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Amundsen EJ, Odsbu I, Skurtveit SO, Gjersing L. Patterns of filled prescriptions and the association with risk of drug-induced death. A population-based nested case-control register study. Pharmacoepidemiol Drug Saf 2024; 33:e5763. [PMID: 38357780 DOI: 10.1002/pds.5763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 12/12/2023] [Accepted: 01/18/2024] [Indexed: 02/16/2024]
Abstract
PURPOSE Opioid analgesics (OA) and other pharmaceuticals have been associated with drug-induced deaths. However, there is a lack of knowledge regarding patterns of use of these pharmaceuticals in the population and regarding such associations. We identify and describe subgroups of people with different patterns of filled prescriptions of OA and other relevant pharmaceuticals and examine associations with drug-induced deaths. In addition, we estimate the proportion of drug-induced deaths with a filled OA prescription and OA as cause of death. METHODS A Norwegian population-based nested case-control register study with cases (drug-induced deaths 2010-2018, N = 2388) and population controls matched for age, gender and year of inclusion (N = 21 465). Patterns of filled prescriptions for opioid analgesics (OA), benzodiazepines and benzodiazepine-related drugs, gabapentinoids, ADHD medication and antidepressants/antipsychotics were explored by k-means cluster analysis. Associations with drug-induced deaths were estimated by conditional logistic regression adjusted for sociodemographic characteristics. Overlap of filled OA prescriptions and OA as cause of death was estimated. RESULTS Five clusters were identified: 'few prescriptions', 'weak OA', 'ADHD medication', 'sedative/psychiatric morbidity' and 'strong OA'. The 'strong OA' cluster had higher socioeconomic status compared to the other groupings. The risk of drug-induced death was also highest in this cluster (OR = 35.5; CI 25.6-49.3) and, for 68% (CI 64-73) of cases, filled prescriptions for OA was indicated as the underlying cause of death. CONCLUSIONS The cluster analysis identified a subgroup with filled prescriptions of OA and other pharmaceuticals and a higher socioeconomic status than other subgroups. This subgroup had a high risk of drug-induced death that needs to be addressed.
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Affiliation(s)
- Ellen J Amundsen
- Department of Alcohol, Tobacco and Drugs, Norwegian Institute of Public Health, Oslo, Norway
| | - Ingvild Odsbu
- Department of Chronic Diseases, Norwegian Institute of Public Health, Oslo, Norway
| | - Svetlana O Skurtveit
- Department of Chronic Diseases, Norwegian Institute of Public Health, Oslo, Norway
- Norwegian Centre for Addiction Research (SERAF), University of Oslo, Oslo, Norway
| | - Linn Gjersing
- Department of Alcohol, Tobacco and Drugs, Norwegian Institute of Public Health, Oslo, Norway
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Domzaridou E, Carr MJ, Millar T, Webb RT, Ashcroft DM. Non-fatal overdose risk associated with prescribing opioid agonists concurrently with other medication: Cohort study conducted using linked primary care, secondary care and mortality records. Addiction 2023; 118:2374-2383. [PMID: 37536685 DOI: 10.1111/add.16306] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 06/25/2023] [Indexed: 08/05/2023]
Abstract
BACKGROUND AND AIMS An apparently protective effect of opioid agonist treatment (OAT) on all-cause and cause-specific mortality risk has been widely reported. Non-fatal overdose (NFO) often precedes subsequent drug-poisoning deaths. We hypothesized that benzodiazepines, gabapentinoids, antipsychotics, antidepressants, Z-drugs or opioids increase the NFO risk when co-prescribed with OAT. DESIGN We conducted a cohort study using the Clinical Practice Research Datalink GOLD and Aurum databases. The cohort was linked to Hospital Episode Statistics admitted patient care data (HES-APC), neighbourhood- and practice-level Index of Multiple Deprivation quintiles and mortality records from the Office for National Statistics. SETTING Primary care in England. PARTICIPANTS We studied patients with opioid use disorder, aged 18-64 years, who were prescribed OAT (15155 methadone and 5743 buprenorphine recipients) between Jan 1, 1998, and Dec 31, 2017. MEASUREMENTS The main outcome examined was NFO risk during co-prescription of OAT with benzodiazepines, antipsychotics, gabapentinoids, antidepressants, Z-drugs or opioids. Overdose was defined according to International Classification of Diseases codes from the HES-APC data set. Negative binomial regression models were used to estimate weighted rate ratios (wRR) for NFO during co-prescription of OAT and benzodiazepines, antipsychotics, gabapentinoids, antidepressants, Z-drugs or opioids with periods of exclusive OAT usage. FINDINGS Among 20 898 patients observed over 83 856 person-years, we found an elevated overdose risk that resulted in hospital admission during co-prescription of OAT with benzodiazepines [wRR: 1.45; 95% confidence interval (CI) = 1.26-1.67], gabapentinoids (wRR = 2.22; 95% CI = 1.77-2.79), Z-drugs (wRR = 1.60; 95% CI = 1.31-1.96), antipsychotics (wRR = 1.85; 95% CI = 1.53-2.25) and opioids (wRR = 1.28; 95% CI = 1.02-1.60). The risk ratio for antidepressant co-prescriptions was below unity (wRR = 0.90; 95% CI = 0.79-1.02) but this result was not statistically significant. CONCLUSION Elevated risk of non-fatal overdose among opioid agonist treatment recipients is associated with concurrent use of medication prescribed for other reasons.
