Quality and variability of osteoporosis data in general practice computer records: implications for disease registers

Public Health (2005) 119, 771–780

Quality and variability of osteoporosis data in general practice computer records: implications for disease registers S. de Lusignana,*, T. Chanb, O. Wooda, N. Haguea, T. Valentinc, J. Van Vlymena a

Department of Community Health Sciences, St George’s Hospital Medical School, London SW17 0RE, UK Surrey and Hampshire Borders NHS Trust, Ridgewood Centre, Old Bisley Road, Camberley, Surrey, UK c St George’s Hospital Medical School, London, UK b

Received 12 May 2004; received in revised form 16 August 2004; accepted 6 October 2004 Available online 11 May 2005

KEYWORDS Osteoporosis; Accidental falls; Computerized medical records; Medical informatics; Medical audit; General practice

Summary Objective. To determine the extent to which routinely collected general practitioner computer data could be used to create disease registers of patients with osteoporosis, and to report any improvement in data quality since previous studies. Study design. Audit using anonymized data extracted from general practice computer records from across England. Methods. Morbidity Query Information and Export Syntax (MIQUEST) software was used to extract structured data from the 78 volunteer practices that participated in the study. The data were aggregated and analysed. Results. There were 100-fold differences in the rates of recording of relevant data. Many patients receiving treatment had no diagnostic codes. Data about secondary causes of osteoporosis and fractures were more consistently recorded than data relating to falls. There were no data to indicate whether fractures were low impact. T-scores, the gold-standard measure of bone density, were very infrequently recorded. Conclusions. Sufficient data about secondary causes of osteoporosis exist, and these could be searched to identify patients at risk. Meanwhile, fracture recoding could be improved, including likely fragility fractures, and T-scores could be added to computer records. A systematic approach is needed to raise the computer records to a standard where they can be used as valid and reliable disease registers. Q 2005 The Royal Institute of Public Health. Published by Elsevier Ltd. All rights reserved.

Introduction * Corresponding author. Tel.: C44 20 8725 5661; fax: C44 20 8767 7697. E-mail address: [email protected] (S. de Lusignan).

Osteoporosis is a common condition with a current lifetime risk of osteoporotic fractures greater than

0033-3506/$ - see front matter Q 2005 The Royal Institute of Public Health. Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.puhe.2004.10.018

772 one in three for women and one in 12 for men.1,2 However, as the population ages, the number of fractures is likely to increase,3 as is the age-related mortality associated with these fractures. 4 Although an important cause of mortality and morbidity, osteoporosis is under-recognized and under-treated, 5–7 although dual-energy X-ray absorptiometry (DEXA) scans provide a reliable method of assessing bone density,8 and effective therapy exists.9 As osteoporotic fractures are usually the result of a fall from standing height or less, the management of falls in the elderly is an integral part of reducing the impact of osteoporosis.10 The costs attributable to osteoporotic fractures in the USA were estimated to be $15 billion in 2002, possibly the same cost as preventing fractures with drugs.11 In the UK, the costs associated with osteoporosis have been estimated as £2.8 million per 100 000 population. Implementation of a casefinding approach to management is estimated to cost £200 000; an equivalent cost to managing nine of the expected 120 hip fractures.12 In the UK, one of the standards within the National Service Framework (NSF) for Older People is the improved management of falls and osteoporosis.13 The NSF proposes integrated management of patients at risk of falling with those with osteoporosis. In the absence of evidence for population-based screening, a case-finding approach is recommended. Patients who are at increased risk are those with fragility fractures, those on oral steroids, those who fall, and those with other defined risk factors.14,15 Recent draft guidelines from the National Institute for Clinical Excellence have suggested that only patients who have had a fragility fracture should be prioritized.16,17 Although a wide range of quality standards has been financially incentivised within new contractual arrangements for general practice,18 falls and osteoporosis have not been included to date. A disease register contains information about all instances of a condition in a defined population. Creation of disease registers is a long-established first step towards improving the quality of the clinical record, and provides a basis for auditing the quality of care.19,20 Whilst the existence of a disease register does not automatically result in quality improvement,21 it at least provides a baseline against which subsequent quality improvement can be measured, and allows comparisons between practices to be made. In general practice, there is a trend from written to computer-based notes promoted by the National Health Service (NHS) information strategy.22,23 We carried out this study to explore whether osteoporosis data,

S. de Lusignan et al. routinely collected in general practice computer systems, is now of sufficient quality to create disease registers.

