Predictive validity of the Beers and STOPP criteria to detect adverse drug events,
hospitalizations, and emergency department visits in the United States
Joshua D. Brown, PharmD1,3; Lisa C. Hutchison, PharmD, MPH2; Chenghui Li, PhD1; Jacob T.
Painter, PharmD, MBA, PhD1; Bradley C. Martin, PharmD, PhD1
1
Division of Pharmaceutical Evaluation and Policy, 2Department of Pharmacy Practice, College
of Pharmacy, University of Arkansas for Medical Sciences; and 3Institute for Pharmaceutical
Outcomes and Policy, University of Kentucky College of Pharmacy.
Corresponding author: Bradley C. Martin, PharmD, PhD, Professor and Head, Division of
Pharmaceutical Evaluation and Policy, College of Pharmacy, University of Arkansas for Medical
Sciences, 4301 West Markham St #522, Little Rock, AR 72205, bmartin@uams.edu, P: (501)
603-1992, F: (501) 686-5156
Alternate Contact: Joshua D. Brown, PharmD, Institute for Pharmaceutical Outcomes and
Policy, University of Kentucky College of Pharmacy, 292 Pharmacy Building, 789 South
Limestone, Lexington, KY 40536, P: (479) 650-8047, F: (501) 686-5156
Funding: The project described was supported by the Translational Research Institute (TRI),
grant UL1TR000039 through the NIH National Center for Research Resources and National
Center for Advancing Translational Sciences.
Meeting submission: This study was presented as a poster presentation at the International
Society for Pharmacoeconomics and Outcomes Research (ISPOR) 17th Annual European
Congress, November 8-12, 2014, Amsterdam, the Netherlands.
Abbreviated title: Comparison of Beers and STOPP in the U.S.
1
1
Abstract
2
OBJECTIVES: To compare the predictive validity of the 2003 Beers, 2012 Beers, and STOPP
3
inappropriate prescribing criteria.
4
DESIGN: Retrospective cohort.
5
SETTING: Managed care administrative claims data from 2006 to 2009
6
PARTICIPANTS: 174,275 commercially insured persons 65 and older in the United States.
7
MEASUREMENTS: Association between adverse drug event, emergency department (ED)
8
visits, and hospitalization outcomes and inappropriate medications using time-varying Cox
9
proportional hazard models. Measures of model discrimination (c-index) and hazard ratios (HR)
10
were calculated to compare unadjusted and adjusted models for associations.
11
RESULTS: The prevalence of inappropriate prescribing was 34.1%, 32.2%, and 27.6% for the
12
2012 Beers, 2003 Beers, and the STOPP criteria. Each criteria modestly discriminated ADEs in
13
unadjusted analyses: STOPP (HR=2.89 [2.68-3.12]; C-index=0.607), 2012 Beers (HR=2.51
14
[2.33-2.70]; C-index=0.603), 2003 Beers (HR=2.65 [2.46-2.85]; C-index=0.605). Similar results
15
were observed for ED visits and hospitalizations. Adjusted analyses increased the c-indices to
16
between 0.65 and 0.70. The kappa for agreement between criteria was 0.80 for the 2003 and
17
2012 Beers, 0.58 for the 2012 Beers and STOPP, and 0.59 for the 2003 Beers and STOPP. For
18
the three outcomes, 2012 Beers had the highest sensitivity (61.2%-71.2%) and the lowest
19
specificity (41.2%-70.7%) while STOPP criteria had the lowest sensitivity (53.8%-64.7%) but
20
the highest specificity (47.8%-78.1%).
2
21
CONCLUSIONS: All three criteria were modestly prognostic for ADEs, EDs, and
22
hospitalizations with STOPP slightly outperforming Beers. With low sensitivity, low specificity,
23
as well as low agreement between the criteria, further updates to each criteria are needed to
24
develop a better predictive tool.
25
Key words: Beers Criteria, STOPP Criteria, inappropriate prescribing, adverse drug events
3
26
27
INTRODUCTION
A potentially inappropriate medication (PIM) exists when the risk of adverse events due
28
to treatment outweighs the clinical benefit (1). PIMs are associated with adverse health and
29
economic outcomes (2-11), making detection and prevention a primary goal of clinicians, payers,
30
and policymakers. Since its development in 1991 (12), the Beers Criteria has become the most
31
widely used and recognized explicit criteria for the detection of PIMs in older adults (8, 13, 14).
32
The criteria were updated in 1997 (15), 2003 (16), and again in 2012 by an American Geriatrics
33
Society (AGS) expert panel and includes drugs to always avoid, drugs to use with caution, and
34
drug-disease interactions (17).
35
The Screening Tool of Older Persons’ Prescriptions (STOPP) Criteria is an alternative
36
criteria developed in 2008 by a European consensus group (1). STOPP is organized by
37
physiological system and includes drugs to avoid, drug-drug and drug-disease interactions, and
38
therapeutic duplication to define PIMs. It is purported to be more effective in a European
39
population where many of the medications considered inappropriate by the Beers Criteria are not
40
available (1, 18). As a result, STOPP has little overlap with the 2012 Beers Criteria – 55% of the
41
65 criteria are not found in 2012 Beers (19).
42
STOPP and the 2003 Beers have been compared in European populations where STOPP
43
identified more PIMs and increased the odds of having a serious adverse drug event (ADE) by
44
85% (20-26). A study conducted in Spain compared the 2003 Beers and STOPP along with the
45
updated 2012 Beers Criteria (27). The PIM prevalence was 24.3%, 35.4%, and 44% for 2003
46
Beers, STOPP, and 2012 Beers and the agreement between 2012 Beers and STOPP was 0.35.
47
That study did not compare the criteria on adverse outcomes.
4
48
Because of the lack of evidence comparing Beers with STOPP in a United States (US)
49
population, a comparison of the ability of each criteria to predict relevant clinical outcomes is
50
warranted (19). Therefore, the current study sought to compare the predictive validity of 2003
51
Beers, 2012 Beers, and STOPP using three outcome measures: 1) ADEs, 2) all cause emergency
52
department (ED) visits, and 3) all cause hospitalizations. Further, the prevalence of PIMs
53
detected with each criteria was investigated as well as measures of agreement between the
54
criteria.
5
55
METHODS
56
Data source
57
The study sample was selected from a 10% random sample of the proprietary Lifelink
58
Health Plans Claims Database comprised of administrative claims from 80 Managed Care
59
Organizations within the US. The data capture the health claims data of the elderly enrolled in
60
health plans offering employer sponsored coverage and Medicare Advantage plans but do not
61
capture data for persons enrolled in traditional Medicare.
62
Study subjects and design
63
We used a retrospective cohort study design. Inclusion into the cohort was based on a
64
person being at least 65 years old and having at least 9 months of continuous medical and
65
pharmacy coverage, including a 6 month pre-index period and a minimum 3 months of follow-up
66
between January 1, 2006 and December 31, 2009. The index date was defined as the first day of
67
the seventh month of continuous eligibility. Individuals were followed until the end of
68
continuous enrollment, the end of the study period, or until an outcome event occurred. Because
69
full medical and pharmacy claims data may not be captured, individuals with the payer identified
70
as “Medicaid” were excluded as this group may have additional insurance or incomplete records.
71
Potentially inappropriate medication exposure
72
Exposure definitions were created according to the 2003 (16) and 2012 Beers Criteria
73
(17) and the STOPP Criteria (1). Therapeutic duplication, present as an over-arching item in
74
STOPP, was excluded as this is not unique to the elderly population and was deliberately
75
excluded from the Beers Criteria (17). Additionally, dabigatran (2012 Beers) was not on the
6
76
market during the time period of this study (2006-2009) and propoxyphene (2003 Beers) was not
77
included because it is no longer on the market. Otherwise, all items from each criteria were
78
included.
79
Drug-only criteria were mapped using the Medi-Span Generic Product Identifier (GPI,
80
Wolters Kluwer Health, Philadelphia, PA) classification system and the American Hospital
81
Formulary Service Pharmacologic-Therapeutic Classification codes (AHFSCC). These
82
hierarchical coding systems allowed for classification from the drug class, individual
83
medications, formulations (e.g. extended release), or dosing of individual products.
84
Disease-dependent PIM definitions were based on International Classifications of
85
Disease, 9th Revision, Clinical Modification (ICD-9-CM) codes in conjunction with the GPI and
86
AHFSCC medication codes. As an initial basis for defining disease concepts, the validated
87
Clinical Classification Software (CCS) codes were used to map ICD-9-CM definitions (28).
88
These codes were compared to other validated coding algorithms used by the Center for
89
Medicare and Medicaid Services (CMS) (29), Agency for Healthcare Research and Quality
90
(AHRQ) (30), and coding algorithms used widely in administrative claims data (31, 32).
91
Identified codes were included if they were present in at least two of these sources.
92
For disease states not defined using the above sources, literature searches were performed
93
on PubMed using “ICD-9” and “administrative claims” with a description of the disease.
94
Additionally, a manual search of an ICD-9-CM dataset and web pages for ICD-9-CM coding
95
were queried using disease specific terms (http://www.cms.gov/medicare-coverage-
96
database/staticpages/icd-9-code-lookup.aspx; http://icd9.chrisendres.com/). A review of all code
97
selections was conducted by two clinical pharmacists with experience in administrative claims
7
98
research and a geriatric pharmacy specialist. (The full details of PIM disease definitions are
99
provided in Supplement 1.)
