Patterns and Predictors of Chronic Opioid Use in Older Adults: A Retrospective Cohort Study

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Patterns and Predictors of Chronic Opioid Use in Older Adults: A Patterns and Predictors of Chronic Opioid Use in Older Adults: A

Retrospective Cohort Study Retrospective Cohort Study

GYeon Oh

University of Kentucky

Erin L. Abner

University of Kentucky

, erin.abner@uky.edu

David W. Fardo

University of Kentucky

, david.fardo@uky.edu

Patricia R. Freeman

University of Kentucky

, [email protected].edu

Daniela C. Moga

University of Kentucky

, daniela.moga@uky.edu

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Patterns and Predictors of Chronic Opioid Use in Older Adults: A Retrospective Patterns and Predictors of Chronic Opioid Use in Older Adults: A Retrospective

Cohort Study Cohort Study

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RESEARCH ARTICLE

Patterns and predictors of chronic opioid use

in older adults: A retrospective cohort study

GYeon Oh

, Erin L. Abner

1,2,3

, David W. Fardo

2,3

, Patricia R. Freeman

, Daniela

C. Moga

1,2,4

1 Department of Epidemiology, University of Kentucky, Lexington, Kentucky, United States of America,

2 Sanders-Brown Center on Aging, University of Kentucky, Lexington, Kentucky, United States of America,

3 Department of Biostatistics, University of Kentucky, Lexington, Kentucky, United States of America,

4 Department of Pharmacy Practice and Science, University of Kentucky, Lexington, Kentucky, United States

of America

* [email protected]

Abstract

Background

Given the controversy around the effectiveness of opioid treatment for chronic pain and the

lack of detailed guidance for prescribing opioids in older adults, the objectives of this study

were to estimate the trajectories and predictors of opioid use in older adults.

Methods

Data were extracted from the National Alzheimer’s Coordinating Center (2005–2017). Group-

based trajectory modeling was used to identify the patterns of opioid use (any or strong)

among participants age 65+. We used multivariable logistic regression with backward selection

to evaluate demographics and comorbidities as potential predictors of trajectory membership.

Results

Among 13,059 participants, four trajectories were identified for the use of both any opioids

and strong opioids (minimal-users, incident chronic-users, discontinuing-users, and preva-

lent chronic-users). For any opioids, female sex (adjusted odds ratio = 1.23; 95% confidence

interval = 1.03–1.46), black vs. white (1.47; 1.18–1.82), year of education (0.96; 0.94–0.99),

type of residence (independent group vs. private: 1.77; 1.38–2.26, care facility vs. private:

1.89; 1.20–2.97), hypertension (1.44; 1.20–1.72), cardiovascular disease (1.30; 1.09–1.55),

urinary incontinence (1.45; 1.19–1.78), dementia (0.73; 0.57–0.92), number of medications

(1 to 4 vs. none: 0.48; 0.36–0.64, 5 or more vs. none: 0.67; 0.50–0.88), and antidepressant

agent (1.38; 1.14–1.67) were associated with incident chronic-use vs. non-use. For strong

opioids, female sex (1.27; 1.04–1.56), type of residence (independent group vs. private:

1.90; 1.43–2.53, care facility vs. private: 2.37; 1.44–3.90), current smoking (1.68; 1.09–

2.60), hypertension (1.49; 1.21–1.83), urinary incontinence (1.45; 1.14–1.84), dementia

(0.73; 0.55–0.97), number of medications (1 to 4 vs. none: 0.46; 0.32–0.65, 5 or more vs.

none: 0.59; 0.42–0.83), and antidepressant agent (1.55; 1.24–1.93) were associated with

incident chronic-use vs. non-use.

PLOS ONE | https://doi.org/10.1371/journal.pone.0210341 January 11, 2019 1 / 14

a1111111111

OPEN ACCESS

Citation: Oh G, Abner EL, Fardo DW, Freeman PR,

Moga DC (2019) Patterns and predictors of chronic

opioid use in older adults: A retrospective cohort

study. PLoS ONE 14(1): e0210341. https://doi.org/

10.1371/journal.pone.0210341

Editor: Danijela Gnjidic, University of Sydney,

AUSTRALIA

Received: September 21, 2018

Accepted: December 20, 2018

Published: January 11, 2019

article distributed under the terms of the Creative

Commons Attribution License, which permits

unrestricted use, distribution, and reproduction in

any medium, provided the original author and

source are credited.

Data Availability Statement: "NACC data are

owned by the National Alzheimer’s Coordinating

Center and are available by request from https://

www.alz.washington.edu/. Two types of data files

are now available, each based on the most recent

data freeze at the time of request. • The QUICK-

ACCESS FULL DATA FILE contains the complete

UDS and Neuropathology data sets from the latest

quarterly data archive. It can be provided more

quickly — shortly after submitting this request and

signing NACC’s Data Use Agreement — but may

require more effort on the part of the investigator

afterward in understanding the data elements. •

Conclusion

Given that chronic opioid use was more prevalent in participants who were more vulnerable

(i.e., older age, with multiple comorbidities, and polypharmacy), further studies should eval-

uate the safety and efficacy of using opioids in this population.

Introduction

Over 50% of the elderly population reported pain in the United States (US) in 2011, and about

75% of those reported pain in multiple sites [1]. Although chronic pain is prevalent in older

adults, appropriate treatment is challenging for this population due to the high rate of poly-

pharmacy and potential of adverse events [2]. Older adults with dementia may be especially

vulnerable due to inherent difficulties in assessing and treating pain [3–5]. Long-term (90

days) opioid prescriptions have dramatically increased over the past decade, though the effec-

tiveness of this therapy for chronic pain is yet to be established [6, 7]. The prevalence of long-

term opioid use in US adults increased from 1.8% in 1999–2000 to 5.4% in 2013–2014 [8].

