A retrospective cohort study assessing patient characteristics and the incidence of cardiovascular disease using linked routine primary and secondary care data.

Abstract

Data linkage combines information from several clinical data sets. The authors examined whether coding inconsistencies for cardiovascular disease between components of linked data sets result in differences in apparent population characteristics.
Retrospective cohort study.
Routine primary care data from 40 Scottish general practitioner (GP) surgeries linked to national hospital records.
240 846 patients, aged 20 years or older, registered at a GP surgery.
Cases of myocardial infarction, ischaemic heart disease and stroke (cerebrovascular disease) were identified from GP and hospital records. Patient characteristics and incidence rates were assessed for all three clinical outcomes, based on GP, hospital, paired GP/hospital (similar diagnoses recorded simultaneously in both data sets) or pooled GP/hospital records (diagnosis recorded in either or both data sets).
For all three outcomes, the authors found evidence (p<0.05) of different characteristics when using different methods of case identification. Prescribing of cardiovascular medicines for ischaemic heart disease was greatest for cases identified using paired records (p≤0.013). For all conditions, 30-day case fatality rates were higher for cases identified using hospital compared with GP or paired data, most noticeably for myocardial infarction (hospital 20%, GP 4%, p=0.001). Incidence rates were highest using pooled GP/hospital data and lowest using paired data.
Differences exist in patient characteristics and disease incidence for cardiovascular conditions, depending on the data source. This has implications for studies using routine clinical data.

Full text

Page 1

A retrospective cohort study assessing
patient characteristics and the incidence
of cardiovascular disease using linked
routine primary and secondary care data
Rupert A Payne,1Gary A Abel,1Colin R Simpson2
ABSTRACT
Objectives: Data linkage combines information from
several clinical data sets. The authors examined
whether coding inconsistencies for cardiovascular
disease between components of linked data sets result
in differences in apparent population characteristics.
Design: Retrospective cohort study.
Setting: Routine primary care data from 40 Scottish
general practitioner (GP) surgeries linked to national
hospital records.
Participants: 240846 patients, aged 20 years or
older, registered at a GP surgery.
Outcomes: Cases of myocardial infarction, ischaemic
heart disease and stroke (cerebrovascular disease)
were identified from GP and hospital records. Patient
characteristics and incidence rates were assessed for
all three clinical outcomes, based on GP, hospital,
paired GP/hospital (similar diagnoses recorded
simultaneously in both data sets) or pooled GP/
hospital records (diagnosis recorded in either or both
data sets).
Results: For all three outcomes, the authors found
evidence (p<0.05) of different characteristics when
using different methods of case identification.
Prescribing of cardiovascular medicines for ischaemic
heart disease was greatest for cases identified using
paired records (p#0.013). For all conditions, 30-day
case fatality rates were higher for cases identified
using hospital compared with GP or paired data, most
noticeably for myocardial infarction (hospital 20%, GP
4%, p¼0.001). Incidence rates were highest using
pooled GP/hospital data and lowest using paired data.
Conclusions: Differences exist in patient
characteristics and disease incidence for
cardiovascular conditions, depending on the data
source. This has implications for studies using routine
clinical data.
BACKGROUND
Primary care data sets are commonly used for
assessment of cardiovascular outcomes. Such
events often are associated with hospital-
isation.1However, it is possible that the
manner in which outcomes are coded and
recorded in electronic health records may
differ between primary and secondary care.
This may result not only in differences in
the apparent incidence of a condition,
depending on whether primary or secondary
care records are used, but also in differences
in the observed characteristics of patients.
Studies have observed that variations in
diagnostic criteria can affect estimates of
disease prevalence,2and the complexities of
clinical coding systems for electronic health-
care records can lead to inconsistent data
recording.3This will lead to uncertainties
To cite: Payne RA, Abel GA,
Simpson CR. A retrospective
cohort study assessing
patient characteristics and
the incidence of
cardiovascular disease using
linked routine primary and
secondary care data. BMJ
Open 2012;2:e000723.
doi:10.1136/
bmjopen-2011-000723
<Prepublication history for
this paper is available online.
To view this file please visit
the journal online (http://dx.
doi.org/10.1136/
bmjopen-2011-000723).
Received 7 December 2011
Accepted 21 February 2012
This final article is available
for use under the terms of
the Creative Commons
Attribution Non-Commercial
2.0 Licence; see
http://bmjopen.bmj.com
1General Practice and
Primary Care Research Unit,
University of Cambridge,
Cambridge, UK
2eHealth Research Group,
Centre for Population Health
Sciences, The University of
Edinburgh, Edinburgh, UK
Correspondence to
Dr Rupert A Payne;
rap55@medschl.cam.ac.uk
ARTICLE SUMMARY
Article focus
- Data linkage allows information to be combined
from different routine clinical data sources.
