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Integrating illness data from different sources to measure progress against targets

Integrating illness data from different sources to measure progress against targets. Alan Spence, UK Health & Safety Executive Workshop on OSH Monitoring Systems Bilbao, 30 September 2002. Outline of presentation. Background: OSH targets in the UK. A model of work-related ill health.

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Integrating illness data from different sources to measure progress against targets

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  1. Integrating illness data from different sources to measure progress against targets Alan Spence, UK Health & Safety Executive Workshop on OSH Monitoring Systems Bilbao, 30 September 2002

  2. Outline of presentation • Background: OSH targets in the UK. • A model of work-related ill health. • Existing data sources, including: • Self-reported Work-related Illness (SWI) surveys • Occupational Disease Intelligence Network (ODIN) surveillance. • Possibilities for integration: fully or semi-quantitative. • Next steps: data collection and peer review.

  3. Background • Revitalising Health and Safety target: “To reduce the incidence rate of cases of work-related ill health by 20% by 2010”. • Statistical Note on Progress Measurement (June 2001): set out technical approach. • “Data from the various sources should be integrated to produce an overall judgement about progress against this target, for individual diseases and in aggregate”.

  4. The ‘integration’ approach • Aim: A consistent set of estimates, exploiting the strengths and minimising the weaknesses of all the available sources. • Uses in other areas of statistics: National Accounts, Labour Accounts. • Starting point: A model of the underlying relationships.

  5. A model for work-related ill health Socio-economic context Awareness, attitudes and behaviours Occ health policies & actions Exposures and working conditions Not perceived Health effects Not work-related Effects Perceived by individual Reported by individual (in survey) Presented to doctor Reported to employer Claim made for compensation Diagnosed / attributed to work by Doctor Awarded by Compensation authorities Recognised / attributed to work by Employer Attributed to work by Self-report

  6. Data sources in the UK

  7. SWI and ODIN • SWI: Household surveys of self-reported work-related illness • linked to the LFS, run in 1990, 95, 99 and 01/02 • “any illness, disability or other physical problem that was caused or made worse by your work”. • ODIN: Voluntary reporting by doctors in the Occupational Disease Intelligence Network • involves occupational physicians and specialists in respiratory, skin, hearing, musculoskeletal, stress/psychological disorders and infections • now “The Health and Occupation Reporting network”.

  8. RIDDOR, IIS and DCs • RIDDOR: Statutory Reports under HSE’s Reporting of Injuries, Diseases and Dangerous Occurrences Regulations • requirement on employers (as for injuries). • IIS: New cases of assessed disablement under the Department for Work and Pensions’ Industrial Injuries Scheme • compensation for prescribed diseases. • DCs: Deaths from occupational lung diseases recorded on Death Certificates • including HSE’s Mesothelioma Register.

  9. Possibilities for integration • Fully quantitative • Four steps: principles and examples. • Semi-quantitative • Role of supporting data / indicators. • Micro-integration • Involves individual record matching • Not directly applicable to work-related ill health in the UK.

  10. Fully quantitative integrationSteps 1 and 2 • Harmonisation / conceptual adjustments: • Converts definitions, classifications, timing etc to a common conceptual basis • e.g. adjust SWI data for self-reported cases not presented to doctors. • Error minimisation / quality adjustments: • Corrects for sampling errors and biases • e.g. adjust SWI estimates for effect of raised awareness.

  11. Fully quantitative integrationSteps 3 and 4 • Balancing / coherence adjustments: • Achieves precise numerical balance in the identity relations • e.g. apply expertise to remove minor differences between estimates. • Aggregation / weighting • Aggregates data for different components • e.g. use population data to weight together different health outcomes.

  12. Semi-quantitative integration:An alternative approach • Would be ambitious to apply integration outside National / Labour Accounts, and limitations are recognised even these areas. • Remember the aim of the exercise: • Statistical Note: “To produce an overall judgement about progress against the target”.

  13. Semi-quantitative integration:Conceptual and quality issues • Still need to understand reasons for conflicting results between sources. • Conceptual issues / harmonisation • e.g. SWI more inclusive. • Quality issues / error minimisation • e.g. ODIN more timely. • Aim to identify a ‘leading source’ for each health outcome.

  14. Health outcomes and sources

  15. Semi-quantitative integration:Use of supporting data • Data on health outcomes not sufficiently robust: • Statistical Note: “Supplementary approaches should be explored, for example collecting data on economic, social and cultural factors”. • Need indicators based on models: • e.g. WHO “surveillance indicators”.

  16. The model revisited Socio-economic context Awareness, attitudes and behaviours Occ health policies & actions Exposures and working conditions Not perceived Health effects Not work-related Effects Perceived by individual Reported by individual (in survey) Presented to doctor Reported to employer Claim made for compensation Diagnosed / attributed to work by Doctor Awarded by Compensation authorities Recognised / attributed to work by Employer Attributed to work by Self-report

  17. Semi-quantitative integration:Examples of indicators Socio-economic context e.g. contracting strategies in affected industries Awareness, attitudes and behaviours e.g. culture towards relevant controls Occ health policies & actions e.g. HSE inspection activities Exposures and working conditions e.g. percentage of workforce exposed Health effects

  18. Fullyquantitative A single answer Applies ‘state of the art’ theory Learns lessons from other areas of statistics. Semi-quantitative A fuller picture Fewer theoretical challenges Draws on models and initiatives relevant to work-related ill health. Recap: fully quantitative versus semi-quantitative approach

  19. Next steps • Data collection: • For conceptual and quality adjustments • Supporting data from other initiatives. • Review by peer group: • Validation of methodology • Expert input to assumptions etc • Consensus on the judgement of progress (for strategy mid-point 2004/05).

  20. Summary of main points • SWI, ODIN and other sources measure different forms of reporting and attribution. • For progress measurement these must be combined in some way. • Lessons can be learned from ‘integration’ in other areas and from ‘indicators’ for OSH. • The fully and semi-quantitative approaches should be followed together. • Consensus and peer review are crucial – at an international level too?

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