100
A time varying approach was used to assess PIM exposure as a monthly binary variable.
101
For drug-only criteria, a subject was only considered exposed to a PIM for the month a
102
medication was dispensed. For PIM definitions based on co-existing disease states, a patient was
103
considered to have that disease in the month of the first inpatient or non-ancillary outpatient
104
claim with a primary or secondary diagnosis for that disease and for all subsequent months of the
105
study.
106
Outcome variables
107
ADEs were based on ICD-9-CM codes previously used for surveillance in hospital claims
108
data (33). A similar manual search strategy was performed with the terms “drug-induced”,
109
“adverse effect”, “caused by”, “poisoning”, “drug”, and “allergy” appearing in code descriptions.
110
ADEs were classified based on the subgroups in the original publication (33) with the addition of
111
those identified through the manual search and the removal of ADEs specific to the perinatal
112
period. (ADEs identified in this study, along with the ICD-9-CM codes and rates, are available
113
in Supplement 2.)
114
All-cause ED visits were defined by procedure and place of service codes and
115
hospitalizations were identified by unique confinement numbers. ADEs, all-cause ED visits, and
116
all-cause hospitalizations were considered separate outcomes; therefore, an individual could
117
experience one or more of the outcomes.
118
Subject characteristics
8
119
Cohort demographics and plan characteristics were determined at the beginning of the
120
post-index period. Age was categorized as 65-74, 75-84, and 85 years and older. Region was
121
classified as South, West, East, and Midwest. Insurance coverage was categorized into five
122
categories based on payer type (Medicare or commercial) and plan type: HMO (health
123
maintenance organization, non-HMO (Preferred Provider Organization, Consumer Directed,
124
Indemnity, Point of Service), or unknown.
125
Comorbidities were based on the Charlson Comorbidity Index using the ICD-9-CM
126
coding algorithms by Quan et al. (32) and were assessed during the 6 month pre-index period.
127
Use of long term care was determined during the pre-index period and included the use of skilled
128
nursing facilities, nursing homes, or hospice care. Additionally, prescription utilization was
129
evaluated separately as the total number of prescription fills and refills annualized to number per
130
12 months as well as the total number of unique drug classes used during the post-index period.
131
Data analysis
132
Baseline variables for the total cohort were compared for those exposed to a PIM from at
133
least one of the three criteria using two-sided Student t-tests for continuous variables and the
134
Chi-square tests for categorical variables. Unadjusted and adjusted Cox proportional hazards
135
models were used to estimate the relationship between PIM exposure and outcomes. In order to
136
preserve the temporal relationship between PIM exposure and outcomes, individuals having an
137
outcome during the pre-index period were excluded in the model assessing the influence of PIMs
138
on that particular outcome but were included in models exploring one of the other two outcomes.
139
Three time-varying models and one time invariant approach were estimated to explore the
140
temporal relationship between PIM exposure and outcome. The primary model assessed PIM
9
141
exposure in month t(i) and looked for an outcome in month t(i+1); providing stronger assurances
142
that the exposure preceded the experience of the outcome event. The alternative time-varying
143
model assessed exposures and outcomes within the same month. An additional third time-
144
varying approach used a once-exposed-always-exposed exposure classification where a subject
145
was considered exposed the first month a PIM was detected and all subsequent months.
146
Dummy variables were created for each covariate. Reference categories were as follows:
147
Age 65-74; Male gender, East region, and Medicare HMO insurance coverage. The Charlson
148
Comorbidity Index diseases were used as individual binary disease states. Prescription
149
utilization variables were considered to possibly exist along the causal pathway and were
150
excluded from the primary analyses. Sensitivity analyses were conducted which considered each
151
prescription utilization variable as a categorical and continuous variable. Additionally, separate
152
models were estimated which stratified the cohort by these measures. The proportionality
153
assumption was evaluated for each covariate in models by specifying interaction terms between
154
each covariate and log-time – where statistically significant coefficients would indicate a
155
violation of the assumption. Hazards ratios and 95% confidence intervals are reported.
156
To compare the predictive validity of the criteria, a c-index specifically developed for
157
time-varying models was used (34). The c-index is analogous to the c-statistic often used with
158
logistic regression and ranges between 0.5 and 1 where a value of 0.5 indicates model prediction
159
no better than chance and a value of 1 indicates a model which predicts events perfectly.
160
Concordance or discordance occur when the predictor score for the individual having an event is
161
greater or lesser than individuals not having an event at that time (34).
10
Additionally, Cohen’s kappa was calculated to assess the person-level agreement
162
163
between all possible pairwise PIM criteria. The sensitivity and specificity of each of the PIM
164
criteria were calculated using each of the outcome measures and a composite outcome measure
165
as gold standards. All analyses were conducted using SAS version 9.3 (SAS Institute, Inc., Cary,
166
NC).
11
167
168
RESULTS
A total of 538,532 individuals were 65 or older during the time period January 1, 2006
169
and December 31, 2009. Applying eligibility inclusion criteria, 257,206 had at least 9 months of
170
continuous medical insurance enrollment and 175,696 also had 9 months of continuous
171
pharmacy benefit enrollment. Combined, 175,581 had at least 9 months continuous enrollment
172
with both medical and pharmacy benefit during the study period. An additional 1,306 (<1%)
173
individuals were excluded having "Medicaid" identified as the payer type. The final cohort
174
consisted of 174,275 individuals representing 32.4% of the original elderly sample (Supplement
175
3). The mean follow up time of the cohort was slightly over 2 years (24.9 months, Median 27.0
176
months, IQR 12-39 months) and the cohort contributed a combined 361,621 person-years.
177
Baseline cohort demographics by PIM exposure are presented in Table 1.
178
Over the entire post-index period, 72,493 (41.6%) of the cohort were exposed to at least
179
one of the criteria and 19.7% were exposed to all three. Exposure to at least one PIM from 2012
180
Beers criteria was 34.1%, 2003 Beers 32.2%, and STOPP 27.6%. Overall exposure for those
181
experiencing outcome events was nearly double that of the total cohort and tended to be 1.5 to 2
182
times more prevalent for individual items. Person-level agreement between each of the PIM
183
criteria, measured by Cohen’s kappa, was “good” between 2012 and 2003 Beers (κ = 0.80, Table
184
2), and “moderate” between STOPP and the 2012 and 2003 Beers (κ = 0.58 and 0.59) (35).
185
The top 5 individual PIMs for each criteria included many of the same medication groups
186
but differed in prevalence because of different definitions of inappropriateness. A “use with
187
caution” criteria which included SSRIs, SNRIs, antipsychotics, and other medications associated
188
with syndrome of inappropriate anti-diuretic hormone (SIADH) was the most prevalent PIM for
12
189
the 2012 Beers (16.2% of cohort). This was followed by benzodiazepines (11.3%), skeletal
190
muscle relaxants (6.6%), non-benzodiazepine hypnotics (5.8%), and NSAIDs (5.4%). The top
191
five 2003 Beers PIMs included anticholinergics and first generation antihistamines (19.4%),
192
SSRIs (“with caution”, 10.5%), benzodiazepines (11.2%), muscle relaxants and antispasmodics
193
(7.4%), and long-term NSAID use (5.1%). STOPP PIMs included NSAIDs (16.2%), opioids
194
(4.8%), beta-blockers (4.7%), corticosteroids (3.8%), and first generation antihistamines (3.8%).
195
(Complete PIM exposure prevalence is available in Supplements 4-6.)
196
A total of 1,911 individuals with a post-index ADE in the cohort (67 ADEs per 10,000
197
person-years, 1.12% of the total cohort) after excluding 3,558 people who had pre-index adverse
198
events. Additionally, 24,614 individuals were excluded who had a pre-index ED visit and an
199
additional 29,864 had a post-index event (140 ED visits per 1,000 person-years, 17.1% of the
200
total cohort). Post-index hospitalizations occurred for 16,444 persons (67 hospitalizations per
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1,000 person-years, 9.4% of the total cohort) with 22,190 individuals excluded with
202
hospitalizations occurring in the pre-index period. The associations of demographic and health-
203
related characteristics with each outcome are shown in Supplement 7.
204
PIM exposure was strongly associated with all study outcomes in both adjusted and
205
unadjusted models (Table 3). In the primary unadjusted model, PIM exposure was associated
206
with a 2 to 3 fold increase risk across all outcomes for 2003 Beers, 2012 Beers, and STOPP. The
207
associations were similar across the three outcome measures. A stronger relationship between
208
PIM exposure with all three of the criteria and each of the three outcomes (HRs: 3.67 – 5.30) was
209
observed in the time varying models that assessed exposure and outcome in the same month.
210
The time dependent once exposed always exposed model found more modest associations
211
between all the PIM criteria (HRs: 1.30 – 1.76), however all remained significant. The hazard
13
212
ratios for the STOPP criteria in the primary time varying model trended higher than those for
213
either of the Beers criteria.
214
For the primary unadjusted model, the c-indices were similar for each of the criteria for
215
each of the outcomes and indicated modest levels of discrimination with c-indices between 0.58
216
and 0.61 (Table 4). When the models included the pre-index covariates, the levels of
217
discrimination increased to 0.65 to 0.70 and were similar across the criteria for each of the
218
outcomes. The model that assessed PIM exposure and outcome in the same month had the
219
highest measures of discrimination than the other models. Inclusion of prescription utilization
220
measures as covariates increased the discrimination of the models less than 1% and stratification
221
had no significant effect. The sensitivity and specificity of the 2012 Beers, 2003 Beers, and
222
STOPP for the separate composite outcomes are shown in Table 5.
