Amongst other things, I’m beginning to tap into a data source I have acquired for my research known as Hospital Episode Statistics (HES). These are datasets which record the particulars of hospital service by patients. Generally they have a bit of a learning curve, and require the gathering of several additional datasets in order to make them useful. Having gathered all this data and put in all within a MySQL database I decided to conduct a basic analysis, using my study site of Southwark as a guinea pig. Essentially I wanted to known whether more people from Southwark were using hospitals of outpatient appointments than we would expect from national (England) figures. There are many reasons why any given area might be using health care services at a greater or lesser rate than other areas, but for the moment I simply wanted to see whether there was any interesting patterns.

In the HES data it is simple to calculate the total number of people using outpatient care, what is more complex is deriving an expected score from the national data. I went about it in the following way:

Firstly, I took the ONS experimental population projections from mid-2008 and calculated the number of people in each Southwark LSOA, and at the national (England) level, for each of the available age bands by men and women. The population projection age bands are quite coarse, giving totals for 5 population groups: 0-15, 16-29, 30-44, 45-64 (for men) or 45-59 (for women) and 65+ (for men) and 60+ for women. This isn’t ideal, but the age bands do roughly correlate with the different groups of mortality causes in the Grim Reaper’s road map (Shaw, Thomas, Smith and Dorling, 2008). Then I calculated the admission totals for all of the age-sex bands nationally (England), with this I could create a ratio of admissions against popualtion nationally. By applying this ratio to the Southwark LSOA population projects I could create an expected value for number of admissions per areas. Finally it is simply a case of dividing the observed admissions by the expected and multipling by 100 to get a score.

I mapped the results as follows, a score of 100 suggests that the area is not different from the national picture, whereas a value higher than 100 suggests that the area has more people using hospitals than we would expect and a value lower than 100 suggests the converse.

In the case of Southwark, the pattern seems to follow those that are often observed in my work on Southwark, in that the Bankside areas, and the southern part of the borough, in addition with the north-eastern former docklands area have levels of admissions that are equivilant too, or lower than what we would expect nationally, whereas the central areas have admission numbers higher than the national level.

I’ve been doing some more analysis on the Southwark GP patient register at the household level. After a fair amount of cleaning and interpretation I’ve arrived at the following distribution of households.

There are a number of interesting things to say about this data, not least in the section that I’ve marked ‘larger social groupings’ as it seems to suggest a possible migrant social network effect, as the larger household groupings tend to be of minority ethnic groups, including Nigerians and other Africans, Hispanics and South-East Asians who are perhaps using cross-country social ties as help in getting established when first arriving in the UK. However, visually the shape of the distribution of household occupancy is very distinctive, and actually is very close to an exponential. Here I’ve taken the log of frequency of occurence and plotted the best-fit line through the plot:

This linear trend means that the model log(y) = -0.1635x + 4.602 is a good predictor of the number of Households we can expect to exist in Southwark for a given value of x, or occupancy.

It is not entirely clear however why this situation is the case. Firstly, it may just be an artifact of the data, either of the matching process that has occured between the patient register and OS AddressLayer2, the way that GPs encode patient addresses in the first place, or the fact that the patient register is only a sample of the total population of Southwark, i.e. those people who register with a doctor. Secondly, it may simply be a reflection of the structure of the built environment in Southwark – i.e. what kind of housing is actually available. However, the distribution is also subject to the choices of individuals or groups.

Currently, I am in the process of dissagregating the above characteristics and looking at trends by different population groups.

Since coming to government, new Conservative Health Secretary Andrew Lansley has sought to fulfil the pledge he made to put an end to local restructurings of NHS service delivery by authorities higher up the NHS hierarchy. Ostensibly he believes that local decision-making will have a better overall effect on the quality of outcomes for patients and hence lead to a better health service. Specifically he wants to provide GPs with an opportunity to work with community leaders and their local authorities to steer local services. The core elements actually do not differ greatly from the outgoing Labour policies, particularly with respect to patient choice; however I will argue that there is a clear danger in engaging to too great an extent with a purely ‘local’ approach, in general there seems to be something of a misconception in Government, particularly in the provision of local services (i.e. schools), that local approaches are somehow ‘better’.

