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

• 批准号: 10308682
• 负责人: NICOLAS A MENZIES
• 金额: $ 49.92万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 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/10308682
• 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; Persons; 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; risk prediction; screening; sex; tool; transmission process; trend; user-friendly; web based interface

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

项目总结/文摘