14
223
DISCUSSION
224
In studies using the Beers Criteria, PIM rates of 40-50% are common and have ranged as
225
low as 12% (7, 25, 36, 37) while rates for STOPP have ranged from 13-70% (20, 21, 23, 38, 39).
226
Our study found 41.6% of the cohort to be exposed to at least one of the criteria. The 2003 and
227
2012 Beers Criteria identified PIMs in 32.2% and 34.1%, and 27.6% of the cohort were
228
classified as having a PIM using the STOPP Criteria. These rates are similar to a study in Spain
229
comparing the three criteria in an ambulatory population (27). Differences between criteria with
230
similar drug classes are due to inherent differences in the criteria definitions.
231
We found that exposure to a PIM from any criteria was associated with an increased risk
232
of ADEs, ED visits, and hospitalizations. Individuals with exposure to PIM from STOPP had
233
slightly higher risks than either of the Beers. Despite the slightly higher risk associations for
234
STOPP compared to Beers, there were only marginal differences in discrimination between the
235
criteria. 2012 Beers performed better in terms of sensitivity across all outcomes but was less
236
specific while STOPP was less sensitive but more specific.
237
For the Beers criteria, the slightly lower performance appears to be a result of higher
238
exposures resulting in more false-positives weakening the association with outcomes. The
239
STOPP detected only 53% of individuals having any outcome while the 2012 Beers detected an
240
additional 7% of individuals having each outcome. When the combined “any criteria” exposure
241
was considered, sensitivity increased for ADEs, ED visits, and hospitalizations. Overall, the
242
combined exposure had a sensitivity of 71.4% and specificity of 67.4% for the composite
243
outcome. Therefore, future updates of the Beers Criteria should consider evidence-based
15
244
refinement of the criteria to include more drug classes that are predictive of serious adverse
245
outcomes (40, 41).
246
AGS has adapted the Beers Criteria into a mobile application and a pocket guide for the
247
practicing clinician who they acknowledge as the target audience (17). However, Beers Criteria
248
have been widely used by researchers, pharmacy benefit managers, and policy-makers – greatly
249
broadening the impact of the Beers Criteria over the last twenty years. For example, the criteria
250
have been used by the CMS and the National Committee for Quality Assurance (NCQA) as
251
quality indicators in long-term care and ambulatory settings (42). There have even been cases of
252
“misuse” of the criteria to deny coverage of medications (43). Given this broad impact and
253
implications beyond education and prescribing, future updates and further research should
254
identify medications which pose the largest safety risk and are the most predictive of important
255
outcomes such as ADEs, ED visits, and hospitalizations.
256
One of the notable limitations of this study is the outcomes measures selected. We used a
257
narrow set of ICD-9-CM codes specific to drug-induced syndromes to define an adverse drug
258
event, some of which are based on supplementary E-codes. These codes were based on previous
259
work which measured the performance of these codes as an ADE surveillance system. They
260
found that the codes had an overall sensitivity and specificity of 55% and 97% for ADEs causing
261
hospital admission and positive predictive value greater than 70% (33). Though these codes may
262
have only detected half of all adverse drug events in that study, the codes can be expected to
263
detect true ADEs. Conversely, the all-cause hospitalizations and ED visits are not specific to
264
ADE events and may have higher sensitivity detecting serious ADEs but will be less specific.
265
For example, up to 31% of hospitalizations may be medication related (44) leaving two thirds
266
that are not. This should be considered when interpreting our findings, particularly when we
16
267
report the sensitivity and specificity which should not be interpreted in the conventional fashion
268
as measures of diagnostic or screen accuracy for a specified verified outcome.
269
This study was strengthened by considering the temporal relationship of exposure and
270
outcomes with a time-varying approach. This allowed for the observation of the initial period of
271
PIM exposure when adverse events may be more apt to occur (45). This method also allows
272
individuals to move to and from exposed and unexposed status taking into account changes,
273
additions, and discontinuations of therapy. Our primary model in which exposure was assessed
274
in a month and outcomes assessed in the following month strongly preserves the temporal
275
relationship where exposure precedes outcomes. Though the month-to-month model provided
276
stronger associations between exposure and outcome, reverse causality may explain the stronger
277
association.
278
Non-prescription medications, such as aspirin or NSAIDs, and prescriptions not
279
processed through claims were not present in the data. For example, inappropriate use of proton
280
pump inhibitors based on STOPP has been highly prevalent and its underrepresentation in our
281
data may bias the associations between STOPP and the outcomes toward the null. The absence
282
of medications considered by all three criteria sets would have a similar but balanced effect.
283
Similarly, disease-dependent PIM definitions may suffer from missing data due to undercoding
284
(46, 47). Thus, our findings are likely conservative as more individuals are likely to be exposed
285
to PIMs than were observed in this study.
286
We excluded the therapeutic duplication criteria from the STOPP PIM definition because
287
this item has been specifically mentioned for exclusion from the Beers Criteria as it is not a
288
problem unique to the elderly (41). While therapeutic duplication has been reported to have a
17
289
prevalence of nearly 5%, it has not been associated with ADEs in published studies (5, 38). Our
290
exclusion of this item may have decreased the exposure prevalence and the association of
291
STOPP with the outcomes.
The most prevalent PIM from the 2012 Beers criteria considered “use with caution”
292
293
medications because of the risk of SIADH. Based on the original wording of 2012 Beers, this
294
criterion did not require an individual to have had previous episodes, compared to STOPP which
295
did require a previous diagnosis of hyponatremia or SIADH. Thus, all individuals exposed to
296
these commonly used medications, including selective serotonin and norepinephrine reuptake
297
inhibitors, were considered exposed to the Beers Criteria. While this may over-estimate the
298
exposed, the time-varying exposure approach accounted for the risk associated with new
299
exposure when persons are at greater risk of experiencing adverse events.
300
The administrative claims capture the healthcare utilization of members enrolled in
301
commercial coverage and Medicare Advantage plans and would be expected to be generalizable
302
to that population. Individuals covered under traditional Medicare are not included. This
303
population may differ from the general Medicare population by demographic characteristics such
304
as income status, education, and health behaviors which could not be compared in the current
305
study.
18
306
CONCLUSIONS
307
This was the first study to compare the predictive validity of the updated Beers Criteria to
308
the STOPP Criteria in a population of older adults as well as the first application of the full Beers
309
Criteria including drug-disease items in the US. Our study showed low agreement and no
310
significant differences between the two iterations of the Beers Criteria and the STOPP Criteria in
311
the level of discrimination for ADEs, ED visits, and hospitalizations, though each was
312
moderately prognostic of these outcomes. Future evidence-guided updates of these widely used
313
tools should identify medications and medication classes that may increase the predictive ability
314
of the criteria.
19
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ACKNOWLEDGEMENTS
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BCM was paid by the International Society for Pharmacoeconomics and Outcomes Research
318
(ISPOR) to teach courses in retrospective database analysis. This study was unrelated to that
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course content and ISPOR had no affiliation or review of the submitted work. BCM received a
320
grant (NIH Grant # 1UL1RR029884) which supported acquisition of the data used in this study.
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CL is a consultant for eMaxHealth Systems on unrelated studies. LCH received a grant from
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MedEdPortal/Josiah Macy Foundation on interprofessional education development and served
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as a consultant for the Arkansas Foundation for Medical Care drug safety quality improvement
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projects. LCH has stock in Cardinal Health and CareFusion and has received royalties from the
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American Society of Healthsystem Pharmacists for a pharmacy textbook which are unrelated to
326
this work.
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JDB is now the University of Kentucky, Humana, Pfizer Doctoral Fellow at the Institute for
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Pharmaceutical Outcomes and Policy in Lexington, KY. This work was completed before taking
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this new position and the aforementioned companies had no involvement in the concept, design,
330
interpretation, or drafting of this manuscript.
331
We do not believe these are potential conflicts of interest, but report them in the interest of
332
full disclosure.
333
334
Author Contributions: Brown: study concept and design, data analysis and interpretation,
335
preparation and editing of manuscript. Hutchison: study concept and design, data interpretation,
336
editing of manuscript. Li: study design, data analysis and interpretation, editing of manuscript.
337
Painter: study concept and design, data interpretation, editing of manuscript. Martin: study
338
concept and design, data analysis and interpretation, preparation and editing of manuscript.
339
Sponsor’s Role: This project was supported by the UAMS Translational Research Institute (NIH Grant #
340
1UL1RR029884) which supported acquisition of the data. The sponsor had no other role in this study.
341
Meeting submission: This study has been accepted as a poster presentation at the International
342
Society for Pharmacoeconomics and Outcomes Research (ISPOR) 17th Annual European
343
Congress, November 8-12, 2014, Amsterdam, the Netherlands.
