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

摘要/摘要 公布的监测数据与数学模型一起清楚地表明，联合国艾滋病规划署2030年的目标(90% 减少艾滋病毒发病率)将不能用目前的支出和资源分配以及 联合国艾滋病规划署2020年的目标(90%的感染者被检测到，90%的被检测到的人与护理有关，90%的人与 医疗保健受到病毒抑制)到2020年将不会得到满足。现有的模型表明艾滋病病毒的感染率增加了两倍 支出(每年128亿至400亿美元)将是实现这些目标所必需的，以及 优化这一支出。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。 为实现联合国艾滋病规划署2030年目标，必须严格评估所有可用工具的作用，并为其量身定制 最大限度地发挥其影响的战略。然而，当前的数学模型忽略了武器库中的重要工具 在资源有限的区域实现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具体干预措施包括短信- 基于预约和/或药物和组合干预的文本提醒，类似于 Link4Health[包括加速用药、基于短信的短信提醒、护理/信息包 +/-非现金财政奖励])，(2)针对高危人群(如酗酒者)的干预措施 使用障碍[AUD]和常见精神障碍[CMD]) 人口结构和政策限制，以及(3)艾滋病毒支出高峰的交替时间(越早越好 因为这会导致“赶在疫情之前”，但可能不太可行)。因此，我们的建议 将这些工具合并到一个数学模型中，以评估广泛范围的 将重点放在津巴布韦和南非国家的艾滋病毒预防组合战略，因为 他们不成比例的艾滋病毒发病率和死亡率负担。

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.