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 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.

Recently I obtained a portion of address layer 2 for Southwark and surrounding boroughs in order to georeference my patient data by household. Currently I have a postcode match with over 99% match between known postcodes and patient reported postcodes. Being able to locate patients by their address, rather than their postcode will allow me to begin to think about patients in terms of ‘households’ rather than as I have been currently doing as (somewhat atomised) individuals. A recent experiment I conducted on choice characteristics of patients which was an evolution of my CASA working paper showed that seeing people as individuals was faulty logic in the context of primary care uptake. Reason tells me that families and people living in the same household are more likely to go to the same GP than different ones, due to factors that might include social network effects, thus in order to test this I need to address geocode the patient list data to a finer standard than postcodes- households.

Addresslayer2 is a rich Ordnance Survey dataset that pinpoints the location of houses, commercial buildings and features of the built environment (such as post boxes). I have an extract of the national dataset covering Lambeth, Lewisham and Southwark which constitutes over 400,000 points of interest which are given an explicit location in space.

The difficulty inherent is to match up the reported patient address with the record in addresslayer2 in order to derive a location for each patient. Patients that overlay each other in space can then be aggregated to a ‘household’ for that location. Often the addresses of patients living in the same house are subtly different so I cannot simply group the list by the addresses given, such a method also says little about the unque location of each household. Thus I have chosen to address geocode allt he aptients first and then derive household information from the spatial component of the data.

Initially I tried using pre-existing geocoding software in both ArcGIS and Manifold GIS, but neither was able to provide a satisfactory result. So i set about doing it myself using the Python Programming language.

One of the main things that has helped me so far is Fuzzy String Matching using Levenshtein Distance. It is rare that a recorded address will exactly match (i.e. match by equality, x == y) an address in addresslayer2. Often differences between two address strings amount to very little, such as capitalisation, abbreviations, punctuation etc. So a fuzzy match can be obtained by comparing the similarity of 2 strings.

The Levenshtein distance computes a value that represents the number of edits (by way of insertion, deletion or substitution of characters) required to turn one string into another. I am using the following algorithm written in python to calculate this value:

def levenshtein(s1, s2):
    if len(s1) < len(s2):
        return levenshtein(s2, s1)
    if not s1:
        return len(s2)

    previous_row = xrange(len(s2) + 1)
    for i, c1 in enumerate(s1):
        current_row = [i + 1]
        for j, c2 in enumerate(s2):
            insertions = previous_row[j - 1] + 1
            deletions = current_row[j] + 1
            substitutions = previous_row[j] + (c1 != c2)
            current_row.append(min(insertions, deletions, substitutions))
        previous_row = current_row

return previous_row[-1]

This algorithm is available, in this form and in many other languages, from wikibooks.

For 2 strings, calling the levenshtein function returns an integer which represents the number of edits required to make the 2 strings the same, however this value is meaningless without reference to the length of the string in the first place – 5 edits on a string 5 characters long suggests a completely different string, whereas 5 edits on a string 50 characters long suggests that 90% of the string was the same and that changes were actually minimal. Thus to get a relative ratio from the levenshtein distance I use the following code:

def ratio(s1,s2):
    edit = levenshtein(s1,s2)
    output = float(edit)/max(len(s1),len(s2))
    return output

This is a simple comparison that I found here and is implemented in a python Levenshtein library here. This library is written in c and hence should be faster than the python implementation, unfortunately I couldn’t get it to compile properly in windows 7. Works really well in Linux though!

Having established the levenshtein and ratio functions, it is simply a case of matching candidate strings with known address strings. I’m using a threshold for similarity so the string has to been similar to a specified degree before it can be considered a match.

Using this algorithm in conjunction with some other basic string operations, strip(), isdigit(), is alnum() and find()/replace() etc. gives me a match rate of around 90%. This is reasonable, but because the dataset of patients is very large it still leaves around 30,000 people unmatched. My next move will be to start subsetting addresses and matching elements of them, and checking which pieces are not matched. This is particularly important with students and people living in social housing where a lot of the address information given is specific to particular subsets of social housing estates, but given to me as a single string. Dissaggregating this data will allow me to match each bit with individual fields in addresslayer2 relevant to social housing.

