Monitoring the effectiveness of our prescription medications

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By Jenna Wong
and Robyn Tamblyn
McGill University

What conditions do you take prescription medications for? Are they working? Have you had negative side-effects?

It may surprise you to know that answers to these critical health questions aren’t well documented for most Canadians. Yet they would provide the crucial information needed to ensure our medications are safe and worth taking after they’ve been approved for use.

Jenna Wong
Jenna Wong

Before prescription drugs are approved in Canada, they‘re tested under controlled conditions on relatively small numbers of patients (several hundred to several thousand) with selective characteristics (patients of certain ages, races, ethnic groups or genders).

But once medications are approved, they aren’t monitored as closely as they should be.

We need to significantly improve our system of post-market surveillance for prescription drugs in Canada to make sure we’re continually monitoring their safety and effectiveness in real-world settings.

Why?

First, adverse drug reactions may surface that were not previously detected in smaller pre-regulatory trials. Recall the unfortunate case of 15-year-old Vanessa Young, who was taking cisapride for her gastrointestinal symptoms and died after suffering a heart arrhythmia – a life-threatening side-effect of the drug that surfaced only after it was released onto the market.

Robyn Tamblyn
Robyn Tamblyn

Second, the use of a drug may broaden over time to include milder forms of the disease or even different medical conditions that were not assessed during the drug’s pre-market trials. Take antidepressants, for example. These medications are increasingly used for conditions other than depressionNew research shows that nearly one in three antidepressant prescriptions is written for unapproved (‘off-label’) conditions – most of which are not backed by sufficient evidence.

To adequately monitor the safety and effectiveness of medications in real-world settings, we need a timely post-market drug surveillance system that can identify the reasons why patients take their medications. It must also follow patients to detect adverse drug reactions and determine if their medications are working.

How are we doing so far?

Not great.

Identifying the reason for drug use

The medical reasons for prescriptions are not often explicitly documented in patient charts, nor is this information required for patients to fill prescriptions or receive reimbursement for drugs.

So, when it comes to drugs like antidepressants that can be prescribed for different medical conditions, not knowing why a patient is taking a drug creates major challenges for assessing the drug’s effectiveness and appropriateness of use (i.e., whether the use is backed by regulatory approval or scientific evidence).

Detecting adverse drug reactions

Canada’s Adverse Drug Reaction Reporting System has many flaws, including the fact that it relies upon voluntary reporting by physicians via a process that’s time-consuming and outside routine procedures.

In fact, it’s estimated that less than five per cent of all adverse drug reactions are reported to Health Canada.

Tracking medication effectiveness

Once drugs are released onto the market, their real-world effectiveness isn’t systematically monitored.

It’s troubling to know that we have no large-scale mechanisms to track whether patients are experiencing the anticipated benefits from their medications.

What’s the solution?

We need a national post-market drug surveillance system that mandates the systematic collection of data on the reasons for drug use, adverse drug reactions and effectiveness.

The system must also govern the use of health information technologies to collect these data.

Health information technologies offer the opportunity to seamlessly collect such data as part of the care process and even enhance patient care. For example, electronic prescribing systems could prompt physicians to record the reason for treatment when drugs are prescribed and alert physicians to potential prescribing errors or present alternative treatment options when prescriptions are not evidence-based.

When prescriptions are cancelled, renewed or modified, electronic medical record systems could prompt physicians to record details about adverse drug reactions and effectiveness, which would also ensure that details about a patient’s treatment history and experiences with past therapies are documented. 

Many Canadian provinces have implemented centralized drug information systems to track all medications that patients receive. If these systems are to contribute toward an effective post-market drug surveillance system, they need to additionally collect information on the reasons medications are being prescribed and the outcomes they produce.

Medications can be life-saving. But they’re only as good as our knowledge about them. It’s time we kept better track of our experiences with medications.

Jenna Wong recently received her PhD from the Department of Epidemiology, Biostatistics and Occupational Health at McGill University. She will begin a post-doctoral research fellowship in the Department of Population Medicine at Harvard Medical School in 2018. Robyn Tamblyn is James McGill professor in the Departments of Medicine and Epidemiology, Biostatistics, and Occupational Health at McGill University.

Jenna and Robyn are Troy Media Thought Leaders. Why aren’t you?

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safe prescriptions, medications worth taking

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By Jenna Wong

Jenna Wong, PhD, MSc is a post-doctoral research fellow and Pyle Fellowship Awardee in the Department of Population Medicine at Harvard Medical School and Harvard Pilgrim Health Care Institute. She received her MSc in Epidemiology from the University of Ottawa and her PhD in Epidemiology from McGill University. Her research focuses on predictive modelling applications in epidemiology using electronic and linked administrative health data. She has created dynamic risk prediction models for in-hospital mortality, risk indexes for post-discharge outcomes, and machine learning algorithms for antidepressant treatment indications. She is conducting her post-doctoral research in the Therapeutics Research and Infectious Disease Epidemiology (TIDE) group under the mentorship of Dr. Darren Toh, where she is exploring the use of machine learning to extract information from unstructured electronic health data to enhance pharmacoepidemiologic research.

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