1. Association of Public Health Observatories Day 3 Session 2 Sources of lifestyle data Andrew Hughes South East Public Health Observatory Spring 2009 Based on material from East Midlands Public Health Observatory

2. Obesogenic Lifestyle Factors

3. Question What’s the best way to estimate the % of people in your local population who smoke?

4. What we will cover in this session what we mean by “lifestyle” the need for local lifestyle data –applications –LOPs (Local Operational Plans) and LAAs (Local Area Agreements) sources of lifestyle data and their comparative strengths and weaknesses –data from commercial/market research organisations –data from primary care –synthetic estimates from national surveys –local surveys

5. Lifestyle? Smoking Physical activity Diet Obesity Alcohol consumption Use of drugs Sexual behaviour Sexually transmitted infections Teenage pregnancy Breastfeeding Blood pressure Cholesterol levels

6. The need for local lifestyle data General data at LA/PCT-level AND below for... –comparison with other LAs/PCTs –within-area comparisons by age, gender, ethnicity, ward, practice, etc –analysis of trends over time and progress towards local targets Specific Local Operational Plans (LOPs) Local Area Agreement (LAAs) Needs Assessments/Service impact/evaluation

7. Lifestyle in LOPs and LAAs Local Operational Plans (LOPs) –based on the “Vital Signs” indicators Local Area Agreement (LAAs) –based on the National Indicator Set (NIS) Vital Signs and NIS lifestyle indicators restricted to: –child obesity –smoking quitters –hospital admissions for alcohol-related harm –adult physical activity levels –school sport –breastfeeding

8. The need for local lifestyle data (cont.) helps to communicate important public health messages...... to the public/local communities... to decision makers/commissioners/funders

9. Different sources: 1.data from primary care 2.data from commercial/market research organisations 3.synthetic estimates from national surveys 4.local surveys local boosts of national surveys regionally coordinated local surveys locally designed and managed surveys

10. Validity Reliability Accuracy Bias Precision Timeliness Cost Some criteria for assessing and comparing different sources

11. 1.Data from primary care records of consultations held on practice computers incentives - the QOF system GP research databases Primary care prescribing Special data collection from GP systems Community health systems

12. The Quality and Outcomes Framework (QOF) is a system of financial rewards to general practices for the provision of high quality care Obesity: The practice can produce a register of patients aged 16 years and over with a BMI greater than or equal to 30 in the last 15 months. Smoking 1: The percentage of patients with any or any combination of the following conditions: coronary heart disease, stroke or TIA, hypertension, diabetes, COPD or asthma whose notes record smoking status in the previous 15 months (except those who have never smoked where smoking status need only be recorded once since diagnosis). Smoking 2: Ditto... but where the notes contain a record that smoking cessation advice or referral to a specialist service, where available, has been offered within the previous 15 months.

13. QOF Smoking indicators, 2008/09

14. MIQUEST queries –may be useful for public health –increasingly automated but has traditionally involved practice visits –examples include: Smoking Obesity Special data collections:

15. Percentage change in classification of BMI in Year6 children from Reception Year, matched cohort, 2006-2007

16. 2.Data from commercial organisations e.g. CACI, Claritas, Experian, Acxiom, Dr Foster large volumes of household survey and consumer data modelled to provide estimates for all areas of the country e.g.: –expenditure on tobacco, food and drink –prevalence of smoking and obesity

17. Acxiom smoking prevalence estimates based on the “National Shoppers Survey” large national sample - some coverage in your area updates available annually adjusted for known biases in the sample e.g. undersampling of young people. relatively cheap 2005 data gave a national prevalence of 17% compared to 23-24% from 2004-5 GHS and 2004 HSE

18. The analysis looked at two surveys (The Health Survey for England and the British Market Research Bureau's TGI quarterly survey of 25,000 Britons) which ask people their Body Mass Index (BMI)*. By linking the postcodes of these respondents to the lifestyle categorisation (MOSAIC**) it was possible to show which types of people tend to have high and low BMIs. Dr Foster Obesity Data

19. Data from commercial organisations The main problem is that detailed methodologies are often not available

20. 3. Synthetic estimates from national surveys (public domain) Neighbourhood Statistics: LA Model-Based Estimates of Healthy Lifestyles Behaviours, 2003-05 Publication date December 17, 2007 http://www.ic.nhs.uk/statistics-and-data-collections/population-and-geography/neighbourhood- statistics/neighbourhood-statistics:-model-based-estimates-of-healthy-lifestyles-behaviours-at-la-level- 2003-05 http://www.ic.nhs.uk/statistics-and-data-collections/population-and-geography/neighbourhood- statistics/neighbourhood-statistics:-model-based-estimates-of-healthy-lifestyles-behaviours-at-la-level- 2003-05

