Interdisciplinary and multi-level approach to estimate the disease burden and outcomes of childhood tuberculous meningitis.

跨学科和多层次的方法来估计儿童结核性脑膜炎的疾病负担和结果。

• 批准号: 10453976
• 负责人: Karen Du Preez
• 金额: $ 8.09万
• 依托单位: STELLENBOSCH UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-02-05 至 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10453976
• 关键词: Adult; Algorithms; Autopsy; Caring; Case Fatality Rates; Cause of Death; Cessation of life; Child; Child health care; Childhood; Clinic; Clinical Management; Cohort Studies; Community Health; Country; Data; Databases; Detection; Development; Diagnosis; Diagnostic; Disease; Disease Management; Disease Outcome; Disease Progression; Drug resistance in tuberculosis; Early Diagnosis; Early treatment; Economics; Family health status; Future; Geography; Goals; HIV; Health; Health Services; Heterogeneity; Hospitals; Incidence; Individual; Infection; International; Intervention; Laboratories; Leadership; Literature; Location; Meningeal Tuberculosis; Meningitis; Mentors; Methods; Modeling; Morbidity - disease rate; Mycobacterium tuberculosis; Neurologic; Neurological outcome; Outcome; Pharmaceutical Preparations; Pharmacy facility; Policies; Population; Prevalence; Prevention; Preventive therapy; Program Evaluation; Records; Reporting; Research; Research Personnel; Research Support; Resources; Risk; Scientist; Severity of illness; South Africa; South African; Symptoms; System; Techniques; Training; Translating; Treatment outcome; Tuberculosis; United States Public Health Service; Universities; Work; World Health Organization; age related; analytical method; attributable mortality; burden of illness; career; career development; chemotherapy; comorbidity; demographics; disability; disorder risk; effective therapy; experience; family burden; high risk; improved; low and middle-income countries; mathematical model; mortality; multiple data sources; novel; prevent; programs; prospective; public health priorities; public health research; skills; social; spatiotemporal; support network; surveillance data; surveillance strategy; tuberculosis treatment

PROJECT SUMMARY Candidate: Dr. Karen du Preez has dedicated herself to a research career focused on improving health care for children in low and middle income countries (LMIC). Her overall career development goal is to become an independent clinician scientist with a comprehensive, interdisciplinary skill set and research network to efficiently answer high priority public health questions, and have the ability to translate research findings into meaningful policy and implementation changes to improve health care for children in South Africa, and globally. This overall goal is supported by three specific training aims: 1) To understand and apply specialized analytical skills to public health research, specifically disease estimation modelling techniques and spatial analytic methods; 2) To strengthen clinical, management and leadership expertise; and 3) To develop the ability to translate research into meaningful policy changes and implementation. Dr. du Preez has the full support of the Desmond Tutu TB Centre, an internationally recognized research center at Stellenbosch University, Cape Town, South Africa and has assembled a mentoring team highly experienced in all facets of pediatric TB research, and actively involved at both national and international level contributing to scientific steering committees and supporting pediatric TB program implementation. Research: The lack of reliable surveillance data for the disease burden and spectrum of pediatric tuberculosis (TB) is a challenge that limits our ability to appropriately manage the disease globally. TB meningitis (TBM) is a severe form of pediatric tuberculosis with very poor outcomes and often consequent lifelong neurological disability if not diagnosed and treated early. Very little childhood TBM surveillance data exist and the overall goal of this research is therefore to determine the burden, incidence and outcomes of childhood TBM at three levels: global, national (South Africa) and sub-national (Cape Town district), whilst identifying opportunities for prevention, earlier diagnosis and treatment. Mathematical modelling techniques will be used to estimate the global burden of childhood TBM disease and attributable mortality, by country and WHO region (Specific Aim 1). Routine TB surveillance data from South Africa, spanning 14 years (2004-2017), will be used to determine the spatiotemporal heterogeneity of the national disease burden and treatment outcomes of childhood TBM and identify population-level drivers of high burden locations and unfavorable outcomes (Specific Aim 2). Prospective, complementary surveillance strategies will identify all diagnosed and undiagnosed childhood TBM cases (<15 years) within the Cape Town health district over a 2-year period. All identified children will be eligible for enrolment in an observational cohort study that will collect data on diagnostic certainty, disease severity, comorbidities, outcomes and missed opportunities for both TB preventive therapy and earlier diagnosis, with the aim of informing earlier diagnosis (diagnostic algorithms) and targeted interventions for improved TBM prevention and care (Specific Aim 3).

项目摘要 候选人：Karen du Preez博士致力于改善医疗保健的研究事业， 低收入和中等收入国家（LMIC）。她的总体职业发展目标是成为一名 独立的临床科学家，具有全面的跨学科技能和研究网络， 回答高度优先的公共卫生问题，并有能力将研究结果转化为有意义的 政策和实施变化，以改善南非和全球儿童的卫生保健。这一总体 这一目标由三个具体的培训目标支持：1）理解并应用专业分析技能，以公众 卫生研究，特别是疾病估计建模技术和空间分析方法; 2） 加强临床，管理和领导专业知识;和3）发展翻译研究的能力 有意义的政策变化和实施。杜Preez博士得到了Desmond Tutu TB的全力支持 该中心是南非开普敦斯泰伦博斯大学的一个国际公认的研究中心， 组建了一支在儿科结核病研究的各个方面都经验丰富的指导团队， 在国家和国际层面，为科学指导委员会做出贡献，并支持儿科结核病 方案实施。 研究：缺乏儿童结核病疾病负担和谱的可靠监测数据 (TB)是一个挑战，限制了我们在全球适当管理这一疾病的能力。结核性脑膜炎（TBM）是一种 一种严重的小儿结核病，结局非常差，通常会导致终身神经系统疾病 如果不及早诊断和治疗，就会造成残疾。很少有儿童期TBM监测数据存在， 因此，本研究的目的是确定3岁时儿童TBM的负担、发病率和结局。 全球、国家（南非）和国家以下一级（开普敦区），同时确定 预防、早期诊断和治疗。数学建模技术将用于估计 按国家和世卫组织区域分列的全球儿童结核性脑膜炎疾病负担和归因死亡率（具体目标1）。 南非14年（2004-2017年）的常规结核病监测数据将用于确定 国家疾病负担的时空异质性和儿童TBM的治疗结果， 确定高负担地区和不利结果的人口水平驱动因素（具体目标2）。 前瞻性的补充监测策略将识别所有确诊和未确诊的儿童结核性脑膜炎 开普敦卫生区2年内的病例（<15岁）。所有被确认的孩子都有资格 入组观察性队列研究，该研究将收集诊断确定性，疾病严重程度， 结核病预防性治疗和早期诊断的共病、结局和错失的机会， 目的是提供早期诊断（诊断算法）和针对性干预措施，以改善TBM 预防和护理（具体目标3）。