Post on 24-Jun-2020
Injuries Among Elderly Canadians: Psychotropic Medications and the Impact of Alcohol
by
Nicole Marie Riley
A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy in Epidemiology
Dalla Lana School of Public Health University of Toronto
© Copyright by Nicole Marie Riley (2011)
ii
Injuries Among Elderly Canadians: Psychotropic Medications and
the Impact of Alcohol
Nicole Marie Riley
Thesis for the Degree of Doctor of Philosophy in Epidemiology
Dalla Lana School of Public Health
University of Toronto
2011
Abstract
Psychotropic medication use is widely implicated as a risk factor for injuries, and it is believed
that the adverse effect profiles of these medications are exacerbated by the consumption of
alcohol. The objectives of this study are (a) to examine the associations between the use of
specific classes of psychotropic medications and injuries among elderly participants of the
National Population Health Survey (NPHS), and (b) to determine whether and how associations
between psychotropic medications and injuries are modified by the consumption of alcohol.
Data from Cycles 1 (1994/95), 2 (1996/97), and 3 (1998/99) of the NPHS household longitudinal
file were used in this study, selecting community-dwelling participants aged 65 years of age and
older in 1994/95. Among antidepressant medications, the magnitude of the risk of injuries was
higher for users of tricyclic derivatives (OR=1.4; 95%CI: 0.7 – 2.9) than SSRIs (OR=0.3;
95%CI: 0.1 – 1.0). Benzodiazepine use for any indication increased the risk of injuries, but that
effect was not consistent across indications. The use of benzodiazepine antianxiety medications
resulted in an increased risk of injuries (OR=2.0; 95%CI: 1.3 – 3.1), but there were no significant
effects on the injury risk among benzodiazepine hypnotic and sedative users (OR=0.8; 95%CI:
0.4 – 1.7). Results pertaining to the second objective of this study raised as many questions as
they resolved. Alcohol consumption decreased the odds of injury among hypnotic and sedative
iii
users, but otherwise, no consistent results were observed. Findings from this study underscore
the importance of identifying appropriate alcohol measures for research among elderly
populations. They also stress the need to separately consider the impact of different classes of
psychotropic medications on injuries (tricyclic antidepressants separate from SSRI
antidepressants and antianxiety benzodiazepines separate from hypnotic and sedative
benzodiazepines).
iv
Acknowledgments
I would like to take this opportunity to thank those who have been instrumental in the completion
of my dissertation. To my thesis committee, Mary Chipman, Susan Bondy, and Mary Tierney, I
am most grateful for your willingness to work through so many years of drafts en route to the
final product. I have enjoyed our late night overseas teleconferences, the occasional video calls,
and our face-to-face meetings both in Canada and Europe. To my friend and editor, Jacqui
Goodall, thank you for your invaluable contributions to my thesis and for being such a lovely
part of our lives while our families have been abroad. I will always appreciate the support and
encouragement of my husband, Tom, over our many years together, and I know I have been very
fortunate in my children, Cassandra and Logan, who loved Mommy even on her tired and
grumpy days.
v
Table of Contents
List of Tables ............................................................................................................................... viii
List of Figures ................................................................................................................................ ix
List of Appendices .......................................................................................................................... x
Chapter 1 ......................................................................................................................................... 1
1 Introduction ................................................................................................................................ 1
1.1 Study objectives .................................................................................................................. 1
1.2 Study rationale .................................................................................................................... 2
1.3 Timeline of research ........................................................................................................... 2
Chapter 2 ......................................................................................................................................... 4
2 Literature Review ....................................................................................................................... 4
2.1 Descriptive epidemiology of injuries in the elderly ............................................................ 4
2.2 Consequences of injuries in the elderly .............................................................................. 4
2.3 Psychotropic medications and potential associations with injuries .................................... 5
2.3.1 Antidepressant medications .................................................................................... 6
2.3.2 Benzodiazepine medications ................................................................................... 7
2.3.3 Antianxiety (anxiolytic) medications ...................................................................... 8
2.3.4 Hypnotic and sedative medications ........................................................................ 8
2.4 Alcohol, injuries, and psychotropic medication use ........................................................... 9
2.4.1 Epidemiology of alcohol use in the elderly .......................................................... 10
2.4.2 Concurrent use of alcohol and psychotropic medications .................................... 10
2.4.3 Effects of alcohol on associations between psychotropic use and injuries ........... 11
2.5 Potential confounders of associations between psychotropic use and injuries ................. 11
2.6 Previous injury studies from the NPHS ............................................................................ 12
Chapter 3 ....................................................................................................................................... 19
vi
3 Research Methods .................................................................................................................... 19
3.1 Subjects ............................................................................................................................. 19
3.1.1 Data source ............................................................................................................ 19
3.1.2 Study population ................................................................................................... 20
3.1.3 Database structure ................................................................................................. 20
3.2 Statistical definitions ......................................................................................................... 21
3.2.1 Sampling weights .................................................................................................. 21
3.2.2 Complex survey designs ....................................................................................... 21
3.2.3 Bootstrap resampling methodology for variance estimation ................................ 22
3.2.4 Generalized estimating equations ......................................................................... 22
3.2.5 Interaction and effect modification ....................................................................... 23
3.3 Variable definitions ........................................................................................................... 23
3.3.1 Injury status ........................................................................................................... 23
3.3.2 Psychotropic medication use ................................................................................. 24
3.3.3 Alcohol consumption ............................................................................................ 25
3.3.4 Potential confounders ............................................................................................ 27
3.4 Analysis ............................................................................................................................. 28
3.4.1 Statistics Canada guidelines .................................................................................. 28
3.4.2 Candidate adopted guidelines ............................................................................... 29
3.4.3 Descriptive analyses .............................................................................................. 30
3.4.4 Regression analyses .............................................................................................. 30
3.5 Sample size, power, and precision .................................................................................... 34
3.6 Candidate‟s role in the design and conduct of the research .............................................. 35
3.7 Currency and continued relevance of these data ............................................................... 37
Chapter 4 ....................................................................................................................................... 41
4 Results ...................................................................................................................................... 41
vii
4.1 Descriptive statistics ......................................................................................................... 41
4.1.1 Study sample ......................................................................................................... 41
4.1.2 General characteristics .......................................................................................... 42
4.1.3 Psychotropic medication use ................................................................................. 42
4.1.4 Representativeness of the study sample relative to the target population ............ 44
4.2 Analytic results ................................................................................................................. 45
4.2.1 Associations between psychotropic medication use and injuries ......................... 45
4.2.2 Modification of psychotropic medication use and injury associations by
alcohol consumption ............................................................................................. 47
4.2.3 Associations between psychotropic use in one cycle and injuries in the next ...... 48
Chapter 5 ....................................................................................................................................... 62
5 Discussion ................................................................................................................................ 62
5.1 Discussion of relationships between psychotropic medication use and injuries .............. 62
5.1.1 Antidepressant medications .................................................................................. 62
5.1.2 Benzodiazepine medications ................................................................................. 63
5.1.3 Strengths and limitations relating to relationships between psychotropic
medication use and injuries ................................................................................... 65
5.2 Discussion of modification of psychotropic medication use and injury associations by
alcohol consumption ......................................................................................................... 73
5.2.1 Potential explanations for these findings .............................................................. 74
5.2.2 Strengths and limitations relating to modification of psychotropic use and
injury associations by alcohol consumption ......................................................... 75
5.3 Public health implications for policy ................................................................................ 80
5.4 Areas for future research ................................................................................................... 81
References ..................................................................................................................................... 84
Appendices .................................................................................................................................. 100
viii
List of Tables
Table 1. Summary of Previously Published Injury Studies using the NPHS .............................. 17
Table 2. Data Collection Period for Key Analysis Variables Relative to the Day of Survey
Administration for Each of the Three NPHS Cycles .................................................................... 39
Table 3. Minimum Detectable ORs for Injury in the Past 12 Months based on Different Methods
of Calculation ................................................................................................................................ 40
Table 4. Baseline Demographic and Health Characteristics of Community-Dwelling Elderly
Canadians (N=2,423 Subjects)...................................................................................................... 50
Table 5. Self-reported Alcohol Consumption of Community-Dwelling Elderly Canadians
(N=2,423 Subjects in 1994/95, 2,174 in 1996/97 and 1,918 in 1998/99) ..................................... 52
Table 6. Psychotropic Medication use of Community-Dwelling Elderly Canadians in any of the
Three Cycles (N=2,423 Subjects) ................................................................................................. 53
Table 7. Psychotropic Medication use of Community-Dwelling Elderly Canadians in
Subsequent (Back-to-Back) Cycles (N=2,423 Subjects) .............................................................. 55
Table 8. Associations between Psychotropic Medication use of Community-Dwelling Elderly
Canadians and Injuries based on GEE Models (N=6,302 Records) ............................................. 56
Table 9. Associations between Psychotropic Medication use of Community-Dwelling Elderly
Canadians and Injuries by Frequency of Alcohol Consumption based on GEE Models (N=6,296
Records) ........................................................................................................................................ 57
Table 10. Associations between Psychotropic Medication use of Community-Dwelling Elderly
Canadians and Injuries by Quantity of Alcoholic Drinks Consumed in the Past Week based on
GEE Models (N=6,300 Records) .................................................................................................. 59
Table 11. Associations between Psychotropic Medication use of Community-Dwelling Elderly
Canadians in one Cycle and Injuries in the Following Cycle based on GEE Models (N=4,352
Records) ........................................................................................................................................ 61
ix
List of Figures
Figure 1. Summary of psychotropic medications as classified by the ATC system used by
Statistics Canada to code medications in the NPHS. .................................................................... 16
x
List of Appendices
Appendix A. Effect of dependent observations on variance ....................................................... 100
Appendix B. Psychotropic medications as classified in the Anatomical Therapeutic Chemical
Classification System for Human Medicines (ATC) .................................................................. 101
Appendix C. Alcohol consumption variables in the NPHS longitudinal full file ....................... 107
Appendix D. Potential confounders considered from the NPHS longitudinal full file ............. 108
Appendix E. Summary of the methods used for the selection of potential confounders ............ 109
Appendix F. Modeling strategy adopted to determine the associations between psychotropic
medications and injuries ............................................................................................................. 110
Appendix G. Modeling methods for estimation of minimum detectable OR ............................. 111
1
Chapter 1
1 Introduction
Psychotropic medication use is widely implicated as a risk factor for injuries, and it is believed
that the adverse effect profiles of these medications are exacerbated by the consumption of
alcohol. Complete abstinence from alcohol is currently recommended when taking any
psychotropic medications, but research in this area, particularly in elderly populations, is limited.
Therefore, health care providers are left with incomplete knowledge regarding the injury risk
profiles for specific psychotropic medications.
1.1 Study objectives
The aim of this study is to investigate associations between psychotropic medications and
injuries in the population of community-dwelling elderly Canadians. To facilitate this aim, the
two primary study objectives are
to examine the associations between the use of specific classes of psychotropic
medications and injuries among elderly participants of the National Population Health
Survey (NPHS) and
to determine whether and how associations between psychotropic medications and
injuries are modified by the consumption of alcohol.
The NPHS, providing longitudinal data from a representative sample of elderly Canadians, is
used as a vehicle to achieve these objectives. To efficiently assess the study objectives, the
following specific steps are undertaken:
1. The prevalence of psychotropic medication use is described in order to present the
epidemiology of psychotropic use in community-dwelling elderly Canadians and to
determine the feasibility of studying specific classes of psychotropics within the NPHS
database.
2
2. The persistent use of psychotropic medications across cycles of the NPHS is described in
order to place study results in context with the way in which psychotropics are used by
the elderly Canadian population over time.
3. The associations between specific classes of psychotropic medications and injuries in the
study population are modeled, and the influence of alcohol consumption on those
associations is examined.
1.2 Study rationale
The prevalence of medication use is higher among the elderly than any other age group,1, 2
and
the proportion of the Canadian population over 65 years of age is growing and will continue to
rise over the coming years.3, 4
Injury in the elderly is a particularly under-researched area. It is a
growing public health concern, given Canada‟s aging population, and it presents significant
issues for individual people. This study will educate health care providers regarding prescribing
practices in the long-term treatment of elderly patients, amend current knowledge by
differentiating between specific classes of psychotropics and isolating specific medications, and
inform elderly Canadians and their health care providers of the effects of alcohol consumption on
the risk of injury when using psychotropic medications.
The elderly are disproportionately affected with the highest death, dysfunction, and disability
rates due to injuries.5-7
Fear of having to bear these consequences if they become injured often
affects the way in which elderly Canadians live.6, 8
Results of this study will impact both clinical
practice and individual lifestyle choices regarding psychotropic medication use, injuries, and
alcohol consumption for elderly Canadians.
1.3 Timeline of research
This research extended over 10 years due to both logistical issues and changes in the candidate‟s
family situation. The thesis protocol was approved by the candidate‟s examiners and thesis
committee in June, 2001, and Statistics Canada granted access to the NPHS database in
September, 2001. The candidate began work on the analysis at the Statistics Canada Research
Office in Toronto and transferred the work to the new Research Data Centre on the University of
Toronto Campus when it opened in October, 2001.
3
A year and a half later, the candidate‟s husband accepted an international assignment with his
company, and as a result, the candidate transferred to Brussels, Belgium in June, 2003. The
candidate continued to work by remote access preparing programs on dummy datasets and
submitting them directly to Statistics Canada to be run. Analysis was completed by November,
2003, and the candidate began full-time employment in January, 2004. One year later, the
candidate‟s family transferred to Geneva, Switzerland, and the candidate restarted work on this
research until the birth of her first child in April, 2006. The candidate then moved to Divonne-
les-Bains, France in April, 2007 and had her second child in December, 2007. Work for this
research was restarted for the final time in April, 2009, the departmental defense occurred in
May, 2011, the senate oral defense occurred in August, 2011, and this dissertation document was
submitted to the School of Graduate Studies in September, 2011.
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Chapter 2
2 Literature Review
This chapter examines the current state of knowledge related to the study objectives for this
research. Specifically, it describes the epidemiology and consequences of injuries, reviews the
current state of knowledge relating to psychotropic medication use, injuries and alcohol
consumption in the elderly, and describes this study in the context of other relevant studies that
have used the NPHS database.
2.1 Descriptive epidemiology of injuries in the elderly
Based on population surveys conducted by Statistics Canada over the past two decades, an
average of 7.7% (range=5.7-9.1%) of elderly Canadians self-reported an injury in the year prior
to being interviewed.9-12
Consistent with injury data from these surveys, recent Canadian
hospital admission data from the National Trauma Registry (NTR) in 2006 and 2007 have shown
that injury risk increases slightly with increasing age among the elderly and that the risk of injury
is higher for elderly women than for men.9-15
Although only 13% of the Canadian population
was 65 years of age or older in 2001,16
the elderly accounted for 40% of all hospital admissions
for injuries from 2001 through 2005,5, 16, 17
and 23% of all hospital admissions for major injuries
in 2004/05.18
Falls are the leading cause of both injuries and hospital admissions for injuries in
the elderly, while motor vehicle crashes (MVCs) are the second most common cause of injury in
this population.3, 5, 15-17, 19
Specifically, data from the 1994 NPHS indicated that falls accounted
for 52.2% of self-reported unintentional injuries in the elderly, while MVCs accounted for
7.9%.7 The NTR indicated that while MVCs accounted for approximately 6% of injury
hospitalizations in the elderly from 1994 through 2001,3, 16
falls accounted for approximately
84% from 1994 through 2005.3, 5, 16, 17, 19
2.2 Consequences of injuries in the elderly
The consequences of injuries among the elderly are severe for both the injured individuals and
society as a whole. Injuries are less common in the elderly relative to younger age groups,20
but
recovery from injury is more difficult and less likely for the elderly,3, 6, 21, 22
thus leading to a
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disproportionate consumption of injury-care health dollars by elderly Canadians.23, 24
According
to data from the National Trauma Registry – Minimum Data Set (NTR-MDS),25
there were
69,077 injury hospitalizations in acute care facilities in Canada among those aged 65 years and
older in 2008/09 (43.9% of all injury hospitalizations). While the average length of stay for
injury hospitalizations among those aged 65 years and under was 6.2 days, the average among
those aged over 65 years was 17.5 days.25
There were 5,261 deaths resulting from injury
admissions among the elderly (83.6% of all injury related hospital deaths). While 1.2% of injury
admissions resulted in death among those aged 65 years and under, 7.6% resulted in death
among the elderly.25
The elderly have consistently been shown to have the highest
hospitalization rates, length of stay, and mortality rates for injury3, 5, 6, 16, 17, 22, 23
as well as
increased disability, decreased mobility, and increased dependence and social isolation after
injuries.3, 6, 7
Even when recovery from injury is complete, elderly people often experience a fear
of recurrence.3, 6
Fear of falling is a common anxiety for the elderly even among those who have
never fallen,8, 26, 27
and elderly people often impose limitations upon themselves to protect
against future falls.3, 6, 7
As the size of the elderly population increases, so will the relative
impact of injuries in this age group.
2.3 Psychotropic medications and potential associations with injuries
Psychotropic medications are prescribed to stabilize or improve mood, mental status or behavior.
Such medications can be classified by their actions in one of two directions: psycholeptic
medications have a depressive action on the brain while psychoanaleptic medications stimulate.
Under this dichotomy, psychotropic medications can be broadly classified. According to the
Anatomic Therapeutic Chemical Classification System for Human Medicines (ATC),
psycholeptics include antipsychotics, antianxieties (or anxiolytics), and hypnotics and sedatives,
while psychoanaleptics include antidepressants and stimulants.28, 29
Statistics Canada used the
ATC system to code the medication data for the NPHS (Section 3.3.2), and Figure 1 summarizes
this classification for the psychotropic medications considered in this study.
There is evidence in the literature of associations between injuries and antidepressants,
antianxieties, and hypnotics and sedatives.30-34
These medications are commonly used in the
elderly Canadian population, and their use is on the rise.35
Therefore, analysis of these
6
medications is technically feasible, and there is a public health utility in their study. The
following sections summarize the literature regarding these medications and injuries.
2.3.1 Antidepressant medications
Antidepressants are the second most commonly prescribed psychotropic medication after
benzodiazepines.35, 36
Among elderly Canadians, the prevalence of antidepressant use increased
from 5.6% in 1993 to 10.9% in 2002.35
The International Consensus Group on Depression and
Anxiety suggests antidepressants, specifically selective serotonin-reuptake inhibitors (SSRIs), as
the first line treatment for anxiety disorders.37-40
Therefore, antidepressants can be expected to
be prescribed and taken long-term to treat both depression and anxiety.41-43
Upon the introduction of SSRIs into the market in 1987,44
it was suggested that the magnitude of
the associations between antidepressant use and injuries may depend upon the specific type of
antidepressant. The two most common types of antidepressants are SSRIs (or bicyclics) and
tricyclics. Tricyclics are commonly associated with injuries and cause cognitive impairment,
sedation, sleep disturbances, dizziness, blurred vision, urinary disturbance, orthostatic
hypotension, and arrhythmogenic effects.30, 32, 41, 45
Approximately 10 to 20 percent of users
experience the more common side effects,46, 47
but the rare side effects such as orthostatic
hypotension and cardiac arrhythmias (which occur in less than three percent of users)46
are still
important to consider due to their seriousness. The tricyclic amitriptyline has the most
pronounced side effects48, 49
and should be avoided when treating depression in the elderly.50-52
Given that the side effects of SSRIs are much less frequent and intense, it was assumed that
SSRIs would not be associated with injuries, but studies have consistently shown that the risk of
falls and fractures in SSRI users is as great as that in tricyclic users.30, 33, 41, 45, 52
It has been
suggested that this could be due to selective prescribing practices where those at higher risk of
hip fracture are prescribed SSRIs rather than tricyclics,32, 44
but this possibility was addressed in
a case-series analysis showing that SSRI use did increase the risk of injuries.53
More recently,
there has been some evidence that a reduction in bone density among SSRI users is to blame, but
current research is not conclusive.41, 45, 54, 55
The literature is controversial regarding the effect of depression on associations between
antidepressant use and injuries. While antidepressants have been consistently associated with
injuries, results are mixed regarding associations between depression and injuries.54
It is unclear
7
in many studies whether treating depression with antidepressants increases the risk of injury or
perhaps changes the reason for the injury risk.41
A root cause of fractures is low bone density,
but it is unknown at this time if or how depression may impact bone density.54
Finally, it is
largely unknown if driving performance of depressed patients is improved when they take
antidepressants because most studies use healthy subjects56
(although one study of depressed
patients did show no improvement in driving skills when taking antidepressants).57
2.3.2 Benzodiazepine medications
Benzodiazepines are the most commonly used psychotropic medications in community-dwelling
elderly populations.35, 36, 58
Among elderly Canadians, the prevalence of benzodiazepine use
decreased slightly from 17% in 1993 to 15% in 2002.35
Continuous use of benzodiazepines for
periods between two and four weeks is generally considered to lead to tolerance of the
medication and dependency,59-61
and benzodiazepines have long been recognized as a risk factor
for falls, hip fractures, and MVCs.30, 31, 33, 34, 58, 62-64
Therefore, expert bodies recommend that
benzodiazepines only be prescribed for short periods of intermittent use.48, 49, 59, 60, 65
The side effect profiles of benzodiazepines have been well established to include sedation,
drowsiness, ataxia, cognitive problems, confusion, forgetfulness, mental slowing, psychomotor
impairment, clumsiness, and loss of coordination.48, 49, 60, 66
It has been suggested that
benzodiazepines with longer half-lives would increase the risk of injury due to the patient being
affected by these adverse effects for longer periods, but the results differentiating long-acting and
short-acting benzodiazepines are not consistent.32, 62, 63, 67
The true half-life of medications
depends on both the chemical composition of the drug and the physiological characteristics of
the individual, so half-lives can vary depending on the biological changes within the aging
person. While this could potentially explain the conflicting results, other researchers have
suggested that perhaps dose or duration of benzodiazepine use plays a more important role in
injury risk.68-71
Finally, given that benzodiazepines are prescribed for a variety of indications
including anxiety, insomnia, depression, epilepsy, and other seizure disorders,72
a further
possibility is that the characteristics of patients diagnosed with the particular indication, rather
than benzodiazepine use itself, may drive the strength of the associations with injuries.67, 73
Research to date has generally assessed the effects of any benzodiazepine use, as a single
exposure class, on injuries.31, 58, 74, 75
There have been some studies which have distinguished
8
between the effects of long and short half-life benzodiazepines,30, 32, 34, 58, 63
but very few have
considered the effects of antianxiety benzodiazepines separately from hypnotic and sedative
benzodiazepines.30, 31
2.3.3 Antianxiety (anxiolytic) medications
Anxiety research is not particularly advanced in elderly populations, so knowledge is
extrapolated from anxiety studies in younger adult populations.76
Although there are non-
benzodiazepine alternatives for treating anxiety that do not result in the sedative effects or
functional impairment that could cause injuries, their use in elderly populations is rare.