Among these long-term opioid users, 25% were adults age 65 years or older [8]. Opioid-related

negative outcomes, such as addiction, misuse, and overdose deaths, have also risen [9–12].

Long-term opioid use has also been associated with opioid overdose-related hospitalization in

older adults [13].

A recent study in Australia showed that opioid initiation with a transdermal formulation,

higher oral morphine equivalents, older age, history of mental health comorbidities, use of

non-opioid analgesics, and use of benzodiazepines were the predictors of persistent prescrip-

tion opioid in adults 18 years and older [14]. A prospective study with participants in a large

nonprofit health care system in Washington State reported that patients’ expectations of long-

term opioid use was the main predictor of using opioids 30 or more days [15]. Although sev-

eral studies reported the predictors of chronic opioid use in different populations, is the evi-

dence is still limited regarding predictors of long-term opioid use in older adults in the US

population. Older adults are more sensitive to negative outcomes (e.g., cognitive impairment,

falls) from opioids, in part due to age-related decreases in liver and kidney function and poly-

pharmacy [2, 9, 10, 12]. The Centers for Disease Control and Prevention (CDC) recently

issued guidelines aimed at improving the safety and effectiveness of chronic pain treatment

[16, 17]. These guidelines recommend increasing monitoring to minimize the risks of opioids

in older adults, yet lack detailed guidance on opioid prescribing [16, 17]. Identifying the char-

acteristics associated with opioid use in older adults can help identify factors that could

improve risk-benefit assessment and prevent inappropriate use. Therefore, the purpose of this

study was to investigate patterns of longitudinal opioid utilization in older adults using group-

based trajectory models and to identify predictors associated with the trajectories indicating

chronic use.

Methods

Study participants

Study data were drawn from the National Alzheimer’s Coordinating Center’s (NACC) Uni-

form Data Set (UDS), which comprises participants enrolled in longitudinal studies at

National Institute on Aging-funded Alzheimer’s Disease Centers (ADC) throughout the US.

Participants included subjects with cognitive status ranging from normal to dementia that are

Long-term opioid use in older adults

PLOS ONE | https://doi.org/10.1371/journal.pone.0210341 January 11, 2019 2 / 14

The CUSTOM FILE is created for the investigator

after he or she has carefully specified the file

criteria, with or without the guidance of NACC’s

research scientists. The custom file is generally

provided less than a week after the criteria are fully

specified. The authors of this study did not enjoy

any special access privileges which would preclude

other researchers from requesting access to these

data. Data are retained beyond each quarter. Other

researchers would be able to request the full data

file used in our study by requesting the September

2017 Uniform Data Set (UDS) data freeze with the

variables listed in our cover letter and also attached

with this submission. Researchers will have to

apply our eligibility criteria; inclusion criteria: (1) 65

years or older at their initial UDS visit, and (2)

medication data recorded at every visit.

Participants with fewer than three visits were

excluded from our study. Additional information

regarding access to these data, including the data

dictionary, can be found at: https://www.alz.

washington.edu/"

Funding: This study was supported in part by grant

R01AG054130 to DCM from the National Institute

on Aging. There was no additional external funding

received for this study. The NACC database is

funded by NIA/NIH Grant U01 AG016976. NACC

data are contributed by the NIA-funded. ADCs: P30

AG019610 (PI Eric Reiman, MD), P30 AG013846

(PI Neil Kowall, MD), P50 AG008702 (PI Scott

Small, MD), P50 AG025688 (PI Allan Levey, MD,

PhD), P50 AG047266 (PI Todd Golde, MD, PhD),

P30 AG010133 (PI Andrew Saykin, PsyD), P50

AG005146 (PI Marilyn Albert, PhD), P50

AG005134 (PI Bradley Hyman, MD, PhD), P50

AG016574 (PI Ronald Petersen, MD, PhD), P50

AG005138 (PI Mary Sano, PhD), P30 AG008051

(PI Thomas Wisniewski, MD), P30 AG013854 (PI

M. Marsel Mesulam, MD), P30 AG008017 (PI

Jeffrey Kaye, MD), P30 AG010161 (PI David

Bennett, MD), P50 AG047366 (PI Victor

Henderson, MD, MS), P30 AG010129 (PI Charles

DeCarli, MD), P50 AG016573 (PI Frank LaFerla,

PhD), P50 AG005131 (PI James Brewer, MD,

PhD), P50 AG023501 (PI Bruce Miller, MD), P30

AG035982 (PI Russell Swerdlow, MD), P30

AG028383 (PI Linda Van Eldik, PhD), P30

AG053760 (PI Henry Paulson, MD, PhD), P30

AG010124 (PI John Trojanowski, MD, PhD), P50

AG005133 (PI Oscar Lopez, MD), P50 AG005142

(PI Helena Chui, MD), P30 AG012300 (PI Roger

Rosenberg, MD), P30 AG049638 (PI Suzanne

Craft, PhD), P50 AG005136 (PI Thomas

Grabowski, MD), P50 AG033514 (PI Sanjay

Asthana, MD, FRCP), P50 AG005681 (PI John

Morris, MD), P50 AG047270 (PI Stephen

Strittmatter, MD, PhD).

recruited through clinician referral, self-referral by patients or family members, active recruit-

ment, and volunteers. Data from subjects and their study partners (co-participants) are col-

lected annually by trained clinicians and other ADC research personnel until they are

deceased or decline to participate. Data collected at initial and annual follow-up visits include

sociodemographic characteristics, family history, medical history, neurological evaluations,

and medication use information [18–20]. For this study, we included participants from 38

ADCs with data available in the September 2017 UDS data freeze, meeting the following inclu-

sion criteria: (1) 65 years or older at their initial UDS visit, and (2) medication data recorded at

every visit. Participants with fewer than three visits were excluded to facilitate assessing trajec-

tory trends with quadratic components; in addition participants with cancer history [21], were

also excluded given that opioid medications are highly prevalent in this population (Fig 1).

