Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, MA, 02120, USA.
Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
BMC Med. 2023 Aug 30;21(1):331. doi: 10.1186/s12916-023-02785-y.
In the United States, the tuberculosis (TB) disease burden and associated factors vary substantially across states. While public health agencies must choose how to deploy resources to combat TB and latent tuberculosis infection (LTBI), state-level modeling analyses to inform policy decisions have not been widely available.
We developed a mathematical model of TB epidemiology linked to a web-based user interface - Tabby2. The model is calibrated to epidemiological and demographic data for the United States, each U.S. state, and the District of Columbia. Users can simulate pre-defined scenarios describing approaches to TB prevention and treatment or create their own intervention scenarios. Location-specific results for epidemiological outcomes, service utilization, costs, and cost-effectiveness are reported as downloadable tables and customizable visualizations. To demonstrate the tool's functionality, we projected trends in TB outcomes without additional intervention for all 50 states and the District of Columbia. We further undertook a case study of expanded treatment of LTBI among non-U.S.-born individuals in Massachusetts, covering 10% of the target population annually over 2025-2029.
Between 2022 and 2050, TB incidence rates were projected to decline in all states and the District of Columbia. Incidence projections for the year 2050 ranged from 0.03 to 3.8 cases (median 0.95) per 100,000 persons. By 2050, we project that majority (> 50%) of TB will be diagnosed among non-U.S.-born persons in 46 states and the District of Columbia; per state percentages range from 17.4% to 96.7% (median 83.0%). In Massachusetts, expanded testing and treatment for LTBI in this population was projected to reduce cumulative TB cases between 2025 and 2050 by 6.3% and TB-related deaths by 8.4%, relative to base case projections. This intervention had an incremental cost-effectiveness ratio of $180,951 (2020 USD) per quality-adjusted life year gained from the societal perspective.
Tabby2 allows users to estimate the costs, impact, and cost-effectiveness of different TB prevention approaches for multiple geographic areas in the United States. Expanded testing and treatment for LTBI could accelerate declines in TB incidence in the United States, as demonstrated in the Massachusetts case study.
在美国，结核病（TB）疾病负担和相关因素在各州之间存在很大差异。虽然公共卫生机构必须选择如何部署资源来对抗结核病和潜伏性结核感染（LTBI），但尚未广泛提供州级建模分析来为政策决策提供信息。
我们开发了一种结核病流行病学的数学模型，该模型与基于网络的用户界面 - Tabby2 相关联。该模型根据美国、每个美国州和哥伦比亚特区的流行病学和人口统计学数据进行校准。用户可以模拟描述结核病预防和治疗方法的预定义方案，或创建自己的干预方案。针对流行病学结果、服务利用、成本和成本效益的位置特定结果作为可下载的表格和可定制的可视化报告。为了展示该工具的功能，我们预测了在没有额外干预的情况下，所有 50 个州和哥伦比亚特区的结核病结果趋势。我们还对马萨诸塞州扩大治疗非美国出生人群的 LTBI 进行了案例研究，涵盖了 2025-2029 年期间目标人群的 10%。
在 2022 年至 2050 年期间，预计所有州和哥伦比亚特区的结核病发病率都将下降。2050 年的发病率预测范围为每 100,000 人 0.03 至 3.8 例（中位数为 0.95）。到 2050 年，我们预计在 46 个州和哥伦比亚特区，大多数（>50%）结核病将在非美国出生的人群中诊断出来；各州的百分比范围为 17.4%至 96.7%（中位数为 83.0%）。在马萨诸塞州，对该人群的 LTBI 进行扩大检测和治疗，预计将使 2025 年至 2050 年期间的累积结核病病例减少 6.3%，结核病相关死亡减少 8.4%，与基础病例预测相比。从社会角度来看，这种干预措施的增量成本效益比为每获得一个质量调整生命年 180,951 美元（2020 年美元）。
Tabby2 允许用户估计美国多个地理区域的不同结核病预防方法的成本、影响和成本效益。如马萨诸塞州案例研究所示，扩大 LTBI 的检测和治疗可以加速美国结核病发病率的下降。
