中华预防医学杂志
中華預防醫學雜誌
중화예방의학잡지
CHINESE JOURNAL OF
2014年
4期
259-264
,共6页
赖圣杰%廖一兰%张洪龙%李小洲%任翔%李夫%余建兴%王丽萍%余宏杰
賴聖傑%廖一蘭%張洪龍%李小洲%任翔%李伕%餘建興%王麗萍%餘宏傑
뢰골걸%료일란%장홍룡%리소주%임상%리부%여건흥%왕려평%여굉걸
传染病%疾病暴发流行%模型,统计学%时间模型%时空模型
傳染病%疾病暴髮流行%模型,統計學%時間模型%時空模型
전염병%질병폭발류행%모형,통계학%시간모형%시공모형
Communicable diseases%Disease outbreaks%Models,statistical%Temporal model%Temporal-spatial model
目的 比较国家传染病自动预警系统(简称预警系统)中时间模型与时空模型的预警效果,为预警模型的进一步改进提供依据.方法 2011-2013年中国CDC通过预警系统在20个省份的208个试点县(区)同时应用时间模型和时空模型,根据发病水平对16种传染病分两类进行预警分析,结合疾病监测信息报告管理系统报告的16种传染病个案数据、突发公共卫生事件报告管理信息系统报告的暴发事件,采用预警信号数、灵敏度、错误预警率和及时性等指标,比较两个模型的暴发探测效果.结果 对于16种传染病整体而言,时间模型与时空模型的灵敏度分别为96.23％(153/159)和90.57％(144/159),差异无统计学意义(Z=-1.604,P=0.109);时间模型的错误预警率(1.57％,57 068/3643 279)高于时空模型(0.64％,23 341/3643 279)(Z=-3.408,P=0.001);两者的暴发探测时间中位数分别为3.0d和1.0d,差异无统计学意义(Z=-1.334,P=0.182).对于发病水平较低的6种Ⅰ类疾病(流行性出血热、流行性乙型脑炎、登革热、流行性脑脊髓膜炎、流行性和地方性斑疹伤寒、钩端螺旋体病),时间模型和时空模型的灵敏度均为100％(8/8,8/8),错误预警率均为0.07％(954/1 367437,900/1 367 437),二者的暴发探测时间中位数分别为2.5和3.0d,时空模型比时间模型减少2.29％(23条)预警信号.对于发病水平较高的10种Ⅱ类疾病(流行性腮腺炎、痢疾、猩红热、流行性感冒、风疹、戊型肝炎、急性出血性结膜炎、甲型肝炎、伤寒和副伤寒、其他感染性腹泻病),时间模型和时空模型的灵敏度分别为96.03％(145/151)和90.07％(136/151),时空模型比时间模型减少59.36％(56 656条)预警信号,各病种信号数和错误预警率均低于时间模型,时空模型暴发探测时间中位数(1.0 d)短于时间模型(3.0 d).结论 总体上时空模型比时间模型预警效果较好,但对于不同发病水平的传染病,时空模型和时间模型的暴发探测效果有所差别,预警系统应根据具体的病种来调整和优化时间模型与时空模型.
目的 比較國傢傳染病自動預警繫統(簡稱預警繫統)中時間模型與時空模型的預警效果,為預警模型的進一步改進提供依據.方法 2011-2013年中國CDC通過預警繫統在20箇省份的208箇試點縣(區)同時應用時間模型和時空模型,根據髮病水平對16種傳染病分兩類進行預警分析,結閤疾病鑑測信息報告管理繫統報告的16種傳染病箇案數據、突髮公共衛生事件報告管理信息繫統報告的暴髮事件,採用預警信號數、靈敏度、錯誤預警率和及時性等指標,比較兩箇模型的暴髮探測效果.結果 對于16種傳染病整體而言,時間模型與時空模型的靈敏度分彆為96.23％(153/159)和90.57％(144/159),差異無統計學意義(Z=-1.604,P=0.109);時間模型的錯誤預警率(1.57％,57 068/3643 279)高于時空模型(0.64％,23 341/3643 279)(Z=-3.408,P=0.001);兩者的暴髮探測時間中位數分彆為3.0d和1.0d,差異無統計學意義(Z=-1.334,P=0.182).對于髮病水平較低的6種Ⅰ類疾病(流行性齣血熱、流行性乙型腦炎、登革熱、流行性腦脊髓膜炎、流行性和地方性斑疹傷寒、鉤耑螺鏇體病),時間模型和時空模型的靈敏度均為100％(8/8,8/8),錯誤預警率均為0.07％(954/1 367437,900/1 367 437),二者的暴髮探測時間中位數分彆為2.5和3.0d,時空模型比時間模型減少2.29％(23條)預警信號.對于髮病水平較高的10種Ⅱ類疾病(流行性腮腺炎、痢疾、猩紅熱、流行性感冒、風疹、戊型肝炎、急性齣血性結膜炎、甲型肝炎、傷寒和副傷寒、其他感染性腹瀉病),時間模型和時空模型的靈敏度分彆為96.03％(145/151)和90.07％(136/151),時空模型比時間模型減少59.36％(56 656條)預警信號,各病種信號數和錯誤預警率均低于時間模型,時空模型暴髮探測時間中位數(1.0 d)短于時間模型(3.0 d).結論 總體上時空模型比時間模型預警效果較好,但對于不同髮病水平的傳染病,時空模型和時間模型的暴髮探測效果有所差彆,預警繫統應根據具體的病種來調整和優化時間模型與時空模型.
