Data linkage: a powerful research tool with potential problems.

Abstract

Policy makers, clinicians and researchers are demonstrating increasing interest in using data linked from multiple sources to support measurement of clinical performance and patient health outcomes. However, the utility of data linkage may be compromised by sub-optimal or incomplete linkage, leading to systematic bias. In this study, we synthesize the evidence identifying participant or population characteristics that can influence the validity and completeness of data linkage and may be associated with systematic bias in reported outcomes.
A narrative review, using structured search methods was undertaken. Key words "data linkage" and Mesh term "medical record linkage" were applied to Medline, EMBASE and CINAHL databases between 1991 and 2007. Abstract inclusion criteria were; the article attempted an empirical evaluation of methodological issues relating to data linkage and reported on patient characteristics, the study design included analysis of matched versus unmatched records, and the report was in English. Included articles were grouped thematically according to patient characteristics that were compared between matched and unmatched records.
The search identified 1810 articles of which 33 (1.8%) met inclusion criteria. There was marked heterogeneity in study methods and factors investigated. Characteristics that were unevenly distributed among matched and unmatched records were; age (72% of studies), sex (50% of studies), race (64% of studies), geographical/hospital site (93% of studies), socio-economic status (82% of studies) and health status (72% of studies).
A number of relevant patient or population factors may be associated with incomplete data linkage resulting in systematic bias in reported clinical outcomes. Readers should consider these factors in interpreting the reported results of data linkage studies.

Full text

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RESEARCH ARTICLEOpen Access
Data Linkage: A powerful research tool with
potential problems
Megan A Bohensky1*, Damien Jolley1, Vijaya Sundararajan2, Sue Evans1, David V Pilcher3, Ian Scott4,
Caroline A Brand1
Abstract
Background: Policy makers, clinicians and researchers are demonstrating increasing interest in using data linked
from multiple sources to support measurement of clinical performance and patient health outcomes. However, the
utility of data linkage may be compromised by sub-optimal or incomplete linkage, leading to systematic bias. In
this study, we synthesize the evidence identifying participant or population characteristics that can influence the
validity and completeness of data linkage and may be associated with systematic bias in reported outcomes.
Methods: A narrative review, using structured search methods was undertaken. Key words “data linkage” and Mesh
term “medical record linkage” were applied to Medline, EMBASE and CINAHL databases between 1991 and 2007.
Abstract inclusion criteria were; the article attempted an empirical evaluation of methodological issues relating to
data linkage and reported on patient characteristics, the study design included analysis of matched versus
unmatched records, and the report was in English. Included articles were grouped thematically according to
patient characteristics that were compared between matched and unmatched records.
Results: The search identified 1810 articles of which 33 (1.8%) met inclusion criteria. There was marked
heterogeneity in study methods and factors investigated. Characteristics that were unevenly distributed among
matched and unmatched records were; age (72% of studies), sex (50% of studies), race (64% of studies),
geographical/hospital site (93% of studies), socio-economic status (82% of studies) and health status (72% of
studies).
Conclusion: A number of relevant patient or population factors may be associated with incomplete data linkage
resulting in systematic bias in reported clinical outcomes. Readers should consider these factors in interpreting the
reported results of data linkage studies.
Background
Reports in the United States [1], Canada [2], United
Kingdom [3] and Australia [4] have recommended
increasing the use of existing data, such as administra-
tive and clinical registry data, to provide comparative
clinical performance data to health services, hospitals,
clinical units and clinicians via internal channels and to
consumers via publicly accessible media. Although a
limited number of patient outcomes, such as in-hospital
mortality, complication and re-admission rates are
currently available from some administrative data
sources, obtaining data from several different databases
pertaining to the one individual or participant using
data linkage is often necessary to ensure adequate risk-
adjustment and examine a more comprehensive range
of outcomes for comparison between organisations.
Data or record linkage has been defined as “a process
of pairing records from two files and trying to select the
pairs that belong to the same entity [5].” Linkage may
be conducted between two distinct data sources or
within a single data-set to identify multiple entries (e.g.
re-admissions) for one person or record unit. The var-
ious uses of linked data in clinical research and the
types of data linkage that can be deployed are briefly
described in additional file 1. In the United Kingdom,
47% of multicentre clinical databases surveyed in 2003
* Correspondence: megan.bohensky@monash.edu
1Centre of Research Excellence in Patient Safety, Dept of Epidemiology &
Preventive Medicine, School Public Health & Preventive Medicine, Monash
University, Melbourne, Victoria, Australia, 3181 http://www.crepatientsafety.
org.au
Full list of author information is available at the end of the article
Bohensky et al. BMC Health Services Research 2010, 10:346
http://www.biomedcentral.com/1472-6963/10/346
© 2010 Bohensky et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly cited.

