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      The recent trend of twin epidemic in the United States: a 10-year longitudinal cohort study of co-prescriptions of opioids and stimulants

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          Summary

          Background

          In recent years, the use of central nervous system stimulant medications has increased among the population already using opioids, referred to as a “twin epidemic.” There is an increasing concern about its harmful outcomes in large populations. However, very few studies examined the co-prescription pattern of these two drug categories over a long period, and there is currently no clear restriction on stimulant prescriptions among patients under opioid treatment in the United States. The objectives of our study were to identify opioid prescription dosage time-dependent patterns and patient subgroups representing distinct trajectories on a national level in the recent 10 years, and to further investigate longitudinal associations between stimulant and opioid prescriptions and the impact of stimulant prescriptions on opioid dosage patterns.

          Methods

          We obtained patient records from MarketScan, one of the largest clinical databases of health insurance in the United States. 10 years (2012–2021) of prescription records and related patient profiles, who received at least two independent opioid prescriptions, were utilized for developing a group-based opioid dose trajectory model.

          Findings

          From an initial cohort including 22 million patients with 96 million opioid prescriptions, we developed a study cohort of 2,895,960 patients with a mean age of 43.9 years (standard deviation [SD] 13.0), of whom 1,244,077 (43%) were male. Significant geographical variations in opioid prescription frequency and dosage among four U.S. regions were observed. The trajectory model identified five distinct opioid dose groups. Stimulant prescription before the initial opioid prescription was positively associated with escalating opioid doses (odds ratio [OR]: 7.58; 95% confidence intervals [CI] 6.14–9.35, opioid dose increasing group compared to the decreasing group). Stimulant co-prescriptions were also associated with increasing opioid doses (OR: 1.73; 95% CI 1.40–2.14) and were identified in patients with a higher prevalence of opioid use disorder.

          Interpretation

          During the recent 10 years, stimulant prescription is positively associated with escalating opioid prescription activities in U.S. healthcare systems, suggesting co-prescriptions of these two types of drugs are an important contributing factor for a national-level twin epidemic. Healthcare leaders and policymakers should pay more attention to this issue and its potential harms.

          Funding

          doi 10.13039/100000057, National Institute of General Medical Sciences; , doi 10.13039/100000026, National Institute on Drug Abuse; , and doi 10.13039/100000001, National Science Foundation; .

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          Most cited references30

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          Trajectory Modelling Techniques Useful to Epidemiological Research: A Comparative Narrative Review of Approaches

          Abstract Trajectory modelling techniques have been developed to determine subgroups within a given population and are increasingly used to better understand intra- and inter-individual variability in health outcome patterns over time. The objectives of this narrative review are to explore various trajectory modelling approaches useful to epidemiological research and give an overview of their applications and differences. Guidance for reporting on the results of trajectory modelling is also covered. Trajectory modelling techniques reviewed include latent class modelling approaches, ie, growth mixture modelling (GMM), group-based trajectory modelling (GBTM), latent class analysis (LCA), and latent transition analysis (LTA). A parallel is drawn to other individual-centered statistical approaches such as cluster analysis (CA) and sequence analysis (SA). Depending on the research question and type of data, a number of approaches can be used for trajectory modelling of health outcomes measured in longitudinal studies. However, the various terms to designate latent class modelling approaches (GMM, GBTM, LTA, LCA) are used inconsistently and often interchangeably in the available scientific literature. Improved consistency in the terminology and reporting guidelines have the potential to increase researchers’ efficiency when it comes to choosing the most appropriate technique that best suits their research questions.
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            Polysubstance use in the U.S. opioid crisis

            Interventions to address the U.S. opioid crisis primarily target opioid use, misuse, and addiction, but because the opioid crisis includes multiple substances, the opioid specificity of interventions may limit their ability to address the broader problem of polysubstance use. Overlap of opioids with other substances ranges from shifts among the substances used across the lifespan to simultaneous co-use of substances that span similar and disparate pharmacological categories. Evidence suggests that nonmedical opioid users quite commonly use other drugs, and this polysubstance use contributes to increasing morbidity and mortality. Reasons for adding other substances to opioids include enhancement of the high (additive or synergistic reward), compensation for undesired effects of one drug by taking another, compensation for negative internal states, or a common predisposition that is related to all substance consumption. But consumption of multiple substances may itself have unique effects. To achieve the maximum benefit, addressing the overlap of opioids with multiple other substances is needed across the spectrum of prevention and treatment interventions, overdose reversal, public health surveillance, and research. By addressing the multiple patterns of consumption and the reasons that people mix opioids with other substances, interventions and research may be enhanced.
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              Use of Commercial Claims Data for Evaluating Trends in Lyme Disease Diagnoses, United States, 2010–2018

              We evaluated MarketScan, a large commercial insurance claims database, for its potential use as a stable and consistent source of information on Lyme disease diagnoses in the United States. The age, sex, and geographic composition of the enrolled population during 2010–2018 remained proportionally stable, despite fluctuations in the number of enrollees. Annual incidence of Lyme disease diagnoses per 100,000 enrollees ranged from 49 to 88, ≈6–8 times higher than that observed for cases reported through notifiable disease surveillance. Age and sex distributions among Lyme disease diagnoses in MarketScan were similar to those of cases reported through surveillance, but proportionally more diagnoses occurred outside of peak summer months, among female enrollees, and outside high-incidence states. Misdiagnoses, particularly in low-incidence states, may account for some of the observed epidemiologic differences. Commercial claims provide a stable data source to monitor trends in Lyme disease diagnoses, but certain important characteristics warrant further investigation.

                Author and article information

                Contributors
                Journal
                Lancet Reg Health Am
                Lancet Reg Health Am
                Lancet Regional Health - Americas
                Elsevier
                2667-193X
                17 February 2025
                April 2025
                17 February 2025
                : 44
                : 101030
                Affiliations
                [a ]Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA
                [b ]Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA
                [c ]Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
                [d ]Department of Anesthesiology, College of Medicine, The Ohio State University, Columbus, OH, USA
                Author notes
                []Corresponding author. Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, USA. wsong@ 123456bwh.harvard.edu
                [∗∗ ]Corresponding author. Department of Biomedical Informatics, Department of Computer Science and Engineering, The Ohio State University, USA. zhang.10631@ 123456osu.edu
                [e]

                These authors contributed equally to this work.

                Article
                S2667-193X(25)00040-7 101030
                10.1016/j.lana.2025.101030
                11876894
                40040818
                ea279bcf-62a7-4949-bb60-aeb6c341bec1
                © 2025 The Author(s)

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 24 July 2024
                : 24 January 2025
                : 3 February 2025
                Categories
                Articles

                twin epidemic,opioid crisis,trajectory analysis,co-prescription pattern,drug overdose,patient safety,stimulant prescription

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