sign in sign up
<<
Home , ATC code N, ATC code R, Dexchlorpheniramine, Ergotamine, Isoxsuprine, Melperone

PRESCRIBING OF POTENTIALLY INAPPROPRIATE PSYCHOTROPIC

MEDICATIONS FOR OLDER, COMMUNITY-DWELLING ADULTS

by

RIAN MARIE EXTAVOUR

(Under the Direction of Matthew Perri, III)

ABSTRACT

Antidepressants, and are used to treat various behavioral, psychiatric and neurological disorders. However, the risk of adverse events challenges prescribing for older adults. The use of most , all sedatives in older adults and the use of antipsychotics in elderly with are considered “potentially inappropriate” by Beers 2012 criteria. This study aims to: (i) describe the prevalence of potentially inappropriate psychotropic (, , ) prescribing for older community-dwelling Americans; (ii) assess the influence of sociological factors on potentially inappropriate psychotropic choices; and (iii) assess the relationship between potentially inappropriate psychotropic choices and emergency outcomes.

Visits by older adults (>65 years) to office-based physicians across the US were extracted from the National Ambulatory Medical Care Survey 2010. Orders for antidepressants, sedatives and antipsychotics were classified based on Beers criteria.

Multivariate logistic regression models were used to assess the influence of the various determinants on the choice of antidepressants, sedatives and the use of antipsychotics in

dementia. Bivariate logistic regressions measured the risk of emergency referrals or

hospitalizations following the visit.

Although office-based physicians in the US rarely prescribe potentially

inappropriate psychotropic to be avoided in older adults, many patients are

exposed to classes that should be avoided in selected conditions due to -disease interactions. The use of electronic medical records is significantly associated with improved quality of antidepressant and sedative prescribing, through selection of more appropriate choices. Patient age, race, asthma, hypertension, obesity, and practice ownership are also associated with improved quality of psychotropic prescribing.

Conversely, depression, income, and specialty are associated with higher risks of inappropriate psychotropic choices. Although increased consultation time was associated with less inappropriate antidepressant prescribing, this was reversed for antipsychotic prescribing in dementia.

Patients receiving tertiary antidepressants and patients with dementia receiving antipsychotics are at risk of emergency referrals or hospitalization. The determinants identified by this study may be used to develop quality indicators, and interventions to improve the quality of prescribing of antidepressants, sedatives and antipsychotics for older adults.

INDEX WORDS: psychotropic, antidepressants, sedatives, ,

antipsychotics, older adults, community, inappropriate, prescribing

quality

PRESCRIBING OF POTENTIALLY INAPPROPRIATE PSYCHOTROPIC

MEDICATIONS FOR OLDER, COMMUNITY-DWELLING ADULTS

by

RIAN MARIE EXTAVOUR

B.Sc., The University of the West Indies, St. Augustine, Trinidad and Tobago, July 1999

M.Sc., Robert Gordon University, Aberdeen, United Kingdom, June 2006

M.Sc., European Programme in Pharmacovigilance and Pharmacoepidemiology,

Bordeaux, France, June 2012

A Dissertation Submitted to the Graduate Faculty of The University of Georgia in Partial

Fulfillment of the Requirements for the Degree

DOCTOR OF PHILOSOPHY

ATHENS, GEORGIA

2015

© 2015

Rian Marie Extavour

All Rights Reserved

PRESCRIBING OF POTENTIALLY INAPPROPRIATE PSYCHOTROPIC

MEDICATIONS FOR OLDER, COMMUNITY-DWELLING ADULTS

by

RIAN MARIE EXTAVOUR

Major Professor: Matthew Perri, III Committee: Glen Nowak Henry Young Randall Tackett

Electronic Version Approved:

Julie Coffield Interim Dean of the Graduate School The University of Georgia May 2015

iv

DEDICATION

To my mother, Ann Mary – for her love, patience, and selflessness.

v

ACKNOWLEDGEMENTS

My heartfelt thanks:

To my friends and family who cheered me on at every step of this journey

To Shada, Samah, Surbhi, Palak, Yu and Shardul for sharing the ups and downs, secrets, smiles and exotic foods in this journey

To Annelie for listening, sharing and guiding

To my advisory committee: Glen Nowak, Henry Young, Randall Tackett for their patience and insightful guidance

To Matthew Perri for mentorship, and for shining light on the joy in research, teaching and the benefits of remaining young at heart

To Perkins for unwavering friendship, love and support across the miles

To Almighty God for all that has been, all that is and all that is yet to come

vi

TABLE OF CONTENTS

Page

ACKNOWLEDGEMENTS ...... v

LIST OF TABLES ...... ix

LIST OF FIGURES ...... xii

CHAPTER

1 INTRODUCTION ...... 1

Background ...... 1

Research Goal and Aims ...... 21

Specific Aims ...... 23

Study Hypotheses...... 25

Expected Findings ...... 33

Study Significance ...... 35

2 LITERATURE REVIEW ...... 43

Search Strategy ...... 43

Prescribing in Older Adults ...... 44

Prescribing of Potentially Inappropriate Medications for Older,

Community-dwelling Adults ...... 60

Determinants of Potentially Inappropriate Psychotropic Use ...... 76

vii

Emergency Outcomes Associated with Potentially Inappropriate

Psychotropic Prescribing ...... 90

Limitations and Gaps in the Literature ...... 91

3 METHODS ...... 131

Study Design ...... 131

National Survey ...... 131

Independent Variables: Patient, Practice and Health-system Factors ...... 133

Potentially Inappropriate Antidepressants ...... 135

Potentially Inappropriate Sedative- ...... 136

Antipsychotics in Dementia ...... 137

Emergency Referrals and Hospitalizations ...... 137

Data Analysis ...... 138

4 RESULTS AND DISCUSSION: POTENTIALLY INAPPROPRIATE

ANTIDEPRESSANTS ...... 163

Results ...... 163

Discussion ...... 168

5 RESULTS AND DISCUSSION: POTENTIALLY INAPPROPRIATE

SEDATIVES ...... 191

Results ...... 191

Discussion ...... 195

viii

6 RESULTS AND DISCUSSION: ANTIPSYCHOTICS IN DEMENTIA ...... 216

Results ...... 216

Discussion ...... 218

7 CONCLUSIONS...... 233

REFERENCES ...... 235

APPENDICES

A Institutional Review Board Application ...... 260

B Institutional Review Board Determination Letter...... 263

ix

LIST OF TABLES

Page

Table 1: AGS/Beers 2012 Potentially Inappropriate Psychotropic Medication-Disease

Combinations and ICD-9-CM Codes...... 38

Table 2: Comparison of Common Explicit Measures for Psychotropic Medications To

Avoid (Disease-Independent) in Older Adults ...... 96

Table 3: Prevalence of Potentially Inappropriate Medications and Predictors in studies

using AGS/Beers 2012 Criteria ...... 98

Table 4: Factors Associated with Potentially Inappropriate Medication Use in

Community-dwelling Older Americans ...... 102

Table 5: Studies of Prescribing of Potentially Inappropriate Psychotropic Medications in

Community-dwelling Older Adults ...... 106

Table 6: Antipsychotic Use in Community-dwelling Older Adults with Dementia ...... 115

Table 7: Factors Associated with Potentially Inappropriate Psychotropic Use in

Community-dwelling, Older Adults ...... 123

Table 8: Determinants of Potentially Inappropriate Medications, Potentially Inappropriate

Psychotropic Medications and Psychotropic Medications among community-

dwelling older adults ...... 129

Table 9: Factors Not Associated with Potentially Inappropriate Psychotropic Use in

Community-dwelling, Older Adults ...... 130

x

Table 10a: Lexicon Multnum Drug and Therapeutic Codes: Tricyclic,

Antidepressants ...... 143

Table 10b: Lexicon Multnum Drug and Therapeutic Codes: SSRIs, SNRIs,

Antagonists ...... 145

Table 11a: Lexicon Multnum Drug and Therapeutic Codes: ...... 147

Table 11b: Lexicon Multnum Drug and Therapeutic Codes: Hydrate, Non-

, Other hypnotics ...... 156

Table 11c: Lexicon Multnum Drug and Therapeutic Codes: ...... 158

Table 12: Lexicon Multnum Drug and Therapeutic Codes: Antipsychotics ...... 160

Table 13: Population Characteristics, Potentially Inappropriate Antidepressants and

Emergency Outcomes of Older Community-dwelling Adults in 2010 ...... 179

Table 14: Antidepressant Orders for Community-dwelling Older Adults in 2010 ...... 184

Table 15: Multinomial Logistic Models of Factors Influencing Potentially Inappropriate

Antidepressant Prescribing for Older Adults ...... 187

Table 16: Population Characteristics, Potentially Inappropriate Sedatives and Emergency

Outcomes of Older Community-dwelling Users in 2010 ...... 205

Table 17: Sedative Orders for Older Community-dwelling Adults in 2010 ...... 210

Table 18: Multinomial Logistic Models for Potentially Inappropriate Sedative Prescribing

for Older Adults ...... 212

Table 19: Population Characteristics, Antipsychotic Orders and Emergency Outcomes for

Older Community-dwelling Adults with Dementia in 2010...... 225

Table 20: Antipsychotic Medication Orders for Community-dwelling Older Adults with

Dementia in 2010 ...... 230

xi

Table 21: Binomial Logistic Regression Models for Antipsychotic Orders for Older

Community-dwelling Adults with Dementia ...... 231

xii

LIST OF FIGURES

Page

Figure 1: Theoretical Model of Factors Influencing Psychotropic Prescribing ...... 37

1

CHAPTER 1

INTRODUCTION

Background

In 2012, adults aged 65 years or older, constituted 13.4% of the population of the

United States.1 This is expected to increase to 16.8% by 2020 and to 20% by 2040, resulting in over 79 million Americans aged 65 years or older.2 Americans are living

longer and life expectancy at birth is expected to steadily increase from 78.7 years in

2010 to 79.5 by 2015, 80.2 by 2020 and 82.7 years by 2040.3,4

Although physiological changes may be gradual with aging and may vary with an

individual’s health and lifestyle, the age at which persons are considered elderly or old varies with cultural norms and expectations. The United Nations describes the ageing index of a population using a cut-off age of 60, but applies the age of 65 in the measurement of the old-age dependency ratio.5 In the US, eligibility for Medicare and

supplemental social security begins at age 65.6 As a result, research among older

Americans frequently use age 65 to define older adults, to minimize variability based on

access to benefits that do not extend to adults 60-64 years old.

With an aging population there is a growing need for care of acute and chronic

conditions over time, which leads to increases in medical costs, including clinical visits,

medications and institutional care. In 2012, Medicare provided health insurance to 92%

2

of the nation’s elderly, with prescription benefit payments totaling $66.5 billion for 37

million enrollees.7 In spite of their benefits in disease state management, ongoing

increased medication use exposes older adults to opportunities for adverse drug events.

Randomized controlled trials that provide evidence of a medicine’s risks and benefits

frequently exclude older adults, resulting in a dearth of information on effectiveness and

safety in this population.8 With aging physiology, pharmacokinetic and

pharmacodynamic functions become less efficient, which may lead to decreased

effectiveness and/or increased vulnerability to adverse drug effects, as drug-handling

ability declines. These changes, along with diminishing cognition for some elderly,

further increase the risk of adverse drug events such as falls. Hence, prescribing for

adults becomes more complex as age increases, as physicians attempt to balance the

benefits and risks.

Psychotropic Medications in Older Adults

Psychotropic medicines, such as sedative-hypnotics, antidepressants, antipsychotics, and affect mood, behavior and cognition. The use of psychotropic medications in older adults spans several indications but is limited by the aging physiology, multiple comorbid illnesses, polypharmacy, and adverse drug events.

3

Antidepressants

Antidepressants are primarily used to treat major depressive disorder but are also

approved for the treatment of panic disorder, generalized disorder, post-traumatic

stress disorder, and obsessive-compulsive disorder. Additional indications include neuropathic pain and pain associated with fibromyalgia, with some antidepressants being used in the management of premenstrual dysphoric disorder, urinary stress incontinence and symptoms of menopause.9 Due to their broad range of indications, many persons are

exposed to antidepressants. Older patients may present with multiple comorbid

conditions that require long-term care. Depression in older adults commonly co-exists

with diabetes, coronary heart disease and stroke, thereby increasing the patient’s burden

of disease and reducing his/her quality of life. Although improvements in symptoms

may be experienced after one to two weeks of therapy, full therapeutic effects may only

be achieved after twelve weeks.10 However, patients may experience adverse effects

before therapeutic effects become apparent. In instances of insufficient response to

monotherapy, augmentation may involve psychotherapy, antipsychotics, sedative- hypnotics or electroconvulsive therapy.10 The lengthy duration of therapy, additional

medications and the emergence of adverse effects may contribute to reduced medication

adherence and/or medication switching, before therapeutic goals are achieved.

Anxiolytics and Sedative-Hypnotics

The term “” is synonymous with the description of sedatives as these reduce anxiety and exert a calming effect.11 Although antidepressants are indicated for treatment of anxiety disorders, benzodiazepines such as may also be used as

4

second-line medications.12 At higher dosages, most sedatives produce effects,

causing a more profound depression of the central , making them useful

for general anesthesia and muscle relaxation, prior to medical and surgical procedures. In

addition to , some sedative-hypnotics, including barbiturates may be used in the

management of seizures. The newer non-benzodiazepine hypnotics lack

properties and are used primarily for disorders.11 For the purposes of this paper,

the term “sedatives” will be used to collectively refer to benzodiazepines, barbiturates,

non-benzodiazepine hypnotics, , and .

In older adults, the duration of sedative effects may be prolonged due to reduced

clearance, and/or accumulation in fat. Although sedatives may relieve anxiety and

promote sleep, their use is limited by the risk of tolerance and dependence with long-term

use, requiring higher doses to achieve the therapeutic effect and a need for gradual

withdrawal. Prolonged exposure in older adults increases the risk of dependence, the need

for higher doses and increases the risk of depression of the central nervous system, which

may lead to impaired coordination, falls, coma and death.11,13

Antipsychotics

Antipsychotics vary in their relative affinities for receptors and

subsequent blockade. As a result they are broadly classified as (i) typical, conventional

or first-generation antipsychotics (FGAs), (ii) atypical or second-generation antipsychotics (SGAs) and (iii) atypical -like antipsychotics.14 The latter agents

are commonly grouped with SGAs. Conditions presenting with psychotic symptoms,

such as , bipolar I disorder senile psychoses and drug-induced psychoses

5

are commonly treated with antipsychotics. Additional indications include Tourette’s

syndrome and disturbed behavior in Alzheimer’s disease. They have also been effective

in reducing anxiety and sleep disturbance but are restricted to persons with psychotic

symptoms. Antipsychotics have been used concurrently with antidepressants to treat

symptoms of depression that accompany schizophrenia and complicate its management.15

Both first- and second-generation antipsychotics show modest reduction in behavioral and psychological symptoms of dementia but lack regulatory approval for this indication. In the management of psychosis or agitation associated with dementia, FGAs have not been found to be superior to SGAs.16 However, the use of antipsychotics is

accompanied by the risk of adverse effects that affect the autonomic nervous system,

central nervous system, cardiovascular and endocrine systems.

Autonomic effects include constipation, difficulty urinating, , dry

mouth and impotence, primarily due to activity. Endocrine effects such

as gynecomastia, galactorrhea and amenorrhea may occur with the use of FGAs, while

weight gain and diabetes mellitus may be seen among users of SGAs. Postural or

orthostatic is an established adverse cardiovascular effect of antipsychotic

use, whereas electrocardiographic changes are common with the use of ,

clozapine and . Effects on the central nervous system primarily involve the

extrapyramidal system, leading to (severe muscle spasms),

(restlessness), tardive (abnormal involuntary movements) and/or

pseudoparkinsonism. These are more common among patients using conventional

antipsychotics. Other adverse effects of antipsychotics include impaired cognition,

sedation and reduction in the seizure threshold. In older adults, these adverse effects

6

complicate the management of schizophrenia and other disorders due to reduced

functional ability, falls, worsening of metabolic and cardiac conditions, and decreased

adherence.14,15

One study by Aparasu et al. among community-dwelling older adults using first-

and second-generation antipsychotics found an increased risk of hospitalization with the

use of first-generation agents.17 In a recent review of literature by Mittal et al. increased risks of adverse cardiovascular events and death were reported in studies of older adults with dementia with the use of FGAs and SGAs. Overall, higher risks of death were found among patients of older age, male gender, severe dementia and functional impairment.18 Regulatory warnings regarding the increased risk of cerebrovascular adverse events, including stroke and transient ischemic attacks among older adults were prompted by findings of clinical trials of versus in older adults with dementia-related psychoses.16 In 2005, warnings were issued regarding the use of SGAs,

but due to the increased risk of death and stroke among older adults with dementia

reported by various studies, the warnings were extended to both FGAs and SGAs in

2008.19,20

Appropriateness of Medications in Older Adults

“Appropriateness” of prescribing has been widely used to describe the quality of

the prescribing decision in terms of the balance of benefits and risks for the patient.

Medication prescribing is considered “inappropriate” when the risks of harm outweigh

7

the benefits of the medication. In its National Service Framework for Older People, the

Department of Health of Great Britain describes medication appropriateness as when the

patient would “gain maximum benefit from the medication” but does not experience

unnecessary illness caused by “excessive, inappropriate or inadequate consumption of

medicines”.21

Unnecessary harms include adverse drug events, which are is defined as “any

untoward occurrence that may present during treatment with a pharmaceutical product

but which does not necessarily have a causal relation to the treatment”.22 This definition

includes the adverse drug reaction (ADR) which is “a response to a drug which is

noxious and unintended and which occurs at doses normally used in man for prophylaxis,

diagnosis, or therapy of disease or for the modification of physiologic function”.22

Adverse drug reactions have been identified as causes of hospital admissions in various

age groups, but with 4 to 7 times higher odds in older adults.23 Medications that are

commonly involved in ADR-related hospitalizations include those acting on the central

nervous system such as benzodiazepines, antidepressants, antipsychotics, anti-dementia and anti- .23

In determining appropriateness (or inappropriateness) of medication use, various

process and outcome measures have been developed. Process measures may be implicit,

where clinical judgment is involved or explicit, where general criteria are applied.24 One

review identified 46 tools used to assess inappropriate prescribing: 28 were explicit

measures, 8 were implicit and 10 applied a mixed approach. Of the explicit tools

reviewed, 25 were designed specifically for use in older patients, where 4 were specific to

in-patients or persons in long-term care facilities. The other tools were considered

8

applicable for review of prescribing for older adults in ambulatory or any (non-specific)

settings.25

Explicit Measures of Medication Appropriateness

The increased risk of adverse drug reactions with the use of some medicines by

the elderly has led to the development of various explicit criteria to guide clinicians in the

prescribing of less harmful alternatives. Most explicit criteria were developed by expert

consensus, with the integration of evidence and clinical experience. Examples of these

are Beers criteria (USA), McLeod’s list (Canada), the Assessing Care of Vulnerable

Elders (ACOVE) Quality Indicators (USA) and the Screening Tool of Older Persons’

Potentially Inappropriate Prescriptions (STOPP, UK).26-33

Beers criteria were developed in 1991 to assess medication appropriateness in nursing home elderly, and have since been updated extending its application to other settings.26-29 According to this criteria, inappropriate prescribing involves the

prescription of medicines that should be avoided in the elderly due to unnecessary risk,

where a safer alternative is available and where use in certain conditions should be

avoided.28 The most recent version was jointly published by authors of earlier criteria

and the American Geriatrics Society (AGS) in 2012. In addition to the Delphi consensus

approach, the developers undertook systematic reviews of the literature, followed by

grading of the evidence that supported their recommendations.29

9

The criteria provide checklists of potentially inappropriate medications (PIMs) for

which the risks of harm outweigh the benefits when used in older adults, but they are

limited as tools for medication use review. This is a result of the exclusion of other

contextual aspects of prescribing, such as the patient’s clinical condition, previous

response to therapies, patient preference, physician’s judgment and medication access.

Limitations of Beers criteria include the exclusion of other types of potentially harmful

prescribing practices that are not restricted to older adults, such as drug-drug interactions,

dosing of renally-excreted medications and excessive treatment duration. Nevertheless,

Beers criteria have been applied in the development of quality measures in the US

including those used by the National Committee for Quality Assurance, the Agency for

Healthcare Research and Quality and by Medicare Quality Improvement Organizations to

assess the quality of health care and service.34,35 Administrative policies informed by

these indicators may restrict prescribing of potentially inappropriate medications to

improve the quality of prescribing and to minimize harm to older adults. Drug plans

may assign potentially inappropriate or high-risk medications to higher tiers (increased

co-pay), or apply coverage rules such restricted quantities, mandatory step therapy or prior authorization by prescribers or formulary exclusion.

As a result of their development in the US, Beers criteria are limited in external

validity, as medications listed may not be marketed in other countries. In order to

address this limitation, health professionals and researchers in other countries have

developed alternative criteria.

10

McLeod’s list of inappropriate prescribing practices for the elderly was developed by a 32-member national panel from academic medical centers across Canada, in order to create a list for Canadian practitioners. In August 1995, a preliminary list of inappropriate practices (contraindicated medicines, drug-drug interactions, drug-disease interactions) was sent to panel members, who rated the clinical significance and agreement with alternatives. The list was developed primarily from tertiary sources

( texts, an expert’s quarterly review of drug interactions).30

In Ireland, the Screening Tool of Older Person’s Potentially Inappropriate

Prescriptions (STOPP) was developed through the Delphi consensus approach, and published in 2008.33 It includes medications widely used in Europe, drug-drug interactions and duplicate classes, which increases its validity, but it is not a comprehensive list of potentially inappropriate medications.21 A second version of the criteria has been published as of January 2015.36

The Assessing Care of Vulnerable Elders (ACOVE) project, also based in the US, developed quality indicators for medication use in the elderly in 2001, which was revised in 2007. These criteria define 392 indicators of care, which include aspects of screening, diagnosis, treatment and monitoring of older adults, rather than a list of medications.

While some medications to be avoided are listed, the indicators are mainly recommendations framed within the management of various conditions, to improve overall care.37

11

Prevalence of Potentially Inappropriate Medications (PIMs)

The use of potentially inappropriate medications (PIMs) in older adults in

ambulatory care, based on explicit criteria has been reported in various studies at

different prevalence rates. In studies published between 1987 and 1994, the prevalence

of PIMs for ambulatory elderly ranged from 5-24%.38 Higher prevalence rates of 11.5-

62.5% were reported in a review of administrative database studies from 1990 to 2010.39

Subsequently, Opondo et al. reviewed 19 studies published between 1950 and 2012,

where explicit criteria were applied to the elderly in primary care, and reported a median

rate of 20.5%.40 In a recent report of PIM use based on Beers 2012 criteria, 40.8% of older Americans received at least one prescription fill involving a potentially inappropriate medication to avoid, between 2009 and 2010.41 Variations in prevalence

rates reported by the studies may be due to differences in sampling frame, index age,

study design (self-reports, patient records or prescription claims) and criteria used.

Outcomes Associated with PIM use

Unintended adverse outcomes such as increased hospitalizations, higher costs and

death have been associated with the use of potentially inappropriate medications.

Medications included on various versions of Beers criteria (1991, 1997, 2003) have been

found to be associated with adverse health outcomes in community settings. One review

of 18 studies (from 1991-2006), found that three-quarters of studies assessing the use of

PIMs and hospitalization found significant associations. However, three of six studies

found an association with health-related quality of life. None of the four studies examining mortality found any association.42 Subsequent studies also identified

12

increased risks of inpatient visits,43 emergency room visits43 and hospitalizations43-45

among community-dwelling elderly receiving PIMs (Beers criteria 2003).

In a retrospective study of community-dwelling elderly, the use of potentially inappropriate medications was a significant predictor of increased healthcare expenditures, which was estimated at $7.2 billion more in annual expenditure in the US in 2001.46 Annual and total healthcare costs for elderly taking the PIMs have been found

to be higher than for elderly not receiving PIMs.47

Limitations of Explicit Measures

The use of explicit criteria enables the determination of the extent of PIM use,

predictors and outcomes across multiple settings, using large databases. However, study

findings may vary given the differences in the scope of the criteria and data sources used.

As new risk information emerges regarding the safety of medications in older adults from

post-marketing research, and as pharmaceuticals are added and removed from markets,

criteria may become outdated or limited in scope.

Explicit criteria are designed as checklists and do not provide a comprehensive review of medication use by individual patients, as a result their application may overestimate “inappropriateness”. For instance, where a medication is selected based on past therapeutic response, patient preference, or the failures of safer alternatives, these cases are not excluded by explicit criteria.

Explicit criteria are primarily developed to improve the quality of prescribing for patients by summarizing evidence, providing recommendations for use and supporting

13 medication warnings. These measures are not designed to be used in place of clinical decision-making but to act as global guides to prescribing for the elderly, rather than strict measures of the quality of prescribing. Nevertheless, their integration into clinical decision support systems may alert health professionals about potential harm to patients.

As guidelines, they facilitate the identification of older adults who may require medication review, patient education and/or closer clinical monitoring.

Potentially Inappropriate Psychotropic Medications

Various psychotropic medications are considered potentially inappropriate for older adults due to the increased risks of adverse drug reactions and hospitalizations.29

Adverse events attributed to effects of these medications on the central nervous system have been found to cause 3.6-20.5% of drug-related hospitalizations in older adults.

Examples include altered mental status, syncope, falls, constipation, respiratory distress, hyponatremia and neuropsychiatric symptoms.23 The use of psychotropic medications in older adults has been associated with a 47% increased risk of falls in one study by

Beier.48 In one meta-analysis, Woolcott et al. reported increased adjusted odds of falls in older adults with the antidepressants (aOR=1.36, 95% C.I. 1.13, 1.76) and benzodiazepines (aOR=1.41, 95% C.I. 1.20, 1.71). Although the use of neuroleptics and antipsychotics were associated with a higher unadjusted odds ratio, this was not significant in the presence of confounders.49

14

Several studies among ambulatory elderly have reported different prevalence rates

of potentially inappropriate psychotropic prescribing, in spite of their risks of falls and

hospitalizations. 46,50-59 The two most common potentially inappropriate psychotropic

classes identified by explicit criteria are long-acting benzodiazepines (e.g. ) and tricyclic antidepressants (e.g. ).46,50-58 Rates of potentially inappropriate

benzodiazepine prescribing range from 0.7-10.6%,56,57,59 whereas rates for tertiary

tricyclic antidepressants range from 1.96-18.1%55,56,59 among older adults in various

studies.

Strong “Avoid” recommendations supported by evidence with “High” quality

rating, accompany the continued inclusion of tertiary tricyclic antidepressants (TCAs) in

Beers 2012 criteria. This is primarily due to their anticholinergic and sedating effects, as

well as orthostatic hypotension. These medications increase the risk of falls in older

adults, particularly in the presence of syncope and a history of falls or fractures.

Secondary tricyclic antidepressants (e.g. ), selective serotonin-reuptake

inhibitors and serotonin- reuptake inhibitors are assigned to PIMs for “Use

with Caution” categories in the 2012 criteria.29

Earlier versions of Beers criteria identified barbiturates and long-acting

benzodiazepines as PIMs, but the revised criteria include short- and intermediate-acting benzodiazepines, as well as non-benzodiazepine hypnotics. Benzodiazepines,

barbiturates, non-benzodiazepine hypnotics and other sedative-hypnotics carry “Avoid”

recommendations due to high rates of , risk of overdose and sedating

properties. However, Beers criteria recommendations for these groups vary: barbiturates

should be avoided unconditionally; benzodiazepines should be avoided for the treatment

15

of , agitation and delirium; and the use of non-benzodiazepine hypnotics should not exceed 90 days. While the quality of evidence regarding the barbiturates and benzodiazepines listed is rated as “High”, the evidence for non-benzodiazepines and meprobamate is “Moderate” and “Low” for chloral hydrate.29

Beers 2012 criteria recommend avoidance of all antipsychotics in the treatment of behavioral disorders due to dementia, due to the increased risk of stroke and death in these patients.29 In 2005, the US Food and Drug Administration (FDA) issued a boxed

warning regarding an increased risk of death associated with atypical (second-generation)

antipsychotics in elderly with dementia. Another warning was later issued for the use of

typical (first-generation or conventional) antipsychotics in June 2008.19,20 The high

anticholinergic activity and risk of QT prolongation with thioridazine has been the

rational for its inclusion in previous and recent versions of the criteria.28,29

Clinical Outcomes of Potentially Inappropriate Psychotropic Medication Use in Elderly

Studies assessing the relationship between potentially inappropriate psychotropic

medications and clinical outcomes have been sparse. In their study of the association

between potentially inappropriate psychotropic medication use and health care utilization,

costs and quality of life of older Americans, Aparasu and Mort found no differences in

the broad measures of these outcomes.60 In a later study among older Australians, Price

et al. examined the relationship between potentially inappropriate medications and the

risk of unplanned hospitalization. They found that exposure to anxiolytics/sedative-

16 hypnotics and antipsychotics were significantly associated with unplanned hospitalizations, when controlling for confounders.45 No other studies have examined associations between potentially inappropriate psychotropic medication classes and adverse health outcomes.

Prescribing as a Complex Therapeutic Decision

The designation of “appropriate” or “inappropriate” prescribing applies a simplified approach to a complex process that is influenced by behaviors of prescribers, patients and other allied health professionals, as well as administrative factors. A few studies of inappropriate prescribing60,61 have applied the Andersen model of access to health services, where predisposing characteristics, enabling resources and need factors are applied. 62,63 However, this model focuses on the patient’s access and use of health services rather than the prescribing decision process. To understand prescribing behavior, various theories have been proposed to help explain or predict therapeutic choices. Examples include social cognitive theories such as the Drug Choice Model by

Denig et al.64 and Eisenberg’s model of socioeconomic influences of decision-making by clinicians.65

Eisenberg’s framework models patient characteristics, physician or prescriber characteristics, the physician’s relationship with the patient and the physician’s interaction with his profession and the health system on clinical decisions – Figure 1.

17

Examples of patient characteristics are the patient’s sex, social class, income and ethnic

background. Characteristics of the physician that may influence decision-making include

specialty, approach to medicine (interventionist vs. health maintenance), physician’s age

and education. Influences under the construct of the physician’s interaction with the

health system include relationships with colleagues and non-physician health

professionals, professional acceptance of new medications, regulatory measures, informal

peer pressure (e.g. group practice), structural and organizational factors. The physician’s

relationship with the patient is also considered to be influential, particularly where the

practice is client-oriented. In this aspect, the personalities of both physician and patient,

as well as the decision-sharing style of the physician used during the encounter may

influence prescribing.65

Various patient and prescriber factors have been associated with PIM use

including female gender, advanced age, polypharmacy and visits to general/family

practitioners.54,57,66 Most studies have focused on patient characteristics more so than on

physician/practice characteristics, the patient-physician relationship or the physician- health-system interaction. Therefore, the extent to which these factors influence the prescribing of potentially inappropriate medications is unknown.

Impact of Electronic Medical Records on Prescribing

Electronic health records (EHRs) or electronic medical records (EMRs) provide patient information to prescribers, including medical history, medications and laboratory results required for diagnosis, monitoring and treatment of conditions. Beyond the

18

communication of information, EMRs may be integrated with computerized decision

support systems (CDSS) to support clinical decision-making, including prescribing. In

the context of the Eisenberg model, such technology may be considered part of the

physician’s interaction with the health-care system, as CDSS apply recommendations

from professional guidelines. In 2009, the US Centers for Medicare and Medicaid

Services introduced financial incentives for practitioners using electronic health record

systems that meet Meaningful Use objectives, under the EHR (EMR) Incentive

Programs.67 The primary goal of this program was to encourage EMR adoption in

medical practices to improve the quality of healthcare provided to US residents. Many

physicians have adopted EMRs into practice since the introduction of the incentive

program. One study among office-based US physicians reported an increase in EMR

adoption rates, from 51% in 2010 to 72% in 2012.68

Evidence of the impact of EMRs on the quality of prescribing or on processes of

care for older adults has been mixed. In a systematic review of trials assessing the use of

technologies such as CDSS on inappropriate prescribing in older adults, the authors

found evidence that showed these technologies have the potential to reduce inappropriate

prescribing. Alternatively, the magnitude of the effects varied with the study design,

systems used, settings and outcomes.69 National ambulatory care surveys in the US assessing the impact of EMRs on quality indicators, inclusive of potentially inappropriate prescribing for older adults using Beers 2003 criteria, failed to demonstrate a significant effect.70,71 But given recent financial incentives and the growing adoption of EMRs, this

finding is expected to change, and subsequently influence prescribing choices.

19

Problem Statement

The prevalence of potentially inappropriate psychotropic medication use and the

increased risk of injury in older adults highlight the need for close review of these medication classes. While several studies have examined prescribing of PIMs overall, research assessing specific PIM groups and the related factors has been minimal.46,54,56-

58,61,66,72-77 Aggregate measures of potentially inappropriate prescribing are limited in the

assessment of predictors that may influence the choice of specific psychotropic drug

classes.

The most recent study of potentially inappropriate psychotropic use among older,

community-dwelling adults in the US was based on 18 year-old data (1996).60 Also, the

most recent study estimating the prevalence of antipsychotic use in older Americans with

dementia used 2002-2004 data.78 Given that thirty-five new psychotropic medications,

including antipsychotics and short- and intermediate-acting benzodiazepines have been added to the Beers 2012 list of PIMs to avoid, these would have been excluded from earlier studies.29 Hence, the findings of these studies would not be relevant to

contemporary practice, which indicates the need for updated information regarding

inappropriate prescribing of these therapeutic classes.

Most studies that examined predictors of potentially inappropriate psychotropic

prescribing or the use of antipsychotics in older adults with dementia focused primarily

on clinical factors. Few studies assessed the influence of other factors, such as physician/practice characteristics.79,80 Although EMRs with clinical decision support

20 systems have the potential to reduce inappropriate prescribing, their influence on potentially inappropriate prescribing is unknown. Therefore, there is a need for information regarding the influence of non-clinical factors on the choice of potentially inappropriate psychotropic classes for older adult users, and on the use of antipsychotics in older adults with dementia. There is limited information regarding the influence of the use of potentially inappropriate psychotropic classes on emergency referral and/or hospitalization following the office visit. Although various studies report associations between PIM use and adverse health outcomes, these studies did not measure the influences of the respective psychotropic classes, in the context of recent criteria.42-44

Study Rationale

Research describing the influence of non-clinical factors on the choice of potentially inappropriate psychotropic medications, using more current criteria and factors influencing the use of antipsychotics in dementia is needed. In addition, the associations between potentially inappropriate psychotropic choices and emergency outcomes of the visit are unknown.

The identification of factors that support or discourage the selection of potentially inappropriate psychotropic medications will provide information on the context of use in office-based practices. This information will support the development or modification of quality measures, patient and professional interventions, clinical decision support tools,

21

prescribing protocols and administrative decisions that are tailored to the context in

which the medications are used.

Research Goal and Aims

The long-term goal of this research is to improve the quality of psychotropic

prescribing for older, community-dwelling adults. The aims of this research are to:

(i) Describe the extent of potentially inappropriate psychotropic prescribing of

antidepressants, sedatives and antipsychotics using AGS/Beers 2012 criteria;

(ii) Identify sociologic influences of potentially inappropriate antidepressant,

sedative and antipsychotic prescribing; and

(iii) Describe the relationship between potentially inappropriate antidepressant,

sedative or antipsychotic prescribing and emergency outcomes among older

community-dwelling adults.

This study is unique in that it applies recently updated criteria, in the framework of a different clinical decision model, to identify factors influencing the choice of antidepressants, sedatives and antipsychotic medications. The findings will be relevant to modern practice as more recently marketed medications are included, such as short- and

intermediate-acting benzodiazepines, non-benzodiazepine hypnotics and all antipsychotic

agents.

22

This study will provide more current information on patient, prescriber and health- system factors that influence the selection of these psychotropic agents in the US. This information will guide health professionals, medical educators and administrators in the design and implementation of more relevant strategies and policies regarding psychotherapy in older adults in primary care.

Patients at increased risk of receiving prescriptions with greater potential for harm may be more closely monitored for clinical and economic outcomes. Predictors related to physician characteristics will identify practitioners or practices that may benefit from revision of prescribing protocols, educational interventions, clinical decision support and/or further research. Health-system interaction factors, including the use of EMRs will identify settings and system aspects that may require closer review and/or updating.

Physicians and practice settings that are more likely to engage in potentially inappropriate selection of antidepressants, sedatives or antipsychotics may receive additional education and be alerted to the need for review of current prescribing practices to minimize harm.

This increased vigilance is expected to increase awareness of updated guidelines and minimize the occurrence of potentially inappropriate prescribing, unless clinically necessary. Non-clinical influences identified will inform the design of further research, such as longitudinal studies and sub-group analyses.

The influence of prescribing of these high-risk medications on the emergency outcomes of the visit will add to knowledge of outcomes associated with the use of potentially inappropriate psychotropic medications, in the context of the revised

AGS/Beers criteria. The findings will describe associations between the selection of potentially inappropriate psychotropic medications and the disposition of the visit as a

23

proxy of patient outcomes. This information will also contribute to assessment of the

predictive validity of the AGS/Beers 2012 criteria.

Specific Aims

Potentially Inappropriate Antidepressant Prescribing

In order to determine the extent of use and predictors of prescribing of potentially

inappropriate antidepressant medications, the specific aims of the proposed research are

to:

1. Determine the prevalence of potentially inappropriate antidepressant prescribing

among older adults receiving antidepressant prescriptions in ambulatory care.

2. Assess the influence of patient characteristics, prescriber/practice characteristics,

the patient-physician relationship and physician-health system interaction

characteristics on the choice of potentially inappropriate antidepressants for older

antidepressant users in ambulatory care.

3. Determine the influence of potentially inappropriate antidepressants to avoid on

emergency room referrals and/or hospital admissions for older ambulatory users,

following the visit.

24

Potentially Inappropriate Sedative Prescribing

To determine the patterns and predictors of prescribing of potentially inappropriate sedative medications in older adults, the specific aims of the proposed research are to:

1. Determine the prevalence of potentially inappropriate sedative prescribing for

older adults receiving these medications in ambulatory care.

2. Assess the influence of patient characteristics, prescriber/practice characteristics,

patient-provider relationship and physician-health system interaction

characteristics on the choice of potentially inappropriate sedatives for older adults

receiving sedative medications in ambulatory practices.

3. Determine the influence of the choice of potentially inappropriate sedatives to

avoid on emergency room referral or hospitalization of older users, following the

visit.

Antipsychotic Prescribing in Dementia

To assess the prevalence and predictors of prescribing of antipsychotic medications in older adults with dementia, the specific aims of the proposed research are to:

1. Determine the prevalence of antipsychotic prescribing for older, community-

dwelling adults with dementia, seen in ambulatory practices.

25

2. Measure the influence of patient characteristics, prescriber/practice

characteristics, patient-provider relationship and physician-health system

interaction characteristics on prescribing of antipsychotic medications for older,

community-dwelling adults with dementia.

3. Assess the influence of antipsychotic prescribing on hospitalization or emergency

department referral, following visits by older, community-dwelling adults with

dementia

Study Hypotheses

For simplicity, the term “potentially inappropriate psychotropic prescribing” will be used in this section to refer to the occurrence of medication orders for potentially inappropriate antidepressants to avoid, potentially inappropriate sedatives among older adults to avoid, or the use of antipsychotics among older adults with dementia.

Patient Characteristics as Predictors of Prescribing

Predictors of potentially inappropriate psychotropic prescribing are: sex, race,

ethnicity, age, income, payment source, injury-related reason for visit, chronic diseases,

Beers potentially inappropriate conditions and the number of medications.

26

Directional Hypotheses:

Female sex, white race, non-Hispanic (non-Latino) ethnicity, younger age, lower income, the number of medications, and the presence of chronic conditions are associated with a greater likelihood of potentially inappropriate psychotropic prescribing. Injury- related visits and Medicaid payment source and Beers potentially inappropriate conditions are associated with a lower likelihood of potentially inappropriate psychotropic prescribing.

Chronic conditions influencing sedative choices, such as the presence of asthma, arthritis, , chronic obstructive pulmonary disease, diabetes, depression, ischemic heart disease, congestive heart failure, insomnia, anxiety or seizures are associated with higher odds of potentially inappropriate sedative prescribing.

Older patients with dementia with a diagnosis of diabetes, depression, cerebrovascular disease, hyperlipidemia or ischemic heart disease are less likely to receive antipsychotic medication. Patients with dementia and schizophrenia or bipolar disorder are more likely to receive antipsychotic medications.

Rationale

Female patients utilize health services more often than men, and may more readily disclose emotional symptoms to physicians.81 This increases their exposure to prescribers and may cause them to be more likely to be diagnosed for mood disorders or conditions requiring treatment with a psychotropic medication. Whites and persons of non-Hispanic ethnicities utilize health services more often than their counterparts.82,83 27

Hence these patients are at higher risk of receiving potentially inappropriate antidepressants.

Persons with low income may have less healthy lifestyles, delay seeking healthcare until the condition is debilitating and may have to use more medications to treat advanced disease. Also, the ability to purchase flexible drug plans may be affected, limiting the range of accessible antidepressant options, including safer agents. Drug coverage under Medicaid may be more strictly managed than other insurance plans, restricting access to potentially inappropriate medications. Also, coverage by Medicaid was associated with a reduced risk of potentially inappropriate psychotropic prescribing in earlier studies, possibly due to formulary restrictions.79,80 Exposure to more medications may increase the likelihood of receiving at least one potentially inappropriate antidepressant.

Depression and anxiety have been associated with chronic diseases such as asthma and diabetes, as factors in the etiology and management of the conditions.84,85

Patients with chronic diseases may also present with depression and require an antidepressant, which increases the opportunities for prescription of a potentially inappropriate antidepressant.

Patients with indications that are commonly treated by the various psychotropic classes are more likely to receive potentially inappropriate psychotropic medications as a result of disease management. For antidepressants, the primary indications are depression and anxiety; for sedatives, these include anxiety and seizures; and for antipsychotics, primary indications include schizophrenia or bipolar disorder.9,11,15

Patients with conditions involving chronic or neuropathic pain, such as cancer and 28

diabetes may be at higher risk of receiving tricyclic antidepressants for off-label use, which would increase rates of potentially inappropriate prescribing of antidepressants to avoid. The presence of insomnia or chronic diseases with symptoms that may disrupt sleep: asthma, arthritis, cancer, chronic obstructive pulmonary disease, diabetes mellitus, depression, ischemic heart disease and congestive heart failure, may also be associated with increased use of sedatives.

For patients seen for an injury-related reason or with Beers potentially inappropriate conditions, physicians may be more cautious in prescribing medications that are known to lead to further injury or disease complications. Conditions that have been identified as potentially inappropriate drug-disease combinations for the various psychotropic classes are listed in Table 1.29 Similarly, physicians may avoid prescribing antipsychotics to patients with diabetes, hyperlipidemia, obesity, or cerebrovascular disease due to the risk of adverse metabolic and cardiovascular effects associated with the use of antipsychotics.15

Indicators of Patient-Physician Relationships as Predictors

Established patient status, time spent with physician and the number of additional

services provided by physicians are significant predictors of potentially inappropriate

psychotropic prescribing. 29

Directional Hypotheses

The odds of potentially inappropriate psychotropic prescribing is lower for established patients (seen before), visits to physicians with more opportunities for continuity of care through extended services, and as consultation time increases.

