Comparative and cost effectiveness of diabetes medications

背景：糖尿病是心血管疾病(CVD)、肾脏和眼部疾病的重要原因。 死亡率。糖尿病及其并发症也产生了大量的医疗保健利用。用药 对糖尿病护理的益处和成本都有贡献。新的糖尿病药物已经被开发出来 并推向市场，但我们对其净收益、风险和成本的理解仍然不匹配。 有效性。这与老年人(≥65岁)尤其相关，他们的收益和风险更微妙 平衡。临床实践指南建议所有年龄段的人都使用二甲双胍进行初步治疗，但 当二甲双胍无效时，不清楚下一步的步骤。这是一种频繁的治疗过渡。用药 例如胰升糖素样肽-1受体激动剂(GLP1)和钠-葡萄糖共转运体-2抑制剂 (SGLT2)对心血管疾病和肾脏疾病有良好的疗效，但也有更大的成本和副作用。在 弗吉尼亚州，他们的使用正在加速。目前尚不确定这些药物是否具有相对于 常用的仿制药，磺脲类(SU)。因此，我们假设糖尿病药物 使用受到患者因素和提供者实践模式的复杂相互作用的影响。我们进一步 假设当使用真实世界的数据进行研究时，GLP1和SGLT2药物将减少主要不良反应 作为二甲双胍的附加治疗，与SU相比，效果更好，成本效益更高。 意义：临床和成本效益高的糖尿病的证据基础存在重大差距 老年退伍军人(≥65岁)的治疗决策，占退伍军人糖尿病队列的70%。 创新和影响：我们将在全国范围内雇用患有双重糖尿病的退伍军人样本 参加退伍军人保险/医疗保险，并应用先进的方法来减少不可测量因素的影响。通过使用 真实世界的数据，我们的研究将增加重要的新信息，为老年退伍军人的护理提供信息 糖尿病和药房计划，以及临床指南。 具体目标： 目的1.确定SU、GLP1和SGLT2的处方模式。我们将检查患者在多大程度上 特点和临床医生处方模式影响启动SU、GLP1或SGLT2作为附加的 二甲双胍。 目的2.评估SU、GLP1和SGLT2作为二甲双胍的附加使用之间的关系 医疗保健利用和不良后果。使用临床医生的处方模式作为工具变量， 我们将衡量对急诊科就诊、住院、医疗费用、 低血糖事件、服药依从性、新的糖尿病并发症和死亡率。 目的3.研究启动SU、GLP1或SGLT2作为二甲双胍的附加剂的成本-效果。 方法：这是一项对患者水平管理的次要数据进行的回溯性观察研究 并声称数据来自VA和Medicare，并使用了涉及工具变量的准实验设计。 我们将使用IQVIA核心糖尿病模型(CDM)v9.0进行成本效益分析，这是一个 经过充分验证的马尔可夫模型。 后续步骤/实施： 我们将与药房福利管理学院细节服务(ADS)和退伍军人管理局专业合作 关怀服务(SCC)贯穿整个项目。在它完成时，我们将与ADS和SCC合作开发 教育和传播计划通知一线临床医生、专科护理负责人和退伍军人政策制定者。

Background: Diabetes is a significant cause of cardiovascular disease (CVD), kidney and eye diseases, and mortality. Diabetes and its complications also generate substantial healthcare utilization. Medications contribute to both the benefits and costs of diabetes care. Newer diabetes medications have been developed and marketed, but there remains a mismatch in our understanding of their net benefits, risks, and cost- effectiveness. This is particularly relevant for older adults (≥65 years), where benefits and risks are more finely balanced. Clinical practice guidelines recommend initial treatment with metformin across all age groups but are unclear about next steps when metformin is ineffective. This is a frequent treatment transition. Medications such as glucagon-like peptide-1 receptor agonists (GLP1) and sodium-glucose co-transporter-2 inhibitors (SGLT2) have favorable effects on CVD and renal disease but also carry greater costs and side-effects. Within VA, their usage is accelerating. It remains uncertain if these medications have specific advantages over the frequently used generic medications, sulfonylureas (SU). Thus, we hypothesize that diabetes medication usage is influenced by a complex interplay of patient factors and provider practice patterns. We further hypothesize that when studied using real-world data, GLP1 and SGLT2 medications will reduce major adverse outcomes and be cost-effective in comparison to SU when used as add-on treatment to metformin. Significance: There are major gaps in the evidence base that informs clinical and cost-effective diabetes treatment decisions among older Veterans (≥65 years), which comprise >70% of the VA diabetes cohort. Innovation and Impact: We will employ a large nationwide sample of Veterans with diabetes who are dually enrolled in VA/Medicare and apply advanced methods to reduce the impact of unmeasured factors. By using real-world data, our study will add significant new information that informs the care of older Veterans, VA’s diabetes and pharmacy programs, and clinical guidelines. Specific Aims: Aim 1. Determine prescribing patterns for SU, GLP1 and SGLT2. We will examine the extent to which patient characteristics and clinician prescribing patterns influence initiating an SU, GLP1 or SGLT2 as add-on to metformin. Aim 2. Estimate the relationships between SU, GLP1, and SGLT2 usage as add-on to metformin with healthcare utilization and adverse outcomes. Using clinician prescribing patterns as an instrumental variable, we will measure the effects on emergency department (ED) visits, hospitalizations, healthcare costs, hypoglycemia events, medication adherence, new diabetes complications and mortality. Aim 3. Investigate the cost-effectiveness of initiating an SU, GLP1 or SGLT2 as add-on to metformin. Methodology: This is a retrospective observational study of secondary data from patient-level administrative and claims data from VA and Medicare, and uses a quasi-experimental design involving instrumental variables. We will conduct the cost-effectiveness analyses using the IQVIA Core Diabetes Model (CDM) v9.0, which is a well-validated Markov model. Next Steps/Implementation: We will collaborate with Pharmacy Benefits Management Academic Detailing Service (ADS) and VA Specialty Care Services (SCC) throughout the project. At its completion we will work with ADS and SCC to develop education and dissemination plans to inform front-line clinicians, specialty-care leads and VA policymakers.