From 90-90-90 to 95-95-95 and beyond: Optimizing and targeting combination HIV prevention for Zimbabwe and Kwazulu Natal

摘要/总结 已公布的监测数据以及数学建模表明，联合国艾滋病规划署2030年目标（90%） 减少艾滋病毒发病率）将无法用目前的支出和资源分配来满足， 联合国艾滋病规划署2020年目标（90%的感染者被发现，90%的被发现者与护理有关，90%的被发现者与治疗有关） 到2020年将无法实现。现有的模型表明， 为实现这些目标，需要支出（每年128亿至400亿美元）， 优化这些支出。事实上，如果没有优化，实现这一目标的必要支出可能会超过 每年520亿美元。 为了实现联合国艾滋病规划署2030年目标，有必要严格评估所有可用工具的作用， 战略，以最大限度地发挥其影响。然而，目前的数学模型忽略了武器库中的重要工具 在资源有限的地区实现2030年目标，包括：（1）针对艾滋病毒的具体干预措施 护理连续体（特别是具有随机对照试验证据的特定干预措施，包括SMS- 基于文本的预约和/或药物提醒以及类似于 Link 4 Health [包括加速药物启动、基于短信的文本提醒、护理/信息包 +/-非现金财政奖励]），（2）针对高危人群（如酗酒者）采取干预措施 使用障碍[AUD]和常见精神障碍[CMD]），特别是与一个地区的 人口统计和政策限制，以及（3）艾滋病毒支出高峰的交替时间（越早越好 因为它导致“领先于流行病”，但可能不太可行）。因此，我们的建议 将这些工具纳入一个数学模型，以评估广泛的配置效率， 艾滋病毒预防综合战略侧重于津巴布韦和南非， 艾滋病毒的发病率和死亡率对这些国家造成了不成比例的负担。

ABSTRACT/SUMMARY Published surveillance data together with mathematical modeling make it clear that UNAIDS 2030 goals (90% reduction in HIV incidence) are not going to be met with current spending and resource allocation, as well as UNAIDS 2020 goals (90% of infected are detected, 90% of detected are linked to care, and 90% of linked to care are virally suppressed) are not going to be met by 2020. Existing models suggest a tripling in HIV spending (from $12.8 billion to $40 billion per year) would be necessary to meet these goals, together with an optimizing of that spending. Indeed, without optimization, the necessary spending for that goal would likely top $52 billion per year. To achieve UNAIDS 2030 goals it will be necessary to critically assess the role of all available tools and tailor strategies to maximize their impact. However, current mathematical models omit important tools in the arsenal for achieving 2030 goals in resource-limited regions, including: (1) Specific interventions that target the HIV care continuum (in particular specific interventions with randomized controlled-trial evidence include SMS- based text reminders for appointments and/or medications and combination interventions similar to Link4Health [including accelerated medication initiation, SMS-based text reminders, care/information package +/- noncash financial incentive]), (2) targeting interventions to high risk populations (such as those with alcohol use disorders [AUDs] and common mental disorders [CMDs]) that are specifically relevant to a region’s demographics and policy constraints, and (3) alternate timing of the peak of HIV spending (earlier is better because it leads to “getting ahead of the epidemic” but may be less feasible). Accordingly, our proposal incorporates these tools into a mathematical model to evaluate the allocative efficiency of a wide spectrum of combination HIV prevention strategies focusing on the countries of Zimbabwe and South Africa because of their disproportionate burden of HIV morbidity and mortality.