Skip to main content
“Chapter 22: Evaluation of Provincial Pharmacy Network” in “Handbook of eHealth Evaluation: An Evidence-based Approach”
Chapter 22
Evaluation of Provincial Pharmacy Network
Don MacDonald, Khokan C. Sikdar, Jeffrey Dowden, Reza Alaghehbandan, Peizhong
Peter Wang, Veeresh Gadag
22.1 Introduction
Adverse drug events (ADEs) are a concern in both inpatient (Evans, Lloyd, Stoddard, Nebeker, & Samore, 2005; Bates et al., 1995; Baker et al., 2004) and outpatient (Budnitz
et al., 2005; Zed et al., 2008) settings. An ADE is defined as an iatrogenic hazard or incident that is created either through
omission or commission of the administration of a drug or drugs (prescription
or non-prescription), harming a patient whose outcome is always unexpected and
unacceptable to the patient and healthcare provider (Tafreshi, Melby, Kaback, & Nord, 1999; Nebeker, Hoffman, Weir, Bennett, & Hurdle, 2005). Such events are a significant cause of morbidity and mortality
(Juntti-Patinen & Neuvonen, 2002; Alexopoulou et al., 2008), and result in significant resource
utilization, including increased emergency room (ER) and physician visits, diagnostic tests, medication use, and hospital
admissions. Studies conducted in the United States estimate that such events
account for 17 million ER visits and 8.7 million hospital admissions each year (Bates et al., 1997;
Johnson & Bootman, 1995). Between 1995 and 2000, costs associated with ADEs rose from US$76.6 billion to over US$177.4 billion (Johnson & Bootman, 1995; Ernst & Grizzle, 2001). It would be expected that if a Pharmacy Network were deployed
and that it included complete medication profiles and automatic drug
utilization reviews, then adverse drug events resulting in an ER visit would be reduced in the population.
22.2 Current State of Evidence
ADEs have been mostly studied among patients admitted to hospital, and it has been
estimated that 5% to 25% of hospital admissions are drug-related (Samoy et al.,
2006; Pirmohamed et al., 2004). However, ADEs occurring in outpatient settings and treated in ERs receive less attention, even though more than 80% of community-dwelling adults
use medications on a weekly basis, and approximately threefold more patients
are treated in ERs for ADEs compared to those admitted to hospital (Budnitz et al., 2005; McCaig & Burt, 2003; Kaufman, Kelly, Rosenberg, Anderson, & Mitchell, 2002). The Institute of Medicine (1999) report, To Err Is Human: Building a Safer Health System, concluded that the solution to preventing medical errors is “building a safer health system” that identifies patient safety as a prerequisite to high-quality care. Despite
widespread recognition of the need for a safer health system, ADEs occurring in community settings remain a substantial cause of ER visits. A Pharmacy Network is a regional drug information system that offers
population-based online, real-time medication profiles and an interactive
database to assist pharmacists and physicians in producing optimal medication
treatment. Such networks would also provide the tools to monitor, track, and
mitigate ADEs and medication errors that occur in the community.
22.2.1 Synthesis of Current Evidence
ADEs are a major public health problem given that such events are the most common
type of injuries experienced by hospitalized patients (Institute of Medicine,
1999). ADEs may lead to hospitalization, or occur during hospitalization and contribute to
an increased length of stay. The recent focus on patient safety and the concern
about the number of negative outcomes resulting from drug use, rather than the
underlying diseases, has prompted health care professionals to take a critical
look at these drug responses.
A series of studies examined ADEs among hospitalized patients in the United States and Australia (Bates et al.,
1995; Lazarou, Pomeranz, & Corey, 1998; Roughead, Gilbert, Primrose, & Sansom, 1998; McDonnell & Jacobs, 2002; Zhang et al., 2009); however, less research is available about
these events in hospitalized patients in Canada. A U.S.-based meta-analysis by Lazarou et al. (1998) revealed that the incidence of
serious ADEs in hospitalized patients was 2.1%, while for those newly admitted to a
hospital it was 4.7%. A subsequent study reported ADEs were between the fourth and sixth leading cause of death (Tafreshi et al.,
1999). Other studies have found ADEs occurred in between 2% and 20% of hospitalized patients (Roughead et al.,
1998; McDonnell & Jacobs, 2002; Zhang et al., 2009). Baker and colleagues (2004) provided a
national estimate of the incidence of adverse events among adult patients in
Canada (7.5 per 100 hospital admissions). After extrapolating to the entire
population of Canada, the number of hospital admissions attributed to adverse
events was estimated between 141,250 and 232,250 in 2000 (Baker et al., 2004).