- Previous work has shown differences between
sources of data but has not examined this at the
patient level.
Key messages
- Patients’ apparent characteristics, and disease
incidence and severity, vary depending on
whether primary care, hospital or combined
definitions of cardiovascular events are used.
- Use of isolated routine primary care or hospital
data may result in biased patient selection.
- This has implications in the public health arena,
clinical trial patient recruitment and validity and
reliability of secondary data in clinical trials.
Strengths and limitations of this study
- The strengths of this study are the novel
analytical approach, using a large routine data
set linked at individual patient level from multiple
GP surgeries.
- Limitations of this study include restricting our
analysis to four coding groups, uncertainty as to
whether GP and hospital events could be
considered toberecorded
potential diagnostic coding inaccuracies and the
relatively small number of GP surgeries, which
may not have been representative.
simultaneously,
Payne RA, Abel GA, Simpson CR. BMJ Open 2012;2:e000723. doi:10.1136/bmjopen-2011-000723
1
Open Access Research

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with respect to disease prevalence and mortality,4impact
on clinical care, have additional health service implica-
tions such as affecting funding5and potentially influ-
ence identification of patients for clinical trials. Previous
studies have compared general practice coding and
disease prevalence with other unlinked data sources,
including paper notes.6
combining information from two sources has not been
previously examined. This study used linked individual
patient electronic health records collected from primary
and secondary care to examine the effect of using data
from different parts of the healthcare service on the
incidence rates, case fatality rates and patient charac-
teristics of myocardial infarction (MI), ischaemic heart
disease (IHD) and cerebrovascular disease (CVD).
7
However, the effect of
METHODS
Data sources
Sixty general practitioner (GP) surgeries take part in the
Scottish national Practice Team Information (PTI)
project, of which 40 self-selected surgeries contributed to
the data set used in this study. Practices involved in the
PTI project provide routine central recording of clinical
activity and morbidity from a sample of GP surgeries
considered reasonably representative of the Scottish
population. Practices are reimbursed to ensure that data
recording is optimal. Clinical coding used the Read code
system. Data are used to calculate national estimates and
used by various organisations (eg, NHS Boards, Scottish
Government) to inform policies and better understand
health in Scotland.
Patient details from the PTI data set were linked to the
corresponding admissions recorded in Scottish national
hospital data (the Scottish Morbidity Record, SMR-01)
using probabilistic matching. Matching was based on
Soundex-encoded name, date of birth, sex, postcode and
a unique nationwide identifier, the Community Health
Index. Experienced human review was used to set
a threshold for linkage. A substantial proportion of
patients in this GP cohort have no hospital admissions,
and as such, it is difficult to know whether the absence of
a match is either due to a genuine lack of corresponding
hospital record or due to a false-negative error. Match
rates are thus difficult to quantify, although the use of
multiple identifiers should improve linkage quality. The
linkage was carried out by the Information Services
Division, NHS National Services Scotland. The work was
approved by the Privacy Advisory Committee of NHS
National Services Scotland. For the 2004e2006 period,
SMR-01 data are considered to be 88% accurate.8SMR-
01 records are generated for all inpatient hospital
medical discharges and transfers. Coding is based on the
International Statistical Classification of Diseases and
Related Health Problems (ICD) system (ICD9 prior to
2000, ICD10 thereafter), with up to six inpatient diag-
noses per record. Accident and emergency, maternity
and psychiatric admissions, along with outpatient atten-
dances, are not recorded in SMR-01. SMR-01 itself is also
routinely linked to national mortality data (General
Registrar’s Office for Scotland, GROS). SMR-GROS data
are also used to generate Scottish National Statistics.
Identification and classification of cases
We first identified all records of MI, IHD and CVD from
both GP and hospital data sets using the following Read
codes (MI: G30%/35%/38%, Gyu34/35/36; IHD: G3%,
Gyu3%; CVD: G6%, Gyu6%, F4236; where % indicates
a ‘wildcard’ match) and ICD codes (MI: ICD10 I21e22,
ICD9 410; IHD including MI: ICD10 I20e25, ICD9
410e414; CVD (stroke) including haemorrhage and
transientischaemic attack
G45e46; ICD9 430e438). Hospital events were identi-
fied from any of the six diagnostic positions. These were
not necessarily first events.
We then found all episodes of a similar GP and hospital
event type occurring within a 30-day period and made
the assumption that these pairings represented the same
clinical event. Where the GP and hospital dates differed
for these paired episodes, the first of the two dates was
taken. The choice of 30 days was a pragmatic one but
supported by visual evaluation of the distribution of time
gaps between similar hospital and GP event types over
a 2-year period. Of note, an event recorded by the GP
does not necessarily require a face-to-face consultation or
a referral to be made; hospital admissions will usually be
retrospectively recorded by the GP, using the admission
date as opposed to the data-entry date.
