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Statistics, Informatics & Qualitative Methods (SIQM) Core

统计学、信息学

基本信息

批准号：
10555007
负责人：
Alistair James O'MALLEY
金额：
$ 38.44万
依托单位：
DARTMOUTH-HITCHCOCK CLINIC
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-04-01 至 2028-01-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10555007
关键词：
Address Area Censuses Characteristics Clinic Collaborations Communities Community Outreach Complement Consultations Data Data Analyses Data Collection Data Science Data Set Data Sources Dependence Development Disease Disparity population Electronic Health Record Ensure Environment Feeds Fostering Funding Future Goals Health Health Care Research Health Insurance Healthcare Heterogeneity Informatics Infrastructure Interview Link Maine Maps Measures Medical center Methodology Methods Modeling Nature New England New Hampshire Pathway Analysis Persons Population Qualitative Methods Reporting Research Research Design Research Methodology Research Personnel Research Project Grants Research Support Resources Rural Rural Community Rural Health Rural Health Centers Rural Population Sample Size Science Scoring Method Services Social Network Source Statistical Methods Statistical Models Structure Surveys System Time Vermont Visualization Work analytical method clinical care community engagement data infrastructure data management data repository data visualization data warehouse design disease registry diverse data health care delivery health care service improved innovation insurance claims interest novel practice-based research network primary care practice repository rural healthcare rural patients social health determinants statistics

项目摘要

PROJECT SUMMARY/ABSTRACT Research Core: The Statistics, Informatics, and Qualitative Methods Core The Statistics, Informatics, and Qualitative Methods (SIQM) Core will promote and support rural health care delivery science. Following an integrated mixed-methods research framework and partnering with the Community Engagement and Outreach (CEO) Core, we seek to optimize the rigor, context, and advancement of research across the Center for Rural Health Care Delivery Science. The SIQM Core’s overarching goal is to improve the rigor of the research on important rural health care topics by integrating quantitative and qualitative data analyses into a two-way system where one feeds into the other. The specific areas of focus include supporting the attainment and development of unique datasets, innovative statistical modeling, the construction of accurate statistical inferences, the expansion of informatics and analytics, and the acuity and impact of data visualization. The Core will support the Center’s researchers by developing a robust research data warehouse that responds to their research goals and dually aids in their development as researchers and ultimate graduation to independent funding. The Core will work with datasets that incorporate health insurance claims, disease registry, US Census, electronic health records, geospatial and other sources and forms of data. To complement this, de novo data will be obtained from community stakeholders using surveys, interviews, Community-Engagement Studios, and other forms of mixed-methods research. The data repository will grow with time in breadth and depth to support community needs and priorities and support researchers by expanding the scope of data, to which they will have access as they further their work and prepare their own independent proposals. In addition to integrating and managing data, the SIQM Core will support projects by developing and refining cutting-edge statistical methods, including hierarchical, longitudinal, and geospatial modeling; causal inference methods to account for confounding in observational data (difference-in-difference designs, propensity score methods); and social network analysis. The Core will create an environment across the Center for identifying, sharing, and applying innovative statistical, geoinformatics, data science, and qualitative methods to assist each Research Project Leader. Once established, the SIQM Core will coordinate data and methods, share novel methodological solutions, and generalize these methods for future use.

项目总结/文摘