ADC study procedures are approved by local Institutional Review Boards (IRBs), and all par-

ticipants provided written informed consent. Research using the NACC database is approved

by the University of Washington IRB. Because the NACC data are de-identified, no additional

IRB approval was necessary for this secondary data analysis.

Opioid use assessment

Medication information was provided by the participant and/or the caregiver/legally autho-

rized representative and was based on each participant’s reported medication use within two

weeks of each study visit. In assessing opioid use, opioid medications used as antitussives were

not considered. Participants were considered to be “any opioid” users if they reported use of

any opioid analgesic medications, and “strong opioid” users were defined among any opioid

users if they reported use of opioid analgesics stronger than or equal to morphine’s potency

[22, 23] (e.g., buprenorphine, fentanyl, hydrocodone, hydromorphone, methadone, morphine,

opium, oxycodone, oxymorphone) (S1 Table).

Participant characteristics

Baseline characteristics of interest were recorded at the participant’s initial UDS visit. Demo-

graphic information included age at enrollment (reference category [ref]: 65–74 years), sex

(ref: male), race (ref: white), years of education, and type of residence (ref: single- or multiple-

family private dwelling). Self-reported medical history information included current smoking,

as well as ever-history of alcohol abuse, and other abused substances; hypertension, diabetes,

urinary incontinence, and cardiovascular conditions. Medication information included num-

ber of medications reported (excluding opioid analgesics); use of nonsteroidal anti-inflamma-

tory medication (NSAID), antidepressant agent, antipsychotic agent (including miscellaneous

antipsychotics, psychotherapeutic combinations, phenothiazine psychotics, thioxanthenes,

and atypical antipsychotics), and anxiolytic, sedative, or hypnotic agent (including barbiturates

and benzodiazepines, and miscellaneous anxiolytics, sedatives, and hypnotics). Reference cate-

gory for all medical history and medication variables was the absence of condition or medica-

tion use. Clinician-determined agitation (ref: no agitation) and cognitive status (ref: no

dementia) were also included in the analysis [see S2 Table for detailed descriptions].

Statistical analysis

Group-based trajectory models (GBTM) [24, 25] were used to identify participants with similar

longitudinal patterns of opioid analgesic use. With this approach, latent trajectories are estimated

by the model, and every individual is assigned a probability of belonging to each trajectory, with

total probability of membership summing up to 1.0; we used maximum probability assignment to

determine group membership. The shapes of each trajectory are defined by polynomial terms

Long-term opioid use in older adults

PLOS ONE | https://doi.org/10.1371/journal.pone.0210341 January 11, 2019 3 / 14

Competing interests: The authors have declared

that no competing interests exist.

(cubic, quadratic, or linear). Since the time scale was study time, and participants could have up to

12 visits, follow-up was truncated when more than 95% of participants did not have data available

for a particular visit. As a result, data from visits 11 and 12 were not included in the analysis.

Models considering between 2 and 6 trajectories were fit to the data, and the optimal final

model was determined by the Bayesian Information Criterion (BIC) with the least negative

value [26, 27]. In addition, for judging model adequacy, we used the approach proposed by

Nagin that the average posterior probability of membership in the assigned group is greater

than 0.7 [26, 27].

Once optimal GBTM models were selected, we assessed face validity by tabulating the pro-

portion of total study visits in each trajectory group where opioid analgesic use was reported

(e.g., participants assigned to the higher use groups should have higher proportion of visits

where opioid analgesic use was reported). We then used multivariable logistic regression with

backward selection to identify participant characteristics significantly associated with trajec-

tory group membership. Our preliminary analyses indicated that there were participants who

could be described as chronic users, such that they reported using the drugs at most visits.

Since our primary interest was in identifying risk factors for this group of participants, the out-

come for the logistic models was membership in a chronic user group vs. membership in a

group that did not use opioid analgesics chronically.

Participants with missing values, including “unknown”, were excluded from this analysis

with the exception of type of residence, which had a large number of participants in the

“unknown or other” category (>600). Here, we treated unknown/other as a category. Adjusted

odds ratios (OR

adj

) with 95% confidence intervals (CI) were obtained from the full and

reduced models. To identify the best fitting logistic models, Akaike’s Information Criterion

(AIC) values were compared between full and reduced models. All data analyses were

Fig 1. Sample selection flowchart.

https://doi.org/10.1371/journal.pone.0210341.g001

Long-term opioid use in older adults

PLOS ONE | https://doi.org/10.1371/journal.pone.0210341 January 11, 2019 4 / 14

conducted using SAS 9.4, and 0.05 was set as the significance level. PROC TRAJ [24] was used

to estimate GBTM, and PROC LOGISTIC was used to fit the logistic regression models.