Analysis was carried out over the period 1 January 2005
to 1 January 2007. The total population was randomly
allocated to one of four methods of identifying cardio-
vascular events: those based on GP events only; those
based on hospital events only; those based on pooled
GP/hospital events, with an event in GP data only,
hospital data only or both the GP and hospital data
(although not necessarily occurring within 30 days); and
those based on paired GP/hospital events (those
recorded in both GP and hospital data within 30 days).
An episode was included as an incident event only if
there was no record of a similar clinical event at any time
prior to 1 January 2005 coded in the same data set(s).
This method of identifying incident events is shown in
figure 1. For example, for an event to be included using
only GP data, the first event would have to be recorded
by the GP during the 2-year period of interest, with no
similar events recorded by the GP prior to 1 January
2005; hospital data are completely ignored in this case. A
similar approach is used for identifying events using
hospital-only data, with GP records ignored in this situ-
ation. For the third method, identifying events using
pooled GP/hospital data, the first event needs to be
recorded by either the hospital or the GP during the
2-year study period; there must be no similar event
recorded in either data set prior to 1 January 2005. For
the final method, the first occurrence of paired (ie,
within 30 days) records in both GP and hospital data sets
constituted an incident event if it occurred during the
2-year period; any unpaired GP or hospital records
occurring prior to 1 January 2005 were ignored.
(TIA):ICD10 I60e69,
2
Payne RA, Abel GA, Simpson CR. BMJ Open 2012;2:e000723. doi:10.1136/bmjopen-2011-000723
Characterising cardiovascular disease using linked routine data

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For each incident event, we determined the patient’s
age, sex, socioeconomic status (Scottish Index of
MultipleDeprivationquintile),9
smoking status, record of hypertension, record of dia-
betes and Charlson Index.10Comorbidities, including
Charlson Index, were determined from the GP data as
the presence of any relevant diagnostic Read code prior
to the incident episode date; the list of codes used is
available from the authors on request. Although we have
not formally evaluated performance of our Charlson
Index Read code list, we match 87% of those events
identified by the method described by Khan et al,11and
as such believe that this represents a reasonable, albeit
pragmatic, measure of comorbidity. Death from any
cause within 30 days of the event was ascertained from
linked national mortality (GROS) data. Drug therapy
recorded in the GP record, starting prior to or within
30 days after the event, and continuing for any period of
time after the event, was ascertained for patients alive at
30 days. Drug classes included were ACE inhibitors
(including angiotensin receptor blockers), b-blockers,
calcium channel blockers, diuretics (including potas-
sium sparing and combination diuretics), nitrates,
statins and antiplatelet agents (aspirin or clopidogrel for
MI or IHD; aspirin or dipyridamole for CVD).
recordedcurrent
Statistical analysis
Incidence rates were calculated excluding patients with
events in the relevant data set(s) prior to 1 January 2005.
Incidence rates are expressed per 100000 patient-years
(based on total number of days of follow-up for each
patient within each respective group). Statistical differ-
ences in patient characteristics (including drug treat-
ment) between coding categories were evaluated using
c2tests (for proportions) and Kruskal-Wallis non-
parametric analysis of variance (for continuous data).
The association between coding and 30-day case fatality
was assessed by logistic regression, including the
covariates age, sex,deprivation,
hypertension, diabetes and Charlson Index. Differences
smokingstatus,
in the four incident rates obtained were examined using
Poisson regression.
Data management was carried out using Microsoft
SQL Server 2000. Statistical analysis was performed using
SPSS V.17 (SPSS Inc.).
RESULTS
Differences in identification of incidence events
There were a total of 240846 patients, evenly distributed
between the four coding groups. Numbers of incident
events are shown in table 1. Incidence rates for the three
conditions are shown in figure 2. There was strong
evidence (p<0.001, Poisson regression) that the inci-
dence rates for all three clinical conditions depends on
which data set(s) are used to identify cases. In all cases,
the pooled GP/hospital data produced the highest
incidence rates (376, 1089 and 767 per 100000 patient-
years for MI, IHD and CVD, respectively), and the paired
GP/hospital data gave the lowest incidence rates (188,
489 and 272 per 100000 patient-years, respectively).
There was no evidence that the incidence rates based on
only GP data differ from those of the hospital data for
either MI (p¼0.14) or CVD (p¼0.27), but there was
strong evidence that they were higher for IHD (975 and
673 events per 100000 patient-years for hospital and GP,
respectively, p<0.001). The pooled GP/hospital data
produced slightly higher incidence rates than hospital
data alone for CVD (p<0.001) and marginally so for MI
(p¼0.048) and IHD (p¼0.066).