Therefore, the research that is available in this area is focused on benzodiazepine use, and due to
their side effect profiles, these antianxiety medications are expected to be associated with an
increased risk of injuries.76
Among studies that distinguished between anxiety medications and
hypnotic and sedative medications, antianxiety benzodiazepine use was associated with an
increased risk in MVCs,31, 77, 78
falls,79, 80
fractures,67
and any injuries.71, 73
Antianxiety medications are generally taken throughout the day, so their effects are present while
people are awake and performing their daily routines. Given the current state of knowledge in
this field, an American consensus panel has specified that chlordiazepoxide, clorazepate, and
diazepam should be avoided completely in the treatment of anxiety in the elderly and that total
daily doses of lorazepam, oxazepam, and alprazolam should not exceed 3mg, 60mg, and 2mg
respectively.51
Similarly, a Canadian consensus panel has deemed long-term prescriptions of
long half-life benzodiazepines to be inappropriate in the treatment of anxiety in the elderly.52
2.3.4 Hypnotic and sedative medications
The two main types of medical insomnia treatments are benzodiazepines, which have been the
mainstay for decades,50, 81
and non-benzodiazepine z-compounds, which were introduced to the
market in the mid-1980s.50, 82
Although the z-compounds have short half-lives and less cognitive
effects than benzodiazepines, they were not in common use in Canada in the 1990s. Therefore,
this review focuses on the benzodiazepine literature for insomnia treatments.
Hypnotic and sedative benzodiazepine use has been associated with falls,34, 74, 80, 83
hip
fractures,74, 84, 85
MVCs,78, 83
and any injuries.71
Insomnia that is either untreated or is
unresponsive to treatment is associated with falls in the elderly,50, 86
and it is unclear whether
9
treatment with hypnotic and sedative medications provides any protective effects against injuries,
particularly if the patient is at high risk of falls or cognitive impairment.50, 83, 86
Hypnotic and sedative medications are generally taken at night, so their effects are present when
people are sleeping. If they remain sleeping, any side effects of these medications throughout
the night would not impact injury risk, but if they get up, the medication side effects could
influence their risk of injury. Sleep experts unequivocally recommend that treatment with
hypnotic and sedative medications be restricted to the short-term.50, 82, 87
An American
consensus panel has indicated that flurazepam should be avoided completely in the treatment of
insomnia in the elderly and that total daily doses of triazolam and temazepam should not exceed
0.25mg and 15mg respectively.51
A Canadian consensus panel concurs indicating that long-term
prescriptions of long half-life benzodiazepines, and in particular triazolam, are inappropriate for
the treatment of insomnia in the elderly.52
Both panels have specified that long-term
prescriptions for barbiturates should be completely avoided in the elderly.51, 52
2.4 Alcohol, injuries, and psychotropic medication use
The World Health Organization Global Burden of Disease initiative, 2004 indicated that
unintentional injuries accounted for approximately one quarter of all alcohol attributable
morbidity and mortality worldwide.87
While alcohol consumption has been frequently and
consistently identified as a risk factor for injuries,88-94
current knowledge most commonly
associates injuries with heavy drinking or with consumption that varies greatly relative to
individuals' normal intake.95-99
Alcohol has the potential to interact with at least half of the most commonly prescribed drugs,1,
100, 101 and the elderly are at particular risk of the adverse effects of combining alcohol and
medications.102, 103
These adverse effects are especially of concern when taking antidepressants,
benzodiazepines, and hypnotics and sedatives,101, 104-109
as even light or moderate drinking
exacerbates the effect of these medications by increasing their sedative effects.1, 72, 91, 103, 109
The consumption of alcohol is contra-indicated with psychotropic medications, and low-risk
drinking guidelines commonly advise those taking psychotropic medications in particular to
abstain from consuming any alcohol.110, 111
Therefore, a measure of particular relevance when
assessing how alcohol consumption may affect associations between psychotropic medications
10
and injuries is any consumption rather than heavy consumption. Studies in this field generally
focus on younger adult populations with elderly populations being difficult to study because
heavy and variable drinking is relatively uncommon.112-116
In fact, studies of moderate drinking
and injuries in any age group are rare.96, 117
2.4.1 Epidemiology of alcohol use in the elderly
Cross-sectional studies both in Canada and abroad show that elderly populations are less likely to
drink than younger adults.112, 116, 118-120
Typically, those elderly persons who do drink are more
likely to drink daily112, 118
but also to consume less per drinking day than younger adults.112, 113
The elderly have low rates of heavy drinking1, 101, 112, 113, 121
and high rates of abstinence.116, 119, 120
Across age groups, men drink larger amounts and more frequently than women,119, 122
and after
early adulthood when drinking patterns become established, the differences between men and
women in terms of alcohol consumption measures remain consistent.123
2.4.2 Concurrent use of alcohol and psychotropic medications
Statistics regarding concurrent use of alcohol and psychotropic medications in the elderly are
rare. NPHS data indicated that 20% of Canadians 65 years of age and older who were multiple
medication users in 1994/95 were also daily drinkers.1 Specifically regarding psychotropic
medications, studies in this area generally focused on benzodiazepines which are the most
commonly used psychotropics. A British survey in 1985 indicated that 42% of adult
benzodiazepine users in the past week also consumed alcohol.124
A Spanish population study in
1997 indicated that 6.4% of adults used benzodiazepines in a 2-week period, and 16% of those
people drank daily.125
An American population survey of community-dwelling elderly in
1989/90 indicated that antianxiety benzodiazepine users drank less per week than non-
benzodiazepine users but that the difference was negligible when considering hypnotic and
sedative benzodiazepine users.126
Canadian survey data of adults in 2002, including elderly
subjects, supported the American results.108
An American study of elderly prescription drug
users indicated that 16% of antidepressant and antipsychotic users and 17% of antianxiety,
sedative, and hypnotic users also consumed some alcohol.103
Finnish survey data from 1998 to
2001 on subjects aged 53 to 73 years indicated that 11.5% used psychotropics regularly, and
17% of those users drank at least twice per week, 20% were binge drinkers (5 or more drinks on
one occasion for men and 4 or more for women), and 11% were heavy drinkers (14 or more
11
drinks per week for men and 7 or more for women).111
Finally, a Swiss study of all drivers
suspected of driving under the influence of drugs in 2005 showed that 6% of these subjects had
taken benzodiazepines, and 19% of those had also consumed alcohol.127
2.4.3 Effects of alcohol on associations between psychotropic use and injuries
Although there is very little research specifically focused on the elderly, there is some
information available regarding how alcohol consumption may affect associations between
psychotropic medication use and injuries in adult populations. A study of Swedish adults
including elderly subjects indicated that the risk of injury from falls was higher for hypnotic and
sedative users who consumed at least 500 grams of 100% ethanol per month (approximately 37
standard drinks per month in Canada) relative to those who consumed less.61
A Spanish study of
fatally injured drivers from 1991 through 2000 who were suspected of drug or alcohol use
showed that benzodiazepines were detected in 3% of subjects, and 34% of those benzodiazepine
users had consumed alcohol concurrently.118
A study of adults involved in MVCs and admitted
to an American trauma hospital showed that 17% of subjects were using benzodiazepines, and
16% of those subjects had also consumed alcohol.128
A Dutch study of drivers involved in a
crash and suspected of drug or alcohol use showed that 12% were using benzodiazepines,
barbiturates, or tricyclic antidepressants, and 83% of those psychotropic users had consumed
alcohol concurrently.129
A study of people injured in MVCs from 2004 to 2006 in northern
Sweden indicated that among the 200 fatal and nonfatal injuries, 8.5% were using psychotropic
medications at the time of the crash, and 47% of those had also consumed alcohol.130
Finally, a
review of studies of alcohol, drug use, and MVCs concluded that benzodiazepines and alcohol
consumed together generally increase the risk of injuries relative to either substance alone.91
2.5 Potential confounders of associations between psychotropic use and injuries
Psychotropic medication use is the primary exposure of interest in this study, but there are other
variables which may confound the associations between injuries in elderly populations and
psychotropic medication use. Previous studies in this field generally considered very few if any
of these potential confounders, but many indicated that they should be controlled for in future
studies. Some of these variables are available for analysis in the NPHS database including age,
12
sex, health status, concurrent medication use, cognitive impairment, depression, and alcohol
consumption. Given the objectives of this study, alcohol consumption is considered as a
potential effect modifier as discussed in Section 3.3.3.
2.6 Previous injury studies from the NPHS
The NPHS is a longitudinal, population-based survey of Canadians conducted by Statistics
Canada every two years beginning in 1994.131
As evidenced by the more than 300 published
articles indexed on Medline, the NPHS has been widely used for a variety of research purposes
such as the validation of measurement tools, calculation of basic rates, and performance of
descriptive and regression analyses on a broad range of topics. This section provides a synopsis
of the previous NPHS injury studies which are relevant to the current research and details the
unique contributions of this research relative to prior work on the NPHS.
A review of previous NPHS research conducted in November, 2010 revealed 12 studies7, 132-142
specifically on injuries (Table 1). Nine of these injury studies either focused directly on the
elderly or included elderly subjects:
Raina, Wong, et al. (1999) published a cross-sectional study including subjects over 65
years of age using the data from the first NPHS cycle, 1994/95.7 They employed
multiple logistic regression to examine the associations between injury status and the
potential risk factors of sex, household size, marital status, income, education, smoking,
frequency of physical activity, frequency of alcohol use (less than once versus once or
more per week), and the number of chronic illnesses (none, one, two, or three or more).
Also in 1999, Raina, Dukeshire, et al. published a similar study on the same subjects
using multiple linear and logistic regression models of cross-sectional data to explore the
associations between any injury and self-reported health care use while controlling for the
same potential confounders.136
Wilkins (1999) published a cross-sectional study of fall-related fractures in subjects over
65 years of age using data from the second NPHS cycle, 1996/97.141
She used multiple
logistic regression to investigate the associations between fall-related fractures and the
use of antidepressants, diuretics/antihypertensives, heart medication, sleep medication,
13
and tranquilizers in the past month while controlling for age, sex, income, alcohol use
(less than daily versus daily), chronic conditions (arthritis/rheumatism, diabetes, effects
of stroke, urinary incontinence, and impaired vision), and body mass index (BMI).
In 1999, Wilkins also published a study of the association between injurious falls in
subjects over 65 years of age and subsequent entry into health care (14 nights
hospitalization, formal home care, or living in an institution) while controlling for sex,
age, marital status, income, performance of activities of daily living (ADL), and chronic
conditions (cancer, diabetes, effects of stroke, heart disease, and high blood pressure).142
The first two NPHS cycles were restructured to obtain one record per subject with data
on entry into health care from the second cycle, 1996/97, and the independent variable
data from the first cycle, 1994/95. These data were analyzed with multiple logistic
regression models.
In 2003, Tjepkema published a study of repetitive strain injuries in adults using five
cycles of the NPHS and CCHS.138
Elderly subjects were included in the study, but the
prevalence of these injuries was relatively low in this age group. The longitudinal data
files were restructured to obtain one record per subject. Independent variables were
obtained from the cycle in which the first repetitive strain injury was reported, and
dependent variables were computed as the changes in chronic pain and psychological
distress between that cycle and the next.
In 2007, Vingilis and Wilk published a study using data from five cycles of the NPHS to
investigate the association between MVC injuries in subjects aged 12 to 85 years and four
potential risk factors: (a) binge drinking in the past 12 months, (b) self-reported health
status, (c) psychological distress in the past month, and (d) the number of medications
(pain relievers; tranquilizers; antidepressants; codeine, Demerol, or morphine; and
sleeping pills) used in the past month controlling for age, sex, and immigration status.139
In order to use multiple logistic regression techniques, they restructured the database so
that there was one record per subject. If the subject reported an MVC during Cycles 2
(1996/97) through 5 (2002/03), the first reported MVC was selected for analysis, and the
potential risk factors were obtained from the prior cycle. If no MVC was reported, the
potential risk factors were obtained from the first cycle, 1994/95.
14
In 2008, Vingilis and Wilk published a study on the same restructured dataset where path
analysis techniques were used, and the results were stratified by three age groups
including one 60 to 85 years of age.140
Roberts, Vingilis et al., (2008) published a study comparing self-reported MVC injury
rates in two cycles of the NPHS to official police reports in Transport Canada‟s Traffic
Accident Information Database.137
Comparisons were made by sex and age including a
group aged 65 years and older.
Patten, Williams et al., (2010) published a study of major depressive episodes (MDEs)
and injury risk among subjects 12 years of age and older using proportional hazards
models for five cycles of the NPHS.134
They modeled both injury as a risk factor for
MDEs and MDEs as a risk factor for injury. In the second analysis, the authors
controlled for age, sex, and the following time-varying covariates measured every two
years: recreational activity pattern; occupational type; BMI; and the use of
antidepressants, antipsychotics, and benzodiazepines in the past two days.
All the research performed using the NPHS is considered secondary data analysis, but this study
boasts three valuable accomplishments over the other injury studies:
1. While there were four studies of the NPHS data that did examine any injury,7, 134-136
only
this study and that of MDEs and any injury134
examine any injury using the longitudinal
data files. The analytical techniques used in this study and that of MDEs and any
injury134
provide an opportunity to address the temporal sequence of events that is
impossible to pursue with cross-sectional data and provide stronger causal evidence.
2. Although several NPHS studies of specific injury types used the longitudinal data files,
they all restructured the database into one record per subject by selecting the
independent variables from one cycle and the dependent variable from a subsequent
cycle.132, 138-141
By maintaining multiple records of data for each subject and using
repeated measures analysis techniques, this study fully exploits the longitudinal nature
of the database while maximizing the power of the study.
15
3. A number of NPHS studies on topics other than injury did use the medical inventory
data of specific drugs taken within the past two days.2, 143-150
However, NPHS injury
studies that included medication use as a variable in the analyses only considered the
broad categories of medication self-reported in the past month139-141
with the exception
of the study of MDEs and any injury.134
While the NPHS data on medication intake in
the past month was obtained purely through subject recall, medication intake in the past
two days was obtained by medical inventory which is considered a superior method of
collecting accurate medical consumption data relative to self-report.151-153
Therefore,
this study features an important methodological improvement in terms of data quality, as
it does not rely on subject recall and knowledge for medication use data.
The NPHS has been an important source of data for epidemiological, public health, and social
research. This study uses the NPHS data to thoroughly pursue novel research objectives by fully
exploiting the longitudinal nature of the database and incorporating the valuable detailed
medication use data that has so rarely been employed in previous research.
16
Figure 1. Summary of psychotropic medications as classified by the ATC system used by
Statistics Canada to code medications in the NPHS.
Note. Adapted from “Anatomical Therapeutic Chemical Classification System” by the World Health Organization. Available at: Statistics Canada by electronic transmission.29 aSpecific benzodiazepine medications classified as antianxieties within the ATC system are diazepam, chlordiazepoxide, oxazepam, clorazepate dipotassium, lorazepam, bromazepam, ketazolam, alprazolam, and chlordiazepoxide+clidinium. bSpecific benzodiazepine medications classified as hypnotics and sedatives within the ATC system are flurazepam, nitrazepam, triazolam, temazepam, midazolam, and estazolam.
Psychotropics
Psycholeptics
Antipsychotics
Not considered in this study
Antianxieties
Benzodiazepinesa
Dephenylmethane derivatives
Carbamates
Azaspirodecanedione derivatives
Others
Hypnotics and sedatives
Benzodiazepinesb
Barbiturates
Aldehydes and derivatives
Cyclopyrrolones
Others
Psychoanaleptics
Antidepressants
Tricyclics
SSRIs
Mao inhibitors
Mao type a inhibitors
Others
Stimulants
Not considered in this study
17
Table 1. Summary of Previously Published Injury Studies using the NPHS
Authors, year Subjects Dependent variable Independent variables Analysis
Raina et al,
1999
1 NPHS cycle;
2 age groups:
55 - 64 yrs,
65+ yrs
any injury household size, marital status,
income, education, smoking,
frequency of physical activity,
alcohol use, chronic conditions
multiple logistic regression of
cross-sectional data
Raina et al,
1999
1 NPHS cycle;
2 age groups:
55 - 64 yrs,
65+ yrs
average no. of contacts;
any contact with each
type of health care
professional in past 12
months
any injury, age, sex, household
size, co-morbidity, marital status,
income, education, smoking,
alcohol use, frequency of
physical activity
multiple linear regression;
multiple logistic regression of
cross-sectional data
Wilkins,
1999
1 NPHS cycle;
2 age groups:
65 - 74 yrs,
75+ yrs
fracture due to a fall medication use, age, sex,
income, alcohol use, chronic
conditions, body mass index
multiple logistic regression of
cross-sectional data
Wilkins,
1999
2 NPHS
cycles;
2 age groups:
65 - 74 yrs,
75+ yrs
entry into health care
system
injury due to fall, sex, age,
marital status, income,
performance of activities of daily
living, chronic conditions
multiple logistic regression:
independent variables in 1
cycle modeled against
dependent variable in next
cycle
Tjepkema,
2003
4 NPHS cycles
and 1 CCHS;a
all adults aged
20+ yrs
change in chronic pain;
psychological distress
between cycles
repetitive strain injury, age,
marital status, education, income,
work status, obesity, leisure time
physical activity, smoking,
arthritis, diabetes, thyroid
condition
multiple logistic regression:
independent variables in 1
cycle modeled against
dependent variable change
to the next cycle
Cole et al,
2005
4 NPHS
cycles;
4 age groups
of adults
18 - 64 yrs
repetitive strain injury
due to work-related
activity in Cycle 4
Cycle 3: sex, age, marital status
education, income; Cycles 1, 2
or 3: back problems, arthritis,
activity limitations, smoking,
leisure time physical activity
univariate logistic regression:
independent variables in
Cycles 1, 2 and 3 modeled
against dependent variable
in Cycle 4
Potter et al,
2005
1 NPHS cycle;
3 age groups:
12 - 14 yrs,
15 - 17 yrs,
18 - 19 yrs
any injury; recreation
injury; non-recreation
injury
income: household,
neighbourhood; education:
neighbourhood, parental; age,
sex, parental occupation,
rural/urban status, geographic
region, living with parents
multiple logistic regression of
cross-sectional data
Gordon et al,
2006
1 NPHS cycle;
3 age groups:
0 - 14 yrs,
15 - 34 yrs,
35+ yrs
injury due to concussion age, sex descriptive statistics; bivariate
analysis
18
Table 1. Summary of Previously Published Injury Studies using the NPHS (continued)
Authors, year Subjects Dependent variable Independent variables Analysis
Vingilis et al,
2007
5 NPHS
cycles;
12 - 85 yrs
injury due to MVC binge drinking, health status,
psychological distress,
medication use, age, sex,
immigration status
multiple logistic regression:
independent variables in 1
cycle modeled against
dependent variable in next
cycle
Roberts et al,
2008
2 NPHS
cycles;
7 age groups:
15 - 19 yrs to
65+ yrs.
TRIADb
injury due to MVC age, sex comparison of MVC injury
rates by age and sex
between NPHS and TRAIDb
Vingilis et al,
2008
5 NPHS
cycles;
3 age groups:
12 - 29.9 yrs,
30 - 59.9 yrs,
60 - 85 yrs
injury due to MVC binge drinking, health status,
psychological distress,
medication use, age, sex,
immigration status
path analysis: independent
variables in 1 cycle modeled
against dependent variable
in next cycle
Patten et al,
2010
5 NPHS
cycles;
5 age groups:
12 - 18 yrs,
19 - 25 yrs,
26 - 45 yrs,
46 - 85 yrs,
66+yrs
any injury major depressive episode, age,
sex, recreational activity pattern,
occupational type, BMI,
medication use
proportional hazards models
with time varying covariates
Note. aCCHS=Canadian Community Health Survey; MVC=Motor Vehicle Crash; bTRIAD=Transport Canada's Traffic
Accident Information Database.
19
Chapter 3
3 Research Methods
This chapter addresses the details of design and analysis for this study. Specifically, the study
population and NPHS design and structure are described followed by definitions of statistical
concepts relevant to the analyses and brief descriptions of the key analysis variables. The
Statistics Canada guidelines which impacted the analysis plan and those adopted by the
candidate in order to provide consistency to analyses of this extensive database are presented.
This is followed by an account of the specific analyses performed, the magnitude of the odds
ratios (ORs) that one could expect to detect statistically with the available sample size, the role
of the candidate in conducting this research, and the relevance of this research in today‟s
landscape.
