Optimal targeting for individual and population-level TB prevention

个人和人群层面结核病预防的最佳目标

基本信息

批准号：
9913107
负责人：
NICOLAS A MENZIES
金额：
$ 52.9万
依托单位：
HARVARD SCHOOL OF PUBLIC HEALTH
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-12-05 至 2024-11-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9913107
关键词：
Address Adverse event Age Area Benefits and Risks Categories Centers for Disease Control and Prevention (U.S.)Characteristics Cities Clinical Clinical Management Communicable Diseases Communities Companions Computer Models Cost Control Costs and Benefits Country Data Decision Aid Decision Making Demography Diabetes Mellitus Diagnosis Disease Disease Progression End stage renal failure Epidemiology Face Future HIV Health Health Benefit Health Services Accessibility Homelessness Immunocompetence Impairment Imprisonment Incidence Individual Infection International Internet Intervention Location Modeling Mycobacterium tuberculosis NIH Program Announcements Online Systems Outcome Patient-Focused Outcomes Patients Patterns of Care Policies Policy Maker Policy Making Population Population Group Population Sizes Prevalence Prevention Preventive service Preventive therapy Preventive treatment Probability Reporting Research Risk Risk Factors Series Services Software Tools Specificity Testing Time To specify Training Translating Treatment outcome Tuberculosis United States United States National Institutes of Health Variant Visualization Work base clinical decision-making computerized tools cost cost effectiveness cost estimate cost-effectiveness ratio data tools disorder risk evidence base health economics high risk population immune function improved incremental cost-effectiveness interest intervention effect latent infection mathematical model models and simulation mortality novel predictive modeling prevention service programs reactivation from latency residence screening sex tool transmission process trend user-friendly web based interface

项目摘要

PROJECT SUMMARY/ABSTRACT Within the same community, TB risks can differ by several orders of magnitude due to differences in infectious exposure and immune competence, and TB control depends heavily on targeting services to those most at risk. Priority groups described by the CDC and other agencies capture major TB risk factors, but these broad categories include many individuals with low TB risk, and exclude others who would benefit from screening. Our long-term objective is to provide individually- and locally-tailored evidence on TB risks and intervention effects, to optimize TB prevention services. In prior work we have demonstrated the feasibility of estimating TB risks for small population groups, and in Aim 1 we will create granular estimates of TB risk for the US population, via a Bayesian evidence synthesis combining time series data on TB cases and population size, prevalence of latent infection (LTBI), and the fraction of cases due to recent infection. This analysis will allow us to produce individually-tailored risk predictions to better target preventive services, and provide patients with quantitative information on the risks they face. The number of patients to whom this applies is substantial—approximately half of all US residents have been tested for LTBI, and of those testing positive only half initiate treatment. This represents a large number of people facing decisions about LTBI testing and treatment. Aim 2 will directly address these questions, creating highly-disaggregated estimates of the costs, harms, and benefits of LTBI testing and treatment. To do so we will construct a Markov microsimulation model of LTBI screening and treatment. Using this model we will estimate long-term patient-level outcomes, including changes in TB risk, survival, costs, and adverse events. Based on these analyses we will develop a user-friendly web tool to provide patients and clinicians prompt, validated, and individually-tailored information on possible treatment outcomes. We will also conduct analyses and develop a companion tool that will report the impact and cost-effectiveness of LTBI screening for user-defined target groups for the purpose of guiding program decision-making. To increase the reach and impact of these tools we will adapt them for other countries with TB incidence below 20 per 100,000. In Aim 3 we will develop a transmission-dynamic simulation model to predict long-term outcomes for a broad set of TB control options (including but not limited to LTBI treatment) and risk factor trends. The model will be calibrated for multiple jurisdictions, and a web-based interface will allow users to specify scenarios and visualize outcomes. By identifying how current and novel interventions can be most effectively deployed to improve health, this research addresses the NIH’s highest priority area of health economics research, and responds directly to the need for computational tools and models to better understand and respond to infectious disease risks. 1

项目概要/摘要在同一社区内，由于结核病风险的差异，结核病风险可能相差几个数量级。传染性接触和免疫能力，结核病控制在很大程度上取决于有针对性的服务疾病预防控制中心和其他机构描述的高危人群是主要的结核病风险人群。因素，但这些广泛的类别包括许多结核病风险较低的人，并且排除了其他结核病风险低的人我们的长期目标是提供个性化和本地化的定制服务。在之前的工作中，我们有关于结核病风险和干预效果的证据，以优化结核病预防服务。评估小群体结核病风险的可行性已得到证明，在目标 1 中，我们将通过贝叶斯证据综合结合，对美国人口的结核病风险进行精细估计结核病病例和人口规模、潜伏感染率 (LTBI) 和比例的时间序列数据该分析将使我们能够做出个性化的风险预测。更好地定位预防服务，并为患者提供有关其风险的定量信息这种情况适用的患者数量很大——大约占美国居民的一半。已接受 LTBI 检测，其中只有一半检测结果呈阳性，这代表已开始治疗。目标 2 将直接解决大量面临 LTBI 检测和治疗决定的人。这些问题，对 LTBI 的成本、危害和收益进行了高度分类的估计为此，我们将构建 LTBI 筛查和治疗的马尔可夫微观模拟模型。使用该模型，我们将估计患者的长期结果，包括结核病的变化。根据这些分析，我们将开发一个用户友好的网络。为患者和支持者提供有关可能的及时、经过验证和单独定制的信息的工具我们还将进行分析并开发一个报告治疗结果的配套工具。为指导目的而对用户定义的目标群体进行 LTBI 筛查的影响和成本效益为了扩大这些工具的影响范围和影响力，我们将对其进行调整以供其他工具使用。结核病发病率低于每 10 万人中 20 人的国家在目标 3 中，我们将制定传播动态。模拟模型来预测一系列广泛的结核病控制方案的长期结果（包括但不包括仅限于 LTBI 治疗）和风险因素趋势。该模型将针对多个司法管辖区进行校准，基于网络的界面将允许用户通过识别来指定场景并可视化结果。这项研究如何最有效地部署当前和新颖的干预措施来改善健康解决 NIH 卫生经济学研究的最高优先领域，并直接响应需要计算工具和模型来更好地理解和应对传染病风险。 1