J R Soc Med 2003;96:328-332
doi:10.1258/jrsm.96.7.328
© 2003 Royal Society of Medicine
Hidden diabetes in the UK: use of capture–recapture methods to estimate total prevalence of diabetes mellitus in an urban population
Geoffrey V Gill MD
Aziz A Ismail PhD
Nicholas J Beeching MB
Sarah B J Macfarlane MSc
Mark A Bellis PhD 1
Division of Tropical Medicine, Liverpool School of Tropical Medicine, Pembroke
Place, Liverpool L3 5QA
1
School of Health, Liverpool John Moores University, 79 Tithebarn Street,
Liverpool L2 2ER, UK
Correspondence to: Dr G V Gill E-mail:
g.gill{at}liv.ac.uk
 |
SUMMARY
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An early requirement of the UK's Diabetes National Service Framework
is
enumeration of the total affected population. Existing estimates
tend to be
based on incomplete lists. In a study conducted
over one year in North
Liverpool, we compared crude prevalence
rates for type 1 and type 2 diabetes
with estimates obtained
by capture–recapture (CR) analysis of multiple
incomplete
patient lists, to assess the extent of unascertained but diagnosed
cases. Patient databases were constructed from six sources—a
hospital
diabetes centre; general practitioner registers; hospital
admissions with a
diagnosis of diabetes; a hospital diabetic
retinal clinic; a research list of
patients with diabetes admitted
with stroke; and a local children's hospital.
Log linear modelling
was used to estimate missing cases, hence total
prevalence.
The crude prevalence of diabetes was 1.5% (95% confidence interval [CI]
1.41, 1.52), compared with a CR-adjusted rate of 3.1% (CI 3.03, 3.19).
Age-banded CR-adjusted prevalence was always higher in males than in females
and the difference became more pronounced with increasing age. Among males,
CR-adjusted prevalence rose from 0.4% at age 10–19 years to 18.3% at 80+
years; in females the corresponding figures were 0.4% and 9.3%.
The gap between crude and CR-estimated prevalence points to a rate of
'hidden diabetes' that has substantial implications for future
diabetes care.
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INTRODUCTION
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Concerned about the morbidity and mortality of diabetes mellitus,
the UK
Government has published a Diabetes National Service
Framework aimed at
improving outcomes over the next decade.
A major and early requirement is to
enumerate all people with
diabetes. This is difficult, first because type 2
diabetes
commonly goes undiagnosed and, second, because many patients
attend
for health care irregularly if at all. Standard methods
of diabetes prevalence
estimation include population surveys,
postal questionnaires, house-to-house
surveys, clinic or hospital
records, and computerized diabetes
registers.
1–4
All
these are labour-intensive and tend to undercount. Thus
'ascertainment
correction' has been used to adjust crude prevalence
rates
for patients not counted by the customary
methods.
5 The
technique,
known as capture–recapture (CR), was originally developed
by
zoologists to count animal populations but is applicable
in specific
diseases.
6,7
Independent lists or registers of
patients are used as 'captures'.
Those people who appear on
two lists or more are, in effect,
'recaptured'.
6–8
CR
has been used for type 1 (insulin dependent) diabetes and other
diseases,
9–11
but has seldom been applied to prevalence
estimation of both type 1 and type 2
diabetes. We have used
CR to determine total diabetes prevalence in North
Liverpool.
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METHODS
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When applied to epidemiology, the CR technique assumes that
lists of people
with the disease in question will not include
every person with the disease.
When various lists are examined,
high degrees of overlap suggest that most
people with the disease
are being 'captured' and the reverse may
point to a substantial
number of 'missing' cases. By use of multiple
lists and statistical
software, accurate estimates of the 'true'
diabetic population
(with confidence intervals) can be obtained. Provided the
total
general population is known, these results can be transposed
to
prevalence figures as a percentage of the local population.