Firstly, let us consider something that the Government seems to do without fail, something that I, as a Geographer, find to be a grave sin of omission. That is the apparently indiscriminate use of spatial qualifiers without so much of an explanation as to their meaning. The use of ‘local’ and ‘community’ are spectacularly misleading without qualification, and yet they are often used because people seems to think they understand what is meant by them – everyone considers themselves part of a community, and local to a service – but will these personal feelings about their socio-spatial connections actually translate to the ability to input on healthcare decision making? My investigation of access and registeration of patients to GPs in Southwark has shown that a) primary care is a very location based service and without fail each doctor exhibits a characteristic distance decay function that describes the pattern of registration with a GP suggest to some socio-economic criteria, but also that b) patients overlap to a large extent in a densely-populated urban context, the suggestion being that activity-spaces (i.e. retail areas, workplace and schools) has a distorting effect on patterns of registration for some people. To this end I suggest that a ‘community’ can be defined independently for individual GPs based upon the patterns of patient uptake unique to that service, although there may be some strong correlations with residential, workplace, educational etc. communities that overlap it (of course for some GPs the profile of its registered community may be greatly divergent from its observed local (defined by proximity to a GP) community). The following map is an example of this kind of complexity:

Here it is clear that any definition of locality or community based upon an arbitrary areal basis yields groups of people who could be registered to as many as 29 different Southwark GPs in only a very small area. This is in fact a very good, simple, illustration of patient choice in action. There are a lot of questions to ask Mr Lansley about how he views ‘local’ or ‘community’, and whether he is willing to enshrine that definiton in policy before we actually consent to doing anything with provision of services.

Further still, I have claimed that GPs are very much location based services – they are, over a certain distance (in Southwark this is about 6 -10km) no one is registered with a GP, choosing instead a closer service. In many ways this was constrained by the pre-existing system of ‘catchment areas’, however these were set to be removed by the end of the year in the quest for patient choice, thus the potential for registration is opened up to people using doctors near their place of work (for instance) rather than than near their home, thus should these people have a say in provison of services in the area within which they do not live – they are part of the GP’s ‘community’ but not of the residential one. A good illustration of this  is actually the polyclinic system – Southwark is geared up to introduce 3 polyclinics – one which already exists as a large GP-led health centre in the centr eof the borough, and two in the north connected to hospitals, the biggest difficulty faced at the moment is in estimating the daytime population (i.e. transient workforce) of the Southbank in order to account for likely polyclinic usage – a huge number of people who do not live in Southwark but will likely have some part of their healthcare provided for by Southwark PCT.

It is also unclear what Mr Lansley refers to when he talks about ‘top-down’: is it the Strategic health authorities and the DoH itself? It cannot be the PCTs as Mr Lansley claims that the new criteria will have the support of ‘GP commissioners’ and it is the PCTs that actually do the commissioning, further the idea of GPs working with local authorities is largely the same of GPs working with PCTs now, as PCTs and LAs are generally coterminous.

Whilst it is pleasing to see a politician quoting the need for an evidence based appraoch to restructuring, it is unclear what evidence he might base GP quality on, the current payment method (QoF) is based on GP reporting of pre-specified target outcomes to a centralised authority, surely GPs will simply follow these directives in order to bring in as much money as possible. Indeed, these stats are strong recommended not to be used as measures of GP quality as they are by-and-large patchy in what they cover, and include little demographic data. Indeed, had the previous government not already cut the NHS IT initiative that would have made reporting of outcomes actually feasible nationally, the new government would have no doubt cut it anyway.

The final worry I have is one of equity, something upon which the NHS is founded – the provision of a fair service contingent on those that need it, that is free at point of service. Surely such an atomistic approach to healthcare provision as Mr Lansley seems to specify, is liable to deepen the perceived ‘social gradient’ in health care, as without a careful (top-down) hand, the GPs and communities best-equiped to play an active role in orchestrating GP services will get increasingly better provision: most likely to be the wealthier areas of the country. There needs to be at least some form of national accountability for a national health service.