344
22
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26
Table 1: Baseline Characteristics for Cohort and Those Exposed to 2012 Beers, 2003
Beers, and STOPP criteria
Total Cohort
2012 Beers
2003 Beers
STOPP
N=174,275
N=59,426
N=56,144
N=48,121
No. (%)
No. (%)
No. (%)
No. (%)
65-74 128,306 (73.6)
37,150 (62.5)
36,603 (65.2)
30,951 (64.3)
75-84
34,637 (19.9)
17,098 (28.8)
14,991 (26.7)
13,098 (27.2)
85 and older
11,332 (6.5)
5,178 (8.7)
4,550 (8.1)
4,072 (8.5)
94,588 (54.3)
34,779 (58.5)
33,997 (60.6)
27,809 (57.8)
Medicare HMO
22,570 (13.0)
11,071 (18.6)
9,907 (17.7)
8,630 (17.9)
Medicare non-HMO
24,992 (14.3)
8,995 (15.1)
8,357 (14.9)
7,248 (15.1)
Commercial HMO
20,432 (11.7)
5,449 (9.2)
5,311 (9.5)
4,755 (9.9)
Commercial non-HMO
96,412 (55.3)
31,116 (52.4)
29,868 (53.2)
25,214 (52.4)
9,869 (5.7)
2,795 (4.7)
2,701 (4.8)
2,274 (4.7)
Characteristics
Age (years)*
Female*
Insurance Type*
Unspecified
Region
27
East
35,987 (20.7)
11,739 (19.8)
11,333 (20.2)
10,233 (21.3)
Midwest
57,514 (33.0)
20,388 (34.3)
19,219 (34.2)
16,688 (34.7)
South
43,528 (25.0)
14,294 (24.1)
13,393 (23.9)
11,508 (23.9)
West
37,246 (21.4)
13,005 (21.9)
12,199 (21.7)
9,692 (20.1)
1.3 (1.7)
1.6 (1.9)
1.6 (1.8)
1.8 (2.0)
117,690 (67.5)
35,013 (58.9)
33,803 (60.2)
27,111 (56.3)
39,736 (22.8)
16,513 (27.8)
15,265 (27.2)
13,744 (28.6)
16,849 (9.7)
7,900 (13.3)
7,076 (12.6)
7,266 (15.1)
12.1 (36.5)
20.5 (17.7)
20.0 (16.3)
19.4 (16.3)
4.4 (5.3)
8.8 (5.5)
8.7 (5.5)
8.8 (5.7)
3,682 (2.1)
2,287 (3.9)
2,088 (3.7)
2,126 (4.42)
Charlson Co-morbidity
Index (Pre-index)
Mean (SD)*
0-1
2-3
3+
Prescription utilization
Total prescription fills
per 12 months
Mean (SD)*
Unique drug classes
Mean (SD)*
Long term care
28
Follow-up time
(months)
Mean (SD)*
Median
IQR
24.9 (13.2)
29.1 (11.8)
29.4 (11.6)
30.1 (11.2)
27.0
36.0
36.0
36.0
12-39
18-39
19-39
12-39
*p<0.01 for comparison between “Any Exposure” and Total Cohort.
Significant differences were not observed between criteria
Abbreviations: HMO (health maintenance organization); SD (standard deviation); IQR
(inter-quartile range)
29
Table 2 - Inappropriate prescribing criteria person-level
concordance and agreement
Exposure to inappropriate prescribing
No. (% of cohort)
N=174,275
Any exposure to criteria
72,493 (41.6)
Exposed to more than one criteria
58,915 (32.7)
Exposed to all criteria
34,283 (19.7)
Concordance Between Criteria
No. (% Agree)
2012 Beers*2003 Beers
50,182 (84.4)
2012 Beers*STOPP
38,006 (64.0)
2003 Beers*2012 Beers
50,182 (89.4)
2003 Beers*STOPP
37,293 (66.4)
STOPP*2012 Beers
38,006 (79.0)
STOPP*2003 Beers
37,293 (77.5)
2012 Beers*All Criteria
59,426 (82.0)
2003 Beers*All Criteria
56,144 (77.4)
STOPP*All Criteria
48,121 (66.4)
Agreement Between Criteria
Cohen’s Kappa
2012 Beers*2003 Beers
0.80
2012 Beers*STOPP
0.58
2003 Beers*STOPP
0.59
2012 Beers*All Criteria
0.84
2003 Beers*All Criteria
0.80
STOPP*All Criteria
0.70
30
Table 3: Adjusted and Unadjusted Hazards ratios for the 2012 Beers, 2003 Beers, and STOPP criteria for time varying
and non-time varying models.
Unadjusted models (exposure only)
Adjusted modelsa
Time-varying monthly lag (Primary Model)b
Criteria
ADEs
Emergency
Hospitalization ADEs
Emergency
Hospitalization
2012 Beers
2.51 (2.33-2.70) 2.21 (2.16-2.25) 2.25 (2.20-2.30) 2.17 (2.01-2.34) 2.00 (1.96-2.04) 2.03 (1.98-2.07)
2003 Beers
2.65 (2.46-2.85) 2.29 (2.25-2.34) 2.31 (2.26-2.37) 2.33 (2.16-2.52) 2.14 (2.10-2.19) 2.16 (2.11-2.21)
STOPP
2.89 (2.68-3.12) 2.66 (2.60-2.72) 2.80 (2.74-2.87) 2.43 (2.24-2.63) 2.38 (2.32-2.43) 2.46 (2.40-2.52)
Time-varying month to monthc
ADEs
Emergency
Hospitalization ADEs
Emergency
Hospitalization
2012 Beers
4.33 (4.11-4.56) 4.38 (4.31-4.44) 4.27 (4.20-4.34) 3.67 (3.48-3.87) 3.93 (3.87-3.99) 3.75 (3.68-3.81)
2003 Beers
5.01 (4.75-5.28) 4.89 (4.81-4.97) 4.76 (4.68-4.84) 4.30 (4.08-4.54) 4.51 (4.44-4.58) 4.32 (4.25-4.40)
STOPP
5.21 (4.91-5.52) 5.18 (5.09-5.28) 5.30 (5.20-5.41) 4.18 (3.92-4.44) 4.52 (4.43-4.60) 4.47 (4.38-4.56)
Time-dependent once exposed, always exposedd
ADEs
Emergency
Hospitalization ADEs
Emergency
Hospitalization
2012 Beers
1.71 (1.57-1.87) 1.45 (1.42-1.48) 1.46 (1.42-1.49) 1.43 (1.31-1.56) 1.32 (1.29-1.35) 1.30 (1.26-1.33)
2003 Beers
1.66 (1.53-1.81) 1.39 (1.36-1.42) 1.38 (1.35-1.42) 1.45 (1.33-1.58) 1.32 (1.29-1.35) 1.30 (1.26-1.33)
STOPP
1.76 (1.62-1.91) 1.50 (1.46-1.53) 1.54 (1.51-1.58) 1.47 (1.35-1.60) 1.37 (1.34-1.40) 1.38 (1.34-1.42)
Ever exposuree
ADEs
Emergency
Hospitalization ADEs
Emergency
Hospitalization
2012 Beers
3.06 (2.77-3.37) 2.34 (2.28-2.39) 2.58 (2.51-2.65) 2.60 (2.35-2.88) 2.08 (2.03-2.13) 2.27 (2.21-2.34)
2003 Beers
2.83 (2.57-3.12) 2.18 (2.13-2.23) 2.33 (2.27-2.39) 2.49 (2.25-2.74) 2.01 (1.97-2.06) 2.15 (2.09-2.21)
STOPP
3.11 (2.83-3.42) 2.44 (2.38-2.49) 2.71 (2.64-2.78) 2.64 (2.39-2.91) 2.18 (2.13-2.23) 2.38 (2.32-2.45)
a
Covariates included: Age, gender, insurance status, region, long term care, and Charlson comoribidities
b
Outcome events associated with time-varying exposure in the preceding month (i.e. March outcome associated with
February exposure
c
Outcome events associated with time-varying exposure in the same month
d Once
e
exposed to a criteria, always exposed whether or not exposure status changes
Exposed at any point during the post-index follow up period
Sample size for final model excluding pre-index events: ADEs (170,717); ED visits (147,661); Hospitalizations (152,085)
Table 4: C-indices and 95% confidence intervals for 2003 Beers, 2012 Beers, and STOPP criteria for the time varying and
non-time varying models
Adjusted Modela*
Unadjusted Model (exposure only)
Time-varying monthly lag (Primary Model)b
Criteria
ADEs
Emergency
Hospitalization
ADE
Emergency
Hospitalization
0.585 (0.583-
0.590 (0.588-
0.688 (0.677-
0.652 (0.649-
0.673 (0.670-
0.587)
0.592)
0.700)
0.655)
0.677)
0.585 (0.583-
0.588 (0.586-
0.695 (0.684-
0.653 (0.650-
0.673 (0.670-
0.611)
0.587)
0.590)
0.706)
0.656)
0.676)
0.607 (0.601-
0.590 (0.588-
0.599 (0.597-
0.695 (0.685-
0.661 (0.658-
0.683 (0.680-
0.614)
0.592)
0.601)
0.706)
0.664)
0.686)
Emergency
Hospitalization
2012 Beers 0.603 (0.5970.609)
2003 Beers 0.605 (0.599-
STOPP
Time-varying month to monthc
ADEs
Emergency
Hospitalization
ADE
2012 Beers 0.642 (0.639-
0.635 (0.634-
0.636 (0.634-
0.733 (0.723-
0.709 (0.706-
0.720 (0.717-
0.636)
0.638)
0.744)
0.712)
0.723)
0.635 (0.634-
0.637 (0.636-
0.741 (0.730-
0.708 (0.705-
0.721 (0.718-
0.650)
0.636)
0.638)
0.751)
0.711)
0.724)
0.642 (0.638-
0.626 (0.625-
0.634 (0.633-
0.741 (0.730-