Results

A total of 13,059 participants were included in our analyses after applying inclusion and exclu-

sion criteria (Fig 1). The mean (SD) number of follow-up visits was 5.4 (2.2), ranging between

3 and 10 visits. The mean (SD) baseline age was 75.8 (6.9) years. The majority of participants

were female (56.7%), white (83.1%), and resided in private dwellings (89.9%). The most com-

mon comorbidity was hypertension (55.0%), and 55.8% of participants reported taking 5 or

more medications. At the initial visit, there were 498 (3.8%) users of any opioid and 284 (2.2%)

users of strong opioids (Tables 1 and 2).

Using GBTM, four trajectories were identified for both any opioid use and strong opioid

use (Fig 2). The optimal number of trajectories was determined based on the BIC in combina-

tion with the requirement that the average posterior probability in all assigned trajectory

groups was at least 0.70 [26, 27]. The shapes of the trajectories for any opioid use were qua-

dratic or cubic, and the parameter estimates of the quadratic or cubic function for each trajec-

tory were all statistically significant (S3 Table). For strong opioid use, the shapes of the

trajectories were all quadratic, and the parameter estimates of the quadratic function for each

trajectory were significant in 3 of the 4 groups. The final optimal models were adequate based

on the criterion of the average posterior probability [26, 27] (S3 Table).

GBTM for any opioids

A majority of participants (90.4%) were assigned to the “minimal-users” group, meaning they

reported no use or low use over time. Participants (5.0%) who did not report opioid use at

their first visit, but initiated use during the study period and continued their use during fol-

low-up were assigned to the “incident chronic-users” group. “Discontinuing-users” were par-

ticipants who used opioids at the first visit but discontinued during follow-up (2.2%).

“Prevalent chronic-users” (2.4%) were participants who reported opioid use at baseline and

consistently during follow-up (Fig 2). The median percentage (IQR) of UDS visits with any

opioid use were 0% (0–0), 33.3% (25.0–40.0), 40.0% (33.3–60.0), and 85.7% (75.0–100) for

minimal-users, incident chronic-users, discontinuing-users, and prevalent chronic-users,

respectively. Participant characteristics for each group are presented in Table 1.

GBTM for strong opioids

Similar trajectories—minimal-users (94.3%), incident chronic-users (3.4%), discontinuing-

users (0.9%), and prevalent chronic-users (1.4%)—were identified for use of strong opioids.

The median percentage (IQR) of UDS visits with strong opioid use were 0% (0–0), 33.3%

(25.0–40.0), 50.0% (33.3–60.0), and 83.3% (70.0–100.0) for minimal-users, incident chronic-

users, discontinuing-users, and prevalent chronic-users, respectively. Participant characteris-

tics for each group are presented in Table 2.

Predictors of prevalent chronic-use trajectory membership

Multivariable logistic regression models were used to identify t predictors of prevalent

chronic-use trajectory membership compared to discontinuing-users or minimal-users for

both, any and strong opioids. All variables listed in Table 1 were considered for inclusion (full

model) (S4 and S5 Tables), and the reduced models are reported in Tables 3 and 4. After back-

ward selection, urinary incontinence was negatively associated with prevalent chronic-use vs.

Long-term opioid use in older adults

PLOS ONE | https://doi.org/10.1371/journal.pone.0210341 January 11, 2019 5 / 14

discontinuing-use for both any opioids (OR

adj

= 0.66 [95% CI = 0.45, 0.98]) and strong opioids

(0.45 [0.25, 0.80]).

Several factors emerged as significant predictors of prevalent chronic-use vs. minimal-use

in both models (any opioid and strong opioids): age (any opioid: 1.83 [1.28, 2.61]; strong

Table 1. Baseline characteristics across trajectory groups of any opioid use.

Baseline Characteristics Pattern of Any Opioid Use

Minimal



(N = 11,806)

Discontinuing

(N = 287)

Incident chronic

(N = 657)

Prevalent chronic

(N = 309)

Baseline age

65–74 5,479 (46.4) 131 (45.6) 266 (40.5) 132 (42.7)

75–84 4,976 (42.2) 108 (37.6) 294 (44.8) 121 (39.2)

85+ 1,351 (11.4) 48 (16.7) 97 (14.8) 56 (18.1)

Female 6553 (55.5) 203 (70.7) 423 (64.4) 226 (73.1)

Race

White 9,869 (83.7) 216 (75.5) 508 (77.6) 236 (76.6)

Black 1,453 (12.3) 59 (20.6) 131 (20.0) 67 (21.8)

Other

464 (3.9) 11 (3.9) 16 (2.4) 5 (1.6)

Education, mean (SD) 15.3 (3.4) 14.5 (3.6) 14.6 (3.4) 14.5 (3.4)

Type of Residence

Private

10,371 (87.9) 247 (86.1) 531 (80.8) 243 (78.6)

Independent group

854 (7.2) 28 (9.8) 88 (13.4) 44 (14.2)

Care facility

228 (1.9) 5 (1.7) 25 (3.8) 14 (4.5)

Unknown 353 (3.0) 7 (2.4) 13 (2.0) 8 (2.6)

Current smoking 377 (3.2) 18 (6.3) 26 (4.0) 17 (5.5)

Ever alcohol abuse 513 (4.4) 19 (6.6) 31 (4.7) 19 (6.2)

Ever other abused substances 63 (0.5) 4 (1.4) 4 (0.6) 5 (1.6)

Agitation 652 (5.5) 10 (3.5) 31 (4.7) 16 (5.2)

