Elderly patients with diabetes are at high risk of polypharmacy because of multiple coexisting diseases and syndromes. Polypharmacy increases the risk of drug–drug and drug–disease interactions in these patients, who may already have age-related sensory and cognitive deficits; such deficits may delay timely communication of early symptoms of adverse drug events. Several glucagon-like peptide-1 receptor agonists (GLP-1 RAs) have been approved for diabetes: liraglutide, exenatide, lixisenatide, dulagluatide, semaglutide, and albiglutide. Some are also approved for treatment of obesity. The current review of literature along with clinical case discussion provides evidence supporting GLP-1 RAs as diabetes medications for polypharmacy reduction in older diabetes patients because of their multiple pleiotropic effects on comorbidities (e.g. hyperlipidemia, hypertension, and fatty liver) and syndromes (e.g. osteoporosis and sleep apnea) that commonly co-occur with diabetes. Using one medication (in this case, GLP-1 RAs) to address multiple conditions may help reduce costs, medication burden, adverse drug events, and medication nonadherence.

Background When older adult patients with hip fracture (HFx) have unplanned hospital readmissions within 30 days of discharge, it doubles their 1-year mortality, resulting in substantial personal and financial burdens. Although such unplanned readmissions are predominantly caused by reasons not related to HFx surgery, few studies have focused on how pre-existing high-risk comorbidities co-occur within and across subgroups of patients with HFx. Objective This study aims to use a combination of supervised and unsupervised visual analytical methods to (1) obtain an integrated understanding of comorbidity risk, comorbidity co-occurrence, and patient subgroups, and (2) enable a team of clinical and methodological stakeholders to infer the processes that precipitate unplanned hospital readmission, with the goal of designing targeted interventions. Methods We extracted a training data set consisting of 16,886 patients (8443 readmitted patients with HFx and 8443 matched controls) and a replication data set consisting of 16,222 patients (8111 readmitted patients with HFx and 8111 matched controls) from the 2010 and 2009 Medicare database, respectively. The analyses consisted of a supervised combinatorial analysis to identify and replicate combinations of comorbidities that conferred significant risk for readmission, an unsupervised bipartite network analysis to identify and replicate how high-risk comorbidity combinations co-occur across readmitted patients with HFx, and an integrated visualization and analysis of comorbidity risk, comorbidity co-occurrence, and patient subgroups to enable clinician stakeholders to infer the processes that precipitate readmission in patient subgroups and to propose targeted interventions. Results The analyses helped to identify (1) 11 comorbidity combinations that conferred significantly higher risk (ranging from P<.001 to P=.01) for a 30-day readmission, (2) 7 biclusters of patients and comorbidities with a significant bicluster modularity (P<.001; Medicare=0.440; random mean 0.383 [0.002]), indicating strong heterogeneity in the comorbidity profiles of readmitted patients, and (3) inter- and intracluster risk associations, which enabled clinician stakeholders to infer the processes involved in the exacerbation of specific combinations of comorbidities leading to readmission in patient subgroups. Conclusions The integrated analysis of risk, co-occurrence, and patient subgroups enabled the inference of processes that precipitate readmission, leading to a comorbidity exacerbation risk model for readmission after HFx. These results have direct implications for (1) the management of comorbidities targeted at high-risk subgroups of patients with the goal of pre-emptively reducing their risk of readmission and (2) the development of more accurate risk prediction models that incorporate information about patient subgroups.

ObjectiveOpioid and benzodiazepine co-prescribing is associated with a substantial increase in opioid overdose deaths. In this study, we examine the prescribing trends of substitutes of opioids and benzodiazepines alone or in combination, compared with opioids and benzodiazepines.DesignRetrospective cohort study.SettingData were collected using a 20% national sample of Medicare beneficiaries from 2013 to 2018.Participants4.1–4.3 million enrollees each year from 2013 to 2018.InterventionNone.Primary outcomeWe employ a generalised linear mixed models to calculate ORs for opioid use, benzodiazepine or Z-drug (benzos/Z-drugs) use, opioid/benzos/Z-drugs 30-day use, gabapentinoid use and (selective serotonin reuptake inhibitors (SSRI) and serotonin norepinephrine reuptake inhibitors (SNRIs)) use, adjusted for the repeated measure of patient. We then created two models to calculate the ORs for each year and comparing to 2013.ResultsOpioid and benzos/Z-drugs use decreased by 2018 (aOR 0.626; 95% CI 0.622 to 0.630) comparing to 2013. We demonstrate a 36.3% and 9.9% increase rate of gabapentinoid and SSRI/SNRI use, respectively. Furthermore, combined gabapentinoid and SSRI/SNRI use increased in 2018 (aOR 1.422; 95% CI 1.412 to 1.431).ConclusionLittle is known about the prescribing pattern and trend of opioid and benzodiazepine alternatives as analgesics. There is a modest shift from prescribing opioid and benzos/Z-drugs (alone or in combination) towards prescribing non-opioid analgesics—gabapentinoids with and without non-benzos/Z-drugs that are indicated for anxiety. It is unclear if this trend towards opioid/benzos/Z-drugs alternatives is associated with fewer drug overdose death, better control of pain and comorbid anxiety, and improved quality of life.

