Exploring Place-Based Differences in Suicide and Suicide-Related Outcomes Among North Carolina Adolescents and Young Adults.

PURPOSE: Suicide is an ongoing public health crisis among youth and adolescents, and few studies have investigated the spatial patterning in the United States among this subpopulation. Potential precursors to suicide in this vulnerable group are also on the rise, including nonfatal self-injury. METHODS: This study uses emergency department data, death certificates, and violent death reporting system data for North Carolina from 2009 to 2018 to investigate spatial clusters of self-injury and suicide. RESULTS: Findings show that the demographic characteristics of individuals committing fatal and nonfatal self-injury are quite different. Self-injury and completed suicides exhibited different geographical patterns. Area-level measures like micropolitan status and measures of racial and income segregation predicted the presence of high-risk suicide clusters. Suicides among Native Americans and veteran status/military personnel also were associated with higher risk suicide clusters. DISCUSSION: Future interventions should target these specific high-risk locations for immediate reductions in adolescent and youth suicides.

Inequalities in Life Expectancy Across North Carolina: A Spatial Analysis of the Social Determinants of Health and the Index of Concentration at Extremes.

Health inequalities are characterized by spatial patterns of social, economic, and political factors. Life expectancy (LE) is a commonly used indicator of overall population health and health inequalities that allows for comparison across different spatial and temporal regions. The objective of this study was to examine geographic inequalities in LE across North Carolina census tracts by comparing the performance of 2 popular geospatial health indices: Social Determinants of Health (SDoH) and the Index of Concentration at Extremes (ICE). A principal components analysis (PCA) was used to address multicollinearity among variables and aggregate data into components to examine SDoH, while the ICE was constructed using the simple subtraction of geospatial variables. Spatial regression models were employed to compare both indices in relation to LE to evaluate their predictability for population health. For individual SDoH and ICE components, poverty and income had the strongest positive correlation with LE. However, the common spatial techniques of adding PCA components together for a final SDoH aggregate measure resulted in a poor relationship with LE. Results indicated that both metrics can be used to determine spatial patterns of inequities in LE and that the ICE metric has similar success to the more computationally complex SDoH metric. Public health practitioners may find the ICE metric's high predictability matched with lower data requirements to be more feasible to implement in population health monitoring.

Analyzing the spatial determinants of local Covid-19 transmission in the United States.

The Coronavirus Disease 19 (COVID-19) has quickly spread across the United States (U.S.) since community transmission was first identified in January 2020. While a number of studies have examined individual-level risk factors for COVID-19, few studies have examined geographic hotspots and community drivers associated with spatial patterns in local transmission. The objective of the study is to understand the spatial determinants of the pandemic in counties across the U.S. by comparing socioeconomic variables to case and death data from January 22nd to June 30th 2020. A cluster analysis was performed to examine areas of high-risk, followed by a three-stage regression to examine contextual factors associated with elevated risk patterns for morbidity and mortality. The factors associated with community-level vulnerability included age, disability, language, race, occupation, and urban status. We recommend that cluster detection and spatial analysis be included in population-based surveillance strategies to better inform early case detection and prioritize healthcare resources.