3.1 Subjects
3.1.1 Data source
The NPHS is the ongoing survey of the population‟s health in Canada131
where initial data
collection occurred in 1994/95 and has been repeated every two years.154
The goals of the NPHS
have been clearly defined,131, 155
and the content and structure of this survey provide the
flexibility to address a wide range of issues of interest to particular researchers and
organizations. Additionally, the longitudinal nature of the NPHS allows one to describe the
dynamic process of health and illness in addition to cross-sectional estimates.131, 155
The “Enquête sociale et de santé” (ESS) in Quebec and the Labour Force Survey (LFS) in the
rest of Canada used comparable sampling designs and were used as the sampling frames in Cycle
1 of the NPHS (1994/95) from which a representative sample of households was drawn with a
minimum of 1,200 households per province.131, 154-156
In the first cycle of the NPHS, one member of each household provided general demographic
and health information for all residents. Then, one member of each household was randomly
selected to participate in a more detailed survey and become part of the permanent longitudinal
sample to be reinterviewed every two years. If the selected individual was unable to provide
20
these data themselves, data were collected from a proxy informant. Data in 1994/95 were
primarily collected at the selected residences through face-to-face interviews, whereas data in the
1996/97 and 1998/99 cycles were collected mainly through telephone interviews unless the
participant did not have a telephone or preferred a personal interview.156
In 1996/97 and 1998/99, general and health information were again provided by one member of
the household for all residents, and the individual participant selected for the longitudinal sample
in 1994/95 again completed the more detailed surveys. If the longitudinal respondents living in a
residence eligible for inclusion in the household longitudinal study in 1994/95 moved to an
ineligible residence (i.e., an institution or temporarily out of the country) in the latter cycles, that
individual was contacted, and their data were included in the NPHS whenever possible.156
3.1.2 Study population
The target population for this study is the community-dwelling Canadian population aged 65
years of age and older in 1994/95. Data from Cycles 1 (1994/95), 2 (1996/97), and 3 (1998/99)
of the NPHS household longitudinal file (the longitudinal full file) were used in this study. The
study database includes the subset of subjects who responded in all cycles or who died before the
second or third cycles, only includes records for the cycles in which respondents lived in the
community,154
and does not include additional subjects who were surveyed in 1998/99 to
reestablish the 1994/95 sampling distribution.155
Those living on Indian reserves, military bases,
and in some remote areas of northern Ontario and Quebec were excluded from this database.155-
157
3.1.3 Database structure
The NPHS longitudinal database was originally structured as three cross-sectional databases
merged together into a single record for each subject. Each record began with variables collected
in 1994/95, and those were followed by the variables from the second and third cycles. In order
to exploit the longitudinal nature of the database and account for the correlation between
repeated measurements of the same individuals, Generalized Estimating Equation (GEE)
methodology was needed (Section 3.2.4). This statistical technique required that the database be
structured so that each record represented all the data for a participant from a single cycle.158, 159
Therefore, the candidate restructured the original database resulting in a database with up to
21
three records per subject where each record contained (a) an identification variable needed to
link measurements from the same individual, (b) a cycle variable needed to order the successive
measurements for each individual, and (c) a set of variables containing the responses to all the
survey questions.
3.2 Statistical definitions
3.2.1 Sampling weights
In order to most accurately represent the target population of community-dwelling Canadians,
Statistics Canada provided sampling weights in the longitudinal full file which reflect the
probability of selecting each participant into the longitudinal study sample in 1994/95.154
As per
Statistics Canada guidelines, all descriptive population estimates are weighted. When applying
the sampling weights, records that represent a higher percentage of the target population are
weighted higher and thus contribute proportionally more to the results of analyses. The design of
the NPHS (Section 3.2.2) and the analysis techniques required for multivariate modeling of the
longitudinal data (Section 3.2.4) precluded the use of sampling weights in all regression analyses
used in this study as per Statistics Canada instruction.
3.2.2 Complex survey designs
As the NPHS employed a multistage complex survey design incorporating both stratification and
clustering, the basic formulae for variance calculations do not apply.160-162
The NPHS is
statistically less efficient than a simple random sample (SRS) design of the same sample size:
when the variances of the point estimates are correctly calculated, they are larger than those that
would be used if one incorrectly assumed an SRS design.160
Ideally, all design elements must be taken into account when computing variances for point
estimates in surveys with complex sampling designs, but this posed a significant confidentiality
problem for Statistics Canada.160, 162
Therefore, Statistics Canada developed a bootstrapping
methodology which aimed to protect confidentiality while allowing data users a reasonable
approximation of design-specific variances.160, 162
22
3.2.3 Bootstrap resampling methodology for variance estimation
Bootstrapping is a method which allows one to approximate the variance of a point estimate for
which there is no known variance formula. The basic principle of this methodology is to draw
multiple samples, called replicates, from the population; compute the point estimate of interest
for each of the replicates; and calculate the variance of these point estimates.163
Rather than
undertaking the costly process of drawing multiple samples from the population (i.e. repeating
the entire NPHS protocol multiple times), the bootstrapping methodology draws multiple
replicates from the original NPHS sample as though that sample was the population.160, 162
This is a complex methodology to expect data users to correctly employ on their own, and there
are several points during which confidentiality could be breached. Therefore, Statistics Canada
provides data users with a file containing 500 bootstrap weight variables from which the data
users can properly compute the bootstrap variance estimator.160, 161
Using the Statistics Canada
utility, the point estimate remains unchanged, but the bootstrapped variance is applied. In this
study, bootstrapped variances were used to calculate 95% confidence intervals (CIs) around
point estimates in the descriptive analyses, but according to Statistics Canada guidelines (Section
3.4.1), bootstrapping techniques are not used for repeated measures analyses.
3.2.4 Generalized estimating equations
Longitudinal datasets are comprised of multiple measurements of the same subjects over time,
frequently referred to as repeated measures. Unlike databases that contain a single record per
subject, repeated measures databases contain multiple records for each subject that are correlated
with each other. Thus, it is necessary to account for the dependency structure between multiple
observations from the same individual in the longitudinal NPHS data (Appendix A) to obtain
valid standard errors of parameter estimates for hypothesis testing.158, 164
Generalized estimating equation (GEE) methodology allows for the analysis of correlated data
using valid standard errors that would otherwise be modeled using generalized linear models
(GLMs).158, 165, 166
To define a GLM, one needs to specify the distribution of the dependent
variable (from the exponential family), the link function, and the independent variables.158, 159
To define a GEE, in addition to the above three criteria, it is also necessary to specify the
correlation structure of the repeated measurements.158, 159, 165
In this study (a) the dependent
23
variable was binary (injured/not injured); (b) the link function was the logit; (c) independent
variables included the psychotropic medications, potential confounders (Section 3.3.4), and in
some models, alcohol consumption and its cross product interaction term with psychotropic
medication use; and (d) the working correlation matrix was unstructured in order to place no
assumptions on the correlation between repeated measures and to allow the data to drive the
matrix values. In this study, GEEs were used to conduct multivariate modeling, accounting for
the correlated data structure in the regression analyses. As stated in Section 3.2.1, none of the
regression analyses were weighted, thus preserving statistical precision.
3.2.5 Interaction and effect modification
Inclusion of the cross-product interaction term between alcohol consumption and psychotropic
medication use in the logit based GEE model allows for a test of statistical interaction.167
If
inclusion of this interaction term improves the goodness of fit of the model, it implies that the
observed OR for the cross-product interaction term is not equal to zero and that the expected OR
for one main effect when the other is also present is equal to the effect attained when the
independent effects are multiplied together.168, 169
The relevant concern for this study is an
assessment of potential OR modification (effect modification). To interpret how alcohol
consumption potentially modifies the effect of psychotropic medication use on injuries, stratum
specific ORs must be considered to determine whether or not there is homogeneity of ORs across
levels of alcohol consumption.168, 169
3.3 Variable definitions
Complete variable definitions are provided in the documentation of the NPHS available at
Statistics Canada.154, 161
The following sections detail those variables considered in the final
analysis of this study. Table 2 depicts the data collection periods for each of the three NPHS
cycles relative to the day the survey was administered for each of the key analysis variables.
3.3.1 Injury status
The dependent variable for this study was whether or not the subject self-reported an injury
serious enough to limit normal activities in the 12 months prior to the survey. There are no
supporting data available which allow researchers to confirm the accuracy of these self-reports,
and there are no questions within the NPHS survey to determine the severity of the reported
24
injury. As there is a two year interval between each cycle, the NPHS longitudinal database
provides injury data retrospectively for the year prior to each cycle, but provides no information
on injury status for the year following each cycle.
3.3.2 Psychotropic medication use
The primary independent variables for this study were psychotropic medication use in the past
two days and the general categories of medication taken in the past month. In both cases, there
were no data regarding the amount or timing of medication doses, only whether or not subjects
reported taking the medications (Section 5.1.2). Data were collected for all medications whether
prescription or over the counter.
Psychotropic use in the past two days – medical inventory
The independent variables of primary interest were the specific medications subjects took in the
two days prior to the survey. Subjects were asked to look at their bottles, tubes, or boxes and
provide the exact name of the medications taken in the past two days. Drug codes were then
given to these exact names by Statistics Canada according to the ATC,170
and these ATC codes
are available for analysis in the NPHS database. Documentation of the ATC codes was obtained
from Statistics Canada,29
and dichotomous variables were created by the candidate to indicate
whether or not subjects took various broad and narrow categories of psychotropic medications as
well as the specific psychotropic medications themselves (Appendix B).
Psychotropic use in subsequent cycles – medical inventory
Based on the dichotomous variables of psychotropic use in the past two days, an additional set of
dichotomous variables was created to indicate whether or not the subject took each medication in
subsequent cycles: (a) 1994/95 and 1996/97; (b) 1996/97 and 1998/99; or (c) 1994/95, 1996/97
and 1998/99.
General psychotropic use in the past month – self-report
Finally, the NPHS contained a section concerning general categories of medication. Of
relevance to this study, subjects self-reported whether or not they took tranquillizers,
antidepressants, and sleeping pills in the past month.
25
Reliability and validity of data collection methods for medication use
There is general consensus that medical inventory methods are superior to self-report recall
methods for the collection of medication use data.152, 153, 171
Studies on the reliability and
validity of this data in surveys are not common, and most focus on areas of reproductive
health.153
There has been some work among elderly populations, particularly in the area of
cardiovascular disease (CVD), from the Cardiovascular Health Study.153, 172
Among CVD
medications, these studies have shown modest levels of agreement between medical inventory
and self-report recall methods ranging from Kappa=0.44 for reserpine to Kappa=0.72 for beta-
agonists. Assessments of the validity of these two methods in relation to physiologic measures
indicated that medical inventory methods of data collection for CVD medications were far
superior to self-report recall methods.153
Given this conclusion, it is also interesting to note that
agreement was reasonable comparing medical inventory data and serum levels ranging from
Kappa=0.43 for propranolol to Kappa=0.94 for digoxin.172
These conclusions have been further
corroborated with data from the National Social Life, Health and Aging Project indicating that
medical inventory data corresponds well (a) for gender-specific medications, (b) with the
presence of self-reported diseases, and (c) with differences in physiologic measures affected by
medication use.171
3.3.3 Alcohol consumption
Alcohol consumption is being considered in this study as a potential effect modifier168
of
associations between psychotropic medication use and injuries. As such, alcohol consumption
variables are being chosen to define strata rather than to be independent variables in their own
right. The candidate evaluated all the original NPHS survey variables, plus those derived by
Statistics Canada (Appendix C), to determine those most relevant to the potential effect
modification of associations between psychotropic medication use and injuries.
The candidate derived two dichotomous variables for analysis in this study. The first captures
frequency of alcohol consumption in the past 12 months: regular drinker (drinking at least once
per month) versus occasional drinker, former drinker, and abstainer. The second captures
quantity of alcohol consumed in the past seven days: at least one drink versus no drinks. Both
dichotomous variables capture light alcohol consumption, but Kappa=0.70 indicates that while
agreement between the two measures is on the high end of moderate, there is not perfect
26
colinearity between the two measures. They do not capture the same information about subjects,
and analytical results are likely to show different effects as they are, in some respects, measuring
different things.
Statistically, a continuous measure of alcohol consumption would have been preferred given the
potential of a continuum of effect and that continuous measures have the greatest power.173
However, the two continuous measures derived from the 7-day diary variables (weekly total
consumption and average daily consumption) are less appropriate than the candidate‟s chosen
measure: over 1,820 of the 6,515 records available for analysis had missing data, and less than
five percent of those available records indicated consumption of even two or more drinks in the
past week.
While measures of heavy and variable drinking are often used in injury studies among younger
study populations, they are not appropriate as measures of alcohol consumption in this study for
several reasons. First, heavy and variable drinking are rare in elderly, community-dwelling
populations. Additionally, alcohol consumption is not being considered as an independent risk
factor in this analysis. Finally, since any alcohol consumption is contra-indicated with
psychotropic medications, it is not of primary interest to stratify the study population on the basis
of heavy or variable drinking: stratification on the basis of light or moderate alcohol
consumption is of particular interest in this study (Section 2.4) even if sample size and the
distribution of alcohol intake were adequate to permit further stratification.
The literature argues that researchers should differentiate between abstainers and former drinkers
because former drinkers could well have quit drinking due to health problems that themselves
put them at risk of injuries.174-176
At the same time, there is evidence from longitudinal surveys
that the distinction between the two strata cannot be trusted, as former drinkers often report that
they are lifetime abstainers in later surveys.175
Statistics Canada derived a 4-category variable
capturing frequency of consumption in the past 12 months, and the candidate further collapsed
occasional drinkers, former drinkers, and abstainers into a single category. To address any
potential confounding that may have resulted by selecting the dichotomous variable of regular
drinker versus occasional drinker, former drinker, or abstainer in the past 12 months as a
potential effect modifier, the candidate included three additional variables in the analyses
27
(Section 3.3.4): (a) self-reported general health, (b) an indicator for any of seven different
chronic conditions related to injuries, and (c) cognitive impairment.
In the NPHS, subjects were asked if they had drunk any alcohol in the past 12 months thereby
including the lightest and most infrequent of drinkers in the drinking stratum. Given the
importance of capturing subjects who drank and used psychotropic medications concurrently,
this variable was eliminated from further consideration for this study.
Although there were a number of alcohol consumption variables available in the NPHS, only two
were used in the analyses in order to (a) put some limits on multiple comparisons, (b) provide
adequate sample sizes for analyses within strata, and (c) result in parameterizations which are
relevant to both clinical and policy considerations.
3.3.4 Potential confounders
A complete list of potential confounders considered in this study is available in Appendix D, and
a description of the methods used to identify the variables controlled for in these analyses is
found in Section 3.4.4. In this study, sex and age (as a continuous variable) were used directly
from the database,154
and health status was captured by three different variables:
Self-reported general health was measured on a 5-point scale from „Excellent‟ to
„Poor‟.154
The candidate created an indicator variable identifying subjects who had any of seven
chronic conditions (heart disease, stroke, cancer, Alzheimer‟s/dementia, chronic
bronchitis/emphysema, arthritis/rheumatism, and migraine headaches) as selected based
on the literature and GEE analyses of the NPHS database. Data for these seven chronic
conditions were obtained for all members of selected households by one contact
individual for each household.154
Therefore, if the household member selected to
participate in the longitudinal survey was the household contact individual, they provided
their own data, otherwise, these data were collected by proxy for the participant in the
longitudinal survey.
The cognitive impairment function code161
is a sub-component of the Health Utility
Index177
that was derived by concatenating the values from two self-reported variables
(ability to remember things, and ability to think and solve problems). This variable for
cognitive impairment available in the NPHS is measured on a 6-point scale:
28
o No cognitive problem
o A little difficulty thinking
o Somewhat forgetful
o Somewhat forgetful/a little difficulty thinking
o Very forgetful/a great deal of difficulty thinking
o Unable to remember or to think
The candidate collapsed this variable into a cognitive impairment measure on a 3-point
scale due primarily to issues of power. This three level variable for cognitive
impairment was the third health status variable considered as a potential confounder in
this study:
o No cognitive problem/only a little difficulty thinking
o Only somewhat forgetful/both somewhat forgetful and a little difficulty thinking
o Very forgetful or a great deal of difficulty thinking/unable to remember or think
3.4 Analysis
Analyses for this study were performed with programs written by the candidate using the SAS
System.178
Descriptive analysis programs obtained bootstrap variance estimates by incorporating
SAS-based macros and bootstrapping weights files written and provided by Statistics Canada.
3.4.1 Statistics Canada guidelines
Researchers using Statistics Canada survey data must conform to various analysis and reporting
restrictions. Descriptions of the guidelines which are relevant to this study are
Weighting – relevant to analysis and reporting: Sampling weights provided by Statistics
Canada for the longitudinal full file were applied to all frequencies and percentages
reported thereby representing the community-dwelling elderly Canadian population.
Weighted descriptive statistics cannot be presented on categorical data if any of the
categories presented have an unweighted sample size of less than 10 subjects,154
therefore, categorical variables of interest were collapsed where necessary.
Rounding – relevant to reporting: In accordance with Statistics Canada requirements,
frequencies were rounded to the nearest 100 and percentages to the first decimal place.154
29
Coefficient of variation (CV) – relevant to reporting: A measure of variation inherent in
a point estimate, the CV is calculated by dividing the standard deviation by the point
estimate itself and multiplying the result by 100 to express the ratio as a percentage.154
The bootstrapping program provided by Statistics Canada computed CVs based on
bootstrapping methodology for each point estimate of interest for population estimates
used in this study.160
Estimates with a CV greater than 33.3 were considered to be of
unacceptable quality and, following Statistics Canada guidelines, were not presented in
the results of this study. Such estimates were identified in footnotes to the tables. While
CVs between 16.6 and 33.3 indicate estimates with high sampling variability, they were
considered to be of marginal quality and, according to Statistics Canada, may be
presented. Such estimates were identified throughout the results tables by the letter „M‟
for marginal.154
Bootstrapping – relevant to analysis: Point estimates were not bootstrapped. Statistics
Canada recommended that sampling weights be applied and the point estimates be
computed from the original NPHS sample data.179
Bootstrapping techniques were used
to calculate the variances for descriptive statistics (i.e. variances for the point estimates),
so corresponding CIs were based on bootstrapped variances.179
Statistics Canada does
not provide a bootstrapping program, nor does it recommend the employment of
bootstrapping techniques, for repeated measures analyses.160
Therefore, statistical tests
and CIs from regression analyses did not make use of bootstrapping methods for variance
calculations.
3.4.2 Candidate adopted guidelines
The independent variable of primary interest in this study was the use of psychotropic
medications in the past two days as collected by medical inventory methods. These data were
sufficiently detailed to allow for the derivation of indicator variables for over 150 broad
categories and specific psychotropic medications (Appendix B). However, many of these
psychotropics were rarely used by the study participants. Therefore, modeling guidelines were
adopted by the candidate for two purposes: (a) to obtain sample sizes that were adequate for the
various stages of modeling and (b) to provide a structure within which the vast number of
30
independent variables could be efficiently and uniformly investigated. Following are
descriptions of the guidelines adopted by the candidate for this study:
Collapsing of variables: While the Statistics Canada restriction of not reporting on
categories with unweighted frequencies of less than 10 does not apply to the presentation
of results from statistical models, modeling techniques are inherently unstable with very
small sample sizes. Therefore, if variables were collapsed to comply with the Statistics
Canada requirement, only the collapsed version of these variables was used in further
analyses.
Descriptive analyses: Appendix B identifies medications that had less than 10 recorded
instances of use in any of the three cycles (unweighted). Descriptive analyses of
psychotropic medication use were only performed on the subset of medications with at
least 10 recorded instances of use in any of the three cycles. Further analyses were
restricted to this subset of medications.
Modeling: Modeling of the associations between psychotropic medication variables and
injuries was only performed on the subset of medications with over 35 recorded instances
of use in any of the three cycles. Using this standard, the majority of this subset of
medications had adequate sample size to model univariate associations, to account for
other variables in multivariate models, and to stratify those multivariate models by
alcohol consumption as a potential effect modifier.
3.4.3 Descriptive analyses
In preparation for statistical modeling to address the aim of this study (Section 1.1), descriptive
procedures including frequency tables and univariate statistical procedures159
were employed.
This provided an understanding of the statistical features of the study sample, guided the
derivation of new variables, characterized the study population, and examined the consistency of
reporting of psychotropic medication use across subsequent cycles.
3.4.4 Regression analyses
Associations between psychotropic medication use and injuries were described by ORs and 95%
CIs resulting from analyses using GEE methods for repeated measures. 159
Multivariate GEE
31
models which included the cross-product interaction term of alcohol consumption and
psychotropic medication use, tested for statistical interaction. To test specifically for inequality
in stratum specific ORs,168, 169
the multivariate models were then stratified by alcohol
consumption level. The potential effect modification of the ORs for the associations between
psychotropic medication use and injuries were then described by the stratum specific ORs and
95% CIs.
Identification of potential confounders
Based on the literature, candidate decisions, and the variables available for analysis in the NPHS
longitudinal database, a relatively long list of variables was compiled for consideration as
potential confounders to the associations between psychotropic medication use and injuries
(Appendix D). There is general consensus that, while there are many acceptable approaches to
selecting confounders for control, none are perfect.180, 181
In light of the long list of potential
confounders and the similarly long list of psychotropic medications to investigate, this is a
particularly relevant issue for this study. Therefore, methods were established by the candidate
to efficiently determine a set of variables to be controlled for when modeling the associations
between injuries and each psychotropic medication.
Associations between each potential confounder and injuries were modeled using GEE
methodology. When the p-value of the associations between these potential confounders and
injury was ≤ 0.20, their confounding effect was assessed on the associations between injury and
three categories of psychotropic medication (any psychotropic, any psycholeptic, and any
psychoanaleptic) using GEE methodology. Those potential confounders that resulted in a
change in the β estimate of the association between injury and medication use of ≥ 10% were
identified as variables to be controlled for in this study. Those variables not identified were not
variables of interest in their own right but rather had been considered as potential confounders to
the primary associations of interest in the study: they did not have additional confounding effect,
and including them would have decreased the sample size.
Missing values were a particular issue for the measures of depression and health utility index
(HUI). Therefore, the confounding effect of these variables on the main ORs was tested on
reduced databases where none of the records had missing values for the variable of interest.
32
Since these variables did not affect the main ORs further than other variables being considered in
the multivariate models, they were not included in the final multivariate models.
A summary of this strategy can be found in Appendix E. Based on the literature and these
methods, the following variables were controlled for in all multivariate models of the
associations between injuries and psychotropic medication use (Section 3.3.4):
age,
sex,
self-reported general health,
any of the seven identified chronic conditions, and
cognitive impairment.
Associations: psychotropic use and injuries
In order to determine the associations between injuries and psychotropic medications, the same
modeling strategy was employed for the use of each psychotropic medication being studied
(Appendix F). All analyses employed GEE methodology. Where independent ORs for
psychotropic medications appeared to be quite different (as noted in the results), the statistical
significance of the differences was confirmed following the Wald contrast method described by
Altman and Bland (2003).182
This method was also used to confirm differences between stratum
specific ORs in the analyses for effect modification of alcohol consumption.
Timeline: injuries versus independent variables
As the NPHS data were collected longitudinally, there were two approaches for their analysis.
Since injuries were reported over the entire year prior to the survey, and medication use was
reported in either the two days or one month prior to the survey (Table 2), it is possible that the
injury event preceded medication use in each cycle. Therefore, these data could be analyzed to
determine (a) if medication use reported in each cycle was associated with injuries reported in
the same cycle, or (b) if medication use reported in one cycle was associated with injuries in the
next cycle.
In the first case, results are based on the assumption that the medication was being used at the
time of the injury which could have occurred up to one year before the report of medication use.
