Methodological Approaches to Understanding Causes of Health Disparities.
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| Title: | Methodological Approaches to Understanding Causes of Health Disparities. |
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| Authors: | Jeffries, Neal (AUTHOR), Zaslavsky, Alan M. (AUTHOR), Diez Roux, Ana V. (AUTHOR), Creswell, John W. (AUTHOR), Palmer, Richard C. (AUTHOR), Gregorich, Steven E. (AUTHOR), Reschovsky, James D. (AUTHOR), Graubard, Barry I. (AUTHOR), Choi, Kelvin (AUTHOR), Pfeiffer, Ruth M. (AUTHOR), Zhang, Xinzhi (AUTHOR), Breen, Nancy (AUTHOR) |
| Source: | American Journal of Public Health. 2019 Suppl 1, Vol. 109, pS28-S33. 6p. |
| Subjects: | Health planning, Health policy, Policy sciences, Health equity, Statistical models |
| Abstract: | 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. [ABSTRACT FROM AUTHOR] |
| Copyright of American Journal of Public Health is the property of American Public Health Association and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Psychology and Behavioral Sciences Collection |
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| Abstract: | 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. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 00900036 |
| DOI: | 10.2105/AJPH.2018.304843 |