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Affiliation(s)
- Eleni Domzaridou
- National Institute for Health and Care Research Greater Manchester Patient Safety Translational Research Centre, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
- Centre for Pharmacoepidemiology and Drug Safety, Division of Pharmacy and Optometry, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Matthew J Carr
- National Institute for Health and Care Research Greater Manchester Patient Safety Translational Research Centre, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
- Centre for Pharmacoepidemiology and Drug Safety, Division of Pharmacy and Optometry, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Tim Millar
- Centre for Mental Health and Safety, Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Roger T Webb
- National Institute for Health and Care Research Greater Manchester Patient Safety Translational Research Centre, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
- Centre for Mental Health and Safety, Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Darren M Ashcroft
- National Institute for Health and Care Research Greater Manchester Patient Safety Translational Research Centre, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
- Centre for Pharmacoepidemiology and Drug Safety, Division of Pharmacy and Optometry, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
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Brett J, Gillies MB, Buckley NA, Pearson S, Zoega H. Patterns of suboptimal antipsychotic use and misuse in Australia: What can routinely collected data tell us? Br J Clin Pharmacol 2023; 89:3411-3420. [PMID: 37309058 PMCID: PMC10953398 DOI: 10.1111/bcp.15821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 06/01/2023] [Accepted: 06/05/2023] [Indexed: 06/14/2023] Open
Abstract
AIMS There are increasing concerns about harms related to suboptimal antipsychotic use. Here we describe recent population-based trends in antipsychotic use and harms in Australia and identify population groups exhibiting patterns of use likely to contribute to these harms. METHODS Using population-based data from the Australian Pharmaceutical Benefits Scheme (2015-2020), poisoning calls to the New South Wales (NSW) Poisons Information Centre (2015-2020) and poisoning deaths in all coronial records (2005-2018) in Australia, we measured trends in the prevalence of antipsychotic use and related deaths and poisonings. We applied latent class analyses to identify patterns of antipsychotic use that may contribute to harms. RESULTS Quetiapine and olanzapine had the highest prevalence of use between 2015 and 2020. Noteworthy trends included increases of 9.1% and 30.8% in quetiapine use and poisonings, while olanzapine use decreased by 4.5% but poisonings increased by 32.7%. Quetiapine and olanzapine poisonings and related deaths had the highest rates of co-ingestion of opioids, benzodiazepines and pregabalin compared to other antipsychotics. We identified six distinct population groups using antipsychotics: (i) ongoing high-dose use with sedatives (8%), (ii) ongoing use (42%), (iii) ongoing use with analgesics and sedatives (11%), (iv) long-term low-dose use (9%), (v) sporadic use (20%) and (vi) sporadic use with analgesics (10%). CONCLUSION Ongoing potentially suboptimal antipsychotic use and associated harms highlight the need to monitor such patterns of use, for example through prescription monitoring systems.