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by Black et al reported that they undertook routine data
linkage to other databases [6]. A review by Evans et al
reported that 68% of Australian clinical registries routi-
nely undertook some form of data linkage to obtain out-
come information, such as death or disease status, and
to assess data quality [7]. The use of data linkage in
research studies has increased almost 6 fold within the
last two decades. A search by one of the authors (MAB)
of the term “data linkage” and of the heading “medical
record linkage” in study abstracts and titles on Medline
identified only 161 studies between 1992 and 1997,
compared to 951 studies between 2002 and 2007. This
proliferation of data linkage is reflected in the establish-
ment of data linkage research centres and initiatives in
Australia [8,9], North America [10,11] and the United
Kingdom [12,13].
While data linkage is an important tool in observa-
tional research, it may be associated with various types
of error. When linking two data-sets, there is a propor-
tion of cases that will match and a proportion that will
remain unmatched. Error arises if data sources do not
consistently capture the same cases, records that corre-
spond to the same person do not link due to missing or
inaccurate data (false negatives), or unrelated records
are mistakenly linked (false positives).
It is often difficult to assess the quality of a linkage
when the patient outcome which is being linked to
other variables is unknown or there is not an expected
one-to-one relationship between one variable and
another. For example, when linking records to a death
registry to determine a patient’s survival status, it is dif-
ficult to know which matches have been missed if it is
unknown whether the patient is alive or dead. A sys-
tematic review of probabilistic linkage accuracy identi-
fied only six articles that had complete data on
summary measures of linkage quality and found the sen-
sitivity of linkage (ie the proportion of truly matched
records detected) to range from 74% to 98% and specifi-
city (ie the proportion of truly unmatched records
detected) to range from 99 to 100% [14]. In the studies
with lower sensitivity, findings may be biased leaving the
results and their interpretation open to question.
There is a need for clinicians and policymakers to
understand the limitations of linkage for outcome mea-
surement. Methods for evaluating the quality of existing
data sources and operating data linkage services have
been previously reported [15-18]. While these publica-
tions offer a framework for evaluating existing data
sources and managing data linkage services, there is a
paucity of literature that discusses the quality and lim-
itations of research using linked data.
The purpose of this study was to synthesize the evidence
through a structured narrative review of patient or popula-
tion characteristics that may be associated with changes in
sensitivity and specificity of data linkage, thereby introdu-
cing systematic bias into reported outcomes.
Methods
A structured, narrative review of the literature was con-
sidered appropriate for this form of observational
research. Medline, EMBASE and CINAHL databases
were search using the search strategies reported below:
• MEDLINE
“data linkage” as keywords in title, abstract, name of
substance word, subject heading word
“medical record linkage"[Mesh]
Limited to years 1991-2007, the English Language and
studies involving humans.
• CINAHL
Data linkage OR Medical record linkage
Limited to years 1991-2007, the English Language and
studies involving humans
• EMBASE
Data AND Linkage
Medical AND Record AND Linkage
Limited to years 1991-2007, the English Language and
studies involving humans, excluding letters, notes,
commentaries and editorials.
A priori criteria for abstract inclusion were that the
article empirically evaluated methodological issues relat-
ing to data linkage and reported on patient characteris-
tics in matched versus unmatched records. Articles were
excluded if they did not involve data linkage, such as a
discussion of another form of health information tech-
nology; if they did not involve data, such as a commen-
tary, letter or discussed a data linkage methodology
without an empirical evaluation or if they presented a
linkage project without comparing characteristics in
matched and unmatched records. We excluded studies
with an area of focus in genetics from our EMBASE
search, as our search term was detecting genome linkage
projects. We reviewed a subset of these studies (n = 105,
10%) to determine if any of these articles met our study
inclusion criteria and confirmed they did not.
Study titles, abstracts and full articles were screened
and the review and evaluation of the studies were per-
formed by one researcher (MAB). Participant character-
istics influencing the completeness of data linkage were