33
There will undoubtedly be some cases where the medication taken in the 2-day observation
period was not taken at the time of the injury and other cases where subjects who did not report
medication use in the 2-day observation period actually were taking the medication at the time of
the injury. Assuming that the errors are non-differential between the injured and non-injured
groups, such scenarios of classification error would serve to artificially deflate the magnitude of
the observed associations between psychotropic medication use and injuries. This would result
in conservative estimates of association.183, 184
If the misclassification is differential (for
example if being injured makes one more likely to start taking psychotropic medications), then
there is no way to determine in which direction the observed association would be affected.183
In the second case, as each survey was two years apart, results are based on the assumption that a
medication being used in one cycle was still being used at the time of the injury reported in the
next cycle (which would have been between one and two years after reported medication use).
While the possibility of the differential misclassification described in the first modeling strategy
is eliminated in this case, the same two scenarios of measurement error are still applicable.
Given that one would be less confident that those reporting medication use were actually still
taking the medication at the time of the injury in this second modeling strategy, it can be
expected that the observed associations will be even more conservative.183
In order to fully exploit the longitudinal data, investigation of the associations between
psychotropic medications and injuries was accomplished by running the entire modeling strategy
(Appendix F) on both of the data structures as described above. Augmenting these analyses is
the investigation into the use of psychotropic medications in subsequent cycles.
Validity of associations
In addition to the medical inventory of specific medications subjects took in the two days prior to
the survey, the NPHS also collected self-reported general categories of medication data
regarding the time period one month prior to the survey (Section 3.3.2). The modeling strategy
outlined in Appendix F was repeated for three of these general categories of medications
(tranquilizers, antidepressants, and sleeping pills). Results from analyses of the two types of
medication use data were compared: self-reported use of general categories of medication during
the past month versus use of specific medications during the past two days based on a medical
inventory.
34
3.5 Sample size, power, and precision
Given there is a set sample size of 2,423 elderly subjects available for analysis in the NPHS,
methods were undertaken to estimate the minimum detectable ORs for models of the
associations between each psychotropic medication and injuries. As previously noted in the
section detailing the analysis guidelines adopted by the candidate (Section 3.4.2), psychotropic
medications were not considered for analysis if the database contained fewer than 35 recorded
instances of use. Regardless, reported use varies among the list of psychotropic medications, and
those more rarely used are easily identified by the inflated minimum detectable ORs presented in
Table 3. Two methods were employed for these calculations, and both are briefly described
below.
First, the minimum detectable ORs were estimated using the „Unmatched Case-Control Study‟
procedures in EpiInfo with a 2-tailed significance level of 5% and power equal to 80%.185
These
procedures required additional information for calculations, so NPHS data from 1994/95 were
used to estimate the prevalence of injuries in the population and the prevalence of the use of each
medication in controls. The minimum detectable ORs obtained from these procedures are crude
estimates because (a) the associations tested did not account for other variables in the models,
and (b) records were considered to be independent thereby not accounting for the repeated
measures structure of the data.
Secondly, a more accurate method for estimating the minimum detectable ORs was employed by
rerunning the univariate repeated measures models for each medication and changing the injury
status of records where medication was used until the significance of the association between the
medication use and injuries reached the 5% threshold (Appendix G). This method also assumed
that the associations tested were univariate, but it did account for the dependency structure of
these data by using modeling methods for repeated measures. Since variance calculations were
influenced by whether or not records whose injury status was changed in this procedure belonged
to the same subject, estimates from this method were dependent upon the order in which records
appeared in the dataset. Therefore, while this method produced more accurate results than the
EpiInfo procedures, both methods provide crude estimates of the minimum detectable ORs.
Table 3 includes only those psychotropic medications for which there were at least 35 reports of
use, indicates the estimated prevalence of use in 1994/95, and provides crude estimates of the
35
smallest ORs that can be detected statistically based on the two methods previously described.
ORs for injury will be the most difficult to detect statistically for specific medications, as the
minimum detectable ORs are well above 2.0, but categories of psychotropics (antianxieties,
hypnotics and sedatives, and antidepressants) and benzodiazepines will be more feasible, as
minimum detectable ORs range around and below 2.0.
3.6 Candidate’s role in the design and conduct of the research
In preparation for this study, the candidate performed a broad literature search in the general area
of injuries in the elderly to formulate a valuable research question. With this question in mind,
potential study designs and the data sources that could be employed to efficiently and effectively
address the research question were investigated. Once the research question and data source
were selected, the candidate investigated potentially useful statistical techniques and determined
upon a method appropriate to the question and the structure of the available data.
With the main components in place, a full study protocol was written, presented, and
successfully defended. Following approval from the examiners and the candidates‟ thesis
committee members, a proposal was submitted to Statistics Canada, and access to the first three
cycles of the longitudinal full file of the NPHS database was granted.
The statistical work was performed independently over a period of two years. The candidate
performed the analyses and was responsible for their interpretation. Analysis was begun at the
Statistics Canada Research Office location in Toronto, and when Statistics Canada opened the
new Research Data Centre at the University of Toronto, the candidate transferred all the analysis
work to the new site. Finally, after moving with her family to Brussels, Belgium, the candidate
completed the analysis by remote data access preparing programs on dummy datasets and
submitting them directly to Statistics Canada to be run. During this time, the candidate
restructured, prepared, and analyzed the NPHS database.
Extensive work was required to obtain data on psychotropic medication use in a format that
could be used for analysis. For illustrative purposes, suppose a subject reported taking two
medications. In the NPHS database, the first drug code listed was entered as the value for the
first drug variable, and the second drug code listed was entered as the value for the second drug
36
variable. If a subject reported taking 10 medications, then that subject had drug codes entered in
10 different drug variables in the database.
The ATC codes obtained from Statistics Canada, as described in Section 3.3.2, indicated the
specific name of the medication associated with each drug code and indicated which of those
drug codes were included in the broad categories of psychotropic medications. Therefore, in
order to determine if a subject took a specific medication, the candidate wrote macros to search
each drug variable for the specific drug code. A new variable was created for each specific
medication, and if the drug code was found in any of the drug variables, the new variable was
assigned the value of 1 to indicate that the medication was taken. Since broad categories of
psychotropic medications could contain many specific medications, macros were also written to
search all of the drug variables for any of the drug codes included in a broad category of
psychotropic medications, and new variables were created to indicate if each subject took any of
the medications in a particular category. Each new variable created was labeled and formatted
accordingly by the candidate.
Next, restructuring the NPHS database required selecting the records for subjects at least 65
years of age in 1994/95, splitting the longitudinal database with one record per subject into three
separate datasets (one for each of the three cycles), and creating a new database from these
datasets with one record for each cycle for each subject (Section 3.1.3). This was achieved by
adding a variable to each dataset to indicate the cycle number, deleting the variables that would
not be used in the analysis, and renaming the remaining variables to common names in each
dataset before setting the datasets for all three cycles “one after the other” into a single database.
The final step required in preparing the database for analysis involved collapsing the categorical
variables to conform to Statistics Canada‟s guidelines as described in Section 3.4.1. Frequency
distributions were produced for all categorical variables that may have been used in the analysis.
If any of the unweighted cell sizes were less than 10, categories of the variable were collapsed.
For example, instead of a 5-category variable for income level, the candidate may have merged
adjacent categories to create a 3-category variable. Again, the new variables were formatted and
labeled, and the original variables were deleted from the database. This process was repeated
every time new variables were created.
37
The restructured database was analyzed by the candidate as described throughout Sections 3.4.3
and 3.4.4. The candidate then independently prepared the results and discussion chapters over a
period of six years while living abroad during which time she worked full time in Belgium, had
her first child in Switzerland, and had her second child in France. Finally, this thesis document
was edited and updated as a whole in preparation for presentation and defense.
3.7 Currency and continued relevance of these data
Statistics Canada conducts a number of national surveys which collect data on various
combinations of topic areas relevant to this study, but the NPHS is the only such survey which
collects injury, medication use, and alcohol consumption data for its entire sample.186
The
General Social Survey (GSS) which began in 1985, stopped collecting its heath component data
upon the commencement of the NPHS in 1994, and prior to that it did not collect particularly
detailed medication use data. The Canadian Community Health Survey (CCHS) began in 2001
and currently has six cycles of population-based data, but there are no data regarding the use of
psychotropic medications, and the data on injury and alcohol consumption are optional as
decided by each health region or province. The Canadian Health Measures Survey (CHMS) was
launched in 2007, and although it does not collect injury data, it does collect relevant alcohol
consumption data and potentially better quality medication use data than the NPHS: medication
use is collected by medical inventory over the past 30 days and contains information on dosage.
The household component of the longitudinal NPHS began with its first cycle in 1994/95 and
currently has eight cycles of survey data available for analysis though 2008/09. It is the only
national, population-based data available in Canada with information on injury, psychotropic
medication use, and alcohol consumption. As such, research on these data addresses questions
that cannot be otherwise investigated. This study could be replicated on all eight cycles of data,
and the resulting analyses would have greater power and could provide insights into newer
medications that were not widely available in the 1990s. At the time this study was conducted,
only data though 1998/99 were available to researchers. Therefore, this study speaks to
associations between psychotropic medication use and injury through the 1990s. Since this time,
non-benzodiazepine z-compounds have made their way into the psychotropic treatment regime
in Canada, but otherwise, most of the medications used through the study period are still
extensively used today. Therefore, results from this study remain particularly relevant to this
38
field and should indeed be reviewed when considering the current knowledge regarding
psychotropic medications, injury, and alcohol consumption in the elderly.
39
Table 2. Data Collection Period for Key Analysis Variables Relative to the Day of Survey
Administration for Each of the Three NPHS Cycles
Variable Data collection period
Self-reported injuries in the past year
In the past 12 months, did you have any injuries serious enough to limit your normal activities?
Medical inventory of psychotropic medication use in the past two days
During the past two days, yesterday and the day before yesterday, what are the exact names of
the medications that you took? (Look at the bottle, tube, or box.)
Self-reported use of general categories of psychotropic medications in the past month
In the past month, did you take any of the following medications? (Read list of medications.)
Age and sex current
Self-reported general health current
In general, would you say your health is excellent, very good, good, fair, or poor?
Self-reported cognitive impairment current
Derived by Statistics Canada from the following two questions:
How would you describe your usual ability to remember things? remember most things, somewhat
forgetful, very forgetful, or unable to remember anything at all?
How would you describe your usual ability to think and solve day-to-day problems? able to think
clearly and solve problems, having a little difficulty, having some difficulty, having a great deal of
difficulty, or unable to think or solve problems?
Self-reported chronic medical conditions ever in the past
Do you have any of the following long-term conditions that have been diagnosed by a health
professional? (Read list of conditions.)
Self-reported frequency of alcohol consumption (0 vs 1+ drink) in the past 7 days
Derived by Statistics Canada from the following question:
Starting with yesterday, how many drinks of beer, wine, liquor or any other alcoholic beverage did
you have on each of the last seven days? (Collect number of drinks for each day)
Self-reported quantity of alcohol consumption (regular vs occasional, former, abstainer) in the past year
Derived by Statistics Canada from the following question:
During the past 12 months, how often did you drink alcoholic beverages? Everyday, 4-6 times a
week, 2-3 times a week, once a week, 2-3 times a month, once a month or less than once a month?
Note. Adapted from "National Population Health Survey Derived Variables Documentation Cycles 1 to 4." by Statistics
Canada (2002) and from "NPHS Cycle 3: Data Dictionary - Master File - Longitudinal Full Response" by Statistics Canada
(2000). Available at: Statistics Canada by electronic transmission.
Potential effect modifying variables
Potential confounders
Dependent variable
Primary independent variables
40
Table 3. Minimum Detectable ORs for Injury in the Past 12 Months based on Different Methods
of Calculation
Epi-Infob Univariate model
11.3 1.76 1.47
0.8 4.54 2.10
10.7 1.78 1.45
8.6 1.87 1.56
5.8 2.07 1.67
5.7 2.08 1.68
Oxazepam 1.2 3.71 2.41
Lorazepam 2.8 2.60 2.00
2.8 2.60 1.65
2.1 2.91 1.80
Temazepam 0.8 4.54 2.25
3.3 2.46 1.85
3.3 2.46 1.85
2.3 2.80 1.86
Amitriptyline 0.9 4.27 2.76
0.8 4.54 2.35
7.7 1.92 1.57
8.0 1.90 1.57
aPopulation prevalence estimated by percentage of subjects reporting medication use in 1994/95. bCalculations based on 9
percent prevalence of injuries as reported in 1994/95, 5 percent level of significance and 80 percent power. cIncluding
hypnotics and sedatives, antiepileptics and antianxieties. dIncluding hypnotics and sedatives, antiepileptics, antianxieties and
antiarrythmics.
Note. Data source: Statistics Canada, 1994/95, 1996/97 and 1998/99 National Population Health Survey, longitudinal full file.
Psychotropic medication
Psycholeptics
Antianxieties
Benzodiazepine derivatives
Hypnotics and sedatives
Benzodiazepine derivatives
Psychoanaleptics
Any benzodiazepine or barbiturated
Antidepressants
Tricyclic derivatives
Bicyclic derivatives
Any benzodiazepinec
Minimum detectable OR
Psychotropics (including antiepileptics)
Antiepileptics
Psychotropics (excluding antiepileptics)
Estimated prevalence of medication use (% )a
41
Chapter 4
4 Results
This chapter presents the descriptive and analytic results of this study reflecting the description
of the study methods in Chapter 3. While the descriptive section provides general information
detailing the characteristics of the study sample, the analytic section provides the results
specifically addressing the study objectives.
4.1 Descriptive statistics
The following sections provide an overview of the study sample from the NPHS. This overview
begins by presenting information regarding response rates in the study and descriptions of the
study population in terms of injury status, demographics, health status, and alcohol consumption.
In preparation for the analytic results, this section describes (a) the prevalence of psychotropic
use in order to determine the feasibility of studying specific classes and (b) the use of
psychotropics persisting across cycles in order to place study results in context with the way in
which these medications were used. Finally, this section presents data which speak to the
representativeness of the study sample relative to the target population.
4.1.1 Study sample
In the NPHS database, 2,423 subjects contributed 6,515 records to the analysis of community-
dwelling elderly Canadians. This represented 8,646,400 records from a population of 3,181,200
people. Since the longitudinal full file provides access only to the subset of subjects who
responded in all cycles or died between the first and third cycles,154
the data required to calculate
response rates among the elderly population are unavailable. Statistics Canada has reported
response rates for subjects of all ages in the longitudinal survey:154
1994/95: Based on all eligible persons invited to participate, the response rates were
86.0% for the general component and 83.6% for the health component.
42
1996/97: Based on all responders (longitudinal panel members) in 1994/95, the panel
response rates were 93.6% for the general component and 92.8% for the health
component.
1998/99: Based on the longitudinal panel members in 1994/95, the panel response rates
were 88.9% for the general component and 88.2% for the health component.
Weighted frequency distributions of elderly participants indicated that data were collected by
proxy for 22% of respondents in 1994/95, 14% in 1996/97, and 14% in 1998/99.
4.1.2 General characteristics
Results of GEE analyses indicated that there was a statistical difference in the reporting of injury
between cycles (χ2
2 = 7.73; p = 0.02). Weighted results indicate that 9.3% of the study sample of
the elderly Canadian population was injured in 1994/95 (95% CI: 7.8-10.9%), 6.3% in 1996/97
(95% CI: 5.0-7.7%), and 7.5% in 1998/99 (95% CI: 5.9-9.1%).
Table 4 provides a descriptive view of some general baseline characteristics of the study sample.
This table is based on 2,423 records (unweighted) available in the database in 1994/95. Sample
estimates indicated that 19% of the elderly Canadians in the NPHS were 80 years of age or older
and 43% were male. While 88% reported having very few or no cognitive difficulties, only 40%
reported being in very good or excellent health.
Table 5 describes alcohol consumption in each of the three cycles as defined in Section 3.3.3.
This table is based on 2,423 records (unweighted) available in the database in 1994/95, 2,174
records in 1996/97, and 1,918 records in 1998/99. Results of GEE analyses indicated that the
proportion of the elderly NPHS sample who reported being regular drinkers differed between
cycles (χ2
2 = 14.10; p = 0.0009), but there was no difference between cycles in the proportion
reporting one or more drinks in the past week (χ2
2 = 1.88; p = 0.3903). As expected, a higher
proportion of subjects reported being regular drinkers in each of the three cycles than having
consumed at least one drink in the past week.
4.1.3 Psychotropic medication use
Table 6 describes psychotropic medication use in any of the three cycles. This table is based on
6,515 records from 2,423 subjects (unweighted) available in the database over 1994/95, 1996/97,
43
and 1998/99. If antiepileptics being used by those without epilepsy are considered as
psychotropic medications, 19% of the community-dwelling elderly Canadians in the NPHS took
psychotropics in at least one of the three cycles. Excluding antiepileptics for non-epileptic
indications, that proportion drops slightly to 18%. Psycholeptics were more commonly used
than psychoanaleptics (14% and 6% respectively). Antianxieties were the most commonly used
category of psychotropics in any of the three cycles (10%), followed by antidepressants (6%),
and hypnotics and sedatives (4%). Benzodiazepines for any indication were used by 13% of the
community-dwelling elderly Canadians in at least one of the three NPHS cycles, whereas
barbiturates for any indication were used by less than 1%.
There was a lack of consistency between the medical inventory data in the two days prior to the
survey and the self-reported medication use data recalled over the past month. If subjects took
medications in the past two days, they should also have reported taking them in the past month:
Tranquilizers: 40% of subjects who took specific medications classified as tranquilizers
(antianxieties) in the past two days also reported taking medications in the general
category of tranquilizers in the past month.
Antidepressants: 61% of those who took specific antidepressant medications in the past
two days also reported taking medications in the general category of antidepressants in
the past month.
Sleeping pills: 75% of those who took specific hypnotic and sedative medications in the
past two days also reported taking sleeping pills as a general category in the past month.
Table 7 describes the persistent use of psychotropic medications across subsequent cycles. This
table is based on 2,423 subjects (unweighted) available in the database in 1994/95. The first
column of results indicates the percentage of the elderly Canadian study sample from the NPHS
who reported taking the specific medication in at least one cycle and who participated in more
than one cycle. The last column indicates the percentage of those subjects who reported taking
the medication in subsequent cycles. This results in relatively small sample sizes when, for
example, 3.4% of those who participated in more than one cycle reported taking tricyclic
antidepressants, and 28.8% of those reported taking them in subsequent cycles.
44
Hypnotics and sedatives were the most commonly reported psychotropic medication category
taken across subsequent cycles (42%). One third of those who used antianxieties (33%) and
antidepressants (30%) also reported using these medications in subsequent cycles.
Benzodiazepine use for any indication occurred in subsequent cycles in 42% of benzodiazepine
users.
4.1.4 Representativeness of the study sample relative to the target population
By design, the NPHS study sample for this research is inherently comparable to the target
population of community-dwelling elderly Canadians (Section 3.1). To demonstrate, several
data points can be compared between this study and Canadian census data. Baseline data from
1994/95 (Table 4) and data from the 1996 Canadian census show similar distributions of males
(43.1% versus 42.2%) and elderly age groups (65-74 yrs: 61.5% versus 58.4%; 75+yrs: 38.5%
versus 41.6%).187
A slightly higher percentage of elderly participants in the NPHS were living
married or common-law in 1994/95 (57.9%) relative to the percentage reported in the 1996
Canadian census (55%).188
The percentage of elderly participants in the NPHS who were white
(94.9%) was slightly higher than that in the population in both 2001 and 2007 (92.8%) according
to the 2007 Canadian Yearbook.189
Additionally, several markers can be identified which indicate that the NPHS study sample used
psychotropic medications as expected for the community-dwelling elderly Canadian population
in the 1990s:
Antipsychotics were rarely used among the elderly NPHS subjects with only 34 study
participants reporting use in any of the three cycles (data not shown). This was
consistent with expectations, as antipsychotics are generally only used among elderly
patients in institutions as a last resort in the management of behavioral problems.
Similarly, only 12 study participants reported using barbiturates, and less than five
reported using antianxiety medications other than benzodiazepines (data not shown).
These medication groups have particularly deleterious side effects and were not
expected to be in common use.
45
The non-benzodiazepine z-compounds were not yet used by the elderly NPHS study
participants.
A study of the Ontario Drug Benefits (ODB) database provided prevalence estimates of
psychotropic medication use based on filled prescriptions during 3-month intervals for
all community-dwelling elderly residents of Ontario from 1993 to 2002.35
The ODB
showed that tricyclics comprised a notably higher percentage of all antidepressant
medications than did SSRIs during 1994/95 in particular: at the beginning of 1994,
approximately 80% of all antidepressant prescriptions were for tricyclics, and 20% were
for SSRIs, while at the end of 1995, approximately 65% of all antidepressant
prescriptions were for tricyclics, and 35% were for SSRIs. The NPHS data from this
study showed antidepressant use in the 1994/95 cycle to be well within the ranges
reported in the ODB: 70% of all antidepressants used over a 2-day period in 1994/95
were tricyclics, and 24% were SSRIs (as calculated from Table 3).
4.2 Analytic results
The following sections provide the analytic results directly pertaining to the study objectives
(Section 1.1). This section begins by presenting the GEE modeling results of the associations
between psychotropic medication use and injuries in the elderly Canadian population (Section
3.2.4). This is followed by the presentation of the GEE modeling results stratified by alcohol
consumption levels to address the second objective. The results section concludes by presenting
the associations between psychotropic use and injuries where medication use in one cycle is the
independent variable and injury status in the next cycle is the dependent variable in order to
further address the first study objective.
4.2.1 Associations between psychotropic medication use and injuries
Table 8 characterizes the univariate and multivariate associations between specific categories of
psychotropic medication use and injuries using GEE modeling methods for repeated measures.
Both the univariate and multivariate results are based on the subset of 6,302 records
(unweighted) with no missing values for the variables being controlled for in the multivariate
models (Section 3.3.4). The magnitude of the univariate associations changed when controlling
for variables in the multivariate models for all psychotropic medications. Since this is an
46
indication of confounding, while both adjusted and unadjusted results are presented in the table,
the following will focus on the multivariate results.
While, as a broad category, the odds of being injured in this study sample were increased
approximately one and a half times among psychotropic users relative to nonusers (OR=1.5;
95%CI: 1.0 - 2.1), specific types of psychotropics differed in their associations with injuries.