Patient characteristics
Patient characteristics are shown in table 1 for all three
clinical conditions. There was no evidence that rates of
diabetes and hypertension, or the distribution of sex or
deprivation, varied between coding groups. Greater
numbers of smokers were found in the paired GP/
hospital group for patients with MI (45% in the paired
group compared with 28%e34% in the other groups,
p¼0.028) and IHD (35% compared with 24%e27%,
p¼0.021). The level of comorbidity for all conditions, as
Figure 1
incident events. The figure shows
how incident events can be
identified from linked general
practice (GP) and hospital data
sets, for eight hypothetical
patients, illustrating some of the
potential coding combinations.
Circles correspond to the
presence of a GP (B) or hospital
(C) clinical code, with numbers
illustrating the order. Immediately
adjacent circles represent codes occurring within 30 days of one another. It can be seen that, for any given patient, it is possible to
classify them as having an incident event in up to four ways: GP data only, hospital data only, paired GP/hospital and pooled GP/
hospital; the code that identifies an incident event for each of these methods is shown on the right of the figure. Codes do not count
as incident events if a further, similarly classified, event has occurred prior to the start of the study period. In our study, patients
were randomly allocated to one of the four coding methods. For instance, if patient E was allocated to ‘hospital only’ coding, they
would not be classified as having had an event; in contrast, they would be classified as having had an event if they were allocated
to any of the other three coding methods.
Identification of
Payne RA, Abel GA, Simpson CR. BMJ Open 2012;2:e000723. doi:10.1136/bmjopen-2011-000723
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measured by the Charlson Index, is lower in the paired
GP/hospital group (1.8, 1.3 and 1.9 for MI, IHD and
CVD, respectively) and higher in the hospital group (2.2,
1.7 and 2.4, respectively, p#0.014). For IHD and CVD,
there is evidence that patients identified using solely GP
or solely hospital data were slightly younger.
Prescribing
Differences in prescribing rates were observed between
coding groups (table 2). These were most marked for
IHD, where rates of prescribing of ACE inhibitors,
b-blockers, nitrates, statins and antiplatelet agents were
higher in the paired group (p#0.013). However, this
findingdid not appear to be replicated for MI specifically.
For CVD, prescribing rates for statins and antiplatelet
agents were lower in the hospital group (p#0.022).
case fatality
Considerable 30-day case fatality rate differences exist for
all three conditions depending on the coding used
(p#0.002, table 3). Rates for all conditions are highest in
patients coded only in hospital and lower in the GP and
Table 1Variation of patient characteristics with different methods of identifying cases
GPHospital Paired GP/hospitalPooled GP/hospitalp Value
Myocardial infarction
N
Men (%)
Age, mean (SD)
Deprivation quintile (%)
1
2
3
4
5
Smokers (%)
Diabetes (%)
Hypertension (%)
Charlson Index, mean (SD)
145
65
68 (13.8)
171
59
67 (13)
105
60
68.4 (13.8)
209
64
68.8 (14.9)
0.68
0.51
19
15
26
15
24
33
15
39
2.5 (1.7)
11
25
17
23
24
34
12
44
2.2 (1.6)
10
26
29
21
14
45
8
38
1.8 (1.4)
12
17
31
22
17
28
11
44
2.0 (1.6)
0.55
0.028
0.29
0.52
<0.001
Ischaemic heart disease
N
Men (%)
Age, mean (SD)
Deprivation quintile (%)
1
2
3
4
5
Smokers (%)
Diabetes (%)
Hypertension (%)
Charlson Index, mean (SD)
362
56
66.2 (12.7)
529
55
65.8 (11.6)
270
61
66.9 (13.4)
585
56
68.4 (12.8)
0.38
0.007
17
18
29
17
20
27
11
42
1.5 (1.6)
13
20
23
22
23
27
15
47
1.7 (1.6)
11
20
27
24
19
35
13
44
1.3 (1.3)
13
21
26
20
19
24
10
45
1.5 (1.5)
0.25
0.011
0.091
0.51
0.002
Cerebrovascular disease
N
Men (%)
Age, mean (SD)
Deprivation quintile (%)
1
2
3
4
5
Smokers (%)
Diabetes (%)
Hypertension (%)
Charlson Index, mean (SD)
302
48
70.3 (14.1)
330
47
70.8 (13.6)
153
46
72 (12.9)
424
47
73 (13.6)
0.97
0.031
9
23
29
24
15
26
13
46
2 (1.7)
12
18
29
22
20
28
16
49
2.4 (1.7)
8
22
32
24
14
29
13
53
1.9 (1.6)
11.6
19.1
23.6
23.3
22.3
25
13
46
2.1 (1.7)
0.72
0.68
0.47
0.40
0.014
Patient characteristics for myocardial infarction, ischaemic heart disease and cerebrovascular disease, identified using GP, hospital, paired GP/
hospital and pooled GP/hospital data. Deprivation quintile 1 is least deprived. Significant differences are calculated by c2test or Kruskal-Wallis
analysis of variance.
