Outline

1. GIS and HealthMartin Charlton1Ronan Foley2Dennis Pringle21National Centre for Geocomputation2Department of GeographyNUI Maynooth

2. Outline • Preliminary observations on GIS and health • Examples of GIS in Health: research and practice • Mapping people not places

3. Patterns in Health Data • Three main areas: • Mapping of disease incidence • Relative risk estimation • Identification of disease clustering • Surveillance • Ecological analysis • Relationship between incidence and covariates (Lawson et al, 2003)

4. Health Data • Typically patient data is available in two forms • Individual – perhaps anonymised to postcode/zip code level • Aggregate – counts are available for a set of zones • The nature of the data affects the nature of the analysis

5. Geographic Information Systems • GIS is often confused with mapping • A range of activities which includes inter alia mapping and analysis • Will typically involve several datasets • May also involve several different pieces of software

6. Mapping: EpiINFO • This is a rather simple analysis and mapping program • It can be downloaded from the internet • It doesn’t provide much in the way of spatial data manipulation… • … nor are its analytical capabilities very advanced • It is easy to use

8. Spatial Data Manipulation • GIS isn’t merely concerned with data display or query • It’s useful in bringing together different spatial datasets in the first stages of an analysis • This may well require a range of different geometrical and database operations

9. Analysis with Spatial Data • Statistical analysis usually involves some separate software. • Spatial data have some properties which mean that SPSS isn’t always appropriate. • There is now a range of specialist software – much of it available for download

10. GIS may be ideal for data manipulation, display and query, but it’s not always ideal for analysis – in epidemiology a variety of analytical software is available … WinBUGS, GeoBUGS (MRC Biostatistics Unit, Cambridge)

11. Software to implement particular methods – for example scan statistics - can be downloaded quickly and easily from the Internet. Kulldorf’s SaTScan system implements a range of different scan statistical models

12. GIS in Health in Practice • Facility location – dialysis units • Facility location – obs & gyn • Data integration – schizophrenia in Cavan • Data manipulation and display – GMS card holders in Maynooth

13. 1: Provision of Dialysis Units • There are 5 dialysis units for renal patients in a region with a population of about 3,000,000 • Major population centre has two units about 5 miles apart • Where would you locate an extra one? • What would happen if one was closed?

14. Location-allocation • Location-allocation problems deal with optimum location of facilities • Attempt to minimise total weighted travel time/distance from users to facilities • What data is needed?

15. Data requirements • Digitised road network with classification of road types • Mean travel speeds on each type for urban/non-urban roads • Extents of urban areas • Anonymised postcoded patient records • Postcode->grid reference lookup • Locations of hospitals in region (both with and without dialysis units)

16. GIS processing • Data integration • Determine which segments of road network are urban • Determine travel times using road type urban/non-urban speeds • Tally numbers of patients at each node in the road network • Find nearest nodes in network to each hospital • Code hospitals as ‘existing’ or ‘potential’ units

17. Modelling/Analysis • Carried out using standard GIS functions • Convenient ‘front end’ written using system’s macro language (save a lot of typing) • Explore a range of alternatives

19. Hospital with dialysis unit Study region showing hospital locations and urban areas

20. Patients requiring dialysis (tallied to road nodes)

21. Classified road network – includes roads outside study area

22. Some alternative scenarios: • Optimal allocation of patients • Provide 1 extra unit • Provide 2 extra units • Close 1 unit in city and reallocate its patients

23. Role of GIS • Used to integrate data • Used to process data • Used to model and analyse • Used to generate alternative scenarios • Used to display the results

24. 2: Facility Location • Two hospitals both provided facilities for obstetrics, gynaecology, paediatrics and a special care baby unit. • Was there a case for rationalisation and improving the facilities at one hospital only? • Would closure disadvantage any patients?

