases, the predicted error by Time Series was 0.94%, while the predicted error by Grey Model was 1.50%. On prediction of monthly incidence cases, the predicted error by Time Series was 4.46%, whilst the predicted error by Grey Model was 23.36%.
3.Factors affected survival of HAI case
The probability of survival might increase by 1.03 fold for every 1 year decrease in the age of HAI case . The probability of survival might also increase by 1.20 fold for every 1 day decrease in the time interval from onset of symptoms until hospitalization. In addition, comparing with the patients came from un-family aggregation, the survival probability of the cases belonged to family cluster might increase by 2.71 fold. .
Conclusion
1. Epidemiological surveillance of HAI
No marked change in the epidemic pattern of HAI has been found over the past 6 years: both men and women in each age group can be infected; the peak seasons were stably in winter and spring; Family aggregations also has no increasing tendency by calendar years.
2. Prediction effect of monthly number of HAI case
Time Series (ARIMA) was suitable to predict number of HAI case in short term.
3. Factors affected survival of HAI case
Age of HAI patients, time interval from onset of symptoms until hospitalization, and family aggregation of HAI case may effect on survival of HAI cases.
方法收集2003年12月至2009年2月全球15个国家向WHO报告的实验室确诊人甲型流感(H5N1)病例的资料建立数据库;通过对全球人禽流感病例的逐年的年龄和性别分布、发病至住院的中位时间及发病至死亡的中位时间、家庭聚集性变化、季节趋势和病死率等指标进行分析,判断人禽流感发病模式有无变化;用灰色动态模型和时间序列ARIMA模型对人禽流感病例月发病例数和月累计发病例数进行预测;运用多水平logstic模型和单水平logstic回归筛选可能影响病例死亡的因素。
结果
1.人禽流感发病模式监测
各年龄组男女均可发病,男女性别比为0.9:1,各年份间和各年龄段间性别比差异无统计学意义;发病到住院的中位时间为4天(范围0～20天),发病到死亡的中位时间为9天(2～31天),各年份间差异无统计学意义;发病高峰期为11月20日～3月6日,不同年份和不同国家间差异无统计学意义;总病死率为62.3%,且不随时间而变化;历年家庭聚集性也无增加趋势。
2.人禽流感病例数预测效果
对月累计发病例数进行预测,时间序列的中位预测误差为0.94%,灰色模型的中位预测误差为1.5%;对月发病例数进行预测,时间序列的中位预测误差为4.46%,灰色模型的中位预测误差为23.36%。
3.影响人禽流感病例存活的相关因素
病例年龄每减少1岁,存活的可能性将增加1.03倍;病例发病到住院时间间隔每减少1天,病例存活可能性将增加1.20倍;有家庭聚集性的人禽流感病例,其存活的概率是无家庭聚集性病例的2.71倍。
结论
1.人禽流感发病模式监测
全球人禽流感H5N1发病模式过去6年没有重大改变:各年龄段男女均可发病;冬春季节高发的特征比较稳定;历年家庭聚集性无增加趋势。
2.人禽流感病例数的预测效果
时间序列ARIMA模型是对人禽流感H5N1月发病数进行预测的比较理想方法。
3.影响人禽流感病例存活的相关因素
年龄、发病到住院天数和有无家庭聚集性可能对病例的存活有影响。
Background & Objectives Human avian influenza (HAI) is an infectious disease with a higher fatality rate caused by exposure to secretion or excretion polluted by avian influenza virus (H5N1). Respiratory tract infection and mucosal hyperemia are the main symptoms. Some patients infected by highly pathogenic avian influenza virus may appear respiratory failure, multiple organ damage . During Dec 2003 to Feb 2009, HAI (H5N1) epidemic has spread in 15 countries worldwide, covering Asia, Europe, Africa, America, for a total of 408 cases, in which 254 people died, the fatality rate was as high as 62.3%. Therefore, it’s necessary to analyze the data of laboratory-confirmed human cases of H5N1 to monitor changes in the epidemic patterns, and explore potential factors affected survival of HAI cases and methods of forecasting number of cases occurred in short term for guiding the prevention and control work in future.
