Racial/Ethnic Disparities in the Performance of Prediction Models for Death by Suicide After Mental Health Visits.

Importance: Clinical prediction models estimated with health records data may perpetuate inequities. Objective: To evaluate racial/ethnic differences in the performance of statistical models that predict suicide. Design, Setting, and Participants: In this diagnostic/prognostic study, performed from January 1, 2009, to September 30, 2017, with follow-up through December 31, 2017, all outpatient mental health visits to 7 large integrated health care systems by patients 13 years or older were evaluated. Prediction models were estimated using logistic regression with LASSO variable selection and random forest in a training set that contained all visits from a 50% random sample of patients (6 984 184 visits). Performance was evaluated in the remaining 6 996 386 visits, including visits from White (4 031 135 visits), Hispanic (1 664 166 visits), Black (578 508 visits), Asian (313 011 visits), and American Indian/Alaskan Native (48 025 visits) patients and patients without race/ethnicity recorded (274 702 visits). Data analysis was performed from January 1, 2019, to February 1, 2021. Exposures: Demographic, diagnosis, prescription, and utilization variables and Patient Health Questionnaire 9 responses. Main Outcomes and Measures: Suicide death in the 90 days after a visit. Results: This study included 13 980 570 visits by 1 433 543 patients (64% female; mean [SD] age, 42 [18] years. A total of 768 suicide deaths were observed within 90 days after 3143 visits. Suicide rates were highest for visits by patients with no race/ethnicity recorded (n = 313 visits followed by suicide within 90 days, rate = 5.71 per 10 000 visits), followed by visits by Asian (n = 187 visits followed by suicide within 90 days, rate = 2.99 per 10 000 visits), White (n = 2134 visits followed by suicide within 90 days, rate = 2.65 per 10 000 visits), American Indian/Alaskan Native (n = 21 visits followed by suicide within 90 days, rate = 2.18 per 10 000 visits), Hispanic (n = 392 visits followed by suicide within 90 days, rate = 1.18 per 10 000 visits), and Black (n = 65 visits followed by suicide within 90 days, rate = 0.56 per 10 000 visits) patients. The area under the curve (AUC) and sensitivity of both models were high for White, Hispanic, and Asian patients and poor for Black and American Indian/Alaskan Native patients and patients without race/ethnicity recorded. For example, the AUC for the logistic regression model was 0.828 (95% CI, 0.815-0.840) for White patients compared with 0.640 (95% CI, 0.598-0.681) for patients with unrecorded race/ethnicity and 0.599 (95% CI, 0.513-0.686) for American Indian/Alaskan Native patients. Sensitivity at the 90th percentile was 62.2% (95% CI, 59.2%-65.0%) for White patients compared with 27.5% (95% CI, 21.0%-34.7%) for patients with unrecorded race/ethnicity and 10.0% (95% CI, 0%-23.0%) for Black patients. Results were similar for random forest models, with an AUC of 0.812 (95% CI, 0.800-0.826) for White patients compared with 0.676 (95% CI, 0.638-0.714) for patients with unrecorded race/ethnicity and 0.642 (95% CI, 0.579-0.710) for American Indian/Alaskan Native patients and sensitivities at the 90th percentile of 52.8% (95% CI, 50.0%-55.8%) for White patients, 29.3% (95% CI, 22.8%-36.5%) for patients with unrecorded race/ethnicity, and 6.7% (95% CI, 0%-16.7%) for Black patients. Conclusions and Relevance: These suicide prediction models may provide fewer benefits and more potential harms to American Indian/Alaskan Native or Black patients or those with undrecorded race/ethnicity compared with White, Hispanic, and Asian patients. Improving predictive performance in disadvantaged populations should be prioritized to improve, rather than exacerbate, health disparities.

State-Level Data on Suicide Mortality During COVID-19 Quarantine: Early Evidence of a Disproportionate Impact on Racial Minorities.

The unprecedented impact of COVID-19 has raised concern for the potential of increased suicides due to a convergence of suicide risk factors. We obtained suicide mortality data to assess completed suicides during the period of strict stay-at-home quarantine measures in Connecticut and compared this data with previous years. While the total age-adjusted suicide mortality rate decreased by 13% during the lockdown period compared with the 5-year average, a significantly higher proportion of suicide decedents were from racial minority groups. This finding may provide early evidence of a disproportionate impact from the social and economic challenges of COVID-19 on minority populations.

Risk factors observed in health care system 6 months prior completed suicide.

Ten percent of all deaths in Greenland are caused by suicide. The aim of this study was to explore if applicable risk factors could be identified among the suicide victims within the health care system up to 6 months prior to the suicide. The study was performed as an age- and gender-matched case control study including all suicides in Greenland from 2012 to 2015, based on review of medical records for risk factors including suicide ideation, suicide attempts, incidence of alcohol intoxication, incidence of violence and treatment for psychiatric illness within the 6 month period leading up to the suicide. In total, 160 cases and 160 controls were included. Presence of any risk factors were observed in around a third of all suicide cases compared a tenth among the controls. The highest odds ratios for suicide were observed for suicide ideation and suicide attempts. However, no contact with the health care system was observed for two thirds of the suicides victims. Thus, focus on suicide ideation and suicide attempts among patients could help health care professionals to assess suicide risk and initiate prevention. Additional preventive strategies targeting the majority without contact to the health care system need to be explored.