Furthermore, Canadian incident reporting data indicated a 35% increase of adverse
reactions from 2008 to 2009 (Health Canada, 2010).
ADEs are common and can have serious consequences in an older population. According
to recent population estimates, Canadians 65+ population grew by 12% between
2001 and 2006 and this demographic now represents about 16% of the total
population (Statistics Canada, 2007; Canadian Institute for Health Information,
2010; CBC News, 2015). Elderly individuals are vulnerable to ADEs because of their multiple drug consumption patterns and biologic changes,
which may restrict their drug consumption and inhibit physiological processes
they take to manage multiple comorbid conditions and because of
pharmacokinetics and pharmacodynamics changes (Zhang et al., 2009; Bates,
1998). Furthermore, ADEs can be recurrent events, in that an individual may experience one or more such
events over a period of time. It is important to identify the magnitude of ADEs in this high-risk group to aid physicians in their decisions about
prescribing, delivering, administering, and monitoring drug therapies. If
predictive factors can be identified, this would allow providers to identify
early symptoms of ADEs and to offer rapid response to the patient (Field et al., 2001).
Although prior research (Zhang et al., 2009; Field et al., 2001; French, 1996;
Fialová et al., 2005; Onder et al., 2002; Onder et al., 2003; Chrischilles, VanGilder,
Wright, Kelly, & Wallace, 2009) has identified several risk factors for the occurrence of ADEs among older adults (e.g., age, sex, and drug regimen), little is known about
the risk factors associated with recurrent ADEs. For public health planning and the evaluation of quality management programs,
it is important to study recurrent ADEs, rather than only the first event (Donaldson, Sobolev, Cook, Janssen, & Khan, 2009). Given the risk of both health service utilization and the patient’s burden of illness increasing with each subsequent ADE, the number of ADEs is a more robust indicator of risk than a single event (Glynn & Buring, 1996).
22.2.2 Summary of Key Findings
ADEs have been mostly studied among patients admitted to hospital, and it has been
estimated that between 5% and 25% of hospital admissions are drug-related.
However, ADEs occurring in outpatient settings and treated in ERs receive less attention, even though more than 80% of community-dwelling adults
use medications on a weekly basis, and approximately threefold more patients
are treated in ERs for ADEs compared to those admitted to hospital.
22.3 Selected Case Study – Adverse Drug Events in Adult Patients Leading to ER Visits
22.3.1 Setting and Study Population
The study setting was two adult acute care hospitals, the Health Science Centre
(HSC) and St. Clare’s Mercy Hospital (SCMH), both of which deliver tertiary care in the capital city of St. John’s in Newfoundland and Labrador (N.L.), Canada. These two hospitals serve a catchment area of approximately 280,000
residents, and together have an average of 28,000 acute separations and 80,000 ER visits per year. Both hospitals capture electronic summary data on all ER visits in an emergency room triage database. Eligible subjects for this study
included all patients aged 18 years or over that were residents of N.L. and presented to one of the two ERs between January 1, 2005 and December 31, 2005.
ER visits with a high probability of not being due to an ADE (e.g., motor vehicle accident, substance abuse, drug abuse, attempted suicide,
cut- or burn-related injuries, etc.) were excluded. It should be noted that
conditions such as attempted suicide and drug abuse would not likely be the
presenting complaint. Therefore, these ER visits may not have been excluded from the sampling frame; rather they were
excluded later (if selected) from the study sample during the chart review
phase of the study. Patients who presented to ERs through a referral process, but were subsequently identified as a valid ER visit, were included in this study.
