1. Generating local intelligence where routine data is not available Simon Packer, Field Epidemiology Service (FES), Public Health England (PHE)

2. Introduction Why do it ? When is it required ? What does it involve ? Who carries it out ? How is it different from routine data collection ? Examples: Tuberculosis cohort review. Outbreak investigation. 2Generating local intelligence where routine data is not available

3. Why FES do it ? When is it required? To guide public health action and/or improve service delivery. Specific question / concern. Outbreak investigation. Larger than expected increase in the incidence of a health threat. Service improvement. 3Generating local intelligence where routine data is not available

4. What does it involve ? Developing a protocol (even if it is simple). Aims Data items Data Sources Data collection materials Validation rules Data collection and transfer. Data validation. Analysis. Recommendations and interventions. 4Generating local intelligence where routine data is not available

5. Who carries it out ? Do it yourself. Clinical staff. Environmental health officers. Service staff. Considerations Training / guidance materials. Standardised data collection materials. Co-ordination. Additional work – ensure that data collection is as low burden as possible 5Generating local intelligence where routine data is not available

6. How is it different from routine data collection ? Routine Data CollectionBespoke Data Collection Broad nationally relevant questionSpecific local question Established systemsNeed to develop all aspects of system Complicated systemsBasic systems recommended Built into servicesAdditional work for staff Feeds into national / regional dataLocal impact Continual data collectionOne off or periodic data collection Historical data availableNo historical comparisons available 6Generating local intelligence where routine data is not available

7. Outbreak Investigation Timely investigation into a event of public health significance. Standard case definition (count cases). Data collected by: Clinical staff, EHO’s, local health protection teams and FES staff. Data collection using interviews / questionnaire / case note review. Telephone questionnaires. Web based questionnaire. Clinical staff. Data input and validation. Data used to develop public health intervention to prevent further cases. 7Generating local intelligence where routine data is not available

8. Tuberculosis cohort review Periodic data collection to improve service provision All cases of TB in a specific region are presented at a quarterly meeting, where the care provided is examined. Standardised data collection forms. Clinical staff collect data – guidance is provided. Data system used to process, validate, and export data. Data provides local intelligence on areas in the TB treatment pathway that can be improved. 8Generating local intelligence where routine data is not available

9. Conclusion Non-routine data can provide vital data for public health action and service improvement. Wide range of uses and applications. Rubbish in rubbish out. Important considerations: Plan it. Develop standard materials and definitions. Respect data collector time. Validate you data. Ensure that data collected can be used to improve public health. 9Generating local intelligence where routine data is not available