grouped thematically after reviewing studies that met
the inclusion criteria. As no quality grading tool exists
for studies utilising linked data, we assessed the quality
of the studies by examining the participant characteris-
tics that were assessed and the methods used for evalu-
ating potential sources of bias.
Results
Using the search strategy described above, the search of
Medline identified 1451 articles, CINAHL identified 317
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articles and EMBASE identified 42 articles not identified
in the MEDLINE searches. Of the 1810 studies identi-
fied in the search, 1416 abstracts (78.2% of all studies)
were screened. Of these, 33 (1.8%) articles met the
inclusion criteria (see Figure 1 for search process). A
review of the references of the included studies did not
identify any additional articles meeting our inclusion cri-
teria. All articles were grouped thematically according to
patient or population characteristics that were compared
in matched and unmatched records (listed in additional
file 2). Below, we describe characteristics of the identi-
fied studies and present a qualitative synthesis of the
differences in matching rates according to each of the
identified patient or population characteristics: age, sex,
ethnic/racial group, geographical/hospital site, socio-eco-
nomic status and health status. Selected case study
descriptions are provided in additional file 3.
Study characteristics
While a number of studies used data linkage methods
(n = 612), we identified only 33 studies that examined
characteristics of matched versus unmatched records.
Identified studies were from a range of countries (12
from the United States, 8 from Australia, 5 from the
United Kingdom, 4 from Canada and 1 each from the
Netherlands, New Zealand, Switzerland and Taiwan)
demonstrating the increasing popularity of these meth-
ods. Among the included studies, there was broad het-
erogeneity of reported characteristics. Some studies only
reported one patient characteristic while others reported
up to six. The methods for assessing the completeness
of linkage were also inconsistent. Some studies com-
pared proportions of different patient characteristics
among matched and unmatched records, while others
examined the odds of a successful match using regression
analysis. The influence of missed links on the measure
of association between exposure and outcome is difficult
to quantify where data are unlinked (i.e. if the data with
the exposure measure are not linked to the correspond-
ing data with the outcome of interest). However, some
studies did attempt to account for this through a sensi-
tivity analysis of possible outcomes. Common reasons
for the linkage errors have been categorised in addi-
tional file 4 along with a framework for potential
reporting items that can assist in developing greater
standardisation in this area.
Age
Eighteen studies looked at the relationship between par-
ticipants’ age and linkage rate. Five of these studies
found that no significant relationship between age and
linkage errors [19-23]. There was a trend for older parti-
cipants to be less likely to consent to record linkage
[24-26]. Where consent was not sought, the association
between age group and correctly matched records was
less clear with both younger and older participants hav-
ing lower linkage rates [27-35]. Adams et al found that
mothers aged under 18 years were more likely than
other age groups to have missing social security num-
bers (21.4% vs 3.2% for 25-29 year olds; p < 0.00), which
was a key linkage variable used to match their records
to their child’s birth records [27]. As younger maternal
age has been associated with greater risk of adverse
pregnancy outcomes, the higher potential for missed
linkage when relying on social security number could
underestimate this as a risk factor.
Gender
Ten of the identified articles compared gender in linked
and unlinked records. In five of these articles, the rela-
tionship was not found to be significant [19,21,22,25,31].
In three studies, males had lower linkage rates
[24,28,29] and in two studies women had lower linkage
rates [34,35]. Males were found to be less likely to con-
sent to record linkage in one study [24]. As women’s
names are more likely to change over their lifespan,
linkage relying on surnames may explain the discre-
pancy in at least one of the studies [34]. In the other
study, the linkage utilised participants’ social security
numbers, which more women than men may not have if
they have never been employed [35].
Ethnic/racial groups
Differences in linkage rates among ethnic or racial
groups were examined in fourteen studies with five find-
ing no variations in rates of linkage error according to
ethnic group [21,23,25,34,36]. In seven studies, it was
found that people in minority groups had lower linkage