The overall 1.6-fold increase in the odds of being injured among psycholeptic users (OR=1.6;
95%CI: 1.1 - 2.3) was not consistent among the psycholeptic categories: the odds of being
injured among antianxiety users were two times higher than among nonusers (OR=2.0; 95%CI:
1.3 - 3.1), while the use of hypnotics and sedatives resulted in a protective but nonsignificant
effect (OR=0.6; 95%CI: 0.3 – 1.3). The OR for antianxiety users was 3.2 times as large as the
OR for hypnotic and sedative users, and this ratio of ORs (ROR) was statistically significant
(ROR=3.2; 95%CI: 1.4 – 7.2).
Benzodiazepine derivatives of antiepileptics, antianxieties, and hypnotics and sedatives were all
available in Canada in the 1990s, but antiepileptic benzodiazepines for non-epileptic indications
were rare in this study with only 15 subjects reporting use in any of the three cycles (data not
shown). While the odds of being injured were 1.7 times higher among benzodiazepine users for
any indication (OR=1.7; 95%CI: 1.2 – 2.5), this association differed appreciably by indication.
The odds of being injured among antianxiety benzodiazepine users were significantly higher than
among nonusers (OR=2.0; 95%CI: 1.3 - 3.1), while use of hypnotic and sedative
benzodiazepines showed no association with injury (OR=0.8; 95%CI: 0.4 – 1.7). The OR for
antianxiety benzodiazepine users was 2.6 times as large as the OR for hypnotic and sedative
benzodiazepine users, and this ROR was statistically significant (ROR=2.6; 95%CI: 1.1 – 6.4).
Overall, antidepressant use showed no association with injuries (OR=1.2; 95%CI: 0.6 - 2.3), but
ORs were statistically significantly different between the two main categories of antidepressants:
the OR for tricyclic users was 5.1 times as large as the OR for bicyclic users (ROR=5.1; 95%CI:
1.2 – 21.7). While tricyclic use showed no significant association with injuries (OR=1.4;
95%CI: 0.7 – 2.9), bicyclic use resulted in a signigicantly protective effect (OR=0.3; 95%CI: 0.1
– 0.9).
47
Following are the results for self-reported general categories of medication taken in the last
month and the corresponding results from the medical inventory of specific medications taken in
the past two days:
Tranquilizers: The odds of being injured were twice as high among those who used
antianxiety (tranquilizer) medications in the past two days relative to nonusers (OR=2.0;
95%CI: 1.3 - 3.1), but the odds of being injured were not significant for those who
reported using tranquilizers in the past month (OR=1.3; 95%CI: 0.8 - 2.2).
Antidepressants: The odds of being injured were not significantly different between
those who did and did not use antidepressants in the past two days (OR=1.2; 95%CI: 0.6
– 2.3), but there was a significant 2-fold increase in the odds of being injured among
those who reported using antidepressants in the past month relative to those who did not
(OR=2.1; 95%CI: 1.1 – 3.9).
Sleeping pills: While both ORs were nonsignificant, their associations were in opposite
directions. The odds of being injured were slightly lower among those who used
hypnotics and sedatives in the past two days relative to those who did not (OR=0.6;
95%CI: 0.3 – 1.3), while the odds were slightly higher among those who reported using
sleeping pills in the past month relative to those who did not (OR=1.5; 95%CI: 1.0 – 2.4).
4.2.2 Modification of psychotropic medication use and injury associations by alcohol consumption
To evaluate potential effect modification, Table 9 reports the associations between each
psychotropic medication and injury stratified by type of drinker. Additionally, this table
provides the p-values of the cross-product interaction terms between frequency of alcohol
consumption in the past 12 months and each psychotropic medication where the interaction term
p-values test for statistical interactions.168, 169
These results are based on the subset of 6,296
records (unweighted) with no missing values for the variables being controlled for in the
multivariate models (Section 3.3.4) plus the variable for frequency of alcohol consumption in the
past 12 months.
Table 9 indicates that there is little evidence of effect modification. The odds of injury were
significantly lower among hypnotic and sedative users who were regular drinkers relative to
48
those who were occasional drinkers, former drinkers, and abstainers (ROR=0.1; 95%CI: 0.01 –
0.9). Otherwise, there was no other statistically significant effect modification. The power for
these analyses was low: effect modification could not be statistically detected for the other
psychotropic medications even though the odds of being injured were 40 to 90% lower among
medication users who were regular drinkers relative to those in the lesser alcohol consumption
stratum for
any psychotropics, psycholeptics, and psychoanaleptics;
any antianxieties, benzodiazepine antianxieties, and lorazepam;
any hypnotics and sedatives, benzodiazepine hypnotics and sedatives, and temazepam;
any antidepressants, and tricyclic antidepressants; and
any benzodiazepines from all categories.
Table 10 reports the associations between each psychotropic medication and injury stratified by
the quantity of alcohol consumption in the past week in order to evaluate potential effect
modification using the second alcohol consumption variable. Additionally, this table provides
the p-values of the cross-product interaction terms between the number of drinks consumed in
the past week and each psychotropic medication where the interaction term p-values test for
statistical interactions.168, 169
These results are based on the subset of 6,300 records (unweighted)
with no missing values for the variables being controlled for in the multivariate models (Section
3.3.4) plus the alcohol consumption variable for quantity of alcohol consumed in the past week.
Table 10 shows that effect modification was not clearly evident across psychotropic medications
when stratifying the results on the number of drinks consumed in the past week. Power was low
for these analyses, and the magnitude of the modification of ORs was generally less than that
seen using the other alcohol consumption variable (Table 9). None of the stratum specific ORs
were statistically significantly different, and the direction of the stratum specific ORs was not
consistent among the psychotropic medications.
4.2.3 Associations between psychotropic use in one cycle and injuries in the next
Table 11 presents results supporting the first study objective (Section 1.1). This table is identical
in structure to Table 8, except that rather than presenting models where medication use in one
cycle is the independent variable for injuries in the same cycle, Table 11 presents models where
49
medication use in one cycle is modeled against injuries in the next cycle (medication use in
1994/95 is modeled against injuries in 1996/97, and medication use in 1996/97 is modeled
against injuries in 1998/99). Therefore by design, the number of records available for analysis is
reduced by approximately one third relative to the analyses presented in Table 8. These results
are based on 4,352 records (unweighted).
Although none of the multivariate associations presented in Table 11 yielded statistically
significant results, some similar patterns were seen in both tables:
Those who used any psychotropic had an increased risk of injury.
The increased injury risk among all psycholeptic users was not consistent among
categories (i.e., antianxiety users had an increased risk of injury, but hypnotic and
sedative use showed a protective effect).
Benzodiazepine use for any indication and use of antianxiety benzodiazepines showed an
increased risk of injury, but use of hypnotic and sedative benzodiazepines showed a
protective effect.
Although the generally observed patterns were similar between Table 8 and Table 11, none of
the associations in Table 11 were statistically significant, due not only to the reduced sample size
for these analyses, but also due to the decreased magnitude of the observed effect for most of the
medications. Table 11 showed a 20 to 40% decrease in the ORs in these analyses relative to
Table 8 for all antianxieties, benzodiazepine derivative antianxieties, all psychoanaleptics, all
antidepressants, and tricyclic antidepressants. Additionally, while amitriptyline was rarely used
in the population (Table 6), the odds of being injured among amitriptyline users relative to
nonusers decreased from OR=1.6 in the analysis of Table 8 to OR=0.2 in this analysis.
50
Table 4. Baseline Demographic and Health Characteristics of Community-Dwelling Elderly
Canadians (N=2,423 Subjects)
Canadian born 73.9 [71.5, 76.3]
White race/colour 94.9 [93.0, 96.8]
Male 43.1 [42.8, 43.5]
Age (yrs)
65 - 69 32.7 [30.2, 35.2]
70 - 74 28.8 [26.2, 31.3]
75 - 79 20.0 [17.9, 22.2]
80+ 18.5 [16.5, 20.5]
Married/common-law 57.9 [55.3, 60.5]
Highest education level
< Secondary 53.1 [50.0, 55.9]
Secondary graduate 13.0 [10.9, 14.7]
Other post-secondary 16.5 [14.5, 18.1]
College/university graduate 17.4 [15.0, 19.6]
Self-reported general health
Excellent 12.9 [10.9, 15.0]
Very good 27.0 [24.8, 29.1]
Good 34.1 [31.3, 36.9]
Fair 19.6 [17.3, 21.9]
Poor 6.4 [5.0, 7.8]
Self-reported cognitive impairment
No problems/a few problems 88.0 [86.2, 89.6]
Forgetful/difficulty thinking 8.6 [7.1, 10.1]
Very forgetful/can't remember 3.4 [2.4, 4.3]
Characteristic
Distribution of study samplea
% [95% CI]
51
Table 4. Baseline Demographic and Health Characteristics of Community-Dwelling Elderly
Canadians (N=2,423 Subjects) (continued)
Self-reported medical conditions
Alzheimer's disease/other dementia 0.4 [0.2, 0.7]
Heart disease 16.6 [14.7, 18.6]
Effects of stroke 3.7 [2.7, 4.7]
Arthritis/rheumatism 41.3 [38.6, 44.0]
Chronic bronchitis/emphysema 7.1 [5.7, 8.4]
Cancer 5.1 [3.9, 6.3]
Migraine headaches 4.2 [3.2, 5.2]
Any of above 7 chronic medical conditions 56.1 [53.4, 58.8]
Self-reported use of ≥ 5 medications in past 2 days 11.6 [9.6, 13.5]
Note. Data source: Statistics Canada, 1994/95, 1996/97 and 1998/99 National Population Health Survey, longitudinal full file.
aPercentage of elderly Canadian study sample reporting characteristic in 1994/95.
Characteristic
Distribution of study samplea
% [95% CI]
52
Table 5. Self-reported Alcohol Consumption of Community-Dwelling Elderly Canadians
(N=2,423 Subjects in 1994/95, 2,174 in 1996/97 and 1,918 in 1998/99)
Survey year
1994/95
1996/97
1998/99
Any of 3 cycles
1994/95
1996/97
1998/99
Any of 3 cycles
Distribution of study sample with highest consumption levela
% [95% CI]
39.5 [37.1, 41.9]
36.7 [34.1, 39.4]
Type of drinker (regular versus occasional/former/abstainer)
aPercentage of elderly Canadian study sample self-reporting highest consumption level.
36.6 [33.7, 39.5]
47.8 [45.5, 50.2]
30.1 [27.7, 32.5]
29.9 [27.4, 32.5]
32.4 [29.4, 35.3]
43.2 [40.5, 45.8]
Consumption of ≥ 1 drink in the past week (yes/no)
Note. Data source: Statistics Canada, 1994/95, 1996/97 and 1998/99 National Population Health Survey, longitudinal full file.
53
Table 6. Psychotropic Medication use of Community-Dwelling Elderly Canadians in any of the
Three Cycles (N=2,423 Subjects)
Psychotropics (including antiepileptics) 18.9 [16.6, 21.2]
Antiepileptics 2.3(M) [1.3, 3.2]
Hydantoin derivatives 0.8(M) [0.4, 1.3]
Phenytoin/sodium 0.8(M) [0.4, 1.3]
Psychotropics (excluding antiepileptics) 17.7 [15.6, 19.9]
Psycholeptics 14.4 [12.4, 16.4]
Antianxieties 10.1 [8.3, 11.9]
Benzodiazepine derivative 9.9 [8.2, 11.7]
Diazepam 0.8(M) [0.5, 1.2]
Oxazepam 2.5(M) [1.5, 3.4]
Lorazepam 5.6 [4.3, 6.9]
Alprazolam 1.0(M) [0.4, 1.7]
Hypnotics and sedatives 4.0 [3.0, 5.0]
Benzodiazepine derivatives 2.9 [2.0, 3.8]
Flurazepam/15/30 0.6(M) [0.2, 0.9]
Temazepam 1.5(M) [0.8, 2.2]
Cyclopyrrolones 0.7(M) [0.4, 1.1]
Psychoanaleptics 6.4 [5.1, 7.7]
Antidepressants 6.4 [5.0, 7.7]
Tricyclic derivatives 3.8 [2.8, 4.8]
Amitriptyline 1.5(M) [0.9, 2.2]
Bicyclic derivatives 2.2(M) [1.4, 2.9]
Paroxetine 0.7(M) [0.3, 1.1]
Any benzodiazepineb 12.5 [10.6, 14.5]
Any barbituratec 0.6(M) [0.2, 1.0]
Any benzodiazepine or barbituratec 12.9 [10.9, 14.8]
Psychotropic medication
Specific medication taken in past two days
Distribution of study samplea
% [95% CI]
54
Table 6. Psychotropic Medication use of Community-Dwelling Elderly Canadians in any of the
Three Cycles (N=2,423 Subjects) (continued)
Tranquilizers 10.4 [8.6, 12.1]
Antidepressants 8.1 [6.5, 9.7]
Sleeping pills 15.7 [13.7, 17.8]
Distribution of study samplea
Note. (M)=weighted estimates of marginal quality (16.6 < CV ≤ 33.3). Results not presented if ≤ 10 subjects reported use of
the medication in any of the three cycles (unweighted). Results not presented for medications where weighted estimates do
not meet Statistics Canada's quality standards (CV > 33.3): antiepileptic benzodiazepine derivatives; antiepileptic carboxamide
derivatives; antianxiety benzodiazepine derivative bromazepam; hypnotic and sedative barbiturates, plain; hypnotic and
sedative benzodiazepine derivatives nitrazepam and triazolam; antidepressant tricyclic derivatives trimipramine, nortriptyline
and doxepine HCL; antidepressant bicyclic derivatives fluoxetine HCL and sertraline; other antidepressant medication
trazodone hydrocloride. Data source: Statistics Canada, 1994/95, 1996/97 and 1998/99 National Population Health Survey,
longitudinal full file.
aPercentage of elderly Canadian study sample reporting medication use in any of the three cycles. bIncluding hypnotics and
sedatives, antiepileptics and antianxieties. cIncluding hypnotics and sedatives, antiepileptics, antianxieties and antiarrythmics.
Psychotropic medication
General medication category taken in past month
% [95% CI]
55
Table 7. Psychotropic Medication use of Community-Dwelling Elderly Canadians in
Subsequent (Back-to-Back) Cycles (N=2,423 Subjects)
and participating in ≥ one cycle in subsequent cycles
% % [95% CI]
Psychotropics (including antiepileptics) 17.3 44.6 [37.8 - 51.4]
Psychotropics (excluding antiepileptics) 16.2 42.0 [34.8 - 49.2]
Psycholeptics 13.3 39.9 [31.5 - 48.4]
Antianxieties 9.3 33.4 [23.6 - 43.2]
Benzodiazepine derivatives 9.2 33.8 [23.9 - 43.8]
Lorazepam 5.1 31.2 (M) [19.8 - 42.5]
Hypnotics and sedatives 3.7 41.7 [29.8 - 53.7]
Benzodiazepine derivatives 2.7 45.8 [31.1 - 60.5]
Psychoanaleptics 5.6 29.8 (M) [19.6 - 40.0]
Antidepressants 5.5 30.0 (M) [19.7 - 40.3]
Tricyclic derivatives 3.4 28.8 (M) [16.5 - 41.1]
Any benzodiazepineb 11.6 42.3 [33.3 - 51.3]
Any benzodiazepine or barbituratec 11.8 43.1 [34.1 - 52.1]
Tranquilizers 9.4 24.4 [17.6 - 31.3]
Antidepressants 7.4 27.8 [18.1 - 37.5]
Sleeping pills 14.3 34.3 [27.6 - 41.1]
Distribution of study sample reporting medication usea
Note. (M)=weighted estimates of marginal quality (16.6 < CV ≤ 33.3). Results not presented if ≤ 10 subjects reported use of
the medication in subsequent cycles (unweighted). Results not presented for medications where weighted estimates do not
meet Statistics Canada's quality standards (CV > 33.3): for all medications considered in this study, refer to full list inTable 6.
Data source: Statistics Canada, 1994/95, 1996/97 and 1998/99 National Population Health Survey, longitudinal full file.aPercentage of elderly Canadian study sample. bIncluding hypnotics and sedatives, antiepileptics and antianxieties. cIncluding
hypnotics and sedatives, antiepileptics, antianxieties and antiarrythmics.
Psychotropic medication
Specific medication taken in past two days
General medication category taken in past month
56
Table 8. Associations between Psychotropic Medication use of Community-Dwelling Elderly
Canadians and Injuries based on GEE Models (N=6,302 Records)
Univariate modela Multivariate modelb
Psychotropics (including antiepileptics) 1.68 [1.20 - 2.36] 1.41 [1.01 - 1.98]
Antiepileptics 0.92 [0.31 - 2.74] 0.85 [0.29 - 2.50]
Psychotropics (excluding antiepileptics) 1.77 [1.26 - 2.50] 1.47 [1.04 - 2.07]
Psycholeptics 1.98 [1.37 - 2.86] 1.63 [1.13 - 2.34]
Antianxieties 2.42 [1.57 - 3.71] 2.00 [1.31 - 3.05]
Benzodiazepine derivative 2.44 [1.59 - 3.76] 2.02 [1.32 - 3.08]
Oxazepam 2.41 [1.07 - 5.42] 1.93 [0.85 - 4.41]
Lorazepam 2.62 [1.48 - 4.65] 2.11 [1.24 - 3.58]
Hypnotics and sedatives 0.72 [0.37 - 1.42] 0.63 [0.31 - 1.27]
Benzodiazepine derivatives 0.83 [0.39 - 1.79] 0.77 [0.35 - 1.71]
Temazepam 0.87 [0.28 - 2.69] 0.85 [0.27 - 2.70]
Psychoanaleptics 1.42 [0.69 - 2.93] 1.17 [0.60 - 2.27]
Antidepressants 1.42 [0.69 - 2.94] 1.17 [0.60 - 2.28]
Tricyclic derivatives 1.58 [0.81 - 3.08] 1.43 [0.70 - 2.90]
Amitriptyline 1.78 [0.54 - 5.82] 1.63 [0.44 - 6.03]
Bicyclic derivatives 0.35 [0.10 - 1.16] 0.28 [0.08 - 0.96]
Any benzodiazepinec 2.02 [1.36 - 3.00] 1.72 [1.17 - 2.54]
Any benzodiazepine or barbiturated 1.96 [1.33 - 2.91] 1.67 [1.14 - 2.46]
Tranquilizers 1.55 [0.95 - 2.53] 1.27 [0.75 - 2.17]
Antidepressants 2.11 [1.17 - 3.79] 2.05 [1.07 - 3.90]
Sleeping pills 1.63 [1.11 - 2.38] 1.53 [0.97 - 2.42]
Note. Results not presented if ≤ 35 subjects reported use of the medication in any of the three cycles (unweighted): for all
medications considered in this study, refer to full list in Table 6. Data source: Statistics Canada, 1994/95, 1996/97 and
1998/99 National Population Health Survey, longitudinal full file.aBased on subset of records with values for all variables in the multivariate model. bControlling for age, sex, self-reported
general health, any of seven chronic conditions (heart disease, stroke, cancer, Alzheimer's/dementia, chronic
bronchitis/emphysema, arthritis/rheumatism or migraine headaches) and cognitive impairment. cIncluding hypnotics and
sedatives, antiepileptics and antianxieties. dIncluding hypnotics and sedatives, antiepileptics, antianxieties and antiarrythmics.
Psychotropic medication
OR [95% CI]
Specific medication taken in past two days
General medication category taken in past month
57
Table 9. Associations between Psychotropic Medication use of Community-Dwelling Elderly
Canadians and Injuries by Frequency of Alcohol Consumption based on GEE Models (N=6,296
Records)
Regular
% No.b (N=2,160)d (N=4,136)d
Psychotropics (including antiepileptics) 24.8 > 150 0.0964 0.78 [0.33 - 1.81] 1.73 [1.19 - 2.51]
Psychotropics (excluding antiepileptics) 25.7 > 150 0.1038 0.82 [0.35 - 1.91] 1.78 [1.22 - 2.61]
Psycholeptics 24.7 101-150 0.1758 0.93 [0.35 - 2.49] 1.96 [1.32 - 2.90]
Antianxieties 23.0 51-100 0.3800 1.30 [0.41 - 4.15] 2.27 [1.44 - 3.59]
Benzodiazepine derivative 22.7 51-100 0.3886 1.32 [0.41 - 4.24] 2.29 [1.45 - 3.62]
Oxazepam 37.7 ≤ 25 0.9847 1.76 [0.35 - 8.89] 1.96 [0.72 - 5.31]
Lorazepam 19.5 26-50 0.0597 0.57 [0.14 - 2.25] 2.62 [1.51 - 4.57]
Hypnotics and sedatives 30.9 26-50 0.0261 0.09 [0.01 - 0.65] 0.90 [0.43 - 1.91]
Benzodiazepine derivatives 28.1 26-50 0.0693 0.14 [0.02 - 1.07] 1.05 [0.45 - 2.45]
Temazepam 21.1 ≤ 25 0.4201 0.42 [0.05 - 3.32] 1.03 [0.29 - 3.65]
Psychoanaleptics 28.1 51-100 0.2426 0.63 [0.18 - 2.21] 1.39 [0.66 - 2.91]
Antidepressants 28.2 51-100 0.2394 0.63 [0.18 - 2.21] 1.39 [0.66 - 2.93]
Tricyclic derivatives 25.5 26-50 0.4684 0.97 [0.22 - 4.37] 1.61 [0.73 - 3.54]
Amitriptyline 18.2 ≤ 25 0.6872 2.87 [0.26 - 31.18] 1.43 [0.30 - 6.76]
Bicyclic derivatives 33.9 ≤ 25 0.6022 0.40 [0.04 - 3.75] 0.23 [0.05 - 1.07]
Any benzodiazepinee 23.9 101-150 0.1969 0.96 [0.33 - 2.84] 2.08 [1.37 - 3.16]
Any benzodiazepine or barbiturate f 24.8 101-150 0.1551 0.90 [0.31 - 2.61] 2.06 [1.36 - 3.11]
OR [95% CI]
Distribution of medication
users and drinkersa
Psychotropic medication
Specific medication taken in past two days
Occasional/
Former/Abstainer
Statistical
interaction
p-valuec
58
Table 9. Associations between Psychotropic Medication use of Community-Dwelling Elderly
Canadians and Injuries by Frequency of Alcohol Consumption based on GEE Models (N=6,296
Records) (continued)
Regular
% No.b (N=2,160)d (N=4,136)d
Tranquilizers 32.1 101-150 0.8350 1.15 [0.48 - 2.78] 1.27 [0.74 - 2.18]
Antidepressants 28.2 51-100 0.0924 0.83 [0.30 - 2.24] 2.05 [1.07 - 3.91]
Sleeping pills 32.1 > 150 0.3513 1.01 [0.45 - 2.28] 1.53 [0.97 - 2.43]
aDistribution of elderly Canadian medication users in the study sample who reported being regular drinkers. bUnweighted
number from the study sample. cTest for statistical interaction. dUnweighted number of records in all cycles. eIncluding
hypnotics and sedatives, antiepileptics and antianxieties. fIncluding hypnotics and sedatives, antiepileptics, antianxieties and
antiarrythmics.