21. Estimating local obesity from the HSfE

22. Model-based estimates of healthy lifestyle IC-funded project HSfE 2003-2005 data on smoking, obesity, binge drinking, fruit and veg consumption statistical modelling to identify social and demographic predictors of these aspects of lifestyle LA- and MSOA-level estimates (and CIs) based on the social and demographic characteristics of their populations. validated against other survey data e.g. Merseyside boost of the HSfE

23. Example: LA-level smoking model Area characteristics statistically associated with prevalence of smoking high % households with no car low % of people 16-74, professional & managerial occupations high % males, 16-34, white ethnic origin low life expectancy (females) low % with limiting long-term illness

24. AreaPopulation 16+ Estimated smoking prevalence 16+ Estimated smokers 16+ Portsmouth, Area 5640042.52720 Southampton, Area 3513037.51924 Portsmouth, Area 1471030.81451 Milton Keynes, Area 2667030.72048 Milton Keynes, Area 1607028.81748 Cherwell Valley, Area 4833025.62132 Mole Valley, Area 34840251210 Tunbridge Wells, Area 4588020.11182 Tunbridge Wells, Area 5632019.41226 Southampton, Area 5531018.2966 Oxford, Area 4590017.41027 Cherwell Vale, Area 2524017.1896 South Bucks, Area 26330171076 Chiltern, Area 1496016.5818 Chiltern, Area 3604014.7888 MSOA smoking estimates

25. Health Warnings –The models estimate the expected prevalence of health behaviours in an MSOA or LA area given the social and demographic characteristics of the local population. –Wide confidence intervals at MSOA-level –Extremely unwise to use synthetic estimates for an area at two points in time to infer trends in that area http://www.ic.nhs.uk/statistics-and-data-collections/

26. 4.Local surveys local boosts of national surveys regional surveys local surveys

27. Particular strengths of a locally designed and managed survey 1. Generates real local data 2. Flexibility and control over eg: –population to be surveyed - area, age, sex, ethnicity, etc –sample size - trading statistical power v cost –survey design - cross-sectional/longitudinal, census/sample, etc –method - phone, internet, interview, postal –subject matter - can be anything!!

28. Particular difficulties with locally designed and managed survey –lack of comparability with other areas/benchmarks –securing permissions –labour intensive –cost

29. Different purposes... different aspects of lifestyle.. different sources Between area comparisons Within-area comparisons Trends over time Evaluating local services/initiatives Smoking Physical Activity Diet Obesity Alcohol consumption

30. Source ApplicationPrimary care data Data from commercial organisations Published synthetic estimates Local surveys Comparing your area with other LAs Analysis of within-area inequalities by age, gender, ethnicity and area of residence. Monitoring trends over time Measuring the impact of services/initiatives Smoking prevalence in your local authority area Rate the different sources for different applications

31. Question What’s the best way to estimate the % of people in your local population who smoke?

32. What we have covered in this session what we mean by “lifestyle” the need for local lifestyle data –applications –LOPs and LAAs sources of lifestyle data and their comparative strengths and weaknesses –data from primary care –data from commercial/market research organisations –synthetic estimates from national surveys –local surveys

33. Source ApplicationPrimary care data Data from commercial organisations Published synthetic estimates Local surveys Comparing your area with other LAs Analysis of within-area inequalities by age, gender, ethnicity and area of residence. Monitoring trends over time Measuring the impact of services/initiatives Smoking prevalence in your local authority area Different sources for different applications

34. Health profiles Neighbourhood Statistics: LA Model-Based Estimates of Healthy Lifestyles Behaviours, 2003-05 Publication date December 17, 2007 SW Healthy Schools Mapping Tool http://www.noo.org.uk/uploads/doc168_2_noo_NCMPrep ort1_110509.pdf Data Sources

35. User’s Guide to Data Collected in Primary Care in England: www.erpho.org.uk/Download/Public/128 99/1/erpho%20Primary%20Care.pdf Reports on: www.healthknowledge.org.uk/Health%2 0Information/HK%203c1.htm Other resources for primary care