Estimating vital rates in the developing world: A Bayesian process modeling approach

估计发展中国家的生命率：贝叶斯过程建模方法

• 批准号: 9061759
• 负责人: Tyler McCormick
• 金额: $ 12.29万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-05-01 至 2020-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9061759
• 关键词: Address; Advisory Committees; African; Area; Bayesian Modeling; Birth; Birth History; Censuses; Cessation of life; Characteristics; Complex; Country; Data; Data Collection; Data Sources; Demographic and Health Surveys; Demography; Developing Countries; Development; Development Plans; Ecology; Economic Factors; Economics; Environment; Estimation Techniques; European; Event; Family dynamics; Fertility; Fertility Rates; Fertility Study; Future; Goals; Health; Health Care Surveys; Healthcare; Household; Human Resources; Income; Individual; Infant Mortality; Information Systems; Interview; Link; Measurement; Measures; Mentors; Methods; Modeling; Outcome; Pattern; Policies; Population; Population Dynamics; Population Research; Population Sizes; Process; Public Health; Reading; Recommendation; Recording of previous events; Reporting; Reproductive Behavior; Research; Research Infrastructure; Research Personnel; Research Proposals; Research Training; Respondent; Role; Sampling; Sampling Errors; Series; Site; Social Sciences; Source; South Africa; Special Event; Staging; Statistical Methods; Statistical Models; Supervision; Surveys; System; Techniques; Testing; Time; Training; Tweens; Uncertainty; Update; Validation; Variant; Visit; Woman; Work; base; burden of illness; catalyst; child bearing; data collection methodology; design; experience; insight; meetings; migration; novel; programs; reproductive; skills; social; statistics; tool; trend

 DESCRIPTION (provided by applicant): My ultimate goal to establish an independent research agenda that develops novel statistical methods for population research in developing nations and other data-constrained environments. I focus specifically on developing estimates for vital indicators, which are especially critical to understanding population dynamics, developing public programs, and implementing or evaluating public health actions. In most parts of the developing world, there is massive uncertainty about even the most basic indicators. Achieving this objective requires an interdisciplinary skill-set that has three components: (i) expertise in statistical modeling, (ii) an understanding the historical, social/cultural and economc underpinnings of core themes in demography and (iii) experience with the complex realities of collecting demographic data in developing countries. After doctoral work in statistics, I am prepared for the first of these three components. My training and development plan proposes a series of activities to address the second two components. My mentoring team consists of Stewart Tolnay (mentor), Sam Clark (co- mentor), Adrian Raftery (advisory committee) and Basia Zaba (advisory committee). First, I will pursue training to understand, and eventually contribute to, substantive questions in demography and ecology. I will work to understand how various social, cultural, and economic factors relate to individuals' demographic outcomes and how these outcomes relate to population dynamics. Though social science questions motivate my study of statistics, I have no formal training in demography and my only formal training in the social sciences is at an undergraduate level. I address this gap in my current training through coursework and directed readings with a highly skilled and experience mentoring team. Second, my statistical training leaves me unprepared to address the complex realities of data collection in developing nations. My statistical training emphasizes analysis tools for data already collected, often under restrictive assumptions. Data used for demographic research in developing nations, however, often violates these assumptions and nonsampling error is rampant. I address this gap through coursework as well as fieldwork experiences. I propose two substantial (consisting of approximately 6-8 weeks each) fieldwork experiences at the Agincourt Health and Demographic Surveillance System in the northeast of South Africa. The Agincourt site, which features prominently in both my development and training plans, includes annual census and special events updates (systematic recording of all births, deaths and migrations), making Agincourt one of the very few places with both high-quality validation data and infrastructure to implement and evaluate new data collection methodologies. During my visits I will, under the supervision of my mentoring team, observe interviews, meet key survey research personnel, and discuss the findings and ideas of my research proposal with Agincourt investigators. My experiences in Agincourt are a tangible link between the research and training components of my proposal. The research proposal focuses on estimating fertility in such situations and understanding the key drivers of changes in national and regional fertility patterns. Fertility is an important determinant of population size and composition. Quality information about fertility is key for formulating national and regional policy, developing public programs, and implementing and evaluating public health actions. I propose a technique for estimating fertility in developing countries that emphasizes the relationship between data collection, model, and outcome. An overarching Bayesian modeling framework incorporates nonsampling error, draws strength from similar respondents, and naturally shares uncertainty between different data sources. The proposed methods would reduce bias by adjusting for variability introduced through nonsampling errors, provide statistically principled measures of uncertainty for national and subnational estimates and generate recommendations for efficient survey design. Using the same modeling framework, I will also evaluate specific hypotheses about observed and projected trends in fertility. Aim 1 develops a model to estimate national and subnational fertility rates in developing nations and evaluates that model using both DHS and Agincourt data. Aim 2 proposes a microsimulation environment that facilitates testing hypotheses about fertility patterns and dynamics at an individual or household level. This environment also facilitates testing hypotheses about measurement error, which will again be evaluated extensively using Agincourt and DHS data. Aim 3 develops models to project future fertility rates that incorporate uncertainty in the underlying individual-level covariates that are associated with changes in national and regional rates. I will also make projections using both past Agincourt data (a census has been in place for approximately 20 years) and make actual predictions of future fertility rates in Agincourt that I will evaluate at the end of the project priod.