General medication category taken in past month
Occasional/
Former/Abstainer
OR [95% CI]
Distribution of medication
users and drinkersa
Psychotropic medication
Statistical
interaction
p-valuec
Note. Frequency of alcohol consumption based on question 'During the past 12 months, how often did you drink alcoholic
beverages?' where 'Regular'=at least once per month and 'Occasional/Former/Abstainer'=less than once per month.
Multivariate stratified models controlled for age, sex, self-reported general health, any of seven chronic conditions (heart
disease, stroke, cancer, Alzheimer's/dementia, chronic bronchitis/emphysema, arthritis/rheumatism or migraine headaches)
and cognitive impairment. Excludes medications for which there was insufficient sample size for multivariate stratified models:
for all medications considered in this study, refer to full list in Table 6. Data source: Statistics Canada, 1994/95, 1996/97 and
1998/99 National Population Health Survey, longitudinal full file.
59
Table 10. Associations between Psychotropic Medication use of Community-Dwelling Elderly
Canadians and Injuries by Quantity of Alcoholic Drinks Consumed in the Past Week based on
GEE Models (N=6,300 Records)
1+ drinks 0 drinks
% No.b (N=1,741)d (N=4,559)d
Psychotropics (including antiepileptics) 23.8 > 150 0.9291 1.49 [0.69 - 3.22] 1.40 [0.96 - 2.02]
Psychotropics (excluding antiepileptics) 23.9 > 150 0.8127 1.62 [0.74 - 3.51] 1.42 [0.97 - 2.08]
Psycholeptics 22.9 101-150 0.8302 1.47 [0.63 - 3.45] 1.66 [1.11 - 2.49]
Antianxieties 22.4 51-100 0.7502 2.25 [0.87 - 5.84] 1.90 [1.17 - 3.06]
Benzodiazepine derivative 22.1 51-100 0.7352 2.31 [0.88 - 6.03] 1.91 [1.19 - 3.09]
Oxazepam 38.5 ≤ 25 0.9257 1.68 [0.33 - 8.70] 1.95 [0.72 - 5.28]
Lorazepam 18.7 26-50 0.9322 2.20 [0.70 - 6.90] 2.08 [1.13 - 3.83]
Hypnotics and sedatives 29.2 26-50 0.6531 0.46 [0.11 - 1.87] 0.70 [0.31 - 1.58]
Benzodiazepine derivatives 25.7 ≤ 25 0.6994 0.57 [0.10 - 3.12] 0.85 [0.34 - 2.08]
Temazepam 22.9 ≤ 25 0.5248 1.55 [0.25 - 9.52] 0.68 [0.15 - 3.18]
Psychoanaleptics 23.8 51-100 0.9345 1.33 [0.33 - 5.33] 1.15 [0.55 - 2.42]
Antidepressants 23.9 51-100 0.9398 1.33 [0.33 - 5.33] 1.16 [0.55 - 2.43]
Tricyclic derivatives 24.5 26-50 0.4417 2.45 [0.58 - 10.43] 1.20 [0.56 - 2.55]
Amitriptyline 14.1 ≤ 25 0.1106 10.71 [0.66 - 174.2] 0.89 [0.25 - 3.23]
Bicyclic derivatives 26.7 ≤ 25 0.9400 0.27 [0.03 - 2.30] 0.28 [0.06 - 1.26]
Any benzodiazepinee 22.9 101-150 0.9308 1.77 [0.75 - 4.21] 1.72 [1.11 - 2.66]
Any benzodiazepine or barbiturate f 23.7 101-150 0.9369 1.61 [0.68 - 3.83] 1.70 [1.10 - 2.61]
Specific medication taken in past two days
OR [95% CI]
Distribution of medication
users and drinkersa
Psychotropic medication
Stastical
interaction
p-valuec
60
Table 10. Associations between Psychotropic Medication use of Community-Dwelling Elderly
Canadians and Injuries by Quantity of Alcoholic Drinks Consumed in the Past Week based on
GEE Models (N=6,300 Records) (continued)
1+ drinks 0 drinks
% No.b (N=1,741)d (N=4,559)d
Tranquilizers 26.9 51-100 0.8757 1.16 [0.46 - 2.96] 1.26 [0.75 - 2.10]
Antidepressants 26.9 51-100 0.5323 1.24 [0.37 - 4.11] 1.85 [0.98 - 3.48]
Sleeping pills 26.2 101-150 0.8516 1.45 [0.63 - 3.34] 1.33 [0.84 - 2.10]
aDistribution of elderly Canadian medication users in the study sample who reported drinking at least one drink in the past
week. bUnweighted number from the study sample. cTest for statistical interaction. dUnweighted number of records in all
cycles. eIncluding hypnotics and sedatives, antiepileptics and antianxieties. fIncluding hypnotics and sedatives, antiepileptics,
antianxieties and antiarrythmics.
General medication category taken in past month
Stastical
interaction
p-valuec
OR [95% CI]
Distribution of medication
users and drinkersa
Psychotropic medication
Note. Quantity of alcoholic drinks consumed in the past week calculated as the sum of the total number of drinks consumed on
each of the seven days prior to the survey where '1+ drinks'=at least 1 drink consumed in the past week and '0 drinks'=no
drinks consumed in the past week. Multivariate stratified models controlled for age, sex, self-reported general health, any of
seven chronic conditions (heart disease, stroke, cancer, Alzheimer's/dementia, chronic bronchitis/emphysema,
arthritis/rheumatism or migraine headaches) and cognitive impairment. Excludes medications for which there was insufficient
sample size for multivariate stratified models: for all medications considered in this study, refer to full list in Table 6. Data
source: Statistics Canada, 1994/95, 1996/97 and 1998/99 National Population Health Survey, longitudinal full file.
61
Table 11. Associations between Psychotropic Medication use of Community-Dwelling Elderly
Canadians in one Cycle and Injuries in the Following Cycle based on GEE Models (N=4,352
Records)
Univariate modela Multivariate modelb
Psychotropics (including antiepileptics) 1.49 [0.94 - 2.36] 1.37 [0.85 - 2.20]
Antiepileptics 1.08 [0.31 - 3.74] 1.11 [0.33 - 3.82]
Psychotropics (excluding antiepileptics) 1.55 [0.96 - 2.48] 1.42 [0.87 - 2.31]
Psycholeptics 1.66 [0.99 - 2.79] 1.53 [0.90 - 2.61]
Antianxieties 1.69 [0.90 - 3.18] 1.55 [0.78 - 3.09]
Benzodiazepine derivative 1.71 [0.91 - 3.22] 1.57 [0.79 - 3.12]
Oxazepam 0.97 [0.32 - 2.96] 0.75 [0.22 - 2.52]
Lorazepam 1.79 [0.69 - 4.59] 1.83 [0.67 - 4.99]
Hypnotics and sedatives 0.73 [0.31 - 1.73] 0.72 [0.31 - 1.70]
Benzodiazepine derivatives 0.75 [0.28 - 2.04] 0.76 [0.28 - 2.07]
Temazepam 0.72 [0.16 - 3.13] 0.69 [0.15 - 3.09]
Psychoanaleptics 0.84 [0.40 - 1.79] 0.80 [0.37 - 1.69]
Antidepressants 0.84 [0.40 - 1.79] 0.80 [0.37 - 1.69]
Tricyclic derivatives 0.95 [0.42 - 2.13] 0.91 [0.40 - 2.04]
Amitriptyline 0.17 [0.02 - 1.30] 0.17 [0.02 - 1.29]
Bicyclic derivatives 0.70 [0.13 - 3.84] 0.64 [0.12 - 3.50]
Any benzodiazepinec 1.60 [0.94 - 2.74] 1.50 [0.84 - 2.67]
Any benzodiazepine or barbiturated 1.54 [0.90 - 2.64] 1.45 [0.82 - 2.56]
Tranquilizers 1.28 [0.70 - 2.33] 1.17 [0.64 - 2.14]
Antidepressants 1.09 [0.52 - 2.31] 1.01 [0.48 - 2.11]
Sleeping pills 1.15 [0.69 - 1.92] 1.07 [0.64 - 1.80]
aBased on subset of records with values for all variables in the multivariate model. bControlling for age, sex, self-reported
general health, any of seven chronic conditions (heart disease, stroke, cancer, Alzheimer's/dementia, chronic
bronchitis/emphysema, arthritis/rheumatism or migraine headaches) and cognitive impairment. cIncluding hypnotics and
sedatives, antiepileptics and antianxieties. dIncluding hypnotics and sedatives, antiepileptics, antianxieties and antiarrythmics.
OR [95% CI]
Psychotropic medication
Specific medication taken in past two days
General medication category taken in past month
Note. Results not presented if ≤ 35 subjects reported use of the medication in any of the three cycles (unweighted): for all
medications considered in this study, refer to full list in Table 6. Data source: Statistics Canada, 1994/95, 1996/97 and
1998/99 National Population Health Survey, longitudinal full file.
62
Chapter 5
5 Discussion
This chapter presents a discussion of the two study objectives: (a) the assessment of associations
between psychotropic medication use and injuries, and (b) the investigation into whether and
how those associations are modified by alcohol consumption. Specifically, this chapter places
the key study results in context with current knowledge in the field, presents design issues
relating to each study objective, assesses the policy implications of this research, and proposes
areas for future study.
5.1 Discussion of relationships between psychotropic medication use and injuries
The findings of this study clearly indicate that the risk of injury differs between the various types
of psychotropic medications. Among antidepressant medications, the magnitude of the risk of
injuries was higher for users of tricyclic derivatives than SSRIs. Benzodiazepine use for any
indication increased the risk of injuries, but that effect was not consistent across indications. The
use of benzodiazepine antianxiety medications resulted in an increased risk of injuries, but there
were no significant effects on the injury risk among benzodiazepine hypnotic and sedative users
with all the observed risks being protective but not statistically significant.
5.1.1 Antidepressant medications
This study showed that SSRI users not only had a lower risk of injury than tricyclic users
(OR=0.3; 95%CI: 0.1 – 1.0 and OR=1.4; 95%CI: 0.7 – 2.9 respectively), but SSRI use yielded a
significantly protective effect. There is no obvious biological mechanism of injury for SSRIs,41
so when they were first introduced into the market, they were considered to be a safer alternative
to tricyclics.32, 45
Regardless, studies in the literature consistently showed that the risk of injury
was at least as high among SSRI users as it was among tricyclic users.30, 32, 41, 45, 55
Therefore,
based on the current state of knowledge, results from this study were surprising.
It is certainly possible that these study results simply confirm the original belief that, with no
biological mechanism for injury, SSRIs are a safer alternative to tricyclics. As studies began to
63
show SSRI users with injury risks comparable to tricyclic users, publication bias may have
started to impact the literature resulting in researchers not publishing protective effects of SSRIs.
Another explanation is that there could be confounding by indication within this study that was
not adequately controlled. In the late 1990s, the International Consensus Group on Depression
and Anxiety suggested SSRIs as the first line treatment for anxiety disorders.37-40
Given the data
available in this study, it was not completely feasible to address this potential confounding.
There was no information regarding the indication for which medications were prescribed, so it
was unknown, in this study, whether antidepressants were being taken for depression or anxiety
disorders. If subjects with anxiety disorders were being prescribed SSRIs, perhaps these results
reflect a treatment for anxiety that provides good control of deleterious side effects.
Additionally, the NPHS did assess depression, but it was unclear whether or not it should be a
confounder in this study. The depression measure was riddled with missing values, and when
depression was added to the models in this study, it did not meaningfully increase the
confounding effect of the other potentially confounding variables. Other studies showed mixed
results for associations between depression and falls, depression and fractures, and depression
and bone density.54
Neither previous studies nor this study adequately answer the question of
whether or not depression or other indications could account for some residual confounding, so
they should be considered in future studies.
5.1.2 Benzodiazepine medications
This study showed significant differences between the odds of injury among antianxiety
benzodiazepine users and hypnotic and sedative benzodiazepine users: the odds of injury were
significantly increased among benzodiazepine antianxiety users (OR=2.0; 95%CI: 1.3 – 3.1), but
benzodiazepine hypnotic and sedative use showed no significant effects on the odds of injury
(OR=0.8; 95%CI: 0.4 – 1.7). Although the side effect profiles of benzodiazepines are such that
one would expect their use to be associated with an increased risk of injuries, this was obviously
not the case for all benzodiazepines in this study.
One explanation for these risk differences is that the indication for which the benzodiazepine was
prescribed was a marker for certain characteristics of the medication users. Perhaps these
characteristics, rather than the medication itself, determine the magnitude of association between
medication use and injury. Those who take antianxiety medications tend to be anxious, worried,
64
neurotic, nervous, agitated, or irritable, and these characteristics could cause people not to pay
attention to the present, to be unaware of their current environment, and to be more vulnerable to
injuries. Few other studies of benzodiazepines and injuries have considered these two
indications separately. It is difficult to compare those that did, as they focused on different
outcomes, considered different individual medications, and yielded inconsistent results. One
study of an adult, Canadian population by Neutel (1995) and another by Neutel, Hirdes, et al.
(1996) suggested that, relative to the use of antianxiety benzodiazepines, hypnotic and sedative
benzodiazepine use resulted in stronger associations with MVCs78
and falls80
respectively.
Vestergaard, Rejnmark et al. (2008) studied fractures among children and adults in Denmark
concluding that antianxiety benzodiazepine use resulted in stronger associations with fractures,
but it should be noted that this study did not include several hypnotic and sedative medications
that are used in Canada.67
A study of British adults indicated that, relative to the use of hypnotic
and sedative benzodiazepines, antianxiety benzodiazepine use resulted in stronger associations
with MVCs, but when restricting the analysis to elderly participants, the sample size was quite
low, and there were no longer any notable differences between the risks.77
Finally, Tamblyn,
Abrahamowicz et al. (2005) completed a study of elderly benzodiazepine users in Quebec,
Canada focusing on individual medications, and there were no obvious patterns distinguishing
the strength of associations between hospitalizations for injuries and benzodiazepine use by
indication.71
Another potential explanation for the differences seen in this study is that the timing of
benzodiazepine doses could dramatically impact the influence of those medications on injury
risk. Antianxiety benzodiazepines would be taken throughout the day, so their side effects could
indeed put one at risk of injury. On the other hand, since hypnotics and sedatives are taken at
bedtime, their side effects could influence the person while they are asleep thereby not causing
undue risk of injuries as long as the person did not get up during the night. Prescribed dosage
could also influence the degree to which subjects are at risk of injuries, as the dose of medication
prescribed is indicative of the intensity of any adverse effects. Antianxiety benzodiazepines are
generally prescribed at higher doses than hypnotic and sedative benzodiazepines, but none of
these data were collected in the NPHS.
While it is biologically plausible that the medication half-life influences the magnitude of the
risk of injury (Section 2.3.2), testing of this hypothesis has yielded inconsistent results in the
65
literature.32, 62, 63, 67
Findings from this study were unable to differentiate between long-acting
and short-acting benzodiazepines since only shorter-acting oxazepam, lorazepam, and
temazepam had sufficient numbers of subjects using the medications.
5.1.3 Strengths and limitations relating to relationships between psychotropic medication use and injuries
Overall, the NPHS was an ideal choice for a large-scale study of injuries and psychotropic
medication use in the elderly, but there were both advantages and disadvantages to this unique
and important data source. This section presents a discussion of the strengths and limitations of
the NPHS as they relate directly to the quality of research for this first study objective.
Longitudinal persistence of psychotropic medication use
The longitudinal nature of the NPHS provided multiple measurements of data on psychotropic
medication use, so this study was able to analyze the persistence of medication use over time.
This investigation accomplished two things: first, it uncovered valuable findings regarding the
long-term use of psychotropic medications among elderly Canadians, and second, it facilitated
the interpretation of the analytic results of this study.
Although it is clear that most psychotropic medications are not meant to be prescribed for long-
term use in elderly populations, 51, 52, 59
this is common practice among the elderly population in
Canada (a result consistent with other literature). Among elderly psychotropic users in this
study, 42% reported any psychotropic use in subsequent cycles two years apart. Similarly, a
British study of adults showed long-term use with 49% of psychotropic users reporting use of
these medications for at least one year.190
Regarding antidepressant use, this study showed 30%
of elderly users reporting antidepressant use in a subsequent cycle two years later, while a study
of elderly Americans showed approximately 60% of antidepressant use was for more than 90
days,58
and a study of British adults showed that 44% was for more than one year.190
Regarding
antianxiety use, 33% of elderly antianxiety users in this study also took antianxiety medications
in a subsequent cycle two years later, while approximately 92% of adults in France, Britain,
Germany, and Italy who took antianxiety medications took them for more than one month,191
and
47% of British adults who took antianxieties used them for more than one year.190
For hypnotics
and sedatives, it is believed that many elderly patients are treated chronically with hypnotics and
66
sedatives for years.192, 193
This study showed that 42% of hypnotic and sedative users reported
use in subsequent cycles two years apart, while the study of British adults showed that 61% of
those who used hypnotics and sedatives used these medications for more than one year,190
and
the study of adults in four European countries showed that 91% of hypnotic and sedative users
reported using these medications for at least one month.191
Finally, this study showed that
among elderly subjects who used any benzodiazepine, 42% reported use in a subsequent cycle
two years later. In other studies, 37% of Dutch adults aged over 55 years who took
benzodiazepines took them continuously,70
52% of Americans residing in seniors buildings who
took benzodiazepines took them continuously,194
85% of community-dwelling elderly American
benzodiazepine users reported use for more than 30 days,58
66% of American adults who used
benzodiazepines used them for more than three months,195
and 33% of community-dwelling
elderly Americans who used benzodiazepines filled at least three prescriptions in six months.73
The results of this study hinge on the presumption that medications used in the past two days
were being taken at the time of injuries reported within the last year. Depending on the
medication, between one third and one half of psychotropic medication users in this study
reported using these medications in the next cycle two years later. Additionally, the international
literature confirms that psychotropic medications are commonly used long-term in various
populations, even though such use is not recommended. These facts strengthen the results of this
study: misclassification of medication use at the time of the injury is less of a concern in light of
the evidence of long-term use of psychotropic medications.
Generalizability of study results
While the primary objectives of this study were analytical in nature, the descriptive component
of this research reported prevalence estimates of psychotropic medication use and investigated
the persistence of that medication use over the 6-year study period. In this context, it would be
valuable to be able to generalize the descriptive results to the population of community-dwelling
elderly Canadians. Studies based on emergency room or institutionalized populations do not
provide estimates of medication use that can be generalized to the wider population: samples
from both types of studies include select groups of injured or otherwise ill people who may well
use psychotropic medications differently from community-dwelling populations. Although
studies based on administrative databases can provide good, generalizable estimates of the
67
prevalence of medication use, it is only known for certain that medications were prescribed.
Therefore, as a population-based survey that used medical inventory methods, the NPHS has the
potential to provide the best prevalence estimates of psychotropic medication use for the
population of community-dwelling elderly Canadians.
Based on the inventory of medications taken in the two days prior to the 1994/95 survey,
approximately 11% reported using at least one psychotropic, 6% used antianxieties, 3% used
hypnotics and sedatives, 3% used antidepressants, and 8% used benzodiazepines for any
indication (Table 3). While there have been population-based surveys of psychotropic
medication use in other countries,190, 191, 195, 196
prevalence rates are not easily comparable. Not
only do the study designs differ in terms of reference periods and methodologies for the
collection of medication use data, but international prevalence rates are dependent upon the
prevalence of mental disorders, the utilization of mental health services, insurance coverage, and
laws regulating prescriptions.196
Although one cannot conclude anything for certain about those who did not participate in the
NPHS, the results presented in Section 4.1.4 suggest that the study sample in the NPHS is
representative of the community-dwelling elderly Canadian population both demographically
and in terms of psychotropic medication use. While this is important to note when interpreting
descriptive statistics, it is often less critical when considering results from regression analyses.197,
198 The NPHS is inherently more representative of the community-dwelling elderly population
than many other studies of psychotropic medication use and injury which use select groups of
elderly subjects from more restricted institutionalized or hospitalized populations. Even so, the
regression analyses for this research assessed the associations between injury and psychotropic
medication use, so the only way in which nonparticipants would affect these associations is if
they were biologically more susceptible to the side effects of psychotropic medications than the
participants.169
Statistics Canada does not provide response rates for elderly participants specifically, but overall
response rates were very strong (Section 4.1.1). Participation in the NPHS among the elderly is
probably related to health status, but researchers do not have access to fieldwork data from the
NPHS which could quantify the extent to which this is an issue. People who were
institutionalized, and arguably in worse health than the community-dwelling elderly population,
68
were not actually eligible for inclusion in this study. Therefore, nonparticipants who were not
living in institutions, and therefore eligible to have participated in the NPHS, were likely to have
been in better health than institutionalized people. Given the evidence available, it is likely that
the study sample of elderly Canadians who participated in the NPHS had an “average”
susceptibility to the effects of psychotropic medications. Therefore, it is reasonable to generalize
these study results to the Canadian population of community-dwelling elderly people.
Medical inventory versus self-report methodologies
The primary medication use data analyzed in this study were collected by medical inventory
which is considered a superior method of collecting accurate data compared to self-report.151-153
Given that the NPHS collected data for some psychotropic medications using both medical
inventory and self-report methods (Section 3.3.2), there was an opportunity to assess the
different methodologies. It is clear from this assessment that study participants did not
necessarily correctly self-report medications or understand the purpose of those medications
(Section 4.1.3).