25. Data • Locations of hospitals in study region • Anonymised postcoded finished consultant episodes for each of the four specialities at each hospital • Classified road network • Speeds associated with each road type

26. Data preparation • Postcoded records allocated grid references using Central Postcode Directory (POSTZON) • Vector data converted to raster • Patient/specialty/hospital counts produced for each raster • Passage times computed for each raster cell based on road classification and speed estimates

27. Roads, urban areas, patient locations, hospitals in study area

28. Processing • Travel time surfaces computed for each hospital • Each raster cell contains time required to reach hospital in minutes • Assumed walking speed in ‘non road’ areas

29. Travel time surface for hospital S – darker = shorter time

30. Travel time surface for hospital T

31. Time differences • If we subtract the travel time surfaces areas nearer one hospital will have positive values for the difference, and areas nearer the other will have negative ones • Boundary between positive and negative values is boundary of catchment for each hospital. • If you’re on the boundary (difference is zero), it doesn’t matter which hospital you choose

32. Roads, patient locations, hospital catchments

33. Indifference • If we identify those rasters where the time difference (ignoring the sign) is, say, 5 – people in those areas are in the 5 minute ‘indifference’ zone. • Patients might not be worried about a 5 minute increase in their travel time, they might be worried about 30 minutes • Indifference zones can be computed quite easily

34. 15-minute indifference zone between the two hospitals

35. Mean access time • For each cell in the raster we can compute the product of the number of patients and the time required to reach each hospital • Dividing this by the number of patients gives a patient weighted average time for each hospital

36. Average travel times for the various specialties. There isn’t much to chose between specialities with the exception of paediatric episodes.

37. Role of GIS • Used to gather data • Used to integrate data • Used to process data • Used to model data • Used to display results • All carried out using raster operations

38. 3: Schizophrenia in Cavan • In 1996 Pringle, Waddington and Youssef reported on evidence for the causes of schizophrenia • Ecological analysis of variation among EDs • Cases varied from 0-11 per ED • Populations varied from 250-2750 • Poisson probabilities suggested that one area had an unusually high number of cases – these can be mapped

39. Male cases superimposed on plot of prevalence by ED Male cases superimposed on rectified Landsat image with ED boundaries

40. 4: GMS card holders: Maynooth • Data on GMS – Medical Card holders drawn under strict data protection protocols from DOHC • Anonymised address lists data matched with GeoDirectory • Address matching rate of around 77% in Maynooth • Interesting range of results around average for the whole ED

41. ED – single value 14.6 % (national average around 30%) • Small Area – Range from 2% to 70% - noticeable internal variation and • good indicator of elderly or poor populations

42. Strategic value in comparing datasets • Important in policy analysis Mapping invites comparison

43. Role of GIS • Data integration • Tally of cases in small areas requires data on locations of cases and small area boundaries • ‘spatial join’ places small area codes on the case records • Counting is a standard database operation • Data display

44. Finally • GIS encourages you to think about space • In a ‘conventional’ map of Ireland areas (EDs, Counties…) are shown in proportion to their physical size • There are alternative ways of displaying maps…

45. In a cartogram the areas are drawn proportional to their populations – this is a “density-equalised” map projection A map of the boundaries of counties and electoral divisions – the areas are drawn proportional to their physical size

46. By comparison, the more deprived western area of Dublin, northern area of Cork and south east of Limerick are clearly apparent in the cartogram. Haase’s deprivation index suggests that the most deprived populations are in peripheral rural areas in the north west.

47. Redrawing the data on the cartogram reveals wide variation in the SMR, and also uncovers the urban dimension which is hidden in the conventional display Patient data matched to Electoral Divisions – hospital admissions for psychiatric problems – the patchwork effect does not suggest any pattern

48. The cartogram reveals the urban nature of the problem – those areas which are more deprived are also those with higher levels of illness/disability Residents aged 15+ who are unable to work through illness or disability: the map pattern suggests that might be a largely rural phenomenon

49. The roads, rail, and urban areas data have been transformed into the same space as the cartogram. Dublin and Cork seem poorly provided with roads… Other spatial information can be transformed to the space of the density equalised projection. Here is the familiar map of main roads and towns.

50. Using a cartogram we see that the incidence of lung cancer is randomly spatially distributed Lung cancer in males in New York State [Gastner & Newman: 2004] Plotting locations of lung cancer cases, we obtain a map which follows the underlying population distribution – there appear to be “clusters”