GP, general practitioner.
4
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paired GP/hospital groups. The most striking differences
were observed for MI, with a 30-day case fatality rate of
20% for the hospital group but only 4% for the GP group.
DISCUSSION
In a world where electronic healthcare data are
becoming increasingly used for the purposes of clinical
trials and epidemiological research, there is a need for
researchers to understand whether additional informa-
tion can be gained by linking two (or indeed more)
electronic health record data sources together. However,
where there is overlap between the constituent data sets,
such as with coding of clinical conditions, the researcher
needs to decide which data set to rely on for identifying
cases, or indeed whether combining information from
both the data sets may be of value. Our study demon-
strates that the method of coding MI, IHD and CVD
appears to result in identification of different types of
patient, in particular as characterised by prescribing and
case fatality rates. Incident rates of disease also vary
depending on the coding method used.
Previous work examining the epidemiology of cardio-
vascular disease has been conducted in Scotland using
routine clinical data. Primary care data have been used
to demonstrate that IHD is a common problem associ-
ated with male gender, increasing age and socioeco-
nomic deprivation.12Yet the recording of IHD data
varies in general practice with different methods used
for case detection.13Furthermore, external factors such
aspayment-for-performance
improvethe recording
have
of
beenshown to
IHD-relatedhealth
Figure 2
years, for different clinical conditions over a 2-year time period
beginning 1 January 2005, based on general practice (GP),
hospital, paired GP/hospital and pooled GP/hospital data.
CVD, cerebrovascular disease; IHD, ischaemic heart disease;
MI, myocardial infarction.
Incidence rates, expressed per 100000 patient-
Table 2Variation of patient characteristics with different methods of identifying cases
GPHospital Paired GP/hospitalPooled GP/hospitalp Value
Myocardial infarction
N
ACE inhibitor/ARB (%)
b-blocker (%)
Calcium channel blocker (%)
Diuretic (%)
Nitrate (%)
Statin (%)
Antiplatelet agent (%)
139
68
68
10
32
46
79
84
137
77
61
10
32
61
81
82
99
77
59
8
28
59
77
85
173
71
61
15
29
55
76
78
0.30
0.50
0.29
0.87
0.065
0.70
0.43
Ischaemic heart disease
N
ACE inhibitor/ARB (%)
b-blocker (%)
Calcium channel blocker (%)
Diuretic (%)
Nitrate (%)
Statin (%)
Antiplatelet agent (%)
353
48
57
21
35
40
67
71
484
48
54
21
30
43
67
71
262
58
62
25
34
60
82
87
541
45
49
19
33
40
63
66
0.013
0.005
0.28
0.57
<0.001
<0.001
<0.001
Cerebrovascular disease
N
ACE inhibitor/ARB (%)
b-blocker (%)
Calcium channel blocker (%)
Diuretic (%)
Nitrate (%)
Statin (%)
Antiplatelet agent (%)
285
38
25
20
32
15
56
54
278
33
19
15
33
14
41
44
145
31
22
13
32
15
53
50
381
36
19
17
33
13
50
55
0.42
0.16
0.27
0.99
0.94
0.006
0.022
The 30-day prescribing rates for myocardial infarction, ischaemic heart disease and cerebrovascular disease, identified using GP, hospital,
paired GP/hospital and pooled GP/hospital data. Patients are those alive at 30 days, and this is reflected by lower numbers of patients than in
tables 1 and 3. Significant differences are calculated by c2test.
ARB, angiotensin receptor blocker; GP, general practitioner.
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indicators.14Such incentivisation was introduced to UK
general practice (but not hospital practice) in 2004, and
so it is possible that this may have reduced the discrep-
ancies between hospital and GP data in our study.
Interestingly, pooling of GP and SMR records has
previously been advocated for detecting MI cases,15and
pooled GP/SMR data from the same data set we used
have demonstrated differences between cohorts of inci-
dent and prevalent MI.16However, the effect of using
only one component of such a data set has been hitherto
unknown.