Certainly, poor subject recall could be problematic in the collection of accurate self-reported
psychotropic medication use data. In fact, it may well be that it is not just what the subject
recalls of the medications they took but also what they understand about those medications that
impacts the quality of these data. As presented in Section 4.1.3, results from this study suggest
that approximately 60% of subjects who do take tranquilizer medications either would not call
them tranquilizers or do not know that is what they are. Similarly, 40% of subjects who take
antidepressants would not classify or report them as such. With only 25% of subjects who took
hypnotics and sedatives not reporting use of sleeping pills, the consistency between data
collection methodologies was better than that for tranquilizers and antidepressants, but analytic
results showed few similarities (Section 4.2.1). On the other hand, comparing analytic results
between sleeping pills in the past month (OR=1.5. 95%CI: 1.0 – 2.4) and benzodiazepines for
any indication in the past two days (OR=1.7; 95%CI: 1.2 – 2.5) showed remarkable consistency
(Table 8). Since benzodiazepines are known and often taken for their sedative effects, perhaps
part of the inconsistency could be explained if subjects reported some of their antianxiety
medications as sleeping pills which may, in fact, have been a perfectly reasonable understanding
of the purpose of those medications.
69
An additional concern could be the social stigma attached to mental disease: Olfson and Picus
(1994) explained that the social desirability effect can impact the quality of psychotropic
medication use data on several levels.195
If study participants know that they suffer from a
mental health condition, they may be less likely to volunteer this information in surveys thereby
not reporting use of medications to treat those types of indications. Similarly, physicians may
well explain the purpose of psychotropics in more socially desirable ways in order to make the
medications more acceptable to their patients. For example, a physician may prescribe
something to “calm your heart” or “help you sleep” rather than “reduce your anxiety”. Both of
these situations could seriously impact the quality of data obtained from a simple survey question
such as, “In the past month, did you take antidepressants?” In fact, in their study of American
adults, Olfson and Picus (1994) reported that the reasons for using benzodiazepines that were
perceived by the study participants were not consistent with recognized uses for 47% of
benzodiazepine purchases.195
In 43% of these misperceptions, subjects reported that they were
using benzodiazepines for general medical conditions rather than mental health conditions: the
most common misperceptions being that the benzodiazepines were prescribed for hypertension
and circulatory system diseases.
Regardless of the reason, it is clear in this study that there was a notable amount of
misclassification in the self-report medication use data. Therefore, results presented and
discussed focus on psychotropic medication use data collected through medical inventory.
Measurement of primary dependent variable: injuries
Given the nature of surveys, and the injury question from the NPHS in particular, the most
severe injuries which result in death or institutionalization are not addressed in this study. While
no single data source includes the complete range of injury consequences, this study investigated
injuries that may have required medical attention, but in the very least, limited the person‟s
normal activities. Injuries captured in the NPHS are particularly relevant to the community-
dwelling elderly population, as even “minor” injuries have important consequences to elderly
health, quality of life, and dependence.7, 199
The NPHS relies heavily on self-reported data from elderly participants, and the general
perception is that the quality of these data are at risk due to memory impairments, cognitive
difficulties, and poor health; factors that become more problematic with increasing age.200, 201
70
This study focused on the community-dwelling elderly, so those with more severe health issues
that may lead to poor self-reported data were likely to be institutionalized and therefore ineligible
for this study. Even so, commonly occurring or mundane events such as falls or injuries are
subject to poor recall through both forgetting events and reporting events that occurred outside
the recall period.200, 202, 203
It has been suggested that a 12-month recall window, such as in the
NPHS, yields better data than a shorter window because the elderly have difficulty placing
events like falls in a particular period of time.203
It is hard to assess the degree of recall issues
for self-reports of any injury because self-reports are often the only source of these data. If
measurement error of self-reported injury data was non-differential with respect to psychotropic
medication use, then ORs would be biased towards the null, but this bias is not as great for
commonly occurring events such as injuries compared to events which are more rare.202
Control of potential confounding
Prior to this research, studies of psychotropic medications and injuries in the elderly controlled
for very few if any other variables in analyses, but they often suggested potential confounders to
be considered in future research. Variables were selected for consideration in this study based on
the literature and candidate decisions (Appendix D) in order to avoid a fishing expedition
through the entire NPHS database for potential confounders (Section 3.4.4). Final multivariate
models controlled for age, sex, self-reported general health, cognitive impairment, and any of
seven chronic conditions (heart disease, stroke, cancer, Alzheimer‟s/dementia,
bronchitis/emphysema, arthritis/rheumatism, and migraine headaches). While this list does
include some variables highlighted in previous studies, it also excludes a number of potential
confounders that some may consider important, namely depression, other medication use, and
environmental factors.
As discussed in Sections 5.1.1 and 5.1.2, the NPHS data were inadequate to control for several
potentially confounding variables including the indication for which medications were taken,
depression, and the timing and dosage of medication use. Further, there is evidence that
measures such as cumulative medication dose and total duration of medication use can improve
the fit of models which analyze the impact of medication use on injuries, over the more common
measures of current or baseline medication use or dose.68
None of these data were available in
the NPHS.
71
Previous studies have suggested controlling for the use of other medications, but this was highly
correlated with self-reported general health and the list of chronic conditions. Therefore, neither
the total number of medications taken nor taking five or more medications added meaningfully to
the confounding effect of the other variables.
Finally, environmental factors such as stairs, availability of safety railings, and ice are often
considered in injury studies. While these are certainly risk factors for injuries in the elderly, the
question related to this study is whether such factors would confound the observed associations
between injuries and psychotropic medication use. In order to result in such confounding, these
factors would have to have an association with the use of psychotropic medications in addition to
injuries. Since such a relationship is unlikely, it is believed that such factors would not have
contributed to residual confounding in these analyses.
This study had a great advantage over many other studies in this field in that data for multiple
potential confounders were available for analysis. Emergency room studies generally rely on
chart extractions, so data available for research is inconsistent, only including what the attending
medical personnel believe to be relevant to each case. Studies of administrative databases would
not include behavioral factors or relevant disease and health status variables. Surveys of
institutionalized and community-dwelling populations theoretically have the potential to collect
data on potential confounders, but these surveys are often limited by restricted resources. The
NPHS had no such restrictions, and in controlling for age, sex, health status, chronic diseases,
and cognition, these analyses accounted for potential confounding that other researchers
proposed as important, and they still found associations of interest. Even so, there always
remains the possibility that there is some degree of residual confounding that is not accounted for
entirely.204
Causality
In studies using secondary data analysis, it is common that the original data source does not
precisely collect the information that is most useful. In this case, the NPHS was not designed to
collect data on the timing of medication use relative to the reported injuries. One of the main
criteria to establish causality over simple association is that the hypothesized cause must occur
before the resulting effect:205
in the case of this study, the psychotropic medication must have
been taken at the time that the injury occurred. Given that the NPHS did not collect sufficient
72
data to establish the proper temporal sequence of events, studies utilizing these data are saddled
with the temporal bias that is typical in cross-sectional surveys206
and which results in a
reduction of the observed risk of injury.207
This NPHS study used the longitudinal database, so
effectively, there were six years of cohort data with which to address issues of temporal bias.
Two lines of research were pursued to further strengthen the evidence of these study results.
First, utilizing two different modeling strategies (Section 3.4.4), issues of the temporal
sequencing of events were investigated. Similar results were obtained whether modeling
medication use and injury in the same cycle, or ensuring that medication use occurred before the
injury by modeling medication use in one cycle against injury in the next cycle one to two years
later (Sections 4.2.1 and 4.2.3). This suggests that psychotropic medication use reported in the
past two days was generally reflective of use at the time of the injury. Second, this study
evidenced the persistent long-term use of psychotropic medications in the study population
(Section 4.1.3). These study results, in conjunction with additional literature evidencing long-
term psychotropic medication use patterns from comparable study populations (Section 5.1),
serve to minimize the concerns of the sequencing of events.
From a public health perspective, studies addressing biological, epidemiological, and ecological
associations support each other in forming a comprehensive theoretical model of causality.208
This NPHS study is a strong, longitudinal, population-based survey which allows the study of the
associations between psychotropic medication use and injury. As such, the observed
associations from this study provide a valuable contribution to the current literature in this field.
Multiple comparison bias
Pharmacoepidemiology studies of large administrative databases are frequently subject to
multiple comparison bias resulting in an increase in the Type I error rate. This is of great
concern when researchers have simply embarked on fishing expeditions, or data dredging, for the
purpose of finding significant associations.209
On the other hand, it is commonly held that
adjustments are not needed for multiple comparisons in directed exploratory analyses.210-212
Rather, it is preferable to empower scientific research by presenting all, or some reasonable
proportion, of the study results in order to raise hypotheses that can be followed up by others in
the field.210, 212-214
73
The analyses in this study were borne of a set of predetermined objectives based on previous
literature (Section 1.1). This study exploited the opportunity to investigate the associations
between many different psychotropic medications and injury that occurred in a real life setting
for community-dwelling elderly Canadians. This is not an opportunity that arises in other study
designs, so it was essential to develop a complete analysis including as many psychotropic
medications as there was power to investigate. Rather than emphasizing any one particular
result, this study presented and discussed the observed trends that were apparent between the
different psychotropic medications.
Simply attempting to control for multiple hypothesis testing would not allow conclusive
statements to be made that were based solely on this study, and would, in fact, bias any review
that other researchers would make of these results.213
Therefore, this study presents as much of
the relevant information regarding the study as possible, provides the candidate‟s interpretation
of the results, and cautions readers to make inferences considering the broader knowledge to
which this study makes a contribution.
5.2 Discussion of modification of psychotropic medication use and injury associations by alcohol consumption
Those taking psychotropic medications are advised to abstain from consuming any alcohol
(Section 2.3.3), so it was expected that even light to moderate amounts of alcohol would increase
injury risk among psychotropic medication users. Research regarding this expectation was
scarce, particularly in the elderly, so this study pursued the investigation of this under-researched
objective.
Arguably, results pertaining to the second objective of this study raised as many questions as
they resolved due to inconsistencies within the results across medication categories and measures
of alcohol consumption. Regular drinking in the past year decreased the odds of injury among
hypnotic and sedative users, but otherwise, no consistent results were observed. Given the
dearth of available literature in this area, this section proposes potential explanations for this
study‟s seemingly inconsistent findings and discusses the strengths and limitations of the
research regarding this second study objective.
74
5.2.1 Potential explanations for these findings
There are many nuances to consider from these study results beginning with why drinkers who
took hypnotics and sedatives had a decreased risk of injury. This result may simply come down
to the location and timing of concurrent alcohol and medication use. Hypnotics and sedatives
would normally be taken at home before going to bed, so the increased sedation of combining
alcohol and medication in this case, could reasonably make one sleep more soundly thereby
decreasing the risk of injury relative to those only experiencing the sedative effects of the
medication.
Amitriptyline is a tricyclic antidepressant, and it was expected that concurrent use of alcohol and
this medication would result in increased sedation due to increased availability of the drug.102
Given that antidepressants would be taken during the day, this increased sedation could
reasonably increase the risk of injury.
While these may be reasonable explanations for hypnotics and sedatives and amitriptyline in
particular, results for other antidepressants and antianxieties are confusing. One possible
explanation may be that the two alcohol consumption variables are simply measuring different
things. The direction of stratum specific ORs in the amitriptyline results is the same for both
alcohol consumption variables, but the magnitude of the modification differed greatly. Although
the analyses had low power and the effect modification was not statistically significant, drinking
in the past week resulted in an extreme 11-fold increase in injury risk, while there was a much
more modest 2-fold increase among regular drinkers in the past year. When considered
alongside the results for antianxiety and tricyclic antidepressant use, where the direction of the
stratum specific ORs actually differs, it is possible that the two alcohol consumption variables do
not measure the same thing.
Both alcohol consumption variables captured light drinking (Section 3.3.3), but the lightest
drinkers were more likely to be captured in the group of regular drinkers (those who drank at
least once per month in the past year) than in the group who drank at least one drink in the past
week. Thus overall, the group of drinkers in the past week were heavier drinkers, by definition,
than the group of regular drinkers in the past year. Therefore, it could be that effect modification
is stronger when considering drinking in the past week due to the exclusion of lighter drinkers in
that group.
75
It is also possible that the measure of alcohol consumed in the past week is more reflective of
behavior while taking the medication than the measure of alcohol consumption patterns
throughout the past year. Given that both antianxiety and tricyclic antidepressant medications
would be taken during the day while people are doing their daily activities, the increased sedative
effects of concurrent medication and alcohol use could indeed increase the risk of injury.
Additionally, it has been proposed that drinkers develop tolerance to their regular levels of
consumption. If this were the case, an occasional drinker would be more likely to experience the
adverse effects of alcohol than a regular drinker. Although data regarding the epidemiology of
alcohol consumption among older adults are limited, the elderly have been shown to drink small,
consistent amounts,112
a drinking pattern conducive to developing tolerance to these quantities
with low levels of impairment. In fact though, this proposed effect is not guaranteed, as
tolerance can decrease in the elderly such that the effects of alcohol become more evident as a
person ages while continuing to consume the same low levels of alcohol.119
Perhaps it was expected that concurrent users of psychotropic medications and alcohol would be
at increased risk of injury because previous studies focused on younger adult populations rather
than specifically on the elderly. Relative to the elderly, younger adult drinkers include a larger
proportion of heavy drinkers, the quantity and frequency of their drinking is more variable, and
they tend to consume alcohol in more high risk, unfamiliar environments which are all factors
conducive to injury. It may well be that alcohol consumption does not modify the effects of
associations between psychotropic medications and injury in the elderly due to the elderly being
more mature in their drinking decisions and tending to drink in risk averse ways. Given that
elderly populations do not share the same high risk drinking patterns as younger adults, it may be
inappropriate to extrapolate the findings of currently available research to the elderly.
5.2.2 Strengths and limitations relating to modification of psychotropic use and injury associations by alcohol consumption
The NPHS provided an exceptional opportunity to study alcohol consumption among a
population that is rarely studied. It was a unique source of population-based data for the elderly
containing information on injuries, medication use and alcohol consumption. This section
presents a discussion of the strengths and limitations of the NPHS as they relate directly to the
quality of research for this second study objective.
76
Generalizability of study results
As discussed in Section 5.1.3, while generalizability is important for descriptive analyses, it is
often of less concern for hypothesis testing. In any case, the level of alcohol consumption
captured in the NPHS, is inherently more reflective of the behavior of community-dwelling
elderly Canadians than studies which mostly capture heavy drinking episodes using emergency
room populations – the primary source of current knowledge regarding injury, medication use,
and alcohol consumption.
Control of potential confounding
The literature proposes that the “sick quitters” hypothesis may explain study results in which the
higher drinking group is at lower risk of injury than the lower drinking group. Specifically, this
hypothesis suggests that former drinkers, who are captured in the lower drinking group, may
have quit drinking due to health problems that are themselves risk factors for injuries.174, 176, 215
Additionally, more recent concerns suggest that abstainers and former drinkers may differ from
moderate drinkers in terms of sociodemographic characteristics, dietary habits, and other health
behaviors which may affect health status.216
If either case were true, then including former
drinkers in the control group could bias the results such that the lower drinking group had a
higher risk of injury.
With their study of alcohol consumption and mortality among older adults over a 20-year period,
Holahan et al. (2010) controlled for a wide range of potential confounders to address whether or
not including abstainers and former drinkers in the control group would bias results in studies
regarding associations between alcohol consumption and various health outcomes.216
While
their study did show considerable confounding associated with nondrinkers, confounding did not
completely account for the differences in mortality between moderate drinkers and nondrinkers.
As discussed in Section 5.1.3, the analyses in this study were designed to control for potential
confounding by poor health by including variables for self-reported general health status,
cognitive impairment, and any of seven chronic health conditions considered to be risk factors
for injuries. Therefore in this study, the sick quitters effect should be diminished but may not be
controlled for entirely.
77
Sample size and power
Although the sample size in the NPHS was quite large, there were, in fact, power issues in the
effect modification analysis. This was mostly an artifact of the epidemiology of alcohol
consumption among community-dwelling elderly people in that drinking more than one drink per
week was relatively rare. Even dichotomizing the alcohol consumption variable provided
inadequate subsample sizes of those who consumed alcohol and used certain medications.
Additionally, while it would have been ideal to further separate former drinkers and abstainers
from occasional drinkers, more precise stratification of the alcohol consumption variables was
not feasible.
Causality
In order to establish causality, the temporal sequencing of events must be respected. In the case
of this second objective, both medication use reported in the past two days and reported alcohol
consumption must be reflective of behavior at the time of the injury. The NPHS did not collect
data on the timing of alcohol and medication use relative to reported injuries, but it was argued in
Section 5.1.3 that, while concerns of temporal bias could not be completely eliminated, it was a
fair assumption that psychotropic medication use in the past two days was reflective of use at the
time of the injury.
Just as medication use data were collected multiple times over the course of the NPHS, data on
alcohol consumption also benefited greatly from the longitudinal study design. In fact, it was
unique to have more than two measures of alcohol consumption within a study that did not focus
on adolescent populations.217
Practically speaking, the availability of multiple measures of
alcohol consumption, rather than simply a baseline measure, made the assumption of concurrent
use of both alcohol and medications possible. Regardless, the two measures of alcohol
consumption did cover completely different recall windows, so each must be discussed
separately.
While the first alcohol consumption measure (reporting drinking at least once per month in the
past year) covers the same recall window as that for reported injuries, it is difficult to argue that
being a regular drinker translates into having consumed alcohol and psychotropic medications
concurrently at the time of the injury. Being a regular drinker implies having generally drunk at
78
least a little bit every month throughout the year, and the chance that these drinks were
consumed on the day that the subject was injured is unlikely. Instead, this measure is more
indicative of lifestyle and behavioral characteristics potentially associated with injury.61, 218
On the other hand, the second alcohol consumption measure (having had at least one drink in the
past week) is more clearly reflective of concurrent use with psychotropic medications taken in
the past two days. It can also be better argued that this concurrent use is reflective of behavior at
the time of the injury reported during the past year. As long as the week before the survey was
not a special week, then the literature indicates that 1-week diary measures are indeed good
measures of consumption during other weeks throughout the year in general survey
populations.219
Given the evidence in the literature of the consistency and lack of variability of
drinking patterns among the elderly, it is reasonable to expect that alcohol use in the past week is
reflective of consumption patterns throughout the past year.
Measurement of alcohol consumption
Much research has been done on the validity and reliability of alcohol consumption measures
particularly in general population surveys. This study employed two alcohol consumption
variables which measure different things over different recall periods: (a) reporting of drinking at
least once per month, as determined by data on the frequency of consumption over the past year
and (b) reporting of at least one drink in the past week, as determined by daily diary recall in
each of the past seven days (Section 3.3.3). With all recall methods of collecting alcohol
consumption data, some amount of error is to be expected, but in general population surveys, it is
not considered to be substantial enough to affect research results.219
Reporting of drinking at least once per month in the past year provides reliable information about
average consumption but is not a particularly good measure of heavy or variable drinking. Since
heavy and variable drinking are not relevant to this study, or common among community-
dwelling elderly populations, this measure provides good data on average consumption patterns
which capture the indirect influence of alcohol on injuries. Drinking patterns can reflect lifestyle
and behavior likely to be associated with injury.61, 218
Daily diary records are believed to provide more accurate individual consumption data compared
to measures of average intake in the past year. Less misclassification of drinkers is normally
79
expected within a general population survey using this second measure, but this is not
necessarily the case with elderly subjects. Infrequent drinkers are more likely to report no drinks
during short diary periods, so the diary records collected over the past seven days in the NPHS
could potentially pose a notable source of misclassification among the elderly who are known to
be occasional drinkers.115, 219
Despite the vast literature supporting the validity and reliability of self-reported alcohol
consumption data, there remains a misconception that people in general population surveys
underreport their alcohol use.219
This stems from research indicating that population-based
consumption levels obtained from these surveys do not reflect total sales figures of alcoholic
beverages from those same populations. Research has shown that this is less of a concern when
using alcohol measures that allow reporting of drinks on weekdays separate from weekends.
These measures more accurately reflect the variability of actual drinking which can be masked if
subjects are asked for average levels of consumption.219
The daily diary records employed by
this study collected the number of drinks consumed in each of the past seven days, so potential
underreporting was addressed in this way.
The elderly have also been shown to report more socially desirable responses in survey data,201
so for this age group in particular, it is possible that alcohol consumption was underreported in
the original NPHS alcohol consumption questions. Regardless, this was unlikely to have had
much impact on the variables selected for this study, as the dichotomizations separated light to
moderate drinkers from those who consumed even less. Therefore, even if heavier drinkers
underreported their consumption to more socially desirable levels, they would still be likely to
have been captured in the drinking groups, and those who actually drank low levels were
unlikely to try and hide this.
The vast majority of alcohol research and the development of methodologies to measure alcohol
consumption has been done on adolescent and adult populations younger than the elderly
subjects that were included in this study. As it currently stands, measures for alcohol
consumption are inadequate for studies in elderly populations. The results of this study highlight
the difficulties in performing research in the elderly given the alcohol consumption measures
available in these types of general population surveys. There is a definite need to design and
80
validate better alcohol consumption measures for research in elderly populations who are known
to consume alcohol differently than their younger counterparts.
5.3 Public health implications for policy
There have been few studies which have been able to address injury risk and psychotropic
medication use in community-dwelling populations. As such, this study provides an essential
contribution to current knowledge in this field. The sheer scope of the NPHS provided a unique
perspective addressing all ranges of injury which impact health and well being, investigating not
only broad categories of psychotropics but also specific medications, and completing this
research on a very large, population-based, representative group of community-dwelling elderly
Canadians.
This study identified significant differences in injury risk between categories of psychotropic
medications. These results can and should be added to current knowledge and used to inform
prescribing practices: practitioners can be more aware of treatment choices with lower injury
risks and better prepare patients to minimize injury risks associated with prescribed treatments.
Current strategies recommending against long-term use of benzodiazepines and hypnotics and
sedatives in particular appear to be inadequate. This study adds to the growing body of evidence
that they are, indeed, being used for long-term treatments among the elderly despite the fact that
long-term use can lead to tolerance of these medications and dependency. In a health care
system that is overstressed, practitioners do not necessarily have the time to address the
underlying problems, so they may rely too heavily on inappropriate long-term treatments that
appear to resolve the symptoms. This underscores the importance of identifying the risks of
long-term treatment with these psychotropic medications and effectively communicating these
risks to both practitioners and elderly patients. Elderly patients need to stop unnecessary long-
term psychotropic treatments, and if such treatments are necessary, they need to be aware of and
minimize the risks of injury.
The prevalence rate of psychotropic drug use (11.2%), the injury risk (9.3%), and the adjusted
OR for the association between the two (OR=1.47) that were observed in this study population
lead to a crudely estimated attributable fraction of injuries among those exposed to psychotropic
medications of 33.4%. This implies that approximately 14,000 injuries were associated with
81
psychotropic medications in the community-dwelling elderly Canadian population in 1994/95.