22.3.2 Study Sample
Charts were selected from the sampling frame using a stratified random sampling
design. There were six strata based on patients’ sex and age at ER visit (Male 18 to 44, Male 45 to 64, Male 65+, Female 18 to 44, Female 45 to
64, and Female 65+). Evidence in the literature regarding the prevalence of ADEs in admissions was found to be inconsistent, ranging from 5% to 25%, which can
be mostly attributed to differences in study designs and patient demographics
(Kaufman et al., 2002; Institute of Medicine, 1999; Lazarou et al., 1998). We
estimated 10% of ER visits would be attributed to ADEs in patients aged 18 years and older. To achieve a 95% confidence interval (±4%), we determined that we would need a sample size of 217 ER visits for each stratum, resulting in a total of sample 1,302 ER visits. To reduce the sampling error, and to compensate for the exclusion of ER visits that would be attributed to suicide attempts and drug abuse, we added a
10% over-sample to the sample. After the chart review was completed, the final
sample size for the study was 1,458, resulting in a 12% over-sample. This
difference of 2% was attributed to inclusion of ER visits through referrals. For patients with multiple ER visits during the study period, only one visit was selected at random as the
index visit for review.
22.3.3 Outcomes and Definitions
An ADE is defined as any undesirable effect caused by the interaction of a drug
(prescription or non-prescription) with a patient (Morimoto, Gandhi, Seger,
Hsieh, & Bates, 2004). Events may be the result of normal or inappropriate use of a
medication, and could range from minor reactions such as a skin rash to serious
and life-threatening events, even death. Medication errors (MEs) are mishaps that occur during prescribing, transcribing, dispensing,
administering, adherence, or monitoring a drug. Medication errors are more
common than adverse drug events, but result in harm less than 1% of the time,
with about 25% of adverse drug events attributed to medication errors (Nebeker,
Barach, & Samore, 2004). We studied ADEs, defined as “injury resulting from the use of a drug” (Nebeker et al., 2005) that encompasses all traditional adverse effects plus
harm from any MEs. We also used “possible adverse drug event” (PADE), defined as an event that may have been related to a current medication (e.g.,
viral infection), but it could not be confirmed. ADEs and PADEs involving either prescription or over-the-counter drugs were included.
22.3.4 Data Collection
Data collection involved a two-step review of ER charts using the Meditech system. Meditech is a hospital information system
where all electronic patient information, including ER summaries, are scanned and uploaded to the patient’s profile. In the first step, the ER summaries of each selected chart were reviewed by a team consisting of a
physician and a registered nurse using a Trigger Assessment Tool. This tool
listed 39 screening criteria (triggers) known to be sensitive to the occurrence
of ADEs among the adult population. The reviewers combined any triggers found in the ER chart with the patient’s history of medication use, as well as a subjective assessment, to determine if
an ADE was the reason for the ER visit. If it was classified as being a probable ADE, the reviewers through a consensus process coded the reason for the ER visits as having either a high, moderate, low, or very low probability of being
an ADE.
The second step included a full review of all ER charts identified as having “high” and “moderate” probability ADEs, and a random sample of the “low/very low” probability ADEs. As part of the validation exercise, a full review was also carried out on a
sample of those ER visits classified as having “no” probability of being ADE. In Step 2, two ER physicians and two clinical pharmacists independently reviewed each of the
patient’s charts using a data collection tool, which was a modified version of the tool
by Gandhi et al. (2003). The reviewers were blinded to the first step review
that identified probable ADEs. The reviewers first obtained demographic and clinical information, including
presenting complaints, past medical history, drug history, history of allergy,
medication dose, frequency, and reaction for the event, as well as the patient’s most recent laboratory records.
The reviewers used this information to assess whether the ER visit was a result of an ADE, PADE or ME. Each reviewer also classified the event according to its severity and
preventability. Preventability was based on additional information that would
have been available had a Pharmacy Network been available. Using an adapted
version of previously published criteria (Bates et al., 1995; Gandhi et al.,
2003; Gurwitz et al., 2003), severity was classified as being “fatal”, “life threatening”, “serious” or “significant”; and preventability was classified as “error intercepted”, “definitely preventable”, “probably preventable”, “probably not preventable”, or “definitely not preventable”. Disagreements about classification of ADEs, and their severity and preventability were resolved during consensus
meetings. In this analysis we used two data sets: (a) a limited amount of data
collected on all patients from the first review, and (b) detailed information
on the subsample of patients that were collected through the chart review.