Ever hypertension 6,326 (53.7) 200 (69.7) 432 (65.9) 205 (66.6)

Ever diabetes 1,401 (11.9) 54 (18.8) 104 (15.8) 59 (19.2)

Ever cardiovascular disease 2,953 (25.3) 82 (28.9) 214 (32.8) 103 (33.6)

Ever urinary incontinence 1,742 (14.8) 79 (27.5) 147 (22.4) 62 (20.1)

Dementia diagnosis 2,047 (17.3) 41 (14.3) 103 (15.7) 38 (12.3)

Number of medications

0 898 (7.6) 6 (2.1) 76 (11.6) 10 (3.2)

1 to 4 4,489 (38.0) 58 (20.2) 170 (25.9) 61 (19.7)

5 or more 6,419 (54.4) 223 (77.7) 411 (62.6) 238 (77.0)

Antidepressant agent 2,645 (22.4) 109 (38.0) 184 (28.0) 121 (39.2)

Antipsychotic agent 292 (2.5) 4 (1.4) 24 (3.7) 11 (3.6)

Anxiolytic, sedative, or hypnotic agent 1,134 (9.6) 64 (22.3) 83 (12.6) 73 (23.6)

NSAID use 4,022 (34.1) 116 (40.4) 219 (33.3) 143 (46.3)

Any opioid use 121 (1.0) 162 (56.5) 9 (1.4) 206 (66.7)

Strong opioid use 67 (0.6) 90 (31.4) 6 (0.9) 121 (39.2)

(All results presented are N (%) unless otherwise noted). Abbreviations: SD, standard deviation; NSAID, nonsteroidal anti-inflammatory medication. Note

a = American Indian, Alaska Native, Native Hawaiian, Other Pacific Islander, Asian, or Other

b = single-or multiple family private living

c = retirement community, or independent group living

d = assisted living, nursing home, or hospital

e = number of opioids was excluded from the total number of medications; the minimal-use group includes participants who reported no use or low use over time.

https://doi.org/10.1371/journal.pone.0210341.t001

Long-term opioid use in older adults

PLOS ONE | https://doi.org/10.1371/journal.pone.0210341 January 11, 2019 6 / 14

opioids:2.10 [1.34, 3.28]), female sex (1.76 [1.35, 2.30]; 1.71[1.22, 2.40]), black vs. white (1.92

[1.41, 2.61]; 1.97 [1.34, 2.91]), independent group living vs. private living (1.74 [1.21, 2.49];

1.68 [1.06, 2.65]), care facility living vs. private living (2.02 [1.07, 3.83]; 3.46 [1.73, 6.94]), 5 or

more medications vs. none (2.52 [1.25, 5.08]; 4.89 [1.53, 15.65]), use of antidepressant agent

Table 2. Baseline characteristics across trajectory groups of strong opioid use.

Baseline Characteristics Pattern of strong opioid use

Minimal

(N = 12,317)

Discontinuing

(N = 116)

Incident chronic

(N = 444)

Prevalent chronic

(N = 182)

Baseline age

65–74 5,699 (46.3) 57 (49.1) 176 (39.6) 76 (41.8)

75–84 5,192 (42.2) 40 (34.5) 197 (44.4) 70 (38.5)

85+ 1,426 (11.6) 19 (16.4) 71 (16.0) 36 (19.8)

Female 6,904 (56.1) 79 (68.1) 289 (65.1) 133 (73.1)

Race

White 10,232 (83.2) 92 (80.0) 362 (81.7) 143 (78.6)

Black 1,584 (12.9) 20 (17.4) 70 (15.8) 36 (19.8)

Other

479 (3.9) 3 (2.6) 11 (2.5) 3 (1.7)

Education, mean (SD) 15.2 (3.4) 14.6 (3.7) 14.9 (3.4) 14.6 (3.1)

Type of Residence

Private

10,806 (87.7) 99 (85.3) 351 (79.1) 136 (74.7)

Independent group

911 (7.4) 12 (10.3) 64 (14.4) 27 (14.8)

Care facility

236 (1.9) 4 (3.5) 20 (4.5) 12 (6.6)

Unknown 364 (3.0) 1 (0.9) 9 (2.0) 7 (3.9)

Current smoking 391(3.2) 10 (8.7) 23 (5.2) 14 (7.7)

Ever alcohol abuse 537 (4.4) 13 (11.2) 20 (4.5) 12 (6.6)

Ever other abused substances 65 (0.5) 2 (1.7) 6 (1.4) 3 (1.7)

Agitation 672 (5.5) 4 (3.5) 26 (5.9) 7 (3.9)

Ever hypertension 6,673 (54.3) 84 (72.4) 287 (64.8) 119 (65.4)

Ever diabetes 1,505 (12.3) 23 (19.8) 62 (14.0) 28 (15.4)

Ever cardiovascular disease 3,112 (25.5) 40 (34.8) 139 (31.5) 61 (33.9)

Ever urinary incontinence 1,857 (15.1) 37 (31.9) 101 (22.8) 35 (19.2)

Dementia diagnosis 2,124 (17.2) 16 (13.8) 69 (15.5) 20 (11.0)

Number of medications

0 936 (7.6) 1 (0.9) 50 (11.3) 3 (1.7)

1 to 4 4,599 (37.3) 21 (18.1) 119 (26.8) 39 (21.4)

5 or more 6,782 (55.1) 94 (81.0) 275 (61.9) 140 (76.9)

Antidepressant agent 2,795 (22.7) 51 (44.0) 138 (31.1) 75 (41.2)

