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

Obesogenic Lifestyle Factors

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

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

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

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

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

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

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

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

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

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.

QOF Smoking indicators, 2008/09

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

Percentage change in classification of BMI in Year6 children from Reception Year, matched cohort,

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

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 GHS and 2004 HSE

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

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

3. Synthetic estimates from national surveys (public domain) Neighbourhood Statistics: LA Model-Based Estimates of Healthy Lifestyles Behaviours, Publication date December 17, statistics/neighbourhood-statistics:-model-based-estimates-of-healthy-lifestyles-behaviours-at-la-level statistics/neighbourhood-statistics:-model-based-estimates-of-healthy-lifestyles-behaviours-at-la-level

Estimating local obesity from the HSfE

Model-based estimates of healthy lifestyle IC-funded project HSfE 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

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

AreaPopulation 16+ Estimated smoking prevalence 16+ Estimated smokers 16+ Portsmouth, Area Southampton, Area Portsmouth, Area Milton Keynes, Area Milton Keynes, Area Cherwell Valley, Area Mole Valley, Area Tunbridge Wells, Area Tunbridge Wells, Area Southampton, Area Oxford, Area Cherwell Vale, Area South Bucks, Area Chiltern, Area Chiltern, Area MSOA smoking estimates

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

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

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!!

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

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

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

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

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

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

Health profiles Neighbourhood Statistics: LA Model-Based Estimates of Healthy Lifestyles Behaviours, Publication date December 17, 2007 SW Healthy Schools Mapping Tool ort1_ pdf Data Sources

User’s Guide to Data Collected in Primary Care in England: 99/1/erpho%20Primary%20Care.pdf Reports on: 0Information/HK%203c1.htm Other resources for primary care