22.3.5 Statistical Analysis
We generated descriptive statistics including means, standard deviations, and
ranges. The primary outcome variables – ADEs and PADEs – were combined into a single variable of ADEs/PADEs in order to reduce the random error associated with the small number of events
identified. The unit of analysis was the ER visit. Prevalence of ADEs/PADEs was calculated per 100 ER visits and presented with p-values using the binomial proportion test. Each study subject was assigned a
sample weight based on the inverse probability of selection. The overall
prevalence of ADE/PADEs was estimated using sampling weights to adjust for stratification in the
sampling design.
The estimates by age group and sex were kept non-weighted since each patient in
the sample frame had an equal chance of being selected within the corresponding
age/sex stratum. Events that were assessed as error intercepted, definitely, or
probably preventable were merged into one category “preventable”, and those assessed to be definitely or probably not preventable were merged
into one category “not preventable”. The rate of severity and preventability of ADE were derived by dividing the number of events in the respective categories by
the total number of ADEs. Mantel-Haenszel chi-square analysis was performed to determine whether there
was an association between severity and preventability of ADEs. The number of ADEs was extrapolated to the study population by multiplying the overall prevalence
rate by the number of ER visits in the sample frame. Number of preventable ADEs and hospitalization due to ADEs were extrapolated to the study population in a similar manner. All data were
entered and stored electronically using Microsoft Access and were analyzed
using SPSS 15.0 software package (Statistical Package for Social Sciences, Chicago, IL).
22.3.6 Results
During the study period 82,516 adult ER visits to the HSC and SCMH were identified. Of these, 2,749 visits were excluded because they were by
non-residents of N.L. and 12,076 visits were excluded since they did not meet the inclusion criteria,
leaving 67,691 ER visits (41,135 unique patients). The mean age (±SD) of this cohort was 46.9 (±19.6) years, with 54.4% (36,814 out of 67,691) of the visits by females. Of the
1,458 ER visits sampled from the 67,691 visits, 44.8% (653) were identified as having a
high (29), moderate (135), low (218), or very low (271) probability of being
the result of an ADE.
Gastrointestinal symptoms (e.g., nausea, vomiting, and diarrhea) and skin rashes
were found to be the most common manifestations of patients identified as high
or moderate probability of being an ADE. Patients identified as having a “high” (n = 29) or “moderate” (n = 135) probability of having ADEs, along with a random sample of 170 ER visits classified as having a “low” or “very low” probability of having ADEs, were independently reviewed by two ER physicians and two clinical pharmacists. The mean (±SD) number of co-morbidities and current medications for this group were 3.5 (± 1.9) and 5.6 (± 3.6), respectively. Fifty-five of the 334 patients were identified by the team
to either have an ADE (n = 29) or a PADE (n = 26). After weighting for stratification in the sampling design, the overall
prevalence of ADEs/PADEs was 2.8% (95% CI, 2.0-3.7). The mean (±SD) age for patients with ADE/PADE was 69.9 (±14.2); (71.6 ±9.9 for males versus 68.7 ±16.5 for females). No statistically significant difference was found between
genders (P = 0.13). For both males and females, the prevalence of ADEs/PADEs increased with age, peaking at 9.1% for females aged 65 years and older. For
all age groups, the prevalence of ADEs/PADEs was slightly higher among females than males. In this study, 23 of the 55
patients with ADEs/PADEs (41.8%) required hospitalization.
The mean age for patients with ADEs/PADEs was higher than those having no drug-related visits (69.9 versus 63.8 years, p < 0.01). A higher number of co-morbidities and medications were significantly
associated with drug-related visits (p < 0.05 and p < 0.01, respectively). Of the 55 confirmed ADE/PADE patients, one (2%) case was fatal, two (4%) were life-threatening, 25 (46%)
serious, and 27 (49%) identified as significant. Approximately 29% of the 55 ADEs/PADEs identified were considered to be preventable had additional information been
available through a Pharmacy Network. Of the serious, life-threatening, and
fatal events, 35.7% were identified as potentially preventable, compared with
22.2% of the significant events; however, the difference was not statistically
significant. Of the 23 hospitalizations due to ADE/PADEs, eight (35%) were considered preventable.