The target population for this study was the district of South Sefton in
North Liverpool, an urbanized area with a stable population of about 177 000,
mainly of European origin (Merseyside Information Services, Central Operation
Group, personal communication). Six lists of cases were constructed—a
list from 25 computerized general practices; patients attending the local
hospital diabetes centre; hospital admissions with a discharge diagnosis of
diabetes; diabetic patients attending the local hospital retinal clinic; a
research list of patients with diabetes admitted to hospital with stroke; and
patients attending the diabetes service at the local children's hospital.
Lists were obtained from computer printouts obtained over one year. The
diagnoses of diabetes were based on World Health Organization criteria
applicable at the
time.12 Apart from
the local stroke research database (which was small), all the lists were of a
kind likely to be available in other areas of the UK, and our methodology
should be transferable elsewhere.
The information was entered onto computer using the database software
Epi-Info version
6.04.13 The
Statistical Package for the Social Sciences
(SPSS)14 and the
Generalized Linear Interactive Modelling Package (GLIM
4)15 were used for
analysis. Cases were matched between lists by surname, first name, date of
birth and postcode, by use of a sort and aggregate command in SPSS. Records
without surname, first name, date of birth and the first part of the postcode
were removed to ensure accurate matching. The postcode inclusion ensured that
only patients resident in South Sefton were enumerated. To avoid errors due to
list dependency, the final CR analysis was performed on three pooled lists,
composed of all the general-practice lists (list a); the diabetes centre and
the children's hospital list (list b); and the hospital admission list,
retinal clinic list, and the stroke database (list c). Such combinations
reduce interdependence
errors,16,17
and use of more than three lists does not significantly improve the accuracy
of the population
estimate.18 A
stepwise selection procedure of the various combinations of lists was used,
interdependence being tested by goodness of fit (log-linear modelling). The
number of missing cases was estimated from a 26262 contingency table (for the
three combined lists) by use of GLIM software. Asymptotic confidence intervals
were estimated with the same program. Full details of statistical methods have
been published
elsewhere.17–21
The ascertainment rate—a measure of completeness of case
identification—was calculated by dividing the number of cases identified
in each source by the aggregated number of cases identified from all sources.
The prevalence of diabetes was determined by dividing the number of cases
(aggregated cases for crude rate and estimated cases for CR-adjusted rate) by
the total population in the group and subgroup (1991 Census).
The study was approved by the research and ethics committee of the Aintree
Hospitals NHS Trust and Sefton Health Authority. Confidentiality of
information was maintained at all times, according to the UK Data Protection
Act,22 and the
information was anonymized after the matching procedures.
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RESULTS
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A total of 2585 known diabetic patients were identified through
the six
lists.
Table 1 shows the
numbers on each list, with
age and sex ratio details, and the case
ascertainment. For
the three combined lists used for CR analysis, the
distribution
was 1469 for list a, 1314 for list b and 710 for list c. These
details are shown, together with overlap, by Venn diagram in
Figure 1. Log linear modelling
by the GLIM package showed
the estimated number of missing cases to be 2907
yielding a
total diabetic population of 5492 (95% confidence interval [CI]
4870, 6285).
View larger version (8K):
[in this window]
[in a new window]
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Fig. 1. Number of diabetic cases identified by three sources La=general
practice, Lb=diabetes centre and Alder Hey Children's Hospital, Lc=hospital
admission, stroke database and retinal clinic, m=missing cases
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From the known diabetic patients appearing on all the lists (n=2585) and
the population of 176 682, the crude prevalence rate was 1.5% (CI 1.41, 1.52).
However, use of the CR-adjusted number of cases gave a prevalence of 3.1% (CI
3.03, 3.19).
Table 2 shows crude and
CR-adjusted prevalence rates for age bands by decade. Males were consistently
ahead of females, and the excess of CR-adjusted rates over crude rates was
particularly evident for men over 70. Finally, we estimated age-adjusted rates
using 1997 demographic
data.23 This showed
similar figures for crude rates—1.4% (CI 1.2, 1.6) in females, and 1.6%
(1.4, 1.9) in males. Age-adjusted rates by CR were slightly lower at 2.4%
(2.2, 2.7) in females and 3.1% (2.8, 3.4) in males.
