CORE--DATA MANAGEMENT & STATISTICS & COMMUNITY IMPACT MODELING

数据管理、统计和社区影响建模（DSM） 核心构成所有统计和管理的中心资源 需求每个其他核心和研究项目生成的数据 将移交数据库管理股， 集成SIR/DBMS数据管理系统，旨在满足所有需求 在数据的清理、组织和检索方面， 用于计划和临时分析。这些活动将由下列人员进行： 在联网系统上工作的数据管理员和数据录入员， 专门为数据管理操作配置的奔腾PC， 核心主管的直接监督。 统计股将负责协调规划和 执行中心研究、统计和信息中心进行的所有分析 编程人员。研究项目有一定的 共同的特点，使自己tot类似的分析方法。 这些包括一个共享的前瞻性队列，纵向 数据、对较基线值变化的兴趣，以及 定义哮喘和不良反应的二分和连续变量 发展成果的衡量。 除了这些传统的核心功能外， 建模单元包含在DSM核心中，以：（1）提供研究 拥有社区一级信息集中资源的项目;（2） 提供制定社区风险量化措施的专门知识; (3)将这些措施纳入具体项目的统计模型， 在控制了个体风险因素后，确定是否存在不良反应， 儿童的健康结果与社区水平的风险有关;（4） 确定个体风险和不利因素之间的关系 健康结果随社区风险的变化而变化，以及（5） 确定计划的干预措施的效果是否受到以下因素的影响： 社区风险。

The Data Management and Statistics and Community Impact Modeling (DSM) Core constitutes the central resource for all statistical, and management needs. Data generated by each of the other cores and research projects will be transferred to the Database Management Unit for storage in an integrated SIR/DBMS data management system designed to meet all the needs of the Center with respect to cleaning, organization and retrieval of data for planned and ad hoc analyses. These activities will be carried out by a data manager and a data entry clerk working on a networked system of Pentium PCs specifically configured for data management operations, under the direct supervision of the Core Director. The Statistics Unit will be responsible for coordinating the planning and implementation of all analyses conducted by Center research, statistical and programming staff. The proposed research projects have certain features in common that lend themselves tot similar analytic methods. These include a shared prospective cohort, the collection of longitudinal data, interest in change from baseline values, and a combination of dichotomous and continuous variables that defined the asthma and adverse developmental outcome measures. In addition to these traditional core functions, a Community Impact Modeling Unit is included in the DSM core to: (1) provide the research projects with a centralized resource for community-level information; (2) provide expertise in developing quantitative measures of community risk; (3) incorporate these measures into project-specific statistical models to determine whether, after controlling for individual risk factors, adverse health outcomes of children are related to community level risk; (4) determine whether relationships between individual risks and adverse health outcomes vary as a function of community level risk and (5) establish whether the effects of the planned interventions are affected by community level risk.