Reasons for differences in incidence rates and patient
characteristics
Our data do not allow us to determine the exact cause of
our findings, but a number of hypotheses may be
proposed.Incident disease
between GP and hospital groups for MI and CVD. The
lower incidence of IHD for the GP group reflects the fact
that many patients will have had relatively stable coro-
nary disease for a number of years but not necessarily
required acute hospital admission. Thus, many GP
episodes of IHD do not count as true incident cases as
they have had prior contact with the GP, whereas
a higher number of hospital episodes are incident cases
as these patients have never been previously admitted.
The lower incidence rates for the paired GP/hospital
group, and higher incidence rates for the pooled GP/
hospital group, are inevitable consequences of the way in
which the two data sets are united, although the
magnitude of these differences will nonetheless reflect
the degree of inconsistency in coding between the two.
Furthermore, it would appear that because the paired
GP/hospital data considerably underestimate the true
disease incidence, it is probably not a useful method for
identifying cases, even though such cases might be more
rigorously identified. In addition, the increase in inci-
dence rate using the pooled GP/hospital data demon-
strates the potential advantage of combining two data
sets, over use of a single data set, from the perspective of
improving case finding.
isreassuringly similar
The discrepancies in death rates are probably relatively
straightforward to explain. Acute MI admission has
a high case fatality,1but those surviving beyond discharge
have a much lower case fatality subsequently. It seems
likely that the GP may fail to record the cause of death in
patients who do not survive the hospital admission, thus
resulting in the lower case fatality rates observed in the
paired GP/hospital coding group. Furthermore, it is
possible that patients coded only by the GP may repre-
sent ‘less serious’ illness, where hospitalisation is not
deemed necessary by the GP. It is recognised that many
patients suffering relatively minor strokes may not be
admitted to hospital,17resulting in lower case fatality for
CVD in the GP group, although with the growing avail-
ability of active treatment options for ischaemic stroke in
the form of thrombolysis, this may well change. We used
national mortality data to identify deaths from both GP
and SMR data sets, so discrepancies in recording of
death between GP and hospital are unlikely to explain
the differences in case fatality rates observed. Further-
more, the majority of paired events share exactly the
same date, suggesting that retrospective date entry by the
GP of the hospital event is common, and thus, there is no
reason why this could not be carried out for fatal events.
The higher prescribing rates for IHD in the paired
coding group are probably due to GPs responding
appropriately to secondary care instigated intervention,
reflected in appropriate treatment. That such differ-
ences were not observed for MI may be due to better
communication and awareness for this specific condition
compared with other IHD, such as angina, meaning that
prescribing in the hospital group appears just as good as
for the paired GP/hospital group. However, fewer MI
events may have left us underpowered to detect differ-
ences. The lack of difference in the GP and paired
groups for CVD may reflect poorer awareness of stroke
management guidelines18in comparison with coronary
heart disease, and so prescribing rates are consequently
no higher in the paired group. The lower prescribing
rates of statins and antiplatelet agents in the CVD
hospital group may reflect the GP being unaware of
Table 3 Variation of case fatality rates with different methods of identifying cases
GPHospitalPaired GP/hospital Pooled GP/hospital p Value
Myocardial infarction
N
30-day case fatality rate (%)
145171
20
105209
17460.001
Ischaemic heart disease
N
30-day case fatality rate (%)
362529270 585
29380.002
Cerebrovascular disease
N
30-day case fatality rate (%)
302330
16
153424
10650.001
The 30-day case fatality rates for myocardial infarction, ischaemic heart disease and cerebrovascular disease, identified using GP, hospital,
paired GP/hospital and pooled GP/hospital data. The significance of the differences between coding methods is adjusted for confounding
factors using logistic regression (see text for details).
GP, general practitioner.
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these patients’ clinical need resulting in undertreat-
ment; this is supported by the higher prescribing rates in
the paired group. The differences in other patient
characteristicsdspecifically smoking and comorbidityd
are less easy to understand but may represent increased
disease severity and mortality in hospitalised smokers
and multimorbid patients. The small differences in age
(<3 years) seem unlikely to be clinically relevant,
although may be pertinent from the public health
perspective. Finally, it may be that miscoding of diag-
noses may explain some of the above differences; for
instance, heart failure may be used as an alternative but
incorrect code for MI.19Furthermore, the introduction
of sensitive troponin assays has influenced MI detection
rates20; it is possible that lack of familiarity among some
clinicians for the resulting terms (eg, non-ST elevation
MI, acute coronary syndrome) may result in inaccurate
diagnoses being recorded.
