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.
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.
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 prevalent 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.
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 evaluate the safety and efficacy of using opioids in this population.
Digital Object Identifier (DOI)
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).
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. 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/
S1 Table. List of drugs included in “any opioids” and “strong opioids”. https://doi.org/10.1371/journal.pone.0210341.s001 (PDF)
S2 Table. Description of variables used in the study. https://doi.org/10.1371/journal.pone.0210341.s002 (PDF)
S3 Table. Description of estimated trajectories and number of participants in each trajectory. https://doi.org/10.1371/journal.pone.0210341.s003 (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). https://doi.org/10.1371/journal.pone.0210341.s004 (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). https://doi.org/10.1371/journal.pone.0210341.s005 (PDF)
S6 Table. The frequency distribution across trajectory groups of any opioid use among Alzheimer’s Disease Centers (ADC). https://doi.org/10.1371/journal.pone.0210341.s006 (PDF)
S7 Table. Participant characteristics: Included participants vs. participants excluded for having fewer than 3 visits. https://doi.org/10.1371/journal.pone.0210341.s007 (PDF)
Oh, GYeon; Abner, Erin L.; Fardo, David W.; Freeman, Patricia R.; and Moga, Daniela C., "Patterns and Predictors of Chronic Opioid Use in Older Adults: A Retrospective Cohort Study" (2019). Epidemiology Faculty Publications. 63.
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