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Affiliation(s)
- Jonathan Brett
- Medicines Intelligence in Health, School of Population Health, Faculty of Medicine and HealthUNSW SydneySydneyNew South WalesAustralia
- Clinical Therapeutics DepartmentSt Vincent's HospitalSydneyNSWAustralia
- New South Wales Poisons Information CentreWestmead Children's HospitalSydneyNSWAustralia
| | - Malcolm B. Gillies
- Medicines Intelligence in Health, School of Population Health, Faculty of Medicine and HealthUNSW SydneySydneyNew South WalesAustralia
| | - Nicholas A. Buckley
- New South Wales Poisons Information CentreWestmead Children's HospitalSydneyNSWAustralia
- Clinical Pharmacology and Toxicology Research Group, Biomedical Informatics and Digital HealthUniversity of SydneySydneyAustralia
| | - Sallie‐Anne Pearson
- Medicines Intelligence in Health, School of Population Health, Faculty of Medicine and HealthUNSW SydneySydneyNew South WalesAustralia
| | - Helga Zoega
- Medicines Intelligence in Health, School of Population Health, Faculty of Medicine and HealthUNSW SydneySydneyNew South WalesAustralia
- Centre of Public Health Sciences, Faculty of MedicineUniversity of IcelandReykjavíkIceland
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Beyene K, Fahmy H, Chan AHY, Tomlin A, Cheung G. Predictors of persistent opioid use in non-cancer older adults: a retrospective cohort study. Age Ageing 2023; 52:afad167. [PMID: 37659093 DOI: 10.1093/ageing/afad167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 07/19/2023] [Indexed: 09/04/2023] Open
Abstract
BACKGROUND Long-term opioid use and associated adverse outcomes have increased dramatically in recent years. Limited research is available on long-term opioid use in older adults. OBJECTIVE We aimed to determine the incidence and predictors of long-term or persistent opioid use (POU) amongst opioid-naïve older adults without a cancer diagnosis. METHODS This was a retrospective cohort study using five national administrative healthcare databases in New Zealand. We included all opioid-naïve older adults (≥65 years) who were initiated on opioid therapy between January 2013 and June 2018. The outcome of interest was POU, defined as having continuously filled ≥1 opioid prescription within 91-180 days after the index opioid prescription. Multivariable logistic regression was used to examine the predictors of POU. RESULTS The final sample included 268,857 opioid-naïve older adults; of these, 5,849(2.2%) developed POU. Several predictors of POU were identified. The use of fentanyl (adjusted odds ratio (AOR) = 3.61; 95% confidence interval (CI) 2.63-4.95), slow-release opioids (AOR = 3.02; 95%CI 2.78-3.29), strong opioids (AOR = 2.03; 95%CI 1.55-2.65), Charlson Comorbidity Score ≥ 3 (AOR = 2.09; 95% CI 1.78-2.46), history of substance abuse (AOR = 1.52; 95%CI 1.35-1.72), living in most socioeconomically deprived areas (AOR = 1.40; 95%CI 1.27-1.54), and anti-epileptics (AOR = 2.07; 95%CI 1.89-2.26), non-opioid analgesics (AOR = 2.05; 95%CI 1.89-2.21), antipsychotics (AOR = 1.96; 95%CI 1.78-2.17) or antidepressants (AOR = 1.50; 95%CI 1.41-1.59) medication use were the strongest predictors of POU. CONCLUSION A significant proportion of patients developed POU, and several factors were associated with POU. The findings will enable healthcare providers and policymakers to target early interventions to prevent POU and related adverse events.
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Affiliation(s)
- Kebede Beyene
- Department of Pharmaceutical and Administrative Sciences, University of Health Sciences and Pharmacy, St. Louis, MO, USA
- School of Pharmacy, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand
| | - Hoda Fahmy
- School of Pharmacy, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand
| | - Amy Hai Yan Chan
- School of Pharmacy, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand
| | - Andrew Tomlin
- School of Pharmacy, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand
| | - Gary Cheung
- Department of Psychological Medicine, School of Medicine, The University of Auckland, Auckland, New Zealand