Method Sample Seventy-eight participating practices, with a combined list size of approximately 600 000 patients, volunteered to take part in an audit-based educational programme about the management of osteoporosis in primary care. This was run by the Primary Care Informatics Group at St George’s Hospital Medical School. Practices that had participated in a previous ischaemic heart disease programme, from which we had successfully collected good-quality data, were invited to participate. Seventy-eight were recruited from across the UK. The data for the study were extracted from general practice computer records using Morbidity Information Query and Export Syntax (MIQUEST) software.24 The data shown here are the baseline data collected between August 2003 and February 2004 from participating practices in London and north-east, north-west, south-west and south-east England.

Study aims The aim of the data extraction was to identify and characterize those people with a diagnosis of osteoporosis and osteopenia, their risk factors and current therapy.

Data extraction We adopted a broad data extraction policy, taking data from the whole population, rather than our usual practice of extracting data from a subset with the diagnosis or on treatment for the condition.20 We did this because an initial pilot study carried out in six general practices within a primary care research network suggested that the recording of diagnosis, risk factors and treatment was variable.25 Examples of the Read codes searched are provided in Box 1. This illustrates different ways that a clinician could imply that a patient might have osteoporosis. There is a similar number of coding possibilities for most other data; space precludes publication of an exhaustive coding list. MIQUEST, a Department of Health-sponsored computer programme, was used to extract the data from general practice databases. Only

Osteoporosis data in GP computer records Box 1 Example of range of Read terms that may imply a diagnosis of osteoporosis Five byte

Four byte

N330 N3318 to B 9Od

M61/ N330 N3318 to B 9Od

1409 9N0h 679F

1409 9N0h 679F

58E4

58E4

58EA 58EM

58EA 58EM

Diagnosis of osteoporosis Pathological fractures due to osteoporosis Osteoporosis monitoring administration At risk of osteoporosis Seen in osteoporosis clinic Health education osteoporosis Forearm DXA result osteoporotic Heel DXA result osteoporotic Lumbar DXA result osteoporotic

structured data can be extracted using MIQUEST. In the UK, general practice computer systems use Read codes to record structured data; other countries use different systems although all primary care computer systems include some mechanism to record structured data26 (Box 2). Free text or narrative data cannot be searched by MIQUEST, and therefore information still in paper records, or in text, was not included in the searches. MIQUEST allows the same searches to be run on the different types of general practice computer systems. Customized searches or, more properly, queries, were written for the study (NJH)

Box 2

773 using MIQUEST in its ‘remote’ setting, which allows only anonymized data to be extracted. One GP computer system uses British National Formulary (BNF27) chapter headings, rather than Read codes. Therefore, special queries had to be written for this system, and often groups of codes are not precisely the same. For example, the Read code for HRT is fh1 (menstrual symptoms: combined oestrogen and progestogen preparations); the ‘not quite equivalent’ BNF chapter heading is 6.4.1.1 (Oestrogens and HRT).

Data aggregation, processing and analysis Data were exported from the practice systems, imported into a bespoke database, and exported into the Statistical Package for Social Sciences Version 12. The data were cleaned by removing duplicates and by manual translation of out-ofrange data (e.g. one computer system exported patient’s height in centimetres while others used metres), and analysed. The process of extracting, aggregating and analysing the data has been developed and validated in other disease areas.21,28

The dataset There were four parts to the dataset extracted. 1. Data that defined the denominator population— the age—sex profile of the practice. This enabled us to restrict much of the analysis to people aged over 50 years. This is because most osteoporosis