Rationale

Both the medical and medication histories of established patients are expected to be more familiar to the physician and/or other providers than for new patients. As a result, physicians may be less likely to order potentially harmful medications for these patients. Similarly, the regular provision of extended services by physicians (e.g. weekend consultations, hospital visits, telephone or email consultations) increases opportunities for continuity of care, communication and patient monitoring.

As patients spend more time consulting with physicians, there is more opportunity for development of rapport, information retrieval and problem identification, including experiences with adverse drug events and living conditions. This is expected to contribute to the selection of less harmful psychotropic choices.

Physician and/or Practice Characteristics as Predictors

Practice location, geographic region, physician specialty and the involvement of non-physician providers are predictors of potentially inappropriate psychotropic prescribing. 30

Directional Hypotheses

Southern and/or metropolitan practices, and visits to psychiatrists/psychologists or neurologists are associated with higher odds of potentially inappropriate psychotropic prescribing. Visits involving a non-physician provider are associated with lower odds of the use of potentially inappropriate psychotropic prescribing.

Rationale

Location in Southern states of the US has been associated with higher odds of potentially inappropriate psychotropic prescribing by Aparasu et al.79 Northeastern practices indicated mixed findings in studies by Aparasu et al.79 and Mort et al.80 These may be due to the distribution of physician practices, whereby more physicians or practices are located in metropolitan areas, and in Northern and Western states. With a greater range of experience, rates of inappropriate prescribing would be lower. Practices in metropolitan areas may have a wider range of specialties and may facilitate more patients than non-metropolitan areas, thereby increasing the use of psychotropic medications and the likelihood of potentially inappropriate antidepressant prescribing.

Psychiatrists and psychologists primarily treat patients with severe behavioral and mood, and are expected to prescribe more medications for the treatment of these conditions. The odds of potentially inappropriate antidepressant or sedative prescribing are expected to be higher for these practices. Neurologists frequently manage patients with neurodegenerative disorders, such as Alzheimer’s disease, and neurological conditions, such as seizures. Hence the odds of potentially inappropriate antipsychotic and sedative use would be higher in these practices. 31

The involvement of non-physician providers, such as physician assistants and nursing personnel, may supplement the interaction with the patient. These may facilitate the identification of problems or issues that are not disclosed to physicians, thereby reducing the risk of potentially inappropriate prescribing.

Physician-Health System Interactions as Predictors

Solo practice, practice ownership and the use of electronic medical records are influential factors in prescription of potentially inappropriate psychotropic medications.

Directional Hypotheses

Group practices and practices that use electronic medical records (EMRs) are less likely to engage in potentially inappropriate psychotropic prescribing. Practices that are owned by physicians or a physician group are more likely to prescribe potentially inappropriate psychotropic medications.

Rationale

Within group practices, multiple physicians work at the same location(s) and may influence each other through collaboration or professional protocols. The presence and interactions with peers may support prescribing decisions and minimize inappropriate prescription choices. Practices owned by physicians may facilitate greater autonomy and 32 less administrative restrictions within the practice. As a result, the prevalence of potentially inappropriate psychotropic prescribing may be higher in these practices. The use of EMRs facilitates therapeutic decision-making by providing patient and medication information at the point of care. These systems enable reviews of laboratory data, facilitate clinical monitoring, and may provide warnings, thereby reducing opportunities for inappropriate medication choices.

Influence of Potentially Inappropriate Psychotropic Prescribing on Emergency Outcomes

The use of potentially inappropriate antidepressants, sedatives or antipsychotics in dementia is associated with higher risks of hospital admission or referral to emergency rooms following the visit.

Rationale

Older adults receiving potentially inappropriate antidepressants or sedatives to avoid, and older patients with dementia who receive antipsychotics may present more often with adverse drug events during the visit. Hence, these patients are more likely to be referred to the emergency room or hospitalized following the visit.

Beers criteria recommend avoidance of tertiary tricyclic antidepressants for all elderly, irrespective of comorbidities due to the risk of falls, fractures and cognitive impairment.29

Based on the risk of adverse events, patients receiving tertiary TCAs are expected to be 33

referred to emergency departments or hospitalized more often than patients receiving

other antidepressants.

Beers criteria recommend avoidance of barbiturates in older adults and the

avoidance of benzodiazepines for treatment of insomnia, agitation or delirium, whereas

recommendations for non-benzodiazepine use relate to duration of use (>90 days), based

on available evidence. Hence, older adults receiving barbiturates or benzodiazepines

may be more likely to be referred to emergency rooms or admitted to hospital after the

visit, relative to non-benzodiazepine users.

Older patients with dementia receiving prescriptions for antipsychotics may be

more likely to be referred to emergency rooms or admitted to hospital following the visit,

for treatment of adverse events, such as arrhythmias, stroke or hyperglycemia.

Expected Findings

Prescribing of Potentially Inappropriate Antidepressants

A lower prevalence of prescribing of tertiary tricyclic antidepressants for older adults requiring antidepressants is expected (10-15%), compared with earlier studies.

This would be due to the preferential use of safer alternatives such as selective serotonin reuptake inhibitors (SSRIs), serotonin-norepinephrine reuptake inhibitors (SNRIs), or serotonin reuptake inhibitors (e.g. ). In 2010, most of these 34

classes would have also been available as generic formulations. Factors that are expected

to be influential in the selection of potentially inappropriate antidepressants are: sex, race,

age, established patient status, visits to solo practice and practitioners with non-office

availability (extended services). Patients receiving tertiary tricyclic antidepressants are

expected to have a higher risk of emergency visits or hospitalization, following the visit

than patients receiving other antidepressants.

Prescribing of Potentially Inappropriate Sedative-Hypnotics

The expected prevalence of potentially inappropriate sedative choices for older

adult users will be greater than in previous studies, primarily due to the addition of short- acting and intermediate-acting benzodiazepines to the list. These medications were previously considered “appropriate” for use in older adults. Given the lower potential for abuse and greater advertising efforts, the non-benzodiazepine sedative-hypnotics are

expected to surpass the prevalence rates of other sedative-hypnotic groups. Influential factors that are expected to be identified by the model would be: sex, race, age, number of medications, established patient and visits to solo practice. The patients receiving barbiturates or benzodiazepines are expected to have a higher risk of emergency visits or hospitalization following the visit, than patients receiving non-benzodiazepine sedatives. 35

Prescribing of Antipsychotics for Older Adults with Dementia

It is expected that between 55-65% of visits by older adults with dementia will involve the prescription of antipsychotic medications, mostly atypical agents. Although the use of antipsychotics for the treatment of behavioral and psychiatric symptoms of dementia is off-label, their benefits may be deemed to outweigh the risks. Predictors of

antipsychotic prescribing in this group will be: sex, number of medications, patient

income, age, race, established patient and solo practice. Patients receiving antipsychotic

prescriptions are expected to be at a higher risk of emergency referrals or hospitalization

following the visit than non-users.

Study Significance

This study is novel as it examines psychotropic prescribing choices, in view of

physician, practice and health-system factors. It will apply more valid criteria for

medication appropriateness than earlier studies, which has been based on expert

consensus, literature review and grading of evidence and recommendations. In addition,

medications marketed subsequent to Beers 2003 criteria are included. The findings of

this study will provide more current information on prescribing prevalence, as well as

patient, physician, visit and system factors that influence the prescribing of potentially

inappropriate psychotropic medications. This information may be used as baseline

prevalence for future research and evaluation of prescribing protocols. Indicators of 36

prescribing quality will subsequently be applied and/or interpreted in light of the context

of use based on identified determinants. Interventions designed to improve prescribing

may be modified, such as medication use reviews within selected specialties and the use

of clinical decision support systems.

This study will identify factors that are influential in the prescribing process.

This will add to current knowledge of the influence of health-system factors, as well as

guide subsequent evaluation, selection, use and modification of electronic medical record

systems and other health technologies by institutions and practitioners. Practitioners

and/or administrators may use this information to modify clinical support systems and/or

prescribing procedures. Interventions aimed at improving the quality of prescribing of

psychotropic medications may be designed to strengthen practices where the use of

potentially inappropriate psychotropic medications may be more likely.

Given the potential for harm that each of the therapeutic classes may pose, this

study may identify patients who require close monitoring for adverse events and

physicians or practices that may require further professional education. The increased

vigilance and awareness of updated criteria, inclusive of new medication classes, may

minimize the prescribing of potentially inappropriate psychotropic medications, where

safer alternatives are available and may be tolerated. Patients at higher risk of receiving

prescriptions with greater potential for harm may be more closely monitored for clinical

outcomes. At a population level, the information will add to the post-marketing

information on the use of antidepressants, sedative-hypnotics and antipsychotic medications among older adults. 37

•Age •Sex •Race/Ethnicity Patient •Income Factors •Payment •Injury/Conditions

•Established patient •Extended services Patient- •Number of meds physician •Time spent Antidepressant / Sedative / Antipsychotic Prescription

•Specialty •Non-physician provider Physician •Metropolitan Location •Geographic Region

•Solo Physician- •Ownership •EMR use H/system

Figure 1: Theoretical Model of Factors Influencing Psychotropic Prescribing 38

Table 1: AGS/Beers 2012 Potentially Inappropriate Psychotropic Medication-Disease Combinations and ICD-9-CM Codes86

Disease or Syndrome ICD-9-CM Code and Title Medications

564.00 Unspecified constipation Antipsychotics 564.01 Slow transit constipation TCAs: amitriptyline, Chronic Constipation 564.02 Outlet dysfunction constipation , , 564.09 Other constipation ,

290.11 Presenile dementia with delirium All TCAs 290.3 Senile dementia with delirium Benzodiazepines 290.41 Vascular dementia with delirium Delirium 291.0 withdrawal delirium Sedative hypnotics 292.81 Drug-induced delirium Thioridazine 293.0 Delirium due to conditions classified elsewhere (TCAs, APs) 293.1 Subacute delirium

290.0 Senile dementia uncomplicated 290.10 Presenile dementia uncomplicated Antipsychotics 290.11 Presenile dementia with delirium Dementia and Benzodiazepines 290.12 Presenile dementia with delusional features Cognitive Impairment TCAs 290.13 Presenile dementia with depressive features 290.20 Senile dementia with delusional features 290.21 Senile dementia with depressive features 39

Disease or Syndrome ICD-9-CM Code and Title Medications 290.3 Senile dementia with delirium 290.40 Vascular dementia, uncomplicated 290.41 Vascular dementia, with delirium 290.42 Vascular dementia, with delusions 290.43 Vascular dementia, with depressed mood 291.2 Alcohol-induced persisting dementia 291.82 Drug-induced persisting dementia 294.10 Dementia in conditions classified elsewhere without behavioral disturbance 294.11 Dementia in conditions classified elsewhere with behavioral disturbance 294.20 Dementia, unspecified, without behavioral disturbance 294.21 Dementia unspecified, with behavioral disturbance 331.19 Other frontotemporal dementia 331.82 Dementia with Lewy bodies 331.0 Alzheimer’s Disease

Anticonvulsants, Antipsychotics Benzodiazepines, E929.3 Late effects of accidental fall History of Falls (“Z”) V15.88 History of fall hypnotics SSRIs, TCAs

40

Disease or Syndrome ICD-9-CM Code and Title Medications V13.51 Personal history of pathologic fracture V13.52 Personal history of stress fracture V15.51 Personal history of traumatic fracture Anticonvulsants, Antipsychotics Benzodiazepines, V54.10, V54.11, V54.12 - Aftercare for healing traumatic fracture of arm Nonbenzodiazepine (“Z”) unspecified, upper arm, lower arm hypnotics V54.13, V54.14, V54.15, V54.16 - Aftercare for healing traumatic SSRIs, TCAs fracture of hip, leg unspecified, upper leg, lower leg History of Fracture V54.17, V54.19 - Aftercare for healing traumatic fracture of vertebrae, other V54.20, V54.21, V54.22 – Aftercare for healing pathologic fracture of arm unspecified, upper arm, lower arm V54.23, V54.24, V54.25, V54.26 – Aftercare for healing pathologic fracture of hip, leg unspecified, upper leg, lower leg V54.27, V54.29 - Aftercare for healing pathologic fracture of vertebrae, other bone V67.4 - Following treatment of healed fracture

094.82 Syphilitic Parkinsonism Antipsychotics (all except for Parkinson’s Disease 332.0 Paralysis agitans and clozapine) 332.1 Secondary Parkinsonism 41

Disease or Syndrome ICD-9-CM Code and Title Medications 345.00, 345.01 Generalized, non-convulsive epilepsy 345.10, 345.01 Generalized, convulsive epilepsy 345.40, 345.41 Focal, partial epilepsy with complex partial seizures Chlorpromazine 345.50, 345.51 Focal, partial epilepsy with simple partial seizures Clozapine 345.70, 345.71 Epilepsia partialis continua Chronic Seizures or 345.80, 345.81 Other forms of epilepsy Buproprion Epilepsy 345.90, 345.91 Epilepsy unspecified 780.31 Febrile convulsions, simple Thioridazine 780.32 Complex febrile convulsions Thiothixene 780.33 Post-traumatic Seizures 780.39 Other convulsions

Chlorpromazine 780.2 Syncope and Collapse Olanzapine Syncope 992.1 Heat Syncope Thioridazine Tertiary TCAs

594.8, 594.9 Calculus of lower urinary tract: other, unspecified Antidepressants (amitriptyline, Lower Urinary Tract 599.0 Urinary tract , site not specified , clomipramine, symptoms, 599.89 Other specified disorders of urinary tract , doxepin, imipramine, Benign prostatic 599.9 Unspecified disorder of urethra and urinary tract nortriptyline, , hyperplasia 600.00, 600.01 Hypertrophy (benign) of prostate without/with urinary , trimipramine) obstruction and other lower urinary tract symptoms (LUTS) 42

Disease or Syndrome ICD-9-CM Code and Title Medications 600.20, 600.21 Benign localized hyperplasia of prostate without/with Antipsychotics (chlorpromazine, urinary obstruction and other LUTS clozapine, , , 619.0 Urinary-genital tract fistula female olanzapine, , , prochlorperazine, thioridazine, thiothixene, ) Key: ICD-9-CM: International Classification of Disease, version 9, Clinical Module; RFV: reason for visit; SSRI: Selective Serotonin

Reuptake Inhibitors; TCA: Tricyclic antidepressants 43

CHAPTER 2

LITERATURE REVIEW

Search Strategy

Background searches involved theories and models used to describe the prescribing process or clinical decision-making, potentially inappropriate prescribing in older adults and psychotropic medication prescribing in older adults. More specific searches were done for potentially inappropriate prescribing of psychotropic medications in older adults. The electronic databases searched were PubMed, CINAHL, PsychInfo and Web of Science. Most studies were identified through PubMed and Web of Science.

Keywords, terms and synonyms were combined in accordance to the areas being searched. These primarily included: inappropriate (OR potentially inappropriate OR quality); models (theory); criteria (OR indicator* OR standard); prescribing (OR prescription OR medication OR use); decision making (OR clinical decision); older (OR elderly OR geriatric OR aged); community (OR ambulatory OR non-institutional* OR primary care); psychotropic (OR anxiolytic OR antianxiety OR antidepressant OR antipsychotic OR neuroleptic); factors (OR predictors OR determinants); dementia (OR

Alzheimer’s); emergency (OR hospital*). 44

Prescribing in Older Adults

The Prescribing Process and Therapeutic Decision Making: Theoretical Constructs

The discussion of appropriateness of prescribing is inseparable from the

prescribing process, as an aspect of therapeutic decision-making. Prescribing is a prominent clinical behavior of health professionals due to its impact on immediate and future individual health, public health and subsequent healthcare costs. It involves making a therapeutic decision in light of available demographic, clinical and pharmaceutical information, epidemiology and administrative contexts. Unlike other clinical behaviors, it results in a document that selects and directs medication use. The prescriber is considered the gatekeeper of medical care and the main health professional who determines use.87 However, prescribing decisions may vary according to the prescriber’s qualifications, experience and specialty.

Factors influencing decision-making by health professionals have been examined via various cognitive, behavioral theories. The most commonly applied theory identified in a systematic review of studies based on social cognitive theories evaluating health professionals’ behaviors is the Theory of Reasoned Action (TRA) and its extension, the

Theory of Planned Behavior (TPB).88 This theory describes behavior as a consequence

of intention, which is informed by attitude toward the behavior, subjective norms (e.g.

peer influences) and perceived behavioral control.89 When compared to the operant

learning theory (OPT) and the social cognitive theory (SCT) in one systematic review,

TRA/TPB was found to be the strongest model for prediction of health professionals’ 45

behaviors, but this review excluded prescribing models.88 However, other theoretical models examining prescribing or therapeutic decision-making as a process have emerged.

These include Eisenberg’s model of sociologic influences of clinical decision-making,65

Denig’s Drug Choice Model based on expectancy-value theories,64 Raisch’s Model of

Methods of Influencing Prescribing90 and Bissessur’s Therapeutic Reasoning model.91

The Eisenberg model describes four categories of sociologic influences and

relationships involved in decision-making by clinicians, including physicians and other

prescribers: (i) patient characteristics (e.g. income, race/ethnicity), (ii) physician

characteristics (age, gender, qualifications), (iii) physician-patient relationship (rapport,

confidence) and (iv) physician-health system interaction (peer expectations, professional

guidelines, prescribing restrictions) – Figure 1.65 Unlike the Drug Choice Model and

Theory of Planned Behavior, Eisenberg’s model includes the physician-patient

relationship and physician-health system interactions. The latter considers the use of

administrative tools like electronic health records, without the complexity seen in

Raisch’s model. In a factorial experimental design, McKinlay et al. applied three of the

four Eisenberg influence categories in a factorial experiment among physicians, and

demonstrated that patient characteristics, physician characteristics and setting influence

medical decision-making.92

Research applications of Eisenberg’s model in Psychotropic Prescribing

Various studies of non-clinical influences on prescribing of psychotropic

medications have examined associations between patient and/or physician factors and

their respective prescription choices.93-99 Four studies reviewed factors associated with 46 antidepressant prescribing,93-95,98 two evaluated influences on sedative prescribing,96,97 and one reported on neuroleptic prescribing;99 all of which applied the Eisenberg theory as framework.

Sleath et al. reviewed patient encounters across the United States and Canada to determine how patient characteristics (race, rating of physical and emotional health, expression of physical, emotional and social problem symptoms) and physician perceptions influence psychotropic prescribing decisions. The expression of emotional symptoms and physician’s perceptions of the patient’s emotional health were found to significantly influence psychotropic prescribing to whites, but only patient expression of emotional symptoms was influential in non-whites.98 In a subsequent assessment of sociologic influences on antidepressant prescribing using the 1998 National Ambulatory

Medical Care Survey (NAMCS) in the US, Sleath and Shih found that factors in all four

Eisenberg categories affected prescribing choices. Influential factors identified were depressive diagnosis, source of payment, membership in a health maintenance organization (HMO), HMO capitation, region and visit to psychiatrist.93 Lai et al. used the 2006 NAMCS to examine factors associated with off-label antidepressant prescribing for insomnia and subsequently found physician specialty, office settings and payment method to be influential.94 Another study by Lin et al. assessed factors influencing the prescription of selective serotonin reuptake inhibitors (SSRIs) and selective serotonin- norepinephrine reuptake inhibitors (SNRIs), using 1993-2007 NAMCS data. Significant associations were found between antidepressant choice and race/ethnicity, primary source of payment, physician ownership status and practice regions.95 47

In a national study using the 1996-2001 NAMCS dataset, Balkrishnan et al.

assessed patient and physician characteristics associated with choice of medication for

sleep disorders in outpatients. Significant associations were identified between

prescribing choice and visits by established patients, payment type or source, and age

over 65 years.97 In a further analysis of the same dataset, Rasu et al. evaluated the

influence of socioeconomic and clinical factors on the prescribing of sleep medications

with high abuse potential. Gender and the presence of psychiatric comorbidities were

found to be associated with prescribing of sleep medications with high abuse potential.96

The sole non-US study among those evaluating patient and physician factors influential

in psychotropic prescribing was done in by a survey of 1342 physicians. Case

vignettes were presented to physicians with variations in patient compliance and

socioeconomic status and subsequent antipsychotic choices were evaluated. Patient

compliance and socio-economic status, as well as physician’s age and practice setting

were found to be influential in antipsychotic treatment choice.99

Although the studies each present different limitations given their methodologies, the findings validate the use of the theoretical constructs postulated by Eisenberg.

Eisenberg’s model provides a suitable framework for the examination of influential factors, including the use of clinical decision support tools on prescribing. 48

Appropriate or Inappropriate Prescribing

The term “appropriate prescribing” describes a spectrum of prescribing quality,

where it is considered as the use of medication where patients receive maximum benefit

with minimal harm caused by “excessive, inappropriate or inadequate consumption of

medicines.”21 Inappropriate prescribing in older adults is described as the “use of

medicines that introduce a significant risk of an adverse drug-related event where there is evidence for an equally or more effective but lower-risk alternative therapy available for treating the same condition.”100 The term may include under-prescribing, overprescribing

and mis-prescribing where a needed medication is incorrectly prescribed,24 as well as the

presence of potentially harmful drug-drug or drug-disease interactions.100

Challenges to medication use in the elderly arise from altered

and due to age-related physiological changes and multiple

comorbidities. The exclusion of older adults with multiple chronic illnesses from clinical

trials adds to the challenge of drug therapy as no information is generated on a

medication’s clinical risks and benefits in this group. To confound this further, these

conditions require treatment with various medications resulting in polypharmacy and

increased risk of drug interactions.100 This leaves clinicians with limited evidence and

information based on shared experiences, published case-reports and observational

studies. 49

Potentially Inappropriate Prescribing Criteria

Various measures of appropriateness of prescribing for older adults have been

developed to support safer prescribing. Criteria may be implicit (judgment-based) or explicit (criterion-based).

Implicit criteria, such as the Medication Appropriateness Index, require the clinician to apply professional judgment to individual patient cases, but may be time consuming. Explicit criteria provide lists of medications or classes or dosages that may increase the risk of adverse events in older adults. These are usually developed through literature reviews, expert opinion and/or by consensus to create a list that may be applied with little or no clinical judgment to prescribing for older adults in various healthcare settings.24,101 Explicit criteria are not meant to replace clinical judgment but should be

used to supplement it by identifying elderly patients who may require closer monitoring.

However, the absence of the full clinical review of the patient, which enables convenient

application across large datasets, limits the utility of explicit criteria at the individual

level. For instance, where a medication is identified as inappropriate, it may be

appropriate to the physician given past therapeutic failures or patient tolerance. Hence,

the word “potentially” is applied to indicate the presence of possible harm but not

necessarily a prediction of harm.

One explicit tool developed to identify potentially inappropriate medication (PIM)

use in older adults in nursing homes in the US is Beers criteria. This list was first

published in 1991,26 where a Delphi consensus method was used and has been

subsequently updated in 1997,27 2003,28 and 2012.29 The updated criteria are now 50

applicable to older adults across various settings. Shortcomings of the Beers criteria

include its inability to distinguish from cases where the medication may be of benefit,

where more implicit judgment is required. For example, where previous therapies have

proven ineffective or poorly tolerated by the patient, a “potentially inappropriate”

medication may the best option for the individual case. The general list approach may

lead to overestimation of the problem, but may identify cases where closer monitoring is

warranted. To remain relevant, explicit criteria need to be clinically and geographically

relevant, periodically updated, comprehensive, evidence-based and validated as intervention tools.21

The 2012 update of Beers criteria was jointly published by the American Geriatric

Society (AGS) and the criteria’s developers where the list of potentially inappropriate

medications was modified, removing irrelevant PIMs and redefining categories using an

evidence-grading approach.29 During its development, the approaches used included a

comprehensive review of evidence and a system of grading of evidence related to

medication-related problems and adverse events experienced by the elderly. The rating

of the quality of evidence and the strength of recommendations were novel approaches in

the development of this version. Fifty-three medications or classes are listed across three categories of inappropriate medications for the elderly: (i) 34 PIMs and classes to avoid irrespective of condition, (ii) PIMs to avoid in elderly with certain diseases or syndromes and (iii) PIMs to use with caution in the elderly.29

The updated criteria removed PIMs no longer on the market (e.g. propoxyphene)

from the list of “potentially inappropriate medications to avoid”, as well as medications

that lacked adequate evidence of unacceptable adverse effects (e.g. ) 51

and where problems were not unique to the elderly (e.g. ). Newly added

medications to avoid include glyburide, , sliding-scale insulin, short- acting benzodiazepines, and first- and second-generation antipsychotics. Reclassified medications include barbiturates, non-cyclooxygenase selective non-steroidal anti- inflammatory drugs (NSAIDs) and nitrofurantoin. Modifications to the drug-disease interaction list have also been made, such as the addition of acetylcholinesterase inhibitors with syncope, selective serotonin reuptake inhibitors in falls or fractures and pioglitazone or rosiglitazone in heart failure.14,29 These modifications are expected to

improve the content validity of the criteria.

Beers 2012 Modifications Involving Psychotropic Medications

Modifications from the Beers 2003 to the 2012 version involved removal of

, anorexic agents and daily fluoxetine from the “medications to avoid” list.

Medications that remain common to both the 2003 and AGS 2012 versions are older

dementia treatments, long-acting benzodiazepines, meprobamate, and

thioridazine. Although included on the 2012 list, mesoridazine has been withdrawn from

the US market. Therapeutic classes or medications that remain on both lists but have

been expanded from specific medications to include all agents in the class are: first

generation , barbiturates (previous exclusion of ), short- and

intermediate-acting benzodiazepines and tertiary tricyclic antidepressants. New agents to

avoid are: anticholinergics for control of extrapyramidal side effects of neuroleptic

medications, first- and second-generation antipsychotics in dementia, chloral hydrate and

chronic use of non-benzodiazepine hypnotics.14 52

Beers 2012 criteria overlap to some extent with the process measure for use in

European countries, the Screening Tool for Older Persons’ Prescriptions (STOPP) 2008.

In comparison with STOPP, the Beers 2012 criteria share some “drugs-to-avoid” such as

NSAIDs, but differ where Beers criteria list all benzodiazepines, in contrast to STOPP,

which includes solely long-acting benzodiazepines. Table 2 is an excerpt of a

comparison of potentially inappropriate psychotropic medications identified in Beers

2003, STOPP 2008 and Beers 2012 criteria by Marcum et al.102 Among drug-disease interactions, both criteria agree to avoid tricyclic antidepressants in patients with falls/fractures and dementia or cognitive impairment. However, they disagree on the inclusion of the use of first generation antihistamines in falls, which is found in STOPP but not in the Beers criteria.14,29 Differences also occur in criteria development, market approvals of medications, prescribing context (epidemiologic differences, protocols) and outcomes. One study of community-based elderly in Spain compared PIM prevalence rates using Beers 2003, STOPP and Beers 2012 criteria with rates of 24.3%, 35.4% and

44%, respectively. The PIMs identified varied as expected with some overlap. A kappa agreement of 0.35 (95% C.I=0.25-0.44) between STOPP and Beers 2012, suggests that both tools may be complementary.103

Although literature has provided information on the prescribing of potentially

inappropriate medications in elderly using earlier criteria, updated versions integrating

new medications and evidence would provide more reliable information to clinicians and

administrators, with respect to one aspect of the quality of prescribing in the elderly.

Since the publication of the AGS/Beers 2012 criteria, studies are gradually emerging to

update the prevalence of PIMs in various countries. These include studies conducted in 53

Brazil,104 Italy105, Taiwan,106 New Zealand,107 Spain,103 Thailand,108 Ireland109 and in the

US.41

Prevalence of Inappropriate Prescribing Among Community-Dwelling Elderly

In a review of 19 studies published between 1980 and 2012, rates of potentially

inappropriate prescribing for older adults in ambulatory settings, irrespective of

diagnoses ranged from 2.9-38.5%, with a median rate of 20%. Eight different criteria had

been used, with Beers criteria applied in 15 of 19 studies. Others applied Modified Beers

criteria, Zhan’s criteria, Health plan Employer Data and Information Set (HEDIS) criteria

and More & Romsdal Prescription Study (MRPS) List. Seven studies done in the US

reported prevalence rates of potentially inappropriate prescribing ranging from 4.5-33.3%

and a median rate of 19.6% for older adults, using Beers criteria (6 studies) and the

HEDIS criteria (1 study).40

Studies published between 2007 and 2013 assessing potentially inappropriate prescribing among older Americans applied Beers 2003 criteria. These reported the proportions of older adults who received potentially inappropriate medications at 16% for the MEPS 2000-2001, 44.2% for outpatient veterans in Iowa (including dose and disease- dependent criteria), 16.8% in National Hospital Ambulatory Medical Care Survey

(NHAMCS) 2000-2006 surveys, 13.84% in MEPS 2007 (including disease-dependent)

and 31.9% in Texas Medicare Part D study.46,53,55,110,111 The higher prevalence seen

among older veterans in Iowa was a baseline established as part of a study comparing 54

Beers criteria with the Medication Appropriateness Index (implicit measure). The design

allowed the inclusion of dosages and inappropriate drug-disease combinations to be

included, which increased rates of PIM identification. In addition, this sub-group of older

adults may present with different psychopathologies requiring medication, than non-

veteran groups.110 The study by Holmes et al. among Texans under Medicare Part D

reported a higher rate than nationwide studies assessing disease-independent PIMs. This may have been due to plan coverage and regional prescribing differences.111

Prevalence Using Beers 2012

To date seven studies in various countries have been found whereby Beers 2012

criteria were applied to determine the prevalence of potentially inappropriate medication

use among older community-dwelling adults – Table 3.41,103,104,107-109,112 Various data

collection methods were used including live patient interviews, medical or pharmacy

records, household surveys and pharmacy claims data.

Among elderly outpatients (>60 years) in Ribeirao, Brazil, 59.2% of interviewed

patients had used at least one PIM using Beers 2012 criteria.104 Another survey of

community-based elderly (>75 years) in Dunedin, New Zealand, reported a prevalence of

42.7%.107 The prevalence among older Irish fallers (>70 years) presenting at emergency

departments nationwide, was 44% for medication use 12 months prior to the fall.112 A

later nationwide prospective study among Irish community-dwelling older adults (>65

years) found that 33.1% of patients received at least one PIM of the Beers 2012 subset in

2011-2012.109 Among Spanish residents (>65 years) of the Canary islands, the

prevalence of PIMs using Beers 2012 was found to be 44% of elderly.103 In Thailand, 55

49.8% of elderly outpatients seen in 2012 received at least one PIM based on hospital

records.108 In the US, a study of the 2006-2010 Medical Expenditure Panel Survey

(MEPS) found 42.6% of community-dwelling elderly (>65 years) had one or more

PIM.41

Although the same criteria were applied in the various studies among community-

dwelling adults, the definition of elderly, sampling frame and data collection methods

varied. In addition, medications available to prescribers in the countries may also differ,

making it more difficult to estimate an aggregate prevalence. None of the healthcare

systems, health insurance plans or the pharmaceutical markets in these studies are

comparable to those in the US. As a result the prevalence of inappropriate prescribing

using Beers 2012 is expected to differ among American elderly.

Common Potentially Inappropriate Medications (PIMs)

In the UK, commonly prescribed PIMs identified using STOPP criteria include

psychoactive medicines (first generation antihistamines, tricyclic antidepressants and

long-acting benzodiazepines), medications that increase the risk of falls

(benzodiazepines, vasodilators, neuroleptics), use of non-steroidal anti-inflammatory drugs (NSAIDs) and opiates, and duplicate class prescribing.33 In the review by Opondo

et al., 19 studies of PIM use in ambulatory elderly, based on various criteria were

assessed. The four most frequently prescribed PIMs, and the respective rates and ranges

were: propoxyphene 4.52% (0.10-23.3%), 3.96% (0.32-15.7%), 56

3.30% (0.02-4.40%) and amitriptyline 3.2% (0.05-20.5%). Within

the antidepressant group, amitriptyline was the most prescribed, while diazepam was the

most commonly prescribed of the sedative-hypnotics at 2.74% (0.05-30.05%). No other

psychotropic groups were evaluated in the review. Diphenhydramine and other non-

prescription antihistamines were included in the studies based on physician orders and

criteria used.40

In another review of administrative database studies reporting potentially

inappropriate prescribing in the ambulatory elderly, rates ranged from 11.5-62.5%. Of

the 19 studies published between 1990 and 2010, 15 were from the US and 7 studies were

based on nationwide databases in various countries. Explicit criteria were applied in all

studies with 14 using versions of Beers criteria (1991-2003). The most common individual PIMs identified were amitriptyline (6/7 studies), propoxyphene (6/7) and (4/7).39

Common PIMs Using Beers 2012 Criteria

All studies using Beers 2012 criteria found PIM prescribing to be prevalent

among older adults, with rates ranging from 33-59%. However the study by Moriarty et

al applied a subset of the criteria (81%).109 Medication classes that were common to all

studies were benzodiazepines and non-cyclooxygenase selective (non-selective) NSAIDs.

Tertiary tricyclic antidepressants (TCAs) were prevalent in 6 of the 7 studies (see Table

3). PIMs common among Brazilian outpatients were diclofenac, ,

diazepam, amitriptyline, , and .104 In New Zealand,

the common PIM classes were non-selective NSAIDs, tertiary tricyclic antidepressants 57

(TCAs), and benzodiazepines. Individual medicines included doxazosin, diclofenac,

, , amitriptyline, doxepin, , , and

clonazepam.107 Among older Irish fallers seen at emergency departments and among

those living in the community, the common PIMs included benzodiazepines, non-

selective NSAIDs, tertiary TCAs, non-benzodiazepine hypnotics and alpha-blockers or doxazosin.109,112 In Spain, common PIMs identified in older adults were benzodiazepines, first- and second-generation antipsychotics in dementia, sulfonylureas, non-selective NSAIDs and non-benzodiazepine hypnotics.103 For older adult outpatients

seen in a hospital in Thailand, the most frequently prescribed PIMs were

benzodiazepines, NSAIDs, tertiary tricyclic antidepressants and digoxin.108 In addition to NSAIDS, tertiary TCAs and benzodiazepines, other PIMs that were frequently ordered for older Americans between 2009-2010 included alpha-1 blockers, estrogen, sulphonylureas, relaxants, and antihistamines.41

With the updated criteria, the identification of potentially inappropriate

medications is more relevant to contemporary practice. The inclusion of newly added

PIMs such as short- and intermediate acting benzodiazepines, non-benzodiazepine

hypnotics, sulfonylureas and antipsychotics is expected as these were not considered

inappropriate by earlier criteria. Their addition may also change the overall prevalence of

PIMs and identified predictors of use. The different definitions of older adults used by

the studies would create variations in prevalence rates. Given that each study used a

different starting age, their prevalence rates are not directly comparable. 58

Factors Associated with Potentially Inappropriate Medication Use

Six studies in older Americans assessed the influence of various factors on the use

of Beers potentially inappropriate medications, using multivariate logistic regression

models.53-55,57,58,111 The study by Pugh et al. among older veterans in Texas applied a

modified version of Beers 2003 criteria and is therefore included. All studies, except

Holmes et al., used data from national surveys: NAMCS/NHAMCS (2), MEPS (2), and

Veterans Affairs (1) - Table 4.

Among the studies, female sex was a significant predictor in five of these, in

which higher adjusted odds of PIM use were consistently found.53-55,57,111 All six studies

found higher adjusted odds of PIM use with increasing numbers of medications. As a

predictor of PIM use, age produced conflicting findings. Two studies identified lower

odds among persons of more advanced age groups,57,58 while a third study found higher odds among persons with more advanced age.53 Only two studies found income to be

significant with higher odds among middle-income patients (Zhang et al.) and among

those eligible for lower income subsidies (Holmes et al).55,111 Race/ethnicity was

associated with PIM use in three studies with mixed findings.54,58,111 Zhan et al. found that odds of PIMs were higher for whites and other races compared with blacks.54 Pugh et al. found higher odds among Hispanic males and females and lower odds among black males compared with whites. Holmes et al. found higher odds for blacks, and lower odds for Hispanics and Asians compared with whites.111 Poor self-rated health status was associated with higher odds of PIM use by two of the studies, using MEPS data.54,55

Other patient-related factors associated with PIM use identified in the studies were: the 59

presence of psychiatric comorbidities, the presence of two or more comorbidities and

hospitalization in year preceding the study.58,111

Of the visit-related factors, the number of medications was common to all six

reports, where increasing numbers of medications were associated with higher odds.

Practices located in the Northeast and in non-metropolitan areas were significant

predictors of PIM use, as well as visits to the emergency department of a “for-profit”

hospital.53,55 Other statistically significant factors identified by the studies include: nature

of visit, physician specialty, physician seen, number of different prescribers and the visit

year.

Discrepancies between the studies may be due to variations in: Beers criteria,

reference categories for analyses (e.g. 65-69 years, 65-74 years), sample frame used

(office-based physicians, emergency departments), survey designs (MEPS, NAMCS) and periods of study (single year vs multiple).

Factors Associated with Prescribing of Potentially Inappropriate Medications Using

Beers 2012

In five of seven studies using Beers 2012 criteria, factors associated with PIM use

were assessed. The increasing number of medications was identified as a significant

predictor in all studies.103,104,107,108,112 The use of psychotropic medications was found to

be a significant factor in the studies by Baldoni et al.,104 Nishtala et al.107 and McMahon

et al.112 - Table 3.

Additional factors associated with the use of PIMs among Brazilian elderly in the

study by Baldoni et al. were: female sex, absence of a partner, self-medication, use of 60

over-the-counter medications, adverse event complaints, use of sex hormones and

medications for musculoskeletal, respiratory, nervous system and genitourinary

disorders.104 Nishtala et al. found that the risk of taking a PIM decreased significantly

with increasing age,107 but this was not found among the other studies using Beers 2012 criteria.103,104,112 McMachon et al. found a significant linear association between the

number of comorbidities and PIMs in older fallers, before and after appearing at the

emergency department.112 Pannoi et al. found that patient age, prescriber age and the

number of medications to be positively associated with PIM use. Conversely, the number

of outpatient visits was negatively associated with PIM use.108

None of the studies were conducted in American populations, making it difficult

to extrapolate the findings to the US, given variations in the healthcare settings, which

may influence prescribing patterns. This limitation points to a need for more recent

information on PIM prevalence and risk factor for the US population.

Prescribing of Potentially Inappropriate Psychotropic Medications for Older,

Community-dwelling Adults

Psychotropic Medication Use in Community-Dwelling Elderly

The use of psychotropic medications, primarily antidepressants and anxiolytics,

occurs in older adults around the world with varying prevalence rates. In a systematic 61 review of studies published between 1990-2001, reporting psychotropic drug use among community-dwelling older adults, Voyer et al. found that the prevalence among 9 studies ranged from 11.8-42.5%, with an average of 29%.113 In a 1998 US study, Aparasu et al. reviewed 1995 NAMCS data to determine the prevalence of psychotropic prescribing among older adults. A lower rate was observed, where 7.15% (12.02 million) of visits by elderly involved prescription of psychotropic medications. Common classes were: antidepressants 55.06%, antianxiety medications 36.9%, sedatives and hypnotics 15.5% and antipsychotic/antimanic medications 10.91%.79 This study was not included in the review by Voyer et al.

In a subsequent study using 1996 MEPS data, Aparasu et al. reported that 19% of

American community-dwelling elderly (over 6 million) were using psychotropic medications. Antidepressants were used by 9.1% (almost 3 million), antianxiety agents were used by 7.5% (almost 2.5 million), sedative-hypnotics by 4.8% (1.5 million) and antipsychotics by 1.8% (almost 0.6 million). Among antidepressants, the most common classes were tertiary TCAs (49%) and SSRIs (42.9%), while short-acting benzodiazepines (64.6%) and long-acting benzodiazepines (33.4%) dominated antianxiety agents. Among the sedative-hypnotic class, the predominant groups were

30.9% of users received short-acting benzodiazepines, 15.9% received barbiturates and

6.7% used long-acting benzodiazepines. Approximately half of sedative-hypnotic users received miscellaneous sedatives, which were not identified by the authors. Almost all patients receiving antipsychotics were prescribed first-generation agents.114

More recent studies outside the US reported similar prevalence rates of psychotropic medications. In the United Kingdom in 2009, 10.3% of older community 62

adults had been prescribed antidepressants at least three months prior to the end of the

study. With the exclusion of low-dose TCAs for neuropathy, this rate was 7.3%. Among those receiving antidepressant prescriptions, 56.9% received SSRIs, 25.4% received

TCAs, and 5.7% received SNRIs.115 In a study of psychotropic prescribing for older

adults transitioning from community to care home residence, in Northern Ireland in 2009,

8.4% received hypnotics, 4.2% received anxiolytics and 1.3% of those in community

received antipsychotics.116 The latter study reflected a slight shift in the pattern where

anxiolytic use surpassed that of hypnotics in most studies. Antipsychotics remained the

class with the lowest prevalence of use among community-dwelling elderly. This may have been due to their use in institutionalized patients who require direct supervision or restraint in nursing homes due to behavioral disturbances.

Potentially Inappropriate Psychotropic Medications

Several studies of inappropriate prescribing for community-dwelling elderly have

identified psychotropic medication classes among common PIMs, primarily tricyclic

antidepressants and long-acting benzodiazepines.41,52,53,55-57,59,108,109,117,118 A search of

studies assessing the prevalence of potentially inappropriate psychotropic medication use

among older community-dwelling adults with psychotropic prescriptions, identified three

nationwide, cross-sectional studies done in the US, involving the same principal

investigators (Aparasu and Mort) – Table 5.60,79,80 Two studies used patient records from

the National Ambulatory Medical Care Survey (NAMCS) for 1995 and 1996 among 63

office-based physicians.79,80 The latter study also added data from outpatient department visits in the National Hospital Ambulatory Care Survey (NHAMCS). The third study collected data from the 1996 Medical Expenditure Panel Survey (MEPS).60

The prevalence of potentially inappropriate psychotropic use among community- dwelling elderly in these studies varied with the lowest rate found in 1995 NAMCS data, followed by 1996 NAMCS/NHAMCS and the highest rate in the 1996 MEPS-based study.60,79,80 The prevalence rate in ambulatory settings in the 1996 NAMCS/NHAMCS

study reflected over a quarter of visits involving potentially inappropriate psychotropics,

with higher rates in office-based visits compared with outpatient department visits.80

Disease-independent potentially inappropriate psychotropic use exceeded disease- dependent cases in NAMCS/NHAMCS study, and the majority of potentially inappropriate psychotropic orders in the MEPS study of the same year. Disease-

dependent cases accounted for over one-tenth of potentially inappropriate psychotropic

visits in the NAMCS/NHAMCS study, and just over a quarter of those identified in the

MEPS study.60,80

The variation in prevalence rates is expected given the differences in dataset

survey designs. The NAMCS/NHAMCS study assessed the most records involving

psychotropic use of the three studies (1373: estimating 16.55 million visits), in

comparison with the early NAMCS study (704: est. 12.02 million visits) and 1996 MEPS

study (471: est. 6.09 million visits). This, in addition to differences between the time

periods (one year vs two-year panel), sampling frames and procedures used, and/or

criteria applied, may explain differences in prevalence rates. Disease-independent

psychotropic prescribing based on Beers 1991 criteria26 were assessed in the 1995

64

NAMCS study, whereas disease-independent and disease-dependent cases based on the

1997 criteria27 were identified in the study of 1996 data. With use of the more updated

list and expanded scope, the number of potentially inappropriate psychotropic

medications detected in both databases increased.

