Evaluating the Impact of a HIV Low-Risk Express Care Task-Shifting Program: A Case Study of the Targeted Learning Roadmap.

Q3 Mathematics Epidemiologic Methods Pub Date : 2016-12-01 Epub Date: 2016-11-10 DOI:10.1515/em-2016-0004
Linh Tran, Constantin T Yiannoutsos, Beverly S Musick, Kara K Wools-Kaloustian, Abraham Siika, Sylvester Kimaiyo, Mark J van der Laan, Maya Petersen
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Abstract

In conducting studies on an exposure of interest, a systematic roadmap should be applied for translating causal questions into statistical analyses and interpreting the results. In this paper we describe an application of one such roadmap applied to estimating the joint effect of both time to availability of a nurse-based triage system (low risk express care (LREC)) and individual enrollment in the program among HIV patients in East Africa. Our study population is comprised of 16,513 subjects found eligible for this task-shifting program within 15 clinics in Kenya between 2006 and 2009, with each clinic starting the LREC program between 2007 and 2008. After discretizing follow-up into 90-day time intervals, we targeted the population mean counterfactual outcome (i. e. counterfactual probability of either dying or being lost to follow up) at up to 450 days after initial LREC eligibility under three fixed treatment interventions. These were (i) under no program availability during the entire follow-up, (ii) under immediate program availability at initial eligibility, but non-enrollment during the entire follow-up, and (iii) under immediate program availability and enrollment at initial eligibility. We further estimated the controlled direct effect of immediate program availability compared to no program availability, under a hypothetical intervention to prevent individual enrollment in the program. Targeted minimum loss-based estimation was used to estimate the mean outcome, while Super Learning was implemented to estimate the required nuisance parameters. Analyses were conducted with the ltmle R package; analysis code is available at an online repository as an R package. Results showed that at 450 days, the probability of in-care survival for subjects with immediate availability and enrollment was 0.93 (95% CI: 0.91, 0.95) and 0.87 (95% CI: 0.86, 0.87) for subjects with immediate availability never enrolling. For subjects without LREC availability, it was 0.91 (95% CI: 0.90, 0.92). Immediate program availability without individual enrollment, compared to no program availability, was estimated to slightly albeit significantly decrease survival by 4% (95% CI 0.03,0.06, p<0.01). Immediately availability and enrollment resulted in a 7 % higher in-care survival compared to immediate availability with non-enrollment after 450 days (95% CI-0.08,-0.05, p<0.01). The results are consistent with a fairly small impact of both availability and enrollment in the LREC program on incare survival.

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评估艾滋病低风险快速护理任务转移计划的影响:目标学习路线图案例研究》。
在对感兴趣的暴露进行研究时,应采用系统的路线图将因果问题转化为统计分析并解释结果。在本文中,我们介绍了这样一种路线图的应用,它适用于估算东非艾滋病患者中护士分流系统(低风险快速护理(LREC))的可用时间和个人加入该计划的共同影响。我们的研究对象包括 2006 年至 2009 年间肯尼亚 15 家诊所中符合任务分流计划条件的 16513 名受试者,每家诊所都在 2007 年至 2008 年间启动了 LREC 计划。在将随访时间离散为 90 天的时间间隔后,我们将人群平均反事实结果(即死亡或失去随访机会的反事实概率)的目标设定为在最初获得 LREC 资格后的 450 天内,在三种固定的治疗干预下。这三种情况分别是:(i) 在整个随访期间没有提供计划;(ii) 在最初符合条件时立即提供计划,但在整个随访期间没有注册;(iii) 在最初符合条件时立即提供计划并注册。我们还进一步估算了在假定干预措施阻止个人参与计划的情况下,立即提供计划与不提供计划相比所产生的直接控制效果。我们使用基于最小损失的目标估计法来估计平均结果,同时使用超级学习法来估计所需的干扰参数。分析使用 ltmle R 软件包进行;分析代码作为 R 软件包可从在线存储库中获取。结果显示,在 450 天时,立即可用且注册的受试者的护理生存概率为 0.93(95% CI:0.91,0.95),而立即可用且从未注册的受试者的护理生存概率为 0.87(95% CI:0.86,0.87)。而对于没有 LREC 的受试者,这一比例为 0.91(95% CI:0.90,0.92)。据估计,与不提供项目相比,不进行个人注册但可立即提供项目的受试者的存活率会略微降低 4%(95% CI 0.03,0.06,P<0.05)。
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来源期刊
Epidemiologic Methods
Epidemiologic Methods Mathematics-Applied Mathematics
CiteScore
2.10
自引率
0.00%
发文量
7
期刊介绍: Epidemiologic Methods (EM) seeks contributions comparable to those of the leading epidemiologic journals, but also invites papers that may be more technical or of greater length than what has traditionally been allowed by journals in epidemiology. Applications and examples with real data to illustrate methodology are strongly encouraged but not required. Topics. genetic epidemiology, infectious disease, pharmaco-epidemiology, ecologic studies, environmental exposures, screening, surveillance, social networks, comparative effectiveness, statistical modeling, causal inference, measurement error, study design, meta-analysis
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