An overview of the Read codes

Data are recorded in GP computer systems in two forms: structured data (Read coded) and as free text or narrative. Structured data are used because there are multiple ways that the same clinical concept can be represented in free text (e.g. broken hip bone, fractured neck of femur). It is also needed because words used in an individual medical record are often contextualized (e.g. the existence of the word ‘fracture’ within a medical record does not necessarily imply that the person has had a fracture. Examples as to how it might be contextualized: not fractured; worried it might be a fracture, at the same age her mother had a fracture, etc.). ‘Coding systems’ or terminologies are used to represent clinical concepts in computer records. There are a number of these: ICD (International Classifications of Disease, produced by the World Health Organization), the ‘Read codes’ used in the UK, and ICPC (International Classification of Primary Care-owned by the World Organization of National Colleges and Academies of Family Medicine) used in much of Europe. ‘Read codes’ version 2 is the most common structured data currently used in the UK. It comes in two forms: an older four-character (or four-byte) set, and a newer and more extensive five-character (or five-byte) set. The five-byte set is the most widely used. Both are organized like a family tree with parent and child codes (e.g. Chapter N is the musculoskeletal chapter- and N330 osteoporosis is several steps down the coding hierarchy). The four-byte set has about 100 000 medical concepts and the five-byte set has about double this number. Many ‘codes’ (or more properly ‘terms’) exist for each concept, making the coding system too large to print. Read version 2 is due to be replaced by Read version 3, also known as ‘Clinical Terms version 3’ (CT v3). This is to be amalgamated with an American College of Pathologists’ terminology called SNOMED RT (Systematized Nomenclature of Medicine-Reference Terminology) to form SNOMED CT (CT for ‘Clinical Terms’). This is set to become the transatlantic norm with much of Europe remaining with ICPC and ICD.

774 is post-menopausal and data linking low bone mineral density to fractures is reported from this age.29 2. Codes that suggest a diagnosis of osteoporosis or osteopenia. These include codes for osteoporosis monitoring, ‘at risk’ of osteoporosis and T-scores diagnostic of osteoporosis as well as the more straightforward diagnostic codes. 3. Risk factors for osteoporosis. There is consensus about these,11,14,15 and the dataset includes searching for prescribed steroids, low-impact fractures, early menopause (either natural or surgical), low body mass index, smoking and diseases that can cause osteoporosis (referred to as ‘secondary causes’, such as rheumatoid arthritis, transplants, coeliac disease and many more).30 Falls data were also extracted because those with osteoporosis who fall are at increased risk of fracture.10,31 4. Therapy for osteoporosis. General practitioner (GP) prescribing records are generally complete, especially for long-term prescriptions, as electronic generation of prescriptions is one of the few aspects of working with computers that saves GPs time.32 We looked for prescriptions of hormone replacement therapy (HRT), calcium and vitamin D preparations, bisphosphonates and selective oestrogen re-uptake modulators.

Statistical methods used Means and standard deviations are quoted for the practice population and population over 50 years of age. They are quoted where a histogram of the variable approximated to a normal distribution. Where a variable does not have a normal distribution, the median, 25 and 75th centiles, and range are quoted. To develop an understanding of any relationship between practice list size, population over 50 years of age, and diagnosis of osteoporosis and osteopenia, we produced scatter plots of these independent variables and the known risk factors (dependent variables) and treatment for osteoporosis. These graphs are not shown. As the relationship was linear, we calculated the Pearson correlation; the appropriate bivariate correlation for this type of association. A correlation coefficient under 0.2 is very weak, 0.2–0.39 weak, 0.4–0.59 moderate, 0.6–0.79 strong, and 0.8 and above very strong. However, correlation does not necessarily mean that there is causation. We tested for significance to test whether the association

S. de Lusignan et al. described above occurred by chance. P values based on this test are presented in Table 2.

Feedback to and from participants Summary data was provided to each participating practice, and ‘local’ queries were run in each practice to inform them of the patients who might need diagnostic clarification, investigation or treatment. The baseline data from this intervention were presented at an ‘Osteoporosis Forum’, made up of programme participants. These 2-day residential workshops are a feature of all our programmes, and enable users to drive a programme’s development. They also provide an opportunity to explore the face validity of the findings. A small multidisciplinary group from practices and localities are invited to comment on what they feel the data means, what is useful and what could be done better within the programme. This forum met in March 2004, and its comments are included in the results.

Results Sample practices The 78 practices that participated in this audit varied considerably in size and in their population profiles; the smallest practice had a list of under 2000 patients and the largest nearly 20 000; their combined age–sex distribution was close to the national average33 (Fig. 1). The mean size of the practices was 7649 (SDZ4266). The combined list sizes were 596 642. Approximately one-third of the study population was drawn from the north of England, one-third from London and the other third from Kent, Surrey and Sussex. Three of the

Figure 1 Age–sex distribution of the study population compared with the national average.

