Early Warning System for CD

1. Early Warning System for CD Dr Venanzio Vella, Senior Epidemiologist, CEU

2. Objective of EWS for CD • Early detection of outbreaks; • Timely outbreak response and control; • Monitoring trends; • Evaluate impact of control activities.

3. How the EWS should work LAB The central level aggregates the aggregates of the intermediate level Feedbacks 52 weekly reports per year per health unit are aggregated at intermediate level Feedbacks Feedback Feedback Feedback As soon as a health worker see a patient with symptoms matching the case definition of diseases X, Y. Z tally the case on a weekly form Outbreak control Private & public hospitals, clinics, etc GPs etc patients

4. Reasons for the problematic data The central level has difficulties in deciding is an increase in aggregate numbers is real of due to vagaries of reporting LAB can only detect a few diseases No Feedbacks The % of expected weekly reports that are sent to the intermediate level might vary accross time periods Feedbacks Not all epidemics are investigated No Feedback Feedback Feedback Some health worker stop reporting all cases and the decision of what and when to report becomes subjective. Private & public hospitals, clinics, etc GPs etc patients

5. Basic Principles • Legislative Framework; • Standard Operating Procedures (SOP); • Data Management and piloting; • Training; • Quality control of data and feedback; • Timely Monitoring to identify, investigate and control outbreaks; • Evaluation of results.

6. Legislation • Listofdiseasestobenotified; • Case Definition; • Roleofeachinstitution; • Actionsto investigate and control; • Enforcementmechanisms;

7. SOP • Case Definition; • Data entry and data flow; • Roleofphysicians and Institution; • Data quality and Interpretation; • Thresholds and expectedaction

8. Data Management and piloting • Access database to standardize the data entry on CDs at PHI level; • Flow of data to the FPHI and the RS PHI; • Availability of statistical reports and other relevant documents; • Training on using the database; • Piloting to evaluate implementation of data management and data flow.

9. Training • Assessment against guidelines; • Gaps in procedures, skills, resources; • Training to fill gaps;

10. Quality control of data and feedback • Completeness of weekly reports; • Timeliness of weekly reports; • Trend analysis; • Outliers spotting; • Feedback to periphery to confirm, justify, correct, incentivise.

11. Timely Monitoring to assess change • Quality control to avoid vagaries of reporting; • Threshold levels • Fixed values e.g. One case of Cholera, Polio, Measles is sufficient to issue an outbreak alert (likelihood of individual cases resulting in an outbreak), haemorrhagic fevers (severity of the disease); • Values exceeding expected trends for disease with a foreseable action plan

12. Speedy confirmation of outbreaks • Laboratory capacity; • Training; • Human & other resources; • costs.

13. Speedy investigation & control • Field Epidemiology Training; • Identification of Source; • Tracing and testing contacts; • Lab conformation • Management and containment of spread of outbreaks • Quarantine control; • Logistics and supplies; • Coordinated multisectoral action; • Communication.

14. Evaluate what is going on in the field • If EWS is complying with criteria -Timeliness (e.g. X% units reporting on time) - Completeness (e.g. X% expected reports received) - Feedback (e.g. X% GPs receiving feedback) • If action is taken (e.g. % events investigated) • If action taken has it been effective (indicators of actions taken and effects on epidemic curve)

15. ExpectedResults • Standardization (case definition, data management); • Better interpretation of data; • Evaluation of implementability with better understanding of constraints, including behavioural practices of reporting (incentive, feedbacks, interets, etc), enforcement of legal obligations; • Evaluation of effectiveness of EWS in disease control;