목적 비교국가전염병자동예경계통(간칭예경계통)중시간모형여시공모형적예경효과,위예경모형적진일보개진제공의거.방법 2011-2013년중국CDC통과예경계통재20개성빈적208개시점현(구)동시응용시간모형화시공모형,근거발병수평대16충전염병분량류진행예경분석,결합질병감측신식보고관리계통보고적16충전염병개안수거、돌발공공위생사건보고관리신식계통보고적폭발사건,채용예경신호수、령민도、착오예경솔화급시성등지표,비교량개모형적폭발탐측효과.결과 대우16충전염병정체이언,시간모형여시공모형적령민도분별위96.23％(153/159)화90.57％(144/159),차이무통계학의의(Z=-1.604,P=0.109);시간모형적착오예경솔(1.57％,57 068/3643 279)고우시공모형(0.64％,23 341/3643 279)(Z=-3.408,P=0.001);량자적폭발탐측시간중위수분별위3.0d화1.0d,차이무통계학의의(Z=-1.334,P=0.182).대우발병수평교저적6충Ⅰ류질병(류행성출혈열、류행성을형뇌염、등혁열、류행성뇌척수막염、류행성화지방성반진상한、구단라선체병),시간모형화시공모형적령민도균위100％(8/8,8/8),착오예경솔균위0.07％(954/1 367437,900/1 367 437),이자적폭발탐측시간중위수분별위2.5화3.0d,시공모형비시간모형감소2.29％(23조)예경신호.대우발병수평교고적10충Ⅱ류질병(류행성시선염、이질、성홍열、류행성감모、풍진、무형간염、급성출혈성결막염、갑형간염、상한화부상한、기타감염성복사병),시간모형화시공모형적령민도분별위96.03％(145/151)화90.07％(136/151),시공모형비시간모형감소59.36％(56 656조)예경신호,각병충신호수화착오예경솔균저우시간모형,시공모형폭발탐측시간중위수(1.0 d)단우시간모형(3.0 d).결론 총체상시공모형비시간모형예경효과교호,단대우불동발병수평적전염병,시공모형화시간모형적폭발탐측효과유소차별,예경계통응근거구체적병충래조정화우화시간모형여시공모형.
Objective For providing evidences for further modification of China Infectious Diseases Automated-alert and Response System (CIDARS) by comparing the early-warning performance of the temporal model and temporal-spatial model in CIDARS.Methods The application performance for outbreak detection of temporal model and temporal-spatial model simultaneously running among 208 pilot counties in 20 provinces from 2011 to 2013 was compared; the 16 infectious diseases were divided into two classes according to the disease incidence level; cases data in nationwide Notifiable Infectious Diseases Reporting Information System was combined with outbreaks reported to Public Health Emergency Reporting System,by adopting the index of the number of signals,sensitivity,false alarm rate and time for detection.Results The overall sensitivity of temporal model and temporal-spatial model for 16 diseases was 96.23％ (153/159) and 90.57％ (144/159) respectively,without significant difference (Z =-1.604,P =0.109),and the false alarm rate of temporal model (1.57％,57 068/3 643 279) was significantly higher than that of temporalspatial model (0.64％,23 341/3 643 279) (Z=-3.408,P =0.001),while the median time for detection of these two models was not significantly different,which was 3.0 days and 1.0 day respectively (Z =-1.334,P =0.182).For 6 diseases of type Ⅰ which represent the lower incidence,including epidemic hemorrhagic fever,Japanese encephalitis,dengue,meningococcal meningitis,typhus,leptospirosis,the sensitivity was 100％ for both models (8/8,8/8),and the false alarm rate of both temporal model and temporal-spatial model was 0.07％ (954/1 367 437,900/1 367 437),with the median time for detection being 2.5 days and 3.0 days respectively.The number of signals generated by temporal-spatial model was reduced by 2.29％ compared with that of temporal model.For 10 diseases of type Ⅱ which represent the higher incidence,including mumps,dysentery,scarlet fever,influenza,rubella,hepatitis E,acute hemorrhagic conjunctivitis,hepatitis A,typhoid and paratyphoid,and other infectious diarrhea,the sensitivity of temporal model was 96.03％ (145/151),and the sensitivity of temporal-spatial model was 90.07％ (136/151),the number of signals generated by temporal-spatial model was reduced by 59.36％ compared with that of temporal model.Compared to temporal model,temporal-spatial model reduced both the number of signals and the false alarm rate of all the type Ⅱ diseases ; and the median of outbreak detection time of temporal model and temporal-spatial model was 3.0 days and 1.0 day,respectively.Conclusion Overall,the temporalspatial model had better outbreak detection performance,but the performance of two different models varies for infectious diseases with different incidence levels,and the adjustment and optimization of the temporal model and temporal-spatial model should be conducted according to specific infectious disease in CIDARS.