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DISCUSSION
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Since the data collection some 5 years ago, diabetes pr'evalence
may
have increased, but our purpose is to demonstrate that
simple case collection
in a given area and population may seriously
underestimate total diabetes
prevalence. Indeed, the main finding
of our study is that
capture–recapture adjustment of
crude diabetes prevalence rates
increases the figure considerably,
and reveals a large number of
'hidden' cases: the crude prevalence
was 1.5%, but the CR-adjusted
rate was 3.1%. Sex-stratification
showed an increase amongst females from 1.4%
to 2.7% (crude
to CR), and 1.6% to 4.0% in males. Our statistical methodology
also ensured that we minimized co-dependence of lists, which
reduces the
accuracy of
CR
18–21
(for example, we separated
the diabetes centre and retinal clinic lists, since
many patients
would clearly appear on both). Finally, our results are in
accord
with the only similar study, in which Bruno and colleagues in
northern
Italy reported a CR-adjusted total diabetes prevalence
rate of 2.8% (95% CI
2.4, 3.1).
16
Equally, the age-banded
data showed large excesses for CR with advancing age,
as has
been described in other
studies.
24,25
The lower total and
age-specific prevalence rates in females were likewise in
agreement
with other
data,
2,24,25
and in particular with a careful epidemiological
study from South
Wales.
26
In our study, we chose to estimate total, rather than separate type 1 and
type 2, diabetes. There are several reasons for this. CR estimation of type 1
diabetes has already been applied widely—for example in
Britain,9
Australia,27
Spain,28
Italy29 and
Holland.30 Because
of the smaller numbers of type 1 and the greater ease of diagnosis and
identification, lists of type 1 diabetic persons have a high degree of
ascertainment, and CR methods are especially applicable and appropriate. Type
2 diabetes is more problematic since ascertainment is much less complete.
Additionally, many of the existing lists do not specify the type of diabetes
(though it can sometimes be inferred from treatment, age and duration of
disease17). These
difficulties explain why the Italian study referred to
above16 is the only
other attempt at CR estimation of total diabetes prevalence. Obviously,
ascertainment rate variability between sources for type 1 and type 2 diabetes
may introduce the potential for increased error, but our technique of
combining multiple
lists18 will have
reduced this hazard.
District diabetes registers are often constructed from multiple patient
lists31,32—in
particular, hospital clinic and general practitioner lists. Computerized
summation of all patients on such lists is sometimes known as electronic data
linkage, and can be used to assess crude prevalence rates in individual
districts.33 This
was essentially the technique used by ourselves to estimate crude prevalence
in North Liverpool (1.5%). A very high quality group of lists has been used in
Tayside, Scotland (the DARTS/MEMO database), and gave an estimated diabetes
prevalence of
1.9%.33 This
register has so far not been examined by CR, but our results suggest that the
true prevalence will be higher.
Further validation of the CR technique in diabetes epidemiology is needed.
It is said to be rapid and inexpensive, but a detailed cost comparison with
other epidemiological tools has not been done. A direct comparison with a
standard technique such as house-to-house survey would be
valuable.21
Nevertheless, there is already sufficient evidence to support the use of the
technique in diabetes epidemiology, provided that multiple lists are
used18,34
and that these are combined in a way that minimizes co-dependency and allows
similar chances of
capture.35 The
population studied must also be stable in terms of migration and
mortality.36
The large number of patients with 'hidden diabetes' indicated by
this survey has great implications for health resource allocation,
particularly in view of the rising age of the general population. Standard
estimates of prevalence may lead to gross undercounting, and wider use of
capture–recapture techniques should be seriously considered.
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Acknowledgments
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We thank Ms Liz Harnsape of Sefton Health Authority, staff at
the Walton
Hospital Diabetes Centre, Mr Keith Jones for assistance
with the hospital
admission data from Aintree Hospitals, Ms
Susan Kerr and Dr Colin Smith for
providing data from the Alder
Hey Children's Hospital Diabetic Clinic, and Dr
Anil Sharma
and Mr Kevin McDonald for providing data from the Aintree Hospital
Stroke Research Unit database. We also thank Professor Ronald
LaPorte for his
suggestions and comments.
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SUMMARY
INTRODUCTION
