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Healthy Lifestyles Synthetic Estimates Project Shaun Scholes, Kevin Pickering and Claire Deverill.

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Presentation on theme: "Healthy Lifestyles Synthetic Estimates Project Shaun Scholes, Kevin Pickering and Claire Deverill."— Presentation transcript:

1 Healthy Lifestyles Synthetic Estimates Project Shaun Scholes, Kevin Pickering and Claire Deverill

2 Outline of presentation A small-area estimation problem with national surveys such as the Health Survey for England Get around this problem by statistical modelling. HSE data is used in combination with other data sources to generate estimates at fine levels of geography Importance of quality assurance Caveats when using small area estimates

3 Demand for local area data on health Health related behaviour is not uniform across England (place matters) NatCen regularly use HSE data to produce health indicators: England (trend tables) Larger areas such as: GORs (Annual HSE reports); GORs, SHAs & Counties (SHA report 2004, Compendium of Clinical Indicators, forthcoming Chief Medical Officers report on binge drinking)

4 Regional indicators using HSE data

5 Small area estimation problem National surveys such as the HSE are not designed to provide reliable estimates at local level: Sample sizes typically small or zero within small areas, even after pooling years of HSE data together Direct estimates, where we can calculate them, are unbiased - but have low precision due to wide sampling error Local health surveys are an option but various problems: Costs Comparability across surveys Small area estimation techniques are an alternative

6 Healthy lifestyles synthetic estimates project Information Centre for Health and Social Care and Neighbourhood Statistics commissioned NatCen to use HSE data to produce small area estimates of: current smoking (adults) binge drinking (adults) obesity (adults) fruit & vegetables consumption (adults & children separately)

7 Small area estimation methodology used by NatCen Use a statistical model to express the relationship between individual healthy lifestyle behaviour and area-level information Outputs from that model used to generate a model- based estimate for all areas But must be interpreted differently to direct estimates ~ estimates represent the expected prevalence for an area based on its population characteristics

8 Illustration Whether an individual currently smokes from HSE Attach area-level information to HSE dataset: Regional indicator % with no qualifications (Census 2001) Statistical model run on the subset of areas covered by HSE Use terms from model to obtain a predicted estimate for all small areas

9 Area-level characteristics and current smoking

10 Implementation

11 Quality assurance Quality of estimates crucially depend on the quality of the model Need for QA measures to provide evidence on plausibility of estimates Internal checks (examine the residuals, correlation between direct and model-based estimates) External checks (no gold standard!) –correlation with direct estimates from other surveys such as GHS –correlation with local boost surveys –correlation with Index of Multiple Deprivation 2004

12 Residual plot (all areas sampled)

13 Correlation with IMD 2004 (Bolton)

14 Putting estimates in a context

15 Estimates have health warnings Have to be interpreted differently ~ not estimates of actual prevalence Dependent upon quality of the model Method relies on having powerful predictors of health variations between areas. A large amount of unexplained between area variation results in wide CIs: Can compare MSOAs against the national average Cannot meaningfully compare MSOA X against MSOA Y as CIs overlap But we stress that these estimates are not available elsewhere!


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