Suicide Mortality Among Formerly Incarcerated People Compared With the General Population in North Carolina, 2000-2020.

We aimed to compare rates and characteristics of suicide mortality in formerly incarcerated people with those of the general population in North Carolina. We conducted a retrospective cohort study of 266,400 people released from North Carolina state prisons between January 1, 2000, and March 1, 2020. Using direct and indirect standardization by age, sex, and calendar year, we calculated standardized suicide mortality rates and standardized mortality ratios comparing formerly incarcerated people with the North Carolina general population. We evaluated effect modification by race/ethnicity, sex, age, and firearm involvement. Formerly incarcerated people had approximately twice the overall suicide mortality of the general population for 3 years after release, with the highest rate of suicide mortality being observed in the 2-week period after release. In contrast to patterns in the general population, formerly incarcerated people had higher rates of non-firearm-involved suicide mortality than firearm-involved suicide mortality. Formerly incarcerated female, White and Hispanic/Latino, and emerging adult people had a greater elevation of suicide mortality than their general-population peers compared with other groups. These findings suggest a need for long-term support for formerly incarcerated people as they return to community living and a need to identify opportunities for interventions that reduce the harms of incarceration for especially vulnerable groups. This article is part of a Special Collection on Mental Health.

"We adjusted for race": now what? A systematic review of utilization and reporting of race in American Journal of Epidemiology and Epidemiology, 2020-2021.

Race is a social construct, commonly used in epidemiologic research to adjust for confounding. However, adjustment of race may mask racial disparities, thereby perpetuating structural racism. We conducted a systematic review of articles published in Epidemiology and American Journal of Epidemiology between 2020 and 2021 to (1) understand how race, ethnicity, and similar social constructs were operationalized, used, and reported; and (2) characterize good and poor practices of utilization and reporting of race data on the basis of the extent to which they reveal or mask systemic racism. Original research articles were considered for full review and data extraction if race data were used in the study analysis. We extracted how race was categorized, used-as a descriptor, confounder, or for effect measure modification (EMM)-and reported if the authors discussed racial disparities and systemic bias-related mechanisms responsible for perpetuating the disparities. Of the 561 articles, 299 had race data available and 192 (34.2%) used race data in analyses. Among the 160 US-based studies, 81 different racial categorizations were used. Race was most often used as a confounder (52%), followed by effect measure modifier (33%), and descriptive variable (12%). Fewer than 1 in 4 articles (22.9%) exhibited good practices (EMM along with discussing disparities and mechanisms), 63.5% of the articles exhibited poor practices (confounding only or not discussing mechanisms), and 13.5% were considered neither poor nor good practices. We discuss implications and provide 13 recommendations for operationalization, utilization, and reporting of race in epidemiologic and public health research.

Innovations in suicide prevention research (INSPIRE): a protocol for a population-based case-control study.

BACKGROUND: Suicide deaths have been increasing for the past 20 years in the USA resulting in 45 979 deaths in 2020, a 29% increase since 1999. Lack of data linkage between entities with potential to implement large suicide prevention initiatives (health insurers, health institutions and corrections) is a barrier to developing an integrated framework for suicide prevention. OBJECTIVES: Data linkage between death records and several large administrative datasets to (1) estimate associations between risk factors and suicide outcomes, (2) develop predictive algorithms and (3) establish long-term data linkage workflow to ensure ongoing suicide surveillance. METHODS: We will combine six data sources from North Carolina, the 10th most populous state in the USA, from 2006 onward, including death certificate records, violent deaths reporting system, large private health insurance claims data, Medicaid claims data, University of North Carolina electronic health records and data on justice involved individuals released from incarceration. We will determine the incidence of death from suicide, suicide attempts and ideation in the four subpopulations to establish benchmarks. We will use a nested case-control design with incidence density-matched population-based controls to (1) identify short-term and long-term risk factors associated with suicide attempts and mortality and (2) develop machine learning-based predictive algorithms to identify individuals at risk of suicide deaths. DISCUSSION: We will address gaps from prior studies by establishing an in-depth linked suicide surveillance system integrating multiple large, comprehensive databases that permit establishment of benchmarks, identification of predictors, evaluation of prevention efforts and establishment of long-term surveillance workflow protocols.