Cost-effectiveness analysis of a digital diabetes-prevention programme versus an in-person diabetes-prevention programme in people with prediabetes in the United States.

AIM: To assess the cost-effectiveness of a digital diabetes prevention programme (d-DPP) compared with a diabetes prevention programme (DPP) for preventing type 2 diabetes (T2D) in individuals with prediabetes in the United States. METHODS: A Markov cohort model was constructed, simulating a 10-year period starting at the age of 45 years, with a societal and healthcare sector perspective. The effectiveness of the d-DPP intervention was evaluated using a meta-analysis, with that of the DPP as the comparator. The initial cycle represented the treatment period, and transition probabilities for the post-treatment period were derived from a long-term lifestyle intervention meta-analysis. The onset of T2D complications was estimated using microsimulation. Quality-adjusted life years (QALYs) were calculated based on health utility measured by short form (SF)-12 scores, and a willingness-to-pay threshold of $100 000 per QALY gained was applied. RESULTS: The d-DPP intervention resulted in cost savings of $3,672 from a societal perspective and $2,990 from a healthcare sector perspective and a gain of 0.08 QALYs compared with the DPP. The dropout rate was identified as a significant factor influencing the results. Probabilistic sensitivity analysis showed that the d-DPP intervention was preferred in 85.8% in the societal perspective and 85.2% in the healthcare sector perspective. CONCLUSIONS: The d-DPP is a cost-effective alternative to in-person lifestyle interventions for preventing the development of T2D among individuals with prediabetes in the United States.

Medicaid spending and utilization of gene and RNA therapies for rare inherited conditions.

Gene and RNA therapies are promising treatments for many rare diseases. Pediatric populations that could benefit from these drugs are overrepresented among state Medicaid programs. Using Medicaid State Drug Utilization Data, we examined Medicaid spending and utilization of rare disease gene and RNA therapies. Between 2017 and 2022, the number of available gene and RNA therapies increased from 3 to 13, yearly Medicaid spending increased from $148.3 million to $879.7 million, and the number of yearly treatments (a proxy for number of patients) increased from 327 to 1638. Nearly all spending was attributed to spinal muscular atrophy (SMA) and Duchenne muscular dystrophy drugs. States participating in Medicaid pooled purchasing initiatives had 39% higher treatments per 100 000 enrollees with no differences in spending. Compared to states without a carve-out, states that carved SMA drugs out of managed Medicaid contracts had higher utilization (54%). Spending among carve-out states varied according to managed care enrollment, being higher for those with <80% of enrollees in managed care as compared with those with ≥80% of enrollees in managed care. This suggests that multi-state purchasing initiatives and managed care carve-outs can help increase access to gene and RNA therapies among Medicaid beneficiaries, but it is unclear if these strategies are effective at managing spending.

Sociodemographic and spending characteristics of Medicare beneficiaries taking prescription drugs subject to price negotiations.

BACKGROUND: The 2022 Inflation Reduction Act authorizes Medicare to negotiate the prices of 10 drugs in 2026 and additional drugs thereafter. Understanding the sociodemographic and spending characteristics of beneficiaries taking these specific drugs could be important describing the impact of the legislation. OBJECTIVE: To describe sociodemographic and spending characteristics of Medicare beneficiaries who use the 10 prescription drugs ("negotiated drugs") that will face Medicare drug price negotiations in 2026. METHODS: A 20% sample of Medicare Part D beneficiaries from 2020 (n = 10,224,642) was used. Sociodemographic and spending characteristics were descriptively reported for beneficiaries taking the negotiated drugs, including subgroups by low-income subsidy (LIS) status and by drug, and for Part D beneficiaries not taking negotiated drugs. RESULTS: Part D beneficiaries taking a negotiated drug compared with Part D beneficiaries not taking a negotiated drug overall had similar sociodemographic characteristics, more comorbidities (3.9 vs 2.2) and higher mean [median] Medicare ($33,882 [$18,251] vs $12,366 [$3,429]) and out-of-pocket (OOP) spending ($813 [$307] vs $441 [$160]). There was variation in characteristics by LIS status. The mean age was highest among non-LIS beneficiaries taking a negotiated drug compared with LIS beneficiaries taking a negotiated drug and beneficiaries not taking a negotiated drug (76.2 vs 69.9 vs 71.4). Among beneficiaries using negotiated drugs, a higher percentage of LIS beneficiaries compared with non-LIS was female (59.7% vs 48.0%), was Black (20.9% vs 6.6%), and resided in lower-income areas (39.1% vs 20.3%). Mean [median] annual Part D OOP spending for negotiated drugs was $115 [$59] for beneficiaries with LIS and $1,475 [$1,204] for beneficiaries without LIS. There were also differences depending on which negotiated drug was used. Drugs for cancer and blood clots had the highest proportions of White users, whereas type 2 diabetes and heart failure drugs had the highest proportions of Black users and beneficiaries residing in lower-income areas. Annual Part D OOP costs were lowest for sitagliptin (LIS: $104 [$60], non-LIS: $1,391 [$1,153]) and highest for ibrutinib (LIS: $649 [$649], non-LIS: $6,449 [$6,867]). Among non-LIS beneficiaries, 24% (22% to 76%) had more than $2,000 in OOP costs. CONCLUSIONS: Inflation Reduction Act OOP spending caps and LIS expansion will lower prescription drug costs for beneficiaries with OOP costs exceeding $2,000 who are mostly White and live in higher-income areas, insulin users who are disproportionately Black with multiple chronic conditions, and beneficiaries with low incomes. However, these provisions will not impact the 76% of non-LIS beneficiaries using negotiated drugs who have OOP costs that are still substantial but below $2,000. Negotiations could reduce OOP costs through reduced coinsurance payments for this group, which is older and has more chronic conditions compared with beneficiaries not taking negotiated drugs. Part D plan design, spending, and utilization changes should be monitored after negotiation to determine if further solutions are needed to lower OOP costs for this group.

