Sexually Transmitted Infection Epidemics: Bayesian Prevalence and Size Estimates

性传播感染流行病：贝叶斯患病率和规模估计

• 批准号: 8153072
• 负责人: ADRIAN E RAFTERY
• 金额: $ 37.05万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-15 至 2016-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8153072
• 关键词: AIDS/HIV problem; Accounting; Acquired Immunodeficiency Syndrome; Africa South of the Sahara; Anti-Retroviral Agents; Bayesian Method; Behavioral; Categories; Cessation of life; Child; Clinic; Communities; Computer software; Country; Data; Data Collection; Data Sources; Developing Countries; Development; Disease; Enrollment; Epidemic; Expert Opinion; Female; General Population; Goals; Grant; HIV; HIV Infections; Household; Individual; Infection; Joints; Life; Maps; Methodology; Methods; Modeling; Network-based; Orphan; Policies; Policy Maker; Population; Population Sizes; Prevalence; Publishing; Research; Respondent; Risk; Sampling; Sexually Transmitted Diseases; Source; Statistical Methods; Subgroup; Surveillance Program; Survey Methodology; Surveys; Time; Uganda; Uncertainty; United Nations; Update; Work; base; data modeling; epidemiological model; high risk; intravenous drug user; meetings; men who have sex with men; programs; scale up; sex; surveillance data; time use

DESCRIPTION (provided by applicant): The goal of this proposal is to develop new statistical methods for estimating prevalence and the size of at-risk groups in sexually transmitted infection epidemics. We also aim to estimate other policy-relevant quantities such as the number of orphans and children impacted, and treatment needs. We consider two types of epidemic: generalized epidemics, in which the disease is spread throughout the general population, and concentrated epidemics, in which the disease is largely confined to at-risk groups such as intravenous drug users, sex workers and men who have sex with men. Our goal is to develop methods appropriate for countries with sparse data, most of which are developing countries. For generalized epidemics, we propose a susceptible-infected model with a stochastic infection rate. We will develop a Bayesian approach to estimating the model from clinic data over time and sparse household surveys. We will extend the model to take account of changes in treatment availability, and to produce provincial as well as national estimates. For concentrated epidemics, we will first develop new integrated Bayesian methods for estimating the sizes of the main at-risk groups from fragmentary data, including mapping or hotspot data, behavioral surveillance data, program enrollment data and the overlaps between them. Much recent data comes from two relatively new network-based data collection methods, respondent-driven sampling (RDS) and the network scale-up method. We will develop methods for estimating unknown population size from multiple data sources, including RDS and network scale-up. We will then develop methods for estimating at-risk group size and prevalence over time, using a dynamic Bayesian model. We will produce publicly available software to implement our new methods and make them available to the research community and policy-makers. PUBLIC HEALTH RELEVANCE: This project will develop new statistical methods for estimating prevalence and the size of at-risk groups in sexually transmitted infection epidemics as they change over time. It will also estimate policy-related quantities such as the number of orphans and children impacted, and treatment needs. The at-risk groups considered include intravenous drug users, sex workers and men who have sex with men. The methods will be applicable in countries with sparse data of variable quality, many of which are developing countries.

描述（由申请人提供）：本提案的目标是开发新的统计方法来估计性传播感染流行病的患病率和高危人群的规模。我们还旨在估计其他与政策相关的数量，例如受影响的孤儿和儿童的数量以及治疗需求。我们考虑两种类型的流行病：普遍流行病，即该疾病在整个人群中传播；以及集中流行病，其中该疾病主要局限于高危人群，如静脉吸毒者、性工作者和男男性行为者。我们的目标是开发适合数据稀疏国家（其中大多数是发展中国家）的方法。对于普遍的流行病，我们提出了具有随机感染率的易感感染模型。我们将开发一种贝叶斯方法，根据一段时间内的临床数据和稀疏的家庭调查来估计模型。我们将扩展该模型，以考虑治疗可用性的变化，并产生省级和国家级的估计。对于集中流行病，我们将首先开发新的综合贝叶斯方法，根据零散数据估计主要高危人群的规模，包括地图或热点数据、行为监测数据、项目注册数据以及它们之间的重叠。最近的许多数据来自两种相对较新的基于网络的数据收集方法，即受访者驱动抽样（RDS）和网络扩大方法。我们将开发从多个数据源（包括 RDS 和网络扩展）估计未知种群规模的方法。然后，我们将使用动态贝叶斯模型开发估计高危人群规模和患病率随时间变化的方法。我们将开发公开可用的软件来实施我们的新方法，并将其提供给研究界和政策制定者。 公共卫生相关性：该项目将开发新的统计方法，用于估计性传播感染流行病随时间变化的患病率和高危人群的规模。它还将估计与政策相关的数量，例如受影响的孤儿和儿童的数量以及治疗需求。考虑的高危人群包括静脉注射吸毒者、性工作者和男男性行为者。这些方法将适用于数据稀疏、质量参差不齐的国家，其中许多是发展中国家。