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Integration of Epidemiological and Laboratory Data. -- Challenges and Perspectives in China. Dr. Zhen XU Branch of Respiratory Disease Prevention and Control Division for Disease Control and Emergency Response Chinese Center for Disease Control and Prevention June, 2011.

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integration of epidemiological and laboratory data

Integration of Epidemiological and Laboratory Data

--Challenges and Perspectives in China

Dr. Zhen XU

Branch of Respiratory Disease Prevention and Control

Division for Disease Control and Emergency Response

Chinese Center for Disease Control and Prevention

June, 2011

influenza information system
Influenza information system
  • Integration of ILI sentinel surveillance system and ILI outbreak event report system
  • Data input, query and auto analysis
  • Lab info management
  • Surveillance quality monitoring
national ili sentinel surveillance
National ILI sentinel surveillance

5

  • Epidemiology Surveillance
    • Diagnosis and Registry: daily
    • Reporting of ILI case: weekly
    • Collect and analyze the surveillance data
  • Laboratory Surveillance
    • Specimen collection: 5-15 nose & throat swabs from ILI cases per week per hospital
    • Lab detection: PCR test or virus isolation, antigenic and genetic analysis, antiviral resistance
slide6

ILI surveillance information reporting

ILI reporting

ILI sentinel hospitals

Specimens collection

Send specimens to labs

Specimens info & results

Network laboratories

Detection

Isolates to CCDC

Re-identification

CCDC

real time database ili epi data
Real-time Database- ILI epi data
  • Input data of ILI counts and ILI% by week
  • and age group;
  • Data query by date, by week, by age group,
  • by hospital and by area ;
real time database ili lab data
Real-time Database- ILI lab data
  • Input data of PCR and virus isolation results
  • by specimen within 48h;
  • Data query by patient, by date, by week, by
  • hospital and by area ;
real time database ili outbreak
Real-time Database- ILI outbreak
  • event-based report
  • basic info of ILI outbreak: major symptoms, case number, lab results, duration and location of the outbreak
  • progress report of outbreak response;
automatic data analysis
Automatic data analysis

ILI analysis

Virus typing

Surveillance quality monitoring

slide11
Real-time Database Information System for serological surveillance of Occupational exposure population and environment surveillance

environment surveillance data

Serological surveillance data

slide12

Information dissemination: Weekly Report

Influenza Weekly Report

www.cnic.org.cn/eng

www.cnic.org.cn

sari sentinel surveillance
SARIsentinel surveillance
  • To monitor the severity of Pandemic H1N1 and understand the characteristics of SARI cases caused by pdm H1N1
  • Following WHO case definition
  • Support by MOH and EID program
  • Data source:
    • Epi data by case: Respiratory, Internal ICU, and Infectious Departments from 10 hospitals
    • Lab data: 10 network Labs where the hospitals located
  • Data report: EPI Database weekly sent from hospitals and labs by email
  • Data sharing: Brief report sent to hospitals and labs regularly
notifiable disease reporting system
Notifiable disease reporting system
  • Most are clinical cases;
  • No virological info;
  • Limited use for flu surveillance
research of pandemic h1n1
Research of pandemic H1N1

Case patients hospitalized with 2009 pandemic influenza A (H1N1) virus infection, by date of onset, China, Sep 2009–Feb 2010. ICU,

challenges
Challenges
  • Still lack of a comprehensive surveillance system to oversee the virus and diseases caused by flu virus
    • Characteristics and burden of mild, severe and fatal case
  • Maintain the current giant ILI network—cost, willingness and enthusiasm
  • Translating data into practice is much more important than collecting data
    • Surveillance data, research data
  • Risk assessment of surveillance data
  • Quality monitoring didn’t followed by in time quality improvement action
  • SARI surveillance info is independent from ILI system. Mild and severe case caused by flu virus cannot be linked well
challenges1
Challenges
  • Vaccine gap
    • Supply: 30m doses VS 570m population
    • Capacity: 126m doses VS 570m population
  • Vaccination strategy
    • Annual technical recommendations for target population only
    • Flu vaccination not covered by national government or medical insurance
    • Beijing is the only city to provide the free vaccination service to local population: ﹥65yrs, primary and middle school students
future plans
Future plans

Use the data for vaccination policy making

  • Improve the sentinel SARI surveillance
    • Internet-based reporting of epi and lab data
    • Integration with HIS and LIS in hospitals
    • Publication and sharing of SARI findings
  • Modeling the ILI seasonal pattern
  • Application of early warning based on long-term and good-quality flu data in pilot areas
  • Describe the burden of severe and fatal cases caused by flu virus
    • Population-based inpatients surveillance
    • DSP site: ICD code-based deaths analysis
slide20

Integrated information system

Submission

Data Entry

Search

Surveillance Information System

Sequence

Database

Result feedback

Download

Analysis&Report

Share

Information Platform

Security check

Receipt

Lab Data Management

Website

Public propaganda

Take&Return

Technical support

Result Entry

New

Security check

slide21

Thank you!

谢 谢!