Geodemographic classifications are funny things, they report a view of the world which suggests that areas can be split into groups within which all areas share the same or similar characteristics. This is not an inherently bad thing, for large scale analyses it can be a very useful way of simplifying a diverse array of variables into something that characterises the underlying patterns in the distribution of data. However, for smaller scale analyses I am increasingly finding that non-bespoke geodemographics are limited, I attempted to demonstrate this on a national scale by looking at the entropy scores for each OA in the UK with respect to distance from all supergroup cluster centres (here). Recently, Pete Fischer presented some very clever work in this vein at the recent GISRUK 2010 conference, he used fuzzy classification strategies to account for the likelihood that each OA does not fit exactly into any particular grouping, and that different OAs fit differently into the same group. Aidan Slingsby at City also showed this very nicely visually with his ‘OAC Explorer’. Procedings from the conference can be found here.

With this in mind, I wanted to test the variability of the data in Southwark, my study site, with respect to OAC. OAC paints a very flat picture of the population of Southwark as shown below, and had led to me using LOAC, a London specific variant of OAC created by Jacob Petersen a previous research student at UCL and available as a layer on the London Profiler. Using OAC, Southwark is primarily ‘multicultural’, there is more variability in the LOAC classification however, as is evident.

OAC and LOAC classifications for Southwark.

Inspired by some of James Cheshire’s great work with surnames I employed a method called “Multi Dimensional Scaling” or MDS. Multi Dimensional Scaling is great for exploring similarities and dissimilaries in data, rather than clustering data as in the creation of OAC, it reorders it so that similar datapoints have similar values. One of it’s great advantages is that it allows for the scaling of data that has many dimensions, such as the 41 OAC variables, into fewer dimensions representative of those 41, these can subsequently be visualised. Traditional approaches in geography have used MDS to scale many dimensions into 2, using these 2 to adjust spatial coordinates to ‘blow apart’ maps, reordering places that are similar together and dissimilar further apart. Such representations challenge the validity of Tobler’s 1st law – near things are more similar than distant things. In this case however I don’t want to blow up Southwark, so I follow Cheshire’s lead in using the scaling to specify a colour for each area in which similar colours indicate similar areas in terms of OAC variables and different colours represent different areas. I experimented with both greyscale and RGB colour scales for this representation. Firstly though, a note on how I got there:

1. Download the OAC variables from CASWEB, using the ‘recipe’ specified by Vickers et al (2005).
2. Standardise all the variables – I used Z-score without really checking for normality, although in reality this would be preferable – Vickers suggests some other methods of standardisation.
3. Compute a distance matrix for the MDS. This means calculating the similarity of each pair of OAs, given n OAs this thus leads to an n x n size matrix, a size that can rapidly become unmanageable beyond local scales. I used ‘canberra distance’ (an arbitrary choice) to compute the matrix which is given by: where i relates to the value of the first object in a pair and j the second, and k denotes the variable in question.
4. This matrix is then input into an MDS solver, as a python fan I used the fantastic code written using Numpy by Jeremy Stober, although I added to it to do all the standardisation, distance matrix creation etc as part of a logical process.
5. Specifying the number of output dimensions (I used 1 and 3) allows you to reduce the large distance matrix into a vector (1d) or matrix (3d) of values, these can then be scaled between 0 and 255 to be converted into digital numbers for visual display. Thanks to James Cheshire for the ArcGIS script to assign RGB values in Arc.

The results I got from this preliminary exploration were as follows:

MDS Scaling of OAC variables into Greyscale Representation.

This is a very interesting way of looking at the OAC data, as the comfortable uniformity of the seven classes has been lost, instead we can see trends and similarities, but also a fair amount of discountinuity and noise. In the black and white representation a spectrum is presented in which very dark and very light colours are the most dissimilar slowly converging through the spectrum. The resultant mapping clearly displays areas of similarity, the more affluent southern tip of Southwark, the Southbank region in the north of the borough, and the former docklands in the north-west. Counterpoint to these areas is the middle band of Southwark represented by darker hues, and roughly aligned with known areas of deprivation characterised by high-levels of social housing, higher levels of non-white residents, lower level of educational attainment, poorer health etc. What is clear though is that the picture is not uniform as suggested by OAC, and that there exist notable pockets of difference, possibly interpretable as gentrification, particularly around parks. There is also evidence for some fairly notable discontinuities in demographic structure which isn’t immediately obvious in the OAC classification.

I also mapped an MDS output for 3 dimensions onto an RGB colour scale, as below:

MDS Scaling of OAC variables into Colour Representation.