Studies in Other Countries

In a Swedish study of potentially inappropriate psychotropic prescribing among

patients aged 75 years and older, investigators defined appropriateness based on the

guidelines of the National Board of Health and Welfare. Potentially inappropriate

psychotropic prescribing was further categorized into potentially inappropriate

psychotropic substances (PIPS) or potentially inappropriate combinations of

psychotropics (PICP) or duplicate prescribing. The study found almost two fifths of the

elderly were exposed to potentially inappropriate psychotropic prescribing, mostly

substances or PIPS. Approximately 9% were exposed to both PIPS and PICP. The

utilization by individual psychotropic classes was not reported.15 Although this study

adds international perspective, the index age for elderly and the locally-developed

criteria, make comparisons with other studies untenable.

No other studies to date have assessed the prevalence of potentially inappropriate

psychotropic prescribing among elderly as a therapeutic class. However, inappropriate

use of individual psychotropic classes has been assessed by five other studies, discussed

below.117,119-122 65

Potentially Inappropriate Antidepressants

Various studies of potentially inappropriate medication use in community- dwelling elderly have identified tricyclic antidepressants as frequently prescribed medications – Table 5.54,55,57,75 This therapeutic class accounted for more than half of the psychotropic medications used by the American elderly in the 1995 study by Aparasu et al. Of the visits involving potentially inappropriate antidepressants, one in five involved amitriptyline.79 In the 1996 NAMCS/NHAMCS dataset, Mort et al. found that

just under half of the visits involving psychotropic medications were for antidepressants.

Assessing inappropriate disease-independent antidepressant use, the most common

potentially inappropriate antidepressant was amitriptyline, followed by doxepin. Among

disease-dependent cases, the most common combinations were tricyclic antidepressants

(TCAs) in constipation and anticholinergic antidepressants in benign prostatic hyperplasia.80 In the MEPS study, more than half of antidepressant users had received a

potentially inappropriate antidepressant. As found in the NAMCS/NHAMCS data of the

same year, the most common medications were amitriptyline and doxepin.60

One study of psychotropic medication use among residents of community

residential care facilities, in a three-county area in Washington state, reported inappropriate antidepressant prescribing in 1998 and 1999, based on the use of tertiary tricyclic antidepressants (amitriptyline, doxepin and imipramine). At baseline (1998),

19.3% of antidepressant users received an inappropriate antidepressant, which increased to 21.8% the following year.123 However, the absence of validated or consensus-based 66

criteria, restricted sample frame (3 counties), non-probabilistic sampling and small

sample size introduce bias and do not allow generalization of the findings.

Studies in Other Countries

Three studies reported the prevalence of inappropriate antidepressant use among

older adults in community settings or seen in general practices in Nova Scotia, Canada,

the United Kingdom and Italy.119,124,125 A fourth study assessed appropriateness among older Canadians with symptoms of depression.126

The study in Nova Scotia, Canada applied various criteria including Beers 1991

list to identify potentially inappropriate antidepressants across three periods: 1993-1994,

1994-1995 and 1995-1996, based on provincial administrative claims and prescription

databases. Commonly prescribed antidepressant classes in 1995-1996 were tertiary

TCAs, selective serotonin reuptake inhibitors (SSRIs) and secondary TCAs. In 1995-

1996, more than half of antidepressant prescriptions were considered inappropriate. No

descriptions of inappropriate prescribing rates by the individual criteria of classes were

reported, but almost half of the patients had received more than the minimum effective

doses.124 The second study in general practices across the UK reported approximately

one third of older antidepressant users received potentially inappropriate antidepressants

in 2005, using modified Beers 2003 criteria. No information on specific medications was

reported.119

In a third study, dispensing data from three provinces in Italy were reviewed.

Investigators found 5.52% and 11.49% of older Italians received prescriptions for at least

one antidepressant in 2000 and 2007, respectively. Commonly prescribed antidepressants 67 were SSRIs and TCAs. However, criteria or indicators for appropriateness were based on prevalence of use, duration of antidepressant use, and concordance with diagnoses of depressive disorders. Although developed from references, the criteria was not validated by a panel of experts or developed by consensus. As a result, the findings are subject to investigator bias.125

In 2008, Dalby et al. reported potentially inappropriate antidepressant prescribing among elderly living in Community Care Access Centers in Ontario, Canada. McLeod’s list of inappropriate prescribing practices for the elderly was used in this study to review records of residents.30 The most common potentially inappropriate antidepressants were tricyclic antidepressants (mainly amitriptyline), where one in ten patients with depressive symptoms received this class.126

Overall, tricyclic antidepressants (TCAs) are the most commonly prescribed potentially inappropriate psychotropic medications for older adults in community settings. Selective serotonin reuptake inhibitors (SSRIs) are preferred for the treatment of depression in older adults, due to the absence of anticholinergic effects.10 Although daily fluoxetine was the sole SSRI on Beers 2003 criteria,28 it has since been removed from the “Avoid” list in the 2012 updated version.29 The revised criteria list SSRIs and secondary tricyclic antidepressants under “Medications to Use with Caution”. In addition, the 2012 criteria recommends avoidance of SSRIs in patients with a history of falls/fractures, and in adults with syndrome of inappropriate hormone secretion

(SIADH) and/or hyponatremia.29

Given the availability of antidepressants with safer profiles, the use of tertiary tricyclic antidepressants in older adults is expected to decrease. The revised 68

classification of potentially inappropriate antidepressants by the AGS/Beers criteria may

result in a shift in the prevalence of antidepressants to “Avoid”, in favor of

antidepressants to “use with Caution”. An updated prevalence rate of Beers potentially

inappropriate antidepressants among older, community-dwelling Americans is needed, as

the most recent information is from 18 year-old data (1996).

Potentially Inappropriate Anxiolytics/Sedative-Hypnotics

In older Americans in ambulatory care who were prescribed psychotropic

medications, anxiolytics were prescribed in more than one third of visits, and sedative- hypnotics in approximately one in six visits in the NAMCS 1995 study. Among visits involving potentially inappropriate anxiolytics, the most common anxiolytics were , diazepam and meprobamate.79 In the 1996 NAMCS/NHAMCS study, the common potentially inappropriate psychotropics were the same but the rates were higher, in the following order: diazepam, chlordiazepoxide and meprobamate.80 The

MEPS 1996 study reported a similar pattern of common potentially inappropriate

anxiolytics and prescription rates – Table 5.60

In the study by Lakey et al., among residents of community care facilities in Washington

State, the use of long-acting benzodiazepines was considered inappropriate. The prevalence rates among residents receiving sedatives/anxiolytics was as 16.7% in 1998 and 14.1% in 1999. The rates by individual medications were not reported. However the study design limits the generalizability of the findings. 123 69

Studies in Other Countries

Three other cross-sectional studies focused on appropriateness of benzodiazepine

prescribing in older adults in Canada (two studies) and .117,120,122 The earliest

study period was 2005-2006 in the Canadian study of 2320 older adults interviewed in a

Seniors Health Study. Patient interviews were conducted in Quebec, with validation

from medical and pharmaceutical records. Approximately one third of older adults

received benzodiazepine prescriptions. Commonly prescribed benzodiazepines were

, , clonazepam, , alprazolam, , ,

diazepam and , in order of decreasing prevalence. The definition of

inappropriateness included the use of long-acting benzodiazepines, concomitant

prescription of two or more benzodiazepines, possible benzodiazepine-drug interaction

and too high dosing of benzodiazepines, based on recommendations in the literature and

the Advisory Board of Quebec. Less than half of the benzodiazepine users

were exposed to at least one potentially inappropriate benzodiazepine prescription, with

nearly a quarter of users receiving long-acting benzodiazepines, and approximately one in

five with potential interactions and excessive doses respectively.122

In a subsequent study of the same seniors survey in Quebec,117 the criteria used to

identify inappropriate benzodiazepine prescribing was based on Beers 2003 criteria and drug interactions identified by the Quebec Addiction Prevention Center in 2007.8,28 The

investigators reported less than half of the benzodiazepine users received at least one

inappropriate benzodiazepine in the year preceding the study. Of these prescriptions,

more than one-fifth were inappropriate according to Beers 2003 criteria, less than one-

sixth were due to potentially harmful benzodiazepine-drug interactions and less than one- 70

tenth were shared both categories. The most common benzodiazepines involved were

clonazepam and high doses of temazepam. Common benzodiazepine-drug interactions

reported involved diltiazem, , and digoxin.117

One study of general practice databases in the UK in 2005, grouped sedatives and

anxiolytics together to assess appropriateness based on modified Beers 2003 criteria.

Researchers reported almost three-quarters of prescriptions for anxiolytics or sedative-

hypnotics as inappropriate. The study did not report the medications involved.119

The study in Norway reviewed appropriateness of anxiolytic benzodiazepines,

hypnotic benzodiazepines and non-benzodiazepine hypnotics (zolpidem, zopiclone)

prescribed for Norwegians aged 70 to 89 years. The study participants had to have filled

at least two prescriptions for one of the classes of interest. Inappropriate use was defined

using various guidelines, including Beers 1997 criteria. Indicators used were: prolonged

duration of use (more than 30 weeks), exceedingly high dosage (>9 Defined Daily Doses

(DDD) per week or >30 DDD of anxiolytic benzodiazepines over the year) and the use of

hypnotic benzodiazepines. Among users who filled at least two of the classes, two-thirds

received non-benzodiazepines, two-fifths received anxiolytic benzodiazepines and one- tenth received hypnotic benzodiazepines. Zopiclone was prescribed to over half of the patients. Other medications that were regularly prescribed were oxazepam and diazepam.

Inappropriate use was identified among just over one-tenth of elderly Norwegians. All of the hypnotic prescriptions, two-thirds of the non-benzodiazepines prescriptions and just over a quarter of the anxiolytic prescriptions were inappropriate.120

Benzodiazepines continue to be among commonly prescribed psychotropic

medications, although some are considered inappropriate in older adults. This may 71

continue until safer alternatives, with similar or greater efficacy become available.

Although short- and intermediate-acting benzodiazepines and z-hypnotics were previously considered suitable or appropriate alternatives for treatment of insomnia, the

American Geriatric Society/Beers 2012 criteria, now classifies these as potentially inappropriate for older adults. The criteria recommend avoiding the use of benzodiazepines for the treatment of insomnia, agitation or delirium, and avoidance of chronic use (>90 days) of non-benzodiazepine hypnotics. The criteria recommend that barbiturates in older adults should be avoided unconditionally.29 With the addition of

short-acting, intermediate-acting benzodiazepines and non-benzodiazepine hypnotics,

higher prevalence rates of potentially inappropriate sedative-hypnotics are expected due

to the expanded classification. Literature reporting prevalence rates among older

Americans is based on outdated data, which points to the need for more current

information on these prescribing patterns.

Potentially Inappropriate Antipsychotics

No nationwide studies of potentially antipsychotic prescribing in the US have been published to date, mainly as a result of this class of drugs being omitted from the

Beers 1991, 1997 and 2003. One study among community care facilities in a three-

county area of Washington State defined the use of low- conventional (first- generation) antipsychotics as “inappropriate” for older adults, and reported rates of 7.3% in 1998 and 11.3% in 1999, among residents.123 72

The dearth of research into the prevalence of potentially inappropriate use of antipsychotics among community-dwelling elderly may be a shortcoming of earlier criteria, which was based on available evidence. However, with the introduction of various second-generation (atypical) antipsychotics, the use of antipsychotics among older, community-dwelling adults has increased, along with additional post-marketing safety information. In a national study of outpatient antipsychotic use in the US, an increase from 6.2 million visits in 1995 to 14.3 million visit in 2008 was reported.127

Earlier Beers 2003 criteria classified thioridazine and mesoridazine as antipsychotics to avoid,28 but the revised 2012 list extends “Avoid” recommendations to all antipsychotics in older adults with dementia, due to the increased risk of stroke and death. 19,20 For older adults without dementia, AGS/Beers 2012 criteria recommend that all antipsychotics, except thioridazine should be used with caution, mainly due to their anticholinergic effects.29 As a result, the use of antipsychotics in older adults with dementia may be considered to be potentially inappropriate according to AGS/Beers criteria.

Antipsychotic Use In Older, Community-Dwelling Adults with Dementia

The inclusion of first- and second-generation antipsychotics in the revised Beers criteria is based on the increased risks of adverse cardiovascular events and death among elderly with dementia.18 Hence, the use of these agents in older adults in dementia may be considered “potentially inappropriate”. Twelve studies have reported the prevalence of 73

antipsychotic use among older adults with dementia - Table 6.78,128-138 Five studies were

conducted among US populations, of which three applied probability sampling to

generate population estimates.78,129,131,135,137

Studies in the US

Among a sample of community-dwelling older adults with Alzheimer’s disease who submitted claims to a large health care insurer in the southeast US, antipsychotics were used in 27% of enrollees in 1998. Approximately 16.9% of individuals received second-generation antipsychotics (SGAs), 6.5% received first-generation antipsychotics

(FGAs) and 3.6% received both first- and second-generation agents.129

One study was conducted in Maryland among older adults with a diagnosis of

dementia based on the criteria of the National Institute of Neurological and

Communicative Disorders and Stroke/Alzheimer’s Disease and Related Disorders

Association (NINCDS/ADRDA). Among the 285 patients with dementia, 4.9% received

antipsychotics.130

In another US study among 12,697 Medicare beneficiaries within the 2002

Medicare Current Beneficiary Survey, use of antipsychotics was based on self-reports,

inspection of medication containers and/or insurance slips. Among the community-

dwelling adults with Alzheimer’s disease and related dementia, 94.5% were 65

or older. Less than 5% of beneficiaries received first-generation antipsychotic medications and approximately 9% received second-generation antipsychotics. The most commonly used antipsychotics reported were risperidone, olanzapine, , quetiapine and ziprasidone.131 Although this study was nationally representative, it is 74

limited as a result of the use of paid claims data and the inclusion of persons younger than 65 years of age (5.5%).

Between 2002 and 2004, 19.1% of older adults aged 70 years or more, in the

Aging, Demographics and Memory Study were found to have been using antipsychotics.

The most common agents were olanzapine, risperidone, quetiapine and haloperidol.78

A fifth US study assessing the impact of the FDA’s 2005 boxed warning on

antipsychotic prescribing rates for ambulatory elderly, found a significant decrease from

40% to 27% before and after April 2005. The study found that 43.2% of older adults

with dementia seen in a memory clinic in Virginia from 2001-2009, had been prescribed antipsychotics, most often quetiapine, risperidone, olanzapine or haloperidol. However, as a result of the predominance of male participants (99.7%), this limited the findings to males seen at the clinic.135

A later study, using a probabilistic sampling of beneficiaries with Alzheimer’s

and other related dementias (ADRD) evaluated use based on Medicare claims. The study

reported use across various settings, including communities. Among these beneficiaries living with ADRD, 24% had received antipsychotic medications. The antipsychotic classes were not reported.137

Overall, the extent of antipsychotic use among older community dwelling

Americans with dementia reported by two studies reporting population estimates were

19% and 34%.78,137 75

Studies in Other Countries

Antipsychotic use among Swedish elderly aged 81 years and older, living with

dementia, was reported at rates of 21.6% for 87-1989 and 18.1% from 1994 to 1996.128

One study in Italy reported first-generation antipsychotic (FGA) use rates of 10.7 per

10,000 elderly (>65 years) with dementia in 2004, and the use of second-generation

antipsychotics (SGAs) in 9.7 per 10,000 elders with dementia. Clinical information was extracted from electronic medical records in a national health database. The commonly used antipsychotics were haloperidol, and quetiapine.133

Among Finnish elderly, a national cohort study reported 22% of elderly (>65

years) with Alzheimer’s disease (AD) received prescriptions for antipsychotics in 2005.

Second-generation antipsychotics were more common than first-generation

antipsychotics.134 A more recent study spanning 2005-2011 reported a similar

prevalence, where 20% of persons with Alzheimer’s disease used antipsychotic

medications. Second-generation antipsychotics were more commonly prescribed.138

In the Scottish Programme for Improving Clinical Effectiveness – Primary Care

(SPICE-PC) study, Guthrie et al. found that 17.7% of older adults with dementia had

used antipsychotics between 2006 and 2007. More than two-thirds of prescriptions were

for second-generation antipsychotics, mostly quetiapine.132 Another study, using the UK

Clinical Practice Research Datalink to assess prescribing for patients with dementia in

general practices in 2011, reported antipsychotic use among 7.4% of older adults (>60

years). In addition, between 1995 and 2011, the use of antipsychotics in this population

decreased.136 76

Determinants of Potentially Inappropriate Psychotropic Medication Use

Determinants of Psychotropic Medication Use

Most studies that measured factors associated with psychotropic medication use

among older adults have focused primarily on patient characteristics. In a review of

studies published between 1990 and 2001, inclusive of 8 US-based studies, Voyer et al. identified several socio-demographic predictors. Factors found to be statistically significant in 90-100% of papers reviewed were race (7), sleep complaints (5), number of medical consultations (7) and occupation (2). In 66-90% of studies, gender (22/30), proximity to health center (6/7), number of medications (4/6), health perception (7/9), stressful events (2/3), social support (9/13) and mental health (17/22) found significant associations. Language (1/2) and education (7/13) were significant in approximately half the studies, whereas age, marital status, income, insurance and number of illnesses were significant in less than half the studies where these were assessed.113

A subsequent national (US) study of 1996 MEPS data by Aparasu et al. (2003)

examined associations between predisposing, enabling and need factors and psychotropic

medication use (overall and by class) among community-dwelling elderly. Prescription

insurance and health status were associated with psychotropic drug use: higher odds with

Medicaid and private insurance; lower odds with ‘good’ and ‘very good’ health status.

Other factors were found to be associated with variations across any psychotropic use and

individual classes. These were: gender (any psychotropic, antidepressants, anxiolytics),

mental health status (any psychotropic, antidepressants, anxiolytics), region (anxiolytics, 77 sedatives), race (sedatives), education (sedatives), marital status (antidepressants), functional ability (any psychotropic) and living alone (antidepressants).114

Patient Characteristics Associated with Potentially Inappropriate Psychotropic

Prescribing

Patient characteristics that have been found to be associated with potentially inappropriate psychotropic use by two or more studies are gender, age, income, insurance and chronic diseases. Table 7 presents the studies and factors found to be significantly associated with potentially inappropriate psychotropic prescribing, overall and by class.

Table 8 identifies determinants or factors associated with potentially inappropriate medication use, potentially inappropriate psychotropic use and psychotropic use in older adults identified by at least one study. Table 9 lists factors that were not found to be statistically significant.

The studies of aggregated use of potentially inappropriate psychotropic medications among older Americans by Aparasu and Mort applied logistic regression analyses to measure associations with various patient factors.60,79,80 However, the most recent of the three studies applied bivariate logistic regression analyses, reporting unadjusted odds ratios.60 The factors identified in this study may be subject to confounding by other determinants. In , Lesen et al. evaluated the influence of age, sex, income and marital status on the likelihood of potentially inappropriate 78

psychotropic prescribing, using multivariate logistic regression models to control for

confounding.139

Among the four studies evaluating factors associated with individual psychotropic

classes, three applied multivariate logistic regression models: one in Ireland including

anxiolytics/sedative-hypnotics;119 one Canadian study of benzodiazepines122 and one

Canadian study of antipsychotic use.121 In one Canadian study assessing the economic

impact of inappropriate benzodiazepine prescribing, analyses involved tests of

association, but the strengths of the associations identified were not measured.117

Patient characteristics associated with antipsychotic use in community-dwelling

elderly with dementia were assessed in five studies.78,129,130,134,135 Each patient

characteristic relevant to the use of potentially inappropriate psychotropic medication

classes is described in turn.

Sex

Female patients are more likely than males to receive psychotropic medications, with various explanations offered such as expressions of emotional problems, more frequent physician visits and more health problems.113 Among studies assessing the

influence of sex on prescribing of potentially inappropriate psychotropic medications,

two out of seven studies identified a significant association.15,119 One UK study that used a modified Beers 2003 criteria, reported lower odds of potentially inappropriate anxiolytics or sedative-hypnotics use among male users, controlling for age and practice.119 The modification may explain the variation in findings between the studies.

The second study done in Sweden, reported lower odds of potentially inappropriate 79 psychotropic use among women. However, this study applied a national standard based on various criteria, including Beers and adapted to Swedish settings.139 Five studies tested sex or gender as a factor but did not find a statistically significant association with the respective potentially inappropriate psychotropic class – Table 9.60,79,80,117,122

Although sex is reported as an influential factor of potentially inappropriate medication use, it is not well proven that it is a predictor of inappropriate psychotropic medication use among older Americans.

Age

Four articles reported associations between age or age group and potentially inappropriate psychotropic use, with conflicting results. One study in the US found increasing odds with increasing age, 80 while two studies in the US and Sweden found higher unadjusted odds among lower age groups (<75 years) and lower adjusted odds among advanced age groups (>80 years), respectively.15,60 None of the other studies that examined age found it to be a significant predictor of potentially inappropriate psychotropic prescribing.79,117,122

Rhee et al. found lower odds of antipsychotic use among US elderly aged 85 years or more.78 Among Finnish elderly, Laitinen et al. reported an association between age and the use of antipsychotics among adults with Alzheimer’s disease (AD), where the odds of antipsychotic use were higher in older adults with AD than without AD.134

Other factors examined were sex, number of medications, diabetes and psychosis, but the population analyzed included patients under the age of 65. 80

Although some studies of potentially inappropriate medication and/or

psychotropic use reported age as a predictor, the direction of the associations found

varied. Lower odds of potentially inappropriate psychotropic use among persons of

advanced age may be due to prescribers’ efforts to limit the exposure of frail elderly to

medications. Where higher odds of potentially inappropriate psychotropic use were

found among older age-groups, this may be reflective of a greater number of the

comorbid affective conditions, such as depression and anxiety, but this is difficult to

demonstrate without complete clinical information and local epidemiology of mental

disorders.

Income and Insurance

Of the five studies assessing the influence of income on potentially inappropriate psychotropic prescribing, four identified significant associations where lower income was associated with higher odds of exposure.117,121,122,139 Persons with less economic

resources may not seek medical care until absolutely necessary, by which time the

condition may not respond adequately to safer pharmaceutical or non-pharmacologic

alternatives. Further, newer, more advanced therapies with safer profiles may have been

still under patent, with few generics available at lower cost. As a result, older therapies

that may be available as cheaper generics may be prescribed based on the patient’s ability

to pay and prescription insurance coverage.

Insurance type was identified as a predictor of potentially inappropriate

psychotropic prescribing in two of three studies, in older community-dwelling

Americans.60,80 The 1996 NAMCS/NHAMCS study found lower odds of potentially 81

inappropriate psychotropic prescribing among elderly with Medicaid insurance.80 In the

1996 MEPS study, Aparasu et al. found higher unadjusted odds of potentially inappropriate psychotropic use among patients with Medicare but lower odds among those with dual eligibility (Medicare and Medicaid).60 The reduction of odds seen with

the addition of Medicaid seems to support the association between Medicaid and

potentially inappropriate psychotropic prescribing in the NAMCS/NHAMCS study of the

same year, but the MEPS study failed to control for confounders. Differences in the

likelihood of potentially inappropriate psychotropic prescribing may have been due to

differences in prescription access, stricter controls over benefits and required drug

utilization programs under Medicaid.

Health status

Of the two studies that measured health status or perceived health status, the

Canadian study by Dionne et al. found a significant association with potentially inappropriate psychotropic use.60,117 It is unclear the direction or strength of this

relationship as no measure of association was reported. Additional research measuring

the association is needed. In one study that measured the relationship between the use of

antipsychotics in older adults with dementia and physical health rating, higher odds of

antipsychotic use were found among patients with fair or poor physical health rating.130

Chronic Diseases

Two studies among elderly Canadians tested the relationship between the number

of chronic conditions and potentially inappropriate benzodiazepine prescriptions, but 82

different measures of appropriateness were used.117,122 Préville et al. tested the presence

of chronic conditions (0 or >1) on potentially inappropriate benzodiazepine use. The

criteria defined inappropriate benzodiazepine use as the prescription of long-acting

benzodiazepines for over 24 hours, the concomitant benzodiazepine prescriptions for

more than six weeks, the presence of a benzodiazepine-drug interaction, and excessively high benzodiazepine dose. The study failed to find a significant association using a multivariate logistic regression model.122 Dionne et al. tested for a relationship between

the number of chronic conditions (0-2 or >3) and potentially inappropriate

benzodiazepine use based on Beers 2003 criteria and the presence of drug interactions

with benzodiazepines. Analyses identified a significant association between the number

of chronic conditions and inappropriate benzodiazepine prescribing, but no measure of

the association.117 The presence of multiple chronic conditions is common among older adults, and is expected to increase the likelihood of exposure to potentially inappropriate prescribing. However, few articles have reported an association with PIMs, and none of the studies reviewed found an association between multiple chronic diseases and the use of potentially inappropriate psychotropic medications.

The number of chronic diseases was significantly associated with potentially

inappropriate antidepressant prescribing in the study by Dalby et al. among older

Canadians,126 based on McLeod’s list.30 Patients with six or more chronic conditions

were three times as likely to receive an inappropriate antidepressant compared with

persons with less than three conditions. The presence of a psychiatric diagnosis was

associated with a lower risk of potentially inappropriate antidepressant prescribing.126 83

Chan et al. reported higher odds of antipsychotic medication use among patients

with a clinical diagnosis of dementia and patients with fair or poor physical health

rating.130 In the study by Rhee et al., the odds of antipsychotic use were higher among

patients with moderate and severe dementia (vs. mild), as well as among those with

Alzheimer’s disease (vs. vascular dementia).78 The later study by Sapra et al. among

outpatients of a memory clinic found a statistically significant relationship between the

use of antipsychotics and hyperlipidemia.135

Other Factors

Although Dionne et al. found an association between duration of benzodiazepine

use and potentially inappropriate benzodiazepine prescribing, the strength of the

association was not determined.117 None of the studies that tested education60,117,122 or

race60,79,80 found a relationship with potentially inappropriate psychotropic use - Table 9.

The study by Rhee et al. found an association between living with a caregiver and use of

antipsychotics, whereby the odds of antipsychotic use were lower for patients who lived

with a caregiver.78

Visit Factors and Patient-Provider Interactions Associated with Potentially Inappropriate

Psychotropic Prescribing

Visit factors include the nature of the visit, medications ordered and aspects of the patient’s interaction with prescribers and/or the health-system. Although familiarity with 84

the patient (new or established) may relate to the physician-patient relationship under the

Eisenberg model, for purposes of the literature review, they are included here.

Visit factors include medication use, the nature of the visit (injury, surgery),

previous interactions (e.g. repeat visits), referrals and multiple medical consultations. Six

studies tested and identified an association between one or more visit factors and

potentially inappropriate psychotropic prescribing.60,79,80,117,122,126 Four studies reported

adjusted odds ratios,79,80,122,126 whereas one study reported crude odds ratios60 - Table 7.

Factors associated with potentially inappropriate psychotropic use in community-

dwelling elderly by one or more studies are: purpose of visit, “seen before” or repeat

visit, number of prior ambulatory visits, number of medications, psychotropic class,

duration of medication use and number of pharmacies consulted in the preceding year.

Injury-related visits, repeat visits, number of medications, use of antidepressants and

anxiolytics were positively associated with aggregate use of potentially inappropriate

psychotropic medications in two US studies.79,80

Number of medications, Psychotropic Class and Duration of Use

Aparasu et al. in their 1998 study found the number of medications used by CDE was associated with the use of potentially inappropriate psychotropic medications, using a multivariate logistic regression model. A positive relationship was found where the adjusted odds of potentially inappropriate psychotropic prescribing increased with each additional medication.79 In 2004, Aparasu et al. reported a similar trend with a higher likelihood of potentially inappropriate psychotropic prescribing with the use of two or more psychotropic medications, but did not control for confounders.60 In contrast, Mort 85 et al. failed to find a relationship between potentially inappropriate psychotropic prescribing and the number of medications or the number of psychotropic medications.80

Dalby et al. reported lower odds of potentially inappropriate antidepressant use among patients with more than three medications in Canada.126 The variations in the findings may be a result of differences in study design, duplication of cases with more than one psychotropic medication, the nature of conditions, and other unmeasured confounders.

Secondary administrative databases are limited in terms of the scope of factors that may be assessed.

Psychotropic class was identified as a predictor of potentially inappropriate psychotropic prescribing among community-dwelling adults in the US in two studies using multivariate analyses.79,80 In one study, Aparasu et al. found that the use of antidepressants and the use of anxiolytics increased the risk of potentially inappropriate psychotropic prescribing.79 Although the findings reported by Mort and Aparasu also demonstrated higher odds of potentially inappropriate psychotropic prescribing with antidepressant use, lower odds of potentially inappropriate psychotropic prescribing were found among antipsychotic users.80

The study by Dionne et al., which used the same database as the study by Préville et al., albeit with different measures of inappropriateness, reported an association between the duration of use of benzodiazepines and potentially inappropriate benzodiazepine prescribing. The strength of the association was not measured.117 86

Prior visits and purpose of visit

Aparasu et al., using 1995 NAMCS data found that older adults with an injury- related visit had more than three times the likelihood of receiving a potentially inappropriate psychotropic medication (adjusted odds ratios). They suggested that this may have been due to the emergent nature of the visit.79 In contrast, Mort and Aparasu failed to find a significant association between injury or surgical visit and potentially inappropriate psychotropic prescribing in a subsequent study of the 1996

NAMCS/NHAMCS data. In this study, the authors controlled for confounders and identified higher odds of potentially inappropriate psychotropic prescribing for established patients (seen before).80 The findings of the 1996 study may have arisen due

to poor therapeutic responses to safer alternatives, adverse effects or patient preferences.

Hence the prescriber may have weighed risks, benefits, patient response and preferences

in selecting the potentially inappropriate psychotropic medication. In cases where

patients are less familiar, as in referrals or new visits, physicians may be less confident

with initiating potentially inappropriate psychotropic medications.

The number of ambulatory visits and the number of pharmacies consulted in the

year prior to one study were found to be associated with potentially inappropriate

benzodiazepine use, among Canadian elderly. Both factors produced higher odds of

potentially inappropriate psychotropic prescribing among the cohort.122 Older adults who

had fewer than 50 clinic visits in the year preceding the study were slightly more likely to

receive a potentially inappropriate benzodiazepine than those with 50 or more visits. The category boundary used and a possible relationship between number of ambulatory visits and disease severity may have confounded the findings. Patients who consulted two or 87 more pharmacies in the preceding year were more likely to receive a potentially inappropriate benzodiazepine than those with none or with one pharmacy consultation.122

Visits to physicians and/or pharmacies may have been reflective of the illness behavior of older adults. Multiple visits to clinics and pharmacies may be due to patients needing to seek frequent care as a result of ongoing symptoms, development of , addiction or other adverse drug effects. In addition, patients whose conditions are refractory to initial and/or safer therapeutic alternatives may have been prescribed potentially inappropriate psychotropic medications as options became limited. The nature of the database used limited the examination of previous therapies, dosages and clinical progress.

Physician and/or Practice Factors Associated with Potentially Inappropriate Psychotropic

Prescribing

Physician and/or practice factors were assessed by five of the studies under review: three US and two Canadian studies. Factors found to be associated with potentially inappropriate psychotropic use in at least one study were practice location or region and physician specialty – Tables 7-8. Neither physician’s age, physician’s sex, physician type (M.D. vs. D.O.) nor non-physician prescriber were statistically significant factors – Table 9.

Five studies assessed region and/or metropolitan location as a factor with mixed results. Location in the Northeast US was associated with higher odds of potentially 88

inappropriate psychotropic prescribing by Aparasu et al., but lower odds were reported by

Mort and Aparasu in a later study.79,80 Office-based practices in the Southern states of the US were associated with higher odds by Aparasu et al. in 1998.79 This may be

reflective of regional patterns of disease and prescribing. Metropolitan location was

associated with lower odds in the study by Mort and Aparasu.80 This suggests that

physicians working in more urban areas are less likely to engage in inappropriate

prescribing of psychotropic medications.

Physician specialty was evaluated by one study. Prescriptions written by

psychiatrists were associated with lower odds of potentially inappropriate psychotropic

prescribing by Aparasu et al. in a bivariate analysis.79 This may be a result of practitioners in this specialty having more experience with psychotropic medications, their indications and adverse effects, when compared with non-psychiatrists. Other physician variables may influence prescribing choices, such as qualification and years of experience but these factors and any interactions between them have not been examined, mainly as a result of the dataset limitations.

Physician-Health-System Interaction Factors Associated with Potentially Inappropriate

Psychotropic Prescribing

None of the studies assessing potentially inappropriate psychotropic prescribing examined the influence of electronic medical records (EMRs) or other indicators of the physician’s relationship, or interaction with the health system. 89

Two nationally representative studies assessing the impact of EMRs on various indicators of quality of ambulatory care, including avoidance of inappropriate medication in older adults found no association between the use of EMRs (with or without CDSS) and avoidance of PIMs.70,71 The earlier study by Linder et al. used 2003-2004 National

Ambulatory Medical Care Survey (NAMCS) data without adjusting for confounders.71

This study was followed by that of Romano et al. using 2005-2007 NAMCS and National

Hospital Ambulatory Care Survey (NHAMCS), with adjustment for confounders.70 In

2014, Hsiao et al. reported findings from a study using 2008-2009 NAMCS data to evaluate the influence of different levels and features of EMRs on inappropriate prescribing in older Americans. No association was found between the levels of EMRs used and potentially inappropriate prescribing, but EMRs with patient problem lists were associated with higher odds of inappropriate prescribing for older adults.140

Unlike studies by Linder et al.71 and Romano et al.,70 Hsiao et al.140 did not compare rates between EMR users and non-users. Instead, their study assessed levels and features of EMRs used by office-based physicians. None of the studies evaluating the use of EMRs on inappropriate prescribing has applied the Beers 2012 criteria.

Although the earlier studies suggest no association between EMRs and potentially inappropriate prescribing, in light of the growing adoption of EMRs and the introduction of incentives, the influence of these health technologies on prescribing may become more apparent. Information on the influence of EMRs on inappropriate prescribing for older adults, in a modern context is needed. 90

None of the studies assessing factors associated with antipsychotic use among

older adults with dementia assessed the patient-physician relationship, physician/practice characteristics factors or physician-health system interactions as influential factors.

Emergency Outcomes Associated with Potentially Inappropriate Psychotropic

Prescribing

Antidepressants, benzodiazepines and antipsychotics are included among psychotropic drugs that lead to drug-related hospitalizations in older adults, with falls as the leading cause of admissions.23 Few studies have examined emergency room visits or

hospitalizations in the context of the Beers classification of potentially inappropriate

psychotropic medications. None have applied AGS/Beers 2012 criteria in assessing the

influence of these medications on emergency referrals or hospitalizations.

One US study of the influence of potentially inappropriate psychotropic

medication use in older adults and health care utilization in 1996 failed to find an

association between psychotropic medication use and emergency room visits.60

However, one study among older Australians (1993-2005) identified associations

between unplanned hospitalizations and exposure to potentially inappropriate

antipsychotics, sedative-hypnotics and antidepressants. Adjusted odds ratios were higher

for patients who received thioridazine, oxazepam, diazepam and temazepam.45 91

Two cohort studies of administrative databases in Ontario assessed the risks of

hospital admissions due to stroke associated with the use of antipsychotic drugs among

older adults with dementia.141,142 Hermann and Lanctot pooled findings from randomized controlled trials and reported an increased risk of adverse cardiovascular events vs placebo.142 However, Gill et al. reported no significant change in the adjusted

relative risk of hospitalization due to stroke between FGAs and SGAs.141

Limitations and Gaps in the Literature

Limitations of Studies of Potentially Inappropriate Psychotropic Medication Use

Most studies were conducted in developed countries, primarily located in North

America (7) and Europe (4), limiting generalization to these countries. Differences in

health policies between the countries such as payment structure (insurance vs. universal

health care), prevalent mental health disorders, costs and medication access limit

comparisons. As a result prescribing practices would also vary.

All of the studies reporting on inappropriate use of psychotropic medications used

secondary databases to extract medical and/or pharmaceutical information at a population

level. As sources of information, databases may be convenient and allow analysis of

large numbers of persons or records but are limited in various ways. Records may

include data entry errors, diagnostic and/or drug coding errors and may be missing data, 92

which cannot be easily verified or corrected. Also, if the information was collected for

primarily administrative purposes, clinical details may be missing. For instance, it would not be possible to determine prior drug therapy or patient progress since the last visit as clinical notes are not included. Assessment of appropriateness with respect to medication doses, duration of use or matching of indications or contraindications may be difficult if this is not captured. The nature of database studies precludes the examination of previous therapies and clinical progress. The evaluation of factors influencing the use of

antipsychotics in older adults with dementia was restricted by the small number of studies

among community-dwelling elderly.

Variations in study design, particularly sample frame, sampling method, and data

collection methods may limit generalizability of the studies. The three US-based studies used NAMCS, which allows comparison of the findings. Studies by Préville et al. and

Dionne et al. used data that integrated information from patient interviews with medical and pharmacy records.117,122 The inclusion of live interviews may improve the depth of information retrieved, particularly as it may relate to past experience and present medication use habits. But this is limited by non-response bias, where non-participants may differ in medication use behaviors and/or indications. The use of different index ages for older adults and variations in age categories used for analyses limited the comparison of the influence of age across studies. A general positive or negative relationship may be elucidated but the measure of the association would be specific to the study setting and population.

Another limitation of the studies is the application of various criteria or definitions for “inappropriateness”, which were mainly subject to time and country of 93

study. Although Beers criteria were used in more than half of the studies, the list of

medications to avoid evolved over time with updates in 1997 and 2003. Country-specific modifications were made for the list to be relevant to the setting but limits comparisons across countries. In the database study of older adults in Lombardy, Italy, criteria were developed based on matching of prevalence of use, quantity dispensed and prevalence of a depression diagnosis among patients.125 The criteria used was not validated, reviewed by experts or developed by consensus, which limits its validity. Also it served as a check on appropriateness of treatment for depression rather than as a measure of appropriateness for use in older adults.

The use of explicit criteria that has been developed by multiple experts enables application and comparison across geographic regions, settings and countries. Some studies applied dose, duration, drug interactions and/or indication-related criteria, which increases the validity as it improves the identification of “appropriateness” but unless consistently applied by all, the findings are limited to the specific population. One limitation of explicit criteria compared to implicit criteria is the lack of specificity in terms of exceptional cases where use of a medication described as “inappropriate” is suitable based on the patient’s clinical course and response to earlier therapies. Despite the lack of context for most medications, explicit criteria may still be integrated as indicators of the need for protocol and/or health system review.

Most studies applied multivariate logistic regression models to test potential predictors but the variations in the independent variable definitions and reference categories made direct comparison difficult. The reporting of the crude odds ratios may allow comparisons of bivariate relationships across the studies, but these do not control

94

for the influence of confounders. Most studies focused primarily on patient

characteristics, with little emphasis on physician and/or practice factors and visit

characteristics. None assessed physician-health-system interactions. This may have been due to available variables and/or limitations of the scope of the study.

