Geographical Research with the Health Survey for England: Environmental Health Equity

1. Geographical Research with the Health Survey for England:Environmental Health Equity Ben Wheeler & Yoav Ben-Shlomo

2. Outline • Environmental equity • Air pollution & respiratory health • A small area air quality index • Using the HSE with small area data • Respiratory health, social class and air quality

3. Environmental equity • USA – environmental justice movement “…no group of people, including racial, ethnic, or socioeconomic groups, should bear a disproportionate share of the negative environmental consequences resulting from industrial, municipal, and commercial operations…” • UK – more recent developments in research & policy

4. Environment Agency:Position Statement onEnvironmental Inequalities “People who are socially and economically disadvantaged often live in the worst environments.  For example, those living in the most deprived parts of England experience the worst air quality…”

5. Air Pollution &Respiratory Health Inequalities • Numerous studies have found adverse effects of air pollution on respiratory health • Studies usually ‘adjust away’ socio-economic confounding • But this is inherently interesting… • What is the relationship between air quality, environmental equity and health inequalities?

6. The Study • Health Survey for England 1995-97 collected lung function measurements • Large dataset on individuals with relevant outcome and potential confounder variables • But how to estimate ambient air pollution exposure?

7. Small Area Air Quality Index • Constructed as a general relative measure of air quality • National, high resolution estimates of ambient air pollutant concentrations • Annual mean estimates for 1996 for a 1km grid covering the UK • Geographic Information System (GIS) used to allocate to 1991 wards

8. Index calculated for each ward based on 4 values: NO2 SO2 PM10 Benzene

9. The HSE & Geography • Data for mid-1990s • Highest geographic resolution = District Health Authority (n=100, not all covered) • Too big for small area analysis • Solution needs to protect confidentiality

10. Allocating Small Area Data to HSE • Air quality index data supplied to NatCen researchers with standard ward-identifiers • Index quintile across all wards • Continuous variable would have allowed identification of ward of residence • These data allocated to HSE participants’ records based on ward of residence

11. Allocating Small Area Data to HSE • Returned participant IDs with allocated air quality data • Merged back into main HSE datasets from data archive • Each individual in data therefore attributed a categorical AQ index

12. Air Quality & Social Class HSE Households 1995-97  Lower social class households were more likely to be in ‘poor’ air quality wards

13. Respiratory Health Measures • 2 basic measures: • ‘Ever’ diagnosed asthma • Forced Expiratory Volume in 1 second (FEV1) • Age/height standardised FEV1 calculated to give a relative measure • Results are in terms of % difference from what would be expected given age and height.

14. Air Quality Men 16-79 Women 16-79 Social Class

15. Regression Models • Potential confounders: smoking, passive smoking, BMI, urban/rural, survey year, ever had asthma, inhaler use in previous 24 hours. • Social class and air quality index both reduced to binary indicators • Compare equivalent size groups: • poor/good air quality (60%/40%) • low/high social class (62%/38%)

16. Results Men Poor air quality -2.3% (95% CI -1.6,-3.0%) Low social class -2.7% (95% CI -2.1,-3.4%) • Effect estimates on age/height standardised FEV1 • Separate models for air quality/social class Women Poor air quality -1.9% (95% CI -1.3,-2.6%) Low social class -2.7% (95% CI -2.1,-3.3%) All adjusted for confounders

17. ‘Negative’ Results • Coefficients were virtually unchanged following reciprocal adjustment • Does not provide any evidence that air quality inequalities can explain social inequalities in respiratory function • Asthma – no relationship between air quality and asthma prevalence

18. Interaction • FEV1 analysis repeated stratified by social class • For men: • Social class I & II: FEV1 -1.4% (-0.3 to -2.4) • Social class III-V: FEV1-2.8% (-1.9 to -3.7) • p-value for interaction 0.04 • But • not seen for women • gender difference not hypothesised a priori

19. Conclusions • An ‘Inverse Air Law’ (apologies to Tudor Hart) • Possibility that effects on lung function are exacerbated for lower social class groups • But adjusting for air quality doesn’t attenuate social gradient • No evidence of effect on asthma prevalence

20. Limitations 1 • Assumes residence area pollution levels proportional to exposure • Assumes current exposure proportional to long term exposure • Ignores effects of other pollutants, esp. O3 • May be more significant in rural areas • But equity implications are different – non-local sources

21. Surveys & Health Geography • Large sample sizes and detailed information make survey data useful for health geography • Willingness of survey researchers to allow ‘anonymised access’ to small area geography permits research using survey data combined with a variety of geographical datasets

22. Limitations 2 • Limit to precision of geographical data that can be allocated • Categorical version of air quality index only • Mapping and geographical analysis (e.g. spatial statistics) still not possible without actual area identifiers.