1810 articles identified under search
terms


621 full articles reviewed



•
473 excluded based on title and abstract
not relating to data linkage (e.g. e-health,
electronic medical records)

•
385 articles excluded for being
letters/commentaries

•
331 excluded based on title and abstract
not indicating that the study reported
results (e.g. study design, methodological
articles)

•
547 excluded due to use of data linkage
project without evaluation between
matched and unmatched cases

•
32 excluded due to describing a linkage
method without evaluation of the quality

•
9 duplicate reports

33 articles included in the review
Figure 1 Flow diagram of included and excluded studies.
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rates [26,27,35,37-40]. Several studies offered reasons for
this finding including subjects in minority groups being
treated at facilities with less complete data [38], a lower
rate of migrants reporting accidents to the police [40],
higher rates of non-consent to data linkage [26] and
lower likelihood of having social security numbers
recorded [35]. Conversely, in one Australian study, Aus-
tralian-born mothers had lower linkage rates due to
more frequent treatment in private hospitals which
lacked data on mothers’ full names [20]. The remaining
study found that estimations of death varied for ethnic
groups depending on the linkage method [30].
Geographical/hospital sites
As data collection practices and training are likely to
vary between sites, 13 of 14 identified studies found a
relationship between the quality of linkage at different
geographic or hospital sites [25-27,29,33,34,38,40-45]
with only one study finding no relationship [21]. Dar-
lymple et al examined the use of a record linkage sys-
tem to track and identify treatment patterns of patients
with psychiatric conditions in Ontario, Canada. It was
found that the prevalence of linkage error in the com-
munity health sector was five times that of psychiatric
hospitals, possibly due to a higher turnover of data entry
staff with less training [42]. Within community agencies,
misidentification by unique identifier code was approxi-
mately 22% compared to 0.5% for psychiatric hospitals,
making it difficult to accurately track utilisation patterns
of patients treated outside of psychiatric hospitals.
Socio-economic status
Eleven articles examined socio-economic status, and
other proxy measures of social deprivation (assessed by
income level, car availability and insurance status) and
linkage rates. Two studies did not find a relationship
[23,41], while nine studies found people in lower socio-
economic groups and with lower levels of educational
attainment were less likely to have matched records
[21,25-27,32,34,37,46,47]. The differences were mainly
attributedtolower consent
[21,25,26,47,48] and less complete data and reporting
for people in lower socio-economic categories [36,49].
rates forlinkage
Health status
Eighteen studies were identified that examined health
status in matched and unmatched records. Five of the
eighteen studies that examined the relationship between
health status or condition severity found that differences
in health status were evenly distributed between
matched and unmatched records [19,25,39,50,51].
Tromp et al found that twin births with multiple read-
missions had errors in the linkage of readmission
records due to inaccuracies in twin rank data [51].
Generally, people with poorer health tended to have
higher rates of consent for record linkage [21,24,47] and
five studies found that pre-term and low birth weight
children tended to be under-reported or have less accu-
rate data [20,36,45,49,52,53]. Three studies found
patients with greater illness or injury severity had better
quality data, as a result of being treated in hospital as
opposed to lower acuity health services [31,40,42]. Kari-
minia et al found that the sensitivity of reporting drug-
related deaths and suicides was lower compared to
other causes of death [54], while Magliano et al found
that reporting of cancer-related deaths was higher than
deaths due to cardiovascular disease [55].
Discussion
The results of this review have identified a number of
participant characteristics (age, gender, race, setting,
socioeconomic and health status) that are associated
with incomplete data linkage and potential for systema-
tic bias in reported outcomes. As demonstrated in addi-
tional file 2 there is heterogeneity of reported patient
characteristics and methods for assessing and reporting
these differences, which highlights the lack of standardi-
sation in this area. To our knowledge, this is the first
study to synthesize the literature on assessing quality of
data linkage and suggests how it may influence the
validity of research results. For many variables examined
in the reported studies, the evidence for an association
between the variable and rates of data linkage was
inconsistent. This may reflect differences in sampled
populations and other contextual factors.
The reasons for different rates of completeness in data
linkage are varied but can be broadly grouped into the
methods of linkage, governance issues, such as requiring
consent for linkage, and the accuracy and completeness
of the data within each data source. These may be gen-
eral issues related to the linkage variables, such as differ-
ences in linkage rates by gender resulting from female’s
changing their names, or contextual issues related to a
specific study or setting, for example the use of social
security numbers as a linkage variable in the United
States. In countries that lack a national health identifier,
data linkage commonly relies on names or medical
record numbers, and these may be collected with vary-
ing degrees of accuracy and completeness across hospi-
tal sites, as was identified in the study by Ford et al [20].
This study has limitations that should be noted. There
is currently no grading system available to assess studies
utilizing linked data. Existing critical appraisal tools,
such as the STROBE guidelines[56], do not address
issues of bias associated with data linkage. Consequently,
we were unable to apply a validated, standardized tool
to assess the quality of these studies. However, this
review was intended to be an exploratory discussion of