Based on these 55 ADE/PADE patients, we estimate that approximately 1,900 adult patients (95% CI: 1,354-2,505) were treated in the St. John’s region for ADEs/PADEs in the two ERs during the study period (January to December 2005), of which an estimated 550
were preventable. Further, of the 1,900 it is estimated that 800 were
subsequently hospitalized. This estimate is based on all ER visits (n = 67,691), excluding those not attributed to ADEs (e.g., alcohol-related, suicide attempt, car accidents, cut, burn, wound
dressing, etc.). Hematologic complications (e.g., bleeding) were the most
common complications associated with ADEs/PADEs (43.6%), followed by gastrointestinal (32.7%), neurological (14.5%), skin
(12.7%), cardiovascular (12.7%), metabolic (9.1%), respiratory (7.3%), and
renal (5.5%) complications. The medications most frequently associated with ADEs/PADEs, either on their own or in combination with other agents, were such
anti-platelets as aspirin (24%), warfarin (18%), antibiotics (15%),
anti-hypertensive agents (13%), and chemotherapy agents (11%). Warfarin,
divalproex, and chemotherapy agents, medications with a narrow therapeutic
index (NTI) and a high risk for toxicity, were found to be the cause of nearly one-third
(31.7%) of ER-treated ADEs/PADEs in patients aged 65 years or older. Note that, as part of the validation
process, a sample of 192 charts from 805 ER visits classified as “no” probability for ADE visits were reviewed for the validation of the trigger tool exercise. None of
these 192 visits were found to be ADE-related.
22.4 Issues, Guidance and Implications
There is considerable research available on ADEs that occur in hospitals, but considerably less so on those that occur in the
community. This study is one of the few studies in Canada to investigate ADEs among adult patients presenting to ERs. Our study found that adverse drug events accounted for 2.8% of ER visits, of which about a third were considered preventable if a Pharmacy
Network were available. Patients with ADEs/PADEs were found to be older, prescribed more medications, and had a higher number
of co-morbidities. Although there is debate in the literature as to whether age
itself is a risk factor for an ADE-related visits or hospitalization, the mechanism relating age to risk for ADEs may include the administration of multiple drugs in treating multiple
co-morbidities which is more common among the elderly population. In addition,
while an aging population tends to take a higher average number of medications,
they are also less likely to tolerate certain medications for various reasons,
as outlined in the Beers Criteria (Donaldson et al., 2009). In this current
study, medications such as warfarin, divalproex, and chemotherapy agents with NTI and high risk for toxicity caused about one-third of ER-treated ADEs in patients aged 65 years or older.
Comparisons with other studies are challenging since there are many variations
in case definitions (e.g., ADE, PADE, ME, etc.), study designs, and patient populations. It is argued that the benefit
of a Pharmacy Network is its ability to provide a complete patient drug profile
on which the pharmacies’ drug utilization review software can run, and that that this complete drug
profile provides accurate and complete medication information across the
continuum of patient care (i.e., Medication Reconciliation). This argument
carries significant weight in cases when the patient uses multiple pharmacies
when obtaining prescription medications. Conversely, others would argue that
where patients only use one pharmacy for all their prescription medications,
either out of preference (e.g., knowing the pharmacy staff) or necessity (i.e.,
the only pharmacy in the community), the benefits of a Pharmacy Network to the
patient are minimal.
Another expected benefit of a Pharmacy Network is the reduction in double
doctoring, as prescription-dispensing records would be available to all
pharmacies on the network in real time. The other issue sometimes raised is
that there is a usually a cost to the pharmacy for being part of the network
(e.g., hardware and software upgrades, Internet access, lost productivity,
etc.) and that the pharmacy is a private company that for the most part
generates revenue through dispensing medications, not providing additional
patient care. While there can be several valid arguments, both for and against
a Pharmacy Network, ultimately if it provides increased patient safety and
improves quality of care, both government and the private sector need to work
towards the deployment of such a network across their population.
This study faced several limitations. Firstly, using a retrospective chart
review design may underestimate the true frequency of emergency visits as being
caused by an ADE. Ideally, a prospective design with a large sample including patient interviews
and obtaining key information would have increased the accuracy of estimates of
drug-related visits and their preventability. Secondly, compared to patients
aged 65 years or more, we found fewer ADEs in younger age groups, which makes our estimates of ADE prevalence more prone to sampling error in these age groups. Nevertheless, the
prevalence of ADE among elderly patients was 8.4%, which is very close to our pre-study assumption
of 10% considered in the study design.