Gaps Identified in the Literature

From the literature reviewed, the following aspects are the main gaps in the research

stream of potentially inappropriate prescribing of psychotropic medications among

community-dwelling elderly:

i. Updated prevalence rates of potentially inappropriate antidepressant and

anxiolytic/sedative-hypnotic prescribing among older community-dwelling

users in the US in light of updated criteria are needed;

ii. Updated estimates of the influence of physician and health-system factors on

prescribing of potentially inappropriate antidepressants and

anxiolytic/sedative-hypnotics among community-dwelling older Americans

are needed;

iii. The extent to which patient, physician and health-system factors influence the

prescribing of antipsychotic medications for older community-dwelling

Americans with dementia is unknown; 95 iv. Information on the relationship between the use of potentially inappropriate

antidepressants and sedatives to avoid on emergency referrals, following

office-based visits by community-dwelling older users is needed;

v. Information regarding the presence of an association between the use of

antipsychotics and emergency referrals following office visits, by older,

community-dwelling adults with dementia is needed. 96

Table 2: Comparison of Common Explicit Measures for Psychotropic Medications To Avoid (Disease-Independent) in Older Adults14

Therapeutic Class Beers 2003 Beers STOPP 2012 2006 Central Nervous System and Psychotropic Drugs Amphetamines and anorexic agents X -- -- Anticholinergics to treat extrapyramidal side effects of neuroleptic medications (e.g., -- X X benztropine, ) Antipsychotics, first (conventional) and second (atypical) generation (for behavioral problems of -- X -- dementia) Barbiturates (all agents except phenobarbital for seizure control in Beers 2003; all agents in X X -- Beers 2012) Benzodiazepines, short- and intermediate acting (dose limits in Beers 2003; all doses in Beers X X -- 2012) Chloral hydrate -- X -- Dementia treatments, older (i.e. mesylates and in Beers 2003 and Beers 2012; X X -- in Beers 2003) Fluoxetine, daily X -- -- Long-acting benzodiazepines (e.g. clonazepam, diazepam, flurazepam) X X X Long-term neuroleptics for insomnia -- -- X Meprobamate X X -- Mesoridazine X X -- Nonbenzodizepine (“Z”) hypnotics (i.e. , , zolpidem) -- X -- Tertiary TCAs, alone or in combination (amitriptyline and doxepin Beers 2003; all in Beers X X -- 97

2012) Thioridazine X X -- Reference: Table 1 Comparison of the Most Common Explicit Measures for Drugs to Avoid in Older Adults in in Marcum ZA, Hanlon JT. Commentary on the New American Geriatric Society Beers Criteria for Potentially Inappropriate Medication use in Older Adults. Am J Geriatr Pharmacother. 2012 April; 10(2):151-159. Key: STOPP: Screening Tool of Older Persons’ potentially inappropriate Prescriptions, TCA: Tricyclic antidepressants 98

Table 3: Prevalence of Potentially Inappropriate Medications and Predictors in studies using AGS/Beers 2012 Criteria

Data source; PIM Predictors Adjusted ORs Study Sample Size Common PIMs ORs (95% CI) Prevalence (95% CI) (index age) Baldoni et Interviews (self- 59.2% Of elderly: Female gender 2.2 (1.7-2.9) 1.8 (1.3-2.5) al. report) Without partner 1.8 (1.3-2.3) 1.5 (1.1-2.1) 2013104 N=1000 Diclofenac 20.3% Self-medication 2.6 (1.9-3.5) 2.4 (1.7-3.3) (>60y) Dexchlorpheniramine 9.6% Use of OTCs 1.9 (1.4-2.7) 1.8 (1.2-2.5) Brazil Diazepam 7.6% Complaints of ADEs 1.9 (1.4-2.5) 1.8 (1.3-2.3) Amitriptyline 7.3% Psychotropic meds 4.8 (3.5-6.6) 4.5 (3.2-6.2) Clonazepam 6.1% More than 5 meds 2.9 (2.1-3.7) 2.7 (2.0-3.6) Clonidine 5.6% ATC code R 3.71 (2.46-5.57) 3.53 (2.33-5.35) Orphenadrine 5.6% ATC code M 5.17 (3.90-6.84) 4.71 (3.54-6.26) ATC code N 3.37 (2.57-4.40) 3.07 (2.31-4.06) ATC code G 2.53 (1.08-5.90) 2.76 (1.15-6.59) Nishtala et Interviews and 42.7% Of PIMs: Living alone 0.63 (0.40-0.99) 0.63 (0.36-1.11) al. researcher visit NSAIDS 21.1% Polypharmacy 2.64 (1.44-4.84) 2.06 (1.03-4.12) 2013107 N=316 Tertiary TCAs 15.7% Any psychotropic 15.21 (7.88-29.38) 22.05 (5.80- (>75y) Benzodiazepines 15.2% medication 6.97 (3.43-14.20) 83.84) New Antidepressant 9.53 (2.11-43.01) 0.51 (0.12- Zealand Doxazosin 12.5% Antipsychotic 2.36)* Diclofenac 11.4% 1.68 (0.23- Terazosin 9.8% Poisson model: 12.19)* Zopiclone 9.2% Increasing age Amitriptyline 8.7% No. of drugs prescribed Doxepin 7.1% 0.95 (0.91-0.99) 99

Data source; PIM Predictors Adjusted ORs Study Sample Size Common PIMs ORs (95% CI) Prevalence (95% CI) (index age) Triazolam 5.4% 1.11 (1.08-1.15) Ibuprofen 4.3% Naproxen 3.8% Clonazepam 3.3%

McMahon Pharmacy claims 44.0% (pre- Of fallers : Polypharmacy (pre-fall) -- 3.86 (2.84-5.26) et al. N=1016 fall) LA benzodiazepines 15.3% 2014112 (>70y) SA/IA Significant linear benzodiazepines10.5% association found between UK Non-COX NSAID >3mths no. of comorbidities and 8.5% PIP pre- and post-fall Tertiary TCAs 5.3% (p<0.001) Non-BZD hypnotic >90 days 4.8% Alpha-blockers, doxazosin 4.8% Blanco- Interviews, 44% (2012) Of PIMs: Age -- 0.99 (0.97- Reina et al. package reviews, Benzodiazepines 39.4% No. of medications -- 1.02)* 2014103 medical records Antipsychotics 12.8% Male -- 1.21 (1.12-1.32) N=407 Sulfonylureas 9.5% >1 psychiatric disorders 0.89 (0.58- Spain (>65y) Non-COX NSAIDs 8.7% 1.37)* Non-BZD hypnotics 5.8%, 2.91 (1.83-4.66) Pannoi et Medical, pharmacy 214 patients CNS: 45% of PIMs Patient age: 1.018 (1.001-1.035) n/a al. 2014108 records with at least Pain: 27%, Cardiovascular: Outpatient visits (ref. 1-3)

100

Data source; PIM Predictors Adjusted ORs Study Sample Size Common PIMs ORs (95% CI) Prevalence (95% CI) (index age) N=430 1 PIM, 19% 4-6 visits 0.581 (0.408-0.828) Thailand outpatients 2012 49.8% >7 visits 0.704 (0.526-0.943) (>65y) Lorazepam: 17.5% PIMS diclofenac 17.2%, Prescriber age: 1.105 (1.002-1.218) doxazosin 15.2%, Number of medications ibuprofen 9.4%, (ref. 1-4): alprazolam 8%, 5-7 medications 2.491 (1.919-3.234) amitryptiline 7.7%, 8-10 medications 5.133 (3.763-7.001) dipotassium chlorazepate 11-14 medications 6.535 (4.290-9.956) 6.7% >15 medications 25.198 (5.168- 122.87) Davidoff et MEPS 2006-2010 42.6% For 2009-2010 n/a n/a n/a al. 201541 (household survey) (broad NSAIDs: 10.4% N=18,475 definition), Benzodiazepines: 9.0% USA (>65y) 2006-2007: Alpha-1 blocker: 3.9% 45.5% Non-BZD: 3.5% 2009-2010: Sulphonylureas: 3.5% 40.8% First-gen : 3.4% Skeletal : 3.3% Estrogen: 3.2% Moriarty F TILDA (national) Beers: Benzodiazepines: n/a n/a n/a 101

Data source; PIM Predictors Adjusted ORs Study Sample Size Common PIMs ORs (95% CI) Prevalence (95% CI) (index age) et al. N=2,051 30.5% base, Baseline=9.8%, 2015109 (>65y) 33.1% Follow-up= 8.8% follow-up Ireland Baseline (2009- STOPP: Tertiary TCAs: 11), 2-yr follow-up 52.7% Baseline=4.7%, (2011-2012) Follow-up=5.8% Subset used: 81% of NSAIDS: Beers Baseline=4.5% criteria Follow-up=3.9%

Key: (*) not significant at p<0.05 ADE: adverse drug event, ATC: Anatomic-therapeutic classification, BZD: Benzodiazepine, CI: confidence interval, code G: genitourinary system & sex hormones, code M: musculoskeletal system, code N: nervous system, code R: , COX: cyclooxygenase, IA: intermediate-acting, n/a: not available, NSAID: Non-steroidal anti-inflammatory drug, OTC: over-the-counter, PIM: potentially inappropriate medication, SA: short-acting, TCA: , TILDA: The Irish Longitudinal Study on Ageing 102

Table 4: Factors Associated with Potentially Inappropriate Medication Use in Community-dwelling Older Americans

Factors Variable (reference) Adjusted Odds Ratios Studies Data source Criteria (95% C.I.) 1.3 (1.1-1.6) Zhan et al. 200154 1996 MEPS Beers 1997 2.10 (1.68-2.62) Goulding 200457 1995-2000 Beers 1997 NAMCS/NHAMCS 1.56 (1.39-1.75) Meurer et al. 201053 2000-2006 Beers 2003 Sex Female (ref: male) NHAMCS 1.814 (1.413-2.330) Zhang et al. 201155 2007 MEPS Beers 2003 1.33 (1.32-1.35) Holmes et al. 2007-8 Texas Beers 2003 2013111 Medicare

>80 (ref: 65-69y) 0.64 (0.49-0.83) Goulding 2004 1995-2000 Beers 1997 NAMCS/NHAMCS 65-69 M: 1.3 (1.2-1.3) F: 1.3 (1.1-1.6) Pugh et al. 200658 FY 2000 HEDIS Age 70-84 (ref: >85y) M: 1.1 (1.1-1.1) NPCD, VA 2006 >75 (ref: 65-74y) 1.67 (1.35-2.07) Meurer et al. 2010 2000-2006 Beers 2003 NHAMCS

White 1.6 (1.1-2.3) Zhan et al. 2001 1996 MEPS Beers 1997 Other (ref: black) 2.1 (1.0-4.2) Black M: 0.9 (0.9-0.9) F: ns Pugh et al. 2006 FY 2000 HEDIS Race / ethnicity Hispanic M: 1.3 (1.3-1.3) F: 1.4 (1.1-1.8) NPCD, VA 2006 Other M: ns F: 1.4 (1.1-1.8) Unknown (ref: white) M: 0.8 (0.8-0.9) F: 0.9 (0.8-0.9) Black 1.07 (1.05-1.10) Holmes et al. 2013 2007-8 Texas Beers 2003 103

Factors Variable (reference) Adjusted Odds Ratios Studies Data source Criteria (95% C.I.) Hispanic 0.87 (0.86-0.89) Medicare Asian 0.60 (0.58-0.63) Other (ref: white) 0.88 (0.81-0.95)

Middle income (ref: high 1.505 (1.112-2.037) Zhang et al. 2011 2007 MEPS Beers 2003 income) Income / wealth Eligible for low-income 1.03 (1.02-1.05) Holmes et al. 2013 2007-8 Texas Beers 2003 subsidy Medicare

Self-rated Health Poor (ref: Excellent) 5.9 (3.4-10.1) Zhan et al. 2001 1996 MEPS Beers 1997 status Poor (ref: Excellent) 2.317 (1.440-3.729) Zhang et al. 2011 2007 MEPS Beers 2003

SMI (ref: absent) M: 1.7 (1.6-1.8) F: ns Pugh et al. 2006 FY 2000 NPCD, VA HEDIS Other mental illness M: 1.5 (1.5-1.5) F: ns 2006 SMI + Other mental illness M: 1.7 (1.6-1.7) F: 1.3 (1.1-1.5) Medical History Hospitalization in year prior 1.10 (1.08-1.11) Holmes et al. 2013 2007-8 Texas Beers 2003 Medicare >2 comorbidities (ref: 0) 0.89 (0.87-0.91) Holmes et al. 2013 2007-8 Texas Beers 2003 Medicare

Injury-related 1.52 (1.40-1.66) Meurer et al. 2010 2000-2006 Beers 2003 NHAMCS Nature of Visit Immediacy, > 1hr vs. 1.44 (1.29-1.61) Meurer et al. 2010 2000-2006 Beers 2003 immediate NHAMCS 104

Factors Variable (reference) Adjusted Odds Ratios Studies Data source Criteria (95% C.I.)

>14 (<14) 2.9 (2.3-3.6) Zhan et al. 2001 1996 MEPS Beers 1997 2 2.62 (1.85-3.72) Goulding 2004 1995-2000 Beers 1997 3 3.09 (2.16-4.42) NAMCS/NHAMCS 4 3.36 (2.36-4.77) 5 4.15 (2.86-6.03) 6 (vs. 1 Rx) 6.64 (4.64-9.51) 4-6 M: 2.2 (2.1-.2) F:2.3 (1.9-2.7) Pugh et al. 2006 FY 2000 HEDIS No. of medications 7-9 M: 3.8 (3.8-3.9) F: 4.3 (3.7-5.1) NPCD, VA 2006 >10 (ref: 1-3) M:8.2 (8.0-8.4) F:9.6 (8.2-11.2) >2 Rxs 6.62 (6.15-7.14) Meurer et al. 2010 2000-2006 Beers 2003 NHAMCS >25 (ref: <25, incl 0) 1.940 (1.498-2.514) Zhang et al. 2011 2007 MEPS Beers 2003 6-8 2.48 (2.43-2.52) Holmes et al. 2013 2007-8 Texas Beers 2003 9-12 4.37 (4.29-4.54) Medicare 13+ (ref: 1-5) 9.11 (8.93-9.29)

Rest of US (ref: Northeast) 2.05 (1.45-2.91) Meurer et al. 2010 2000-2006 Beers 2003 Region NHAMCS South (ref: Northeast) 2.042 (1.322-3.153) Zhang et al. 2011 2007 MEPS Beers 2003 Metropolitan Non-MSA (ref: MSA) 1.35 (1.17-1.57) Meurer et al. 2010 2000-2006 Beers 2003 location NHAMCS

Physician Family/General practice 1.41 (1.02-1.95) Goulding 2004 1995-2000 Beers 1997 105

Factors Variable (reference) Adjusted Odds Ratios Studies Data source Criteria (95% C.I.) specialty (ref: other specialties) NAMCS/NHAMCS Physician seen See with resident or intern 0.65 (0.55-0.76) Meurer et al. 2010 2000-2006 Beers 2003 (ED) involvement NHAMCS Number of 2 1.18 (1.16-1.20) Holmes et al. 2013 2007-8 Texas Beers 2003 different 3 1.29 (1.27-1.32) Medicare prescribers 4+ (ref: 1) 1.50 (1.47-1.53) For profit hospital 1.19 (1.03-1.38) Meurer et al. 2010 2000-2006 Beers 2003 Hospital owner (ref: nonprofit or NHAMCS (ED) government) Other visit factors Year of visit (vs. 2000, per 0.98 (0.95-0.997) Meurer et al. 2010 2000-2006 Beers 2003 (ED) additional year) NHAMCS Key:

ED: Emergency Department, FY: fiscal year (Sept-Oct), HEDIS: Health Plan Employer Data and Information Set (modified Beers 2003), MEPS:

Medical Expenditure Panel Survey, MSA: metropolitan statistical area, NAMCS: National Ambulatory Medical Care Survey, NHAMCS: National

Hospital Ambulatory Medical Care Survey, NPCD: National Patient Care Database, SMI: Serious mental illness (schizophrenia, bipolar disorder), VA:

Department of Veteran Affairs 106

Table 5: Studies of Prescribing of Potentially Inappropriate Psychotropic Medications in Community-dwelling Older Adults

Authors, Country, Sample Criteria Data source, User Prevalence; Common PIPs Year study year selection, Analyses PI Prevalence sample size Potentially Inappropriate Psychotropic Medications Aparasu et US Pr – cluster Beers 1991 NAMCS – national 16.87% of PD users Classes: al., 1995 9808 records; DI survey database Antidepressants 55.06% 199879 704 visits with Antianxiety drugs 36.90% PD Multivariate LR Sedatives and hypnotics 15.50% (est. Antipsychotic/antimanic drugs 10.91% 12,022,489 visits) DI PIPs: Amitriptyline 20.18% of AD users (10.42% of PD visits), Diazepam 6.47% of AX users (2.35%), Chlordiazepoxide 6.99% of AX (2.54%), Flurazepam 7.89% of SH (1.22%), Meprobamate 0.94% of AX (0.34%) Mort et US Pr – cluster Beers 1997 NAMCS/NHAMCS 27.2% of PD users DI PIPs: al., 1996 1373 visits DI, DD – national survey (27.95% of office Amitriptyline 10.23%, 200080 involved PDs database practices, 19.28% of Diazepam 7.43%, (est. 16.55 OPD visits) Doxepin 3.87%, million visits) Multivariate LR Chlordiazepoxide 2.51% DI: 94.14% of PIP visits DD PIPs: 107

Authors, Country, Sample Criteria Data source, User Prevalence; Common PIPs Year study year selection, Analyses PI Prevalence sample size DD: 12.13% Sedatives & COPD 1.81%, TCAs & constipation 0.53%, Anticholinergic ADs & BPH 0.52% Aparasu et US Pr - cluster Beers 1997 MEPS – national 7.14% of CDE; Antidepressants: al., 1996 2455 records DI, DD survey database 37.86% of PD users DI PIPs: 50.93% of AD users (24.65% 200460 for elderly; of PD users); 471 patients Bivariate LR DI PIP : 32.94% of Amitriptyline 39.03% (18.89%); with PDs (est. users Doxepin 6.22% (3.01%) 6.09 million) DD PIPs : 10.21% DD PIPs use: TCAs & arrhythmias 4.57% of AD users (2.21% of PD); Selected AD’s & insomnia 4.51% (2.18%) TCAs & constipation 2.70% (1.31%)

Anxiolytics: DI PIPs: 31.84% of AX users (12.73% of PD) Diazepam 18.11% (7.24%) Chlordiazepoxide 9.41% (3.76%) Meprobamate 2.98% (1.19%)

DD PIPs: 108

Authors, Country, Sample Criteria Data source, User Prevalence; Common PIPs Year study year selection, Analyses PI Prevalence sample size LA BZDs & falls 3.37% (1.34%)

Sedative-hypnotics : DI PIPs: 23.49% of SH users (5.96% of PD) Flurazepam 6.72% (1.71%) Barbiturates 2.84% (0.72%)

DD PIP: Sedatives & COPD 14.71% (3.73%) Lesen et Sweden, National Swedish National National Swedish 39.2% of PD users Not reported al. 2006 census Board of Health Prescribed Drug 201015 384,712 and Welfare Register; DI PIP: 36.0% Prescription DD PIP: 12.2% database for DI+DD PIP: 9.0% dispensed medications at pharmacies (non- hospital)

Multivariate LR Potentially Inappropriate Antidepressants Rojas- Nova Regional NIH 1992 Provincial medical AD use among Tertiary TCAs: 64%, 57%, 52% 109

Authors, Country, Sample Criteria Data source, User Prevalence; Common PIPs Year study year selection, Analyses PI Prevalence sample size Fernandez Scotia, census Beers 1991 (DI) services database; elderly: SRIs : 19%, 26%, 31% et al. Canada 12,048 – yr1 Blazer 1989 Administrative drug Yr1 : 10.3% Atypical ADs (trazodone, nefazodone, 1999124 1993 – 12,317 – yr2 McCue 1992 database for insured Yr2 : 10.4% venlafaxine) : 7%, 7%, 9% 1996 13,419 – yr3 elderly under Yr3 : 11.2% Secondary TCAs : 6%, 5%, 4% Yr1 : 1993- Total : 37784 Medicare 4 with Rx for PI AD use among Yr2 : 1994- AD AD users 5 Yr1 : 67% Yr3 : 1995- Yr2 : 61% 6 Yr3 : 55% Lakey et US Convenience Tertiary amine State and Medicaid Tertiary TCA use: Prevalence for individual TCAs not al. Washington sample, tricyclic databases; Baseline: 19.3% of reported. 2006123 state (3 voluntary antidepressants : interviews with AD users (6% of county enrolment amitriptyline, patients, caregivers, residents); area) from doxepin, administrative 1998-1999 community imipramine personnel Year 1: 21.7% of residential AD users (9.7% of care facilities residents) n=282 (baseline) (mean age 82.9) n=277 (year 1) 110

Authors, Country, Sample Criteria Data source, User Prevalence; Common PIPs Year study year selection, Analyses PI Prevalence sample size Carey et United Census; Beers 2003 National primary AD use among None reported al. Kingdom 218, 567 (modified) care records of CDE: 12.7% 2008119 2005 records for DI, DD general practices elderly; PI AD: 4.2% of 71,080 CDE Multivariate LR CDE 34.7% of AD users Dalby et Canada Geographic McLeod’s list Records or database PI AD (all): 7.1% Cyclic ADs: 6.2% (all), 10.1% (DS) al. 1999-2001 census (under-treatment, of Community Care Elderly with Atypical ADs: 0.5% (all), 1.7% (DS) 2008126 (Ontario) PI treatment of Access Centers depressive MAOIs: 0.3% (all), 0.2% (DS) Records depression) (CCACs) symptoms (DS): Other ADs: 0.2% (all), 0.2% (DS) 3,321 elderly 12.1% (65+ years) Multivariate LR Parabiaghi Italy Regional In-house Pharmacy database Elderly with at least In 2007: et al. 2000, 2007 Census (not validated) 1 AD: SSRIs 75.7% of AD’s (8.76% of 2011125 Yr1: 520,416 Prevalence and 2000: 5.5% elderly), Yr2: 597,572 incidence of use; 2007: 11.5% TCAs 1.1% of elderly. quantity dispensed; Inadequate trial, prevalence of 2007: 33.9% of AD depression users diagnosis and duration of treatment 111

Authors, Country, Sample Criteria Data source, User Prevalence; Common PIPs Year study year selection, Analyses PI Prevalence sample size Potentially Inappropriate Anxiolytics, Sedative-Hypnotics Lakey et US Convenience Long-acting State and Medicaid LA BZD use: Prevalence for individual BZDs not al. Washington sample, benzodiazepines : databases; Baseline: 16.7% of reported. 2006123 state (3 voluntary chlordiazepoxide, interviews with ASH users (3.5% of county enrolment clonazepam, patients, caregivers, residents); area) from , administrative 1998-1999 community diazepam, personnel Year 1: 14.1% of residential flurazepam, ASH users (5.1% of care facilities , residents) n=282 , (baseline) (mean age 82.9) n=277 (year 1) Carey et United Census; Beers 2003 National primary AX/SH use among None reported al. Kingdom 218, 567 (modified) care records of CDE: 10.2% 2008119 2005 records for DI, DD general practices elderly; PI AX/SH: 7.7% of 71,080 CDE Multivariate LR CDE 35.6% of AX/SH users Neutel et Norway, Census Excessive National Among 2+ BZD-Z Common BZD-Zs (of BZD-z Rxs): 112

Authors, Country, Sample Criteria Data source, User Prevalence; Common PIPs Year study year selection, Analyses PI Prevalence sample size al. 2012120 2008 118,526 duration or prescription users: Zopiclone 52.3% elderly with at Excessive dosage database BZD AX: 40.6% Oxazepam 21.2% least 2 BZD-z (wkly or yrly) or BZD hypnotic: Diazepam 17.3% Rxs Use of BZD No statistical tests 10.6% Nitrazepam 7.2% hypnotics Z-hypnotics: 67.6% Zolpidem 5.3% (25.1% of all elderly 70- (various PI BZD-Z use: Z-hypnotics: 57.6% of Rx’s 89); guidelines incl 12.3% of elderly Anxiolytic BZDs: 40% of Rx’s 739,591 Rxs Beers 1997) 26% of BZD AX Hypnotic BZDs: 9.6% of Rx’s users 100% of hypnotic users 65% of z-hypnotic users

Préville et Quebec, Pr LA BZD for >24 Interviews (survey) BZD use: Common BZDs among users: al. Canada 2320 hrs; and medical and 32.1% of elderly SA BZDs: 2012122 2005-6 Concomitant Rx pharmaceutical (n=744) Lorazepam 41.8% of 2 or more records Oxazepam 23.7% BZDs >42 days; PI BZD use: Temazepam 9.1% BZD-drug Bivariate LR, 48.1% of users Alprazolam 7.5% interaction; Multivariate LR Excessive dose: Bromazepam 7.4% Too high dose 19.8% BZD Duplicate: 3.0% LA BZDs: 113

Authors, Country, Sample Criteria Data source, User Prevalence; Common PIPs Year study year selection, Analyses PI Prevalence sample size (literature, PI Interactions: Clonazepam 12.9% advisory board) 22.7% Flurazepam 5.5% Diazepam 3.9% LA BZDs: 24.3% of Nitrazepam 1.2% users

Dionne et Quebec, Pr Beers 2003 (LA Interviews (survey), BZD use: Common PI BZD Rxs: al. Canada 2320 BZDs, high doses medical and 32.1% of elderly Clonazepam 29% of PI Rx’s (12.9% of 2013117 2005-6 of SA BZDs); pharmaceutical BZD Rxs); Possible drug- records PI Rx’s among High doses of temazepam 13.6% (6%) drug interaction: users: 44.4% Ben Amar Chi-squared tests PI BZD (Beers): PI Interactions: 21.9% Diltiazem PI interactions: Macrolide antibiotics 15.2% Trazodone Both: 7.4% Digoxin Potentially Inappropriate Antipsychotics Lakey et US Convenience Low-potency State and Medicaid Baseline: Prevalence for individual APs not al. Washington sample, conventional databases; 7.3% of AP users reported. 2006123 state (3 voluntary APs (not listed) interviews with (1.1% of residents); county enrolment patients, caregivers, area) from administrative Year 1: 1998-1999 community personnel 11.3% of AD users 114

Authors, Country, Sample Criteria Data source, User Prevalence; Common PIPs Year study year selection, Analyses PI Prevalence sample size residential (2.5% of residents) care facilities n=282 (baseline) (mean age 82.9) n=277 (year 1) Key:

AD: Antidepressant, AP: antipsychotic, AX: anxiolytic, BZD: Benzodiazepine, BZD-Z: benzodiazepines and z-hypnotics, CDE: community-dwelling elderly, DD: Disease-dependent, DI: Disease-independent, LA: long-acting, LR: logistic regression, MEPS: Medical Expenditure Panel Survey, PD: psychotropic drug, PI: potentially inappropriate, SH: sedative-hypnotic, Pr: probabilistic, SA: short-acting, TCA: tricyclic antidepressant, SRI: serotonin 115

Table 6: Antipsychotic Use in Community-dwelling Older Adults with Dementia

Country, Sample selection, Setting(s) AP use and Significant Authors, Year Study Data sources Measures sample size Age Factors year(s) Giron et al. Sweden Population-based Institutional; Subject interviews Proportions with/out 1987-1989: 21.6% 2001128 1987-1989; survey Community or informant (non- dementia receiving 1994-1996: 18.1% 1994-1996 Persons living in institutional) psychotropic institutions and non- Elderly 81y+ Nurses/ medical medications institutions Mean age = records (community) 86.9y (institutional) 1987-9: n=162 w. Prescriptions and dementia (839 drug containers without) 1994-6: n=188 w. dementia (493 without) Kolanowski et Southeast Insurance enrollees Community Blue Cross/ Blue Proportions using Any AP: 27% al. USA with diagnosis of Shield of Georgia FGAs, SGAs, both FGAs: 6.5% 2006129 1998 dementia, 1+ Age : 45y + and none. SGAs: 16.9% prescription claim Both: 3.6% Differences in None: 73% N=959 Comorbidity scores and number of types No differences in comorbidity of non-AP scores. prescriptions Patients with no AP between AP users prescription were dispensed 116

Country, Sample selection, Setting(s) AP use and Significant Authors, Year Study Data sources Measures sample size Age Factors year(s) and non-users fewer types of non-AP drugs (p=.0005) than patients receiving any AP and those receiving SGAs. Chan et al. Baltimore, Randomly selected Community Memory and Demographics; Neuroleptics used by 4.9% 2007130 MD elderly, Baltimore practices Medical Care Baseline use of USA area and rural Study (MMCS) cognitive-enhancers, Association between use of 1998-1999 Maryland Mean age = antidepressants, neuroleptics and BPSD: 81.7y Interviews with neuroleptics, Not significant for any BPSD, Non- knowledge anxiolytic/ hypnotics depression, psychosis or institutionalized informants; and anti-agitation agitation; elderly with medical chart drugs. Physical health rating NINCDS/ADRDA reviews; Medicare poor/fair: OR=4.94 based diagnosis of claims; Univariate LR Clinical dementia: OR=7.93 dementia psychotropic drug Multivariate LR use via pharmacy N=349 with records. dementia; (285 with pharmacy data) Gruber-Baldini USA Pr - cluster Community Medicare Current Weighted, Typical AP (community): et al. 2002 12,697 beneficiaries Beneficiary unweighted Haloperidol 2.3% 2007131 (including LTC) <65y: 5.5% Survey; frequencies Quetiapine 2.3% 65-74 y: 19.6% patient or proxy Ziprasidone 0.2% 117

Country, Sample selection, Setting(s) AP use and Significant Authors, Year Study Data sources Measures sample size Age Factors year(s) CD: 11,593 75-84 y: 45.3% interviews; W. dementia: n=686 >85 y: 29.3% Atypical AP (community): (weighted Medication use: Olanzapine 4.0% n=2,001,780) self-reports, Risperidone 5.1% container review, insurance slips Includes <65y olds

Guthrie et al. Scotland Elderly aged 65y+ General Scottish Prevalence of Rx for AP use: 17.7% 2010132 2006-2007 w. dementia practices Programme for oral antipsychotics, diagnosis and Improving Clinical antidepressants, SGAs: 68% of prescriptions: psychotic illness Effectiveness – hypnotics/anxiolytics Quetiapine 33%; Primary Care Haloperidol 17.5% N=10,058 w. (SPICE-PC) Multilevel LR Amisulpiride 12.7% dementia (prolonged AP Risperidone 12% variations) Chlorpromazine 7.3% Univariate LR Olanzapine 6.6%

Trifiro et al. Italy Pr – cluster General EMRs, Health One-year, monthly Atypical AP (2004): 9.7 per 2010133 2000-2005 sampling practices Search/ Thales prevalence rates 10,000 Database (HSD) among gen pop, Typical AP (2004): 10.7 per Elderly >65 years Age distribution elderly, elderly with 10,000 elderly w. dementia n=3,991 or mean age dementia 118

Country, Sample selection, Setting(s) AP use and Significant Authors, Year Study Data sources Measures sample size Age Factors year(s) Elderly w. dementia (nr) Rates per 10,000 Haloperidol: 27.8 (nr) inhabitants Promazine: 18.0 Quetiapine: 15.7 per 10,000 (2005) Laitinen et al. National census Community Finnish National Chi-squared tests, t- AP use: 2011134 2005 Matched cohort practices Prescription tests for differences Persons w. AD: 22.1% Register, Special between users and Elderly w. AD: 22.0% Persons w. AD <65 y: 2.7% Reimbursement non-users n=28,089 65-74 y: 16.7% Register (Social FGAs: 75-84 y: 57.2% Insurance 4.4% AD group; 4.5% of 65+ Elderly 65+ w. AD: 85+ y: 23.4% Institutions) AD group n=27,325 Mean age: 80 Prescriptions SGAs: (SD=6.8y) dispensed in 18.8% of AD group; 18.7% of community 65+ AD grp settings AD group: Risperidone 10.2% Quetiapine 8.6% 1.5% Haloperidol 1.3% Olanzapine 1.1%

Factors assoc’d w. AP use in 119

Country, Sample selection, Setting(s) AP use and Significant Authors, Year Study Data sources Measures sample size Age Factors year(s) grp w. AD (all ages): female, higher total no. of drugs, diabetes and psychosis. Rhee et al. USA Nationally Living in Aging, Demographic AP use: 19.1% 201178 2002-2004 representative, community Demographics and variables; behavioral longitudinal study Memory Study issues Olanzapine: 7.9% (Health and (Neuropsychiatry Risperidone: 6.6% Persons aged Retirement Study) Inventory); Clinical Quetiapine 3.2% 70yrs+ in Dementia Rating Haloperidol 0.6% community, w. In-person (CDR) Scale; dementia, n=307 evaluation (nurse, physical function Factors associated with AP (sample) neuropsych use: Mean age=84.4 yrs technician, Psychotropic Moderate dementia OR=7.4 respondent, medication use: Severe dementia OR=5.8 vs. informant) antipsychotics, mild Medical records, antidepressants, Alzheimer’s dis OR=6.7 vs. medication anticonvulsants, vascular dementia containers, benzodiazepines Age >85y: OR=0.33 medication file Caregiver depression: Multivariate LR OR=0.03 Living with caregiver: OR=0.19 Sapra et al. Virginia, Clinic census Memory EMRs of Proportions 43.2% of elderly outpatients 120

Country, Sample selection, Setting(s) AP use and Significant Authors, Year Study Data sources Measures sample size Age Factors year(s) 2012135 USA n=407 Disorders outpatients receiving AP during 2005 Clinic (chart review) illness Quetiapine 64.8% of AP users Risperidone 17% Mean age=82 Comparison of Olanzapine 8.5% (SD=6.42) proportions of AP Haloperidol 7.4% Male 99.7% users vs. non-users: 2.3% Fisher’s exact test (association) Factors Associated: Hyperlipidemia, Caregiver education. Martinez et al. United National census General UK Clinical Use of AP at index AP use at index day in 2011: 2013136 Kingdom Matched cohort practices in UK Practice Research day among dementia 7.4% 1995-2011 study Datalink cohort, 2011 Patients >60 y Mean prevalence 1995-2011: n=50,349 Mean age=82.5 12.5% (SD=7.4) In 1995: 19.9% In 2011: 7.4% 2011: 60-64 y: 1.5% 65-69 y: 3.8% 70-74 y: 9.2% 75-79 y: 16.4% 80-84 y: 26.1% 85-90 y: 23.4% 121

Country, Sample selection, Setting(s) AP use and Significant Authors, Year Study Data sources Measures sample size Age Factors year(s) >90 y: 16.6% Rattinger et al. USA National panel Multiple Medicare Current Annual prevalence, Among ADRD group 34.0% 2013137 2008 Pr – cluster (extracted Beneficiary frequencies received AP meds community) Survey; Beneficiaries with Claims Among CDE: 24.0% AD, related Mean age=83.3 dementias (ADRD) (SD=7.4)

n=395,131 (>65 y) Taipale et al. Finland Persons with AD Community- Medication Use Prevalence of AP use among AD: 20% 2014138 2005-2011 diagnosed between dwelling and Alzheimer’s psychotropic drug FGA: 2.5% 2005-2011 persons with Disease use one year after SGA: 18.2% (not Parkinson’s AD (MEDALZ) cohort AD diagnosis; dementia) Compare prevalence AP Among Elderly: Mean age=80y Prescription with non-AD 65-74y: 19% N=69,080 patients register, Special patients; 75-84y: 19% w. AD reimbursement Assess changes in >85y: 22% register, register of prevalence between care at social 2005-2011 institutions, Hospital discharge register 122

Key: AD: Alzheimer’s disease (dementia); ADRD: Alzheimer’s disease or related dementias; AP: antipsychotic; BPSD: behavioral or psychological symptoms of dementia; CDE: community-dwelling elderly; FGA: first-generation antipsychotic; NINCDS/ADRDA: National Institute of Neurological and Communicative Disorders and Stroke/Alzheimer’s Disease and Related Disorders Association; Pr: probabilistic; SD: standard deviation 123

Table 7: Factors Associated with Potentially Inappropriate Psychotropic Use in Community-dwelling, Older Adults

Factors PI PD (any) PI AD PI AX PI SH AP in Dementia Studies ORs (95%CI) ORs (95%CI) ORs (95%CI) ORs (95%CI) ORs (95%CI) Patient Characteristics Gender M: 2.22 (2.07-2.37) M: 0.75 (0.71-0.80) AX/SH Carey et al. 2008119 Women 0.94 (0.93- Lesen et al 0.95) 2010139 F: 0.49 (0.17-1.39) Rhee et al. 201178 Age 65-69: 5.52 (1.78- 4.95 (1.42-17.26)ǂ 4.75 (0.31- 9.00 (0.74- Aparasu et al. 17.16)ǂ 2.88 (0.90-9.24) 72.43)ǂ 109.10) ǂ 200460 70-74: 4.61 (1.58- 1.76 (0.48-6.48) 5.19 (0.35- 20.45 (1.73- 13.51) 0.68 (0.21-2.21) 77.95) 241.91) 75-79: 2.57 (0.87-7.64) Ref >85y 2.25 (0.14- 16.78 (1.4- 80-84: 1.98 (0.59-6.64) 35.69) 201.26) Ref >85y 4.46 (0.27- 10.51 (0.73- 73.67) 151.00) Ref >85y Ref >85y Age 1.02 (1.01-1.05) Mort et al. 200080 <65: 9.49 (5.67- Laitinen et al. 18.87) 2011134 65-74: 8.63 (7.14- 10.44) 75-84: 6.34 (5.80- 124

Factors PI PD (any) PI AD PI AX PI SH AP in Dementia Studies ORs (95%CI) ORs (95%CI) ORs (95%CI) ORs (95%CI) ORs (95%CI) 6.93) 85+: 4.18 (3.70-4.71) Ref: non-AP users 85+: 0.33 (0.14-0.76) Rhee et al. Ref. <85y 201178 70-74: ns Carey et al. 75-79: 1.17 (1.08- 2008119 1.27) 80-85: 1.28 (1.12- 1.39) >85: 1.39 (1.28- 1.51) Ref: 65-69y 65-74: 1.57 (0.81- Dalby et al. 3.06) 2008126 75-84: 2.19 (1.20- 4.00) 85+: 2.19 (1.05- 4.57) Ref: <65y 80-84: 0.93 (0.91-0.94) Lesen et al. 85+: 0.91 (0.90-0.93) 201015 Ref: 75-79y Marital status Never married 1.25 Lesen et al. 125

Factors PI PD (any) PI AD PI AX PI SH AP in Dementia Studies ORs (95%CI) ORs (95%CI) ORs (95%CI) ORs (95%CI) ORs (95%CI) (1.21-1.29) 201015 Divorced 1.18 (1.15- 1.21) Widowed 1.02 (1.01- 1.04) Ref: Married Living w. 0.19 (0.05-0.71) Rhee et al. caregiver 201178 Income Middle 1.08 (1.07-1.10) Lesen et al. Low 1.14 (1.13-1.16) 2010139 Ref: High income Low: 1.85 Préville et al. (1.16-2.96) 2012122 Ref: Χ2 : <15,000 Dionne et al. 2013117 Q1: 1.24 (1.10-1.39) Puyat et al. Q2: 1.09 (1.05-1.34) 2012121 Ref: Q4 Medicaid Yes: 0.59 (0.37-0.95) Mort et al. 200080 Medicare status MC: 0.84 (0.36-1.96)ǂ 1.21 (0.32-4.54)ǂ 0.79 (0.15- 14.87 (1.12- Aparasu et al. MC+MA: 0.97 (0.51- 1.26 (0.50-3.17) 4.15)ǂ 197.44)ǂ 200460 1.84) 0.81 (0.15-4.44) 1.67 (0.53-5.33) 0.16 (0.03-0.91) 126

Factors PI PD (any) PI AD PI AX PI SH AP in Dementia Studies ORs (95%CI) ORs (95%CI) ORs (95%CI) ORs (95%CI) ORs (95%CI) MC+Prv: 0.98 (0.30- 2.91 (0.36- 1.70 (0.04-71.24) 3.21) 23.26) Ref: MC+MA+Prv+other Dementia 7.93 (2.21-28.52) Chan et al. diagnosis 2007130 Psychiatric 0.17 (0.10-0.28) Dalby et al. diagnosis 2008126 Cognitive function Mod: 7.40 (2.12- Rhee et al. 25.89) 201178 Severe: 5.80 (1.30- 26.0) Ref. Mild Alzheimer’s 6.70 (1.11-40.23) Rhee et al. disease 201178 3-5: 1.14 (0.69- Dalby et al. 1.88) 2008126 No. of chronic 6+: 3.00 (1.30-6.88) diseases or Ref: 0-2 comorbidities Χ2 : >3 diseases Dionne et al. 2013117 Poor physical 4.94 (1.03-23.62) Chan et al. health rating 2007130 Self-perceived Χ2 : Med/bad Dionne et al.

127

Factors PI PD (any) PI AD PI AX PI SH AP in Dementia Studies ORs (95%CI) ORs (95%CI) ORs (95%CI) ORs (95%CI) ORs (95%CI) mental health 2013117 Self-perceived Χ2 : Med/bad Dionne et al. general health 2013117 Visit Factors Injury visit 3.57 (1.53-8.34) Aparasu et al. 199879 “Seen before” 2.17 (1.19-3.96) Mort et al. 200080 No. of ambulatory 0-49: 1.06 Préville et al. visits (1.02-1.10) 2012122 1.16 (1.02-1.33) Aparasu et al. 199879 No. of medications >3: 0.24 (0.11-0.51) Dalby et al. Ref: 0-3 2008126 No. of >2: 2.88 (1.80-4.60) ǂ Aparasu et al. psychotropics Ref: 1 200460 5.85 (3.24-10.58) Aparasu et al. 199879 Antidepressant use 2.93 (1.29-6.64) Mort et al. 200080 Anxiolytic use 1.85 (1.08-3.18) Aparasu et al. 199879 Antipsychotic use 0.32 (0.12-0.81) Mort et al. 200080 128

Factors PI PD (any) PI AD PI AX PI SH AP in Dementia Studies ORs (95%CI) ORs (95%CI) ORs (95%CI) ORs (95%CI) ORs (95%CI) Duration of use Χ2 : >90 days Dionne et al. 2013117 No. of pharmacies >2: 1.87 Préville et al. consulted (1.09-3.21) 2012122 Prescriber or Practice Characteristics Region NE 2.15 (1.09-4.23) Aparasu et al. South 2.52 (1.34-4.74) 199879 NE 0.25 (0.15-0.41) Mort et al. 200080 Metropolitan 0.66 (0.47-0.92) Mort et al. location 200080 Physician Psychiatry 0.22 Aparasu et al. Specialty (0.10-0.47) 199879

Key: ǂ: Unadjusted odds ratios reported; all others are adjusted odds ratios with 95% confidence intervals Q1: 1st (lowest) quintile, Q2: 2nd quintile, AP: antipsychotic, AX: anxiolytic, AX/SH: anxiolytics/sedative-hypnotics, MC: Medicare, MA: Medicaid, NE: North-East, PI: potentially inappropriate, Prv: private insurance, SH: Sedative-hypnotic, Χ2 : Significant association using Chi-squared test (p<0.05) 129

Table 8: Determinants of Potentially Inappropriate Medications, Potentially Inappropriate Psychotropic Medications and Psychotropic Medications among community-dwelling older adults

Determinants PIM Use PI Psychotropic Use Psychotropic Use Patient Characteristics Gender    Age   ǂ Race/ethnicity    Education --  ǂ Income   ǂ Marital status or living alone --   Health status    Medical history / chronic illnesses    Mental health or illness    Payment source / insurance --   Prescriber or practice Characteristics Region or MSA location    Specialty or type   -- Physician qualification --  -- Physician age --  -- Visit Factors Purpose or nature   -- Seen before   -- Prior ED or ambulatory visits    No. of medications    Therapeutic classes --  -- Duration of medication use --  -- No. of prior pharmacies consulted --  -- No. of prior prescribers  -- -- Key: : Significant in >1 study testing the variable : Not significant in studies testing this factor ǂ: Not significant in US study by Aparasu et al 2003114 or in US studies reviewed by Voyer et al 2004113 --: Not tested ED: emergency department, MSA: metropolitan statistical area, PI: potentially inappropriate 130

Table 9: Factors Not Associated with Potentially Inappropriate Psychotropic Use in Community-dwelling, Older Adults

Study PIP Model or Test Non-significant factorsa Aparasu et al. Age, sex, race, referral visit, repeat visit, insurance, Any PIP Multivariate LR 199879 qualification (Doctor of Medicine) Sex, race, payment method (managed care), referral visit, injury visit, surgery visit, no. of Mort et al. Any PIP Multivariate LR medications, no. of psychotropic agents, care by 200080 non-physician prescriber, ambulatory setting (office-based) Sex, race, education, metropolitan location, region, Any PIP Medicare status, health status Sex, race, education, location, region, Medicare AD status, number of psychotropics, health status Aparasu et al. Bivariate LR Age group, sex, race, education, location, region, 200460 AX Medicare status, number of psychotropics, health status Sex, race, education, location, region, number of SH psychotropics, health status Carey et al. Age, level of wealth (ACORN index) AP; ASH Multivariate LR 2008119 Age, Gender, education, region of residence, Préville et al. presence of chronic diseases, depressive disorder, BZD Multivariate LR 2012122 anxiety disorder, physician gender, number of BZD prescribers, age of physician Age, gender, marital status, education, area of Dionne et al. Pearson’s χ2 BZD residence, number of physicians, presence of 2013117 (df=1) psychiatric disorders Key: a: Not statistically significant at p<0.05 or 95% confidence interval of odds ratio includes 1. AD: antidepressants, AP: antipsychotics, ASH: anxiolytics/sedative-hypnotics, AX: anxiolytics, BZD: benzodiazepine, LR: logistic regression, PIP: potentially inappropriate psychotropic medication, SH: sedative-hypnotics 131

CHAPTER 3

METHODS

Study Design

A retrospective, secondary database analysis of a national survey of office-based

medical practices was undertaken to assess the choice of antidepressants and sedative-

hypnotics for older adults requiring these agents, and to assess the use of antipsychotics

for older adults with dementia.