0.707 (0.704-
0.726 (0.723-
0.647)
0.628)
0.634)
0.752)
0.710)
0.729)
0.645)
2003 Beers 0.646 (0.643-
STOPP
Time-varying once exposed, always exposed
ADEs
2012 Beers 0.566 (0.5570.574)
2003 Beers 0.563 (0.5540.571)
Emergency
Hospitalization
ADE
Emergency
Hospitalization
0.548 (0.546-
0.551 (0.549-
0.666 (0.654-
0.628 (0.624-
0.653 (0.648-
0.551)
0.554)
0.679)
0.631)
0.655)
0.542 (0.540-
0.544 (0.541-
0.667 (0.655-
0.626 (0.622-
0.651 (0.647-
0.545)
0.546)
0.680)
0.629)
0.654)
STOPP
0.567 (0.559-
0.548 (0.546-
0.554 (0.552-
0.670 (0.658-
0.630 (0.627-
0.657 (0.652-
0.574)
0.551)
0.557)
0.682)
0.634)
0.660)
Ever exposuree
ADEs
Emergency
Hospitalization
ADE
Emergency
Hospitalization
0.548 (0.546-
0.551 (0.549-
0.666 (0.654-
0.628 (0.624-
0.652 (0.648-
0.551)
0.554)
0.679)
0.631)
0.655)
0.542 (0.540-
0.544 (0.541-
0.667 (0.655-
0.626 (0.622-
0.650 (0.647-
0.571)
0.545)
0.546)
0.680)
0.629)
0.654)
0.636 (0.624-
0.599 (0.596-
0.612 (0.608-
0.713 (0.701-
0.659 (0.656-
0.687 (0.683-
0.647)
0.603)
0.615)
0.725)
0.663)
0.691)
2012 Beers 0.566 (0.5570.574)
2003 Beers 0.563 (0.554-
STOPP
*Covariate only model c-indices: ADE 0.664 (0.651-0.676); Emergency 0.606 (0.603-0.610); Hospitalization 0.647 (0.6440.651)
a
Covariates included: Age, gender, insurance status, region, long term care, and Charlson comoribidities
b
Outcome events associated with time-varying exposure in the preceding month (i.e. March outcome associated with
February exposure
c
Outcome events associated with time-varying exposure in the same month
d Once
e
exposed to a criteria, always exposed whether or not exposure status changes
Exposed at any point during the post-index follow up period
Sample size for final model excluding pre-index events: ADEs (170,717); ED visits (147,661); Hospitalizations (152,085)
Table 5: Sensitivity and specificity of PIM criteria predicting study outcomes
Sensitivity (%)
Specificity (%)
ADEs
71.2
41.2
Emergency Visits
61.2
70.7
Hospitalizations
64.3
69.0
Composite Outcome
60.6
73.9
ADEs
67.7
42.8
Emergency Visits
57.8
72.2
Hospitalizations
60.3
70.4
Composite Outcome
57.3
75.4
ADEs
64.7
47.8
Emergency Visits
53.8
78.1
Hospitalizations
57.6
76.3
Composite Outcome
53.4
80.2
ADEs
79.8
30.1
Emergency Visits
71.8
63.2
Hospitalizations
74.8
61.4
Composite Outcome
71.4
67.4
2012 Beers
2003 Beers
STOPP
All Criteria exposure
Study supplement to facilitate review only, not intended for publication or for inclusion in the word or
table count.
Supplement 1 - Beers and STOPP Criteria PIM disease description, ICD-9-CM coding
definitions and source of codes
Disease/Condition and Source
ICD-9-CM Codes
(Multi-level CCS unless specified)
*Additions to CCS from identified
sources
Hypertension (7.1)
Arthritis, osteo- and rheumatoid (13.2.1;
13.2.2)
Arrhythmias (7.2.8, 7.2.9)
401.x- 404.x, 405.01,405.09, 405.11,405.19,
405.91,405.99, 437.2
714.x; 715.x, 720.0, 721.x*, V13.4
426.x, V45.x, V53.3, 427.x, 785.0, 785.1
Glaucoma (6.7.3)*
365.x, 377.14*
Chronic Obstructive Pulmonary Disease
(COPD) (8.2)
Benign Prostatic Hyperplasia, BPH (10.2.1)
490.x-492.x, 494.x, 496.x
Atrial Fibrillation (7.2.9.3)
427.31
Depression (5.8.2)*
293.83, 296.x, 300.4,301.12*, 309.0*,
309.1, 311
260-269,799.4, V12.1
Nutritional Deficiency (3.5)
600.x
Obesity (3.11.2)
403.x*,404.02*,404.03*,404.12*,404.13*,
404.92*,404.93*,580.x-583.x, 585.x- 588.x,
792.5, V42.0, V45.1, V45.11,V45.12,
V56.0, V56.1, V56.2, V56.31, V56.32,
V56.8
278.0x,793.91, V85.21-V85.54
Edema (48)
782.3, 276.6
Chronic Kidney Disease (10.1.1, 10.1.3)*
Heart Failure (7.2.11)
Constipation (9.12.1)
398.91,402.01, 402.11,402.91,
404.01,404.11, 404.91,404.03,
404.13,404.93, 428.x
800-829, 733.x, 905.0x-905.5x,V13.51,
V13.52,V54.1x,V54.2x, V66.4, V67.4
596.5x,599.8x, 625.6, 788.3x,
344.61,596.5x, 599.8, 599.84, 625.6,
788.32, 788.39
564.0x
Gout (51)
274.x
Dementia, cognitive and memory disorders
(5.4)
290.x, 293.0, 293.1, 294.x, 331.x, 797
Insomnia (52)
307.41,307.42, 780.51, 780.52
Breast Cancer (2.5)
174.x, 175.x, 233.0, V10.3
Syncope (17.1.1)
780.2
Falls (2603 single-level CCS)
E880-E888, E968.1,E987.0, E987.1,E987.2,
E987.9,V15.88
286.x, 287.x, 289.81,289.82, 289.84, 782.7
Fractures (16.2, 13.5)
Urinary incontinence (49, 50)
Bleeding Disorder, coagulation and
hemorrhagic disorders (4.2)
Urinary Retention (10.1.8.2)
788.2x
Hypotension (7.4.4.1)
458.x
Diarrhea, Intestinal infection, Enteritis (9.1,
9.6.2)
001.x-009.x, 021.1, 022.2, 555.x-556.x,
564.5*
Extrapyramidal Symptoms (EPS) (53, 54)
333.x
Hyponatremia (55, 56)
276.1
Hypogonadism (57)
257.1, 257.2, 257.8, 257.9+
Deep Vein Thrombosis (58, 59)
451.11,451.19,
451.2,451.81,451.9,453.2,453.40,453.51,45
3.42,453.8,453.9,671.30,671.31,671.33,671.
40,671.42,671.44, 671.9,997.2, 999.2
415.1, 639.6, 996.7
Pulmonary Embolism (58)
Hypoglycemia (60, 61)
250.3, 250.8, 251.0, 251.1, 251.2, 270.3,
775.0, 775.6, 962.3
Stress or mixed urinary incontinence
625.6, 599.82, 788.33
(49, 50)
Epilepsy (6.4)
345.x, 780.3x
Gastric or peptic ulcers (9.4.2, 9.10.1)
Parkinson's Disease (6.2.1)
531.x-534.x, V12.71
332.x
Delirium (Adapted from CCS 5.4)
293.0, 293.1, 290.11,290.30, 290.41
Gastroparesis (62)
536.3
Urinary Catheter
V53.6, V58.82
SIADH (55, 56)
253.6
Anorexia (5.15.2 )
307.1, 307.5x
References to the supplement
1. Medina-Ramon M, Goldberg R, Melly S, Mittleman MA, Schwartz J. Residential exposure to
traffic-related air pollution and survival after heart failure. Environ Health Perspect. 2008
Apr;116(4):481-5.
2. Oliphant SS, Wang L, Bunker CH, Lowder JL. Trends in stress urinary incontinence inpatient
procedures in the united states, 1979-2004. Am J Obstet Gynecol. 2009
May;200(5):521.e1,521.e6.
3. Anger JT, Saigal CS, Madison R, Joyce G, Litwin MS, Urologic Diseases of America Project.
Increasing costs of urinary incontinence among female medicare beneficiaries. J Urol. 2006
Jul;176(1):247,51; discussion 251.
4. Wallace KL, Riedel AA, Joseph-Ridge N, Wortmann R. Increasing prevalence of gout and
hyperuricemia over 10 years among older adults in a managed care population. J Rheumatol.
2004 Aug;31(8):1582-7.
5. Asche CV, Joish VN, Camacho F, Drake CL. The direct costs of untreated comorbid insomnia
in a managed care population with major depressive disorder. Curr Med Res Opin. 2010
Aug;26(8):1843-53.
6. Finkelstein MM, Jerrett M. A study of the relationships between parkinson's disease and
markers of traffic-derived and environmental manganese air pollution in two canadian cities.
Environ Res. 2007 Jul;104(3):420-32.
