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Analysis of National Lab Database to evaluate the HIV treatment rollout in South Africa

分析国家实验室数据库以评估南非艾滋病毒治疗的推广情况

基本信息

批准号：
9223654
负责人：
Matthew Alexander Pease Fox
金额：
$ 31.78万
依托单位：
WITS HEALTH CONSORTIUM (PTY), LTD
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-02-01 至 2020-01-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9223654
关键词：
Address Affect Big Data Boston CD4 Lymphocyte Count Caring Cells Chronic Disease Clinic Clinical Cohort Studies Counseling Country Data Data Set Databases Development Doctor of Philosophy Early treatment Effectiveness Eligibility Determination Evaluation Face Future Government Guidelines HIV Health Heterogeneity Human immunodeficiency virus test Individual Investments Laboratories Learning Light Link Longitudinal cohort Methods Monitor Outcome Patient-Focused Outcomes Patients Play Policies Policy Maker Population Prospective cohort study Public Sector Publishing Quality of Care Recommendation Recording of previous events Research Research Personnel Resource Allocation Resources Role Running Services Site South Africa South African Spatial Distribution System Testing Time Treatment outcome Universities Viral Viral Load result Wit Work antiretroviral therapy base care seeking cohort design epidemiology study experience follow-up immune health improved innovation migration novel programs public health relevance retention rate scale up success treatment planning treatment program trend uptake

项目摘要

DESCRIPTION (provided by applicant): As South Africa's national antiretroviral therapy (ART) treatment program matures, it faces significant challenges in expanding access to harder to reach populations while also maintaining quality of care for those for whom HIV has become a manageable chronic disease. In light of these challenges, monitoring and evaluation of the treatment program is critical to its sustained improvement. However as policy makers face decisions on how to improve their program, key national parameters on which to base decisions remain unknown. The answers to these questions are critical for planning for changes to national treatment guidelines, yet to date there has been no routine way to answer these questions at a national level and without being heavily influenced by losses from care, which can be significant. We propose using South Africa's entire national public-sector laboratory monitoring database to create a national patient-level cohort. We will use this cohort to evaluate the national program while also building capacity in South Africa to analyze big data to answer important questions about the HIV treatment scale-up. The study is designed as a prospective cohort study of over 20 million laboratory observations and over 3 million patients designed to answer four questions: 1) What is the effect of the national HIV testing campaign on linkage to HIV care and treatment?; 2) What are national retention rates before and after ART initiation?; 3) What are the long run treatment outcomes for the national patient cohort?; and 4) What are the effects of immediate vs. deferred ART eligibility at a CD4 count of 350 on retention and clinical outcomes? To answer these questions we will use novel methods for causal inference, including regression discontinuity design and quantitative bias analysis, and will work to increase South African capacity for implementing these methods. This study is significant because South Africa has the largest treatment program in the world, and is investing 1.4 billion dollars annually, yet it is not clear what the benefits are on a national scale. Individual cohort studies that have tried to assess local impacts have all been clouded by loss to follow up from HIV care and transfer between sites. Our study is innovative because it will generate a unique dataset that will be able to evaluate the impact of the South African HIV treatment program on a national scale. It is also innovative because it will allow the first ever comprehensive look at migration between clinics within South Africa and the first evaluation of treatment impact without being affected by programmatic losses. Finally it is innovative because it will combine the data with innovative designs and analytic approaches that will allow us to assess causal effects.