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Wu X, Zhang L, Huang H, Huang L, Lu X, Wang Z, Xiao J. Signal mining and analysis for central nervous system adverse events due to taking oxycodone based on FAERS database. ZHONG NAN DA XUE XUE BAO. YI XUE BAN = JOURNAL OF CENTRAL SOUTH UNIVERSITY. MEDICAL SCIENCES 2023; 48:422-434. [PMID: 37164926 PMCID: PMC10930086 DOI: 10.11817/j.issn.1672-7347.2023.220304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Indexed: 05/12/2023]
Abstract
OBJECTIVES Central nervous system adverse events (AEs) occur when oxycodone is used in combination with benzodiazepines, antidepressants and anticonvulsants. There have been no reports of central nervous system AEs with oxycodone alone or in combination with oxycodone. Based on USA Food and Drug Administration Adverse Event Reporting System (FAERS) data, this study aims to explore the risk signals of central nervous system AEs with oxycodone alone or in combination with benzodiazepines, antidepressants and anticonvulsants, and to provide a reference for the safe and rational use of this drug. METHODS Extracted AEs data from the FAERS for oxycodone alone and in combination with benzodiazepines, antidepressants, and anticonvulsants from Q1 2004 to Q2 2021. The risk signal mining analysis of AEs was performed using the proportional imbalance method and Bayesian method. Number of reports ≥3 and lower 95% CI limit of reporting odds ratio (ROR)>1; number of reports ≥3, proportional reporting ratio (PRR)≥2 and χ2≥4; lower information components (IC) lower 95% CI limit (IC025)>0; empirical Bayes geometric mean (EBGM) lower 95% CI limit (EBGM05)>2, and N>0 were defined as positive signals. RESULTS A total of 5 793 reports of central nervous system AEs with oxycodone alone were tapped, and 366, 622, and 740 reports of combined benzodiazepines, antidepressants, and anticonvulsants, respectively. Consumers and physicians were the main reporting population. The age distribution of oxycodone alone was mainly from 61 to 80 years old. The age distribution of oxycodone in combination with related drugs was mainly from 46 to 60 years old. The risk of AEs was greater in women than in men, and the United States was the predominant reporting country. Oxycodone alone was strongly associated with myoclonus [ROR=2.92, 95% CI 2.28 to 3.76); PRR=2.92, χ2(77.49); IC=1.52, IC025(0.65); EBGM=2.89, EBGM05(2.33)], delirium [ROR=4.69, 95% CI 4.24 to 5.21; PRR=4.66, χ2(1 052.64); IC=2.17, IC025(1.81); EBGM=4.50, EBGM05 (4.13)], mental disorder [ROR=2.95, 95% CI 2.53 to 3.44; PRR=2.94, χ2(206.93); IC=1.56, IC025(0.96); EBGM=2.95, EBGM05(2.58)], and acute central respiratory depression [ROR=2.87, 95% CI 2.68 to 3.08); PRR=2.82, χ2(971.62); IC=1.52, IC025(1.33), EBGM=2.87, EBGM05 (2.76)]. Combination of benzodiazepines was most strongly associated with mental disorder [ROR=10.08, 95% CI 9.38 to 10.78; PRR=9.90, χ2(64.06); IC=3.33, IC025 (1.65); EBGM=10.08, EBGM05(5.61)], and tremor [ROR=3.09, 95% CI 2.76 to 3.42); PRR=3.08, χ2(48.93); IC=1.63, IC025 (1.17); EBGM=3.09, EBGM05(2.34)]. Combination of antidepressants was most strongly associated with delirium [ROR=13.23, 95% CI 12.23 to 14.23; PRR=12.87, χ2(43.86); IC=3.69, IC025(1.36); EBGM=12.23, EBGM05 (5.32)] and somnolence [ROR=6.74, 95% CI 6.15 to 7.33); PRR=6.73, χ2(53.42); IC=2.75, IC025(1.52); EBGM=6.73, EBGM05(4.10)]. Combination of anticonvulsants was most strongly associated with myoclonus [ROR=17.89, 95% CI 17.46 to 18.32; PRR=17.72, χ2(971.39); IC=4.16, IC025(2.70); EBGM=17.89, EBGM05(12.46)] and delirium [ROR=4.86, 95% CI 4.45 to 5.27); PRR=4.82, χ2(69.49); IC=2.28, IC025 (1.51); EBGM=4.86, EBGM05(3.44)]. CONCLUSIONS Based on pharmacovigilance studies of the FAERS database, clinical medication monitoring of oxycodone alone and in combination with benzodiazepines, antidepressants, and anticonvulsants should be strengthened to be alert to the occurrence of central nervous system-related AEs.
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Affiliation(s)
- Xiangping Wu
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008.
- School of Pharmacy, Dali University, Dali Yunnan, 671000.