The colour representation should be a more nuanced reading of the similarities and differences, although it is immediately more challenging to interpret. One of the interesting factors is how the southern area of Southern, most characterised by a blue/purple colour has now been distanced from the southbank and former docklands areas, suggesting they are more distinguishably different than previously. The previously dark area is now a pinkish hue, again suggesting a uniformity in that area, however it is flecked with a variety of colours suggesting that deviations in demographics amongst the areas of high deprivation are not similar to each other, but distinct enclaves each with their own specific character.

This constituted a preliminary study, time permitting I will continue to investigate interesting methods such as this. It is however a computationally intensive process, and a treatment of, for example the UK in this manner is out of the question. Nevertheless, I may update it at different scale in the future.

Acknowledgment: Boundaries Crown Copyright 2010 Ordnance Survey. A UKBorders/JISC Supplied Service. Data from CASWeb.

One of the best things about having address-geocoded an entire popualtion dataset it that you can finally get away from non-uniform areal representation (OAs, Postcodes) and present something that is uniformly disaggregate. Academics such as David Martin have long expounded the value of gridded representation of population data as it is regular and hence spatial unbiased. In fact the current work his group are doing is really interesting stuff, looking at daytime (as opposed to residential) population.

Anyhow, using the address-geocoded patient register for Southwark I was able to create a population density visualisation on a 100m x 100m grid that still preserves patient anonymity to an appropriate level. Of course there are some issues with Patient Registers, notably that they are only complete for people that register with a GP. Nonetheless, they provides a uniquely fine grain view of population in Southwark without resorting to the statistical uncertaintyof a smoothing surface-based density estimation, or an irregular, space-filling administrative/postal areal unit solution.

My colleague James Cheshire’s research deals with understanding and classifying spatial patterns in surnames. He has been able to show, through various techniques, that there exists in the UK a regional geography of surnames. This in mind, I thought I’d interogate my database of NHS patient registrations for Southwark and see what was going on in surname terms there. This first table shows the top 20 most popular surnames in Southwark, ranked by occurance.

Figure 1: Top 20 Surnames in Southwark, by occurance.

Unsurprisingly perhaps, the top places are dominated by surnames native to the UK, classically Smith, Williams, Jones etc. However, in line with Southwark’s reputation as a diverse borough and in light of it’s high inmigration figures, it is also clear that of these top 20 surnames some of them would be connected to inmigrant names: Kamara, Ahmed, Ali, Patel and Khan are all surnames that are increasingly associated with a previous period of migration to the UK. Interestingly the Vietnamese population is very small, less than 1% of the population of Southwark, but around 23% of these have the surname ‘Nguyen’. The ethnicity of the surnames is derived from Onomap.

The frequency distribution of Southwark surnames looks like this:

Figure 2: Surname Frequency Distribution for Southwark, 2009

Note the characteristic long tail, there are a huge number of unique, or almost unique surnames, and considerably fewer surnames which are possessed by a large number of people. Such a distribution seems to obey a power law of some sort.

We can dig deeper into this phenomenon by looking at the number of surnames that comprise a given percentage of the population:

Figure 3: Surnames comprising given percentages of the Southwark Population

As we can see from the above figure, only 56 names account for 10% of the Southwark Population, but that in total there are 88,124 distinct surnames in Southwark. Again there is a characteristic decay to the curve.

Finally, let us consider just the charactersitics of the long-tail of the distribution:

Figure 4: Focus on the long-tail - percentage population for given surname frequencies.

From figure 4 it is clear that almost 25% of the Southwark population have a surname that is share by fewer that 11 people, indeed just over 16% of the Southwark population have a surname unique to the Southwark patient register. The shape of the curve in figure 4 demonstrate the effect of the long tail seen in figure 2.

For more information on surnames research check out James Cheshire’s blog, working paper or Pablo Mateos’ working paper.

]]> http://danieljlewis.org/2010/03/03/analysis-of-surnames-from-southwark-patient-register/feed/ 0 http://danieljlewis.org/2010/02/12/southwark-households-a-preliminary/ http://danieljlewis.org/2010/02/12/southwark-households-a-preliminary/#comments Fri, 12 Feb 2010 19:28:55 +0000 Daniel Lewis http://danieljlewis.org/?p=186

I’ve spent a chunk of time recently address geocoding the Southwark PCT patient register to Ordnance Survey Address Layer 2 data. What this means is that I can start identifying and (later) classifying households, this will allow me to ask questions about how different households approach healthcare. More broadly it allows me an insight into the demographic character of Southwark.