Limitations
This study has highlighted important issues related to
patient coding and linked data, but although it has the
advantage of using a reasonably large routine data set,
linked at the individual patient level, a number of issues
and limitations should be considered. The relatively
small number of GP surgeries (40) may not have been
fully representative. In addition, the number of events is
relatively small, and given the conservative nature of the
c2test, this increases the possibility of type 2 errors; thus,
a larger data set may have identified more differences
between groups. We restricted our analysis to four simple
coding groupsdGP, hospital, paired and pooled GP/
hospital. However, it is clear that there are many further
ways of categorising events, including the presence or
absence of prior or subsequent coding based on the
alternative half of the data set. For instance, an incident
GP event with a historical hospital event may be coded
differently to a GP event with no previous hospital
record. However, we found that many of these theoret-
ical categories have only a handful of cases. Further-
more, even when we examined six or seven separate
smaller coding categories, similar differences in patient
characteristics persisted between groups (data not
shown). Our choice of four main groups was therefore
a pragmatic one, which reflects the choice that would
face a researcher dealing with a similar linked data set.
The decision to use a 30-day limit for pairing data could
also be questioned; we are unable to prove that these two
events are truly the same clinical episode. The choice was
again, therefore, partly pragmatic, although supported
by examination of the distribution of time gaps between
the GP and hospital data. We did not limit the lead-in
time period prior to 1 January 2005 in any way. Length of
GP records is generally greater and more variable than
SMR records, and there is the potential to see a lower
number of new incident events among persons with
longer GP records. Our study used routine GP data, and
it is possible that such profound differences may not be
found with research-standard databases, such as General
Practice Research Database (GPRD).21Nonetheless,
work linking primary care research databases to hospital
(and other) records is ongoing, and the issues raised by
our study must be acknowledged. The SMR data set only
records hospital events in Scotland and thus fails to
capture events in elsewhere in the UK or abroad. Similar
issues face the English equivalent Hospital Episode
Statistics, and a UK-wide hospital events data set would
be valuable. SMR (and Hospital Episode Statistics) also
provide multiple diagnostic codes for a single event. We
elected to use all six diagnostic positions to ensure
maximum capture of relevant hospital events. However,
the robustness of low-priority diagnoses might be ques-
tioned. Nonetheless, we found similar results when we
used only two diagnostic positions (data not shown). We
also did not examine miscoding of eventsdfor example,
a code of angina being used rather than the code for MI.
Coding of SMR is considered 99% complete and 88%
accurate8; corresponding metrics are not available for
PTI data (although the completeness and accuracy of
Read coding of morbidity in Scottish general practice
has been shown previously to be greater than 91%22).
Furthermore, the two data sets use different coding
systems, so completely reliable comparison is not
possible. However, we used relatively broad definitions,
and the Read code system is based on ICD. Nonetheless,
we may in particular have missed some administrative
Read codes, which might have enabled identification of
additional cases in the GP group. Of course, ideally
further validation of the coding should be conducted;
linkage to laboratory data might be one way of achieving
this. Finally, our 30-day limit for prescribing was selected
from a pragmatic perspective. However, it is possible that
patients who were admitted for over 30 days would not
have had a new prescription issued by the GP within the
30-day post-event period, resulting in an apparent
underestimation of prescribing. We believe that these
numbers will be relatively small, however, and unlikely to
alter the overall interpretation of our findings.
Research and policy implications
These results have significant implications for linked
data; the drug management, disease severity and to some
degree the patient characteristics vary depending on
how the disease cohort is defined. They also have
implications for the use of unlinked routine dataduse of
isolated primary or secondary care data may result in
a biased selection of patients. This may affect patient
recruitment as well as the validity and reliability of such
information sources as secondary data in clinical trials,
including clinical outcomes. It is similarly relevant to the
public health environment. Using linked data allows one
to have a more robust definition, by using pairs of GP
and hospital codes only, but it is clear that the apparent
incidence of a disease will be considerably lower. Alter-
natively, linked data enable a looser but more inclusive
disease definition, using both GP and hospital data, but
not relying on the coding occurring simultaneously.
When using separate data from only one source, one
Payne RA, Abel GA, Simpson CR. BMJ Open 2012;2:e000723. doi:10.1136/bmjopen-2011-000723
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Characterising cardiovascular disease using linked routine data

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needs to take into account that patient characteristics
may not be representative of the wider population. It is
difficult to recommend one coding approach over
another, however, and the decision will need to be based
on the specific question being posed.
CONCLUSIONS
In conclusion, patient characteristics vary depending on
whether GP, hospital or combined definitions of
cardiovascular events are used. In particular, disease
severity as measured by mortality varies considerably.
This has important implications for studies using linked
routine primary and secondary care data, and for studies
where information is only available from one of these
sources. These issues should be acknowledged by studies
using routine data as a secondary data source, and
further work is merited to examine whether similar
discrepancies exist for other clinical conditions or within
primary care research databases.
Contributors RAP conceived the study. RAP and GAA contributed to the study
design, analysis and interpretation and to the drafting of the article. CRS
acquired the data and set up the linked database. All authors contributed to the
critical revision of the paper and approval of the final version.
