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The Association of Racial and Ethnic Social Networks with Mental Health Service Utilization Across Minority Groups in the USA.

Sung W ChoiChristal RamosKyungha KimShahinshah Faisal Azim
Published in: Journal of racial and ethnic health disparities (2019)
Though they have comparable prevalence of mental illness, American racial and ethnic minorities are less likely to receive mental health services than white Americans. Minorities are often part of racial and ethnic social networks, which may affect mental health service utilization in two ways. While these networks can encourage service utilization by working as a channel of knowledge spillover and social support, they can also discourage utilization by stigmatizing mental illness. This study examined the association of racial and ethnic social networks with mental health service utilization and depression diagnosis in the USA. Using the 2012 Behavioral Risk Factor Surveillance System (BRFSS) data, a multilevel mixed-effect generalized linear model was adopted, controlling for predisposing, need, and enabling factors of mental health service utilization. The association of racial and ethnic social networks with mental health service utilization and depression diagnosis was significant and negative among African Americans. Despite having a comparable number of bad mental health days, the association was insignificant among Hispanic, Asian, and non-Hispanic white respondents. An African American living in a county where all residents were African American was less likely to utilize mental health services by 84.3-86.8% and less likely to be diagnosed with depression by 76.0-84.8% than an African American living in a county where no residents were African American. These results suggest racial and ethnic social networks can discourage mental health service utilization and should be engaged in efforts to improve mental health, particularly among African American communities in the USA.
Keyphrases
  • african american
  • mental health
  • mental illness
  • depressive symptoms
  • social support
  • risk factors
  • public health
  • artificial intelligence