Large data sets based on demographic information, disease occurrences and prescribing information are now available from several sources for use by trained and competent researchers.
MULTIPURPOSE DATABASES
Large
data sets based on demographic information, disease occurrences and prescribing
information are now available from several sources for use by trained and
competent researchers. We now have extensive populations in diverse
geographical settings for whom routine information about demography, drug
exposure and disease experience are available in reasonably standardised
formats. We have skilled analysts avail-able to review such data sets for
important causal associations between drugs and events. These infor-mation sets
are extremely powerful tools and must be used with skill and great care lest
the results reported turn out to be erroneous. In such circumstances great
damage could be done both to public health and also to the data sets
themselves. It is therefore crucially important that investigators ensure that
validity of their observations by careful scrutiny of at least a sample (if not
all) of the basic records. To rely solely on computer codes for disease
identification without the ability to return to verify basic written records is
likely to lead to significant potential for serious error. Failure to undertake
proper validation could easily lead to inappropriate damage to the reputation
of indi-vidual medicines, or even the parent data set itself.
Recently
there have been some examples of conflicting conclusions emanating from
different investigators reviewing the same topic from within the largest
database in the United Kingdom, the General Practice Research Database. This
may seem surprising at first sight. However, it must be clearly understood that
the worldwide experience in this exciting area is confined to relatively small
groups of investigators, as there are formidable logistical problems to
over-come in entering and conducting research on these data resources. For
example, drug-, symptom- and disease-codes tend to change with time during the
years of data accrual. This is not territory for the amateur or the unwary! One
simply cannot go to these extremely complex information systems and expect to
perform high-quality research overnight. The issues are usually technically
challenging and epidemiolog-ically extremely complex.
Classical
epidemiology is well used to dealing with fixed properties of individual
patients, such as sex, height, weight, parity, smoking habits and so on or
one-off exposures to toxic substances, such as chem-icals or infective agents.
It is not so comfortable dealing with intermittent exposures at varying doses
that are usually the case in drug epidemiology stud-ies. There are some areas
where the exposure status can be somewhat constant. Examples of these would be
the use of oral contraceptives and hormone treat-ments (replacement therapies
with oestrogens, insulin, thyroxine, etc.). Even here, however, patients
regu-larly change individual preparations, and great care must be taken to
ensure accuracy and fairness in data interpretation. In other areas intermittent
exposures are the norm.
In
embarking upon a drug safety study in a large database, the investigator must
clearly specify the hypothesis to be tested. (Such databases are so complex as
to be generally unsuitable for hypothesis generation except under very confined
circumstances arising usually within an individual study.) Once one has defined
the hypothesis, exposure and outcome status have to be assessed accurately. The
nature of the study design has to be identified. Is it a follow-up study, a case–control
study or will it be a nested case– control study within a large group of
subjects exposed to an individual medicine or class of medicines?
Failure
of clarity at this stage could doom the study from the onset. Investigators
interested in a particular hypothesis can often be mesmerised by the apparent
abundance of information available to them. They should keep in mind that it is
crucial to restrict themselves to appropriate comparisons. Thus if one is
looking at the effect of, say, hormone replace-ment therapy on osteoporosis,
the relevant outcome measure available in such databases is generally a
fracture. However, not all fractures are relevant. Indeed, many are irrelevant
to the hypothesis, as they will have an obvious and sufficient cause, such as a
road traffic or other accident, an underlying neoplasm or pre-existing bone
disease. Similarly, not all expo-sures to hormones are relevant. For example,
it would seem unlikely (biologically implausible) that a single prescription
for such treatment would be relevant to the outcome of interest. Trained
epidemiologists are used to thinking of chance, bias and confounding as
explanations for any associations they see in data. Although the items
mentioned above are forms of bias, they tend to be obscure to all but those
trained in the complexities of pharmacoepidemiology. Yet they are crucial
issues to consider before one embarks on a seemingly large and promising study.
Reflect that a negative outcome to a project could be because the study drug
does not cause the outcome of interest. However, it could also arise from the
fact that there is so much ‘noise’ in the system that an investiga-tor cannot
see the true link between drug and disease when it is in front of him because
of inappropriate inclusions in the disease and drug exposure categories and
inappropriate inclusions and exclusions in the comparator population. Finally,
there is the problem of missing information found in all systems, yet
requir-ing particularly careful handling in a multipurpose database. Such
information rarely leads to a false-positive conclusion, but it could result in
missing a key finding. The main safeguard here is familiarity with the data set
itself.
Without
due care and attention, the availability of more and more powerful information
systems could lead to an epidemic of poorly undertaken studies that would
reflect badly on the fledgling science of pharma-coepidemiology. This would be
a matter of great regret, as the subject is of major importance for the future
safety of patients, prescribers, dispensers and manu-facturers alike. All have
different perspectives, yet all share a common goal of getting the safest
medicines to the appropriate patients at the right dose and at the right time.
For a guide to some of the less obvious pitfalls in this type of research, see
the paper by Jick and colleagues in the Lancet (1998; 352: 1767–70).
The
development of pharmacovigilance is now at a critical stage. With powerful new
tools at our disposal we have at last the opportunity to provide the public
with some of the reassurances it requires from the industry and the
professions. Ironically, it has taken over 35
years since David Finney originally recom-mended this approach in a seminal
article in the Jour-nal of Chronic Diseases (1965; 18: 77–98). It is crucial
that we continue to meet this challenge with enthu-siasm and skill, seizing the
opportunities that present in these powerful information systems and
surmount-ing the local difficulties relating to anonymisation of data sets, scientific
rigor and credibility. For once we in the United Kingdom are in possession of a
world-beating facility for research in the form of the General Practice
Research Database, due to the fore-sight of its founding practitioner, the
commitment of large numbers of collaborating general practitioners, and the
realisation among researchers and the Regu-latory Authority in the United
Kingdom that this is a resource beyond value.
Multipurpose
databases generally concentrate infor-mation collection upon prescription
medicines. Over-the-counter (OTC) and alternative medicines are excluded or
dealt with in a non-standard manner. Whilst OTC medicines usually have been
reviewed in detail when they were prescription medicines, the same cannot be
said for alternative medicines such as herbal and homeopathic preparations.
Many of these have been found to be associated with serious health hazards in
the past, and some have also been found to interact with prescription medicines.
We need some method other than relying solely on spon-taneous reporting systems
to be reassured that these preparations are indeed acceptably safe. The
result-ing system need not be as all embracing as the large databases; however,
the work needs to be done, and done both rapidly and cost-efficiently in the
near future. In the United Kingdom, the Herbal Medicines Advisory Committee has
been established to advise the Licensing Authority directly on this issue.
The
large databases are perhaps best developed in the United Kingdom because of its
unique feature of the general practitioner being the gateway through which
patients progress to specialist care. As the system changes and others such as
nurses and pharmacists begin to initiate primary prescriptions in measurable
numbers, these systems could become less effective. There will have to be
careful thought directed towards the best ways in which the relevant
information can be captured economically to ensure the continuing maintenance
and viability of the databases. This can most readily be achieved by channeling
records of all prescriptions through a patient’s practitioner, thereby ensuring
not only continuity of records but also safety in therapy. With advances in
transferable electronic patient records and the use of a unique patient
identi-fier, these tasks become easier, in theory at least.
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