TY - JOUR
T1 - Methodological Approaches to Understanding Causes of Health Disparities
AU - Jeffries, Neal
AU - Zaslavsky, Alan M.
AU - Diez Roux, Ana V.
AU - Creswell, John W.
AU - Palmer, Richard C.
AU - Gregorich, Steven E.
AU - Reschovsky, James D.
AU - Graubard, Barry I.
AU - Choi, Kelvin
AU - Pfeiffer, Ruth M.
AU - Zhang, Xinzhi
AU - Breen, Nancy
N1 - Publisher Copyright:
© 2019 American Public Health Association Inc.. All rights reserved.
PY - 2019/1
Y1 - 2019/1
N2 - 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.
AB - 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.
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U2 - 10.2105/AJPH.2018.304843
DO - 10.2105/AJPH.2018.304843
M3 - Article
C2 - 30699015
AN - SCOPUS:85060905936
SN - 0090-0036
VL - 109
SP - S28-S33
JO - American journal of public health
JF - American journal of public health
ER -