The data actually extends past the Southwark boundary as people in Lambeth, Lewisham, Bromley and Croyden do also to some extent use Southwark primary healthcare services (GPs) this means that although Southwark’s population is only c.300,000 the datset I’m using is for just over 340,000 people. There is some uncertainty in the data naturally, this results from the two datasets used; on the one hand addresses recorded in the Southwark patient register are not all necessarily complete, for example there is sometimes a failure to record which particular subdivision of a house someone lives in, or which flat in a larger block of social housing. On the other hand the AddressLayer2 data, although very rich, is not necessarily complete, this could be due to the prescence of unacknowledged subdivisions in residential housing, and although most social housing estates seem well documented, some commercial developments are not necessarily registered beyond the building level. Similarly, there are a number of instances of social institutions, such as the Salvation Army and St. Mungos, or marinas and dormitories having a single registered address for a high number of residents. This may have the effect of skewing the data slightly. With this in mind I created the following graph from the dataset of Number of households against number of inhabitants per household:

This shows that there is still a major trend for single-person households, but equally that around a quarter of all households are co-habited. The long tail in the graph (which i have truncated here) is caused by a few special cases, some examples of which are acknowledged in the previous paragraph. The average household size of 3.10 is itself higher than the UK average household sizes reported after the 2001 census which was 2.36; at the time the borough of Newham in East London had the highest household occupancy rate at 2.64. Of course there are any number of reasons why these data are not comparable, to start with the census took place 8 years before the Southwark dataset was created, similarly the uncertainty in the Southwark dataset is higher as it was not created with the primary purpose that it be able to successfully locate all patients as more often than not patients go to the Doctor and not vice-versa, whereas the census is distributed at a household level to each individual. The Southwark dataset does also include particularly tranisient communities which are missed by the census, such as the homeless who don’t have a fixed address (and hence may be using shelter or hostel addresses) but still require medical treatment at times.

Nevertheless, an interesting first look. The next steps will involve evaluating and validating the dataset to the best of my ability and then moving on to look at ways of examining and classifying household structure.

In a nod to my colleague James Cheshire‘s fascination with cartograms, I’ve created one from the Spatial Equity data I used in the previous post. A cartogram is a map in which the value of each spatial unit’s area is replaced with a thematic mapping value; thus the mapped representation is warped and distorted to reflect the new thematic variable. Danny Dorling has been particularly active in this field, writing up work on Dorling Cartograms in the CATMOG series, and laterly using the Gastner Newman method to create cartograms for his interesting work in the book: The Atlas of the Real World.

Figure 1: Cartogram of Spatial Equity by Gravity Potential Model

It is clear from figure 1 that the south of Southwark suffers in terms of accessibility to a Southwark GP, whereas the central areas, characterised by a higher population density and more social housing have greater accessibility to healthcare services.

Whilst I’m not sure whether such a representation is entirely appropriate in this context, it does tell an interesting story- the same as the previous post but in a different manner, using the size of areas as well.

NB the map is subject to Crown Copyright 2009 Ordnance Survey. An UKBorders/JISC supplied service.

A little while ago I created some basic measures of spatial equity for my main study site in Southwark, London. Spatial equity in this case relates to a measure of the ‘fairness’ of spatial distribution of services. The NHS as a public institution has a requirement in its universal terms of service to provide a fair service to all.

The following maps aim to show how different areas in Southwark, in this case output areas (OAs), have different characterisitics in terms of: the level of primary care provision available, and the distance to centres of primary healthcare. Following Truelove (1993), Talen and Anselin (1998) and Ricketts et al (1994) the first 3 maps use a buffer-approach to spatial equity, whilst the final shows a gravity model approach.

Figure 1: Spatial equity measured with a 500m buffer around GPs

This first map (figure 1) demonstrates that large parts of Southwark do not have access to healthcare services within 500 metres (euclidian distance), whereas the best served areas have access to more than one GP surgery and as many as 24 individual doctors.

Figure 2: Spatial equity measured with a 750m buffer around GPs

Figure two demonstrates that with a 750m buffer most areas are served, although there are still unserved areas, particularly in the south of the borough. The most well-served areas not have access to as many as 48 doctors.