Osteoporosis data in GP computer records practices came from central England or the southwest. Seventy-two percent (55/78) used a practice system that utilized BNF chapter headings rather than Read codes for drugs, which meant that their use of HRT is likely to be slightly inflated compared with other practices.

Completeness of the data Data are missing for a number of reasons. Some omissions are due to the queries failing to extract data in these practices, either due to a lack of automated coding behind free-text input, or complications with the Quest interpreter.34 The missing data were not confined to one or two practices but spread evenly across all practices. One practice had no data about secondary causes of osteoporosis and that practice also had no fracture data. Two practices’ computer systems printed out nearly all the practice population as having osteopenia, so were discounted. Four practices had no fracture data, and in 10 practices, steroid data failed to extract. All the drug queries failed to run in two practices, and smoking queries in another.

Variation in data recording of diagnostic data There was considerable interpractice variation in the recording of diagnostic codes, risk factors and the use of treatment. For example, the recorded prevalence of osteoporosis (using the diagnostic code N330) varied from zero (in nine practices) to 29 per 1000 patients over 50 years, and that for osteopenia from zero (in 22 practices) to 45 per 1000 patients. In the 73/78 practices where fractures were coded, recording varied nearly 1000-fold, from 0.1 to 97 per 1000. In the 76/79 practices with a record of prescribing calcium and vitamin D preparations, rates ranged from 2.5 to 93 per 1000 registered patients. The variation in recording rates is illustrated in Table 1.

Fracture recording There was very little use of low-impact fracture codes (N331M fragility fracture unspecified—osteoporosis, or 14G6, history of fragility fracture). As a result, proximal radius and ulna, spine and hip fractures were counted. Inevitably some of those recorded will not be fragility fractures.

775 The nearest match is BNF Section 6.4.1.1. (Oestrogens and HRT), and although this drug chapter heading was entered into MIQUEST, it would only extract data at the level of Chapter 6.4.1. (Female sex hormones, which also includes progestogens). This means that for one practice system, the HRT sample was actually a ‘female sex hormones’ sample, and thus overestimated the number of HRT prescriptions by about one-third. Where Read codes were used, there were problems with different preparations sitting in different parts of the coding hierarchy. However, notwithstanding these problems, where data are absent, it is because they were not coded into the clinical record.

Variation in treatment Only a minority of patients being treated for osteoporosis had the diagnosis recorded. Excluding HRT, as it has indications other than osteoporosis, across the 69 practices with diagnosis data, there were 9155 patients receiving treatment without a recorded diagnosis; a mean of 123 (SDZ126) patients per practice. There were just over three times (3.04) as many prescriptions being written for osteoporosis treatments as there were recorded diagnoses; when HRT was included, the ratio was eight. The under-recording of diagnosis for those receiving therapy varied greatly between practices, ranging from 0.85 to 100% when HRT was excluded and from 0.21 to 85% when HRT was included.

Variation in DEXA scan data Recording of DEXA scan results was disappointing, especially as this is the gold-standard assessment regarding whether a patient truly has osteoporosis. Fourteen practices did not have queries run in them to extract these data; of the remainder, only 27% (17/64) had any data. The mean number of DEXA scan records was 77 per practice, with only two practices having over 200 records. Disappointingly, there were no records of the numeric T-score. Two reasons were given for this: firstly, codes have only been introduced recently; and secondly, one of the major computer systems will not accept a negative value being entered.

Use of HRT

The proportion of the practice aged 50 years or more only partially explains the variation in data recording

The technical problems concerned with extracting data about HRT were raised in the Method.

We analysed the data to see the extent to which the size of the population over 50 years of age

776

Table 1

Variation in data recording in the 78 practices.