Thirdly, in preparing the sampling frame, we excluded 12,076 visits from the
study population using pre-defined exclusion criteria. However, based on a
cursory review of these excluded visits, we concluded that the criteria used in
excluding non-drug related visits might not have been as precise as we had
hoped. The main reason for this lack of precision was that the exclusion
criteria were applied to the patients’ self-reported complaint, and not the diagnosis provided by the attending health
professional after the encounter. As such, exclusion of any drug-related visits
may have resulted in leaving out a low-risk subset of ER visits instead of a no-risk subset, and thereby resulted in overestimating the
prevalence of ADEs in the study sample. Fourthly, we did not extrapolate our data to the entire
province, since the HSC and SCMH are located in the capital city of St. John’s and cannot be considered representative of all hospitals in the province.
22.4.1 Summary of Evaluation Issues
The evaluation of a Pharmacy Network presented issues that exist with most
evaluations, in that there is a lack of standards in undertaking evaluations
overall, which limits the amount of comparability one study has. The
methodological approach to evaluations is not new, with most employing age-old
research methods (e.g., surveys, interviews, chart reviews, administrative
data, etc.) to determine whether whatever is being evaluated has met its
objectives. The challenge is getting a consistent approach so that peer-to-peer
comparisons can be made and best practices identified. In the absence of such
comparisons, we are limited to comparing results in the same environment pre-
and post-implementation, with no idea if the pre-intervention indicators are
any better (or worse) than our peers. In the case of this current study, the
team is waiting until the Pharmacy Network has been fully deployed for 12
months before doing the post-Pharmacy Network intervention. This is expected to
occur in early 2018.
22.4.2 Guidance for Future Directions
Evaluating the benefits of a Pharmacy Network is not only resource intensive and
costly, but is delivered within a government’s policy framework and as such is not under the control of the evaluation team.
When evaluating a government intervention, whether it is a policy, a program,
or a new technology, always consider that many issues that will arise will be
out of your control and you must mitigate them as best you can in the design of
your evaluation.
22.4.3 Policy and Practice Implications
In implementing a Pharmacy Network it is in the interest of government to
provide its population with a sustainable, high quality, and safe service in
relation to the usage of prescription medications. Through that lens it seems
logical that a Pharmacy Network would deliver on these three fronts, ignoring
the costs to actually implement the network. However, in the practice
environment it is not so linear, as some pharmacies may not perceive any
benefits if they believe their client population is non-nomadic. If a Pharmacy
Network does not include all pharmacies within the population, health
professionals may not be provided with their patient’s complete drug profile, reducing double doctoring is compromised, and the data
will be incomplete in the development of new policies and programs.
22.5 Summary
Emergency room visits as a result of ADEs are not uncommon. A focus on further education along with the tools need to be
in place so that physicians and pharmacists can collaborate more closely to
improve prescribing practices and monitoring, particularly among high-risk
patients, and thereby contribute to reducing the subset of ADEs that is potentially preventable. The authors believe that if a Pharmacy
Network were deployed it would allow authorized healthcare providers to access
and share information, which would contribute to reducing the frequency of
adverse events related to drugs in the community.
References
Alexopoulou A., Dourakis, S. P., Mantzoukis, D., Pitsariotis, T., Kandyli, A.,
Deutsch, M., & Archimandritis, A. J. (2008). Adverse drug reactions as a cause of hospital
admissions: A 6-month experience in a single center in Greece. European Journal of Internal Medicine, 19(7), 505–510.
Baker, G. R., Norton, P. G., Flintoft, V., Blais, R., Cox, J., Etchells, E., … Tamblyn, R. (2004). The Canadian adverse events study: the incidence of adverse events among
hospital patients in Canada. Canadian Medical Association Journal, 170(11), 1678–1686.
Bates, D. W. (1998). Drugs and adverse drug reactions: How worried should we be?
Journal of the American Medical Association, 279(15), 1216–1217.
Bates, D. W., Cullen, D. J., Laird, N., Petersen, L. A., Small, S. D., Servi,
D., … Edmondson, A. (1995). Incidence of adverse drug events and potential adverse
drug events. Journal of the American Medical Association,274(1), 29–34.