Given the potentially significant reductions on both life expectancy and healthy life expectancy
that can follow injuries in the elderly, the interaction of psychotropic medication use and injury
risk is an important public health concern in Canada. However, it would be inappropriate to
suggest that injuries could be reduced by 30 percent by eliminating psychotropic medication use
among elderly Canadians.
As previously acknowledged, in observational studies such as this, both unmeasured and
imperfectly measured confounders may have residual effects on the apparent size of the
association. Thus the actual proportion of all injuries which realistically and appropriately could
be eliminated through changes in prescribing practices may be somewhat lower than estimated
above. Not all psychotropic medication use in this population is inappropriate. Some degree of
elevated injury risk may have to be accepted for the improvement in quality of life and comfort
afforded by medication appropriately applied. Regardless, recognition of the increased risk of
injury among psychotropic medication users is important for prescribing physicians and patients.
Additional efforts are warranted to ameliorate the risk of injury both through the reduction of
unnecessary psychotropic medication use and by taking extra precautions against injury when
these medications are applied appropriately.
5.4 Areas for future research
Although many psychotropic medications are considered mainstay treatments, the
pharmaceutical landscape does change over time, as some medications fall out of favor, and
others are introduced into the market. Therefore, these large-scale, population-based studies
should be replicated as a kind of watching brief, and the repeated collection of the NPHS data
over the years provides an excellent opportunity to continue this work. As more cycles of data
are included in the NPHS, power will be improved, thus strengthening the study results,
particularly for specific medications.
As researchers continue to work in this field, they need to be wary of relying on self-reported
medication use data, since they depend not only on the study participant‟s memory but also their
knowledge of what their medications are being taken for. In future studies, researchers should
invest the necessary resources into accurate collection of medication use data by medical
inventory, administrative databases, or biological measures as indicated by the study design.
82
The elderly represent a unique group in terms of alcohol research: they consume alcohol
differently than younger age groups, and even the consumption of light to moderate amounts of
alcohol can result in poor health outcomes. While alcohol consumption measures used in
population-based surveys may be appropriate for research in younger age groups, and may
effectively capture high-risk, heavy, and variable drinking behaviors, they are inadequate to
effectively address light and infrequent consumption patterns that are important for research
among the elderly.
Modified versions of measures such as the Timeline Followback and Form 90219
or a graduated
frequency measure220
would allow researchers to more effectively capture the infrequent
drinking behavior common among the elderly, while simultaneously assessing concurrent use of
alcohol and medications. Such an approach may be useful in the design of studies with this
specific focus, but due to the time investment, would be inappropriate in large population-based
surveys. In such surveys, the addition of a question regarding the concurrent use of alcohol and
psychotropic medications would enhance currently available data.
Despite recommendations strongly advising against long-term treatments with psychotropic
medications, there is overwhelming evidence of this practice on an international scale.
Therefore, perhaps the most obvious opportunity for future research lies in a study of the
consequences of inappropriate, long-term treatments with psychotropic medications. There is
general consensus that long-term treatments can lead to tolerance and dependence, but maybe
these consequences seem acceptable if symptoms are relieved. Good studies elucidating the
health, behavioral, and social consequences of tolerance and dependence could provide the
impetus needed to change this practice.
Changing prescribing practices which currently result in inappropriate long-term use of
psychotropic medications, and communicating the injury risks associated with the use of specific
psychotropic medications to health care professionals is a challenge. Systematic reviews of
interventions to change prescribing behavior commonly identify two methodologies which are
often successfully implemented in conjunction with other interventions: (a) audit and feedback
(where summaries of clinical performance over a period of time are provided to health
professionals) and (b) educational outreach visits (where prescribers are visited by a
knowledgeable health care professional to discuss an evidence-based approach to prescribing).221
83
This is an area of burgeoning research, and given the heterogeneity of interventions and
outcomes that have been studied in this field, the next step is to delve deeper into these studies
and determine the specific features of these interventions that yield success.
This study identified a number of important and unresolved issues that raise the question of what
to do next. While some solutions have been proposed, testing the feasibility and effectiveness of
these solutions does not fall within the scope of this thesis. These are seeds for future research.
84
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Appendices
Appendix A. Effect of dependent observations on variance
The data available for analysis contain two or three data records for each subject. If one
incorrectly assumed each observation to be independent, the following equation would be used to
calculate the variance:
nt
ntSST
Y1
..var
where n is the number of subjects, t is the number of observations per subject, and nt is the total
number of observations. Thus, whether t increases or n increases, the total number of
observations would increase thereby decreasing the variance. The smaller the variance, the more
likely one is to find statistical significance.
On the other hand, if one correctly assumed a dependency structure between observations from
the same individual, the following equation would be used to calculate the variance:
ntnY
22
..var
In this case, if n increases, the variance would decrease quickly, but if t increases, it would only
affect the second component of the variance thereby decreasing the variance much less. This
larger variance would yield more appropriately conservative tests of significance.164
101
Appendix B. Psychotropic medications as classified in the Anatomical Therapeutic Chemical
Classification System for Human Medicines (ATC)
Psychotropics (including antiepileptics)
Antiepileptics
Barbiturates and Derivativesa
@Primidone; Apo-primidone; Mysolinea
Hydantoin Derivatives
@Phenytoin/Sodium; Dilantin; Diphenylhydantoin
@Mephenytoin; Mesantoina
Succinimide Derivativesa
@Ethosuximide; Zarontina
@Methsuximide; Celontina
Benzodiazepine Derivatives
@Clonazepam; Alti-Clonazepam; Apo-Clonazepam; Clonapam; Clonazepam/-R; Dom-Clonazepam; Gen-
Clonazepam; Nu-Clonazepam; PMS-Clonazepam; RHO-Clonazepam; Rivotril
@Clobazam; Frisiuma
Carboxamide Derivatives
@Carbamazepine; Apo-Carbamazepine; Novo-Carbamaz; Tegretol/Chew Tabs
Fatty Acid Derivativesa
@Valproate Sodium; Depakene; Valproic Acida
@Divalproex Sodium; Epivala
@Vigabatrin; Sabrila
Other Antiepilepticsa
@Gabapentin; Neurontina
@Lamotrigine;Lamictala
@Topiramate; Topamaxa
Unknown Antiepilepticsa
Medication for Epileptics; Seizure Meds/Pillsa
Psychotropics (excluding antiepileptics)
Psycholeptics
Antipsychotics
Phenothiazine with Dimethylaminopropyl Groupa
@Chlorpromazine; Chlorpromanyl 20; Largactil; Neo-Promine; Novo-Chlorpromazinea
@Promazine HCL; Promazine Inja
@Methotrimeprazine; Apo-Methoprazine; Novo-Meprazine; Nozinan; PMS-Methotrimeprazine a
Psychotropic medication category
102
Appendix B. Psychotropic medications as classified in the ATC (continued)
Phenothiazine with Piperazine Structurea
@Fluphenazine HCL/Deconoate/Enanthate; Apo-Fluphenazine; Modecate; Moditen HCL/Enanthate;
Permitila
@Perphenazine; Apo-Perphenazine; Trilafon/Conc.a
@Prochlorperazine; Stemetila
@Trifluoperazine; Apo-Trifluoperazine; Novo-Flurazine; Solazine; Stelazine; Terfluzinea
@Thioproperazine; Majeptila
@Acetophenazine; Tindala
@Trifluroperazine+, Isopropamide; Darbid; Stelabida
Phenothiazine with Piperidine Structurea
@Pericyazine; Neuleptila
@Thioridazine; Apo-Thioridazine; Mellaril; Novo-Ridazine; PMS-Thioridazinea
@Mesoridazine; Serentila
@Pipotiazine Palmitate; Piportil L4a
Butyrophenone Derivativesa
@Haloperidol/Decanoate; Apo-Haloperidol; Haldol/-LA; Novo-Peridol; Peridola
@Droperidol; Inapsinea
Thioxanthene Derivativesa
@Flupenthixol; Fluanxol; @Flupenthixol Decanoate/Dihydrochloride; Fluanxol/Depota
@Thiothixene; Navanea
@Zuclopenthixol; Acuphase, Clopixol; Clopixol/Acuphase; Clopixal/Depota
Diphenylbutylpiperidine Derivativesa
@Fluspirilene; Imap/Fortea
@Pimozide; Orapa
Dibenzodiazepine and Dibenzoxazepine Derivativesa
@Loxapine HCL/Succinate; Loxapcac; PMS-Loxapinea
@Clozapine; Clozarila
@Olanzapine; Zyprexaa
@Quetiapine; Seroquela
Neuroleptics, in Tardive Dyskinesiaa
@Tetrabenazine; Nitomana
Lithiuma
@Lithium Carbonate; @Lithium Citrate; Carbolith; Duratith; Lithane; Lithizine; PMS-Lithium Citratea
Antimanic drugs; Manic-depressivea
Psychotropic medication category
103
Appendix B. Psychotropic medications as classified in the ATC (continued)
Other Antipsychoticsa
@Risperidone; Risperdala
@Clorprothixene; Tarasana
@Remoxipride; Roxiama
Unknown Antipsychoticsa
Antipsychotic drugs/medsa
Antianxieties
Benzodiazepine Derivatives
@Diazepam; Apo-Diazepam; E-Pam; Novo-Dipam; Valium; Vivol
@Chlordiazepoxide; Apo-Chlordiazepoxide; Librium; Medilium; Novo-Poxide; Soliuma
@Oxazepam; Apo-Oxaxepam; Novoxapam; Oxpam; Serax
@Clorazepate Dipotassium; Novo-Clopate; Tranxenea
@Lorazepam; Apo-Lorazepam; Ativan; Novo-Lorazem; Nu-Loraz
@Bromazepam; Lectopam
@Ketazolam; Loftrana
@Alprazolam; Apo-Alpraz; Novo-Alprazol; Xanax
@Chlordiazepoxide+,Clidinium; Apo-Chlorax; Corium; Librax a
Dephenylmethane Derivativesa
@Hydroxyzine HCL; Apo-Hydroxyzine; Atarax; Multipax; Novo-Hydroxyzina
Carbamatesa
@Meprobamate; Apo-Meprobamate; Equagesic; Equanil; Meditran; Novo-Meproa
282-Mep; @Meprobamate+, Caffeine, Codeine, Acetylsalicylic Acida
Azaspirodecanedione Derivativesa
@Buspirone; Buspara
Other Anxiolyticsa
@Chlormezanone; Trancopala
Hypnotics and Sedatives
'Barbiturates, Plain
@Pentobarbital Sodium; Nembutal; Nova-Rectal; Novo-Pentobarba
@Amobarbital/Sodium; Amytal/Sodiuma
@Seconal Sodium; @Secobarbital Sodium; Novo-Secobarba
Psychotropic medication category
104
Appendix B. Psychotropic medications as classified in the ATC (continued)
@Phenobarbital; Phenobarbital/ICN; Phenobarbitone
@Butabarbital Sodium; Butisol Sodiuma
@Mephobarbitala
@Seconal Sodium+, Amobarbital; Tuinala
@Phenobarbital+, Hyoscyamine Sulfate, AtropineSulfate, Hyoscine HBR; Donnatal *a
Aldehydes and Derivativesa
@Chloral Hydrate; Noctec (USA); Novo-Clorhydrate; PMS-Chloral Hydratea
@Dichloralphenazone; Welldorm (USA)a
@Paraldehydea
Benzodiazepine Derivatives
@Flurazepam/15/30; Apo-Flurazepam; Dalmane/15; Novo-Flupam; Somnol/15/30
@Nitrazepam; Mogadon
@Triazolam; Apo-Triazo; Halcion; Novo-Triolam
@Temazepam; Restoril
@Midazolam; Verseda
@Estazolam; Prosoma
Cyclopyrrolones
@Zopiclone; Apo-Zopiclone; Imovane; Nu-Zopiclone; Rhovane
Other Hypnotics and Sedativesa
@Ethchlorvynol; Placidyla
@Methyprylon; Noludara
Unknown Psycholepticsa
Agitation/Pills; Anxiety Pills; Anxiolytic; Hypnotics; Nerve Pills; Nervosite; Pills for Relaxation; Relaxation
Pills; Sedative/s; Sleeping Pills; Somnifere; Stress; Tranquilizersa
Psychoanaleptics
Antidepressants
Tricyclic Derivatives
@Desipramine; Norpramin; Pertofranea
@Imipramine; Apo-Imipramine; Impril; Novo-Pramine; Tofranila
@Clomipramine HCL; Anafranil; Apo-Clomipraminea
@Trimipramine; Apo-Trimip; Apo-Trimipramine; Novo-Trimipramine; Nu-Trimipramine; Rhotrimine;
Surmontil; Taro-Trimipramine; Trimipramine; @Tripramine; Novo-Tripramine
Psychotropic medication category
105
Appendix B. Psychotropic medications as classified in the ATC (continued)
@Amitriptyline; Apo-Amitriptyline;Elavil/H.S.; Levate; Novo-Triptyn
@Nortriptyline; Apo-Nortriptyline; Eventyl; Dom-Nortriptyline; Nortriptyline; Norventyl; Nu-Nortriptyline;
PMS-Nortriptyline; STCC-Nortriptyline
@Protiptylinea
@Doxepine HCL; Novo-Doxepin; Sinequan; Triadapin
@Amoxapine; Asendina
@Maprotiline HCL; Ludiomila
@Venlafaxine HCLa
@Amitriptyline+,Perphenazine; Apo-Peram; Elavil Plus/; Etrafon; PMS-Levazine; Triavila
Bicyclic Derivatives
@Fluoxetine HCL; PMS-Fluoxetine; Prozac
@Sertraline; Zoloft
@Paroxetine; Paxil
@Fluvoxamine; Alti-Fluvoxamine; Apo-Fluvoxamine; Luvox; Servox a
Monoamine Oxidase (Mao) Inhibitors, Non-selectivea
@Phenelzine Sulfate; Nardila
@Tranylcypromine Sulfate; Parnatea
@Icocarboxazid; Marplana
Monoamine Oxidase (Mao) Type a Inhibitorsa
@Moclobemide; Manerixa
Other Antidepressants
@Trazodone Hydrochloride; Apo-Trazad; Apo-Trazodone; Desyrel/Dividose; Dom-Trazodone; Novo-
Trazodone; Nu-Trazodone; PMS-Trazodone; SYN-Trazodone
@Nefazodone HCL; Serzonea
@Bupropion; Wellbutrin/SR; Zybana
Unknown Antidepressantsa
Antidepressant Pills/Medsa
Stimulantsa
Phenylethylamine Derivativesa
@D-Amphetamine Sulfate; @Dextroamphetamine; Dexedrinea
Bicyclic Compoundsa
@Pemoline; Cylerta
Psychotropic medication category
106
Appendix B. Psychotropic medications as classified in the ATC (continued)
@Methylphenidate HCL; DOM-Methylphenidate; Methylphenidate; PMS-Methylphenidate; Riphenidate;
Ritalin/SRa
Xanthine Derivativesa
@Caffeine; Stay Alert; Stay Awake; Wake Upsa
Tricyclic Compoundsa
Tricyclinesa
Any Benzodiazepineb
Any Barbituratec
Any Benzodiazepine or Barbituratec
Psychotropic medication category
aLess than 10 subjects took the medication in any of the three NPHS cycles (unweighted); analyses of these drugs were not
conducted. bIncluding hypnotics and sedatives, antiepileptics and antianxieties. cIncluding hypnotics and sedatives,
antiepileptics, antianxieties and antiarrythmics.
107
Appendix C. Alcohol consumption variables in the NPHS longitudinal full file
In the past 12 months…
1. did you drink?
Dichotomous: yes/no
2. how often did you drink?
Categorical: every day
4-6 times/week
2-3 times/week
once/week
2-3 times/month
once/month
< once/month
3. type of drinker (derived by Statistics Canada)
Categorical: regular (≥ once/month)
occasional (< once/month but drank in past 12 months)
nondrinker now (none in past 12 months but drank before)
never drank (never had a drink)
4. how many times drank ≥ 5 drinks on one occasion?
Continuous: no. of times
5. what is the highest number of drinks consumed on one occasion?
Continuous: no. of drinks
In the past week…
6. did you drink?
Dichotomous: yes/no
7. how many drinks on each of the past 7 days?
Continuous: no. of drinks
8. weekly total of alcohol consumption (derived by Statistics Canada)
Continuous: no. of drinks
9. average daily alcohol consumption (derived by Statistics Canada)
Continuous: no. of drinks
Ever…
10. did you ever drink?
Dichotomous: yes/no
11. ever drink regularly > 12 drinks/week?
Dichotomous: yes/no
If doesn‟t drink now, but used to…
12. why reduced or quit drinking?
Categorical: list of options
108
Appendix D. Potential confounders considered from the NPHS longitudinal full file
Age (years)
Sex (male/female)
Marital status (married or common-law/not married or common-law)
Highest education level (<secondary/secondary graduate/other post-secondary/college degree)
Income adequacy (low/middle or high)
Country of birth (Canada/not Canada)
Race/colour (white/not white)
Self-reported general health161
(Excellent, very good, good, fair, poor)
Health Utility Index161
(-0.360 to 1.000 in increments of 0.001)
composite index based on eight attributes (vision, hearing, speech, mobility, dexterity,
cognition, emotion, and pain and discomfort)
provides description of overall functional health where perfect health=1.000, death=0.000,
and negative scores reflect health status worse than death
Alzheimer‟s disease or any other dementia diagnosed by health professional (yes/no)
Heart disease diagnosed by health professional (yes/no)
Suffer from the effects of a stroke diagnosed by health professional (yes/no)
Arthritis or rheumatism diagnosed by health professional (yes/no)
Chronic bronchitis or emphysema diagnosed by health professional (yes/no)
Cancer diagnosed by health professional (yes/no)
Migraine headaches diagnosed by health professional (yes/no)
Any of the above seven chronic conditions diagnosed by health professional (yes/no)
Number of the above seven chronic conditions diagnosed by health professional (yes/no)
Cognitive problems (based on Cognition Problem – Function Code of Health Utility Index)161, 177
derived by concatenating values from two variables (ability to remember things, and ability
to think and solve problems)
originally derived on a 6-point scale; considered in this study on a 3-point scale:
- no cognitive problem / only a little difficulty thinking
- only somewhat forgetful / both somewhat forgetful and a little difficulty thinking
- very forgetful or a great deal of difficulty thinking / unable to remember or think
Depression Scale – Short Form161, 222
(score from 0 to 8)
based on a subset of items from the Composite International Diagnostic Interview (CIDI) that
measures major depressive episodes
Number of different medications taken in past 2 days
5 or more medications taken in the past 2 days (yes/no)
109
Appendix E. Summary of the methods used for the selection of potential confounders
1. compiled list of all potential confounders available in
NPHS
'A' set of all potential confounders to be considered
2. modeled association between each variable in 'A' with
injury
'B' subset of 'A' with p ≤ 0.20 for associations with injury
3. modeled confounding effect of each variable in 'B' on
association between injury and psychotropic medication
use
'C' subset of 'B' where confounding effect resulted in a
change of ≥ 10% in β estimate of association
'D' subset of 'B' where confounding effect resulted in a
change of < 10% in β estimate of association
4. modeled combined confounding effect of all variables in
'D' on association between injury and psychotropic
medication use
'D' combined confounding effect of all variables in 'D' was
> 10% in β estimate of association so subset 'D'
remained unchanged
5. candidate used regression techniques to identify the
variables in 'D' that produced the confounding effect
'E' subset of 'D' that together produced the 10% change
in β estimates of association
6. created chonic condition indicator variables 'F' subset of chronic condition indicator variables
7. candidate used regression techniques on potential
confounders in 'C', 'E' and 'F' to identify the variables that
produced the change in β estimates of association
between injury and psychotropic medication use
'G' subset of potential confounders to be controlled for in
multivariate modelling
8. repeated steps 1-7 for 3 categories of psychotropic
medications used in the two days prior to the survey:
any psychotropic, any psycholeptic, any
psychoanaleptic
'H' 'G' for each of the 3 categories of psychotropic
medications had very few differences between them,
therefore, candidate decided to use a single set of
potential confounders for all multivariate models
9. candidate decided to use self-reported general health
rather than health utility index as a general measure of
health
'I' subset 'H' plus self-reported general health
10. candidate decided not to use depression as a potential
confounder
'I' subset of all potential confounders to be controlled for
in multivariate models
Resulting outputAction step
110
Appendix F. Modeling strategy adopted to determine the associations between psychotropic
medications and injuries
Univariate models were first run on the subset of records with values for injury status and the
psychotropic medication variable of interest.
Then, to investigate the effect of potential confounding variables on those associations, a further
subset was created containing records with values for injury status and the psychotropic
medication variable of interest as well as each of the potential confounding variables (Section
3.3.4). Univariate models were run on these subsets of data, and the results were compared with
those from multivariate analyses that controlled for potential confounders.
Finally, the potential effect modification between each medication and alcohol consumption was
investigated to determine if alcohol consumption modified the effect of psychotropic
medications on injury status. The previous subsets of data were further reduced to include only
those records with values for the alcohol consumption variable of interest (Section 3.3.3), and a
series of models were run for each psychotropic medication variable to accomplish this
objective: (1) univariate models, (2) multivariate models controlling for potential confounders,
(3) multivariate models additionally controlling for alcohol consumption, (4) multivariate models
controlling for potential confounders and alcohol consumption including a cross-product
interaction term between the alcohol consumption and psychotropic variables, and (5)
multivariate models controlling for potential confounders stratified by the level of the alcohol
consumption variable. This strategy was repeated for the two alcohol consumption variables
(frequency of alcohol consumption in the past 12 months and quantity of alcohol consumption in
the past seven days) as previously described in the variable definition section on alcohol
consumption (Section 3.3.3).
111
Appendix G. Modeling methods for estimation of minimum detectable OR
Univariate models using repeated measures methods were run to determine the crude association
between psychotropic medication use and injury status in the NPHS data. Assuming medication
use was a risk factor for injuries, the following scenarios describe the procedures used to
estimate the minimum detectable OR for each psychotropic medication:
Scenario 1:
The estimate is positive (OR > 1) and significant (p < 0.05).
The candidate changed the injury status of injured medication users from injured to not
injured until p ≥ 0.05.
Scenario 2:
The estimate is not significant (p < 0.05) or negative (OR < 1) and significant (p < 0.05).
The candidate changed the injury status of not injured medication users from not injured
to injured until the estimate was positive and p ≥ 0.05.