Practices with data Median 25th centile 75th centile Interquartile range Minimum Maximum Range Denominator

Population and diagnosis (%)

Risk factors (%)

Practice population 78 7017 3768 11177 7409

Females at risk (%)

Therapy (%)

Osteoporosis

Osteopenia

At risk of osteoporosis

Steroids

Fractures

Falls

Secondary causes

Smoking habit

Early menopause

Hysterectomy !45 years

HRTC female sex hormones

CaCC and vitamin D

Bisphosphonates

78

78

78

78

78

78

78

75

76

76

78

78

78

6.87 1.81 11.03 9.22

1.23 0.00 2.76 2.76

0.02 0.00 0.08 0.08

1.69 0.98 2.75 1.77

6.54 2.15 9.74 7.58

2.02 0.42 5.98 5.56

2.38 1.56 3.12 1.56

62.6 56.8 68.8 12.03

1691 0 0 0 0 0 19895 29.07 45.34 1.40 6.08 17.38 18204 29.07 45.34 1.40 6.08 17.38 Sum of list for practices with data, total for 78Z596 642

0 31.79 31.79

0 7.97 1.11

4.6 89.7 85.1

1.11 0.42 2.25 1.83

6.97 4.46 8.79 4.33

0 0 7.97 13.25 7.97 13.25 Females for 78 practicesZ301 171

12.5 9.1 15.9 6.75

1.6 0.8 2.5 1.71

0 0 40.1 9.3 40.1 9.3 nZ596 642

1.0 0.6 1.5 0.91 0 2.5 2.5

S. de Lusignan et al.

Osteoporosis data in GP computer records

777 term treatment. In the absence of DEXA scans, data about secondary causes and fractures are appropriate places for GPs to start the case-finding process.

Discussion

Figure 2 Distribution of proportion of practice population over 50 years of age in the study practices.

explained the variation in data recording. The proportion of patients over 50 years varied from 6% of total list size in a university health centre to 52% in a coastal practice with a high elderly population (Fig. 2). The mean population over 50 years of age (32.7%) was close to the national average (33.3%). Practices with more patients over 50 years of age were more likely to have higher levels of recording of diagnosis, risk factors and therapy. The strongest associations with the number of patients over 50 years were with the recording of osteopenia, at risk of osteoporosis, and prescription of calcium and vitamin D preparations. The weaker associations with the number of patients over 50 years were for the recording of fractures, taking steroids, and treatment with HRT and bisphosphonates (Table 2).

Feedback from users The principal finding of the Osteoporosis Forum was the recognition that practices used very different codes to label patients with similar clinical conditions. They proposed a short list of recommended codes (Table 3); these are a subset of those used in the study. It is essential to record T-scores in the clinical record; the technical problems preventing this need to be overcome. More DEXA scanner availability was felt to be essential. Nearly, all the practices represented felt that low availability adversely affected their ability to confirm the diagnosis before commencing long-

There were large variations between GP practices in population profiles and level of recording of clinical data, and many patients receiving treatment had no associated diagnostic code. There were 100-fold differences in the rate of recording of diagnostic codes, risk factors and treatment. For many variables, there was better correlation between data recording and number of patients aged over 50 years than with the diagnosis of osteoporosis. Data about secondary causes of osteoporosis and fractures were more consistently recorded than data about falls. Although fracture recording was more prevalent than other potential causes of osteoporosis, there was great variation in levels of recording and very rarely an indication regarding whether fractures were low impact. The implications for practice are that data quality has not reached the stage where osteoporotic patients can be identified reliably. To take this first step towards quality improvement, it is necessary to identify the patients at risk.20 Together, patients with potential secondary causes of osteoporosis, those with fractures and those on steroids provide sufficient data to make a start. A limited list of codes of the sort proposed by the PCDQ Osteoporosis Forum is required, along with the consistent use of diagnostic codes and other key data. DEXA scan results should be added to computer records as a numeric T-score. Only searching structured (Read coded) data has limitations. Hospital letters and free-text entries into the computer may contain the information, but remained invisible to our searches. Our ability to search was also hampered by the lack of easy-to-use codes that differentiate low-impact fractures. Technical problems with drug chapter headings also caused problems. We have not addressed the issue of the denominator population being incorrect. GPs often have larger lists than the number of patients identified from the census and other sources; so-called ‘list inflation’. Before the direct connection of practice computer systems to the NHS central register, all practices had large numbers of what were termed ‘ghost’ patients. List inflation is a particular problem where there

778

Table 2

Correlation between practice population, population over 50 years of age and a diagnosis of osteoporosis. Population and diagnosis Practice population

Osteoporosis

Osteopenia

Note: correlation is Pearson two-tailed.