National Ambulatory Medical Care Survey

Public use files of the National Ambulatory Medical Care Survey (NAMCS) 2010 administered by the National Center for Health Statistics, Centers for Disease Control and Prevention (NCHS-CDC) were used to identify visits by older adults in the US. The

survey documented visits to office-based practices and community health centers, exclusive of visits to collect previously prescribed medications, specimen delivery, claims or bill payment. Institutions where the patient’s care was the main responsibility of the institution over time were excluded. Actively practicing, non-federal doctors of medicine or osteopathy who were engaged in patient care activities were included. 132

Using lists from the American Medical Association and the American Osteopathic

Association, physicians were selected via multistage, probability cluster sampling. For

2010, 112 geographic segments of counties, groups of counties, county equivalents,

towns or townships within the 50 states and the District of Columbia were selected,

followed by a probability sample of 3,525 practicing physicians from the files of the

respective associations. Sampled physicians were interviewed to ensure they met the following inclusion criteria: office-based practice, engaged mainly in patient care activities, non-federally employed and not practicing in anesthesiology, pathology or radiology specialties.

Eligible physicians (2,406) were interviewed with study participation from 1,482, who were randomly assigned one week for record review. Records of patients seen in the assigned week were randomly selected from the physician’s log by office staff or survey field representatives. Physician and practice information, including specialty, location and type of practice were recorded separately from patient information. From the participating practices 32,229 patient record forms were completed for 1,292 practices: a response rate of 58.9% (57.3% weighted); 54.6% of patient records were abstracted by field representatives.143

Medications were coded by generic and therapeutic classifications using Lexicon

Plus®, by Cerner Multnum, Inc,144 and diagnostic codes followed the International

Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM).145

Missing data were imputed using randomly assigned values from patient record forms

from matching regions, specialties and diagnoses. Data entry and quality control

procedures were conducted in Durham, NC by SRA International, Inc.146 133

An application for ethical review was submitted to the Institutional Review Board

of the University of Georgia (Appendix A), which granted administrative exemption

(Appendix B), given the use of a public data set without traceable patient or physician

identifiers.

Independent Variables: Patient, Practice and Health-system Factors

John Eisenberg’s model of sociologic influences of clinical decision-making was applied as the theoretical framework using characteristics of the patient, the patient- physician relationship, physician and/or practice and physician-health system interactions

- Figure 1.65 Patient variables assessed were age, sex, median household income,

expected payment type, race, ethnicity, injury-related visit, the number of medications ordered, the presence of listed chronic conditions, and any Beers potentially inappropriate

drug-disease combinations.29 Patient race was based on three categories: white, black or

other, and patient ethnicity was identified as Hispanic/Latino or non-Hispanic/non-

Latino. The median household income recorded was based on the patient’s zip code.

Conditions that constituted potentially inappropriate antidepressant-disease or

antipsychotic-disease combinations, by Beers 2012 criteria were: delirium, dementia or

cognitive impairment, history of falls, history of fractures, syncope, constipation, lower

urinary symptoms or benign prostatic hyperplasia. Parkinson’s disease was also

considered an inappropriate condition for concurrent antipsychotic use. For sedative-

hypnotics, inappropriate conditions were delirium, dementia/cognitive impairment, 134

history of falls and/or fractures.29 ICD-9-CM codes for the various inappropriate

conditions are shown in Table 1.

Indicators of the patient-physician relationship were based on the degree of

interaction between the patient and physician, specifically: established patient (seen

before), time spent with physician and the number of extended physician services.65

Visits where no time was spent with the physician (time=0 minutes) were excluded.

Extended services were summed across visits to physicians who, in the last normal week

of practice: saw patients on evenings or weekends, visited hospitals, nursing homes or

other homes and/or held telephone, email or internet consultations.

In the absence of physician demographics, physician or practice characteristics

assessed were physician specialty, inclusion of other non-physician providers, geographic

region and metropolitan location. Physician-health-system relationship indicators were based on aspects that may introduce the influence of professional peers, administrative restrictions and/or clinical protocols.65 Selected variables used were: physician

ownership, solo practice and the use of electronic medical records (EMRs). The use of

EMRs was positive for practices that used EMRs alone or with paper records. 135

Potentially Inappropriate Antidepressants

Visits by adults aged 65 years or more, where an antidepressant was ordered or administered were selected. Visits were classified by the antidepressants ordered, based on the recommendations of the AGS/Beers criteria:

(i) Antidepressants to avoid unconditionally (Avoid): tertiary tricyclic

antidepressants (TTCAs);

(ii) Antidepressants to avoid in selected conditions or to use with caution

(Caution-Avoid): Selective serotonin reuptake inhibitors (SSRIs), serotonin-

norepinephrine reuptake inhibitors (SNRIs), secondary amine tricyclic

antidepressants (STCAs) and ; or

(iii) Antidepressants without specific recommendations or Unrestricted,

specifically buproprion, nefazodone and trazodone.

Visits with multiple antidepressants were cleaned to remove duplicate orders or drug codes. Where antidepressant categories overlapped, the profile was assigned to the

category of stricter recommendation. For instance, visits involving both tertiary TCAs

and antidepressants from the Caution-Avoid or Unrestricted group were classified as a

tertiary TCA profile, based on the recommendation to avoid in all elderly. Visits

involving Caution-Avoid antidepressants and Unrestricted antidepressants were classified

as Caution-Avoid profiles. Multnum® generic equivalent codes used to identify

antidepressants are shown in Tables 10-12. 136

Potentially Inappropriate Sedative-Hypnotics

Visits with orders for sedatives were classified into three groups, based on the nature of the recommendations of the 2012 AGS/Beers criteria:

(i) Avoid unconditionally: barbiturates, chloral hydrate or meprobamate;

(ii) Avoid for treatment of insomnia, agitation or delirium: benzodiazepines; or

(iii) Avoid chronic use (more than 90 days): non-benzodiazepines.

Given that no recommendation was provided for ramelteon, it was classified with non-benzodiazepine hypnotics, which carried the least strict restriction. Visits with more

than one sedative order were cleaned to remove duplicates. Where sedative categories

overlapped in one visit, the profile was assigned to the category of stricter

recommendation. For instance, where barbiturates were ordered along with

benzodiazepines or non-benzodiazepines during the visit, the profile was assigned to the

category, based on the recommendation to avoid in all elderly. Similarly

visits with benzodiazepines and non-benzodiazepine orders were classified as

benzodiazepine profiles. 137

Antipsychotics in Dementia

Patients living with dementia were identified based on the presence of either (i) the

presence of dementia or Alzheimer’s disease, based on ICD-9-CM codes – Table 1, or (ii)

orders for any of the four medications used to treat cognitive symptoms of Alzheimer’s

disease: rivastigmine, donepezil, or .

Visits were classified as either:

(i) Antipsychotic use, where either first-generation or second-generation or a

combination of antipsychotics were ordered, or

(ii) No antipsychotic use.

Antipsychotics were identified from medications recorded based on Multnum® coding.

Emergency Referrals and Hospitalizations

The survey captured outcomes of the visits, including referral to other physicians, under visit disposition. Referrals to emergency rooms or hospital admissions were identified from the dichotomous variable recorded. The reasons for referrals were not recorded by the survey. 138

Visits involving antidepressant orders were grouped as either (i) tertiary tricyclic antidepressants or (ii) other antidepressants, to determine the influence of the use of antidepressants with the severest restrictions (TTCAs) on emergency outcomes.

All sedatives, except ramelteon carry “avoid” recommendations with varying contexts, according to Beers 2012 criteria. To determine the influence of traditionally inappropriate sedative prescribing (benzodiazepines and barbiturates) on emergency outcomes, relative to the use of non-benzodiazepines, sedative orders were classified into two categories: (i) barbiturates, meprobamate or benzodiazepines, and (ii) non- benzodiazepine sedatives.

No adjustment of categories was required for the assessment of the influence antipsychotic use on emergency outcomes, in older adults with dementia.

Data Analysis

Analyses were conducted using the complex samples module of IBM SPSS

Statistics version 22.0 (IBM Inc., Armonk, NY). Descriptive statistics were used to describe categorical distributions of the various psychotropic agents ordered during visits.

The proportions of antidepressants and sedatives ordered were based on the number of visits requiring either of the psychotropic classes. Proportions of antipsychotic orders were based on the number of older adults with dementia. 139

Estimates of population prevalence, measures of association and statistical tests

were calculated using complex sampling analyses, which incorporated patient weights

and the sampling design.146 Measures of central tendency were used to describe the

distributions of continuous variables of age, number of medications, number of extended

physician services and time spent with physician. T-tests were used to compare differences between group means and the population mean for continuous variables.

Crosstabs were used to examine group proportions by antidepressant category, and

Pearson’s tests of independence were used to assess the presence of associations between independent categorical variables, including visit disposition. Where categories resulted in zero cell counts, these were pooled with the reference category or Other, to facilitate maximum likelihood estimation. Associations between interval level variables and psychotropic drug category or antipsychotic use were assessed using adjusted Wald tests

in bivariate logistic regressions, reporting unadjusted odds ratios.

Multivariate Model of Psychotropic Choice or Use

To measure the effects of individual factors or covariates on antidepressant

category prescribed, bivariate, multinomial logistic regression was used. Factors with

likelihood ratio (adjusted F) or modified Wald tests with significance p<0.25 were

selected for inclusion in the multivariate model. To determine the concurrent effects of

influential factors, a multivariate model was developed using forward stepwise entry. To

assess if the variable addition at each step was influential in the model, the model 140

significance, coefficient significance and the McFadden’s pseudo R-square values were

checked. Variables that produced unreliable estimates, indicated by large standard errors

or failure to achieve maximum likelihood estimates, were excluded from the model. The

significance of the model was assessed using the Wald F-test, and individual coefficients

were checked using t-tests and 95% confidence intervals of the adjusted odds ratios.

The multinomial, multivariate model for potentially inappropriate antidepressant or

sedative choice, for older users used:

(1) logit [p(D=1)] = ln [p(D=1)/p(D=3)] = β0 + β1x1 + β2x2 + β3x3 + … + βkxk + ε

(2) logit [p(D=2)] = ln [p(D=2)/p(D=3)] = β0 + β1x1 + β2x2 + β3x3 + … + βkxk + ε

Where p: probability of antidepressant choice or sedative choice (D=1, 2) relative to

reference category D=3, β0: model intercept, βi: effect coefficient for each independent variable (xi), and ε : estimated random error.

The binomial, multivariate model for antipsychotic use among older adults with

dementia used:

(3) logit [p(D=1)] = ln [p(D=1)/p(D=0)] = β0 + β1x1 + β2x2 + β3x3 + … + βkxk + ε

where p: probability of antipsychotic prescribing (D=1) relative to no antipsychotic

prescribing (D=0) 141

Sensitivity Analyses for Multiple Antidepressants or Sedative Orders

To check if the categorization of visits involving multiple antidepressants and

multiple sedatives affected the prevalence and model prediction, visits were reassigned to

the category of lower restriction. Comparisons were made between the revised category

proportions, the proportion of correctly predicted categories produced by the multivariate

model, and the McFadden pseudo R2 values.

Bivariate models of Emergency Outcomes

To measure the influence of antidepressant category, sedative category or the use

of antipsychotics on emergency referrals or hospitalizations, bivariate logistic regression

models were used, to estimate the relative risk ratios:

(4) logit [p(ER=1), x1] = ln [p(ER=1)/p(ER=0)] = β0 + β1x1 + ε where p1: estimated probability of emergency referral or hospitalization (ER=1), β0:

model intercept, β1: effect coefficient for antidepressant choice or sedative choice or

antipsychotic use (x1), and ε : estimated model error

For antidepressants, the association between the prescription of tertiary tricyclic

antidepressants (Avoid) and the probability of emergency referrals among users was

tested, relative to the prescription of Other (Caution-Avoid or Unrestricted)

antidepressants. To determine the influence of the choice of sedatives, the probability of 142

emergency referrals among users of barbiturates or benzodiazepines, relative to users of

non-benzodiazepines were tested.

To assess the influence of antipsychotic use, the probability of antipsychotic visits resulting in emergency referrals or hospitalizations were compared with visits without antipsychotic orders. The effect of psychotropic selection or use was measured using unadjusted relative risk ratios produced by the regression models. Model significance was assessed using the Wald F-test, and the significance of coefficients was assessed using t-tests and 95% confidence intervals of the unadjusted relative risk ratios. 143

Table 10a: Lexicon Multnum Drug and Therapeutic Codes: Tricyclic, Tetracyclic Antidepressants147

Multnum generic NAMCS Generic Name Brand names Therapeutic Classes Level 3 equivalent code drug codes Tricyclic Antidepressants 11065 Elavil 01525, Amitriptyline (3ry) d000146 Amitril, Endep, Enovil 11325, 209 tricyclic antidepressants SK-amitriptyline, Amitriptyline 11383, 28200 01530 Amitriptyline, Limbitrol, Limbitrol DS 10-25 17530, 01009 d03462 079 psychotherapeutic combinations chlordiazepoxide Amitriptyline/Chlordazepoxide 01532 Triavil 32290 Amitriptyline, Etrafon, Triptazine d03463 11920, 32565 079 psychotherapeutic combinations perphenazine Amitriptyline Hcl w/ perphenazine, 01535, 23524 Perphenazine w/ amitriptyline Anafranil 92154 Clomipramine (3ry) d00876 209 tricyclic antidepressants Clomipramine 93451 28085 Sinequan 070 miscellaneous anxiolytics, sedatives 00510, Doxepin (3ry) d00217 Adapin, Zonalon and hypnotics 94153, Doxepin, Doxepin HCl 209 tricyclic antidepressants 10325, 89025 31740 Tofranil 15510, Imavate, Janimine, Imipramine (3ry) d00259 16165, 209 tricyclic antidepressants Norfranil, SK-Pramine, 21285, Imipramine 28295, 15520 Surmontil 30235 Trimipramine (3ry) d00873 209 tricyclic antidepressants Trimipramine 32424 Asendin 02748 Amoxapine d00874 209 tricyclic antidepressants Amoxapine 01628 Norpramin, Pertofrane 21400, 23550 Desipramine d00145 209 tricyclic antidepressants Desipramine 09020 Nortriptyline d00144 Pamelor 22520 209 tricyclic antidepressants 144

Multnum generic NAMCS Generic Name Brand names Therapeutic Classes Level 3 equivalent code drug codes Aventyl HCl 03070 Nortriptyline 21403 Vivactil 34685 Protriptyline d00875 209 tricyclic antidepressants Protriptyline 98110 Tetracyclic, unicyclic Wellbutrin, Wellbutrin SR 61605, 01064 Wellbutrin XL, Zyban 03184, 97034 076 miscellaneous antidepressants Buproprion d00181 Buproprion SR, Budeprion SR 03156, 07081 320 smoking cessation agents Budeprion XL, Aplenzin 07229, 09252 Bupropion XL, Bupropion 09587, 93355 Ludiomil 17978 Maprotiline d00877 307 tetracyclic antidepressants Maprotiline 18373 Remeron 96122 Mirtazapine d04025 307 tetracyclic antidepressants Mirtazapine 97056 145

Table 10b: Lexicon Multnum Drug and Therapeutic Codes: SSRIs, SNRIs, Serotonin Antagonists147

Multnum generic NAMCS Generic Name Brand names Therapeutic Classes Level 3 equivalent code drug codes SSRIs Celexa 98115 Citalopram d04332 208 SSRI antidepressants Citalopram 99074 Lexapro 02119 d04812 208 SSRI antidepressants Escitalopram oxalate 02256 Prozac 25674 Fluoxetine d00236 Sarafem, Serontil 00318, 08533 208 SSRI antidepressants Fluoxetine 91079 Symbyax 04043 Fluoxetine, Olanzapine d04917 079 psychotherapeutic combinations Olanzapine/ fluoxetine 08064 Luvox, Luvox CR 95025, 09978 Fluvoxamine d03804 208 SSRI antidepressants Depromel, Fluvoxamine 09779, 95024 Paxil, Paxil CR 93212, 02316 Paroxetine d03157 208 SSRI antidepressants Pexeva, Paroxetine 04573, 94004 Zoloft 93183 Sertraline d00880 208 SSRI antidepressants Sertraline 93193 not found SSNRIs Desvenlafaxine d07113 Pristiq 08234 Cymbalta 04051 d05355 Duloxetine 04576 d06635 Savella 09854 308 SSNRI antidepressants Effexor, Effexor XR 94070, 01029 Venlafaxine d03181 Flavix-XR 04524 venlafaxine 94014 Serotonin (5-HT2) Antagonists Serzone 95080 Nefazodone d03808 Nefazodone 95141 306 antidepressants Desyrel 40520 Trazodone d00395 Trazodone 31997

146

Key:

3ry: tertiary, NAMCS: National Ambulatory Medical Care Survey, SSRI: selective serotonin reuptake inhibitor, SSNRI: selective serotonin- norepinephrine reuptake inhibitor, SNRI: serotonin-norepinephrine reuptake inhibitor, 5-HT2: serotonin 147

Table 11a: Lexicon Multnum Drug and Therapeutic Codes: Barbiturates147

Multnum generic NAMCS drug Generic Name Brand names Therapeutic Classes Level 3 equivalent code codes Amytal 01725 d00171 Amobarbital 01595 Duobarbital 10625 068 barbiturates Lanabarb No. 1 16985 Amobarbital- d04005 Lanabarb No. 2 16986 Tuinal 32720 Amobarbital- a11193 Bartrate 03630 089 anticholinergics/ & Amytal 11455 Ephedrine sulfate & Amobarbital- ephedrine a11194 11485 131 antiasthmatic combinations amobarbital 01600 Amobarbital- ephedrine Asthmacon Aminophylline amobarbital 02850 d03929 Aminophylline ephedrine 131 antiasthmatic combinations ephedrine 01470 amobarbital Buta-Kay Elixir 05065 Butalix 05105 Butatran 05108 d00923 068 barbiturates Buticaps 05135 Butisol 05145 Butabarbital 05070 Acetaminophen, a11119 Coastalgesic 07085 063 combinations butabarbital, Acetaminophen, a11048 G-3 40765 060 butabarbital, codeine Aluminium hydroxide; aminophylline; 02765 butabarbital; a11245 Asmacol 131 antiasthmatic combinations chlorpheniramine; ; butabarbital; a10350 Sedragesic 27713 110 miscellaneous uncategorized agents 148

Multnum generic NAMCS drug Generic Name Brand names Therapeutic Classes Level 3 equivalent code codes ; phenobarbital; secobarbital Amylase; belladonna; butabarbital; cellulase; a10680 Butibel-zyme 05130 096 miscellaneous GI agents proteinase Atropine; butabarbital; ; ; a11211 Hyonatol 40865 096 miscellaneous GI agents Atropine; butabarbital; hyoscyamine; a11205 Neoquess 20710 089 anticholinergics/ antispasmodics phenobarbital; scopolamine Atropine; butabarbital; Butabell-HMB 05080 a11210 089 anticholinergics/ antispasmodics hyoscyamine; scopolamine Hytonatol-B 15300 Butabarbital; ephedrine; a11175 Quibron Plus Elixir 25990 132 upper respiratory combinations ; theophylline Butabarbital; ; 070 misc anxiolytics, sedatives and a11376 S.B.P. Plus 27215 phenobarbital; secobarbital hypnotics Butabarbital; phenobarbital; Butseco 05150 070 misc anxiolytics, sedatives and a11375 secobarbital Tribarb 32295 hypnotics 00311, 03515, Bupap, Bancap, Marten-Tab, 04149, 05029, Dolgic, Cephadyn, Promacet, 06062, 07403, Acetaminophen- d03456 063 analgesic combinations Tencon, Phrenilin, Bucet, 09898, 24140, Axocet, Phrenilin Forte 93034, 95099, 96004 Acetaminophen; butalbital; G-2 13152 a11049 060 narcotic analgesics codeine Phrenilin w. Codeine No.3 24143 Medigesic, Repan 00087, 01031 Zebutal, Anoquan, Butalbital 01066, 02050 APAP; butalbital; caffeine d03455 063 analgesic combinations w. acet, Geone, 05098, 05136 Tencet, Ezol, Pacaps, Anolor, 06267, 07004 149

Multnum generic NAMCS drug Generic Name Brand names Therapeutic Classes Level 3 equivalent code codes Ide-CET, Arcet, 07226, 08182 Dolgic Plus, Esgic, G-1, 08391, 09747, Margesic, Two-Dyne, 09833, 11688 Fioricet, Isocet, Triad, Esgic 13151, 18430 Plus, 32890, 60595 Butalbital/APAP/Caffeine 92047, 93159, 94149 91009, APAP; butalbital; caffeine; Esgic w. Codeine 11689 d03425 191 narcotic analgesic combinations codeine Fioricet w. Codeine 95178 Codeine; acetaminophen; n08029 Floricet 08439 063 analgesic combinations caffeine; butalbital Aspirin-butalbital d03458 Axotal 03110 063 analgesic combinations 04995 Buff-A-Comp 07655, 12550 Fiortal, Fiorinal, Isollyl ASA; butalbital; caffeine d03457 15980, 17105 063 analgesic combinations Lanorinal, Marnal, Fortabs 18440, 40760, Butalbital/aspirin/caffeine 40250 Fiorinal No.1, Fiorinal No.3 12555, 12565 ASA; butalbital; caffeine; Fiorinal w. codeine 12570, d03426 191 narcotic analgesic combinations codeine Isollyl w. codeine 15983 ABC w. codeine 40020 Fiorinal No.2 12560 ASA; butalbital; caffeine; a10604 Anti Ten, Butalbital w. APC, 02130, 05100 060 narcotic analgesics phenacetin Idenal 15460 Butalbital d03061 Butalbital 05095 068 barbiturates Butalbital w. codeine a11076 Butalbital-codeine 05103 060 narcotic analgesics Nembutal 20505 d00335 068 barbiturates Pentobarbital 23310 068 barbiturates Pentobarbital, ephedrine a11371 Ephedrine and Nembutal-25 11475 127 Pentobarbital, n09169 Pentobarbital / propofol 09539 072 general 150

Multnum generic NAMCS drug Generic Name Brand names Therapeutic Classes Level 3 equivalent code codes Pentobarbital, pyrilamine a11372 Wans Supprette No.2 61600 123 antihistamines Belladonna; caffeine; Migergot P-B 19363 d03461 193 antimigraine agents ; pentobarbital Ergocomp-PB 89026 Mysoline, , 20135, 25055 Primidone d00352 201 barbiturate anticonvulsants Primidone, Sterasoline 25060, 29450 Seconal 27650 Secobarbital d00368 068 barbiturates Secobarbital 96096 Aspirin-Secobarbital a11187 Aspirin-secobarbital 02840 063 analgesic combinations Aladrine 00720 Ephedrine-secobarbital a11412 132 upper respiratory combinations Ephedrine & Seconal sodium 11465 Barbita, Luminal 03570, 18015 Phenobarbital 23845, 23870 068 barbiturates Phenobarbital d00340 Phenobarbital elixir, 23905, 28290 201 barbiturate anticonvulsants Phenobarbital sodium, 28755 SK-Phenobarbital, Solfoton 070 miscellaneous anxiolytics, sedatives ; phenobarbital a10012 Quiess 26010 and hypnotics Aluminium hydroxide; aminophylline; ephedrine; a11379 Dainite 08365 131 antiasthmatic combinations phenobarbital; potassium iodide Alumin hydroxide; atropine; hyoscyamine; a11206 Pink Cocktail 94041 096 miscellaneous GI agents phenobarbital; scopolamine Alumin hydroxide; ; a10469 Anaids 01765 096 miscellaneous GI agents phenobarbital Alumin hydroxide; atropine; magnesium a10467 P.H. Plus 61115 096 miscellaneous GI agents antacids; phenobarbital Alumin hydroxide; a11171 Alerbuf 00855 131 antiasthmatic combinations ephedrine; phenobarbital;

151

Multnum generic NAMCS drug Generic Name Brand names Therapeutic Classes Level 3 equivalent code codes theophylline Aminophylline; a10693 Ruphenamin 27128 110 misc uncategorized agents phenobarbital; rutin Aminophylline; a11380 Aminophylline phenobarbital 01475 131 antiasthmatic combinations phenobarbital Amylase, cellulose, homatropine, methyl a10896 Gustase Plus 13915 096 miscellaneous GI agents , phenobarbital, protease Anisotropine Anisotropine w. phenobarb 02030 methylbromide; a10923 089 anticholinergic/ antispasmodics Valpin 50 PB 61550 phenobarbital Ascorbic acid; calcium carbonate; phenobarbital; a11377 Calsuxaphen 05540 110 miscellaneous uncategorized agents salicylamide Aspirin; caffeine, Febro-bar 12155 a10609 059 miscellaneous analgesics phenacetin; phenobarbital Phenocoid 23735 059 miscellaneous analgesics Aspirbar 02800 Aspirin; phenobarbital a11185 070 miscellaneous anxiolytics, sedative Aspirin w. phenobarbital 02830 and hypnotics Atropine, ethanol; Barophen elixir 03605 hyoscyamine; a11207 096 miscellaneous GI agents Spasquid elixir 29045 phenobarbital; scopolamine Atropine; hyoscine; a10965 Donna-Phen elixir 10190 089 anticholinergics/ antispasmodics hyoscyamine; phenobarbital Atropine; magnesium a10466 Triatrophene 32285 096 miscellaneous GI agents antacids; phenobarbital Antrocol 02275 Atropine; phenobarbital d03490 Atropine & phenobarbital 02935 089 anticholinergics/ antispasmodics Phenobarbital & atropine 23850 Belladonna; ergotamine; Bellergal 03845 d03495 089 anticholinergics/ antispasmodics phenobarbital Bellaspas 06317

152

Multnum generic NAMCS drug Generic Name Brand names Therapeutic Classes Level 3 equivalent code codes Sterabel 29375 Bel-phen-ergot 93444 Phernerbel-S 98052 Belladonna; kaolin; Bellkatal, 03860 a11384 090 antidiarrheals phenobarbital Kaophen 16370 Belap, Belladenal 03770, 03785 Belladenal-S, Bellophen 03790, 03870 Belladonna; phenobarbital a11383 Chardonna-2, Phenobarbital 089 anticholinergics/ antispasmodics 06280, 23855 & belladonna 23920 Phenobella Bellafoline; ergotamine; a10958 Bellergal-S 03850 096 miscellaneous GI agents phenobarbital Calcium carbonate; nux a10538 Pheno Nux 23835 088 antacids vomica; phenobarbital Calcium replacement; guaifenesin; hyoscyamine; a10112 Gylanphen 13920 132 upper respiratory combinations phenobarbital; terpin hydrate Chlorpheniramine; ephedrine; phenobarbital; a11167 Tedral Anti-H 30675 132 upper respiratory combinations theophylline Dehydrocholic acid; homatropine; methyl a10895 G.B.S. 13135 095 laxatives bromide; phenobarbital Dehydrocholic acid; homatropine; ox bile a10549 Bilamide 04265 096 miscellaneous GI agents extract; phenobarbital Dehydrocholic acid; a11389 Cholan HMB 06680 096 miscellaneous GI agents homatropine; phenobarbital Bentyl w. phenobarbital 04005 Dicyclomine; phenobarbital a11387 Dibent PB 09380 089 anticholinergics/ antispasmodics Dicyclomine HCl w. 09460 153

Multnum generic NAMCS drug Generic Name Brand names Therapeutic Classes Level 3 equivalent code codes phenobarbital Ephedrine; ethanol; guaifenesin; phenobarbital; a11170 Bronkolixir (npd) 04915 131 antiasthmatic combinations theophylline 02750 Ephedrine; guaifenesin; Asma syrup, Bronkotabs; 04930 a11169 131 antiasthmatic combinations phenobarbital; theophylline Guiaphed; Tedral expectorant 13858 30685 Ephedrine; isoproterenol; Isolate compound elixir 15970 phenobarbital; potassium a11168 132 upper respiratory combinations Isuprel compound elixir 16125 iodide; theophylline Asma-Lief, Asminyl Azma-Aid, Azpan 02755, 2785 Tedrigen, Ephenyllin 03130, 03223 Phedral, Primatene-P 06042, 11505 Respirol, T.E.P, T-E-P 23630, 25050 Tedral, Tedral Elixir 26445,30435 Tedral SA, Tedral-25 30450, 30670 Ephedrine; phenobarbital; d03280 Theocliman, Theofed 30680, 30690 131 antiasthmatic combinations theophylline Theofedral, Theofenal 30695, 31170, Theophed, Theophenylline 31180, 31185 Theophylline compound 31190, 31215 Theophylline-ephedrine- 31219, 31240 phenobarbital, Theotabs 31260, 31270 Val Tep, Theoph-ephedrine & 33490, 61460 phenob Bowdrin 04610 Ephedrine & phenobarbital Ephedrine; phenobarbital a11378 11460 132 upper respiratory combinations Ephedrine & sodium 11470 phenobarbital Ergotamine; hyoscyamine; a11283 Bellergotal 03855 089 anticholinergics/ antispasmodics phenobarbital Ergotamine; l- of a10983 Bellamine 03376 089 anticholinergics/ antispasmodics 154

Multnum generic NAMCS drug Generic Name Brand names Therapeutic Classes Level 3 equivalent code codes belladonna; phenobarbital Erythrityl tetranitrate- a10305 Cardilate-P 05765 110 miscellaneous uncategorized agents phenobarbital Ethaverine; pentaerythritol; a10590 Papavatral w. phenobarbital 22765 096 miscellaneous GI agents phenobarbital Glycopyrrolate; Robinul-PH 26815 a11385 089 anticholinergics/ antispasmodics phenobarbital Robinul-PH Forte 26820 Hexestrol-phenobarbital a10349 Phenobarbital & hexestrol 23860 110 miscellaneous uncategorized agents Homatropine; hyoscyamine; pancreatin; a11381 Panzyme 22745 096 miscellaneous GI agents phenobarbital; scopolamine Homatropine; kaolin; a11390 Lanokalin 17065 096 miscellaneous GI agents phenobarbital Homalyn 14635 Homatropine; phenobarbital a11391 132 upper respiratory combinations Matropinal 18485 Hyoscyamine; a10586 Somlyn w. phenobarbital 28835 089 anticholinergics/ antispasmodics extract; phenobarbital Hyoscyamine; 070 miscellaneous anxiolytics, sedatives phenobarbital; thiamine; a11382 Sedatans 27705 and hypnotics Anaspaz-PB 01845 Hyoscyamine; Levsin-PB drops 17380 d03494 089 anticholinergics/ antispasmodics phenobarbital Levsinex/ phenobarbital 17395 Levsin/ phenobarbital 60820 Isosorbide dinitrate; a11317 Isordil w. phenobarbital 16100 045 agents phenobarbital Magnesium hydroxide- belladonna; ergotamine; n09027 Maalox / Donnatal 09034 096 miscellaneous GI agents phenobarbital Mannitol hexanitrate; Mannitol hexanitrate w. a10954 18355 053 vasodilators phenobarbital phenobarbital ; phenobarbital a11386 Cantil w. phenobarbital 05605 089 anticholinergics/ antispasmodics 155

Multnum generic NAMCS drug Generic Name Brand names Therapeutic Classes Level 3 equivalent code codes Oxyphenycylimine; a10558 Daricon PB 08465 089 anticholinergics/ antispasmodics phenobarbital Pancreatin; phenobarbital; a11388 Phazyme PB 23615 172 miscellaneous otic agents simethicone Pentaerythritol tetranitrate; P.E.T.N w. phenobarb 22370 a10955 045 antianginal agents phenobarb Peritrate w. phenobarb 23495 Phenobarbital; potassium 070 miscellaneous anxiolytics, sedatives a10632 Neuroval elixir 20825 bromide; and hypnotics Phenobarbital; a11180 Dilantin w. phenobarbital 09590 204 miscellaneous anticonvulsants Pro-banthine w. Phenobarbtial; phenobarbital; 25110 a11374 089 anticholinergics/ antispasmodics proprantheline w. 25480 phenobarbital Phenobarbital; sodium Hypertabs; 15225 a10721 051 miscellaneous cardiovascular agents nitrate Soniphen 28860 Pathilon w. phenobarbital Phenobarbital; 22925 a10782 Pathilon w. phenobarbital 089 anticholinergics/ antispasmodics chloride 22930 sequel

Key:

GI: gastrointestinal, NAMCS: National Ambulatory Medical Care Survey

156

Table 11b: Lexicon Multnum Drug and Therapeutic Codes: Chloral Hydrate, Non-benzodiazepine, Other Sedative- hypnotics147

Multnum generic NAMCS drug Generic Name Brand names Therapeutic Classes Level 3 equivalent code codes Aquachloral supprette 02455 Somnote, Cohidrate 04557, 07255 070 miscellaneous anxiolytics, sedative Chloral hydrate d00147 Kessodrate, Noctec 16565, 21205 hypnotics SK-Chloral hydrate 28230 Chloral hydrate 06440 Coprobate, Equanil 07630, 11585 Kesso-bamate, Meprospan 16545, 18800 070 miscellaneous anxiolytics, sedative Meprobamate d00288 Miltown, Sedabamate 19415, 27680 hypnotics SK-Bamate, Tranmep 28220, 31930 Meprobamate 18795 Equagesic, Meprogesic 11580, 18798 063 analgesic combinations Aspirin-meprobamate d03448 Micrainin 19295 179 skeletal muscle relaxant combinations Meprobabmate w. aspirin 92053 070 miscellaneous anxiolytics, sedative -meprobamate a10042 Deprol 08905 hypnotics Conjugated estrogens; Milprem 19410 d04202 186 combinations meprobamate PMB 24350 070 miscellaneous anxiolytics, sedative Meprobamate; pentaerythritol a10589 Miltrate 19420 hypnotics Milpath, Parothyl 19405, 22888 Meprobamate; tridihexethyl Pathibamate, Spenpath 070 miscellaneous anxiolytics, sedative a10781 22910, 29200 chloride Tridihexethyl Cl 25mg / hypnotics 32350 Meprobamate Non-benzodiazepine hypnotics Lunesta 05033 070 miscellaneous anxiolytics, sedative Eszopiclone d05421 Eszopiclone 09213 hypnotics Sonata 00039 070 miscellaneous anxiolytics, sedative Zaleplon d04452 Zaleplon 02107 hypnotics Zolpidem d00910 Ambien CR, Ambien 06002, 93347 070 miscellaneous anxiolytics, sedative 157

Zolpidem 09614 hypnotics Zolpidem tartrate 94035 Antagonist Rozerem 05244 070 miscellaneous anxiolytics, sedative Ramelteon d05578 Ramelteon 09603 hypnotics 158

Table 11c: Lexicon Multnum Drug and Therapeutic Codes: Benzodiazepines147

Multnum generic NAMCS drug Generic Name Brand names Therapeutic Classes Level 3 equivalent code codes Xanax, Xanax XR 35023, 03185 Alprazolam d00168 Niravam, Tafil, 05164, 07672 069 benzodiazepines Alzolam, Alprazolam 09228, 91062 Librium, 17450, Chlordiazepoxide d00189 ICN Azepox, Libritabs 15425, 17445 069 benzodiazepines SK-LYgen, Chlordiazepoxide 28270, 06495 Chlordiazepoxide, Limbitrol, Limbitrol DS 10-25 17530, 01009 d03462 079 psychotherapeutic combinations amitriptyline Amitriptyline/Chlordazepoxide 01532 Librax 17440 Chlordinium Salts 06503 Clindex, Clipoxide, Chlordiazepoxide, 06930, 06940 d03492 Lidinium, Lidoxide, 089 anticholinergics/ antispasmodics clidinium 17480, 17513 Clinoxide 94057 Chlordiazepox hcl w. clidinium 06500 bromide Chlordiazepoxide, a11218 Menrium 18735 187 miscellaneous sex hormones conjugated estrogens Chlordiazepoxide, d05416 Zebrax 05238 089 anticholinergics/ antispasmodics methscopolamine Tranxene, Tranxene T 31945, 09462 Clorazepate dipotassium Clorazepate d00198 02129, 03125 069 benzodiazepines Azene, Clorazetabs 05139, 06993 Clorazepate Rivotril, Klonopin, Clonopin 08563, 60790 069 benzodiazepines Clonazepam d00197 Clonazepam 06990, 06980 203 benzodiazepine anticonvulsants Valium, Valium Rx Pak 33555, 33558 Dizac, Diastat acudial, 00252, 09964, 069 benzodiazepines Diazepam d00148 Valrelease, Zetran, Diastat, 33574, 35230, 203 benzodiazepine convulsants Diazepam 99213, 09370 d00915 Prosom 92080 069 benzodiazepines

159

Estazolam 92123 Dalmane 08390 Flurazepam d00238 069 benzodiazepines Flurazepam 12810 Paxipam 23006 Halazepam d00904 069 benzodiazepines Halazepam 09517 Ativan, / Ativan 069 benzodiazepines 02900, 07489 Lorazepam d00149 Compound, 198 miscellaneous 17888 Lorazepam 203 benzodiazepine anticonvulsants Versed 61570 d00301 069 benzodiazepines Midazolam HCl 93262 Serax 27855 Oxazepam d00040 069 benzodiazepines Oxazepam 22242 Doral 92022 Quazepam d00917 069 benzodiazepines Dormalin, Quazepam 10257, 92148 Restoril 26453 Temazepam d00384 069 benzodiazepines Temazepam 30756 Halcion, Triaz 13999, 96068 Triazolam d00397 069 benzodiazepines Triazolam 93419

Key:

NAMCS: National Ambulatory Medical Care Survey 160

Table 12: Lexicon Multnum Drug and Therapeutic Codes: Antipsychotics147

Multnum generic Generic Name Brand names NAMCS drug codes Therapeutic Classes Level 3 equivalent code

Second-generation Antipsychotics

Abilify 02203 Aripiprazole d04825 Aripiprazole 04014 d07473 Saphris 09391 Fazaclo 06278 Denzapine 08586 Clozapine d00199 Clozaril 92016 341 atypical antipsychotics Clozapine 92115 Zyprexa Zydis, Zyprexa, 02137, 96173 Olanzapine d04050 Zydis 08166 Olanzapine 97002 Olanzapine + Symbyax 04043 d04917 079 psychotherapeutic combinations Fluoxetine Olanzapine/ fluoxetine 08064 Invega 07076 d06297 paliperidone 09334 Seroquel, Seroquel XR 97168, 08212 Quetiapine d04220 Quetiapine fumarate 99101 341 atypical antipsychotics Risperidal, Risperidal Consta, 94158, 04278 Risperidone d03180 Zoridal 08631 Risperidone 94104 Geodon 01036 Ziprasidone d04747 Ziprasidone HCL 01043 First-generation Antipsychotics 161

Multnum generic Generic Name Brand names NAMCS drug codes Therapeutic Classes Level 3 equivalent code Thorazine 31550 Chloramead 06460 Clorazine 06995 196 antiemetics Chlorpromazine d00064 Ormazine 22045 210 phenothiazine antipsychotics Promapar 25345 Promaz 25350 Chlorpromazine 06620 Prolixin 25330 Prolixin D 00148 Fluphenazine d00237 Motival 07671 210 phenothiazine antipsychotics Permitil 23505 Fluphenazine decanoate 92033 Fluphenazine + n08238 Haloperidol / fluphenazine 08612 251 antipsychotics haloperidol Haldol 14000 Haldol decanoate 01038 Haloperidol d00027 Haloperidol 14040 01193 Haloperidol lactate 91028 077 miscellaneous antipsychotic agents Loxitane 17945 Loxapine d00897 Loxapine 17940 Moban 19575 d00896 Molindone 96099 Trilafon 32395 196 phenothiazine antiemetics Perphenazine d00855 Perphenazine 23523 210 phenothiazine antipsychotics

Triavil 32290 Etrafon 11920 Perphenazine + Triptazine d03463 32565 079 psychotherapeutic combinations amitriptyline Amitriptyline Hcl w/ 01535 perphenazine 23524 Perphenazine w/ amitriptyline 162

Multnum generic Generic Name Brand names NAMCS drug codes Therapeutic Classes Level 3 equivalent code Orap 21958 Pimozide d00898 077 miscellaneous antipsychotics Pimozide 92144 Compro 07307 Compazine 07470 Pazine 23008 196 phenothiazine antiemetics Prochlorperazine d00355 Prochloperazine 25220 210 phenothiazine antipsychotics Prochlorperazine edisylate 89068 Prochlorperazine maleate 89069 Com-Pro-San 07418 Combid 07430 Isopro T.D. Prochlorperazine w. 16010 a10446 Isopropazine 096 miscellaneous GI agents isopropramide 16015 Prochlorbid Lanacap 25218 Prochlorperazine w/ 25223 Prozine 50 25680 Sparine 29015 196 phenothiazine antiemetics Promazine d00356 Sparine syrup 29020 210 phenothiazine antipsychotics Promazine 25355 Mellaril 18670 Thioridazine d00389 Sk-Thioridazine 41465 210 phenothiazine antipsychotics Thioridazine 31543 Navane 20435 Thiothixene d00391 Taractan 30595 280 Thiothixene 31542 Serentil 27865 Mesoridazine d00889 210 phenothiazine antipsychotics Mesoridazine 95146 Key:

GI: gastrointestinal, NAMCS: National Ambulatory Medical Care Survey 163

CHAPTER 4

RESULTS AND DISCUSSION: POTENTIALLY INAPPROPRIATE

ANTIDEPRESSANTS

Results

Population Characteristics

Eight thousand, one hundred and fifty-six sampled visits were extracted from the survey (258,976,010 weighted visits). Antidepressants were ordered in 823 sampled visits, representing 24,942,644 visits (9.6%) to physician offices or community health centers. Patients were aged from 65 to 99 years, with mean population age of 74.8 years

(SE=0.357). Approximately 62% of patients were female, 95% were white and almost

6% were Hispanic – Table 13.

Antidepressants Ordered

Nine hundred and four antidepressant orders were issued during the sampled

visits, estimating 27,277,298 drug mentions. Selective serotonin reuptake inhibitors and

other Caution-Avoid antidepressants were ordered during 20,024,152 (80.3%) visits. 164

Tertiary tricyclic antidepressants were ordered during 2,532,624 visits (10.2%), and

Unrestricted antidepressants were ordered during 3,991,618 visits (16%).

Approximately 2.3 million visits (9.4%) involved more than one antidepressant, including 1,605,750 visits (6.4%) involving antidepressants from multiple categories.

Sertraline, citalopram and escitalopram were the three most frequently mentioned antidepressants, and amitriptyline was the most common tertiary tricyclic antidepressant ordered – Table 14.

After classification of visits, potentially inappropriate antidepressants were ordered during 22,169,531 visits: 88.9% of antidepressant users, or 8.6% of all older adults. Of these 2,532,624 visits (10.2%, SE=1.6) were potentially inappropriate due to orders for tertiary TCAs (Avoid); 19,636,907 visits (79%, SE=2.1) were potentially inappropriate due to orders for SSRIs or other Caution-Avoid antidepressants; and

2,773,113 visits (11%, SE=1.3) were potentially “appropriate” due to orders for

Unrestricted antidepressants. Sensitivity checks following the reclassification of antidepressant profiles, where visits involving tertiary TCAs and SSRIs or other Caution-

Avoid antidepressants were moved to the latter category, resulted in changes of less than

2%: tertiary TCA profiles 8.6%, Caution-Avoid antidepressants 80.3%.