Bates, D. W., Spell, N., Cullen, D. J., Burdick, E., Laird, N., Petersen, L. A.,
… Leape, L. L. (1997). The costs of adverse drug events in hospitalized patients.
Adverse drug events prevention study group. Journal of the American Medical Association,277(4), 307–311.
Budnitz, D. S., Pollock, D. A., Mendelsohn, A. B., Weidenbach, K. N., McDonald,
A. K., & Annest, J. L. (2005). Emergency department visits for outpatient adverse drug
events: Demonstration for a national surveillance system. Annals of Emergency Medicine, 45(2), 197–206.
CBC News. (2015, September 29). More Canadians are 65 and over than under age 15,
StatsCan says. Canadian Broadcasting Corporation News/Business. Retrieved from http://www.cbc.ca/news/business/statistics-canada-seniors-1.3248295
Canadian Institute for Health Information. (2010). Drug use among seniors on public drug programs in Canada, 2002-2008. Ottawa, ON: Author.
Chrischilles, E. A., VanGilder, R., Wright, K., Kelly, M., & Wallace, R. B. (2009). Inappropriate medication use as a risk factor for
self-reported adverse drug effects in older adults. Journal of the American Geriatrics Society, 57(6), 1000–1006.
Donaldson, M. G., Sobolev, B., Cook, W. L., Janssen, P. A., & Khan, K. M. (2009). Analysis of recurrent events: a systematic review of
randomised controlled trials of interventions to prevent falls. Age and Ageing,38(2), 151–155. doi: 10.1093/ageing/afn279.
Ernst, F. R., & Grizzle, A. J. (2001). Drug-related morbidity and mortality: updating the
cost-of-illness model. Journal of the American Pharmaceutical Association (Washington), 41(2), 192–199.
Evans, R. S., Lloyd, J. F., Stoddard, G. J., Nebeker, J. R., & Samore, M. H. (2005). Risk factors for adverse drug events: a 10-year analysis.
Annals of Pharmacotherapy, 39(7), 1161–1168.
Fialová, D., Topinková, E., Gambassi, G., Finne-Soveri, H., Jónsson, P. V., Carpenter, I., … Bernabei, R. (2005). Potentially inappropriate medication use among elderly
home care patients in Europe. Journal of the American Medical Association, 293(11), 1348–1358.
Field, T. S., Gurwitz, J. H., Avorn, J., McCormick, D., Jain, S., Eckler, M., … Bates, D. W. (2001). Risk factors for adverse drug events among nursing home
residents. Archives of Internal Medicine, 161(13), 1629–1634.
French, D. G. (1996). Avoiding adverse drug reactions in the elderly patient:
issues and strategies. Nurse Practitioner, 21(9), 90–105.
Gandhi, T. K., Weingart, S. N., Borus, J., Seger, A. C., Peterson, J., Burdick,
E., … Bates, D. W. (2003). Adverse drug events in ambulatory care. New England Journal of Medicine, 348(16), 1556–1564.
Glynn, R. J., & Buring, J. E. (1996). Ways of measuring rates of recurrent events. British Medical Journal, 312(7027), 364–367.
Gurwitz, J. H., Field, T. S., Harrold, L. R., Rothschild, J., Debellis, K.,
Seger, A. C., …Bates, D. W. (2003). Incidence and preventability of adverse drug events among
older persons in the ambulatory setting. Journal of the American Medical Association, 289(9), 1107–1116.
Health Canada. (2010). Adverse reaction and incident reporting–2009. Canadian Adverse Reaction Newsletter, 20(2), 2–4.
Institute of Medicine. (1999). To err is human: Building a safer health system. Washington, DC: National Academy Press.
Juntti-Patinen, L., & Neuvonen, P. J. (2002). Drug-related deaths in a university central hospital. European Journal of Clinical Pharmacology, 58(7), 479–482.
Johnson, J. A., & Bootman, J. L. (1995). Drug-related morbidity and mortality. A cost-of-illness
model. Archives of Internal Medicine, 155(18), 1949–1956.