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital; Laboratory for Rational and Safe Use of Elderly, Changsha 410008.
| | - Lu Zhang
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital; Laboratory for Rational and Safe Use of Elderly, Changsha 410008
| | - Hangxing Huang
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008
- School of Pharmacy, Dali University, Dali Yunnan, 671000
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital; Laboratory for Rational and Safe Use of Elderly, Changsha 410008
| | - Ling Huang
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital; Laboratory for Rational and Safe Use of Elderly, Changsha 410008
| | - Xikui Lu
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008
- School of Pharmacy, Dali University, Dali Yunnan, 671000
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital; Laboratory for Rational and Safe Use of Elderly, Changsha 410008
| | - Zhenting Wang
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008
- School of Pharmacy, Dali University, Dali Yunnan, 671000
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital; Laboratory for Rational and Safe Use of Elderly, Changsha 410008
| | - Jian Xiao
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008.
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital; Laboratory for Rational and Safe Use of Elderly, Changsha 410008.
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Almodóvar AS, Nahata MC. High opioid doses, naloxone, and central nervous system active medications received by Medicare-enrolled adults. J Am Geriatr Soc 2023; 71:98-108. [PMID: 36289563 PMCID: PMC9870936 DOI: 10.1111/jgs.18102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 09/02/2022] [Accepted: 09/25/2022] [Indexed: 01/26/2023]
Abstract
BACKGROUND A limited number of studies have analyzed prescribing among Medicare-enrolled adults at risk for opioid overdoses. The objectives of this study were to evaluate prescribing for naloxone and central nervous system (CNS) active medications and to determine the relationships of patient characteristics with exposure to these medications. METHODS This was a retrospective cross-sectional analysis of a Medicare-enrolled medication therapy management eligible cohort. Patients were stratified into two cohorts, individuals with a mean daily morphine milligram equivalent (MME) dose <50 and individuals with MME ≥50. Medications assessed included benzodiazepines, skeletal muscle relaxants (SMR), hypnotics, gabapentanoids, selective-serotonin reuptake inhibitors (SSRI), serotonin-norepinephrine reuptake inhibitors (SNRI), tricyclic antidepressants (TCA), antipsychotics, barbiturates, other antiepileptics, hydroxyzine, and naloxone. Chi-square with odds ratios and logistic regressions determined the relationships of medications and patient characteristics with mean daily MME ≥50. Relationship between medications and opioid dose was adjusted for age and sex. RESULTS There were 3452 patients with a daily MME <50 and 1116 with a daily MME ≥50. After adjusting for age and sex, patients with a daily MME ≥50 were more likely to be prescribed hypnotics (OR: 1.41, 95% CI 1.17-1.70), SNRIs (OR: 1.39, 95% CI 1.17-1.64), and naloxone (OR: 3.21, 95% CI 2.49-4.12) (p < 0.001). Nine percent of eligible patients received naloxone. Age groups of persons <85 years of age had 1.58-4.04 (p ≤ 0.004) times the odds of being prescribed a mean daily MME ≥50. CONCLUSION Nearly one-fourth of patients were prescribed a mean daily opioid therapy of MME ≥50. These patients were more likely to be prescribed hypnotics, SNRIs, and naloxone. Patients receiving chronic high-dose opioid therapy were more likely to be in age groups of persons <85 years. Naloxone may be underprescribed among eligible adults. Targeted medication services may ensure optimal prescribing among Medicare patients with chronic opioid therapies.
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Affiliation(s)
- Armando Silva Almodóvar
- Institute of Therapeutic Innovations and Outcomes (ITIO), The Ohio State University College of Pharmacy, Columbus, OH
| | - Milap C Nahata
- Institute of Therapeutic Innovations and Outcomes (ITIO), The Ohio State University College of Pharmacy, Columbus, OH.,Institute of Therapeutic Innovations and Outcomes (ITIO), The Ohio State University College of Medicine, Columbus, OH
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Brandt NJ. Opioid Use Disorder and Older Adults: Navigating Treatment Options. J Gerontol Nurs 2022; 48:4-9. [PMID: 35771070 DOI: 10.3928/00989134-20220607-01] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
The intent of the current article is to highlight via a case vignette challenges faced when managing pain across care transitions in an older adult with multiple comorbidities, including, but not limited to, opioid use disorder (OUD). This case will highlight the role of different medications for OUD, namely buprenorphine/naloxone, methadone, and naltrexone. Furthermore, the case illustrates medication-related considerations in addition to action steps that are needed when working with older adults. [Journal of Gerontological Nursing, 48(7), 4-9.].
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Recognizing and Managing Undisclosed Psychiatric Comorbidity. CANADIAN JOURNAL OF ADDICTION 2021. [DOI: 10.1097/cxa.0000000000000130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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