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potential sources of bias to highlight these issues for
researchers and encourage more systematic assessment
and reporting of linkage methods in the future. In addi-
tion, a proportion of articles (52%) were excluded after
reviewing the abstracts, as they did not appear to discuss
data linkage (e.g. articles about electronic medical
records, e-health) or they commented on methodologi-
cal issues related to data linkage without an empirical
study. This may mean that some articles meeting the
inclusion criteria were missed if the abstract lacked
detail. However, full articles were sought for all articles
where the study title and available information appeared
to be related to data linkage but lacked a detailed
abstract.
When linking existing data sources to measure out-
comes of care, such as mortality rates, differences such
as those described above could underestimate mortality
for participants in any of the groups identified, thereby
skewing perceptions of quality of care. Linkage issues
may be compounded by broader data quality issues,
such as a lack of standardised data definitions and
inconsistent coding practices [57], which will also com-
promise quality of measurement. While this paper has
focused on data linkage, it is important to consider the
influence of data linkage in the broader context of other
data quality issues when using existing data to monitor
quality of care.
To accurately assess for bias due to errors in linkage,
characteristics of unmatched records and a measure of
the quality of linkage, such as the rates of false positives
and false negatives, need to be routinely measured and
reported. Depending on the research question and out-
comes under study, potential bias in study results needs
to be assessed on a case-by-case basis. Biased estimation
of outcomes in either direction will have negative conse-
quences for measuring, and therefore improving, the
quality of health care. Furthermore, unreliable and inva-
lid data will continue to undermine the confidence of
clinicians and other stakeholders in measurement sys-
tems relying on existing data sources.
As a way forward, in additional file 4 we have pre-
sented the different reasons for unlinked records and
suggested some factors that should be considered in the
evaluation of studies using linked data in assessing the
quality of data linkage. Studies should clearly describe
the data-sets being used, the linkage variables and pro-
cess and an assessment of the quality of the linked data-
sets to identify potential sources of bias. Even where
unique identifiers exist, cases may not link due to miss-
ing or inaccurate data or inconsistent data definitions,
inclusion criteria or coding practices. It can be difficult
to determine the impact of these issues when the quan-
tity of missing data and unlinked records are not
reported. An appropriately validated quality assessment
tool specific to data linkage studies would help to
systematically identify and review the specific sources of
bias discussed in this paper. Such a tool would assist
clinicians and policy-makers in interpreting the findings
of clinical research studies based on data linkage and
encourage more consistent reporting by researchers.
Conclusions
Studies reliant on linked data may yield biased findings
due to errors in data linkage which relate to specific
characteristics of patients and clinical settings. Such
errors may not be readily apparent due to inconsisten-
cies in the way researchers evaluate and report them. If
existing data sources and linkage processes are to be
used for assessing quality of care, it is important that
clinicians and policymakers recognise their limitations
in order to avoid misinterpreting their findings. Report-
ing and appraising results based on linked data in a
transparent and consistent manner will help to highlight
limitations of current healthcare data sources and assist
in improving data collection, coding practices and link-
age processes.
Additional material
Additional file 1: Linkage methods. A description of the principles of
data linkage and the methods commonly undertaken.
Additional file 2: Selected Case Studies. Several studies are
highlighted to demonstrate how linkage error can influence findings
when applied to health services research.
Additional file 3: Summary of Study characteristics associated with
unmatched data linkage cases. A table of each study included in the
review and how selected population characteristics were influenced by
unmatched records.
Additional file 4: A framework for evaluating data linkage studies.
Reasons for unlinked records and suggested reporting items for studies
utilising data linkage to identify potential quality issues.
Acknowledgements
The authors gratefully acknowledge their colleagues Dr Adam Elshaug, Dr
Sacha Höttje and Dr Cameron Willis for their thoughtful review and
feedback on drafts of this manuscript. Megan Bohensky receives funding
through an Australian Postgraduate Award scholarship at Monash University.
Author details
1Centre of Research Excellence in Patient Safety, Dept of Epidemiology &
Preventive Medicine, School Public Health & Preventive Medicine, Monash
University, Melbourne, Victoria, Australia, 3181 http://www.crepatientsafety.
org.au.2Department of Health Victoria, 50 Lonsdale Street, Melbourne
Victoria, Australia 3000.3Australian & New Zealand Intensive Care Society,
Centre for Outcomes and Resource Evaluation, 10 Ievers Terrace, Carlton
Victoria, Australia 3053.4Department of Internal Medicine, Princess Alexandra
Hospital, Brisbane, Queensland, Australia 4102.
Authors’ contributions
MAB conducted the searches, analysed the data and wrote the article, CAB
supervised data analysis and reviewed the article, DJ, VS, SE, DVP and IS,
supervised and reviewed the article. All authors read and approved the final
manuscript.
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Competing interests
The authors declare that they have no competing interests.
Received: 31 May 2010 Accepted: 22 December 2010
Published: 22 December 2010
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Cite this article as: Bohensky et al.: Data Linkage: A powerful research
tool with potential problems. BMC Health Services Research 2010 10:346.
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