Spatial and Decision Analytic Models for Addressing Challenges in Pediatric Tuberculosis Control and Care

用于应对儿科结核病控制和护理挑战的空间和决策分析模型

• 批准号: 10397391
• 负责人: Kenneth Suranga Gunasekera
• 金额: $ 5.18万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10397391
• 关键词: Address; Adult; Age; Antitubercular Agents; Area; Caring; Cessation of life; Child; Child Care; Child Mortality; Childhood; Cities; Clinical; Communicable Diseases; Communities; Country; Data; Decision Making; Development; Diagnosis; Diagnostic; Diagnostic tests; Disease; Doctor of Philosophy; Evaluation; Exposure to; Fellowship; Foundations; Genomics; Health; Health care facility; Incidence; Individual; Infectious Disease Epidemiology; Infectious Diseases Research; Intervention; Investigation; Location; Meta-Analysis; Methods; Modeling; Moldova; Molecular; Participant; Pattern; Peripheral; Physicians; Population; Public Health; Research; Research Priority; Resolution; Resources; Sampling; Scientist; Signal Transduction; Signs and Symptoms; Source; South Africa; Study models; Techniques; Testing; Training; Treatment outcome; Tuberculosis; Work; World Health Organization; career; clinical care; clinical diagnosis; cohort; diagnostic accuracy; diagnostic tool; disease diagnosis; genome sequencing; genomic data; health equity; improved; mortality; multiple data sources; national surveillance; novel; pathogen; prevent; programs; response; screening; spatial epidemiology; surveillance data; transmission process; treatment optimization; tuberculosis diagnostics; whole genome

Project Summary/Abstract: Tuberculosis is among the top ten causes of global mortality among children <5 years. Each year, over 1 million tuberculosis cases occur among children <15 years worldwide, and nearly one quarter of those children die. Approximately 80% of those deaths occur among children <5 years. Alleviating the burden of pediatric tuberculosis and mortality requires 1) enhanced efforts to prevent transmission to children and 2) treating more children for tuberculosis. Identifying hotspots of tuberculosis transmission can inform spatially-targeted, community-level tuberculosis screening interventions to limit transmission to children. While national tuberculosis programs maintain tuberculosis surveillance registers which represent a potential source of data to investigate transmission patterns, high local tuberculosis incidence may not provide a reliable signal for transmission. In Aim 1, I will investigate whether local overrepresentation of young children in national tuberculosis surveillance data produces a signal for transmission hotspots. To test this hypothesis, I will interrogate spatial models to identify locations where young children are locally overrepresented in surveillance data from the Republic of Moldova. I will compare alignment of these locations to locations with spatial and genomic evidence of transmission. It is estimated that 96% of global childhood mortality due to tuberculosis occurs among children not receiving treatment. Identifying and treating more children with tuberculosis at peripheral health facilities provides an opportunity to reduce child mortality. Preliminary evidence suggests that a majority of antituberculosis treatment-decisions can be made on the basis clinical signs and symptoms alone. In Aim 2, I will optimize treatment decision-making for pediatric tuberculosis at peripheral health facilities in high-burden settings. I will estimate the contribution of clinical evidence to diagnosis by analyzing pediatric tuberculosis diagnostic evaluation data sourced from multiple cohorts in different settings. I will use decision analytic models to inform when clinical diagnosis is sufficient and when additional diagnostic investigation informs decision-making. This training plan proposes to improve the care of pediatric tuberculosis by applying modeling methods to address questions in pediatric tuberculosis control (Aim 1) and care (Aim 2), reflecting the applicant’s public health MD-PhD training. Upon completion of this fellowship, the applicant will be prepared for a career as an independently productive physician-scientist specializing in pediatric infectious disease. This training in spatial and decision analytic modeling; clinical care for children with tuberculosis and other infectious disease; and research conduct will prepare the applicant to study at the intersection of infectious disease and health equity.

项目摘要/摘要：结核病是全球5岁以下儿童死亡的十大原因之一