Clinical Evidence Supporting FDA Approval of Gene and RNA Therapies for Rare Inherited Conditions.

BACKGROUND: Gene and RNA therapies have potential to transform the treatment of rare inherited diseases, but there are concerns about the evidence supporting their use and high costs. OBJECTIVE: We analyze the evidence supporting Food and Drug Administration (FDA) approval of gene and RNA therapies for rare inherited diseases and discuss implications for clinical practice and policy. METHODS: We conducted a qualitative analysis of FDA documents outlining the basis of approval for gene and RNA therapies approved for rare inherited diseases between 2016 and 2023. For each drug, we gathered five characteristics of the evidence supporting FDA approval (no phase 3 trial, nonrandomized, no clinical endpoint, lack of demonstrated benefit, and significant protocol deviation) and four characteristics of the FDA approval process (prior rejection or complete response, negative committee vote, discrepancy between label and trial population, and boxed warning). The main outcome was the number of drugs with each characteristic. RESULTS: Between 2016 and 2023, 19 gene and RNA therapies received FDA approval to treat rare inherited diseases. The most common limitations in the evidence supporting approval of these drugs were nonrandomized studies (8/19, 42%), no clinical endpoint (7/19, 37%), lack of demonstrated benefit or inconsistent results (4/19, 21%), and no phase 3 trial (4/19, 21%). Half (3/6) of accelerated approvals and 57% (5/9) of drugs with breakthrough designation had nonrandomized trials, and gene therapies with one-time dosing were overrepresented (5/7, 71%) among the drugs with nonrandomized trials. Five of six accelerated approvals (83%) and five of nine pediatric drugs (56%), most of which were indicated for Duchenne muscular dystrophy, had no clinical endpoint. Four of nine (44%) pediatric drugs and four of six (67%) accelerated approvals failed to demonstrate benefit compared with none of the nonpediatric drugs and none of the traditional approvals. Five drugs, which all had different indications and represented a mix of RNA and gene therapies, did not have any of these evidence characteristics. Among drugs that received prior rejections or negative committee opinions, all four had nonrandomized trials and lacked a clinical endpoint, and 75% (3/4) lacked demonstrated benefit. Five of nine (56%) pediatric drugs were indicated for broader age groups according to the drug label compared with the trial populations. Of the three drugs with boxed warnings, two had pediatric indications and nonrandomized studies, and one had no phase 3 trial. CONCLUSIONS: Issues related to trial design, outcome, and data integrity in the evidence supporting FDA approval of rare inherited disease gene and RNA therapies raise questions about whether this evidence is adequate to inform prescribing decisions. Gene and RNA therapies with accelerated approval and pediatric indications were overrepresented among drugs lacking clinical endpoints or demonstrated benefit and should be the focus of efforts to reduce uncertainty in the evidence.

Effectiveness of artificial intelligence vs. human coaching in diabetes prevention: a study protocol for a randomized controlled trial.

BACKGROUND: Prediabetes is a highly prevalent condition that heralds an increased risk of progression to type 2 diabetes, along with associated microvascular and macrovascular complications. The Diabetes Prevention Program (DPP) is an established effective intervention for diabetes prevention. However, participation in this 12-month lifestyle change program has historically been low. Digital DPPs have emerged as a scalable alternative, accessible asynchronously and recognized by the Centers for Disease Control and Prevention (CDC). Yet, most digital programs still incorporate human coaching, potentially limiting scalability. Furthermore, existing effectiveness results of digital DPPs are primarily derived from per protocol, longitudinal non-randomized studies, or comparisons to control groups that do not represent the standard of care DPP. The potential of an AI-powered DPP as an alternative to the DPP is yet to be investigated. We propose a randomized controlled trial (RCT) to directly compare these two approaches. METHODS: This open-label, multicenter, non-inferiority RCT will compare the effectiveness of a fully automated AI-powered digital DPP (ai-DPP) with a standard of care human coach-based DPP (h-DPP). A total of 368 participants with elevated body mass index (BMI) and prediabetes will be randomized equally to the ai-DPP (smartphone app and Bluetooth-enabled body weight scale) or h-DPP (referral to a CDC recognized DPP). The primary endpoint, assessed at 12 months, is the achievement of the CDC's benchmark for type 2 diabetes risk reduction, defined as any of the following: at least 5% weight loss, at least 4% weight loss and at least 150 min per week on average of physical activity, or at least a 0.2-point reduction in hemoglobin A1C. Physical activity will be objectively measured using serial actigraphy at baseline and at 1-month intervals throughout the trial. Secondary endpoints, evaluated at 6 and 12 months, will include changes in A1C, weight, physical activity measures, program engagement, and cost-effectiveness. Participants include adults aged 18-75 years with laboratory confirmed prediabetes, a BMI of ≥ 25 kg/m2 (≥ 23 kg/m2 for Asians), English proficiency, and smartphone users. This U.S. study is conducted at Johns Hopkins Medicine in Baltimore, MD, and Reading Hospital (Tower Health) in Reading, PA. DISCUSSION: Prediabetes is a significant public health issue, necessitating scalable interventions for the millions affected. Our pragmatic clinical trial is unique in directly comparing a fully automated AI-powered approach without direct human coach interaction. If proven effective, it could be a scalable, cost-effective strategy. This trial will offer vital insights into both AI and human coach-based behavioral change strategies in real-world clinical settings. TRIAL REGISTRATION: ClinicalTrials.gov NCT05056376. Registered on September 24, 2021, https://clinicaltrials.gov/study/NCT05056376.