Suicide stands as a major public health problem with complex etiological roots, tremendous societal cost through lost productivity and medical care, and resistance to medical and public health interventions. Previous research has implicated a wide range of risk and protective factors, with strong effects documented for the social environment. However, research has neither 1) identified which levels of the social environment (e.g., interpersonal relationships, families, neighborhoods, macro-environments) are most influential in suicide outcomes for women and men at different stages of the life course nor 2) how social environmental contexts interact with each other and/or with individual behavioral, cognitive, and biological factors (e.g., alcohol and drug use, mental illness, life events). Research tends to be bifurcated along social science/public health and clinical/biological lines due to theoretical, methodological and statistical barriers that have traditionally and uniquely prevented scientific integration of suicide research across disciplinary perspectives. This project proposes an innovative solution to this stalemate by bringing together a trans-disciplinary team of social, behavioral, statistical and biomedical scientists in universities and in government to integrate theoretical advances in social networks, overcome data barriers by linking data from new federal efforts (e.g., CDC's National Violence Data Reporting System, Census Bureau's American Community Study), and employ corrections and analytic techniques that take into account data complexity (e.g., merged data bases, right censored data, nested data levels). Specifically, Aim I tailors the Network Episode Model III-R to develop individual, contextual and multi-level hypotheses linking social environmental levels and suicide.
Aim II compiles a pioneering data set linking relatively new federal data efforts and testing hypotheses using appropriate statistical corrections and novel advanced techniques.
Aim III assesses strengths and limits of the new approach, develops plans to fill remaining data gaps by working with federal agencies and state death registration systems/officials, and works with biomedical ethicists to ensure human subject protections across the entire project. With cooperation already secured from the CDC and other key federal agencies, this research plan eliminates longstanding data and analytic barriers in suicide research, leading to significant advancements in understanding the role of multiple social contexts, a promising theoretical mechanism (social network) linking distal and proximal pathways, and interaction of social and biological/individual factors. Working simultaneously on the three aims allows immediate advancement in data enrichment for testing hypotheses and concurrent attention to planning future federal data collections. The refinement of trans- disciplinary theory, innovation in data compilation, correction and concept operationalization, and the rigorous combination of statistical methods will improve knowledge and scientific foundations for clinical and community-based interventions targeted to suicide and other health outcomes.
Scientific research designed to understand and reduce suicide has been hampered by a longstanding methodological barrier to transformative research. Under an innovative, feasible, social science-based, trans- disciplinary research plan, the influence of the social environment at multiple levels (interpersonal relationships, family, organizational and community) that underlie the complex etiology of suicide can be compiled and analyzed to break through the current stalemate, leading to significant advances in knowledge and interventions.
| Boulifard, David A; Pescosolido, Bernice A (2017) Examining Multi-Level Correlates of Suicide by Merging NVDRS and ACS Data. US Census Bur Cent Econ Stud Res Pap Ser 2017: |
