Division of HIV, ID and Global Medicine, University of California, San Francisco, Zuckerberg San Francisco General Hospital, San Francisco, California, United States of America.
Centre for Infectious Diseases Research in Zambia, Lusaka, Zambia.
PLoS Med. 2019 Oct 29;16(10):e1002959. doi: 10.1371/journal.pmed.1002959. eCollection 2019 Oct.
Retention in HIV treatment must be improved to advance the HIV response, but research to characterize gaps in retention has focused on estimates from single time points and population-level averages. These approaches do not assess the engagement patterns of individual patients over time and fail to account for both their dynamic nature and the heterogeneity between patients. We apply group-based trajectory analysis-a special application of latent class analysis to longitudinal data-among new antiretroviral therapy (ART) starters in Zambia to identify groups defined by engagement patterns over time and to assess their association with mortality.
We analyzed a cohort of HIV-infected adults who newly started ART between August 1, 2013, and February 1, 2015, across 64 clinics in Zambia. We performed group-based multi-trajectory analysis to identify subgroups with distinct trajectories in medication possession ratio (MPR, a validated adherence metric based on pharmacy refill data) over the past 3 months and loss to follow-up (LTFU, >90 days late for last visit) among patients with at least 180 days of observation time. We used multinomial logistic regression to identify baseline factors associated with belonging to particular trajectory groups. We obtained Kaplan-Meier estimates with bootstrapped confidence intervals of the cumulative incidence of mortality stratified by trajectory group and performed adjusted Poisson regression to estimate adjusted incidence rate ratios (aIRRs) for mortality by trajectory group. Inverse probability weights were applied to all analyses to account for updated outcomes ascertained from tracing a random subset of patients lost to follow-up as of July 31, 2015. Overall, 38,879 patients (63.3% female, median age 35 years [IQR 29-41], median enrollment CD4 count 280 cells/μl [IQR 146-431]) were included in our cohort. Analyses revealed 6 trajectory groups among the new ART starters: (1) 28.5% of patients demonstrated consistently high adherence and retention; (2) 22.2% showed early nonadherence but consistent retention; (3) 21.6% showed gradually decreasing adherence and retention; (4) 8.6% showed early LTFU with later reengagement; (5) 8.7% had early LTFU without reengagement; and (6) 10.4% had late LTFU without reengagement. Identified groups exhibited large differences in survival: after adjustment, the "early LTFU with reengagement" group (aIRR 3.4 [95% CI 1.2-9.7], p = 0.019), the "early LTFU" group (aIRR 6.4 [95% CI 2.5-16.3], p < 0.001), and the "late LTFU" group (aIRR 4.7 [95% CI 2.0-11.3], p = 0.001) had higher rates of mortality as compared to the group with consistently high adherence/retention. Limitations of this study include using data observed after baseline to identify trajectory groups and to classify patients into these groups, excluding patients who died or transferred within the first 180 days, and the uncertain generalizability of the data to current care standards.
Among new ART starters in Zambia, we observed 6 patient subgroups that demonstrated distinctive engagement trajectories over time and that were associated with marked differences in the subsequent risk of mortality. Further efforts to develop tailored intervention strategies for different types of engagement behaviors, monitor early engagement to identify higher-risk patients, and better understand the determinants of these heterogeneous behaviors can help improve care delivery and survival in this population.
为了推进艾滋病应对工作,必须提高艾滋病毒治疗的保留率,但针对保留率差距的研究主要集中在单一时间点和人口水平的平均估计上。这些方法无法评估个体患者随时间的参与模式,也无法同时考虑到他们的动态性质和患者之间的异质性。我们应用群组轨迹分析(纵向数据的潜在类别分析的特殊应用)来识别赞比亚新开始抗逆转录病毒治疗(ART)的患者中随时间变化的参与模式,并评估这些模式与死亡率的关系。
我们分析了 2013 年 8 月 1 日至 2015 年 2 月 1 日期间在赞比亚 64 个诊所新开始 ART 的感染艾滋病毒的成年人队列。我们进行了群组基于多轨迹分析,以确定在过去 3 个月内药物持有率(MPR,基于药房补充数据的验证性依从性指标)和失访(最后一次就诊超过 90 天)方面具有不同轨迹的亚组。对于至少有 180 天观察时间的患者,我们使用多变量逻辑回归来确定与特定轨迹组相关的基线因素。我们使用 Kaplan-Meier 估计和 bootstrap 置信区间来分层不同轨迹组的死亡率累积发生率,并使用调整后的泊松回归来估计死亡率的调整发病率比(aIRR)。我们对所有分析应用逆概率权重,以考虑截至 2015 年 7 月 31 日通过追踪随机选择的失访患者来确定更新的结果。总体而言,38879 名患者(63.3%为女性,中位年龄 35 岁[IQR 29-41],中位 CD4 计数为 280 个细胞/μl[IQR 146-431])纳入了我们的队列。分析显示,新开始 ART 的患者中有 6 个轨迹组:(1)28.5%的患者表现出一致的高依从性和保留率;(2)22.2%的患者表现出早期不依从但持续保留;(3)21.6%的患者表现出逐渐降低的依从性和保留率;(4)8.6%的患者表现出早期失访但后来重新参与;(5)8.7%的患者表现出早期失访但没有重新参与;(6)10.4%的患者表现出晚期失访但没有重新参与。确定的组在生存率方面表现出很大的差异:调整后,“早期失访并重新参与”组(aIRR 3.4[95%CI 1.2-9.7],p = 0.019)、“早期失访”组(aIRR 6.4[95%CI 2.5-16.3],p < 0.001)和“晚期失访”组(aIRR 4.7[95%CI 2.0-11.3],p = 0.001)的死亡率均高于始终保持高依从性/保留率的组。本研究的局限性包括使用基线后观察到的数据来识别轨迹组并将患者分类到这些组中,排除了在最初 180 天内死亡或转院的患者,以及数据对当前护理标准的不确定性普遍适用性。
在赞比亚新开始 ART 的患者中,我们观察到 6 个患者亚组,这些亚组随时间表现出不同的参与轨迹,并且与随后死亡率的显著差异相关。进一步努力为不同类型的参与行为制定有针对性的干预策略,监测早期参与以识别高风险患者,并更好地了解这些异质行为的决定因素,可以帮助改善该人群的护理和生存。