Identifying social isolation themes in NVDRS text narratives using topic modeling and text-classification methods
Journal:
arXiv
Published Date:
Jun 18, 2025
Abstract
Social isolation and loneliness, which have been increasing in recent years
strongly contribute toward suicide rates. Although social isolation and
loneliness are not currently recorded within the US National Violent Death
Reporting System's (NVDRS) structured variables, natural language processing
(NLP) techniques can be used to identify these constructs in law enforcement
and coroner medical examiner narratives. Using topic modeling to generate
lexicon development and supervised learning classifiers, we developed
high-quality classifiers (average F1: .86, accuracy: .82). Evaluating over
300,000 suicides from 2002 to 2020, we identified 1,198 mentioning chronic
social isolation. Decedents had higher odds of chronic social isolation
classification if they were men (OR = 1.44; CI: 1.24, 1.69, p<.0001), gay (OR =
3.68; 1.97, 6.33, p<.0001), or were divorced (OR = 3.34; 2.68, 4.19, p<.0001).
We found significant predictors for other social isolation topics of recent or
impending divorce, child custody loss, eviction or recent move, and break-up.
Our methods can improve surveillance and prevention of social isolation and
loneliness in the United States.