Categorical Differences

Statistically significant associations were identified between antidepressant profile and age, household income, depression, osteoporosis, metropolitan location and 165 the use of electronic medical records. The mean consultation time of patients receiving tertiary TCAs was significantly less than the population mean (p=0.004) – Table 13.

Across the four income quartiles, the highest proportion of Avoid antidepressants was found among patients in the first quartile, while the highest proportion of Caution-

Avoid antidepressants occurred in the third quartile. Approximately 68% of visits involving an antidepressant order occurred in the absence of a diagnosis for depression.

Most antidepressant orders were for those in the Caution-Avoid group, but a higher proportion was ordered for patients with depression than without depression. Patients with osteoporosis were present in 8% of antidepressant visits. Significantly less Avoid antidepressants and more Caution-Avoid antidepressants were ordered for this group than expected.

Approximately 87% of visits were to practices in metropolitan locations, which ordered mostly SSRIs and other cautionary antidepressants. However, practices in non- metropolitan areas ordered more Avoid and Unrestricted antidepressants than metropolitan practices. Practices that used EMRs were identified among 59% of visits.

Among these, more Unrestricted antidepressants and less Caution-Avoid antidepressants were ordered than in practices without EMRs. 166

Determinants of Potentially Inappropriate Antidepressant Prescribing

At the bivariate level, an inverse relationship between age and the odds of prescribing of tertiary TCAs and Caution-Avoid antidepressants, compared with

Unrestricted antidepressants was found. Similarly, relative odds ratios for both antidepressant categories were reduced with the use of EMRs. The odds of tertiary TCA versus Unrestricted prescribing were lower in patients of black race and for those living with asthma, depression or osteoporosis. Higher odds ratios for Caution-Avoid

antidepressants were found in injury-related visits, third income quartile and visits to

cardiovascular physicians. The multivariate model included: age, race, household

income, expected payment type, injury visit, asthma, depression, osteoporosis,

consultation time, specialty, metropolitan location and the use of EMRs – Table 15.

Controlling for identified confounders, as the patient’s age increased, the adjusted

odds of receiving tertiary TCAs, compared with Unrestricted antidepressants, decreased

by 6.4% with each additional age-year. Visits by patients with asthma were associated

with 88% lower adjusted odds, and visits by patients of black race were associated with

97.6% lower adjusted odds of orders for tertiary TCAs over Unrestricted antidepressants.

With each additional minute spent with the physician, the adjusted odds of orders for

tertiary TCAs over Unrestricted antidepressants were reduced by 7.6%. The use of

EMRs was associated with 61% lower adjusted odds of tertiary TCA prescribing.

The adjusted odds of Caution-Avoid antidepressant ordering for among patients

with depression were twice that of Unrestricted antidepressants, and 2.8 times higher for

patients in the third income quartile ($40,627-$52,387). Visits to cardiovascular 167

specialties were associated with more than four times higher odds of Caution-Avoid

versus Unrestricted orders, and visits to physicians or practices using EMRs were associated with 73.6% lower odds.

The model correctly predicted 79.7% of the antidepressant profile categories.

Sensitivity analyses of the model using revised assignment of visits with tertiary TCAs

and Caution-Avoid antidepressants, identified a small change in the classification. The revised model correctly predicted 80.6% of the sedative categories (McFadden pseudo

R2=0.152).

Emergency Referrals and Hospitalizations

Emergencies occurred in approximately 168,644 visits, where 75.5% of referrals

involved an order for a tertiary TCA – Table 13. None of the visits resulting in

emergency referral or hospitalization involved Unrestricted antidepressants. Likelihood

ratio tests identified a significant association between prescribing of tertiary TCAs and

emergency outcomes. Prescribing of tertiary TCAs was associated with more than 28

times greater risk of emergency referral or hospitalization (relative risk ratio=28.637,

3.793-216.216, p=0.001) among users than for other antidepressants. 168

Discussion

This study found that SSRIs and other Caution-Avoid antidepressants were frequently ordered for older adult users, with relatively conservative use of tertiary tricyclic antidepressants. Determinants of potentially inappropriate antidepressant prescribing for older users are age, race, depression, asthma, cardiovascular specialty, the

duration of the consultation and the use of electronic medical records (EMRs). A higher

risk of emergency referral or hospitalization following the visit was found among older

adult users of tertiary tricyclic antidepressants.

Most ambulatory visits by older adults, where an antidepressant was ordered,

involved a Beers-defined potentially inappropriate antidepressant for use with caution or

to avoid in selected conditions: SSRIs, SSNRIs or secondary TCAs. This is in

concordance with practice guidelines of the American Psychiatric Association where

SSRIs and other second-generation antidepressants are recommended for use in older

adults.10 Although the serotonin antagonists, nefazodone and trazodone were classified

as Unrestricted, their use in elderly is still cautioned due to sedative effects. Due caution

is still needed with the use of SSRIs and other antidepressants in older adults, including

those without Beers-defined recommendations or restrictions. With the use of quality

indicators based on Beers 2012 criteria, the measurement of appropriateness of

antidepressant use should also include identification of inappropriate drug-disease

combinations to avoid over-estimation.

The overall prevalence of potentially inappropriate antidepressant prescribing exceeds the prevalence found among antidepressant users in 1996, as reported by 169

Aparasu et al. (51%), applying earlier Beers criteria.60 However, a lower prevalence of

tertiary TCA prescriptions was found in this study (10% vs 39%). Physicians may avoid

the use of tertiary tricyclic antidepressants due to the availability of multiple

antidepressants with safer profiles, awareness of earlier criteria, the use of clinical

decision support systems and/or prescription plan restrictions.

Determinants of Potentially Inappropriate Antidepressants

The use of electronic medical records (EMRs) was the strongest non-clinical determinant of inappropriate antidepressant prescribing. Prescribers or practices using

EMRs were less likely to be involved in the prescription of either group of potentially inappropriate antidepressants, compared with Unrestricted antidepressants. With the use of EMRs, the selection of less harmful antidepressant choices for older adults who require pharmacotherapy is supported. This highlights the utility of integrated health

information technologies and clinical decision support tools in preventing antidepressant-

related adverse events in older adults. The finding is consistent with results of clinical

trials148-150 and observational studies151,152 reporting improvements in the quality of

prescribing for older adults in ambulatory care.

The presence of patient information, medication information and clinical decision

support features including safety alerts, in a single interface may improve the knowledge

and efficiency of office-based practitioners at the point of care. With the use of EMRs,

less time would be required to access current information relevant to the patient, thereby 170

improving the efficiency and quality of care provided. With the growing adoption of

EMRs by office-based physicians in the US, as reported by Hsiao et al.,68 prescribing of

potentially inappropriate antidepressants is expected to decrease over time. Widespread

use of electronic medical records in ambulatory care will support prescribers in their assessment of the risks and benefits during the selection of antidepressants for older adults. In the context of mental health care, which involves less objective measures for assessment and management than other chronic conditions, the decision support provided by EMRs will be invaluable. This becomes particularly useful for patients who may not recall medications or adverse events arising from medication use. These technologies may also support therapeutic interventions and patient education by and/or other health professionals in community settings. The investment in EMRs is therefore

expected to reduce the costs of care associated with preventable medication-related harm,

resulting in cost-savings and improved quality of care.

Although some health professionals may differ in the extent of EMR use, this

study’s findings indicate that the use of these technologies may minimize the selection of

potentially harmful antidepressants for older adults. Further assessment of the effects of

different features, physician or prescriber attitudes, utilization habits and related

outcomes of EMR-based decisions may provide additional scope. 171

Determinants of Tertiary Tricyclic Antidepressant Prescribing

The reduced odds of tertiary TCA prescribing among ageing users, suggests that

prescribers are cognizant of the risk of harm such as falls, with increasing age. This is similar to findings by Aparasu et al., where US adults aged 65-69 years were more likely

to receive potentially inappropriate antidepressants (tertiary TCAs/fluoxetine) than

patients over 85 years of age.60 However, younger antidepressant users who receive

tertiary TCAs should be monitored for adverse events, and switched to safer alternatives

if needed.

Patients of the black race were less likely to receive tertiary TCAs than

unrestricted antidepressants. This may be a result of differences in clinical characteristics

(e.g. indications, adverse effects, therapeutic responses), health behaviors, access to

mental health services and/or patient-physician relationships among races. Although

national surveys among Americans by Skaer et al. and Olfson et al. reported lower rates

of depressive diagnosis and antidepressant treatment among blacks than among whites or

Hispanics, no distinction was made between antidepressant classes used.153,154 Further

research examining the influence of underlying factors, including the patient-physician

relationship and socioeconomic factors is needed to explore the relationship between race

and the choice of antidepressant.

Older antidepressant users with asthma were less likely to receive tertiary TCAs

than Unrestricted antidepressants, which may be due to concerns about sedative and/or

anticholinergic side effects of the class.9 Although there are no respiratory warnings or

adverse respiratory reactions directly attributed to tertiary tricyclic antidepressants, the 172

use of sedatives in patients with asthma has been associated with increased risk of

asthmatic complications and death, possibly due to the reduced respiratory drive or non-

adherence.155,156 Also, physicians may also be aware that the addition of tertiary TCAs to

anti-asthma regimens that include anticholinergic agents such as

may increase the risk of anticholinergic side effects.157 These effects include dry mouth,

urinary retention, cognitive impairment and delirium, which may lead to falls, non- adherence and treatment failure. As a result, prescribers may avoid the use of tertiary

TCAs, in older patients with asthma.

Patients receiving tertiary TCAs spent significantly less time with physicians than

patients receiving Caution-Avoid or Unrestricted antidepressants, and as the consultation

time increased, the likelihood of receiving tertiary TCAs decreased. This may be a result

of the exchange of more information between the physician and patient, including patient

preferences and health and medication-related experiences. As consultation time

increases, multiple aspects of patient care may be facilitated including information

retrieval, problem identification, preventive care and patient education. The diagnosis

and treatment of depression and other mood-related disorders is challenged by the

subjective features of the conditions and patient’s lifestyle. Hence additional time may

be needed to allow physicians to fully assess the patient’s needs and to identify problems.

The association between visit duration and indicators of the quality of care, such

as patient satisfaction identified in studies of physicians may be due to the facilitation of

time-intensive activities.158 In a British study within a general practice, physicians with

10-minute visit intervals asked more questions about the patient’s health history, assessed

psychosocial concerns, discussed more problems, explained management and provided 173 health education compared to those with 5 and 7.5-minute bookings.159,160 One Canadian study among family and internal medicine practitioners identified visit duration under 15 minutes as a risk factor of suboptimal management of gastrointestinal complications arising from the use of anti-inflammatory drugs.161 To understand if the effect of time on tertiary TCA use identified in this study is a result of improved rapport, clinical review or information exchange, an assessment of the activities involved during the consultation is needed.

Based on this study’s findings, additional consultation time with older adults who require antidepressants is expected to minimize the use of tertiary tricyclic antidepressants and support the selection of less harmful alternatives. While this may reduce the risk of preventable drug-related harm and improve the quality of care, it may also reduce the number of patients seen by physicians and subsequent revenue for the practice. Prescribers will need to balance the risk of harm with the possible financial losses. This may discourage prescribers from attending to patients with depression or other psychiatric conditions, who may require more office time. As long as a physician’s compensation depends on the quantity of patients seen rather than the quality of care provided, consultation time will not change substantially despite this study’s findings. 174

Determinants of SSRIs and other Cautioned Antidepressant Prescribing

In our study, patients with depression were more likely to receive SSRIs or other

Caution-Avoid antidepressants, suggesting a preference for treating depression in the elderly with antidepressants from this group. This aligns with recommendations of

American College of Physicians and the American Psychiatric Association, which identify SSRIs and other second-generation antidepressants as preferred options for older adults with depression.10,162 Further assessments of antidepressant use and outcomes

among patients with depression are needed to assess other aspects of appropriate use,

including indications, drug-disease interactions, dosing and duration of therapy.

Patients with median household income between $40,627 and $52,387 were more

likely to receive Caution-Avoid antidepressants than Unrestricted antidepressants. More

tertiary TCAs were ordered for patients of the lowest income level, while more Caution-

Avoid and Unrestricted antidepressants were ordered for patients in the upper income

levels. This distribution may have been due to differences in prescription plans

purchased by patients. Although expected payment for the visit was not a significant

predictor in this study, closer review of socioeconomic factors, including prescription

costs and prescription plan payment may provide further insight into this finding.

As patient age increased, the relative odds of prescribing of antidepressants in the

Caution-Avoid group were reduced but this was not statistically significant (p=0.05) in

the presence of other predictors. Nevertheless, physicians may be hesitant to prescribe

SSRIs and other Caution-Avoid antidepressants, as well as tertiary TCAs for patients of

advanced age, due to concerns for safety. This shift in antidepressant prescribing with 175 advancing age will contribute to reducing the incidence of avoidable adverse events in older adults.

The increased likelihood of prescriptions for SSRIs and other Cautioned antidepressants among cardiovascular specialists may reflect a preference for these antidepressants over tertiary TCAs and Unrestricted antidepressants. Patients seen by these physicians present with various cardiovascular indications and/or complications, which may be exacerbated by the tertiary TCAs and serotonin antagonists. These antidepressant classes are cautioned in patients with a history of cardiovascular disease, due to risks of orthostatic hypotension, arrhythmias, myocardial infarction and sudden cardiac death.163-166 In the treatment of depression, SSRIs are considered the safest choice when compared with other antidepressants (excluding nefazodone/trazodone) due to the absence of orthostatic hypotension and conduction abnormalities.167 Reviews of antidepressant prescribing for patients with depression, those with mid-level income and patients seen in cardiology are needed, along with monitoring of these patients for adverse events arising from drug-disease interactions. In addition, patients and care- givers should be educated to identify and report early signs of adverse events.

The quality of antidepressant prescribing for older community-dwelling adults was influenced by various patient, physician and health-system factors. Prescribing policies and related interventions to improve the quality of antidepressant prescribing in older adults may be developed to improve antidepressant use among the identified patient populations and/or physician practices.

176

Risk of Emergency Outcomes

The increased risk of emergency referral or hospitalization following the visit,

among users of tertiary tricyclic antidepressants may have resulted from the presentation

of adverse effects such as syncope, cardiac arrhythmias, or severe cognitive dysfunction,

immediately prior to or during the visit. This finding justifies the continued classification

of this group of antidepressants among potentially inappropriate medications to avoid in

older adults by the AGS/Beers criteria. An earlier study among older Australians also found an increase in the unadjusted odds of unplanned hospitalization with the use of amitriptyline.45 Although this study (NAMCS 2010) found conservative use of tertiary

TCAs among Americans, patients receiving these antidepressants should be monitored

for severe adverse effects that may lead to hospitalization, during and after visits.

Caregivers and/or patients should be taught to recognize these adverse drug effects in

order to seek immediate care.

Limitations and Future Work

The survey facilitated examination of non-clinical factors, such as the use of

EMRs but did not facilitate evaluation of other forms of potentially harmful prescribing such as inappropriate dosing, duration, or drug-disease combinations. Also, assessment of the influences of unlisted conditions, more than eight medications, multiple antidepressants and individual physician demographics (age, sex and experience) on the 177

choice of antidepressants was not feasible. Small counts prevented the assessment of

chronic renal failure and visits to nurse practitioners due to quasi-separation in the model.

The assessment of antidepressant choice on emergency referrals did not control for confounding indications or reasons for referrals.

The association identified between emergency referral and the prescription of tricyclic antidepressants may have been confounded by other factors including age and comorbid conditions. Multivariate analyses inclusive of reason for referral and other clinical details would facilitate measurement of this relationship while controlling for confounding variables.

The findings are based on prescribing in 2010 and may not reflect current practices where significant changes to protocols or prescriber learning have occurred.

The cross-sectional nature of the survey facilitated identification of factors influencing prescription of potentially inappropriate antidepressants, as well as confirmation of an association with emergency outcomes, but did not prove causation.

The Beers criteria are based on evidence that was available during its development, and is therefore subject to maturation effects or lag-time bias. Subsequent medication safety information related to antidepressants would be excluded. In addition, the classification used is not a predictor of harm, but provides a general categorization to facilitate institutional or population studies. Nevertheless, the prevalence and predictors of antidepressant choice identified in this study may be used in longitudinal studies to evaluate the predictive validity of the AGS/Beers 2012 classification of potentially inappropriate antidepressants. In the development and application of quality measures of antidepressant prescribing using Beers 2012 criteria, consideration of the patient, 178

physician and health-system predictors identified would improve validity by providing context. Further work may explore prescriber rationale, the physician-patient and consultation time association, socioeconomic and/or racial factors and polypharmacy, which may mediate or directly influence the choice of antidepressants. Post-visit

emergency referrals may be further assessed in longitudinal studies, controlling for

confounding indications and patient factors. 179

Table 13: Population Characteristics, Potentially Inappropriate Antidepressants and Emergency Outcomes of Older Community-dwelling Adults in 2010

Factors Variables Visit Weighted Visit % (SE) p- Weighted Category % or Mean (SE) Count Visits or Mean (SE valueb TTCA Cautiona Unrestricted (10.2%) (78.7%) (11.1%)

Age 65-99 years 823 24 942 644 74.84 (0.357) 74.54 (0.7) 74.56 (0.4) 77.03 (1.0) 0.024

Sex Female 504 15 526 420 62.2 (2.3) 10.4 79.6 10.0 0.596 Male 319 9 416 224 37.8 (2.3) 9.7 77.3 13.0

Race White 774 23 729 198 95.1 (1.1) 10.4 78.6 11.0 0.220 Black 28 772 153 3.1 (0.8) 1.8 78.7 19.6 Other 21 441 293 1.8 (0.6) 10.3 86.1 3.6

Ethnicity Hispanic 51 1 391 105 5.6 (1.3) 6.5 87.2 6.3 0.563 Not Hispanic 772 23 551 539 94.4 (1.3) 10.4 78.2 11.4

Incomec Quartile 1 167 4 832 679 20.3 (3.1) 17.2 69.3 13.5 0.032 Quartile 2 190 5 828 475 24.5 (3.0) 10.9 79.5 9.5 Quartile 3 205 6 266 758 26.4 (3.1) 7.3 86.4 6.3 Quartile 4 222 6 841 674 28.8 (3.3) 7.4 77.1 15.5

Payment type Private / self 137 4 100 215 16.8 (2.0) 5.8 86.3 7.9 0.116 Medicare 637 19 672 987 80.5 (2.1) 10.8 77.2 12.1 180

Factors Variables Visit Weighted Visit % (SE) p- Weighted Category % or Mean (SE) Count Visits or Mean (SE valueb TTCA Cautiona Unrestricted (10.2%) (78.7%) (11.1%) Medicaid 17 420 426 1.7 (0.7) 20.0 71.9 8.2 Other 10 235 366 1.0 (0.4) 33.8 48.9 17.2

Injury visit Yes 53 1 912 430 7.7 (1.3) 11.5 84.0 4.5 0.147 No 770 23 030 214 92.3 (1.3) 10.0 78.3 11.7

Chronic Conditions Arthritis 194 6 563 544 26.3 (2.7) 11.0 76.8 12.3 0.795 Asthma 46 1 709 791 6.9 (1.2) 2.2 87.1 10.7 0.056 Cancer 146 3 630 817 14.6 (1.7) 12.1 74.4 13.5 0.588 CBVD 42 1 109 131 4.4 (1.1) 12.8 82.3 5.0 0.401 CHF 45 1 139 987 4.6 (1.0) 7.2 77.0 15.8 0.779 CRF 32 1 090 437 4.4 (1.1) -- 98.1 1.9 0.016c COPD 87 3 276 601 13.1 (2.0) 8.7 83.3 8.0 0.677 Depression 291 8 092 696 32.4 (3.0) 3.5 87.2 9.3 <0.001 Diabetes 168 5 285 066 21.2 (1.8) 12.1 73.0 14.9 0.351 Hyperlipidemia 251 8 455 716 33.9 (2.7) 11.5 78.6 9.9 0.713 Hypertension 456 13 520 624 54.2 (2.7) 10.8 78.7 10.4 0.710 IHD 79 2 154 083 8.6 (1.3) 8.6 84.3 7.1 0.637 Obesity 62 1 751 655 7.0 (1.1) 11.0 75.9 13.1 0.902 Osteoporosis 63 1 953 910 7.8 (1.9) 0.6 86.2 13.1 0.008

Beers PI conditions Yes 34 1 037 048 4.2 (1.0) 4.3 84.7 11.0 0.500 No 789 23 905 596 95.8 (1.0) 10.4 78.5 11.1 181

Factors Variables Visit Weighted Visit % (SE) p- Weighted Category % or Mean (SE) Count Visits or Mean (SE valueb TTCA Cautiona Unrestricted (10.2%) (78.7%) (11.1%)

Seen Before Yes 741 22 959 700 92.0 (1.5) 10.2 78.5 11.3 0.814 No 92 1 982 944 8.0 (1.5) 9.4 81.5 9.0

Time spent 1-150 minutes 804 24 596 259 21.8 (0.727) 17.75 (1.4) 22.22 (0.8) 22.50 (1.8) 0.128

Number of meds 1-8 823 24 942 644 6.42 (0.120) 6.53 (0.2) 6.41 (0.1) 6.45 (0.3) 0.900

Extended services 0-6 823 24 517 646 1.39 (0.103) 1.34 (0.2) 1.38 (0.1) 1.56 (0.2) 0.286

Physician specialty General/Family 166 6 181 800 24.8 (3.3) 13.5 74.4 12.1 0.130 Internal medicine 98 6 369 283 25.5 (3.9) 6.2 81.9 11.9 Surgical 47 1 333 316 5.3 (1.1) 23.2 62.6 14.2 Cardiovascular 97 1 385 095 5.6 (0.9) 6.6 89.1 4.3 Dermatology 16 462 113 1.9 (0.5) 27.3 56.4 16.3 Urology 50 723 379 2.9 (0.8) 19.8 62.5 17.7 Psychiatry 70 1 192 665 4.8 (0.9) 6.6 80.6 12.8 Neurology 90 768 765 3.1 (0.6) 9.5 81.0 9.4 Ophthalm/Otolaryn 44 1 517 552 6.1 (2.0) 4.2 89.8 6.0 Oncology 83 1 091 261 4.4 (0.9) 12.0 78.4 9.7 Other 62 3 917 415 15.7 (2.8) 7.4 82.6 10.1

Non-physician PA 28 1 240 239 5.0 (3.3) 9.3 73.2 17.5 0.409 182

Factors Variables Visit Weighted Visit % (SE) p- Weighted Category % or Mean (SE) Count Visits or Mean (SE valueb TTCA Cautiona Unrestricted (10.2%) (78.7%) (11.1%) provider NP/MW 9 246 907 1.0 (0.4) -- 50.3 49.7 0.086d RN/LPN 206 5 952 082 23.9 (3.8) 11.9 75.2 12.8 0.636

Region Northeast 143 3 804 030 15.3 (2.7) 8.7 81.6 9.7 0.347 Midwest 217 5 862 238 23.5 (4.5) 10.7 81.3 8.0 South 237 9 272 699 37.2 (4.7) 12.8 73.5 13.7 West 226 6 003 677 24.1 (3.7) 6.5 82.4 11.1

Metropolitan area Yes 727 21 685 851 86.9 (4.6) 8.9 80.7 10.4 0.020 No 96 3 256 793 13.1 (4.6) 18.3 65.6 16.1

Solo Practice Solo 225 7 137 879 28.6 (4.3) 10.7 80.2 9.1 0.632 Group 597 17 801 309 71.4 (4.3) 9.9 78.1 11.9

Ownership Physician 615 19 994 495 80.2 (3.2) 10.4 78.9 10.7 0.749 Other 208 4 948 149 19.8 (3.2) 9.2 78.0 12.8

EMR use Yes 470 14 687 670 59.4 (4.3) 10.5 74.7 14.8 0.003 No 344 10 047 317 40.6 (4.3) 9.2 85.1 5.7

Emergency referral or hospitalizatione TTCA Caution/Unrestricted Sig. Yes 5 168 644 0.7 (0.4) 75.5 24.5 <0.001 No 818 24 774 000 99.3 (0.4) 9.7 90.3 183

Key: a: Caution group: selective serotonin-reuptake inhibitors, serotonin-norepinephrine-reuptake inhibitors, secondary amine tricyclic antidepressants b: Significance based on Likelihood ratio (adjusted F) tests for categorical predictors, and Wald F tests for interval predictors c: Median household income of patient’s zip code: Quartile 1= less than $32,794, Quartile 2= $32,794-$40,626, Quartile 3= $40,627-$52,387, Quartile

4= $52,388 or more d: Unreliable estimator. Validity of model fit is uncertain. e: Row percent shown (sum vertically), based on visit disposition as dependent variable

CBVD: cerebrovascular disease, CHF: congestive heart failure, COPD: chronic obstructive pulmonary disease, D.O.: Doctor of Osteopathy, ER: emergency room, EMR: electronic medical records, IHD: ischemic heart disease, M.D.: Doctor of Medicine, NP/MW: nurse practitioner/midwife, PA: physician assistant, PI: potentially inappropriate, RN/LPN: registered nurse/licensed practical nurse, SE: standard error, TTCA: tertiary tricyclic antidepressant, Unrestr: antidepressants without Beers restrictions 184

Table 14: Antidepressant Orders for Community-dwelling Older Adults in 2010

Order Frequency Visit Percent† Order Percent Category Antidepressant name (weighted) N=24 942 644 n=27 277 298

SSRIs Sertraline 4 515 051 18.1 16.6

Citalopram 3 706 072 14.9 13.6

Escitalopram 3 659 822 14.7 13.4

Paroxetine 2 409 544 9.7 8.8

Fluoxetine 2 037 588 8.2 7.5

Fluvoxamine 123 405 0.5 0.5

SNRIs/SSNRIs Venlafaxine 1 296 350 5.2 4.8

Duloxetine 1 242 635 5.0 4.6

Milnacipran 246 261 1.0 0.9

Desvenlafaxine 7 020 0.03 0.03

Tertiary TCAs Amitriptyline 1 819 879 7.3 6.7

Amitriptyline + perphenazine 90 200 0.4 0.3 185

Order Frequency Visit Percent† Order Percent Category Antidepressant name (weighted) N=24 942 644 n=27 277 298

Doxepin 400 182 1.6 1.5

Imipramine 189 606 0.8 0.7

Clomipramine 32 757 0.1 0.1

Secondary amine TCAs Nortriptyline 613 597 2.5 2.2

Desipramine 33 939 0.1 0.1

Alpha-2 Mirtazapine 675 382 2.7 2.5

Serotonin antagonists Trazodone 2 901 131 11.6 10.6

Nefazodone 37 677 0.2 0.1

Dopamine reuptake inhibitor Buproprion 1 135 218 4.6 4.2

Other Miscellaneous antidepressants 103 982 0.4 0.4

Key: 186

† : Total visit percentages exceed 100% due to visits involving multiple antidepressant orders

SSRI: selective serotonin reuptake inhibitor, SNRI: selective norepinephrine reuptake inhibitor, SSNRI: selective serotonin-norepinephrine reuptake inhibitor, TCA: tricyclic antidepressant 187

Table 15: Multinomial Logistic Models of Factors Influencing Potentially Inappropriate Antidepressant Prescribing for Older

Adults

Antidepressant Factorsa Values OR 95% CI, OR p-value AORc 95% CI, AOR p-value Groupb

Age 65-99 years TTCA 0.958 0.927-0.990 0.010 0.936 0.902-0.972 0.001

Caution 0.958 0.923-0.994 0.024 0.957 0.915-1.000 0.050

Race White TTCA 0.333 0.029-3.857 0.376 0.155 0.008-3.091 0.220

Caution 0.301 0.055-1.636 0.163 0.634 0.064-6.274 0.695

Black TTCA 0.032 0.001-0.703 0.029 0.024 0.001-0.883 0.043

Caution 0.169 0.021-1.386 0.097 0.360 0.029-4.485 0.424

Asthma Yes TTCA 0.218 0.048-0.992 0.049 0.138 0.025-0.770 0.024

Caution 1.166 0.400-3.398 0.776 0.702 0.197-2.506 0.584

Depression Yes TTCA 0.338 0.120-0.954 0.041 0.356 0.084-1.512 0.160

Caution 1.498 0.808-2.779 0.198 2.007 1.009-3.994 0.047

Osteoporosis Yes TTCA 0.050 0.008-0.310 0.002 0.109 0.010-1.219 0.072

Caution 0.923 0.355-2.398 0.869 1.317 0.292-5.935 0.718 188

Antidepressant Factorsa Values OR 95% CI, OR p-value AORc 95% CI, AOR p-value Groupb

Injury visit Yes TTCA 2.979 0.885-10.03 0.078 2.953 0.681-12.802 0.147

Caution 2.777 1.136-6.789 0.025 2.511 0.874-7.212 0.087

Household Quartile 1 TTCA 2.664 0.730-9.717 0.137 1.615 0.343-7.608 0.542

Income Caution 1.031 0.465-2.285 0.940 1.140 0.424-3.067 0.794

Quartile 2 TTCA 2.397 0.945-6.081 0.065 1.715 0.495-5.943 0.392

Caution 1.671 0.848-3.289 0.136 2.001 0.891-4.496 0.092

Quartile 3 TTCA 2.429 0.888-6.649 0.084 2.579 0.886-7.507 0.082

Caution 2.759 1.213-6.274 0.016 2.781 1.164-6.648 0.022

Payment Private/self TTCA 0.372 0.036-3.794 0.401 0.342 0.021-5.628 0.450

Caution 3.844 0.463-31.92 0.211 5.603 0.796-39.443 0.083

Medicare TTCA 0.454 0.056-3.676 0.456 0.645 0.049-8.447 0.737

Caution 2.252 0.285-17.803 0.439 4.820 0.677-34.336 0.115

Medicaid TTCA 1.238 0.078-19.743 0.879 0.631 0.025-15.629 0.777

Caution 3.088 0.202-47.233 0.415 2.857 0.197-41.509 0.439

Time Spent 1-150 minutes TTCA 0.947 0.897-1.000 0.052 0.924 0.868-0.984 0.014

Caution 0.998 0.976-1.021 0.868 1.000 0.967-1.035 0.985

189

Antidepressant Factorsa Values OR 95% CI, OR p-value AORc 95% CI, AOR p-value Groupb

Metropolitan Yes TTCA 0.754 0.281-2.021 0.572 0.820 0.266-2.527 0.727

Area Caution 1.904 1.033-3.508 0.039 1.624 0.691-3.818 0.264

Specialty General / Family TTCA 1.529 0.34-6.804 0.574 1.945 0.243-15.582 0.528

Caution 0.752 0.271-2.085 0.581 0.817 0.270-2.473 0.719

Internal TTCA 0.706 0.120-4.148 0.698 1.001 0.110-9.087 0.999

Caution 0.840 0.284-2.480 0.750 0.967 0.322-2.907 0.952

Surgical TTCA 2.240 0.297-16.862 0.431 0.894 0.095-8.374 0.921

Caution 0.540 0.125-2/340 0.480 0.494 0.102-2.385 0.377

Cardiovascular TTCA 2.076 0.289-14.920 0.465 2.546 0.232-27.887 0.441

Caution 2.503 0.715-8.770 0.150 4.143 1.010-16.990 0.048

Dermatology TTCA 2.287 0.267-19.596 0.448 0.870 0.080-9.529 0.909

Caution 0.422 0.101-1.759 0.234 0.229 0.032-1.624 0.139

Urology TTCA 1.530 0.326-7.193 0.588 1.123 0.117-10.781 0.919

Caution 0.431 0.130-1.434 0.169 0.505 0.141-1.808 0.291

Psychiatry TTCA 0.703 0.129-3.842 0.683 3.220 0.208-49.815 0.400

Caution 0.770 0.230-2.576 0.670 0.351 0.090-1.366 0.130

Neurology TTCA 1.382 0.242-7.891 0.714 1.977 0.202-19.322 0.555

Caution 1.049 0.328-3.356 0.935 1.029 0.272-3.900 0.966

Ophthalm/Otolaryngology TTCA 0.954 0.088-10.396 0.969 0.516 0.040-6.629 0.609 190

Antidepressant Factorsa Values OR 95% CI, OR p-value AORc 95% CI, AOR p-value Groupb

Caution 1.833 0.289-11.624 0.518 1.880 0.245-14.428 0.541

Oncology TTCA 1.690 0.198-14.441 0.629 1.590 0.153-16.500 0.696

Caution 0.990 0.233-4.212 0.989 0.964 0.240-3.871 0.958

EMR use Yes TTCA 0.434 0.204-0.923 0.030 0.390 0.194-0.781 0.008

Caution 0.335 0.168-0.666 0.002 0.264 0.123-0.566 0.001

Key: a : Redundant parameters are No, except for Race (Other), Income (Quartile 4), and Specialty (Other) b : Antidepressant profile is the dependent variable. Caution group is: selective serotonin-reuptake inhibitors, serotonin-norepinephrine-reuptake inhibitors, secondary amine tricyclic antidepressants. Reference group is Unrestricted. c : Multivariate model information: Population size= 22 756 723. Sampling design degrees of freedom= 134. Test of model Effects: Wald F (52,83) =

11.900, p<0.001. Pseudo R2 (McFadden)=0.157.

AOR: adjusted odds ratio, CI: confidence interval, EMR: electronic medical record, OR: odds ratio, PIAD: potentially inappropriate antidepressant, SE: standard error, TTCA: tertiary tricyclic antidepressant 191

CHAPTER 5

RESULTS AND DISCUSSION: POTENTIALLY INAPPROPRIATE SEDATIVES

Results

Population Characteristics

Eight thousand, one hundred and fifty-six visits to office-based practices by adults aged 65 years or more were extracted from the survey, which estimated 258,976,010 visits. Sedatives were ordered in 708 sampled visits, representing approximately

23,821,026 visits, or 9.2% of visits by older adults. Patient ages ranged from 65 to 96 years, with a mean age of 75.8 years (SE=0.43). Approximately 67.5% of sampled patients were female, 92.8% were white and 4.2% were Hispanic or Latino. Between 1 and 8 medications were ordered during visits, with an average of 6.2 orders (SE=0.1) per visit. Time spent with physician ranged from 5 to 150 minutes, with mean of 20.6 minutes (SE=0.82). Characteristics of the sampled population are shown in Table 16. 192

Sedatives Ordered

Approximately 25,459,371 orders for sedatives were issued during visits, inclusive of visits with multiple sedative orders (6.5%). Benzodiazepines were the most

commonly prescribed sedative class, ordered for more than 16 million patients. Non-

benzodiazepines and ramelteon were prescribed during approximately 7 million visits,

while barbiturates and meprobamate were ordered during more than 2 million visits.

Approximately 1,131,250 visits involved sedatives from multiple classes (4.8%).

Zolpidem, alprazolam and lorazepam were the three most frequently ordered sedatives,

during 71.4% of visits – Table 17.

After classification, 2,064,680 visit profiles were potentially inappropriate due to

barbiturates or meprobamate (8.7%, SE=1.2%); 15,640,839 visits (65.7%, SE=2.7%)

were due to benzodiazepine orders, and approximately 6,115,507 visits (25.7%,

SE=2.6%) involved solely non-benzodiazepine orders. Sensitivity analysis of

classification of profiles where visits involving benzodiazepines and barbiturates were

classified as benzodiazepine visits resulted in minimal changes to proportions:

barbiturates 8.5%, benzodiazepines 65.9%. 193

Categorical Differences

Significant associations were found between the sedative classes ordered and

household income, depression, obesity, and Beers potentially inappropriate conditions –

Table 16.

Among the various income quartiles, patients in the highest income quartile received the most sedatives. However, the highest proportions of benzodiazepine orders were among patients in the lower-middle and upper-middle income quartiles.

Benzodiazepines were prescribed for all patients with anxiety, and for most patients with depression, seizures or Beers potentially inappropriate diagnoses. Among patients with obesity, most received barbiturates or non-benzodiazepine sedatives, and for patients with insomnia, either benzodiazepines or non-benzodiazepines were prescribed. No barbiturates were ordered for patients with congestive heart failure, patients in dermatology, psychiatry or ophthalmology, or for patients seen by nurse practitioners.

Although the significance of the likelihood ratio test for physician specialty exceeded the level for model inclusion (p<0.25), the Wald test of the bivariate model significance indicated that at least one specialty category was influential. Hence it was included in the multivariate model. 194

Determinants of Potentially Inappropriate Sedative Choices

Factors entered into the multivariate model were: income, arthritis, depression, obesity, Beers potentially inappropriate conditions, number of medications, number of extended services, visits involving physician assistants, visits involving registered nurses, solo practice, physician specialty, ownership and the use of EMRs. Controlling for all eligible factors, predictors of sedative prescribing choices were: depression, obesity, income, specialty and the use of EMRs – Table 18.

Barbiturate use was associated with six times the odds of non-benzodiazepine use among patients seen in neurology, and almost four times’ greater odds among those seen in otolaryngology. However the use of electronic medical records was associated with

57% lower odds of barbiturate use. Benzodiazepine use was associated with more than twice the odds of non-benzodiazepine use among patients with depression, but almost

69% lower odds among obese patients. The relative odds of benzodiazepine use were 2.4 and 2.5 times greater among patients with lower-middle income and upper-middle income respectively.

The model correctly predicted 66.9% of the sedative profile categories.

Sensitivity analyses of the model using revised assignment of visits with benzodiazepines and barbiturate orders identified a negligible change in the classification. The revised model correctly predicted 67% of the sedative categories (McFadden pseudo R2=0.098). 195

Emergency Referrals and Hospitalizations

Approximately 1% of visits involving a sedative and none involving multiple sedatives resulted in referral to an emergency room or hospitalization. None of the visits involving barbiturate orders resulted in emergency referrals. No evidence of an

association was found between barbiturate or benzodiazepine prescribing and referral to

emergency room or hospitalization, relative to non-benzodiazepine prescribing – Table

16. In the logistic regression model, the prescription of barbiturates or benzodiazepines

was not associated with significantly different risk of emergency referral or hospital

admission (RR=1.510, 0.254-8.987, p=0.648) than for non-benzodiazepine use.

Discussion

Prescribing of sedatives to avoid unconditionally in older adults occurred less

often than prescribing of benzodiazepines and non-benzodiazepines. Mid-level

household income, the presence of depression, obesity, physician specialty and the use of

electronic medical records influenced the quality of sedative prescribing. Relative to

non-benzodiazepines, the use of barbiturates or benzodiazepines was not associated with

emergency outcomes. 196

Although zolpidem was the most commonly prescribed sedative, benzodiazepines

were the most frequently ordered sedative group. The variety of conditions that are

treated with benzodiazepines, either as primary or adjunct therapies, including anxiety

disorders (with or without depression), seizures and alcohol withdrawal, may explain the

use of these medications in more than two-thirds of patients receiving sedatives. Given

that earlier Beers criteria identified only long-acting benzodiazepines as potentially inappropriate, the addition of shorter-acting alternatives to more recent criteria extends the need for judicious use, additional vigilance and patient/caregiver education to all ageing benzodiazepine users.27-29 Prescribers will need to receive updated medical education to ensure awareness of the revised criteria and/or newly identified harms related to benzodiazepine use.

Orders for non-benzodiazepines or ramelteon may be attributed to their primary indication and preferred risk-benefit profile in the management of sleep disorders, over other sedatives.11 One meta-analysis by Buscemi et al. compared adverse effects

identified in randomized controlled trials involving older adults and found a higher risk

of harm to patients in benzodiazepine-placebo trials than among patients in trials of non-

benzodiazepines.168 However, among older adults with insomnia, more than two-fifths

received benzodiazepines. This may have been due to a lack of physician awareness

regarding recommendations against the use of benzodiazepines for treating insomnia in

the elderly, as the revised AGS/Beers criteria were published after this study.29 An

assessment of prescribing subsequent to the publication of AGS/Beers 2012 criteria

would provide more current information on the use of benzodiazepines in older adults

with insomnia. Although benzodiazepines are among medications to avoid within Beers 197

criteria, the recommendation applies to their use in the treatment of insomnia, agitation or

delirium.29 For all other indications, their use is not restricted. In the application of

quality measures based on Beers 2012 criteria, the indication for benzodiazepine use should be included to avoid over-estimation of inappropriate sedative prescribing.

The relatively conservative use of barbiturates suggests that physicians generally avoid the use of these sedatives, in preference for benzodiazepines or non- benzodiazepines. This may be a result of physicians’ awareness of risks, clinical experiences, patient preference, and declining use due to the availability of safer alternatives for behavioral or mood disorders. The use of barbiturates in older adults may

be primarily reserved for seizure disorders, (in combination with other

analgesics) and in cases refractory to other sedatives.

Lower prevalence rates of potentially inappropriate antianxiety and sedative-

hypnotic medications were reported by Aparasu et al. (31.84% and 23.49%), which may

be due to differences in survey methodology, criteria used (2003 vs 2012), and additional

sedative choices in 2010 versus 1996.60 To determine if the quality of sedative

prescribing has changed over time, comparisons over time using the same criteria is

needed. 198

Determinants of Barbiturate Prescribing

Barbiturates were more likely to be used by neurologists than other sedatives.

This may be a result of the higher prevalence of patients with severe neurological disorders requiring barbiturates, such as chronic epilepsy, and muscle disorders. Based on their clinical experience with managing these conditions, neurologists are expected to have more experience using these medications to manage neurological conditions than other specialists.

Similarly, the use of barbiturates was higher among otolaryngologists, which may be attributed to in-office procedures requiring sedation. Some diagnostic and therapeutic procedures used in this practice involve sedatives for the reduction of anxiety and suppression of reflex responses to facilitate local surgical procedures. However, the increased likelihood of barbiturate use in this specialty may be a result of physicians’ preference for barbiturates with faster onset and shorter duration of action (e.g. thiopental) than benzodiazepine alternatives (e.g. lorazepam).169 A review of physician rationale, relevant indications and patterns of sedative prescribing in neurology and otolaryngology practices may explain this finding. These physicians may benefit from continuing medical education and/or revision of clinical protocols to encourage the use of less harmful sedatives where possible. Patients seen by neurologists and otolaryngologists who receive barbiturates should undergo regular medication reviews, clinical monitoring and receive education to identify and report adverse effects arising from barbiturate use. 199

The use of electronic medical records was associated with a lower risk of barbiturate prescribing by physicians. This may be a result of point-of-care access to

patient information, including past and present conditions and adverse events, and drug

information for available alternatives. This finding is consistent with studies reporting

reductions in inappropriate prescribing for older adults in primary care settings, with the

use of electronic medical records or clinical decision support systems.148,149,151 The use

of electronic medical records should be encouraged to reduce the incidence of barbiturate

prescribing for older patients, where a safer alternative may be available. Health

professionals may be readily alerted and adjust therapy at the point of care with the use of

EMRs, which may improve efficiency and reduce harm. Prospective studies may be

undertaken to determine changes in sedative prescribing over time and outcomes of care

in settings EMRs are used.

Determinants of Benzodiazepine Prescribing

The increased relative risk of benzodiazepine use among patients with depression,

more so than other sedatives, may be a result of their role as adjunctive therapy for

patients who present with catatonic symptoms or anxiety.10 This is supported by the

preferential use of benzodiazepines among patients with a primary diagnosis of anxiety.