7. Oliveria SA, Liperoti R, L'italien G, Pugner K, Safferman A, Carson W, et al. Adverse events
among nursing home residents with alzheimer's disease and psychosis. Pharmacoepidemiol Drug
Saf. 2006 Nov;15(11):763-74.
8. Williams C, Simon TD, Riva-Cambrin J, Bratton SL. Hyponatremia with intracranial
malignant tumor resection in children. J Neurosurg Pediatr. 2012 May;9(5):524-9.
9. Movig KL, Leufkens HG, Lenderink AW, Egberts AC. Validity of hospital discharge
international classification of diseases (ICD) codes for identifying patients with hyponatremia. J
Clin Epidemiol. 2003 Jun;56(6):530-5.
10. Khan N, Abbas AM, Almukhtar RM, Khan A. Prevalence and predictors of low bone mineral
density in males with ulcerative colitis. J Clin Endocrinol Metab. 2013 Jun;98(6):2368-75.
11. Anderson FA,Jr, Wheeler HB, Goldberg RJ, Hosmer DW, Patwardhan NA, Jovanovic B, et
al. A population-based perspective of the hospital incidence and case-fatality rates of deep vein
thrombosis and pulmonary embolism. the worcester DVT study. Arch Intern Med. 1991
May;151(5):933-8.
12. Zhan C, Battles J, Chiang YP, Hunt D. The validity of ICD-9-CM codes in identifying
postoperative deep vein thrombosis and pulmonary embolism. Jt Comm J Qual Patient Saf. 2007
Jun;33(6):326-31.
13. Shorr RI, Ray WA, Daugherty JR, Griffin MR. Incidence and risk factors for serious
hypoglycemia in older persons using insulin or sulfonylureas. Arch Intern Med. 1997 Aug 1125;157(15):1681-6.
14. Ginde AA, Blanc PG, Lieberman RM, Camargo CA,Jr. Validation of ICD-9-CM coding
algorithm for improved identification of hypoglycemia visits. BMC Endocr Disord. 2008 Apr
1;8:4,6823-8-4.
15. Wang YR, Fisher RS, Parkman HP. Gastroparesis-related hospitalizations in the united
states: Trends, characteristics, and outcomes, 1995-2004. Am J Gastroenterol. 2008
Feb;103(2):313-22.
Supplement 2 - Adverse drug event outcome categories, ICD-9-CM coding, and observed
post-index rates
Adverse Drug Event
Classification
ICD-9-CM Codes
All Adverse drug events
Addendum from manual
search (drug-induced
anemia, drug-induced
glaucoma, etc.)
Drug psychosis
Other agents (GI agents,
vaccines)
Agents affection blood
constituents; e.g. Iron,
anti-anemic,
anticoagulants, etc.
Agents affecting the CV
system
Dermatitis
Anti-allergy, anti-emetic,
pH agents, enzymes,
vitamins, other systemic
agents
Hormones, natural and
synthetic
Analgesics, antipyretics,
antirheumatics
N
% of % of
Rate per
all
cohort 10,000
ADEs
personyears
1191
---
1.12
67.2
284.11, 284.12, 285.3,
288.03, 339.3, 359.24,
365.32, 365.32, 357.6,
333.72, 333.85, 528.02,
995.2, 995.23, 995.27
504
42.3
0.30
17.7
292.x
255
21.4
0.15
9.0
909.0, 909.5, 970.x,
971.x, 973.x-979.x,
E858.x, E929.2, E943.xE949.x
247
20.7
0.14
8.7
203
17.0
0.12
7.1
147
12.3
0.09
5.2
121
10.2
0.07
4.3
105
8.8
0.06
3.7
94
7.9
0.06
3.3
73
6.1
0.04
2.6
964.x, E858.2, E934.x
972.x, E942.x
692.3, 693.0, 693.8, 693.9
963.x, E858.1, E933.x
962.x, E858, E932.x
965.x, E850.x, E936.x
Antibiotics and antiinfectives
960.x, 961.x, E856.x,
E857.x, E930.x, E931.x
66
5.5
0.04
2.3
33
2.8
0.02
1.2
Anticonvulsants,
antiparkinsonism
966.x, E855.0, E936.x
Psychotropics
969.x, E853.x, E854.x,
E939.x
26
2.2
0.02
0.9
967.x, E851, E852.x,
E937.x
19
1.6
0.01
0.7
968.x, E855.x, E938.x,
E940.0, E940.x, E941.x
18
1.5
0.01
0.6
Sedatives, hypnotics
CNS depressants,
stimulants, anesthetics
Abbreviation: GPI (generic product identifier); AHFSCC (American Hospital Formulary
Service Pharmacologic-Therapeutic Classification)
Supplement 3 - Application of inclusion criteria to study sample
Age greater than 65 years during
the years 2006 to 2009
538,532
Medical Benefits for at least 9
months during study period
257,206
(281,326, 52.24% excluded)
Pharmacy Benefits for at least 9
months during study period
175,696
(362,836, 67.38% excluded)
Combined Medical and Pharmacy
Benefits for at least 9 months
during study period
175,581
(115, <1% excluded)
Excluded those with payer type
identified as "Medicaid”
(1,306, <1% excluded)
Final Cohort
174,275
Supplement 4 - 2003 Beers PIM Exposure
ADEs
ER Visits
Hospitalizations
Total cohort
(N=1,191)
(N=29,864)
(N=16,444)
(N=174,275)
Criterion Definition
Any Old Beers
Exposure
N
%
N
%
N
%
N
%
727
61.02
17402
58.27
9996
60.79
56151
32.22
Drugs always considered inappropriate
Indomethacin
27
2.30
663
2.22
398
2.42
2422
1.39
Pentazocine
1
0.05
33
0.11
21
0.13
105
0.06
Trimethobenzamide
3
0.27
45
0.15
25
0.15
139
0.08
Muscle relaxants and
antispasmodics
155
13.00
3575
11.97
2036
12.38
12896
7.40
Flurazepam
2
0.18
39
0.13
26
0.16
139
0.08
Amitriptylline
45
3.74
899
3.01
518
3.15
3381
1.94
Doxepin
16
1.32
176
0.59
102
0.62
627
0.36
Meprobamate
2
0.20
39
0.13
25
0.15
174
0.10
236
19.8
5205
17.4
3068
18.7
19554
11.2
Disopyramide
0
0.00
15
0.05
8
0.05
52
0.03
Digoxin >0.125mg/d
32
2.71
741
2.48
487
2.96
2196
1.26
Dipyramidole
2
0.17
78
0.26
44
0.27
227
0.13
Methyldopa
1
0.08
24
0.08
15
0.09
105
0.06
Reserpine >0.25mg
0
0.03
12
0.04
5
0.03
35
0.02
Chlorpropamide
0
0.02
9
0.03
7
0.04
17
0.01
GI antispasmodics
36
3.05
866
2.90
470
2.86
3172
1.82
Benzodiazepines
407
34.19
9276
31.06
5334
32.44
33809
19.40
Ergot mesylates
0
0.02
3
0.01
2
0.01
17
0.01
Iron sulfate
3
0.27
66
0.22
53
0.32
122
0.07
Barbiturates (except
phenobarbital) unless
used for seizures
0
0.02
3
0.01
5
0.03
17
0.01
Meperidine
6
0.47
179
0.60
100
0.61
610
0.35
Ticlopidine
2
0.20
36
0.12
25
0.15
87
0.05
Ketorolac
6
0.54
185
0.62
97
0.59
540
0.31
Amphetamines and
anorexants
41
3.45
1024
3.43
605
3.68
3050
1.75
Long term NSAIDs
81
6.77
2093
7.01
1074
6.53
8905
5.11
Fluoxetine
35
2.98
765
2.56
460
2.80
3154
1.81
Stimulant laxatives
unless used with
opioids
2
0.20
66
0.22
44
0.27
157
0.09
Amiodarone
30
2.56
726
2.43
584
3.55
1603
0.92
Orphenadrine
5
0.40
110
0.37
58
0.35
418
0.24
Guanethidine
0
0.00
0
0.00
0
0.00
0
0.00
Guanadrel
0
0.00
0
0.00
0
0.00
0
0.00
Cyclandelate
0
0.00
0
0.00
0
0.00
0
0.00
Isoxsuprine
0
0.00
3
0.01
0
0.00
0
0.00
Nitrofurantoin
94
7.88
1872
6.27
1179
7.17
5647
3.24
Doxazosin
28
2.33
800
2.68
488
2.97
3242
1.86
Methyltestosterone
0
0.02
0
0.00
2
0.01
17
0.01
Anticholinergics and
first generation
antihistamines
Thioridazine
0
0.00
9
0.03
5
0.03
35
0.02
Mesoridazine
0
0.00
0
0.00
0
0.00
0