In conjunction with my colleague Catherine Jones, soon to be of Portsmouth University, I am arranging a session at the coming years RGS-IBG 2010 annual conference. The session is jointly sponsored by the Geography of Health Research Group (GHRG) and the Quantitative Methods Research Group (QMRG) of the RGS. The details by way of a call for papers are:

The Spatial Dimensions of Health

Session Abstract

There is little doubt that geography and health are linked. Whether geography is considered in terms of the ‘geographies’ of individuals; communities and neighbourhoods; services and resources; or diseases- the linkage persists. In light of this, Gatrell and Elliot (2009) state ‘the subject of “health” is a rich source of material that bears study by the geographer’ (p.3). The importance of such study is highlighted by the steadfast presence of spatial disparities in health and healthcare nationally.

The intention of this session is to bring together research on the spatial dimensions of health, for the purpose of highlighting ongoing and nascent challenges within the diverse spectrum of health and health geography.

The session organisers invite proposals for papers that present empirical contributions within the spatial dimensions of health, ideally with focus on the UK. We welcome proposals that explore:

• The spatial dimensions of health inequalities and health behaviours
• Place, community and neighbourhood health and healthcare
• Spatial methods for developing health statistics
• Web 2.0 and health mapping

Reference
Gatrell, A. C. and Elliot, S. J. (2009) “Geographies of Health: An Introduction”, 2nd Edition, Wiley-Blackwell, Chicester

Key Words: Health, behaviour, inequality, quantitative, space.

Deadline for submitting abstracts is Monday 1^st February 2010

Please send abstracts up to a maximum of 250 words and proposed titles (clearly stating name, institution, and contact details) to Daniel Lewis (d.lewis@ucl.ac.uk) and/or Catherine Jones (kate-emma.jones@ucl.ac.uk).

Details of the full call for papers here.

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/2009/12/04/casa-working-paper-150-now-available/ http://danieljlewis.org/2009/12/04/casa-working-paper-150-now-available/#comments Fri, 04 Dec 2009 12:58:27 +0000 Daniel Lewis http://danieljlewis.org/?p=81

My first CASA working paper is now available. It is the result of a large amount of work I did for my upgrade from MPhil to PhD study at UCL. The topic is “Choice and the Composition of General Practice Patient Registers“. The abstract is as follows:

Choice of general practice (GP) in the National Health Service (NHS), the UKs universal healthcare service, is a core element in the current trajectory of NHS policy. This paper uses an accessibilitybased approach to investigate the pattern of patient choice that exists for GPs in the London Borough of Southwark. Using a spatial model of GP accessibility it is shown that particular population groups make non-accessibility based decisions when choosing a GP. These patterns are assessed by considering differences in the composition of GP patient registers between the current patient register, and a modelled patient register configured for optimal access to GPs. The patient population is classified in two ways for the purpose of this analysis: by geodemographic group, and by ethnicity. The paper considers choice in healthcare for intra-urban areas, focusing on the role of accessibility and equity.

The paper is accessible here.

I have had an abstract accepted for an oral presentation at PopFest 2009. The title of my talk is “Demographic Change in General Practice Patient Populations: a Study of Southwark, London.”

PopFest is a population studies conference that is specifically aimed at and organised by postgraduates. I hope it will be a good opportunity to discuss issues concerned with measuring and analysising changing populations with researchers outside of the arena of geography. I will be a great opportunity to engage with social policy and demography and draw out their opinions and assess how they interact with and relate to geographic study.

My abstract for the presentation is as follows:

The predominant literature in geographies of healthcare point to health as being subject to a ‘postcode lottery’- that is your access to, and quality of, care is primarily related to where you live- this has led to a greater reliance on geographic indicators of deprivation of patients by postcode than perhaps it ought to. Recent targeting techniques in health proved to be more effective when also accounting for wider contextual information including: the ethnicity composition, neighbourhood characteristics, patient demographics and a range of other social and economic population attributes shown to be linked to health.

This study focuses on General Practices and an analysis of registered patients, collected through a national but locally managed information system, ‘Exeter’, which records new patient registration in real time. The study integrates patient demographics from ‘Exeter’, a derived ethnicity classification from ONOMAP based on the registered patient data, as well as neighbourhood characteristics from the ONS Output Area Classification and CACI’s Health Acorn with health prevalence data reported at the GP level via the NHS Quality and Outcomes framework (QoF).

The research asserts that the patients themselves, particularly in Southwark where geographic access to healthcare is less of an issue, at times make specific choices related to which General Practice to use based on preferences for a certain type of doctor, service, or community. Thus healthcare service provision at the GP level should be understood both in terms of who and where, moving away from the strict imposition of a ‘postcode lottery’ to a more patient focused understanding.