A new study carried out by a team of researchers from Weill Cornell Medicine and Columbia University Vagelos College of Physicians and Surgeons has identified three distinct profiles featuring socioeconomic factors that have been linked to a higher risk of suicide.
The findings, published in the latest Nature Mental Health journal, have the potential to improve the efficacy of suicide prevention strategies, with the aim being to help counteract the significant rise in suicide rates in the US that has been seen over the last 20 years.
The study itself uses unsupervised machine learning to analyse social health determinants such as poor housing, poverty, harmful environmental exposure, poor access to healthcare and high family stress, all of which have been found to contribute to the risk of suicide.
This technology is capable of processing huge datasets without guidance or labels to reveal hidden patterns and relationships, free from the bias of partial data or researcher assumptions.
REMOTE cluster
One of the identified clusters – REMOTE – affected those living in mountainous regions or rural areas that relied on energy sources like coal that contribute to pollution. These people were typically elderly and living in areas with high levels of abandoned homes and old poor-quality housing.
COPE cluster
The COPE cluster identified people living with complicated family dynamics and significant social/environmental stressors, such as single parents or grandparents raising children, which created different family structures, with poverty a particular problem. Extreme heat was another environmental factor facing these communities.
DIVERSEcluster
The third cluster – DIVERSE – identified people living in racially and ethnically diverse urban areas on the coasts, with large immigrant populations, high housing costs, extreme income inequality, poor air quality and poor access to healthcare.
Commenting on the results, lead author of the study Yunyu Xiao – assistant professor of population health sciences and psychiatry at Weill Cornell Medicine – said: “Our findings could help public health workers develop more tailored interventions that address the specific and different social determinants of health profiles that each community faces to more effectively lower suicide rates.
“This research moves beyond the idea that suicide is only an individual or medical issue. Instead, it shows that the places we live – shaped by history, policy and economics – play a powerful role in shaping who is at risk.”
Potential interventions to help reduce suicide risks among the REMOTE cluster include driving down social isolation and increasing mental healthcare access. For the COPE cluster, community-based interventions that tackle economic stress and substance abuse could be more effective.
And for the DIVERSE cluster, increasing healthcare accessibility, enhancing air quality and improving culturally adapted mental health programmes could help prevent suicides.
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