AUTHOR=Näher Anatol-Fiete , Rummel-Kluge Christine , Hegerl Ulrich 

TITLE=Associations of Suicide Rates With Socioeconomic Status and Social Isolation: Findings From Longitudinal Register and Census Data

JOURNAL=Frontiers in Psychiatry

VOLUME=Volume 10 - 2019

YEAR=2020

URL=https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2019.00898

DOI=10.3389/fpsyt.2019.00898

ISSN=1664-0640

ABSTRACT=Suicide represents a major challenge to public mental health. In order to provide empirical evidence for prevention strategies, we hypothesized current and accumulated levels of low socioeconomic status (SES) and high social isolation (SI) to be linked to increased district suicide rates in N = 390 administrative districts since SES and SI are associated with mental illness. Effects of SES on suicide rates were further expected to be especially pronounced
in districts with individuals showing high SI levels as SI reduces the reception of social support and moderates the impact of low SES on poor mental health. We linked German Microcensus data to register data on all 149,033 German suicides between 1997 and 2010 in N = 390 administrative districts and estimated Prentice and Sheppard’s model for aggregate data to test the hypotheses. Accumulated effects were calculated by Koyck transforming the initial model. Depending on model specifications, we observed increases in suicide rates by 1.72%, p < 0.01; 1.62%, p < 0.001 and 1.81%, p < 0.001 (accumulated effect) for one percent increases of district unemployment, of 1.87%, p < 0.01; 1.72%, p < 0.05 and 1.92%, p < 0.05 (accumulated effect) for one percent increases in one-person-households and of 0.74%, p < 0.01; 0.71%, p < 0.001 and 0.78%, p < 0.001 (accumulated effect) for one percent decreases in single persons’ incomes. The results represent appropriatestarting points for the development of suicide prevention strategies. For the definition of more precise measures, future work should focus on the causal mechanisms resulting in suicidality incorporating individual level data.