Methods After collecting data of laboratory-confirmed human cases of H5N1, the HAI database was established. x2 test, Kruskal-Wallis test and Spearman’s correlation analysis were performed to analyze the age and sex distribution, time interval from onset of symptoms until hospitalization or that until death, family aggregation, seasonal trends, fatality rates by year to monitor changes in the epidemic patterns of HAI. Time series (ARIMA) and Grey Model (1,1) were performed to predict number of HAI cases in the next month. Multilevel logistic models and single level logistic model were performed to explore potential factors affected survival of HAI cases.
Result
1. Epidemiological surveillance of HAI
Both men and women in each age group can be infected and the sex ratio of male cases to female cases was 0.90:1, with no significant difference among different calendar years or age groups. The median day of time interval from the onset of symptoms until hospitalization or that until death were was 4 days (range 0 - 20 days) or 9 days (range 2 - 31 days), with no significant difference among different years. The peak period of incidence was from November 20 to March 6, there were also no significant difference among different years or countries. While average fatality rate of HAI was 62.3%, there was no increasing tendency of the rate by calendar years. In addition, family aggregation also showed no increasing tendency by calendar years.
2. Prediction effect of monthly number of HAI case
On prediction of monthly accumulative cases, the predicted error by Time Series was 0.94%, while the predicted error by Grey Model was 1.50%. On prediction of monthly incidence cases, the predicted error by Time Series was 4.46%, whilst the predicted error by Grey Model was 23.36%.
3.Factors affected survival of HAI case
The probability of survival might increase by 1.03 fold for every 1 year decrease in the age of HAI case . The probability of survival might also increase by 1.20 fold for every 1 day decrease in the time interval from onset of symptoms until hospitalization. In addition, comparing with the patients came from un-family aggregation, the survival probability of the cases belonged to family cluster might increase by 2.71 fold. .
Conclusion
1. Epidemiological surveillance of HAI
No marked change in the epidemic pattern of HAI has been found over the past 6 years: both men and women in each age group can be infected; the peak seasons were stably in winter and spring; Family aggregations also has no increasing tendency by calendar years.
2. Prediction effect of monthly number of HAI case
Time Series (ARIMA) was suitable to predict number of HAI case in short term.
3. Factors affected survival of HAI case
Age of HAI patients, time interval from onset of symptoms until hospitalization, and family aggregation of HAI case may effect on survival of HAI cases.
引文
[1] http://www.who.int/csr/disease/avian_influenza/country/cases_table_2009_02_18/en/index.html.
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[2] Yuen KY, Chan PK, Peiris M, et al. Clinical features and rapid viral diagnosis of human disease associated with avian influenza A H5N1 virus[J]. Lancet, 1998,351(9101):467-471.
[3] Chotpitayasunondh T, Ungchusak K, Hanshaoworakul W, et al. Human disease from influenza A (H5N1), Thailand, 2004[J]. Emerg Infect Dis, 2005, 11(2):201-209.
[4] Hien TT, Liem NT, Dung NT, et al.Avian influenza A (H5N1) in 10 patients in Vietnam [J]. N Engl J Med, 2004,350(12):1179-1188.
[5]徐小元,王勤环.人禽流感的流行病学和生物学[J].中华医学杂志,2004,84(5):354.
[6]陆如山,王青.禽流感威胁着全球的安全[J].医学情报工作,2006,1:6-9.
[7]郭元吉,孟凡义,王永新,等.从野鸭中分离出正粘和副年病毒[J].中华微生物和免疫学杂志,1981,1:159-163.
[8] Jiang Gu, Zhigang Xie, Zhancheng Gao, et al. H5N1 infection of the respiratory tract and beyond: a molecular pathology study[J]. Lancet 2007,370:1137-45.
[9] Mounts AW, Kwong H Izurieta HS, et al. Case-control study of risk factors for avian influenza A (H5N1) disease, Hong Kong, 1997[J]. infect Dis, 1999,180(2):505-508.
[10] Schultsz C, Dong VC, Chau NVV, et al. Avian influenza H5N1 and healthcare workers [J]. Emerg Infect Dis, 2005,11(7):1158-1159.
[11]郭元吉,王敏,金粉银,等.我国家鸭和野鸭中正粘和副粘病毒的调查[J].中国医学科学院学报,1982,1:32-36.
[12]钟培松,戴依群,钱芳,等.人感染高致病性禽流感H5N1病例流行病学分析[J].上海预防医学杂志,2007,19(3):104.
[13]谭爱军,基于H5N1证据基础的人禽流感流行病学特征[J].华南预防医学2007,33(1):4.
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