Females at risk

At risk of osteoporosis

Steroids

0.369

0.072

0.515

0.005

0.625

0.000

57

48

Fractures

77

Secondary causes

Smoking habit

0.726

0.441

0.718

0.000

0.000

0.000

Falls

74

66

78

Therapy

Hysterectomy !45 years

HRTC female sex hormones

CaCC and vitamin D

Bisphosphonates

0.967

0.656

0.880

0.696

0.509

0.000

0.000

0.000

0.000

0.000

Early menopause

75

78

78

77

68

0.439

0.125

0.552

0.776

0.481

0.723

0.909

0.631

0.864

0.735

0.563

0.001

0.396

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

57

48

77

74

66

78

75

78

78

77

68

0.769

0.198

0.284

0.714

0.448

0.508

0.585

0.654

0.705

0.470

0.592

0.000

0.191

0.017

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

56

1

57

45

70

70

64

70

75

78

78

69

60

0.074

0.042

0.366

0.084

0.126

0.327

0.338

0.385

0.215

0.482

0.647

0.756

0.005

0.544

0.350

0.004

0.002

0.001

0.111

0.000

41

57

57

57

75

78

78

56

51

S. de Lusignan et al.

Practice population Corre1 0.939 0.669 lation Signifi0.000 0.000 cance No prac- 78 78 70 tices Practice population over 50 years Corre0.939 1 0.740 lation Signifi0.000 0.000 cance 78 70 No prac- 78 tices Diagnosis of osteoporosis Corre0.669 0.740 1 lation 0.000 0.000 Significance No prac- 70 70 70 tices Diagnosis of osteopenia Corre0.369 0.439 0.769 lation Signifi0.005 0.001 0.000 cance No prac- 57 57 56 tices

Risk factors

Osteoporosis data in GP computer records Table 3 Recommended essential Read code list for osteoporosis and falls. Five-byte Read code Falls 1. Falls risk assessment complete 2. Falls (16D) a. Number of falls in the past year b. Recurrent falls 3. At risk of falls 4. Referral for falls assessment Osteoporosis 5. Osteoporosis 6. At risk of osteoporosis 7. Osteoporosis risk assessment done 8. DEXA

90g2 16D1 16D2 14OC 66aF N33 14O9 90dA 58E

are large numbers of mobile people;35–37 few of the practices in this study were drawn from areas where this is known to be a particular problem. The variation in data recording between practices in this study was much greater than in our previous study, where the difference in data recording in practices was a tenth of what has been seen in this larger group.25 It is possible that these six research network practices were more aware of osteoporosis and data recording issues because two were involved in osteoporosis research. We expected the converse to be the case with increasing numbers of practices becoming paperless,38 and the new GP contract encouraging improvement in data quality. The practices that took part in this study had all taken part in our cardiovascular programme, where their computer data more closely reflects the quality of care.21 The data extracted are compatible with that reported by Bailey et al., who studied the prevalence of treated osteoporosis by looking at the use of treatment for osteoporosis. They looked at data recording from 210 practices that were members of the General Practice Research Database and noted marked interpractice variation in data recording and use of therapy.39 It would appear that problems with data quality in osteoporosis have not significantly improved since then. Further research is needed to characterize patients who have osteoporosis. Manual searches of patients’ records need to be carried out to understand what is known about the patients on therapy who have no diagnosis, and the patients with a diagnosis to see the basis for it. Both these groups and patients recorded as having a DEXA scan need to have their T-scores extracted. The latter provides the only objective measure that patients really are osteoporotic.

779

Conclusions Practitioners wishing to improve the management of osteoporosis should first search their computer records for patients who have diagnoses likely to cause osteoporosis. Patients with multiple risk factors may be an appropriate place to start, perhaps also looking at those who are also taking steroids. In parallel to assessing these patients, systems that make it easier to identify fragility fractures for fracture recording should be put in place. DEXA scan results should be coded, along with the numerical score. These recommendations reflect the approach used in successful quality improvement programmes, for example, in ischaemic heart disease.21 GP computer data is not yet of sufficient quality to create reliable disease registers, although sufficient data exist to make a start on improving management of high-risk patients.

Acknowledgements The PCDQ-Osteoporosis programme was supported by MSD, through an unconditional educational grant, January 2002 to March 2004. The authors wish to thank Tom Valentin, who used his special study module to work on this data, the volunteer practices who took part in the audit, and the GPs who took part in the Osteoporosis Forum.

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