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Purpose: To describe medication adherence and persistence of HIV PrEP overall and compare between sex and age groups of commercially insured individuals in the United States. Methods: We conducted a national retrospective cohort study of the Merative MarketScan Claims Database from 2011 to 2019 to describe adherence and persistence of PrEP overall and compared between sex and age groups. High adherence was defined as ≥80% of proportion of days covered and persistence was measured in days from initiation to the first day of a 60‐day treatment gap. Results: A total of 29 689 new PrEP users identified. Overall adherence was high (81.9%; 95% confidence interval [CI]: 81.5%–82.3%). Females were more adherent than males (adjusted odds ratio [aOR] 1.87; 95% CI: 1.50–2.34), while those ≥45‐years were less adherent than individuals <45‐years (aOR 0.87: 95% CI: 0.81–0.93). More than half of individuals discontinued therapy within the first year (median 238.0 days; interquartile range 99.0–507.0 days). Females were less persistent than males (hazard ratio [HR] 1.49; 95% CI: 1.34–1.65), and people ≥45‐years old were more persistent (i.e., lower risk of discontinuation) than those <45‐years (HR 0.43; 95% CI: 0.33–0.55). Conclusions: These findings show adherence to daily PrEP is high among commercially insured individuals but the majority still discontinue in the first year. Future research should investigate what factors influence PrEP discontinuation among this population and ways to reduce barriers to therapy maintenance to ensure the population‐level benefits of PrEP treatment. [ABSTRACT FROM AUTHOR]

OBJECTIVES: Adherence to medications is important for the management of chronic diseases. Although the proportion of days covered (PDC) is a common metric for measuring adherence, it may be insufficient to distinguish relevant differences in medication-taking behavior. Group-based trajectory models (GBTMs) have been used to better represent adherence over time. This study aims to examine adherence patterns 1 year after initiation among users of sodium-glucose cotransporter 2 (SGLT2) inhibitors using GBTMs and evaluate the ability of baseline characteristics to predict adherence trajectory. STUDY DESIGN: SGLT2 inhibitor new-user cohort study from 2014 to 2018. METHODS: We calculated 12-month PDC and categorized patients with PDC of 80% or greater as adherent. We performed multivariable logistic regression on adherence status controlling for baseline covariates. GBTMs were fit to identify adherence patterns 12 months following SGLT2 inhibitor initiation. Five multinomial logistic regression models including different subsets of predictors were used to predict adherence trajectory group assignment. RESULTS: In a cohort of 228,363 SGLT2 inhibitor users, the mean PDC was 57%, with 36% of the cohort being adherent. Overall, women and patients with anxiety or depression were less likely to be adherent. Six patterns of SGLT2 inhibitor adherence were identified with GBTMs: 1 fill (PDC = 0.08), early discontinuation (PDC = 0.22), consistently low adherence (PDC = 0.35), moderate adherence (PDC = 0.48), high adherence (PDC = 0.79), and near-perfect adherence (PDC = 0.95). All prediction models showed poor predictive accuracy (0.35). CONCLUSIONS: We found wide variation in adherence patterns among SGLT2 inhibitor users in a national cohort. Predictors from a health care claims database were unable to accurately predict adherence trajectory. [ABSTRACT FROM AUTHOR]

Purpose To report historical patterns of pharmaceutical expenditures, to identify factors that may influence future spending, and to predict growth in drug spending in 2024 in the United States, with a focus on the nonfederal hospital and clinic sectors. Methods Historical patterns were assessed by examining data on drug purchases from manufacturers using the IQVIA National Sales Perspectives database. Factors that may influence drug spending in hospitals and clinics in 2024 were reviewed—including new drug approvals, patent expirations, and potential new policies or legislation. Focused analyses were conducted for biosimilars, cancer drugs, endocrine drugs, generics, and specialty drugs. For nonfederal hospitals, clinics, and overall (all sectors), estimates of growth of pharmaceutical expenditures in 2024 were based on a combination of quantitative analyses and expert opinion. Results In 2023, overall pharmaceutical expenditures in the US grew 13.6% compared to 2022, for a total of $722.5 billion. Utilization (a 6.5% increase), new drugs (a 4.2% increase) and price (a 2.9% increase) drove this increase. Semaglutide was the top drug in 2023, followed by adalimumab and apixaban. Drug expenditures were $37.1 billion (a 1.1% decrease) and $135.7 billion (a 15.0% increase) in nonfederal hospitals and clinics, respectively. In clinics, increased utilization drove growth, with a small impact from price and new products. In nonfederal hospitals, a drop in utilization led the decrease in expenditures, with price and new drugs modestly contributing to growth in spending. Several new drugs that will influence spending are expected to be approved in 2024. Specialty, endocrine, and cancer drugs will continue to drive expenditures. Conclusion For 2024, we expect overall prescription drug spending to rise by 10.0% to 12.0%, whereas in clinics and hospitals we anticipate an 11.0% to 13.0% increase and a 0% to 2.0% increase, respectively, compared to 2023. These national estimates of future pharmaceutical expenditure growth may not be representative of any health system because of the myriad of local factors that influence actual spending. [ABSTRACT FROM AUTHOR]

Objectives: Adherence to medications is important for the management of chronic diseases. Although the proportion of days covered (PDC) is a common metric for measuring adherence, it may be insufficient to distinguish relevant differences in medication-taking behavior. Group-based trajectory models (GBTMs) have been used to better represent adherence over time. This study aims to examine adherence patterns 1 year after initiation among users of sodium-glucose cotransporter 2 (SGLT2) inhibitors using GBTMs and evaluate the ability of baseline characteristics to predict adherence trajectory., Study Design: SGLT2 inhibitor new-user cohort study from 2014 to 2018., Methods: We calculated 12-month PDC and categorized patients with PDC of 80% or greater as adherent. We performed multivariable logistic regression on adherence status controlling for baseline covariates. GBTMs were fit to identify adherence patterns 12 months following SGLT2 inhibitor initiation. Five multinomial logistic regression models including different subsets of predictors were used to predict adherence trajectory group assignment., Results: In a cohort of 228,363 SGLT2 inhibitor users, the mean PDC was 57%, with 36% of the cohort being adherent. Overall, women and patients with anxiety or depression were less likely to be adherent. Six patterns of SGLT2 inhibitor adherence were identified with GBTMs: 1 fill (PDC = 0.08), early discontinuation (PDC = 0.22), consistently low adherence (PDC = 0.35), moderate adherence (PDC = 0.48), high adherence (PDC = 0.79), and near-perfect adherence (PDC = 0.95). All prediction models showed poor predictive accuracy (0.35)., Conclusions: We found wide variation in adherence patterns among SGLT2 inhibitor users in a national cohort. Predictors from a health care claims database were unable to accurately predict adherence trajectory.