Processes and techniques

A num ber of processes will be used for the developm ent of geodem ographic health. The m ore detailed ones will be handled specifically in the context of each case study, b u t the generic processes crossing all the cases will be briefly docum ent in this section. The key processes and developm ent fram ew ork are outlined in Figure 16, which shows the progression from raw in p u t data through data processing to the final o u tp u t and results dissem ination;

w hereby the results are communicated and provided for everyday use w ithin the public health departm ent of Cam den PCT.

The fram ew ork outlined in Figure 16 is comprised of tw o distinct parts. The first p art corresponds to processes associated w ith data acquisition,

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processing and analysis. The com ponent processes of this p art are responsible for transform ing individual facts and data into inform ation w hich in the second p art on the m odel can be consolidated into evidence and know ledge.

H ere the conceptual data m odel (Figure 15) is turned into a local spatial data infrastructure for Cam den, which provides the vehicle for inform ing public health spatial decision-making. This fram ew ork is in line w ith the suggested structure of a su p p o rt infrastructure for decision-m aking in Longley et al.

2001 (page 7).

Following the developm ent of the local spatial data infrastructure (data acquisition) the different datasets can be m anipulated, synthesised and

analysed in a consistent m anner to produce health specific indicators relevant to the organising fram ew ork (Figure 13). The results of w hich provide

inform ation to public health professionals. This section of the fram ew ork is addressed in C hapter 6 and 7 of the thesis.

Thus far the skills know ledge has been prim arily in the h ands of the researcher (see Figure 11). The second p art of the processing and developm ent fram ew ork (Figure 16) corresponds to data sharing and dissem ination of results. This section of the fram ew ork is responsible for transferring the research know ledge into the professional dom ain. It is here th at the inform ation created is turned into public health know ledge. C hapter 9 considers this section of the m odel and sets o u t to address the developm ent of geodem ographic know ledge transfer.

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Local spatial data infrastructure

D ata acquisition, processing and analysis

Results, data sharing and dissem ination Disseminate results

Figure 16: Process diagram and geodemographic development framework of the research design

Assign to GP Practice grid reference

Once the local spatial data infrastructure has been developed, health outcome indicators can be created to profile population sub-groups. In the context of this research, profiles are created for different types of analysis units

representing different social and/or spatial scales; the neighbourhood (postcode unit), general practice (GP) and Prim ary Care Trust (PCT)

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adm inistrative boundary. The index score provides a simple, robust and directly com parable m easure of social m easurem ent to identify social health averages (Longley, 2005). It is for this reason index scores w ere chosen as the m easurem ent technique. Index scores enable sim ultaneous direct com parison of health outcom es at two different levels; the neighbourhood (or other unit of analysis) and a local or national average. They utilise appropriate base categories w hen calculating both spatial and aspatial indicators of social, economic and dem ographic conditions.

The profiles are statistical profiles based on the creation of index scores for different health outcomes. Statistical profiling using index scores is a com m only used technique in geodem ographic analysis, particularly in the field of m arketing w here they are described as 'propensities'. In general an index is used to create a single num erical m easure used to quantify trends or variations, and m ost deprivation m easure create index scores (see section 3.1).

In the case of a geodem ographic index score they can be considered as a tool for social health m easurem ent. The equation below show s the form ula for calculating an index score. The term target population refers to potential participants or population groups of specific interest w hereas the base population refers to the proportion of people in the larger population. For exam ple if each patient w ith heart disease in the UK is ascribed w ith a

geodem ographic Type according to the postcode in w hich they live, an index score can be developed to predict the likelihood for all people living in

particular neighbourhood Type in the UK com pared to average propensity for the UK. The proportion of target population w ould be created by calculating the total count of people in the neighbourhood Type w ith heart disease versus the total num ber of UK residents living in th at particular neighbourhood Type. The proportion of the base p o pulation w ould calculate the p roportion of the total population w ith h eart disease in the UK resident population.

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Index score= Proportion of target population x 100 Equation 1 Proportion of base population

The index score provides a simple, robust and directly com parable m easure of social m easurem ent to identify social averages. They can be created for health data to predict health outcom es by ascribing the geodem ographic characteristics of a neighbourhood to the patient or survey respondent th ro u g h the use of the postcode unit. This is a sim ple six step process, outlined in Figure 17. For each respondent/patient there are data records containing inform ation about their address w ith the full u n it postcode (step 1). The responders/ patients are the target population. The postcode of each respondent/patient is extracted and used as a unique identifier (step2). Using this postcode, the geodem ographic classification is assigned to each postcode in the target population (step 3). The num ber of responders/patients for each geodem ographic G roup/Type is totalled (step 4) and the index value is then created (step 5), using the equation above. The final step involves the analysis of index values (step 6), w hich will be considered in the next chapter.

1

1

Survey responder/patients (individual)

2 Extract postcode to use as

hii m l NW4 6TF _identifier

■ CEH Z]3

104 156

Append geodemographic Type/Group to postcode

Survey responder/patients are collated for each geodemographic Type/Group

89 1165 1170 | 55 1 62 ^{4 5} ^ ■ C a l c u l a t e in d e x s c o re

6 Analysis of geodemographic index values

Figure 17: Method for calculating a neighbourhood (geodemographic) index, using operational health or survey data

In essence an index score is used to compare, for example, the num ber of diabetics in an observed population sub-group (target population) as com pared to the total num ber of diabetics in a population base group (base group). Essentially observational frequencies of behaviour, presence of a disease or a survey response are com pared to the total frequencies in a base population or survey sample. They are useful m easures for identifying differences betw een sub-groups, b ut are not always useful for identifying observational differences actually w ithin Groups/Types. There are a num ber of precautionary notes to be aware of w hen using index scores, some of which are docum ented by Harris et al (2005, page 121). The scores are subject to scaling effects; using a value of 100 to act as the average value means scores below 100 are constrained to 0, but scores above 100 can theoretically stretch to infinity. This creates particular issues on datasets that have very small sam ple sizes, because the resultant scores w ould become less reliable as

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sm all-num ber effects occur. This will be discussed in m ore detail in the context of the case studies.