The Power of Big Data in Public Health: UK Small Area Health Statistics Unit (SAHSU)

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1 The Power of Big Data in Public Health: UK Small Area Health Statistics Unit (SAHSU) Dr Anna Hansell Assistant Director, Small Area Health Statistics Unit Reader in Environmental Epidemiology, School of Public Health, Imperial College London Hon. Consultant, Imperial College Healthcare NHS Trust BIA UK Bioscience Forum 15 October 2015

2 Small Area Health Statistics Unit (SAHSU) Established in 1987 as a recommendation of the Black enquiry into the incidence of leukaemia and lymphoma in children and young adults near the Windscale/ Sellafield nuclear power plant

3 SAHSU Scope of Work To develop and maintain comprehensive health and environmental databases To carry out substantive research studies on environment and health To respond rapidly to ad hoc queries about unusual clusters of disease To develop small-area statistical methodology To act as a centre of expertise and disseminate information of spatial epidemiology The work of the UK Small Area Health Statistics Unit is funded by Public Health England as part of the MRC-PHE Centre for Environment and Health, funded also by the UK Medical Research Council

4 Small Area Health Statistics Unit (SAHSU) 300 million health records (incl. deaths, cancers, births, stillbirths, hospital admissions) 10 million records added each year Approvals from National Research Ethics Service (NRES) and Health Research Authority (Section 251, NHS Act 2006)

5 Geocoding Address point Postcode centroid Major road 0 100m SAHSU currently geocodes ~200,000 addresses/year About 2 seconds per address (4.6 days processing time) Bespoke software is deterministic (i.e. rules based) to avoid problems with fuzzy matching algorithms SAHSU maintains a postcode library as postcodes are used for mail delivery and can change over time

6 Noise: Cardiovascular disease near Heathrow 2001 aircraft daytime (above) and night-time noise contours Hansell et al BMJ m population 189,226 CVD admissions

7 Associations between hospital admissions and aircraft noise Stroke Coronary heart disease Cardiovascular disease Model 1: age, sex; 2 + ethnicity, lung cancer, deprivation

8 EMFs: Mobile phone base stations and childhood cancer: case-control study Tilt a. Cross-section Power density Height Beamwidth (vertical) Distance (metres) Power density b. Plan Beamwidth (horizontal) Centreline of beam (distance) aussian exposure model c. Visibility Briggs et al Sci Tot Environ 2012 Great Britain, , 4 controls per case Elliott et al BMJ 2010

9 EMFs: Adult cancers near high voltage overhead power lines in England and Wales: Corona ions released from high voltage overhead power lines (600 m) and mouth/respiratory cancers - Electric fields (25 m) and nonmelanoma skin cancers Elliott P et al Epidemiology 2013, 24:

10 EMFs: Adult cancers near high voltage overhead power lines in England and Wales: Magnetic fields (1000 m) and selected cancers: 7823 leukaemia, 6781 brain/cns, 9153 malignant melanoma, 29,202 female breast cancer cases and 79,507 age-sex matched cancer controls Elliott P et al Epidemiology 2013, 24:

11 Linking: exposure data to NHS Big Data Assigning exposure values (e.g. air pollution concentrations) based on residential location using existing data Calculating traffic indicators TRAFFIC IN LONDON STUDY BIRTHWEIGHT 187GB OF AIR POLLUTION DATA FOR LONDON ADDRESSES Environmental modelling for individual residential locations

12 Dynamic exposure modelling in GIS: improving assessment of population exposure to air pollutants Example routes modelled from 1 home-oa to multiple work-oas Work - OAs Dynamic exposure modelling using time spent at home, work, and journeys to and from work. Home - OA 838,213 journeys modelled between home and work locations (Output Areas- OAs) in Greater London Differences in long-term, time-weighted exposures: Home Home/Work Home/Work/Travel NO 2 (µg/m 3 ) PM 10 (µg/m 3 )

13 Statistical tools: combine area and individual data Area level data (NHS outcome data, exposure data, some confounders) Small Area 1 Small Area i Small Area K Individual level data Individual 1? Individual 1 (e.g. cohort - additional confounders) How to go from the individual level of the cohort/survey to the area level needed for the small area analysis? Multilevel framework Takes into account within area variability Can account for correlation between confounders Cohort/survey has limited coverage: missing data issues Imputing several variables is methodologically challenging and computational intensive use propensity score

14 Summary SAHSU uses NHS Big Data to provide a powerful means of investigating environment and health risks NHS Big Data offer comprehensive coverage PLUS permit high resolution analyses NHS Big Data are clean, but may still require some processing Linking of Big Data with other datasets offers great opportunities for research. SAHSU is developing statistical methodology to help support this.