Figure 3: Spatial equity measured with a 1000m buffer around GPs

A 1km buffer still shows areas of Southwark which are unserved, particularly in the south. My recent working paper features a map which confirms that residents of these areas are less likely to use Southwark services than those in the more core areas in the centre of the borough.

Figure 4: Spatial Equity measured by Log of the Gravity Potential

This final map uses a distance decay function rather than a buffer to represent spatial equity and is specified thusly (Talen and Anselin, 1998 p.600):

where Sj is the size of a facility (measured by number of doctors, operating capacity etc.) at location j and d is a distance decay factor between area i and facility j with a friction parameter alpha, here set to 2.

The result is not hugely different to the buffered approaches, giving a similar account of affairs. It is notable that in all cases the spatial equity correlates with provision of social housing. In the UK context Southwark is a special case, being amongst the most deprived Local authorities by IMD07 rank, in which fair access to services is skewed towards the needs of the more deprived, whether or not uptake, or ability to uptake actually reflects this is another question.

References

]]> http://danieljlewis.org/2009/12/08/basic-equity-maps-for-southwark/feed/ 0 http://danieljlewis.org/2009/09/22/gp-catchment-areas-at-an-end/ http://danieljlewis.org/2009/09/22/gp-catchment-areas-at-an-end/#comments Tue, 22 Sep 2009 11:46:06 +0000 Daniel Lewis http://danieljlewis.org/?p=61

Recent reports (for instance here and here) have indicated that the government is prepared to scrap the notion of catchment areas for General Practices in order to make it easier for patients to choose an appropriate doctor. The new NHS constitution allows patients increased freedom to choose, however the scrapping of catchment areas is an interesting proposition.

My work focuses on Southwark, London, so I can only really comment on the situation there, which as a densely populated urban area is likely to have a different set of issues than suburban or rural locations, however there are still a number of points to be made here.

Firstly, the idea is to promote ‘choice’ however the right to choose is captured primarily within a geographical context, i.e. proximity to a GP, or desire to use a GP that is close to work or school. However, research has shown that the majority of people (50% plus) do not actually care about their GP beyond it being ‘local’, most claim that they choose to use their closest GP. Those that do choose a GP still value accessibility, but more important factors tend to be related to the doctor’s ethnicity, or spoken language, and to the environment of the GP such as the neighbourhood it is within. Only in America, or Europe is the quality of service important; in the UK important metrics exist for assessing school and hospital quality, however an equivilent in the UK seems to be lacking. Prevalence statistics are available, but these are difficult for the layman to interpret, and they are given without context on the NHS choices website.

The main argument behind removing GP catchment areas seems to be that a) it will increase choice, and b) chocie is good. However, I offer some reasons why this might not be true.

• Catchment areas are primarily used to define the area within which a doctor is willing to make house calls. Thus it is not within a doctors interest to accept patients from too far away should this situtation arise.
• Often the catchment areas defined exogenously by a GP are much larger than the de facto catchment area defined endogenously by the pattern of patient registration (certainly the case in Southwark)
• The NHS pays doctors based on the size of their patient, and has a pretty weak structure for handling list data. A move such as this will make it much more difficult to handle registrations particularly if people want to register for convenience near where they work and then find themselves using a more local doctor on an ‘emergency’ basis when they have got something more than a cold.
• This may well create a hierarchical system of GPs based upon wealth, or some other measure of status. This may be evidenced by pronounced post-residential sorting, which will likely effect the performance of GPs in poor areas which can only attract chronically ill patients.
• How will choice be regulated, will GPs be able to close lists at will, or refuse ‘undesirables’?

My suspicion is that even if such a move went ahead it would be unlikely to create a massive upheaval, access to primary care is a very geographically limited problem as no one wants to travel great distances when ill just to go to the doctor. In many ways this approach seems to somewhat undermine the NHS rhetoric towards community health, in a situation in which everyone is choosing their GP you may find that GPs within a community do not actually reflect the needs of that community, or are not representative of the issues faced by that community because the patient register is itself a completely different community from outside of the immediate locality of the GP.

Finally, much of the agenda surrounding choice relies on the primary care commissioners within each Priamry Care Trust to ‘get it right’. I’m not confident that every PCT will be able to manage such a system, particularly if PCT boundaries become more porous and inequities between GPs develop.