 我的最终目标是建立一个独立的研究议程，为发展中国家和其他数据受限环境的人口研究开发新的统计方法。我特别关注发展重要指标的估计，这对了解人口动态，制定公共计划，实施或评估公共卫生行动特别重要。在大多数发展中国家，即使是最基本的指标也存在巨大的不确定性。要实现这一目标，就需要具备一套跨学科的技能，其中包括三个组成部分：㈠统计建模方面的专门知识; ㈡了解人口学核心主题的历史、社会/文化和经济基础; ㈢在发展中国家收集人口数据的复杂现实方面的经验。在统计学博士毕业后，我为这三个组成部分中的第一个做好了准备。我的培训和发展计划提出了一系列活动，以解决后两个组成部分。我的导师团队由斯图尔特·托尔奈（导师）、萨姆·克拉克（共同导师）、阿德里安·拉夫特里（顾问委员会）和巴西亚·扎巴（顾问委员会）组成。首先，我将继续接受培训，以了解并最终为人口学和生态学的实质性问题做出贡献。我将努力了解各种社会，文化和经济因素如何与个人的人口统计结果以及这些结果如何与人口动态相关。虽然社会科学问题激发了我的统计学研究，但我没有接受过正式的人口学培训，我唯一的社会科学正规培训是在本科阶段。我在目前的培训中通过课程作业和指导性阅读与一个高技能和经验丰富的指导团队来解决这个差距。 其次，我的统计培训使我没有准备好应对发展中国家数据收集的复杂现实。我的统计训练强调分析工具已经收集的数据，通常是在限制性假设下。然而，用于发展中国家人口研究的数据往往违反这些假设，非抽样误差猖獗。我通过课程作业以及实地考察经验来解决这个差距。我建议在南非东北部的阿金库尔卫生和人口监测系统进行两次实质性的实地考察（每次约6-8周）。阿金库尔网站在我的发展和培训计划中占有突出地位，包括年度人口普查和特别活动更新（系统记录所有出生、死亡和移徙），使阿金库尔成为少数几个既有高质量验证数据又有基础设施来实施和评估新的数据收集方法的地方之一。在访问期间，我将在我的指导小组的监督下，观察访谈，会见主要的调查研究人员，并与阿金库尔调查人员讨论我的研究建议的结果和想法。 我在阿金库尔的经历是我的建议中的研究和培训部分之间的一个具体联系。研究建议的重点是估计这种情况下的生育率，并了解国家和区域生育模式变化的主要驱动因素。生育率是人口规模和组成的一个重要决定因素。有关生育的高质量信息是制定国家和地区政策、制定公共计划以及实施和评估公共卫生行动的关键。我提出了一种技术，估计生育率在发展中国家，强调数据收集，模型和结果之间的关系。总体贝叶斯建模框架包含非抽样误差，从类似的受访者中汲取力量，并自然地在不同的数据源之间共享不确定性。拟议的方法将通过调整非抽样误差带来的变异性来减少偏差，为国家和国家以下一级的估计数提供不确定性的统计原则性措施，并为有效的调查设计提出建议。使用相同的建模框架，我还将评估有关生育率观察和预测趋势的具体假设。 目标1开发了一个模型，以估计发展中国家的国家和国家以下各级的生育率，并使用人口与健康调查和阿金库尔数据对该模型进行评价。目标2提出了一个微观模拟环境，便于在个人或家庭层面检验关于生育模式和动态的假设。这一环境还有助于检验关于测量误差的假设，这将再次利用阿金库尔和国土安全部的数据进行广泛评估。目标3开发了预测未来生育率的模型，这些模型将不确定性纳入了潜在的个人水平协变量， 与国家和地区利率的变化相关。我还将使用过去的阿金库尔数据（人口普查已经进行了大约20年）进行预测，并对阿金库尔未来的生育率进行实际预测，我将在项目结束时进行评估。