The use of benzodiazepines in these patients is justified based on clinical benefit, but

with the ageing physiology, health professionals, patients and caregivers should continue 200

to be vigilant for adverse sedative effects. In addition, sedative use reviews among older

adults with depression are needed to identify other factors, potential medication errors

and to monitor clinical outcomes.

In a qualitative study of attitudes of primary care physicians in Philadelphia

towards benzodiazepine use in older adults, none of the respondents considered the long-

term use of benzodiazepines in older adults as a significant public health or clinical

problem. The rationale expressed by the focus groups included the low addiction

potential of benzodiazepines, compassionate use, competing medical problems that were

of greater priority, effectiveness in the management of sleep disorders, anticipated patient resistance and/or withdrawal, and limited time.170

This study also found that patients with median household income in the lower-

middle and upper-middle quartiles were more likely to receive benzodiazepines than non-

benzodiazepines. This may be due to the increased financial burden with the exclusion of

benzodiazepines under Medicare Part D.171 As a result, only patients with supplemental

insurance would have had access to these medications. Hence, benzodiazepine use would

be less prevalent among patients with low income, who lack supplemental prescription

insurance. A comparison of prescription plans was beyond the scope of this study.

The increased likelihood of benzodiazepines among patients in the second and

third quartiles contrasts with findings of the 2005-6 study by Préville et al. This study

found that older Canadians in the lower of two income categories (<$25,000 Can =

<$21,000 USD) were more likely to receive potentially inappropriate benzodiazepine

prescriptions.122 Although other studies have identified associations between low 201

income or socioeconomic status and depression, no association was found among older

adults in the lowest income quartile.172,173

Assessments of the relationship between income, prescription plan coverage

and/or other socioeconomic factors and benzodiazepine use would provide additional

insight. However, with recent changes in the Medicare Modernization Act, where

benzodiazepines are no longer excluded, prescription plan coverage may not be a

significant predictor of benzodiazepine use in older adults.

Nevertheless, patients with depression and those with mid-level income who require benzodiazepines should be educated about adverse events. These patients also require medication use reviews and clinical monitoring for adverse events arising from benzodiazepine use.

The reduced likelihood of benzodiazepine use among older adults with obesity may be due to the lipophilic nature of these agents, and the need for dose adjustment to balance therapeutic and adverse effects.174 Obesity has been associated with increased

prevalence and risk of falls among older adults, as well as lower health-related quality of

life and a higher risk of disability after a fall.175,176 Hence, physicians may avoid

benzodiazepines in obese patients to minimize the occurrence of adverse sedative effects,

including confusion, falls, and hospitalization. 202

Risk of Emergency Outcomes

The relative risk of emergency referral or hospitalization following the visit, for

patients receiving barbiturates or benzodiazepines was not significantly greater or less

than the risk among patients receiving non-benzodiazepine sedatives. All community-

dwelling elderly patients receiving sedatives should be monitored for adverse effects,

irrespective of sedative choice.

Limitations and Future Work

Analyses of visits with insomnia, anxiety, seizures, delirium, falls and dementia were limited to their presence among the three primary diagnoses recorded. These conditions may have been secondary to depression or other chronic conditions, or perceived as lower priority. The recording of a maximum of eight medications restricted analyses of nine or more medications. The additive risk of harm with the use of multiple sedatives was not assessed as a result of the classification of visits into mutually exclusive categories. The absence of duration of use and matched indications limited the assessment of therapeutic use, such as temporary sedation for same-day procedures.

Zero cell counts among patients with congestive heart failure, insomnia, anxiety and seizure disorders prevented the measurement of these associations. 203

Visit outcomes were based on emergencies identified during the office visit,

excluding referrals from places of residence directly to the emergency room, which may

be of greater clinical severity. The measure of risk was limited as there may have been

influence by uncontrolled confounders such as age, sex, comorbidity and multiple

sedatives on the outcome. Assessment of the use of sedatives (single or multiple) on

emergency referrals or hospitalization, irrespective of origin, may be done using data

from emergency departments, controlling for confounders.

The findings of this study are based on prescribing in 2010 and may not reflect

the most recent patterns of sedative use, as some physicians may change prescribing

based on learning over time. The cross-sectional nature of the survey did not facilitate the assessment of patient outcomes over time, and is limited to physician offices and community health centers. However, the study identified prevalence rates that may be compared with later surveys, as well as associations that may be included in longitudinal studies assessing causal relationships. These include socioeconomic factors, obesity, neurological disorders, electronic medical records, and physician specialty.

Beers 2012 criteria are based on evidence available at the time of its revision.

Any additional research regarding the safety of sedatives in older adults published after the criteria were developed would be excluded. The criteria may be applied as an initial step in the identification of patients who may be at risk of medication-related harm, prior to further assessment of the context of sedative use. Although the criteria is a convenient list of medications to avoid, its application as an indicator of prescribing quality regarding sedative use is limited in the absence of the clinical context. The determinants identified highlight patient groups and/or physician practices that would benefit from 204 further medication use reviews. They may also guide the application of sedative prescribing quality indicators, related interventions and future research. 205

Table 16: Population Characteristics, Potentially Inappropriate Sedatives and Emergency Outcomes of Older Community- dwelling Users in 2010

Factors Values Visit Weighted Visit % (SE)a Category % or Mean (SE) Count Visits or Mean (SE) BAR (8.7%) BZD (65.7%) NBZ (25.7%) p-valueb Age 65-99 years 708 23 821 026 75.78 (0.4) 75.98 (1.2) 75.62 (0.6) 76.1 (0.7) 0.876

Sex Female 458 16 086 138 67.5 (2.7) 8.4 64.5 27.1 0.646 Male 250 7 734 888 32.5 (2.7) 9.2 68.1 22.7

Race White 658 22 095 922 92.8 (1.3) 8.3 66.0 25.8 0.728 Black 35 1 412 441 5.9 (1.3) 15.0 58.4 26.7 Other 15 312 663 1.3 (0.5) 7.8 77.7 14.4

Ethnicity Hispanic 38 1 001 334 4.2 (1.0) 18.2 59.1 22.7 0.271 Not Hispanic 670 22 819 692 95.8 (1.0) 8.2 65.9 25.8

Incomec Quartile 1 143 5 129 718 21.5 (2.7) 10.3 66.5 23.2 0.033 Quartile 2 152 5 310 949 22.3 (2.9) 4.5 75.7 19.9 Quartile 3 170 4 869 696 20.4 (2.4) 9.1 71.3 19.6 Quartile 4 217 7 976 043 33.5 (3.6) 9.9 55.3 34.8

Payment type Private / self 109 3 092 323 13.0 (1.9) 9.1 65.4 25.5 0.612 Medicare 552 19 319 032 81.1 (2.1) 8.1 65.7 26.2 Medicaid 18 509 763 2.1 (0.7) 5.7 66.9 27.4 Other 6 242 429 1.0 (0.7) 37.7 45.3 17.0 206

Factors Values Visit Weighted Visit % (SE)a Category % or Mean (SE) Count Visits or Mean (SE) BAR (8.7%) BZD (65.7%) NBZ (25.7%) p-valueb

Injury visit Yes 35 1 449 114 6.1 (1.2) 15.5 68.6 15.8 0.376 No 673 22 371 912 93.9 (1.2) 8.2 65.5 26.3

Chronic Arthritis 165 6 173 450 25.9 (3.1) 11.7 69.9 18.4 0.069 Conditions Asthma 38 1 596 938 6.7 (2.0) 17.4 66.2 16.5 0.316 Cancer 144 3 628 602 15.2 (2.0) 4.1 69.3 26.7 0.279 CBVD 29 888 639 3.7 (0.8) 8.1 56.4 35.6 0.648 CHF 40 296 137 1.2 (0.8) -- 69.5 30.5 0.330 CRF 17 888 013 3.7 (0.4) 4.9 66.7 28.5 0.797 COPD 63 3 106 689 13.0 (1.5) 10.5 72.1 17.4 0.324 Depression 144 4 019 159 16.9 (2.0) 4.9 79.1 16.0 0.036 Diabetes 107 3 753 854 15.8 (2.0) 8.3 65.4 26.3 0.985 Hyperlipidemia 206 7 659 272 32.2 (2.7) 5.9 66.6 27.5 0.392 Hypertension 372 12 772 088 53.6 (2.7) 8.7 68.0 23.4 0.436 IHD 80 2 328 115 9.8 (1.9) 4.9 61.9 33.2 0.278 Obesity 33 855 816 3.6 (0.8) 21.6 39.6 38.8 0.042 Osteoporosis 51 1 916 712 8.0 (1.8) 5.8 76.6 17.6 0.444 Other diagnoses Anxiety 24 897 723 3.8 (1.0) -- 100 -- 0.001 Seizures 6 131 555 0.6 (0.1) 25.4 74.6 -- 0.017 Insomnia 14 718 895 3.0 (1.0) -- 41.2 58.8 0.054

PI conditions Yes 8 271 371 1.1 (0.5) 1.5 89.5 9.0 0.037 No 700 23 549 655 98.9 (0.5) 8.7 65.4 25.9 207

Factors Values Visit Weighted Visit % (SE)a Category % or Mean (SE) Count Visits or Mean (SE) BAR (8.7%) BZD (65.7%) NBZ (25.7%) p-valueb Seen Before Yes 637 21 640 729 90.8 (1.6) 8.7 65.8 25.4 0.883 No 71 2 180 297 9.2 (1.6) 8.0 63.8 28.2

Time spent 1-150 minutes 689 23 384 029 20.61 (0.8) 20.22 (1.8) 20.54 (0.8) 20.93 (1.3) 0.883 Medications 1-8 708 23 821 026 6.23 (0.1) 6.69 (0.2) 6.09 (1.3) 6.42 (0.2) 0.086 Services 0-5 708 23 821 026 1.32 (0.2) 1.57 (0.2) 1.38 (0.2) 1.09 (0.2) 0.063

Specialty General/Family 128 5 569 515 23.4 (3.5) 9.1 69.3 21.6 0.265 Internal medicine 74 5 592 488 23.5 (3.5) 6.5 63.8 29.7 General surgery 20 365 080 1.5 (0.4) 4.4 58.9 36.7 Obs/Gynecology 9 278 968 1.2 (0.5) 12.8 71.8 15.3 Orthopedic surg 18 826 333 3.5 (1.1) 27.7 56.2 16.1 Cardiovascular 95 1 482 334 6.2 (1.0) 6.9 62.7 30.3 Urology 45 702 342 2.9 (1.1) 6.3 49.7 44.1 Neurology 69 621 192 2.6 (0.5) 20.5 71.8 7.7 Otolaryngology 14 154 720 0.6 (0.2) 26.0 56.1 17.9 Oncology 89 1 295 818 5.4 (1.2) 2.5 71.1 26.4 Other 147 6 932 236 29.1 (4.6) 8.1 66.4 25.5

Non-physician PA 26 996 208 4.2 (2.2) 2.9 56.1 41.0 0.083 provider NP/MW 8 165 041 0.7 (0.3) 0 81.3 18.7 0.552 RN/LPN 202 7 133 161 29.9 (5.2) 11.6 69.7 18.6 0.054

Region Northeast 122 3 746 159 15.7 (2.0) 6.4 64.2 29.4 0.695 Midwest 176 5 341 518 22.4 (3.8) 6.1 72.0 21.9

208

Factors Values Visit Weighted Visit % (SE)a Category % or Mean (SE) Count Visits or Mean (SE) BAR (8.7%) BZD (65.7%) NBZ (25.7%) p-valueb South 236 9 950 072 41.8 (4.7) 9.4 65.3 25.3 West 174 4 783 277 20.1 (3.5) 11.8 60.5 27.7

Metropolitan Yes 638 20 963 863 88.0 (4.2) 8.3 65.1 26.5 0.454 area No 70 2 857 163 12.0 (4.2) 11.1 69.5 19.4

Solo Practice Solo 173 6 486 410 27.2 (4.4) 10.6 58.3 31.1 0.177 Group 533 17 314 106 72.7 (4.4) 8.0 68.4 23.7

Ownership Physician 535 18 770 489 78.8 (4.1) 9.7 63.9 26.4 0.115 Other 172 5 033 483 21.1 (4.1) 4.8 72.1 23.2

EMR use Yes 423 14 199 817 59.6 (3.7) 6.6 66.9 26.4 0.202 No 281 9 531 366 40.1 (3.7) 11.8 63.9 24.3

ER/Hospital Visit % (SE) BAR/BZD NBZ p-value Yes 8 247 929 1.0 (0.5) 81.3 18.7 0.635 No 700 23 573 097 99.0 (0.5) 74.3 25.7 Key: a: Based on valid visits (N=23,821,026). Total percentages may not equal 100% due to missing values b: Significance level based on Likelihood ratio (adjusted F) tests or modified Wald (F) test (age, number of medications, time spent, extended services) c: Median household income of patient’s zip code: Quartile 1= less than $32,794, Quartile 2= $32,794-$40,626, Quartile 3= $40,627-$52,387, Quartile

4= $52,388 or more

209

BAR: barbiturates / meprobamate, BZD: benzodiazepines, CBVD: cerebrovascular disease, CD: chronic disease, CHF: congestive heart failure, COPD: chronic obstructive pulmonary disease, CRF: chronic renal failure, EMR: electronic medical record, ER: emergency referral, IHD: ischemic heart disease, PA: physician assistant, NBZ: non-benzodiazepine hypnotics / ramelteon, NP/MW: nurse practitioner/midwife, PI: potentially inappropriate,

RN/LPN: registered nurse or licensed practical nurse, SE: standard error 210

Table 17: Sedative Orders for Older Community-dwelling Adults in 2010

Percent of visits Percent of orders Sedative Group Sedative Name Estimated visits N=23 821 026 n=25 459 371

Benzodiazepines Alprazolam 5,349,972 22.5 21.0

Lorazepam 5,108,848 21.4 20.1

Clonazepam 1,945,966 8.2 7.6

Diazepam 1,246,320 5.2 4.9

Temazepam 923,993 3.9 3.6

Chlordiazepoxide 780,405 3.3 3.1

Midazolam 335,503 1.4 1.3

Clorazepate 167,831 0.7 0.7

Oxazepam 102,711 0.4 0.4

Estazolam 62,781 0.3 0.2

Triazolam 62,604 0.3 0.2

Group Total 16,086,934 67.5 63.2

Non-benzodiazepines, Ramelteon Zolpidem 6,541,365 27.5 25.7

Eszopiclone 559,647 2.3 2.2

Zaleplon 61,350 0.3 0.2

Ramelteon 33,292 0.1 0.1

Group Total 7,195,654 30.2 28.3 211

Percent of visits Percent of orders Sedative Group Sedative Name Estimated visits N=23 821 026 n=25 459 371

Barbiturates, Meprobamate Butalbital 768,017 3.2 3.0

Primidone 668,221 2.8 2.6

Phenobarbital 543,459 2.3 2.1

Meprobamate 103,595 0.4 0.4

Butabarbital 93,491 0.4 0.4

Group Total 2,176,783 9.1 8.6 212

Table 18: Multinomial Logistic Models for Potentially Inappropriate Sedative Prescribing for Older Adultsa

Sedative Factors Values OR 95%CI p-valuec AOR 95% CI, AOR p-valued groupb

Arthritis Yes BAR 2.345 0.948-5.797 0.065 1.634 0.561-4.754 0.365

BZD 1.666 1.005-2.761 0.048 1.466 0.757-2.840 0.254

Depression Yes BAR 0.900 0.347-2.333 0.827 0.786 0.226-2.730 0.703

BZD 2.166 1.079-4.348 0.030 2.342 1.189-4.614 0.014

Obesity Yes BAR 1.713 0.433-6.782 0.440 2.144 0.435-10.580 0.346

BZD 0.385 0.143-1.033 0.058 0.312 0.099-0.987 0.047

PI conditions Yes BAR 0.501 0.043-5.870 0.579 0.455 0.028-7.266 0.575

BZD 3.935 0.670-23.097 0.128 3.687 0.434-31.364 0.230

Income Quartile 1 BAR 1.561 0.623-3.909 0.339 1.352 0.548-3.338 0.510

BZD 1.804 0.902-3.607 0.094 1.961 0.934-4.117 0.075

Quartile 2 BAR 0.786 0.303-2.037 0.617 0.697 0.233-2.088 0.516

BZD 2.390 1.235-4.624 0.010 2.414 1.163-5.011 0.018

Quartile 3 BAR 1.635 0.603-4.438 0.331 1.748 0.562-5.434 0.332

BZD 2.291 1.239-4.235 0.009 2.460 1.285-4.708 0.007

Medications 1-8 BAR 1.074 0.912-1.266 0.388 1.139 0.931-1.393 0.203

BZD 0.927 0.838-1.025 0.137 0.926 0.818-1.048 0.221

Services 0-5 BAR 1.257 0.959-1.648 0.097 1.290 0.904-1.839 0.159

213

Sedative Factors Values OR 95%CI p-valuec AOR 95% CI, AOR p-valued groupb

BZD 1.121 0.990-1.269 0.071 1.122 0.972-1.295 0.114

Physician Assist. Yes BAR 0.201 0.034-1.170 0.074 0.213 0.043-1.048 0.057

BZD 0.517 0.238-1.121 0.094 0.731 0.318-1.681 0.458

RN/LPN Yes BAR 2.412 1.068-2.981 0.034 2.385 0.909-6.260 0.077

BZD 1.678 1.000-2.816 0.050 1.526 0.868-2.684 0.141

Specialty General/ Family BAR 1.331 0.569-3.115 0.507 1.121 0.418-3.008 0.819

BZD 1.234 0.681-2.236 0.486 0.868 0.466-1.617 0.653

Internal BAR 0.690 0.190-2.502 0.569 0.769 0.230-2.564 0.666

BZD 0.823 0.347-1.951 0.657 0.747 0.360-1.552 0.432

General surgery BAR 0.377 0.033-4.263 0.428 0.198 0.012-3.390 0.261

BZD 0.615 0.217-1.749 0.360 0.510 0.160-1.623 0.252

Obs/Gynecology BAR 2.626 0.312-22.100 0.372 2.461 0.261-23.170 0.428

BZD 1.798 0.575-5.620 0.311 1.571 0.461-5.351 0.467

Orthopedic surg BAR 5.409 0.831-35.222 0.077 3.616 0.324-40.385 0.294

BZD 1.343 0.428-4.209 0.611 0.974 0.230-4.114 0.971

Cardiovascular BAR 0.714 0.172-2.975 0.642 0.610 0.140-2.649 0.506

BZD 0.794 0.440-1.430 0.439 0.868 0.421-1.789 0.699

Urology BAR 0.449 0.069-2.898 0.397 0.540 0.075-3.872 0.537 214

Sedative Factors Values OR 95%CI p-valuec AOR 95% CI, AOR p-valued groupb

BZD 0.433 0.201-0.932 0.033 0.462 0.181-1.179 0.105

Neurology BAR 8.336 1.795-38.711 0.007 6.243 1.373-28.397 0.018

BZD 3.569 1.058-12.031 0.040 2.927 0.928-9.238 0.067

Otolaryngology BAR 4.562 1.092-19.047 0.038 3.789 1.036-13.855 0.044

BZD 1.204 0.292-4.960 0.795 0.699 0.174-2.809 0.612

Oncology BAR 0.302 0.046-1.961 0.208 0.339 0.048-2.390 0.275

BZD 1.035 0.392-2.735 0.944 0.754 0.265-2.147 0.594

Solo practice Yes BAR 1.017 0.445-2.322 0.968 1.005 0.400-2.524 0.991

BZD 0.650 0.398-1.062 0.085 0.642 0.385-1.069 0.088

Ownership Physician BAR 1.789 0.766-4.179 0.177 1.897 0.694-5.182 0.210

BZD 0.779 0.460-1.320 0.351 1.010 0.548-1.861 0.974

EMR use Yes BAR 0.517 0.265-1.010 0.053 0.431 0.189-0.979 0.045

BZD 0.962 0.571-1.621 0.883 0.821 0.505-1.335 0.424

Key: a: Model Information: Population size= 23,176,053; Pseudo R-square (McFadden)=0.099; Test of Model Effects: Wald F = 5.970 (44, 80) p=<0.001 b: Reference group is Non-benzodiazepine hypnotics c: Significance based on t-test with 132 degrees of freedom d: Significance based on t-test with 127 degrees of freedom 215

ASH: anxiolytics/sedative-hypnotics, AOR: adjusted odds ratio, BAR: barbiturates/chloral hydrate; BZD: benzodiazepines, CI: confidence interval,

CVD: cardiovascular disorders, EMR: electronic medical records, RN/LPN: registered nurse or licensed practical nurse, OR: odds ratio, PI: potentially inappropriate 216

CHAPTER 6

RESULTS AND DISCUSSION: ANTIPSYCHOTICS IN DEMENTIA

Results

Population characteristics

Two hundred and seven sampled visits were by older adults with at least one

indicator of dementia, which represented 5,141,532 weighted visits (2% of older adults).

Ages ranged from 65 to 96 years, with a mean of 79.5 years. Just over half of patients

were female, 87% were white, 93% were non-Hispanic and less than 1% had a primary diagnosis of schizophrenia or bipolar disorder – Table 19.

Antipsychotics were ordered for 253,288 (4.6%) older adults with dementia, involving mostly second-generation agents (3.4%). Quetiapine was the most frequently prescribed antipsychotic, accounting for 44.7% of orders for 2% of the visits, followed by haloperidol (26.8% of orders) - Table 20. 217

Determinants of Antipsychotic use

Statistically significant associations between antipsychotic use and the following

factors were found: diabetes, hyperlipidemia, hypertension, schizophrenia or bipolar

disorder, practice ownership and time spent with physician – Table 19. Few patients

living with diabetes, hyperlipidemia or hypertension received antipsychotics, whereas

most patients with schizophrenia/bipolar disorder received an antipsychotic. Physician-

owned practices were conservative in the use of antipsychotics in these patients. Patients

who received antipsychotics spent an average of five additional minutes with physicians

but this was not significantly different from the population mean. None of the following

patient groups received antipsychotic medications: patients with injury-related visits, new

patients, patients with ischemic heart disease or osteoporosis, seen in non-metropolitan areas, general surgery, orthopedic surgery, dermatology, urology, ophthalmology, otolaryngology or oncology.

Bivariate analyses found that as time with physicians increased, the odds of antipsychotic use also increased. The multivariate model included sex, payment type, depression, diabetes, hyperlipidemia, hypertension, schizophrenia/bipolar disorder, potentially inappropriate conditions, time spent with physician, ownership and the use of

EMRs – Table 21. The estimate for visits involving physician assistants was unreliable.

Controlling for identified confounders, depression, hypertension, time with physician and practice ownership were determinants of antipsychotic use among older adults with dementia. The presence of depression was associated with almost 8.5 times greater odds of antipsychotic prescription than for patients without the condition. Also, 218

for each additional minute spent with the physician, the odds of antipsychotic use

increased by 6.3%. Conversely, the presence of hypertension was associated with 17

times lower odds, and visits to physician-owned practices were associated with 19.7

times lower odds of antipsychotic prescribing.

Risk of Emergency Outcomes

A statistically significant association was found between antipsychotic prescribing and emergency-related visit outcomes. Among patients who received antipsychotics, approximately half were referred to an emergency room or hospitalized. Visits involving antipsychotic prescriptions were associated with 22 times greater risk of emergency referral or hospitalization (RR=22.01, 95% CI: 2.621-184.855, p=0.005).

Discussion

Antipsychotic prescribing on older adults with dementia was infrequent, involving primarily second-generation agents. Factors influencing the use of antipsychotics include depression, hypertension, consultation time and visits to physician-owned practices. The

use of antipsychotics in patients with dementia was associated with a higher risk of

emergency referral or hospitalization during or following the visit. 219

Less than one in twenty older adults with dementia received a prescription for an antipsychotic medication in 2010, most often quetiapine or haloperidol. This is lower than population estimates reported in earlier US studies by Rhee et al. (in 2000: 19.1%) and Rattinger et al. (in 2008: 24%).78,137 Prevalence rates for quetiapine and haloperidol

were also lower than those reported by Gruber-Baldini et al. (in 2002: 2.3% for both)

among Medicare beneficiaries.131 In a recent report by the Government Accountability

Office, almost 14% of Medicare Part D enrollees residing outside of nursing homes received an antipsychotic in 2012.177 However, the comparisons between these studies

are limited as the cohorts involved vary based on survey methodology, setting and data used (e.g. claims data vs patient records).

The relatively conservative use of antipsychotics found in this study may be a

result of prescriber learning over time, awareness of regulatory warnings issued in 2005 and 2008, adverse patient outcomes and/or prescribing restrictions. Although various initiatives to reduce the use of antipsychotics among nursing home elderly with dementia, including provider education, have been implemented by the US Department of Health and Human Services, similar efforts are notably absent in other care settings. In response to the report by the Government Accountability Office, the Department of Health and

Human Services agreed with the need for efforts to reduce antipsychotic use in non- institutional settings.177 The findings of this study further reiterate the need for

assessment and interventions, to minimize harm arising from the use of antipsychotics

among community-dwelling older adults with dementia. Office-based prescribers may

need additional support through continuing medical education, review of prescribing

protocols and clinical decision support systems. 220

Determinants of Antipsychotic Prescribing

Antipsychotic use was not observed among patients with injury-related visits,

ischemic heart disease or osteoporosis, among new patients or among those seen in non-

metropolitan locations. This suggests that prescribers avoid the use of these agents in

unfamiliar patients and for patients with conditions where the risks of falls, fractures,

and/or adverse cardiovascular events, including death may be increased. The absence of

orders among patients seen in non-metropolitan areas may be a result of referral of

patients to specialists in metropolitan areas, where access to emergency care and/or social

support may be greater. However, these could not be assessed as predictors with

statistical significance.

The most influential patient-related predictors of antipsychotic use were the

presence of depression or hypertension. The increased likelihood of use of

antipsychotics among patients with depression is expected, given that it is one of the

behavioral symptoms of dementia.178 In addition, antipsychotics may also be used as

adjunctive therapy for the management of psychotic features of bipolar depression or

major depressive disorders.10,179 Nevertheless, patients with depression who receive

antipsychotics require medication education as well as regular follow-up by health

professionals to identify and report adverse effects when they arise. Medication use reviews of antipsychotic use among patients with depression are needed to identify inappropriate use and to assess clinical outcomes. These patients and their caregivers should be advised about adverse antipsychotic effects that may arise. 221

Conversely, antipsychotic use was less likely among patients with hypertension.

This may be due to physicians’ awareness of the role of hypertension and other stroke

factors as risk markers for vascular cognitive impairment and as possible risk markers for

Alzheimer’s disease.180 The use of antipsychotic medications in these patients will

further increase the risk of adverse cardiovascular complications or events.

Although, an earlier study of US elders with dementia, identified age above 85

years as a predictor of antipsychotic use, increasing age was not a predictor of antipsychotic use in this study.78

Among the proxy indicators of the patient-physician relationship, time spent with the physician was the sole indicator with an effect on antipsychotic prescribing. As the consultation time increased, the odds of receiving an antipsychotic increased incrementally. Physicians may require additional time to conduct neuropsychiatric assessments, evaluate therapeutic outcomes, and/or trace events with the patient or caregiver before prescribing antipsychotic therapy. As consultation time increases, multiple aspects of patient care may be facilitated including information retrieval, problem identification, preventive care and patient education. Patients with dementia may require additional consultation time to ensure a comprehensive assessment. Visits where insufficient time is spent to determine the need for antipsychotics may result in sub-optimal treatment.

The association between visit duration and indicators of the quality of care, such as patient satisfaction identified in studies of physicians may be due to the facilitation of time-intensive activities.158 In a British general practice study, physicians with 10-minute

visit intervals asked more questions about the patient’s health history, assessed 222 psychosocial concerns, discussed more problems, explained management and provided health education compared to those with 5 and 7.5-minute bookings.159,160 Subsequent to the identification or clarification of problems, physicians may determine that the need for antipsychotic medication outweighs the risk of harm. Qualitative studies among physicians may help to describe the time and procedures involved in assessment of elderly with dementia, and the rationale involved in the decision to prescribe an antipsychotic medication.

Physician-owned practices were less likely to be involved in antipsychotic prescribing for older adults with dementia. This may be due to differences in clinical protocols, prescribing restrictions and/or concerns for professional liability, in comparison to non-physician-owned practices. Comparisons of prescribing protocols within institutions with different proprietary relationships and their influence on antipsychotic use may provide greater insight into this relationship. Practices that are not physician-owned may need to review prescribing protocols regarding the use of antipsychotics among older adults with dementia to reduce the risk of preventable harm.

In these practices, patients who may benefit from alternative approaches to treat behavioral and psychological symptoms of dementia should be switched from antipsychotic medications.

223

Emergency Outcomes

Following the visit, patients who received antipsychotic orders were more likely to be referred to an emergency room or hospitalized than patients without antipsychotic orders. Referred patients may have presented with aggressive behaviors or adverse cardiovascular, metabolic or autonomic effects during the visit, requiring acute care or hospitalization. This is similar to the findings of other studies reporting increased risks of adverse cardiovascular events and death with the use of antipsychotics in older adults with dementia in other settings.18 Older adults with dementia seen in physician offices who receive antipsychotic medication should be monitored for severe adverse events during and after visits. Patients and caregivers should be educated to recognize symptoms of these adverse drug effects and to seek immediate medical care.

Limitations

The findings of this study are applicable to patients with a primary diagnosis of dementia and patients receiving medications for the treatment of Alzheimer’s disease, seen in office-based practices.

The scope of the survey limited the ability to identify and assess the influence of unlisted chronic conditions, secondary diagnoses and nine or more medications. In addition, indicators of the patient-physician relationship and physician characteristics were based on proxy indicators. Measures of patient satisfaction, physician age and 224

experience were not feasible. Investigation of other patterns of antipsychotic use, such

as dosages, duration and related indication was not undertaken due to the absence of

clinical and medication-related details. The study focused on the decision to prescribe

antipsychotics, but did not include the influence of patient tolerability or response to

earlier therapeutic efforts. Although Beers 2012 criteria provide the most up-to-date version available, it is subject to time-lag and publication bias, as studies published after its development would be excluded. Similarly, prescribing patterns may change over time and with physician learning. Therefore periodic assessments of antipsychotic

prescribing are needed to determine changes in prescribing over time.

The association between emergency outcomes and antipsychotic use was

measured in the absence of confounding factors, such as number of disease severity. As

a result, the increased risk may not be attributed solely to the use of antipsychotic

medications. The reasons for emergency referral were not provided, thereby restricting

identification of the various outcomes.

Although the study identified associations, which may be included in future

research, the findings do not prove causation. The information may guide the application

of measures of antipsychotic prescribing quality and related strategies to minimize

inappropriate use. The determinants identified highlight sub-populations that require

medication use reviews and/or monitoring of clinical outcomes. Further studies may be

developed to assess the patient-physician relationship between physicians and older

adults with dementia, and to describe prescribing rationale and variations in antipsychotic

prescribing within non-physician organizations.

225

Table 19: Population Characteristics, Antipsychotic Orders and Emergency Outcomes for Older Community-dwelling Adults with Dementia in 2010

Weighted %a or Factors Values Visit Count Weighted Visits Antipsychotic Orders p-valueb Mean (SE) Yes (4.6%) No (95.4%) Age 65-96 years 211 5 141 532 79.54 (0.7) 80.35 (4.1) 79.50 (0.7) 0.842

Sex Female 109 2 882 303 56.1 (4.9) 2.8 97.2 0.194 Male 102 2 259 229 43.9 (4.9) 6.8 93.2

Race White 178 4 458 458 86.7 (3.4) 4.9 95.1 0.478 Black 22 503 304 9.8 (3.0) 1.9 98.1 Other 11 179 770 3.5 (1.5) 3.1 96.9

Ethnicity Hispanic 24 364 652 7.1 (2.2) 4.3 95.7 0.946 Not Hispanic 187 4 776 880 92.9 (2.2) 4.6 95.4

Incomec Quartile 1 35 931 690 18.1 (4.4) 2.1 97.9 0.268 Quartile 2 58 1 161 288 22.6 (3.7) 1.9 98.1 Quartile 3 48 996 216 19.4 (4.6) 9.4 90.6 Quartile 4 60 1 882 813 36.6 (4.7) 5.4 94.6

Payment type Medicare 182 4 504 878 87.6 (3.5) 3.3 96.7 0.117 Medicaid 6 249 840 4.9 (2.5) 6.3 93.7 Other 17 291 784 5.7 (2.0) 25.0 75.0 226

Weighted %a or Factors Values Visit Count Weighted Visits Antipsychotic Orders p-valueb Mean (SE) Yes (4.6%) No (95.4%)

Injury visit Yes 6 308 301 6.0 (2.6) -- 100 0.465 No 205 4 833 231 94.0 (2.6) 4.9 95.1

Chronic Arthritis 44 1 033 332 20.1 (4.9) 5.6 94.4 0.761 Conditions Asthma 7 137 284 2.7 (1.5) 3.2 96.8 0.736 Cancer 23 530 281 10.3 (2.7) 10.0 90.0 0.448 CBVD 20 394 443 6.8 (1.9) 5.7 94.3 0.679 CHF 15 318 722 6.2 (2.2) 5.5 94.5 0.866 CRF 10 225 072 4.4 (2.0) 2.5 97.5 0.543 COPD 8 210 898 4.1 (2.0) 7.8 92.2 0.580 Depression 39 602 320 11.7 (2.2) 13.3 86.7 0.105 Diabetes 47 1 021 897 19.9 (4.1) 2.0 98.0 0.043 Hyperlipidemia 66 1 848 978 36.0 (4.8) 1.3 98.7 0.003 Hypertension 121 3 468 633 67.5 (4.3) 1.2 98.8 0.000 IHD 22 483 233 9.4 (2.8) -- 100.0 0.207 Obesity 7 367 558 7.1 (3.1) 6.6 93.4 0.622 Osteoporosis 15 493 752 9.6 (3.4) -- 100.0 0.233

Schizophrenia Yes 3 32 836 0.6 (0.4) 82.8 17.2 0.000 Or BPD No 208 5 108 696 99.4 (0.4) 4.1 95.9

PI conditions Yes 24 379 872 7.4 (1.5) 19.2 80.8 0.134 227

Weighted %a or Factors Values Visit Count Weighted Visits Antipsychotic Orders p-valueb Mean (SE) Yes (4.6%) No (95.4%) No 187 4 761 660 92.6 (1.5) 3.4 96.6

Established Yes 187 4 698 630 91.4 (3.0) 5.0 95.0 0.424 patient No 24 442 902 8.6 (3.0) -- 100.0

Time spent 5-60 minutes 207 5 052 355 20.13 (1.1) 25.32 (3.1) 19.88 (1.0) 0.024

Medications 1-8 211 5 141 532 6.04 (0.3) 6.49 (0.5) 6.02 (0.3) 0.338

Services 0-5 211 5 141 532 1.62 (0.2) 1.85 (0.8) 1.61 (0.2) 0.768

Specialty General/ Family 27 1 157 877 22.5 (4.6) 5.0 95.0 0.645 Internal 24 1 808 048 35.2 (5.1) 4.0 96.0 Cardiovascular 27 372 553 7.2 (1.6) 3.3 96.7 Psychiatry 4 59 490 1.2 (0.2) 29.6 70.4 Neurology 74 584 902 11.4 (2.1) 7.9 92.1 Other 55 1 158 662 22.5 (3.4) 2.5 97.5

Non-physician Physician Assistant 7 343 323 6.7 (4.4) 1.3 98.7 0.186 provider NP/MW 5 49 541 1.0 (0.6) 19.4 80.6 0.331 RN/LPN 43 1 375 599 26.8 (7.0) 5.0 95.0 0.892

Region Northeast 36 709 586 13.8 (2.4) 2.5 97.5 0.414 Midwest 53 1 619 532 31.5 (5.0) 6.7 93.3

228

Weighted %a or Factors Values Visit Count Weighted Visits Antipsychotic Orders p-valueb Mean (SE) Yes (4.6%) No (95.4%) South 64 1 656 477 32.2 (5.5) 5.6 94.4 West 58 1 155 937 22.5 (4.7) 1.5 98.5

Metropolitan Yes 181 4 463 689 86.8 (5.6) 5.3 94.7 0.248 area No 30 677 843 13.2 (5.6) -- 100.0

Solo Practice Solo 75 1 712 085 33.3 (6.1) 2.9 97.1 0.326 Group 136 3 429 447 66.7 (6.1) 5.4 94.6

Ownership Physician-owned 164 3 892 934 75.7 (5.0) 1.9 98.1 0.001 Other 47 1 248 598 24.3 (5.0) 12.8 87.2

EMR use Yes 110 3 264 990 63.5 (6.8) 2.5 97.5 0.114 No 98 1 792 629 34.9 (6.4) 8.5 91.5

Emergencyd Yes 2 29 567 0.6 (0.3) 49.9 50.1 0.014 (dependent) No 209 5 111 965 99.4 (0.3) 4.3 95.7 Key: a: Weighted percentages based on valid visits (N=5,141,532). Dependent variable is Antipsychotic use. Total percentages may not equal 100% due to missing values. b: Based on Likelihood ratio (adjusted F) for categorical data, or modified Wald F test for interval data: age, number of medications, time spent, extended services. 229 c: Median household income of patient’s zip code: Quartile 1= less than $32,794, Quartile 2= $32,794-$40,626, Quartile 3= $40,627-$52,387, Quartile

4= $52,388 or more d: Weighted percentages based on antipsychotic use. Dependent variable is Emergency referral or hospitalization (yes/no).

BPD: bipolar disorder, CBVD: cerebrovascular disease, CD: chronic disease, CHC: community health center, CHF: congestive heart failure, COPD: chronic obstructive pulmonary disease, CRF: chronic renal failure, EMR: electronic medical record, ER: emergency room, IHD: ischemic heart disease,

PA: physician assistant, NP/MW: nurse practitioner/midwife, PI: potentially inappropriate, RN/LPN: registered nurse or licensed practical nurse, SE: standard error 230

Table 20: Antipsychotic Medication Orders for Community-dwelling Older Adults with Dementia in 2010

Percent of visitsa Percent AP visitsa Class Antipsychotic Weighted visits n=5 141 532 n=235 688

First Generation Haloperidol 63 129 1.2 26.8

Pimozide + quetiapine 17 600 0.3 7.5

Second Generation Quetiapineb 87 642 1.7 37.2

Risperidone 21 708 0.4 9.2

Olanzapine 21 270 0.4 9.2

Clozapine 14 741 0.3 6.3

Ziprasidone 9 598 0.2 4.1

Total visits with antipsychotics 253 288 4.6 100

Visits without antipsychotics 4 905 844 95.4

Key: a: Total percentage may exceed 100% due to rounding. b: Excludes visits with pimozide + quetiapine

AP: antipsychotic 231

Table 21: Multivariate Logistic Regression Models for Antipsychotic Orders for Older Community-dwelling Adults with

Dementiaa

Factor Values OR 95% CI, OR p-value AORa 95% CI, AOR p-value Sex Female 0.393 0.091-1.705 0.207 1.343 0.316-5.709 0.684 Male 1.000 1.000 Depression Yes 1.295 0.842-22.302 0.078 8.464 1.805-39.686 0.008 No 1.000 1.000 Diabetes Yes 0.369 0.123-1.106 0.074 0.593 0.137-2.565 0.477

No 1.000 1.000

Hyperlipidemia Yes 0.194 0.054-0.702 0.013 0.336 0.073-1.549 0.158 No 1.000 1.000 Hypertension Yes 0.094 0.026-0.337 0.000 0.058 0.006-0.530 0.013 No 1.000 1.000 PI conditions Yes 6.709 0.721-62.455 0.093 1.847 0.424-8.050 0.406 No 1.000 1.000 Payment type Medicare 0.101 0.010-1.017 0.052 0.450 0.093-2.168 0.312 Medicaid 0.200 0.010-4.167 0.292 3.102 0.172-56.076 0.436 Other 1.000 1.000 Physician Assistantb Yes 0.253 0.019-3.378 0.292 -- No 1.000 Time with physician 5-60 mins 1.046 1.006-1.088 0.025 1.063 1.008-1.120 0.024 Ownership Physician-owned 0.135 0.039-0.466 0.002 0.051 0.011-0.236 0.000 Other 1.000 1.000 EMR use Yes 0.278 0.054-1.442 0.125 0.431 0.158-1.176 0.099 No 1.000 1.000 232

Key: a: Multivariate model Information: population = 4 873 412. Wald F (11,39)=14.332, p=0.000. Pseudo R2 (McFadden)=0.451. T-test of coefficients based on 49 degrees of freedom. b: Unreliable estimator in multivariate model

AOR: adjusted odds ratio, BPD: bipolar I disorder, CI: confidence interval, EMR: electronic medical records, OR: odds ratio, PI: potentially inappropriate conditions 233

CHAPTER 7

CONCLUSIONS

Although office-based physicians in the US rarely prescribe potentially inappropriate psychotropic medications to be avoided unconditionally in older adults, most patients are exposed to classes that should be avoided in conditions that present drug-disease interactions and increase the risk of adverse events. For antidepressant users, prescribers preferentially order selective serotonin reuptake inhibitors and other antidepressants to use with caution, while minimizing the use of tertiary tricyclic antidepressants. Among the sedatives, benzodiazepines are the most frequently ordered class for older adult users. Antipsychotic prescribing for patients with dementia is infrequent among office-based physicians, involving mostly second-generation antipsychotic medications.

The most significant predictor of inappropriate antidepressant and sedative prescribing is the use of electronic medical records, which is associated with improved quality of prescribing. Other factors related to lower risks of inappropriate psychotropic prescribing of various classes were patient age, race, asthma, hypertension, obesity, and practice ownership. Conversely, income, depression, and physician specialty were associated with inappropriate psychotropic prescribing choices. Although increased consultation time is associated with a lower risk of inappropriate antidepressant prescribing, this relationship is reversed for antipsychotic prescribing in dementia. 234

Prescribing of either tertiary tricyclic antidepressants or of antipsychotics for patients with dementia is associated with increased risks of emergency referrals or hospitalization following visits. The determinants identified by this study may be used to develop quality indicators, policies and interventions to improve the quality of prescribing of antidepressants, sedatives and antipsychotics for older adults. 235

REFERENCES

1. U.S. Census Bureau. Current Population Survey, Annual Social and Economic

Supplement 2012. 2013.

2. U.S. Census Bureau, Population Division. Table 3. Percent Distribution of the

Projected Population by Selected Age Groups and Sex for the United States: 2015

to 2060 (NP2012-T3). 2012.

3. U.S. Department of Health and Human Services, Centers for Disease Control and

Prevention, National Center for Health Statistics. Health, United States, 2012 -

with Special Feature on Emergency Care. 2012.

4. U.S. Census Bureau, Population Division. Table 10. Projected Life Expectancy at

Birth by Sex, Race, and Hispanic Origin for the United States: 2015 to 2060

(NP2012-T10). 2012.

5. United Nations Department of Economics. World population ageing, 1950-2050.

New York: United Nations; 2002.

