Neal Jeffries is with the National Heart, Lung, and Blood Institute, National Institutes of Health (NIH), Bethesda, MD. Alan M. Zaslavsky is with the Department of Health Care Policy, Harvard Medical School, Boston, MA. Ana V. Diez Roux is with the Dornsife School of Public Health, Drexel University, Philadelphia, PA. John W. Creswell is with the Department of Family Medicine, University of Michigan, Ann Arbor. Richard C. Palmer, Kelvin Choi, Xinzhi Zhang, and Nancy Breen are with the National Institute on Minority Health and Health Disparities, NIH, Bethesda. Steven E. Gregorich is with the Department of Medicine, University of California, San Francisco. James D. Reschovsky is with Mathematica Policy Research, Washington, DC. Barry I. Graubard and Ruth M. Pfeiffer are with the National Cancer Institute, NIH, Bethesda. Richard C. Palmer and Nancy Breen are also Guest Editors for this supplement issue.
Am J Public Health. 2019 Jan;109(S1):S28-S33. doi: 10.2105/AJPH.2018.304843.
Understanding health disparity causes is an important first step toward developing policies or interventions to eliminate disparities, but their nature makes identifying and addressing their causes challenging. Potential causal factors are often correlated, making it difficult to distinguish their effects. These factors may exist at different organizational levels (e.g., individual, family, neighborhood), each of which needs to be appropriately conceptualized and measured. The processes that generate health disparities may include complex relationships with feedback loops and dynamic properties that traditional statistical models represent poorly. Because of this complexity, identifying disparities' causes and remedies requires integrating findings from multiple methodologies. We highlight analytic methods and designs, multilevel approaches, complex systems modeling techniques, and qualitative methods that should be more broadly employed and adapted to advance health disparities research and identify approaches to mitigate them.
了解健康差异的原因是制定消除差异的政策或干预措施的重要第一步，但这些原因的性质使得确定和解决它们具有挑战性。潜在的因果因素往往相互关联，使得难以区分它们的影响。这些因素可能存在于不同的组织层次（例如，个体、家庭、社区），每个层次都需要进行适当的概念化和测量。产生健康差异的过程可能包括与反馈循环和动态特性的复杂关系，而传统的统计模型很难表示这些关系。由于这种复杂性，确定差异的原因和补救措施需要整合来自多种方法的发现。我们强调了应该更广泛地采用和适用于推进健康差异研究并确定减轻这些差异的方法的分析方法和设计、多层次方法、复杂系统建模技术和定性方法。