Antipsychotic agent 307 (2.5) 3 (2.6) 16 (3.6) 5 (2.8)

Anxiolytic, sedative, or hypnotic agent 1,219 (9.9) 29 (25.0) 56 (12.6) 50 (27.5)

NSAID 4,235 (34.4) 42 (36.2) 141 (31.8) 82 (45.1)

Any opioid use 259 (2.1) 81 (69.8) 28 (6.3) 130 (71.4)

Strong opioid use 82 (0.7) 78 (67.2) 3 (0.7) 121 (66.5)

(All results presented are N (%) unless otherwise noted). Abbreviations: SD, standard deviation; NSAID, nonsteroidal anti-inflammatory medication. Note

a = American Indian, Alaska Native, Native Hawaiian, Other Pacific Islander, Asian, or Other

b = single-or multiple family private living

c = retirement community, or independent group living

d = assisted living, nursing home, or hospital

e = number of opioids was excluded from the total number of medications.

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(1.89 [1.46, 2.44]; 1.89 [1.36, 2.63]), use of anxiolytic, sedative, or hypnotic agent (2.26 [1.69,

3.02]; 2.51 [1.76, 3.57]), and dementia (0.46 [0.32, 0.68]; 0.39 [0.23, 0.65]) (Tables 3 and 4).

Predictors of incident chronic-use trajectory memberships

Four multivariable logistic regression models were used to identify predictors associated with

incident chronic-users compared to discontinuing-users or minimal-users for both any and

strong opioid groups. Use of anxiolytic, sedative, or hypnotic agent (any opioids: 0.57 [0.39,

0.83]; strong opioids: 0.53 [0.31, 0.90]), 1 to 4 medications vs. none (0.26 [0.11, 0.62]; 0.12

[0.02, 0.95]), and 5 or more medications vs. none (0.17 [0.07, 0.39]; 0.07 [0.01, 0.52]) were sig-

nificant predictors of incident chronic-use vs. discontinuing-use in both models (Tables 3

and 4).

Several factors emerged as significant predictors of incident chronic-use vs. minimal-use in

both models (any opioid and strong opioids): female sex (any opioid: 1.23 [1.03, 1.46]; strong

opioids: 1.27 [1.04, 1.56]), independent group living vs. private living (1.77 [1.38, 2.26]; 1.90

[1.43, 2.53]), care facility living vs. private living (1.89 [1.20, 2.97]; 2.37 [1.44, 3.90])hyperten-

sion (1.44 [1.20, 1.72]; 1.49 [1.21, 1.83]), urinary incontinence (1.45 [1.19, 1.78]; 1.45 [1.14,

1.84]), use of antidepressant agent (1.38 [1.14, 1.67]; 1.55 [1.24, 1.93]), 1 to 4 medications vs.

none (0.48 [0.36, 0.64]; 0.46 [0.32, 0.65]), 5 or more medications vs. none (0.67 [0.50, 0.88];

0.59 [0.42, 0.83]) and dementia (0.73 [0.57, 0.92]; 0.73 [0.55, 0.97]) (Tables 3 and 4).

Discussion

This study investigated the patterns of opioid analgesics (any opioid or strong opioids) use and

identified predictors of inclusion in different use trajectories over 10 years of follow-up in

older adults. The prevalence of any opioid use (3.8%) at enrollment was lower than that

reported in a previous study (6.5%) using National Health and Nutritional Examination Sur-

vey (NHANES) from 1999 to 2014 [8]. In addition, the prevalence of any opioid use in our

study was lower than that reported in other countries. A previous study from Canada has

reported that the prevalence of prescription opioid use was 16.7% in the population aged 65

Fig 2. Estimated group-based trajectories for any opioid and strong opioid use in National Alzheimer’s Coordinating Center (NACC) participants (2005–2017).

https://doi.org/10.1371/journal.pone.0210341.g002

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+ in 2009 [28]. A recent study of Australians conducted by Lalic et al. has reported that the

prevalence of prescription opioid analgesic use in people without cancer (ages 18–99 years)

was 15.37% in 2016–2017 [29]. Another study has examined that 14.1% of residents (aged 65

+) in the State of Victoria, Australia, filled the prescription of oxycodone in 2013 [30]. This

could be due to the different definition of identifying opioid use (reported medications used

within two weeks of visit vs. prescription opioid use in the past 30 days) and using different

study population. Our study identified four longitudinal trends—minimal-users, incident

chronic-users, discontinuing-users, and prevalent chronic-users—for use of both any and

strong opioids. We found that participants who were older, female, black, residing in indepen-

dent group living or care facilities, or taking antidepressant agents were more likely to be

chronic-users compared to minimal-users in both the “any opioid” and “strong opioid” user

Table 3. Predictors associated with chronic-use (prevalent or incident) vs. discontinuing-use or minimal-use of any opioid in multivariable logistic regression

model (reduced models adjusted for the covariates retained by backward selection).

Prevalent chronic-use Incident chronic-use

vs. discontinuing-use

vs. minimal use

vs. discontinuing-use

vs. minimal-use

Baseline age

65–74 - Ref. - -

75–84 - 1.11 (0.85, 1.44) - -

85+ - 1.83 (1.28, 2.61) - -

Female vs. male - 1.76 (1.35, 2.30) 0.72 (0.52, 0.98) 1.23 (1.03, 1.46)

Race

White - Ref. - Ref.

