Rasu Rafia S, Hunt Suzanne L, Dai Junqiang, Cui Huizhong, Phadnis Milind A, Jain Nishank
University of North Texas Health Science Center, Fort Worth, USA.
University of Kansas Medical Center, Kansas City, USA.
Hosp Pharm. 2021 Oct;56(5):451-461. doi: 10.1177/0018578720918550. Epub 2020 Jun 2.
Pharmacy administrative claims data remain an accessible and efficient source to measure medication adherence for frequently hospitalized patient populations that are systematically excluded from the landmark drug trials. Published pharmacotherapy studies use medication possession ratio (MPR) and proportion of days covered (PDC) to calculate medication adherence and usually fail to incorporate hospitalization and prescription overlap/gap from claims data. To make the cacophony of adherence measures clearer, this study created a refined hospital-adjusted algorithm to capture pharmacotherapy adherence among patients with end-stage renal disease (ESRD). The United States Renal Data System (USRDS) registry of ESRD was used to determine prescription-filling patterns of those receiving new prescriptions for oral P2Y12 inhibitors (P2Y12-I) between 2011 and 2015. P2Y12-I-naïve patients were followed until death, kidney transplantation, discontinuing medications, or loss to follow-up. After flagging/censoring key variables, the algorithm adjusted for hospital length of stay (LOS) and medication overlap. Hospital-adjusted medication adherence (HA-PDC) was calculated and compared with traditional MPR and PDC methods. Analyses were performed with SAS software. Hospitalization occurred for 78% of the cohort (N = 46 514). The median LOS was 12 (interquartile range [IQR] = 2-34) days. MPR and PDC were 61% (IQR = 29%-94%) and 59% (IQR = 31%-93%), respectively. After applying adjustments for overlapping coverage days and hospital stays independently, HA-PDC adherence values changed in 41% and 52.7% of the cohort, respectively. When adjustments for overlap and hospital stay were made concurrently, HA-PDC adherence values changed in 68% of the cohort by 5.8% (HA-PDC median = 0.68, IQR = 0.31-0.93). HA-PDC declined over time (3M-6M-9M-12M). Nearly 48% of the cohort had a ≥30 days refill gap in the first 3 months, and this increased over time ( < .0001). Refill gaps should be investigated carefully to capture accurate pharmacotherapy adherence. HA-PDC measures increased adherence substantially when adjustments for hospital stay and medication refill overlaps are made. Furthermore, if hospitalizations were ignored for medications that are included in Medicare quality measures, such as Medicare STAR program, the apparent reduction in adherence might be associated with lower quality and health plan reimbursement.
药房管理索赔数据仍然是一个可获取且高效的来源,用于衡量经常住院患者群体的药物依从性,而这些患者群体被系统地排除在具有里程碑意义的药物试验之外。已发表的药物治疗研究使用药物持有率(MPR)和覆盖天数比例(PDC)来计算药物依从性,并且通常未能纳入索赔数据中的住院情况和处方重叠/间隔。为了使依从性测量的嘈杂情况更加清晰,本研究创建了一种经过改进的医院调整算法,以获取终末期肾病(ESRD)患者的药物治疗依从性。使用美国肾脏数据系统(USRDS)的ESRD登记册来确定2011年至2015年间接受口服P2Y12抑制剂(P2Y12-I)新处方患者的处方填充模式。对未使用过P2Y12-I的患者进行随访,直至死亡、肾移植、停药或失访。在标记/审查关键变量后,该算法对住院时间(LOS)和药物重叠进行了调整。计算了医院调整后的药物依从性(HA-PDC),并与传统的MPR和PDC方法进行了比较。使用SAS软件进行分析。该队列中有78%(N = 46514)发生了住院。中位住院时间为12天(四分位间距[IQR] = 2 - 34天)。MPR和PDC分别为61%(IQR = 29% - 94%)和59%(IQR = 31% - 93%)。在分别对重叠覆盖天数和住院时间进行调整后,HA-PDC依从性值在该队列中的变化分别为41%和52.7%。当同时对重叠和住院时间进行调整时,HA-PDC依从性值在该队列中的68%发生了变化,变化幅度为5.8%(HA-PDC中位数 = 0.68,IQR = 0.31 - 0.93)。HA-PDC随时间下降(3个月 - 6个月 - 9个月 - 12个月)。该队列中近48%的患者在最初3个月内有≥30天的重新填充间隔,且这种情况随时间增加(P <.0001)。应仔细调查重新填充间隔,以获取准确的药物治疗依从性。当对住院时间和药物重新填充重叠进行调整时,HA-PDC测量显著提高了依从性。此外,如果在医疗保险质量指标(如医疗保险星级计划)所涵盖的药物中忽略住院情况,依从性的明显降低可能与较低的质量和健康计划报销相关。