Funding This research received no specific grant from any funding agency in
the public, commercial or not-for-profit sectors.
Competing interests None.
Provenance and peer review Not commissioned; externally peer reviewed.
Data sharing statement There are no additional data available.
REFERENCES
1.Scarborough P, Bhatnagar P, Wickramasinghe K, et al. Coronary
Heart Disease Statistics. 2010 edn. London: British Heart Foundation,
2010.
2.Erkinjuntti T, Ostbye T, Steenhuis R, et al. The effect of different
diagnostic criteria on the prevalence of dementia. N Engl J Med
1997;337:1667e74.
3.Rollason W, Khunti K, de Lusignan S. Variation in the recording of
diabetes diagnostic data in primary care computer systems:
implications for the quality of care. Inform Prim Care
2009;17:113e19.
4.Boyle CA, Dobson AJ. The accuracy of hospital records and death
certificates for acute myocardial infarction. Aust N Z J Med
1995;25:316e23.
5.Cheng P, Gilchrist A, Robinson KM, et al. The risk and consequences
of clinical miscoding due to inadequate medical documentation:
a case study of the impact on health services funding. HIM J
2009;38:35e46.
Jordan K, Porcheret M, Croft P. Quality of morbidity coding in general
practice computerized medical records: a systematic review. Fam
Pract 2004;21:396e412.
Khan NF, Harrison SE, Rose PW. Validity of diagnostic coding within
the General Practice Research Database: a systematic review. Br J
Gen Pract 2010;60:e128e36.
NHS Hospital Data Qualitydtowards Better Data from Scottish
hospitals. An assessment of SMR01 and Associated Data
2004e2006. Edinburgh: ISD Scotland, NHS National Services
Scotland, 2007.
Scottish Index of Multiple Deprivation 2009 Technical Report. Office
of the Chief Statistician. Edinburgh: Scottish Government, 2010.
Charlson ME, Pompei P, Ales KL, et al. A new method of classifying
prognostic comorbidity in longitudinal studies: development and
validation. J Chron Dis 1987;40:373e83.
Khan NF, Perera R, Harper S, et al. Adaptation and validation of the
Charlson Index for Read/OXMIS coded databases. BMC Fam Pract
2010;11:1.
Murphy NF, Simpson CR, MacIntyre K, et al. Prevalence, incidence,
primary care burden and medical treatment of angina in Scotland:
age, sex and socioeconomic disparities: a population-based study.
Heart 2006;92:1047e54.
Moher M, Yudkin P, Turner R, et al. An assessment of morbidity
registers for coronary heart disease in primary care. ASSIST
(ASSessment of Implementation STrategy) trial collaborative group.
Br J Gen Pract 2000;50:706e9.
McGovern MP, Boroujerdi MA, Taylor MW, et al. The effect of the UK
incentive-based contract on the management of patients with
coronary heart disease in primary care. Fam Pract 2008;25:33e9.
Donnan PT, Dougall HT, Sullivan FM. Optimal strategies for
identifying patients with myocardial infarction in general practice. Fam
Pract 2003;20:706e10.
Buckley BS, Simpson CR, McLernon DJ, et al. Considerable
differences exist between prevalent and incident myocardial infarction
cohorts derived from the same population. J Clin Epidemiol
2010;63:1351e7.
Gibbs RG, Newson R, Lawrenson R, et al. Diagnosis and initial
management of stroke and transient ischemic attack across UK
health regions from 1992 to 1996: experience of a national primary
care database. Stroke 2001;32:1085e90.
Jagadesham VP, Aparajita R, Gough MJ. Can the UK guidelines for
stroke be effective? Attitudes to the symptoms of a transient
ischaemic attack among the general public and doctors. Clin Med
2008;8:366e70.
Austin PC, Daly PA, Tu JV. A multicenter study of the coding
accuracy of hospital discharge administrative data for patients
admitted to cardiac care units in Ontario. Am Heart J
2002;144:290e6.
Parikh NI, Gona P, Larson MG, et al. Long-term trends in myocardial
infarction incidence and case fatality in the National Heart, Lung, and
Blood Institute’s Framingham Heart study. Circulation
2009;119:1203e10.
The General Practice Research Database. http://www.gprd.com/
academia/primarycare.asp (accessed 29 Jun 2011).
Murphy NF, Simpson CR, McAlister FA, et al. National survey of the
prevalence, incidence, primary care burden, and treatment of heart
failure in Scotland. Heart 2004;90:1129e36.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
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Payne RA, Abel GA, Simpson CR. BMJ Open 2012;2:e000723. doi:10.1136/bmjopen-2011-000723
Characterising cardiovascular disease using linked routine data