0.00
Nifedipine IR
0
0.03
24
0.08
12
0.07
52
0.03
Clonidine
40
3.35
920
3.08
567
3.45
2858
1.64
Mineral oil
0
0.00
3
0.01
2
0.01
0
0.00
Cimetidine
9
0.75
137
0.46
74
0.45
488
0.28
Ethacrynic acid
2
0.18
18
0.06
12
0.07
35
0.02
Dessicated thyroid
6
0.54
161
0.54
74
0.45
749
0.43
Estrogens (oral)
50
4.24
1063
3.56
582
3.54
6640
3.81
Inappropriate prescribing in the presence of disease
Heart Failure:
disopyramide
0
0.00
0
0.00
0
0.00
0
0.00
Hypertension:
phenylpropanolamine,
pseudoephedrine,
anorexants, and
amphetamines
13
1.07
284
0.95
173
1.05
1098
0.63
Ulcers: NSAIDs and
aspirin >325 mg
2
0.18
54
0.18
43
0.26
139
0.08
Seizures: some typical
antipsychotics
0
0.02
6
0.02
2
0.01
17
0.01
Clotting disorders:
aspirin, NSAIDs,
dipyridamole,
ticlopidine, clopidogrel
56
4.74
1078
3.61
806
4.90
2370
1.36
Lower urinary tract
symptoms of BPH:
anticholinergics,
antihistamines, GI
antispasmodics, muscle
22
1.86
481
1.61
326
1.98
1063
0.61
relaxants, oxybutynin,
flavoxate,
antidepressants,
decongestants, and
tolterodine
Stress Incontinence:
alpha-1 blockers,
anticholinergics,
TCAs, long-acting
benzodiazepines
10
0.85
197
0.66
143
0.87
645
0.37
Arrhythmias:
imipramine, doxepin,
amitriptylline
9
0.77
173
0.58
120
0.73
366
0.21
Insomnia:
decongestants,
theophylline,
methylphenidate,
MAOIs, amphetamines
0
0.02
3
0.01
2
0.01
0
0.00
Parkinson’s:
metoclopramide,
typical antipsychotics,
tacrine
5
0.42
69
0.23
53
0.32
157
0.09
Cognitive impairment:
barbiturartes,
anticholinergics,
antispasmodics, muscle
relaxants, CNS
stimulants
25
2.11
523
1.75
372
2.26
1028
0.59
Depression:
benzodiazepines,
methyldopa, reserpine,
guanethidine
0
0.02
3
0.01
3
0.02
17
0.01
Anorexia/malnutrition:
CNS stimulants,
fluoxetine
3
0.27
42
0.14
25
0.15
105
0.06
Syncope/falls:
benzodiazepines, TCAs
15
1.27
287
0.96
179
1.09
505
0.29
SIADH: SSRIs
229
19.24
5023
16.82
3014
18.33
18369
10.54
Obesity: olanzapine
0
0.00
3
0.01
2
0.01
0
0.00
COPD:
benzodiazepines,
propanolol
6
0.47
119
0.40
81
0.49
296
0.17
Constipation: CCBs,
anticholinergics, TCAs
23
1.93
436
1.46
301
1.83
976
0.56
Supplement 5 - 2012 Beers PIM Exposure
Criterion Definition
Any New Beers
Exposure
ADEs
ER Visits
(N=1,191)
(N=29,864)
Hospitalizatio
ns
Total cohort
(N=174,275)
(N=16,444)
N
%
N
%
N
%
N
%
753
63.15
18375
61.53
10634
64.67
59,42
6
34.10
Drugs always considered inappropriate
First generation
antihistamines (single
or combination
products
90
7.56
2123
7.11
1199
7.29
6884
3.95
Antiparkinsons agents:
benztropine,
trihexyphenidyl
3
0.23
48
0.16
36
0.22
192
0.11
Antispasmodics:
belladonna, clidiniumchlordiazepoxide,
dicyclomine,
hyoscyamine,
propantheline,
scopolamine
43
3.64
1033
3.46
561
3.41
3973
2.28
Dipyridamole - oral
short acting
2
0.17
78
0.26
44
0.27
227
0.13
Ticlopidine
2
0.20
36
0.12
25
0.15
87
0.05
Nitrofurantoin: long
term or Stage 3+ CKD
11
0.89
188
0.63
140
0.85
349
0.20
Alpha-1 blockers:
doxazosin, prazosin,
terazosin
52
4.37
1436
4.81
844
5.13
5943
3.41
Central alpha agonsists:
clonidine, guanabenz,
guanfacine,
methyldopa, reserpine
(>0.1 mg/d)
42
3.49
980
3.28
602
3.66
3120
1.79
Antiarrhythmic: Class
IA, IC, III
50
4.24
1245
4.17
926
5.63
3189
1.83
Dronedarone
0
0.00
0
0.00
0
0.00
0
0.00
Digoxin >0.125 mg/d
32
2.71
741
2.48
487
2.96
2196
1.26
Nifedipine IR
1
0.03
24
0.08
12
0.07
52
0.03
Spironolactone >25
mg/d with CrCl<30
mL/min
3
0.28
78
0.26
62
0.38
174
0.10
Tertiary TCAs
79
6.67
1445
4.84
832
5.06
5333
3.06
Antipsychotics: 1st and
2nd generation
35
2.97
657
2.20
455
2.77
1220
0.70
Thioridazine,
mesoridazine
0
0.00
9
0.03
5
0.03
35
0.02
Barbiturates
2
0.15
30
0.10
18
0.11
157
0.09
Benzodiazepines
233
19.6
5143
17.2
3032
18.4
19600
11.3
Chloral hydrate
4
0.34
39
0.13
15
0.09
105
0.06
Meprobamate
2
0.20
39
0.13
25
0.15
174
0.10
139
11.71
2822
9.45
1801
10.95
10125
5.81
Ergot mesylates,
isoxsuprine
1
0.02
6
0.02
3
0.02
17
0.01
Androgens
8
0.64
137
0.46
76
0.46
593
0.34
Dessicated thyroid
6
0.54
161
0.54
74
0.45
749
0.43
Non-benzodiazepine
hyponotics
Estrogens, oral or patch
58
4.86
1189
3.98
648
3.94
7616
4.37
Growth hormone
0
0.00
3
0.01
0
0.00
0
0.00
Insulin, sliding scale
32
2.70
738
2.47
479
2.91
2213
1.27
Megestrol
6
0.47
63
0.21
44
0.27
122
0.07
Chlorpropamide,
glyburide
28
2.31
956
3.20
534
3.25
3520
2.02
Metoclopramide, unless
for gastroparesis
45
3.79
1030
3.45
673
4.09
2649
1.52
Mineral oil, oral
0
0.00
3
0.01
2
0.01
0
0.00
Trimethobenzamide
3
0.27
45
0.15
25
0.15
139
0.08
Meperidine
6
0.47
179
0.60
100
0.61
610
0.35
Non-COX selective
NSAIDs, oral, >75 y/o
OR taking oral/IV
corticosteroids,
anticoagulants,
antiplatelets
117
9.80
2918
9.77
1751
10.65
8400
4.82
Indomethacin,
Ketorolac
17
1.46
370
1.24
235
1.43
924
0.53
Pentazocine
1
0.05
33
0.11
21
0.13
105
0.06
136
11.35
3118
10.44
1735
10.55
11485
6.59
Skeletal muscle
relaxants: carisoprodol,
chlorzoxazone,
cyclobenzaprine,
metaxalone,
methocarbamol,
orphenadrine
Inappropriate prescribing in the presence of disease
Heart Failure: NSAIDs,
CCBs, TZD, cilostazole,
dronedarone
59
4.99
1242
4.16
932
5.67
2492
1.43
Syncope: AChEIs,
alpha-1 blockers, TCAs,
some antipsychotics
22
1.88
561
1.88
367
2.23
1011
0.58
Chronic seizures or
epilepsy: bupropion,
antipsychotics,
tramadol
4
0.34
63
0.21
41
0.25
122
0.07
Delirium: TCAs,
*Anticholinergics,
benzodiazepines,
chlorpromazine,
corticosteroids, H2RA,
meperidine, sedative
hypnotics, thioridazine
16
1.34
239
0.80
196
1.19
366
0.21
Dementia and cognitive
impairment:
*Anticholinergics,
benzodiazepines,
H2RA, zolpidem,
antipsychotics
71
5.95
1496
5.01
1039
6.32
2876
1.65
History of falls or
fractures:
anticonvulsants,
antipsychotics,
benzodiazepines,
hypnotics, TCAs, SSRIs
114
9.59
2416
8.09
1478
8.99
5054
2.90
1
0.08
6
0.02
5
0.03
35
0.02
Insomnia:
decongestants,
stimulants,
theobromines
Parkinson’s Disease:
antipsychotics,
antiemetics
10
0.84
146
0.49
107
0.65
296
0.17
Chronic constipation:
antimuscarinics, CCBs,
1st generation
antihistamines,
anticholinergics,
antispasmodics4
76
6.42
1224
4.10
811
4.93
2876
1.65
History of
gastric/duodenal ulcers:
NSAIDs
7
0.59
179
0.60
123
0.75
436
0.25
CKD Stage 4+:
NSAIDs, triamterene
2
0.17
69
0.23
43
0.26
122
0.07
Urinary incontinence:
oral and transdermal
estrogen
9
0.72
143
0.48
104
0.63
593
0.34
Lower urinary tract
symptoms, BPH:
inhaled anticholinergic,
*anticholinergics
(except
antimuscarinics)
37
3.12
899
3.01
607
3.69
2056
1.18
Stress/mixed urinary
incontinence: alpha-1
blockers
1
0.08
12
0.04
13
0.08
52
0.03
Use with caution
Aspirin for primary
prevention: age >=80
31
2.58
765
2.56
480
2.92
1725
0.99
Dabigatran (not on
market during study
period)
0
0.00
0
0.00
0
0.00
0
0.00
Prasugrel: >=75 y/o
0
0.00
0
0.00
0
0.00
0
0.00
Antipsychotics,
cisplatin, carboplatin,
mirtazapine, SNRIs,
SSRIs, TCA, vincristine
384
32.26
7890
26.42
4775
29.04
28302
16.24
Vasodilators: syncope
21
1.73
430
1.44
294
1.79
802
0.46
Supplement 6 - STOPP Criteria PIM Exposure
Criterion Definition
ADEs
ER Visits
(N=1,191)
(N=29,864)
Hospitalizatio
ns
Total cohort
(N=174,275)
(N=16,444)
N
%
N
%
N
%
N
%
Any STOPP Exposure
674
56.59
16168
54.14
9531
57.96
60055
34.46
Colchicine, long term
use
17
1.39
457
1.53
298
1.81
1603
0.92
Corticosteroids: COPD
maintenance, over 3
months for arthritis
136
11.40
2323
7.78
1557
9.47
6605
3.79
NSAIDs: h/o ulcer or
bleed without receiving
PPI, H2RA, or
misoprostol, w/
hypertension, heart
failure, >3m with
arthritis, with GFR<50
mL/min, for gout, with
warfarin
270
22.66
6564
21.98
3554
21.61
28163
16.16
Opioids: TCAs, with
enteritis, falls, with
constipation w/o
laxative, with dementia
172
14.43
3279
10.98
2233
13.58
8435
4.84
Aspirin: warfarin or
ulcer disease and not
receiving protective
therapy; without
indication; bleeding
disorder
1
0.10
18
0.06
12
0.07
35
0.02
Beta blocker: COPD,
verapamil, in diabetes
with hypoglycemia
139
11.66
3043
10.19
2110
12.83
8208
4.71
Calcium channel
blocker: constipation,
TCA, diltiazem or
verapamil with Class
III+ HF, verapamil
with beta blocker
76
6.40
1574
5.27
1075
6.54
4026
2.31
Cimetidine: warfarin
1
0.05
9
0.03
5
0.03
17
0.01
Clopidogrel: bleeding
disorder
17
1.41
224
0.75
173
1.05
488
0.28
Digoxin: >0.125 mg/d
and GFR <50 mL/min
3
0.22
57
0.19
35
0.21
122
0.07
Dipyridamole:
monotherapy
prevention, bleeding
disorder
0
0.00
0
0.00
0
0.00
0
0.00
Loop diuretic: edema
w/o HF, monotherapy
w/ hypertension
40
3.37
941
3.15
650
3.95
2161
1.24
Thiazides: gout
4
0.35
84
0.28
49
0.30
209
0.12
Vasodilators:
orthostatic hypotension
5
0.40
75
0.25
62
0.38
192
0.11
Warfarin: bleeding
disorder, NSAIDs, with
aspirin w/o protective
agent
19
1.61
269
0.90
214
1.30
575
0.33
Anticholinergics: EPS
associated with
antipsychotics, with
dementia, chronic
constipation, BPH,
glaucoma
13
1.12
257
0.86
174
1.06
523
0.30
Antihistamines, 1st
generation: falls
90
7.56
2123
7.11
1199
7.29
6553
3.76
Benzodiazepines: longacting more than one
month, falls
64
5.32
1500
5.02
806
4.90
4392
2.52
Antipsychotics:
parkinsonism, epilepsy,
falls
8
0.64
110
0.37
82
0.50
227
0.13
Promethazine: epilepsy,
parkinsonism, falls
29
2.41
663
2.22
426
2.59
1568
0.90
SSRIs: SIADH
4
0.37
60
0.20
48
0.29
139
0.08
Tricyclic
antidepressants:
dementia, glaucoma,
arrhythmias,
constipation, opioids,
CCBs, BPH, urinary
retention
8
0.69
155
0.52
112
0.68
349
0.20
Chlorpropamide:
diabetes
0
0.00
0
0.00
0
0.00
0
0.00
Estrogens: h/o breast
cancer or VTE
1
0.05
6
0.02
3
0.02
17
0.01
Glyburide: diabetes
3
0.27
116
0.39
61
0.37
261
0.15
Antispasmodics with
anticholinergic effects:
constipation
4
0.37
63
0.21
46
0.28
139
0.08
Diphenoxylate: enteritis
1
0.05
12
0.04
8
0.05
17
0.01
Loperamide: enteritis
1
0.05
6
0.02
3
0.02
17
0.01
Metoclopramide:
parkonsonism
1
0.02
6
0.02
5
0.03
17
0.01
Prochlorperazine:
parkinsonism
1
0.02
0
0.00
0
0.00
0
0.00
Proton pump inhibitor:
full dose >8w
82
6.90
2010
6.73
1334
8.11
4810
2.76
Ipratropium nebulized:
narrow angle glaucoma
1
0.03
12
0.04
10
0.06
17
0.01
Theophylline: COPD
0
0.00
0
0.00
0
0.00
0
0.00
Alpha-blockers:
urinary incontinence in
men, catheter,
hypotension
7
0.59
134
0.45
100
0.61
279
0.16
Urinary
antispasmodics,
anticholinergics
2
0.13
27
0.09
21
0.13
52
0.03
Supplement 7 - Unadjusted hazards ratios and 95% confidence intervals of baseline
covariates for ADEs, Emergency visits, and Hospitalizations.
ADEs
ED Visits
Hospitalization
65-74
Ref.
Ref.
Ref.
75-84
1.39 (1.25-1.54)
1.55 (1.51-1.59)
1.64 (1.59-1.69)
85+
1.26 (1.08-1.49)
2.07 (1.99-2.15)
2.01 (1.92-2.10)
1.23 (1.12-1.35)
1.00 (0.97-1.02)
0.89 (0.87-0.92)
Ref.
Ref.
Ref.
Midwest
1.10 (0.97-1.26)
1.08 (1.05-1.12)
1.15 (1.11-1.20)
South
1.14 (0.98-1.32)
0.98 (0.94-1.02)
1.00 (0.96-1.04)
West
1.12 (0.97-1.30)
0.79 (0.76-0.82)
0.85 (0.82-0.89)
Ref.
Ref.
Ref.
Medicare non-HMO
1.03 (0.88-1.21)
0.93 (0.89-0.97)
1.11 (1.05-1.16)
Comm. HMO
0.97 (0.80-1.17)
0.91 (0.86-0.95)
0.90 (0.85-0.95)
Comm. non-HMO
0.86 (0.76-0.98)
0.96 (0.93-0.99)
0.92 (0.89-0.96)
Unknown
0.48 (0.35-0.65)
0.50 (0.47-0.54)
0.50 (0.46-0.54)
Long term care
1.71 (1.41-2.08)
2.10 (1.97-2.24)
4.02 (3.77-4.29)
Myocardial Infarction
1.49 (1.14-1.97)
1.27 (1.13-1.43)
1.35 (1.19-1.53)
Congestive Heart Failure
1.57 (1.33-1.85)
1.41 (1.33-1.48)
1.51 (1.42-1.60)
Peripheral Vascular Disease
1.06 (0.87-1.29)
1.15 (1.10-1.22)
1.24 (1.17-1.32)
Cerebrovascular Disease
1.15 (0.94-1.40)
1.32 (1.24-1.40)
1.31 (1.23-1.40)
Age
Female
Region
East
Insurance
Medicare HMO
Dementia
1.02 (0.70-1.50)
1.17 (1.04-1.31)
0.76 (0.67-0.87)
Chronic Pulmonary Disease
1.25 (1.09-1.43)
1.41 (1.36-1.47)
1.47 (1.41-1.53)
Connective Tissue Disease
2.26 (1.82-2.81)
1.38 (1.27-1.49)
1.39 (1.27-1.51)
Gastric Ulcers
0.76 (0.42-1.37)
1.28 (1.08-1.51)
1.23 (1.01-1.49)
Mild Liver Disease
1.44 (0.94-2.22)
1.29 (1.12-1.49)
1.23 (1.04-1.44)
Diabetes w/o complications
1.32 (1.18-1.49)
1.25 (1.21-1.30)
1.30 (1.26-1.35)
Diabetes w/ complications
1.01 (0.81-1.27)
1.17 (1.09-1.24)
1.21 (1.13-1.30)
Paraplegia, hemiplegia
1.58 (0.92-2.71)
1.67 (1.32-2.10)
1.56 (1.20-2.03)
Renal Disease
1.39 (1.13-1.72)
1.35 (1.26-1.45)
1.49 (1.39-1.61)
Cancer
1.74 (1.52-2.01)
1.25 (1.20-1.30)
1.32 (1.26-1.39)
0
1.76 (1.10-2.82)
3.00 (1.97-4.55)
3.80 (2.87-5.03)
1.17 (0.99-1.37)
1.39 (1.16-1.66)
AIDS/HIV
0
1.40 (0.70-2.80)
0.91 (0.34-2.41)
Depression
1.57 (1.32-1.87)
1.29 (1.22-1.37)
1.19 (1.11-1.26)
Hypertension
1.27 (1.15-1.39)
1.16 (1.13-1.18)
1.20 (1.17-1.24)
Skin ulcers/Cellulitis
1.25 (1.03-1.50)
1.26 (1.19-1.34)
1.20 (1.13-1.28)
Moderate/Severe Liver Disease
Metastatic carcinoma
Abbreviations: HMO (health maintenance organization); ADE (adverse drug event)