Study identifies social steps to mitigate mental illness

Mental illnesses are thought to be caused by both biological and environmental factors in complex interaction. Among the environmental contributors are a wide range of social, economic, and demographic factors known as “social determinants.”

Researcher Adam Skinner and colleagues have used dynamic Bayesian network analysis to infer the complex causal networks that link social determinants to mental health in a nationally representative sample, consisting of around 25,000 participants in the Household, Income and Labor Dynamics in Australia (HILDA) Survey. The research is published in the journal PNAS Nexus.

The authors identified variables that directly predicted mental health problems: physical health, loneliness, satisfaction with the local community, and satisfaction with personal finances. In addition, the authors found that mental health problems influenced many of these same variables, suggesting feedback loops.

According to the authors, these results can be used to identify the social and economic interventions that could most efficiently and effectively reduce mental illness at a population scale. For example, if every participant was satisfied with their financial situation, rates of mental illness would drop by around 3 percentage points, eliminating almost a third of mental illness in Australia.

Significant public health gains could also be achieved by promoting local community engagement, physical well-being, and participation in volunteer or charity work and paid employment, perhaps through a nationally funded job guarantee program.

Provided by
PNAS Nexus

Mental illnesses are thought to be caused by both biological and environmental factors in complex interaction. Among the environmental contributors are a wide range of social, economic, and demographic factors known as “social determinants.”

Researcher Adam Skinner and colleagues have used dynamic Bayesian network analysis to infer the complex causal networks that link social determinants to mental health in a nationally representative sample, consisting of around 25,000 participants in the Household, Income and Labor Dynamics in Australia (HILDA) Survey. The research is published in the journal PNAS Nexus.

The authors identified variables that directly predicted mental health problems: physical health, loneliness, satisfaction with the local community, and satisfaction with personal finances. In addition, the authors found that mental health problems influenced many of these same variables, suggesting feedback loops.

According to the authors, these results can be used to identify the social and economic interventions that could most efficiently and effectively reduce mental illness at a population scale. For example, if every participant was satisfied with their financial situation, rates of mental illness would drop by around 3 percentage points, eliminating almost a third of mental illness in Australia.

Significant public health gains could also be achieved by promoting local community engagement, physical well-being, and participation in volunteer or charity work and paid employment, perhaps through a nationally funded job guarantee program.

Provided by
PNAS Nexus