Black - 1.92 (1.41, 2.61) - 1.47 (1.18, 1.82)

Other - 0.44 (0.18, 1.08) - 0.62 (0.37, 1.04)

Education (1-year difference) - 0.95 (0.91, 0.98) - 0.96 (0.94, 0.99)

Type of Residence

Private - Ref. - Ref.

Independent group - 1.74 (1.21, 2.49) - 1.77 (1.38, 2.26)

Care facility - 2.02 (1.07, 3.83) - 1.89 (1.20, 2.97)

Unknown - 0.88 (0.42, 1.81) - 0.66 (0.37, 1.19)

Hypertension - - - 1.44 (1.20, 1.72)

Diabetes - 1.44 (1.05, 1.97) - -

Cardiovascular disease - - - 1.30 (1.09, 1.55)

Urinary incontinence 0.66 (0.45, 0.98) - - 1.45 (1.19, 1.78)

Number of medications

None - Ref. Ref. Ref.

1 to 4 - 1.33 (0.65, 2.71) 0.26 (0.11, 0.62) 0.48 (0.36, 0.64)

5 or more - 2.52 (1.25, 5.08) 0.17 (0.07, 0.39) 0.67 (0.50, 0.88)

Antidepressant agent - 1.89 (1.46, 2.44) - 1.38 (1.14, 1.67)

Anxiolytic, sedative, or hypnotic agent - 2.26 (1.69, 3.02) 0.57 (0.39, 0.83) -

NSAID - 1.36 (1.06, 1.75) - -

Dementia - 0.46 (0.32, 0.68) - 0.73 (0.57, 0.92)

Note: Where the reference category is not specified, the comparison is either yes vs. no or ever vs. never.

a = Number of observations used in the model is 578 (prevalent chronic-user: 298 and discontinuing-user: 280)

b = Number of observations used in the model is 11,458 (prevalent chronic-user: 298 and minimal-users: 11,458)

c = Number of observations used in the model is 920 (incident chronic-user: 640 and discontinuing-user: 280)

d = Number of observations used in the model is 12,098 (incident chronic-user: 640 and minimal-users: 11,458

https://doi.org/10.1371/journal.pone.0210341.t003

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groups. These results are consistent with previous studies that reported that older adults and

women experience pain more frequently than younger adults and men [13, 31–33], and that

older women have a higher prevalence of long-term opioid use [34]. Also, previous studies

have shown that long-term opioid use is highly prevalent in nursing home residents compared

to people in a community setting [35], and having depression was associated with long-term

opioid use in older adults [13].

We found that taking anxiolytic, sedative, or hypnotic agents (including barbiturates and

benzodiazepines) was significantly associated with prevalent chronic-use in both the any opi-

oid and strong opioid user groups compared to minimal-use. We also observed that the preva-

lence of taking benzodiazepines was higher in prevalent chronic-users (2.9%) than in

minimal-users (0.7%). In a recent study including adult participants of the NHANES, long-

term use of opioids was associated with concurrent benzodiazepine use [8]. Similarly, a study

Table 4. Predictors associated with chronic-use (prevalent or incident) vs. discontinuing-use or non-use of strong opioids in multivariable logistic regression model

(reduced models adjusted for the covariates retained by backward selection).

Prevalent chronic-use Incident chronic-use

vs. discontinuing-use

vs. non-users

vs. discontinuing-use

vs. non-users

Baseline age

65–74 - Ref. - -

75–84 - 1.13 (0.80, 1.59) - -

85+ - 2.10 (1.34, 3.28) - -

Female vs. male - 1.71 (1.22, 2.40) - 1.27 (1.04, 1.56)

Race

White - Ref. - -

Black - 1.97 (1.34, 2.91) - -

Other - 0.54 (0.17, 1.73) - -

Type of Residence

Private Ref. Ref. - Ref.

Independent group 2.20 (1.00, 4.85) 1.68 (1.06, 2.65) - 1.90 (1.43, 2.53)

Care facility 3.14 (0.83, 11.91) 3.46 (1.73, 6.94) - 2.37 (1.44, 3.90)

Unknown 6.53 (0.77, 55.73) 1.32 (0.60, 2.87) - 0.67 (0.33, 1.36)

Current smoking - 2.34 (1.31, 4.16) - 1.68 (1.09, 2.60)

Alcohol abuse - - 0.44 (0.20, 1.00) -

Hypertension - - - 1.49 (1.21, 1.83)

Urinary incontinence 0.45 (0.25, 0.80) - - 1.45 (1.14, 1.84)

Dementia - 0.39 (0.23, 0.65) - 0.73 (0.55, 0.97)

Number of medications

None - Ref. Ref. Ref.

1 to 4 - 2.59 (0.79, 8.45) 0.12 (0.02, 0.95) 0.46 (0.32, 0.65)

5 or more - 4.89 (1.53, 15.65) 0.07 (0.01, 0.52) 0.59 (0.42, 0.83)

Antidepressant agent - 1.89 (1.36, 2.63) - 1.55 (1.24, 1.93)

Anxiolytic, sedative, or hypnotic agent - 2.51 (1.76, 3.57) 0.53 (0.31, 0.90) -

NSAID 1.68 (1.01, 2.78) - - -

Note: Where the reference category is not specified, the comparison is either yes vs. no or ever vs. never.

a = Number of observations used in the model is 290 (prevalent chronic-user: 178 and discontinuing-user: 112)

b = Number of observations used in the model is 12,131 (prevalent chronic-user: 178 and minimal-users: 11,953)

c = Number of observations used in the model is 545 (incident chronic-user: 433 and discontinuing-user: 112)

d = Number of observations used in the model is 12,386 (incident chronic-user: 433 and minimal-users: 11,953)

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from Australia reported that previous use of benzodiazepines was one of the predictors of per-

sistent opioid use [14]. Considering the overdose risk of co-prescribing benzodiazepine and

opioids [36], the CDC guidelines suggest avoiding the use of opioids and benzodiazepines

together [16, 17]. Therefore, further studies are needed to investigate the effect of using opioids

and benzodiazepines together on opioid-related adverse outcomes in older adults.

