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Defining Multimorbidity Space: Structural Characteristics, Spatial Variation of Inpatient Multimorbidity Networks (IMN), and Coronary Heart Disease

机译：定义多发病率空间：住院病人多发病率网络（IMN）的结构特征，空间变异和冠心病

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摘要

Adults in the United States suffer from two or more chronic conditions at the same time (i.e. multimorbidity). Multiple chronic illnesses, such as coronary heart disease, cancer, and diabetes, dramatically shorten life expectancy and present the individual and healthcare system with numerous challenges. To date, no study has assessed multimorbidity and how it varies spatially using quantitative network analysis (QNA), exploratory spatial data analysis (ESDA), and the State Inpatient Discharge Database (HCUP SID). The goals of this study are first, to test the application of QNA as a complementary visualization and analytical tool; second, to explore the geographic variation of multimorbidity and coronary heart disease at the sub-state or county level; lastly, to examine if patterns differ based on gender, race and ethnicity. A cross sectional study design was implemented using the North Carolina HCUP SID. Visualization of multimorbidity networks was successfully demonstrated using QNA. Differences were detected between gender, race and ethnicity impatient multimorbidity networks (IMN). Relationships were observed between underlying social determinants of health and the average weighted degree of coronary heart disease. Multimorbidity varied spatially and average weighted degree of IMN was not distributed randomly; characteristics of multimorbidity space. Mecklenburg, Guilford and Wake Counties had the highest average weighted degree for non-Hispanic white and non-Hispanic black IMN. Limitations include endogeneity, quality of data, missing data, and selection bias. Causal inference cannot be made based on pattern layout of node interactions or generalization to other populations. In conclusion, QNA, network visualization and ESDA are useful exploratory and descriptive tools for studying multimorbidity. This study contributes to new measures and improved understanding of the geographic burden of multimorbidity at the sub-state level.
机译：美国的成年人同时患有两种或多种慢性病（即多发病）。多种慢性疾病，例如冠心病，癌症和糖尿病，极大地缩短了预期寿命，并给个人和医疗系统带来了众多挑战。迄今为止，尚无研究使用定量网络分析（QNA），探索性空间数据分析（ESDA）和州住院患者出院数据库（HCUP SID）来评估多发病率及其在空间上的变化。这项研究的目标首先是测试QNA作为补充的可视化和分析工具的应用。其次，探索在州或县以下的多发病率和冠心病的地理变化；最后，根据性别，种族和种族，检查模式是否不同。使用北卡罗莱纳州HCUP SID实施了横断面研究设计。使用QNA已成功演示了多发病率网络的可视化。在性别，种族和种族不耐烦多发病网络（IMN）之间发现了差异。观察到健康的基本社会决定因素与冠心病平均加权程度之间的关系。多发病率在空间上变化，IMN的平均加权度不是随机分布的；多发病率空间的特征。梅克伦堡，吉尔福德和维克县的非西班牙裔白色和非西班牙裔黑色IMN的平均加权程度最高。局限性包括内生性，数据质量，数据缺失和选择偏见。无法根据节点交互的模式布局或对其他总体的概括来进行因果推理。总之，QNA，网络可视化和ESDA是研究多发病的有用的探索性和描述性工具。这项研究有助于采取新措施，并改善了对次州一级多发病率地理负担的了解。

著录项

  • 作者

    Farrow-Chestnut, Tonya E.;

  • 作者单位

    The University of North Carolina at Charlotte.;

  • 授予单位 The University of North Carolina at Charlotte.;
  • 学科 Geography.;Public health.;Public policy.
  • 学位 Ph.D.
  • 年度 2018
  • 页码 324 p.
  • 总页数 324
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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