6. Understanding Supplemental Security Income (SSI) Eligibility Requirements.

2014; 2014 edition:http://www.ssa.gov/ssi/text-eligibility-ussi.htm. Accessed Feb

22nd, 2015.

7. Klees BS, Wolfe CJ. Brief Summaries of Medicare & Medicaid: Title XVIII and

Title XIX of the Social Security Act. Centers for Medicare & Medicaid Services;

Nov 1, 2013 2013. 236

8. Van Spall HC, Toren A, Kiss A, Fowler RA. Eligibility criteria of randomized

controlled trials published in high-impact general medical journals: A systematic

sampling review. JAMA. 2007;297(11):1233-1240.

9. DeBattista C. Chapter 30. Antidepressant Agents. In: Katzung BG, Masters SB,

Trevor AJ, eds. Basic & Clinical Pharmacology, 12e. New York, NY: The

McGraw-Hill Companies; 2012.

10. American Psychiatric Association. Practice guideline for the treatment of patients

with major depressive disorder. American Psychiatric Association; 2010.

11. Trevor AJ, Way WL. Chapter 22. Sedative-Hypnotic Drugs. In: Katzung BG,

Masters SB, Trevor AJ, eds. Basic & Clinical Pharmacology, 12e. New York,

NY: The McGraw-Hill Companies; 2012.

12. Melton ST, Kirkwood CK. Chapter 53. Anxiety Disorders I: Generalized Anxiety,

Panic, and Social Anxiety Disorders. In: DiPiro JT, Talbert RL, Yee GC, Matzke

GR, Wells BG, Posey LM, eds. Pharmacotherapy: A Pathophysiologic Approach,

9e. New York, NY: The McGraw-Hill Companies; 2014.

13. Dopp JM, Phillips BG. Chapter 55. Sleep Disorders. In: DiPiro JT, Talbert RL,

Yee GC, Matzke GR, Wells BG, Posey LM, eds. Pharmacotherapy: A

Pathophysiologic Approach, 9e. New York, NY: The McGraw-Hill Companies;

2014.

14. Crismon L, Argo TR, Buckley PF. Chapter 50. Schizophrenia. In: DiPiro JT,

Talbert RL, Yee GC, Matzke GR, Wells BG, Posey LM, eds. Pharmacotherapy:

A Pathophysiologic Approach, 9e. New York, NY: The McGraw-Hill Companies;

2014. 237

15. Meltzer H. Chapter 29. Antipsychotic Agents & Lithium. In: Katzung BG,

Masters SB, Trevor AJ, eds. Basic & Clinical Pharmacology, 12e. New York,

NY: The McGraw-Hill Companies; 2012.

16. Jeste DV, Blazer D, Casey D, et al. ACNP White Paper: update on use of

antipsychotic drugs in elderly persons with dementia. Neuropsychopharmacology

: official publication of the American College of Neuropsychopharmacology.

2008;33(5):957-970.

17. Aparasu RR, Jano E, Johnson ML, Chen H. Hospitalization risk associated with

typical and use in community-dwelling elderly patients.

The American journal of geriatric pharmacotherapy. 2008;6(4):198-204.

18. Mittal V, Kurup L, Williamson D, Muralee S, Tampi RR. Review: Risk of

Cerebrovascular Adverse Events and Death in Elderly Patients With Dementia

When Treated With Antipsychotic Medications: A Literature Review of

Evidence. American Journal of Alzheimer's Disease and Other Dementias.

2011;26(1):10-28.

19. Food and Drug Administration. Atypical Antipsychotic Agents. April 11 2005.

20. Food and Drug Administration. Antipsychotics, Conventional and Atypical. June

16 2009.

21. Penge J, Crome P. Appropriate prescribing in older people. Reviews in Clinical

Gerontology. 2013:1-20.

22. Edwards IR, Aronson JK. Adverse drug reactions: definitions, diagnosis, and

management. Lancet. 2000;356(9237):1255-1259.

238

23. Salvi F, Marchetti A, D’Angelo F, Boemi M, Lattanzio F, Cherubini A. Adverse

drug events as a cause of hospitalization in older adults. Drug safety : an

international journal of medical toxicology and drug experience. 2012;35(1):29-

45.

24. Spinewine A, Schmader KE, Barber N, et al. Appropriate prescribing in elderly

people: how well can it be measured and optimised? Lancet. 2007;370(9582):173-

184.

25. Kaufmann C, Tremp R, Hersberger K, Lampert M. Inappropriate prescribing: a

systematic overview of published assessment tools. European journal of clinical

pharmacology. 2014;70(1):1-11.

26. Beers MH, Ouslander JG, Rollingher I, Reuben DB, Brooks J, Beck JC. Explicit

criteria for determining inappropriate medication use in nursing home residents.

Archives of Internal medicine. 1991;151(9):1825.

27. Beers MH. Explicit criteria for determining potentially inappropriate medication

use by the elderly: an update. Arch Intern Med. 1997;157(14):1531-1536.

28. Fick DM, Cooper JW, Wade WE, Waller JL, Maclean JR, Beers MH. Updating

the Beers criteria for potentially inappropriate medication use in older adults:

results of a US consensus panel of experts. Archives of Internal Medicine.

2003;163(22):2716-2724.

29. Campanelli CM. American Geriatrics Society Updated Beers Criteria for

Potentially Inappropriate Medication Use in Older Adults. Journal of the

American Geriatrics Society. 2012;60(4):616-631. 239

30. McLeod PJ, Huang AR, Tamblyn RM, Gayton DC. Defining inappropriate

practices in prescribing for elderly people: a national consensus panel. Canadian

Medical Association Journal. 1997;156(3):385-391.

31. Shekelle PG, MacLean CH, Morton SC, Wenger NS. Assessing care of

vulnerable elders: methods for developing quality indicators. Annals of internal

medicine. 2001;135(8 Pt 2):647-652.

32. Wenger NS, Shekelle PG. Assessing care of vulnerable elders: ACOVE project

overview. Annals of internal medicine. 2001;135(8 Pt 2):642-646.

33. Gallagher P, O’Mahony D. STOPP (Screening Tool of Older Persons’ potentially

inappropriate Prescriptions): application to acutely ill elderly patients and

comparison with Beers’ criteria. Age and ageing. 2008;37(6):673-679.

34. HEDIS 2015 Measures: Summary Table of Measures, Product Lines and

Changes. Vol 2. Washington, DC: National Committee for Quality Assurance;

2015.

35. Kronick R, Clancy C, Munier W, et al. National Healthcare Quality Report.

Rockville, MD: U.S. Department of Health and Human Services;2014.

36. O'Mahony D, O'Sullivan D, Byrne S, O'Connor MN, Ryan C, Gallagher P.

STOPP/START criteria for potentially inappropriate prescribing in older people:

version 2. Age and Ageing. 2015;44:213-218.

37. Wenger NS, Roth CP, Shekelle P, Investigators A. Introduction to the assessing

care of vulnerable elders-3 quality indicator measurement set. Journal of the

American Geriatrics Society. 2007;55 Suppl 2:S247-252. 240

38. Aparasu RR, Mort JR. Inappropriate prescribing for the elderly: beers criteria-

based review. The Annals of pharmacotherapy. 2000;34(3):338-346.

39. Guaraldo L, Cano FG, Damasceno GS, Rozenfeld S. Inappropriate medication use

among the elderly: a systematic review of administrative databases. BMC

geriatrics. 2011;11:79.

40. Opondo D, Eslami S, Visscher S, et al. Inappropriateness of medication

prescriptions to elderly patients in the primary care setting: a systematic review.

PloS one. 2012;7(8):e43617.

41. Davidoff AJ, Miller GE, Sarpong EM, Yang E, Brandt N, Fick DM. Prevalence of

Potentially Inappropriate Medication Use in Older Adults Using the 2012 Beers

Criteria. Journal of the American Geriatrics Society. 2015.

42. Jano E, Aparasu RR. Healthcare outcomes associated with Beers' criteria: a

systematic review. Annals of Pharmacotherapy. 2007;41(3):438-448.

43. Fick DM, Mion LC, Beers MH, J LW. Health outcomes associated with

potentially inappropriate medication use in older adults. Research in nursing &

health. 2008;31(1):42-51.

44. Lin H-Y, Liao C-C, Cheng S-H, Wang P-C, Hsueh Y-S. Association of

potentially inappropriate medication use with adverse outcomes in ambulatory

elderly patients with chronic diseases: experience in a Taiwanese medical setting.

Drugs & Aging. 2008;25(1):49-59.

45. Price SD, Holman CAJ, Sanfilippo FM, Emery JD. Association Between

Potentially Inappropriate Medications From the Beers Criteria and the Risk of 241

Unplanned Hospitalization in Elderly Patients. Annals of Pharmacotherapy.

2014;48(1):6-16.

46. Fu AZ, Jiang JZ, Reeves JH, Fincham JE, Liu GG, Perri M, 3rd. Potentially

inappropriate medication use and healthcare expenditures in the US community-

dwelling elderly. Medical Care. 2007;45(5):472-476.

47. Stockl KM, Lisa L, Shaoang Z, Harada ASM. Clinical and Economic Outcomes

Associated With Potentially Inappropriate Prescribing in the Elderly. American

Journal of Managed Care. 2010;16(1):e1-e10.

48. Beier MT. Updated 2012 Beers Criteria: What’s Noteworthy and Cautionary?

Journal of the American Medical Directors Association. 2012;13(9):768-769.

49. Woolcott JC, Richardson KJ, Wiens MO, et al. Meta-analysis of the impact of 9

medication classes on falls in elderly persons. Archives of Internal Medicine.

2009;169(21):1952-1960.

50. Barnett K, McCowan C, Evans JM, Gillespie ND, Davey PG, Fahey T.

Prevalence and outcomes of use of potentially inappropriate medicines in older

people: cohort study stratified by residence in nursing home or in the community.

BMJ Quality & Safety. 2011;20(3):275-281.

51. Cahir C, Fahey T, Teeling M, Teljeur C, Feely J, Bennett K. Potentially

inappropriate prescribing and cost outcomes for older people: a national

population study. British journal of clinical pharmacology. 2010;69(5):543-552.

52. Chang CB, Chen JH, Wen CJ, et al. Potentially inappropriate medications in

geriatric outpatients with polypharmacy: application of six sets of published

explicit criteria. British journal of clinical pharmacology. 2011;72(3):482-489.

242

53. Meurer WJ, Potti TA, Kerber KA, et al. Potentially inappropriate medication

utilization in the emergency department visits by older adults: analysis from a

nationally representative sample. Academic emergency medicine : official journal

of the Society for Academic Emergency Medicine. 2010;17(3):231-237.

54. Zhan C, Sangl J, Bierman AS, et al. Potentially inappropriate medication use in

the community-dwelling elderly: findings from the 1996 Medical Expenditure

Panel Survey. JAMA. 2001;286(22):2823-2829.

55. Zhang YJ, Liu WW, Wang JB, Guo JJ. Potentially inappropriate medication use

among older adults in the USA in 2007. Age Ageing. 2011;40(3):398-401.

56. Curtis LH, Ostbye T, Sendersky V, et al. Inappropriate prescribing for elderly

Americans in a large outpatient population. Archives of Internal Medicine.

2004;164(15):1621-1625.

57. Goulding MR. Inappropriate medication prescribing for elderly ambulatory care

patients. Archives of Internal Medicine. 2004;164(3):305-312.

58. Pugh MJV, Hanlon JT, Zeber JE, Bierman A, Cornell J, Berlowitz DR. Assessing

potentially inappropriate prescribing in the elderly Veterans Affairs population

using the HEDIS 2006 quality measure. J Manag Care Pharm. 2006;12(7):537.

59. Al-Omar HA, Al-Sultan MS, Abu-Auda HS. Prescribing of potentially

inappropriate medications among the elderly population in an ambulatory care

setting in a Saudi military hospital: trend and cost. Geriatrics & gerontology

international. 2013;13(3):616-621. 243

60. Aparasu RR, Mort JR. Prevalence, correlates, and associated outcomes of

potentially inappropriate psychotropic use in the community-dwelling elderly.

The American journal of geriatric pharmacotherapy. 2004;2(2):102-111.

61. Fu AZ, Liu GG, Christensen DB. Inappropriate medication use and health

outcomes in the elderly. Journal of the American Geriatrics Society.

2004;52(11):1934-1939.

62. Aday LA, Andersen R. A framework for the study of access to medical care.

Health services research. 1974;9(3):208.

63. Andersen RM. Revisiting the Behavioral Model and Access to Medical Care:

Does it Matter? Journal of Health and Social Behavior. 1995;36(1):1-10.

64. Denig P, Haaijer-Ruskamp F, Zijsling D. How physicians choose drugs. Social

science & medicine. 1988;27(12):1381-1386.

65. Eisenberg JM. Sociologic influences on decision-making by clinicians. Annals of

internal medicine. 1979;90(6):957-964.

66. Stuck AE, Beers MH, Steiner A, Aronow HU, Rubenstein LZ, Beck JC.

Inappropriate medication use in community-residing older persons. Archives of

Internal Medicine. 1994;154(19):2195-2200.

67. Centers for Medicare & Medicaid Services. EHR Incentive Programs.

https://www.cms.gov/Regulations-and-

Guidance/Legislation/EHRIncentivePrograms/index.html?redirect=/ehrincentivep

rograms/. Accessed Feb 26, 2014.

244

68. Hsiao CJ, Jha AK, King J, Patel V, Furukawa MF, Mostashari F. Office-based

physicians are responding to incentives and assistance by adopting and using

electronic health records. Health affairs. 2013;32(8):1470-1477.

69. Clyne B, Bradley MC, Hughes C, Fahey T, Lapane KL. Electronic prescribing

and other forms of technology to reduce inappropriate medication use and

polypharmacy in older people: a review of current evidence. Clinics in geriatric

medicine. 2012;28(2):301-322.

70. Romano MJ, Stafford RS. Electronic health records and clinical decision support

systems: impact on national ambulatory care quality. Archives of Internal

Medicine. 2011;171(10):897-903.

71. Linder JA, Ma J, Bates DW, Middleton B, Stafford RS. Electronic health record

use and the quality of ambulatory care in the United States. Archives of Internal

Medicine. 2007;167(13):1400-1405.

72. Aparasu RR, Sitzman SJ. Inappropriate prescribing for elderly outpatients.

American journal of health-system pharmacy : AJHP : official journal of the

American Society of Health-System Pharmacists. 1999;56(5):433-439.

73. Caterino J, Emond J, Camargo C. Inappropriate medication administration to the

acutely ill elderly: a nationwide emergency department study, 1992-2000. Journal

of the American Geriatrics Society. 2004;52(11):1847-1855.

74. Chang C-M, Liu P-Y, Yang Y-H, Wu C-F, Lu F-H. Potentially inappropriate drug

prescribing among first-visit elderly outpatients in Taiwan. Pharmacotherapy.

2004;24(7):848-855. 245

75. Rigler SK, Jachna CM, Perera S, Shireman TI, Eng ML. Patterns of potentially

inappropriate medication use across three cohorts of older Medicaid recipients.

The Annals of pharmacotherapy. 2005;39(7-8):1175-1181.

76. Maio V, Hartmann CW, Poston S, Liu-Chen X, Diamond J, Arenson C.

Potentially inappropriate prescribing for elderly patients in 2 outpatient settings.

American journal of medical quality : the official journal of the American College

of Medical Quality. 2006;21(3):162-168.

77. Hustey FM, Wallis N, Miller J. Inappropriate prescribing in an older ED

population. The American journal of emergency medicine. 2007;25(7):804-807.

78. Rhee Y, Csernansky JG, Emanuel LL, Chang CG, Shega JW. Psychotropic

medication burden and factors associated with antipsychotic use: an analysis of a

population-based sample of community-dwelling older persons with dementia.

Journal of the American Geriatrics Society. 2011;59(11):2100-2107.

79. Aparasu RR, Mort JR, Sitzman S. Psychotropic prescribing for the elderly in

office-based practice. Clinical therapeutics. 1998;20(3):603-616.

80. Mort JR, Aparasu RR. Prescribing potentially inappropriate psychotropic

medications to the ambulatory elderly. Archives of Internal Medicine.

2000;160(18):2825-2831.

81. Bertakis KD, Azari R, Helms LJ, Callahan EJ, Robbins JA. Gender differences in

the utilization of health care services. Journal of Family Practice.

2000;49(2):147-152. 246

82. Mutchler JE, Burr JA. Racial differences in health and health care service

utilization in later life: the effect of socioeconomic status. Journal of Health and

Social Behavior. 1991:342-356.

83. Keyes KM, Martins S, Hatzenbuehler M, Blanco C, Bates L, Hasin DS. Mental

health service utilization for psychiatric disorders among Latinos living in the

United States: the role of ethnic subgroup, ethnic identity, and language/social

preferences. Social psychiatry and psychiatric epidemiology. 2012;47(3):383-394.

84. Chapman DP, Perry GS, Strine TW. The vital link between chronic disease and

depressive disorders. Prev Chronic Dis. 2005;2(1):A14.

85. Katon W, Lin EHB, Kroenke K. The association of depression and anxiety with

medical symptom burden in patients with chronic medical illness. General

hospital psychiatry. 2007;29(2):147-155.

86. US Public Health Service. International Classification of Diseases, Ninth

Revision. Washington, DC: US Government Printing Office; 2003.

87. Haaijer-Ruskamp F, Hemminki E. The social aspects of drug use. WHO

REGIONAL PUBLICATIONS EUROPEAN SERIES. 1993:97-97.

88. Godin G, Bélanger-Gravel A, Eccles M, Grimshaw J. Healthcare professionals'

intentions and behaviours: A systematic review of studies based on social

cognitive theories. Implementation science : IS. 2008;3(36):1-12.

89. Ajzen I. The theory of planned behavior. Organizational behavior and human

decision processes. 1991;50(2):179-211.

247

90. Raisch DW. A model of methods for influencing prescribing: Part II. A review of

educational methods, theories of human inference, and delineation of the model.

DICP : the annals of pharmacotherapy. 1990;24(5):537-542.

91. Bissessur SW, Geijteman EC, Al‐Dulaimy M, et al. Therapeutic reasoning: from

hiatus to hypothetical model. Journal of evaluation in clinical practice.

2009;15(6):985-989.

92. McKinlay JB, Potter DA, Feldman HA. Non-medical influences on medical

decision-making. Social science & medicine. 1996;42(5):769-776.

93. Sleath B, Tina Shih Y-C. Sociological influences on antidepressant prescribing.

Social science & medicine. 2003;56(6):1335-1344.

94. Lai LL, Tan MH, Lai YC. Prevalence and factors associated with off-label

antidepressant prescriptions for insomnia. Drug, healthcare and patient safety.

2011;3:27-36.

95. Lin HC, Erickson SR, Balkrishnan R. Physician prescribing patterns of innovative

antidepressants in the United States: the case of MDD patients 1993-2007.

International journal of psychiatry in medicine. 2011;42(4):353-368.

96. Rasu RS, Shenolikar RA, Nahata MC, Balkrishnan R. Physician and patient

factors associated with the prescribing of medications for sleep difficulties that

are associated with high abuse potential or are expensive: an analysis of data from

the National Ambulatory Medical Care Survey for 1996-2001. Clinical

therapeutics. 2005;27(12):1970-1979. 248

97. Balkrishnan R, Rasu RS, Rajagopalan R. Physician and patient determinants of

pharmacologic treatment of sleep difficulties in outpatient settings in the United

States. Sleep. 2005;28(6):715-719.

98. Sleath B, Svarstad B, Roter D. Patient race and psychotropic prescribing during

medical encounters. Patient education and counseling. 1998;34(3):227-238.

99. Franz M, Ranger J, Hanewald B, Gallhofer B, Lay B. Influences on Therapist’s

Decisions for Neuroleptic Treatment in Schizophrenia: The Role of

Characteristics of the Patient and the Physician. Pharmacopsychiatry.

2012;45(07):261-268.

100. Gallagher P, Barry P, O'Mahony D. Inappropriate prescribing in the elderly. J

Clin Pharm Ther. 2007;32(2):113-121.

101. O'Connor MN, Gallagher P, O'Mahony D. Inappropriate prescribing: criteria,

detection and prevention. Drugs & aging. 2012;29(6):437-452.

102. Marcum ZA, Hanlon JT. Commentary on the new American Geriatric Society

Beers criteria for potentially inappropriate medication use in older adults. The

American journal of geriatric pharmacotherapy. 2012;10(2):151.

103. Blanco‐Reina E, Ariza‐Zafra G, Ocaña‐Riola R, León‐Ortiz M. 2012 American

Geriatrics Society Beers Criteria: Enhanced Applicability for Detecting

Potentially Inappropriate Medications in European Older Adults? A Comparison

with the Screening Tool of Older Person's Potentially Inappropriate Prescriptions.

Journal of the American Geriatrics Society. 2014;62(7):1217-1223.

104. Baldoni AD, Ayres LR, Martinez EZ, Dewulf ND, Dos Santos V, Pereira LR.

Factors associated with potentially inappropriate medications use by the elderly

249

according to Beers criteria 2003 and 2012. International journal of clinical

pharmacy. 2013;36:316-324.

105. Napolitano F, Izzo MT, Di Giuseppe G, Angelillo IF, Collaborative Working G.

Frequency of inappropriate medication prescription in hospitalized elderly

patients in Italy. PloS one. 2013;8(12):e82359.

106. Chang CB, Lai HY, Yang SY, et al. Patient- and Clinic Visit-Related Factors

Associated with Potentially Inappropriate Medication Use among Older Home

Healthcare Service Recipients. PloS one. 2014;9(4):e94350.

107. Nishtala PS, Bagge ML, Campbell AJ, Tordoff JM. Potentially inappropriate

medicines in a cohort of community-dwelling older people in New Zealand.

Geriatrics & gerontology international. 2014;14(1):89-93.

108. Pannoi T, Chapman RS, Panza A. Prevalence of potentially inappropriate

medication (PIM) and factors associated with PIM in elderly outpatient

prescriptions at a district hospital in the southern region of Thailand. J Health

Res. 2014;28(2):101-108.

109. Moriarty F, Bennett K, Fahey T, Kenny RA, Cahir C. Longitudinal prevalence of

potentially inappropriate medicines and potential prescribing omissions in a

cohort of community-dwelling older people. European journal of clinical

pharmacology. 2015;71(4):473-482.

110. Lund BC, Carnahan RM, Egge JA, Chrischilles EA, Kaboli PJ. Inappropriate

prescribing predicts adverse drug events in older adults. The Annals of

pharmacotherapy. 2010;44(6):957-963. 250

111. Holmes HM, Luo R, Kuo YF, Baillargeon J, Goodwin JS. Association of

potentially inappropriate medication use with patient and prescriber

characteristics in Medicare Part D. Pharmacoepidemiology and drug safety.

2013;22(7):728-734.

112. McMahon CG, Cahir CA, Kenny RA, Bennett K. Inappropriate prescribing in

older fallers presenting to an Irish emergency department. Age Ageing.

2014;43(1):44-50.

113. Voyer P, Cohen D, Lauzon S, Collin J. Factors associated with psychotropic drug

use among community-dwelling older persons: A review of empirical studies.

BMC nursing. 2004;3(1):3.

114. Aparasu RR, Mort JR, Brandt H. Psychotropic prescription use by community-

dwelling elderly in the United States. Journal of the American Geriatrics Society.

2003;51(5):671-677.

115. Harris T, Carey IM, Shah SM, DeWilde S, Cook DG. Antidepressant prescribing

in older primary care patients in community and care home settings in England

and Wales. J Am Med Dir Assoc. 2012;13(1):41-47.

116. Maguire A, Hughes C, Cardwell C, O'Reilly D. Psychotropic medications and the

transition into care: a national data linkage study. Journal of the American

Geriatrics Society. 2013;61(2):215-221.

117. Dionne PA, Vasiliadis HM, Latimer E, Berbiche D, Preville M. Economic impact

of inappropriate benzodiazepine prescribing and related drug interactions among

elderly persons. Psychiatric services (Washington, D.C.). 2013;64(4):331-338. 251

118. Fu AZ, Tang AS, Wang N, Du D, Jiang JZ. Effect of Medicare Part D on

potentially inappropriate medication use by older adults. Journal of the American

Geriatrics Society. 2010;58(5):944-949.

119. Carey IM, De Wilde S, Harris T, et al. What factors predict potentially

inappropriate primary care prescribing in older people? Analysis of UK primary

care patient record database. Drugs & aging. 2008;25(8):693-706.

120. Neutel CI, Skurtveit S, Berg C. What is the point of guidelines? Benzodiazepine

and z-hypnotic use by an elderly population. Sleep medicine. 2012;13(7):893-897.

121. Puyat JH, Law MR, Wong ST, Sutherland JM, Morgan SG. The essential and

potentially inappropriate use of antipsychotics across income groups: an analysis

of linked administrative data. Canadian journal of psychiatry. Revue canadienne

de psychiatrie. 2012;57(8):488-495.

122. Préville M, Bosse C, Vasiliadis HM, et al. Correlates of potentially inappropriate

prescriptions of benzodiazepines among older adults: results from the ESA study.

Canadian journal on aging = La revue canadienne du vieillissement.

2012;31(3):313-322.

123. Lakey S, Gray S, Sales AEB, Sullivan J, Hedrick S. Psychotropic use in

community residential care facilities: A prospective cohort study. The American

journal of geriatric pharmacotherapy. 2006;4(3):227-235.

124. Rojas-Fernandez C, Thomas VS, Carver D, Tonks R. Suboptimal use of

antidepressants in the elderly: a population-based study in Nova Scotia. Clinical

therapeutics. 1999;21(11):1937-1950.

252

125. Parabiaghi A, Franchi C, Tettamanti M, et al. Antidepressants utilization among

elderly in Lombardy from 2000 to 2007: dispensing trends and appropriateness.

European journal of clinical pharmacology. 2011;67(10):1077-1083.

126. Dalby DM, Hirdes JP, Hogan DB, et al. Potentially inappropriate management of

depressive symptoms among Ontario home care clients. International journal of

geriatric psychiatry. 2008;23(6):650-659.

127. America VPN. Mysoline (primidone) Tablets. Label and Approval History 2009;

http://www.accessdata.fda.gov/scripts/cder/drugsatfda/index.cfm?fuseaction=Sear

ch.Label_ApprovalHistory#labelinfo. Accessed Sept 9, 2014.

128. Giron MS, Forsell Y, Bernsten C, Thorslund M, Winblad B, Fastbom J.

Psychotropic drug use in elderly people with and without dementia. International

journal of geriatric psychiatry. 2001;16(9):900-906.

129. Kolanowski A, Fick D, Waller JL, Ahern F. Outcomes of Antipsychotic Drug Use

in Community-Dwelling Elders With Dementia. Archives of Psychiatric Nursing.

2006;20(5):217-225.

130. Chan DC, Kasper JD, Black BS, Rabins PV. Clinical diagnosis of dementia, not

behavioral and psychologic symptoms, is associated with psychotropic drug use

in community-dwelling elders classified as having dementia. Journal of geriatric

psychiatry and neurology. 2007;20(2):100-106.

131. Gruber-Baldini AL, Stuart B, Zuckerman IH, Simoni-Wastila L, Miller R.

Treatment of dementia in community-dwelling and institutionalized medicare

beneficiaries. Journal of the American Geriatrics Society. 2007;55(10):1508-

1516. 253

132. Guthrie B, Clark SA, McCowan C. The burden of psychotropic drug prescribingin

people with dementia: a populationdatabase study. Age Ageing. 2010;39(5):637-

642.

133. Trifiro G, Sini G, Sturkenboom MC, et al. Prescribing pattern of antipsychotic

drugs in the Italian general population 2000-2005: a focus on elderly with

dementia. International clinical psychopharmacology. 2010;25(1):22-28.

134. Laitinen ML, Bell JS, Lavikainen P, Lonnroos E, Sulkava R, Hartikainen S.

Nationwide study of antipsychotic use among community-dwelling persons with

Alzheimer's disease in Finland. International psychogeriatrics / IPA.

2011;23(10):1623-1631.

135. Sapra M, Varma A, Sethi R, et al. Utilization of Antipsychotics in Ambulatory

Elderly With Dementia in an Outpatient Setting. FEDERAL PRACTITIONER.

2012.

136. Martinez C, Jones RW, Rietbrock S. Trends in the prevalence of antipsychotic

drug use among patients with Alzheimer's disease and other dementias including

those treated with antidementia drugs in the community in the UK: a cohort study.

BMJ open. 2013;3(1).

137. Rattinger GB, Burcu M, Dutcher SK, et al. Pharmacotherapeutic management of

dementia across settings of care. Journal of the American Geriatrics Society.

2013;61(5):723-733.

138. Taipale H, Koponen M, Tanskanen A, Tolppanen AM, Tiihonen J, Hartikainen S.

High prevalence of psychotropic drug use among persons with and without

Alzheimers disease in Finnish nationwide cohort. European 254

neuropsychopharmacology : the journal of the European College of

Neuropsychopharmacology. 2014;24(11):1729-1737.

139. Lesen E, Andersson K, Petzold M, Carlsten A. Socioeconomic determinants of

psychotropic drug utilisation among elderly: a national population-based cross-

sectional study. BMC public health. 2010;10:118.

140. Hsiao CJ, Marsteller JA, Simon AE. Electronic medical record features and seven

quality of care measures in physician offices. American journal of medical quality

: the official journal of the American College of Medical Quality. 2014;29(1):44-

52.

141. Gill SS, Rochon PA, Herrmann N, et al. Atypical antipsychotic drugs and risk of

ischaemic stroke: population based retrospective cohort study. Bmj.

2005;330(7489):445.

142. Herrmann N, Lanctot KL. Do atypical antipsychotics cause stroke? Mol Diag

Ther. 2005;19(2):91-103.

143. National Center for Health Statistics. 2010 NAMCS Micro-Data File

Documentation. Ambulatory Health Care Data 2012;

http://www.cdc.gov/nchs/ahcd/ahcd_questionnaires.htm. Accessed May 12th,

2014.

144. National Center for Health Statistics. NAMCS Data Collection and Processing.

2011;

http://www.cdc.gov/nchs/ahcd/ahcd_data_collection.htm#namcs_collection.

Accessed May 12th, 2014. 255

145. Centers for Disease Control and Prevention, Centers for Medicare & Medicaid

Services. ICD-9-CM: International Classification of Diseases, 9th Revision,

Clinical Modification, sixth edition. Vol DHHS pub.; no. (PHS) 09-1260. Atlanta,

GA: US Dept of Health and Human Services, Centers for Disease Control and

Prevention; Centers for Medicare and Medicaid Services; 2009.

146. National Center for Health Statistics. 2010 NAMCS Micro-Data File

Documentation. In: Statistics NCfH, ed2012.

147. National Center for Health Statistics, Centers for Disease Control and Prevention.

The New Ambulatory Care Drug Database System. 2012;

http://www2.cdc.gov/drugs/applicationnav1.asp. Accessed August, 2014.

148. Raebel MA, Charles J, Dugan J, et al. Randomized trial to improve prescribing

safety in ambulatory elderly patients. Journal of the American Geriatrics Society.

2007;55(7):977-985.

149. Tamblyn R, Huang A, Perreault R, et al. The medical office of the 21st century

(MOXXI): effectiveness of computerized decision-making support in reducing

inappropriate prescribing in primary care. CMAJ : Canadian Medical Association

journal = journal de l'Association medicale canadienne. 2003;169(6):549-556.

150. Weber V, White A, McIlvried R. An electronic medical record (EMR)-based

intervention to reduce polypharmacy and falls in an ambulatory rural elderly

population. Journal of general internal medicine. 2008;23(4):399-404.

151. Smith DH, Perrin N, Feldstein A, et al. The impact of prescribing safety alerts for

elderly persons in an electronic medical record: an interrupted time series

evaluation. Archives of Internal Medicine. 2006;166(10):1098-1104. 256

152. Simon SR, Smith DH, Feldstein AC, et al. Computerized prescribing alerts and

group academic detailing to reduce the use of potentially inappropriate

medications in older people. Journal of the American Geriatrics Society.

2006;54(6):963-968.

153. Skaer TL, Sclar DA, Robison LM, Galin RS. Trends in the rate of depressive

illness and use of antidepressant pharmacotherapy by ethnicity/race: an

assessment of office-based visits in the United States, 1992-1997. Clinical

therapeutics. 2000;22(12):1575-1589.

154. Olfson M, Marcus SC. National patterns in antidepressant medication treatment.

Archives of general psychiatry. 2009;66(8):848-856.

155. Ryan G, Musk AW, Perera DM, Stock H, Knight JL, Hobbs MS. Risk factors for

death in patients admitted to hospital with asthma: a follow-up study. Australian

and New Zealand journal of medicine. 1991;21(5):681-685.

156. Joseph KS, Blais L, Ernst P, Suissa S. Increased morbidity and mortality related

to asthma among asthmatic patients who use major tranquillisers. Bmj.

1996;312(7023):79-82.

157. Cancelli I, Beltrame M, Gigli GL, Valente M. Drugs with anticholinergic

properties: cognitive and neuropsychiatric side-effects in elderly patients.

Neurological sciences : official journal of the Italian Neurological Society and of

the Italian Society of Clinical Neurophysiology. 2009;30(2):87-92.

158. Dugdale DC, Epstein R, Pantilat SZ. Time and the patient–physician relationship.

Journal of general internal medicine. 1999;14(S1):34-40. 257

159. Morrell DC, Evans ME, Morris RW, Roland MO. The "five minute" consultation:

effect of time constraint on clinical content and patient satisfaction. British

medical journal (Clinical research ed.). 1986;292(6524):870-873.

160. Roland MO, Bartholomew J, Courtenay MJ, Morris RW, Morrell DC. The "five

minute" consultation: effect of time constraint on verbal communication. British

medical journal (Clinical research ed.). 1986;292(6524):874-876.

161. Tamblyn R, Berkson L, Dauphinee WD, et al. Unnecessary prescribing of

NSAIDs and the management of NSAID-related gastropathy in medical practice.

Annals of internal medicine. 1997;127(6):429-438.

162. Qaseem A, Snow V, Denberg TD, Forciea MA, Owens DK, Clinical Efficacy

Assessment Subcommittee of American College of P. Using second-generation

antidepressants to treat depressive disorders: a clinical practice guideline from the

American College of Physicians. Annals of internal medicine. 2008;149(10):725-

733.

163. Cohen HW, Gibson G, Alderman MH. Excess risk of myocardial infarction in

patients treated with antidepressant medications: association with use of tricyclic

agents. The American Journal of Medicine. 2000;108(1):2-8.

164. Ray WA, Meredith S, Thapa PB, Hall K, Murray KT. Cyclic antidepressants and

the risk of sudden cardiac death[ast]. Clin Pharmacol Ther. 2004;75(3):234-241.

165. TEVA Pharmaceuticals Ind. Ltd. Nefazodone Hydrochloride Tablets USP.

Sellersville, PA: TEVA Pharmaceuticals USA; 2014.

166. Angelini Pharma Inc. Oleptro (trazodone hydrochloride) extended-release tablets.

Gaithersburg, MD: Angelini Pharma Inc.; 2014. 258

167. Mavrides N, Nemeroff C. Treatment of depression in cardiovascular disease.

Depression and anxiety. 2013;30(4):328-341.

168. Buscemi N, Vandermeer B, Friesen C, et al. The efficacy and safety of drug

treatments for chronic insomnia in adults: a meta-analysis of RCTs. Journal of

general internal medicine. 2007;22(9):1335-1350.

169. Ellers H, Yost S. General Anesthetics. In: Katzung BG, Trevor AJ, eds. Basic &

Clinical Pharmacology, 13e. 13 ed. New York: McGraw-Hill; 2015.

170. Cook JM, Marshall R, Masci C, Coyne JC. Physicians' perspectives on

prescribing benzodiazepines for older adults: a qualitative study. Journal of

general internal medicine. 2007;22(3):303-307.

171. Chen HMDP, Nwangwu APMS, Aparasu RMP, Essien EMDD, Sun SP, Lee KP.

The Impact of Medicare Part D on Psychotropic Utilization and Financial Burden

for Community-Based Seniors. Psychiatric Services. 2008;59(10):1191-1197.

172. Martikainen P, Adda J, Ferrie JE, Davey Smith G, Marmot M. Effects of income

and wealth on GHQ depression and poor self rated health in white collar women

and men in the Whitehall II study. Journal of epidemiology and community

health. 2003;57(9):718-723.

173. Zimmerman FJ, Katon W. Socioeconomic status, depression disparities, and

financial strain: what lies behind the income-depression relationship? Health

economics. 2005;14(12):1197-1215.

174. Leykin Y. Pharmacokinetic considerations in the obese. Bailliere's Best practice

& research. Clinical anaesthesiology. 2011;25(1):27-36.

259

175. Fjeldstad C, Fjeldstad AS, Acree LS, Nickel KJ, Gardner AW. The influence of

obesity on falls and quality of life. Dynamic Medicine. 2008;7:1-6.

176. Himes CL, Reynolds SL. Effect of obesity on falls, injury, and disability. Journal

of the American Geriatrics Society. 2012;60(1):124-129.

177. United States Government Accountability Office. Antipsychotic Drug Use: HHS

Has Initiatives to Reduce Use among Older Adults in Nursing Homes, but Should

Expand Efforts to Other Settings. Washington, DC: US Government

Accountability Office; 2015.

178. Gareri P, De Fazio P, Manfredi VG, De Sarro G. Use and safety of antipsychotics

in behavioral disorders in elderly people with dementia. Journal of Clinical

Psychopharmacology. 2014;34(1):109-123.

179. American Psychiatric Association. Practice Guideline for the Treatment of

Patients with Bipolar Disorder (revision). The American journal of psychiatry.

2002;159(4 Suppl):1-50.

180. Gorelick PB, Scuteri A, Black SE, et al. Vascular contributions to cognitive

impairment and dementia: a statement for healthcare professionals from the

american heart association/american stroke association. Stroke; a journal of

cerebral circulation. 2011;42(9):2672-2713. 260

APPENDIX A

INSTITUTIONAL REVIEW BOARD APPLICATION

Basic Title: Potentially Inappropriate Prescribing of Psychotropic Medications for community-dwelling Older Adults using Updated Criteria

Short Title: Inappropriate Psychotropic Prescribing for Community Elderly

Brief Description:

This study aims to determine the prevalence of potentially inappropriate prescribing of psychotropic medications to community-dwelling elderly in the U.S.A. and associated factors. Public use files of the National Ambulatory Care Medical Survey of the National Center for Health Statistics, Center for Disease Control and Prevention will be sourced. No patient or physician information is captured. Descriptive and inferential statistics will be used, as well as logistic regression models. Administrative (exempt) approval is sought.

Principal Investigator: Matthew Perri III

Does PI have a financial interest related to this research? No

Will an external IRB act as the IRB on record for this study? No

UGA IRB Protocol Form Submission:

Protocol Title Potentially Inappropriate Prescribing of Psychotropic Medications for Community-dwelling Older Adults Using Updated Criteria

Research Design and Methods Research Design: Retrospective, cross-sectional secondary database analysis. Specific Aims: 261

1. To determine the prevalence of potentially inappropriate psychotropic medication (antidepressants, anxiolytics/hypnotics, antipsychotics) prescribing for older adults seen in office-based practices and 2. To assess the influence of patient, visit, prescriber and system characteristics on prescribing of potentially inappropriate psychotropic medications for older adults seen in office-based practices.

Methods: Secondary data analysis of patient visits collected by the National Ambulatory Care Surveys (2010/2012), administered by the National Center for Health Statistics, Center for Disease Control and Prevention will be undertaken. These are available as public use files on the CDC website. If 2012 data is not available at the time of data collection, 2010 data will be used. The survey employs a multistage, probability sampling method using lists of office-based physicians provided by the American Medical Association and the American Osteopathic Association. Records of visits by adults aged 65 and older to office-based physicians across the USA, who received at least one psychotropic medication will be abstracted for analyses. Potentially inappropriate psychotropic medications and/or inappropriate drug-disease combinations will be identified using the American Geriatric Society Beers 2012 criteria. The findings of the study will be presented in a written dissertation and an oral defense at the College of Pharmacy in December 2014. Abstracts will be submitted for presentation at a medical or pharmaceutical conference (to be identified). Two or three manuscripts will be developed from the study findings for publication in medical/pharmaceutical journals.

Study Timelines a) Describe the anticipated duration of participation for an individual subject.

b) If known, the duration anticipated to enroll all study subjects and the estimated date for the investigators to complete this study (complete primary analyses)

The study subjects will not be directly involved as the survey has been completed and secured by an external agency (NCHS/CDC). The data analyses will begin as soon as IRB approval is acquired and is expected to be completed within a month of approval; no later than October 15th 2014.

Procedures Involved N/A: The data has been collected and secured by NCHS staff on Patient Record Forms. It has been de-identified for both patients and physicians. Hence it is anonymous to the investigators. No further steps are needed to ensure confidentiality. 262

Data and Specimen Banking Data will be downloaded from the website of the NCHS to a desktop computer, secured by a username and password. The dataset is publicly available but the data analysis for the study will only be accessible to the researchers. The dataset will be retained for further analyses for a maximum of two years.

Data Analysis Inappropriate medications and drug-disease combinations among older adults will be counted and used to determine prevalence by therapeutic class. Sub- group analyses of antidepressants, anxiolytics, sedative-hypnotics and antipsychotics will be done. Confidence intervals will be determined at 95% to make population inferences. Chi-squared tests of association and Wald tests will be used to determine independence of variables and goodness of fit, at significance level of 0.05. Independent predictors of inappropriate use will be determined by bivariate and multivariate logistic regression model procedures. Odds ratios and respective 95% confidence intervals will be assessed for significance. A multivariate logistic regression model will be used to determine the effect of different variables on the odds of inappropriate prescribing. The final multivariate model will be determined using a stepwise elimination approach. Model checking procedures will include Deviance and/or Pearson residuals. 263

APPENDIX B

INSTITUTIONAL REVIEW BOARD DETERMINATION LETTER

629 Boyd Graduate Studies Research Center  Athens, Georgia 30602-7411 An Equal Opportunity/Affirmative Action Institution

NOT HUMAN RESEARCH DETERMINATION

August 22, 2014

Dear Matthew Perri:

The University of Georgia Institutional Review Board (IRB) reviewed the following protocol on 8/22/2014:

Type of Review: Initial Study Title of Study: Potentially Inappropriate Prescribing of Psychotropic Medications for Community-Dwelling Older Adults using Updated Criteria Investigator: Matthew Perri IRB ID: STUDY00001296 Funding: None Grant ID: None

The IRB determined that the proposed activity is not research involving human subjects as defined by DHHS and FDA regulations because data that will be analyzed are publicly available and not individually identifiable.

University of Georgia (UGA) IRB review and approval is not required. This determination applies only to the activities described in the IRB submission and does not apply should any changes be made. If changes are made and there are questions about whether these activities are research involving human subjects, please submit a new request to the IRB for a determination.

Sincerely, Larry Nackerud, PhD University of Georgia Institutional Review Board Chairperson

Office of the Vice President for Research Human Subjects Office Phone 706-542-3199 Fax 706-542-3660