We found that patients with dementia were less likely to become chronic users of either any

or strong opioids compared to non-users. This trend might be due to inherent difficulties in

assessing and treating pain in these patients [4, 5], as well as potential concerns about the

added burden of cognitive impairment and risk of other adverse events from opioids. Given

the concern about serious problems (e.g., depression, anxiety, and agitation) that could result

from under-treating pain in older adults [33, 37–40], future studies are required to thoroughly

address the patterns of opioid use in patients with dementia.

Reporting a higher number of medications was positively associated with prevalent

chronic-use of both any opioid and strong opioids; however, with respect to incident chronic-

use, the results showed that participants with higher number of medications were less likely to

be incident chronic-users compared to discontinuing-users or minimal-users. Since ADC par-

ticipants may be more likely to receive medical care than the general population through their

contacts with ADC clinicians, there is a possibility that the participants with polypharmacy

were monitored more closely with regard to newly prescribed opioids. Thus, this result may

not be generalizable to all older adults in the US.

Neither comorbidities nor number of medications significantly predicted prevalent

chronic-use vs. discontinuing use. A recent prospective study concluded that neither baseline

chronic pain risk score nor depression were predictors of long-term opioid use; rather, a

patient’s expectation of long-term opioid use was the strongest predictor [15]. In a recent

study, long-term opioid use was significantly associated with physicians who have high-inten-

sity of prescribing opioids [41]. We also examined group percentages of discontinuing- and

chronic-users among the different ADCs (S6 Table) and observed that some centers had a

higher proportion of discontinuing users than others. Thus, it is possible that clinicians at dif-

ferent ADCs implement varying approaches in the management of pain and the de-escalation

and discontinuation of opioids in participants who use these medications chronically. Future

studies that include other factors (e.g., clinician characteristics or patient’s expectation) are

needed to fully understand how the prevalent chronic opioid-user group is different from the

discontinuing group.

This study has several limitations. First, because opioids were identified by reported medi-

cations used within two weeks of UDS visit, we could not classify participants by continuous

long-term use of opioids. Given the short exposure window, participants could be misclassified

if they used opioids only between visits. However, with up to 10 years of annual assessments,

we believe that we have meaningful information regarding longitudinal use patterns. Addi-

tionally, ADC participants tend to be highly educated, which may limit generalizability [18].

Also, participants who were excluded from the study had a higher rate of comorbidities and a

higher rate of using any opioid/strong opioids at baseline (S7 Table). The selection bias from

this exclusion criterion may result in an underestimate of opioid usage in this cohort. How-

ever, since the mean number of visits for each trajectory group in any opioid users was similar

(minimal user: 5.38; incident chronic user: 5.75; discontinuing-user: 5.13; prevalent chronic

user: 5.06), participants with less of follow up didn’t affected the participants being in their tra-

jectory group. Finally, we did not consider time-varying covariates, which may have resulted

in different associations.

In conclusion, the present study showed that potentially inappropriate opioid use was dis-

proportionately prevalent among vulnerable NACC participants (i.e., older age, with multiple

Long-term opioid use in older adults

PLOS ONE | https://doi.org/10.1371/journal.pone.0210341 January 11, 2019 11 / 14

comorbidities and polypharmacy). Further studies are required to thoroughly address the risk

and benefit of using opioids in older adults, and it is essential to provide evidence-based guide-

lines for opioid use in this population.

Supporting information

S1 Table. List of drugs included in “any opioids” and “strong opioids”.

(PDF)

S2 Table. Description of variables used in the study.

(PDF)

S3 Table. Description of estimated trajectories and number of participants in each trajec-

tory.

(PDF)

S4 Table. Factors associated with chronic-use (prevalent or incident) vs. discontinuing-use

and chronic-use (prevalent or incident) vs. non-use of any opioids in multivariable logistic

regression model (full model).

(PDF)

S5 Table. Factors associated with chronic-use (prevalent or incident) vs. discontinuing-use

and chronic-use (prevalent or incident) vs. non-use of strong opioids in multivariable

logistic regression model (full model).

(PDF)

S6 Table. The frequency distribution across trajectory groups of any opioid use among

Alzheimer’s Disease Centers (ADC).

(PDF)

S7 Table. Participant characteristics: Included participants vs. participants excluded for

having fewer than 3 visits.

(PDF)

Acknowledgments

We thank Dr. I-Chen Chen and Dr. Bobby Jones for assistance with GBTM modeling.

Author Contributions

Conceptualization: GYeon Oh, Erin L. Abner, Daniela C. Moga.

Formal analysis: GYeon Oh.

Funding acquisition: Daniela C. Moga.

Methodology: GYeon Oh, Erin L. Abner, David W. Fardo, Patricia R. Freeman, Daniela C.

Moga.

Supervision: Erin L. Abner, David W. Fardo, Patricia R. Freeman, Daniela C. Moga.

Writing – original draft: GYeon Oh.

Writing – review & editing: Erin L. Abner, David W. Fardo, Patricia R. Freeman, Daniela C.

Moga.

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