Kaufman, D. W., Kelly, J. P., Rosenberg, L., Anderson, T. E., & Mitchell, A. A. (2002). Recent patterns of medication use in the ambulatory
adult population of the United States: the Slone survey. Journal of the American Medical Association, 287(3), 337–344.
Lazarou, J., Pomeranz, B. H., & Corey, P. N. (1998). Incidence of adverse drug reactions in hospitalized
patients: a meta-analysis of prospective studies. Journal of the American Medical Association, 279(15), 1200–1205.
McCaig, L. F., & Burt, C. W. (2003). National hospital ambulatory medical care survey: 2001 emergency department
summary (Advance Data from Vital and Health Statistics, No. 335). Hyattsville, MD: National Center for Health Statistics, Department of Health and Human
Services.
McDonnell, P. J., & Jacobs, M. R. (2002). Hospital admissions resulting from preventable adverse
drug reactions. Annals of Pharmacotherapy, 36(9), 1331–1336. doi: 10.1345/aph.1A333
Morimoto, T., Gandhi, T. K., Seger, A. C., Hsieh, T. C., & Bates, D. W. (2004). Adverse drug events and medication errors: detection and
classification methods. Quality and Safety in Health Care, 13(4), 306–314.
Nebeker, J. R., Barach, P., & Samore, M. H. (2004). Clarifying adverse drug events: a clinician’s guide to terminology, documentation, and reporting. Annals of Internal Medicine, 140(10), 795–801.
Nebeker, J. R., Hoffman, J. M., Weir, C. R., Bennett, C. L., & Hurdle, J. F. (2005). High rate of adverse drug events in a highly computerized
hospital. Annals of Internal Medicine, 165(10), 1111–1116. doi: 10.1197/jamia.M155
Onder, G., Landi, F., Vedova, C., Atkinson, H., Pedone, C., Cesari, M.,
Bernabel, R., & Gambassi, G. (2002). Moderate alcohol consumption and adverse drug reactions
among older adults. Pharmacoepidemiology and Drug Safety, 11(5), 385–392.
Onder, G., Penninx, B. W., Landi, F., Atkinson, H., Cesari, M., Bernabel, R., & Pahor, M. (2003). Depression and adverse drug reactions among hospitalized
older adults. Archives of Internal Medicine, 163(3), 301–305.
Pirmohamed, M., James, S., Meakin, S., Green, C., Scott, A. K., Walley, T. J., … Breckenridge, A. M. (2004). Adverse drug reactions as cause of admission to
hospital: prospective analysis of 18,820 patients. British Medical Journal,329(7456), 15–19.
Roughead, E. E., Gilbert, A. L., Primrose, J. G., & Sansom, L. N. (1998). Drug-related hospital admissions: a review of Australian
studies published 1988-1996. Medical Journal of Australia, 168(8), 405–408.
Samoy, L. J., Zed, P. J., Wilbur, K., Balen, R. M., Abu-Laban, R. B., & Roberts, M. (2006). Drug-related hospitalizations in a tertiary care internal
medicine service of a Canadian hospital: a prospective study. Pharmacotherapy, 26(11), 1578–1586.
Statistics Canada. (2007, Winter). Census snapshot of Canada – Population (age and sex). Catalogue 11.008. Canadian Social Trends, 84, 37–38. Ottawa: Author.
Tafreshi, M. J., Melby, M. J., Kaback, K. R., & Nord, T. C. (1999). Medication-related visits to the emergency department: a
prospective study. Annals of Pharmacotherpy, 33(12), 1252–1257.
Zed, P. J., Abu-Laban, R. B., Balen, R. M., Loewen, P. S., Hohl, C. M.,
Brubacher, J. R., … Purssell, R. A. (2008). Incidence, severity and preventability of
medication-related visits to the emergency department: a prospective study. Canadian Medical Association Journal, 178(12), 1563–1569.
Zhang, M., Holman, C. D. J., Price, S. D., Sanfilippo, F. M., Preen, D. B., & Bulsara, M. K. (2009). Comorbidity and repeat admission to hospital for adverse
drug reactions in older adults: Retrospective cohort study. British Medical Journal,338(a2752), 1–9. doi: 10.1136/bmj.a2752
EPUB
Manifold uses cookies
We use cookies to analyze our traffic. Please decide if you are willing to accept cookies from our website. You can change this setting anytime in Privacy Settings.