ObjectiveExamine the association between the co-prescribing of opioids, benzodiazepines, gabapentinoids (pregabalin and gabapentin) and selective serotonin reuptake inhibitors/serotonin and norepinephrine reuptake inhibitors (SSRI/SNRIs) in different combinations and the risk of falls and fractures.DesignRetrospective cohort study from 2015 to 2018.SettingMedicare enrolment and claims data.ParticipantsMedicare beneficiaries with both chronic pain and anxiety disorders in 2016 with continuous enrolments in Parts A and B from 2015 to 2016 who were prescribed any combination of opioid, benzodiazepine, gabapentinoid and SSRI/SNRI in 2017 for ≥7 days, as documented in their Medicare Part D coverage.InterventionsAny combination of use of seven drug regimens (benzodiazepine +opioid; benzodiazepine +gabapentinoid; benzodiazepine +SSRI/SNRI; opioid +gabapentinoid; opioid +SSRI/SNRI; gabapentinoid +SSRI/SNRI; ≥3 drug classes).Main outcomesFirst event of fall and the first event of fracture after the index date, which was the first day of combination drug use that lasted ≥7 days in 2017.ResultsA total of 47 964 patients (mean [SD] age, 75.9 [7.1]; 78.0% woman) with diagnoses of both chronic pain and anxiety were studied. The median (Q1–Q3) duration of drug combination use was 26 (14-30) days. After adjusting for demographic characteristics, chronic conditions and history of hospitalisation and fall or fracture, the co-prescribing of ≥3 drugs (adjusted HR [aHR], 1.38; 95% CI 1.14 to 1.67) and opioid plus gabapentinoid (aHR, 1.18; 95% CI 1.02 to 1.37) were associated with a high fall risk, compared with benzodiazepineplus opioid co-prescribing, findings consistent with the secondary analysis using inverse probability of treatment weighting with propensity scores. The co-prescribing of benzodiazepine plus gabapentinoid (aHR, 0.76; 95% CI 0.59 to 0.98) was associated with lower fracture risk compared with the co-prescribing of benzodiazepine plus opioid, though this finding was not robust.ConclusionsOur findings add to comparative toxicity research on different combinations of gabapentinoids and serotonergic agents commonly prescribed with or as substitutes for opioids and benzodiazepines in patients with co-occurring chronic pain and anxiety.

Background and objectives U.S. Latinos are a heterogeneous population with unique characteristics related to individual-level socioeconomic and contextual factors based on nativity status and country of origin. Population aging and greater public awareness of dementia may contribute to an increasing prevalence of self-reported cognitive impairment. However, population-level trends in self-reported cognitive impairment among Latinos are unclear and it is unknown whether there are differences among Latino subgroups. Thus, this study aims to examine heterogeneity in self-reported cognitive impairment among older U.S. Latino subgroups. Research design and methods We used data from the 1997-2018 National Health Interview Survey to document age-specific patterns in self-reported cognitive impairment among U.S.-born Mexican, foreign-born Mexican, island-born Puerto Rican, foreign-born Cuban, and U.S.-born non-Latino Whites aged 60 and older. We estimated hierarchical age period–cohort cross-classified random-effects (HAPC-CCREM) models to isolate age patterns in self-reported cognitive impairment across disaggregated Latino subgroups and U.S.-born non-Latino Whites. Results The overall prevalence of self-reported cognitive impairment increased from 6.0% in 1997 to 7.1% in 2018. This increase was evident among U.S.-born non-Latino Whites and U.S.-born and foreign-born Mexicans but not other Latino subgroups. Fully-adjusted HAPC-CCREM estimates indicated that Latinos were more likely to self-report cognitive impairment than U.S-born non-Latino Whites (b=0.371, p&lt;0.001). When disaggregated by Latino subgroup, the difference in the likelihood for self-reported cognitive impairment compared to U.S.-born non-Latino Whites was greatest for island-born Puerto Ricans (b=0.598, p&lt;0.001) and smallest for foreign-born Cubans (b=0.131, p&gt;0.05). Discussion and implications We found evidence of considerable heterogeneity in the age-patterns of self-reported cognitive impairment among U.S. Latino subgroups. We also detected large differences in the likelihood for self-reported cognitive impairment between U.S. Latino subgroups compared to U.S.-born non-Latino Whites. These results underscore the importance of differentiating between unique Latino subpopulations when studying population-level trends in cognitive function.