The Experience of Survivors of Firearm Suicide Attempts: A Retrospective Case Series.

Objective: We sought to identify people who survived firearm suicide attempts to describe the acute stressors, substance use, and mental health conditions related to the attempt. Background: Most firearm deaths in the United States are the result of suicide. Because firearm suicide attempts have a case fatality rate of approximately 90%, little is known about the precipitating factors that lead to firearm suicide attempts. Methods: We conducted a retrospective case series of patients admitted to a large hospital system between 2000 and 2019 who survived intentional, self-inflicted gunshot wounds to the head. Through the electronic medical record, we collected information about acute stressors, substance use, and mental health diagnoses before or at the time of the suicide attempt. Results: Thirty-four patients were included in the study cohort. Patients were predominantly White (74%) and male (88%), with a mean age of 44 (range, 14-82). Nineteen (56%) patients were acutely intoxicated with alcohol upon hospitalization and 17 (50%) patients had a positive urine drug screen. Acute stressors involving interpersonal relationships (53%), work/school (32%), and legal disputes (18%), among others, were documented in 82% of patients. Most patients (65%) had been diagnosed with depression before their index hospitalization. Most patients were discharged to an acute rehabilitation center (41%) or an inpatient psychiatric facility (41%). Conclusions: Acute stress and alcohol intoxication were common in this cohort of patients who attempted suicide using firearms. These data offer an ability to learn from the experience of survivors of firearm suicide attempts, a rare population.

Estimating Community Disruption from Nighttime Gunshots in 6 U.S. Cities, 2015 to 2021.

BACKGROUND: Gunshots affect those directly involved in an incident and those in the surrounding community. The community-level impact of nighttime gunshots, which may be particularly disruptive to the sleep of nearby community members, is unknown. OBJECTIVE: Our aim is to estimate the number of people potentially affected by nighttime gunshots and the relationship between nighttime gunshots and median household income in the USA. DESIGN: We collected publicly available data on the timing and location of gunshots in six U.S. cities (Baltimore, MD; Boston, MA; Washington, D.C.; New York, NY; Philadelphia, PA; and Portland, OR) from 2015 to 2021. We then analyzed the data by computing rate ratios (RRs) to compare the frequency of gunshots during nighttime hours (6:00 pm to 5:59 am) versus daytime hours (6:00 am to 5:59 pm). Additionally, we used geospatial mapping to create choropleth maps to visualize the variation in nighttime gunshot density across cities. We estimated, using city-wide population, person-nights potentially impacted by the sound of gunshots within areas of 0.2- (low) and 0.5-mile (high) radius. Finally, for five of six cities where data on median household income were available by census tract, we built nonlinear regression models to estimate the relationship between the number of nighttime gunshots and median household income. KEY RESULTS: We analyzed 72,236 gunshots. Gunshots were more common during the nighttime than daytime (overall RR = 2.5). Analyses demonstrated that the low estimates for the mean annual number of person-nights impacted by nighttime gunshots were 0.4 million in Baltimore and Portland, 1.3 million in Philadelphia, 1.6 million in Boston, 2.9 million in New York City, and 5.9 million in Washington. The number of nighttime gunshots was inversely related to median household income. CONCLUSIONS: Nighttime gunshots are prevalent, particularly in low-income neighborhoods, and may have under-recognized effects on the surrounding community.

How Treatment Effect Heterogeneity Works.

How Treatment Effect Heterogeneity WorksThis Stats, STAT! animated video explores the concept of treatment effect heterogeneity. Differences in the effectiveness of treatments across participants in a clinical trial is important to understand when deciding how to apply clinical trial results to clinical practice.

How Censoring Works.

How Censoring WorksA common challenge in clinical research is determining the time to occurrence of a given event. This animated video explores the concept of censoring in survival analysis and how investigators deal with ambiguity in the time of an event's occurrence.

Of Climate Change and Competing Risks.

Of Climate Change and Competing Risks This Stats, STAT! animated video explores the concept of competing risks - and discusses why it is so important for investigators to consider whether the occurrence of one event can prevent or change the likelihood of the occurrence of another.

Good Intentions to Treat.

Good Intentions to Treat This Stats, STAT! animated video explores common approaches to analyzing data from randomized controlled trials, including intention-to-treat, per-protocol, and as-treated analyses.

How Statistical Power Works.

How Statistical Power WorksThis Stats, STAT! animated video explores the concept of statistical power and explains how clinical investigators determine how many participants to enroll in a randomized trial.

Bayesian Way.

Bayesian WayThis animated video explores two possible approaches to analyzing data in a randomized controlled trial: "Frequentist" versus "Bayesian."

Large Language Models.

Large Language ModelsIn the latest edition of Stats, STAT!, Fralick and colleagues explain the statistics behind large language models - used in chat bots like ChatGPT and Bard. While these new tools may seem remarkably intelligent, at their core they just assemble sentences based on statistics from large amounts of text.

Threats to Blinding.

Blinding is a critical strategy used to limit certain types of bias in randomized controlled trials. This animated video explores the rationale and examines potential threats to keeping group allocation concealed - from study participants and investigators.

The Case of the Missing Data.

The Case of the Missing DataThis animated video explores how investigators approach missing data in clinical trials.

Subgroup Analyses: Subpar or Sublime?

Subgroup Analyses: Subpar or Sublime?This animated video explores some of the potential pitfalls of performing subgroup analyses in randomized controlled trials and explains how to approach potential findings with caution.

The Problem of Multiple Comparisons.

The Problem of Multiple Comparisons This animated video reviews the problem of multiple comparisons in research studies and explains how performing multiple statistical hypothesis tests can produce associations simply by chance.

Is Noninferior Not Inferior?

Is Noninferior Not Inferior? What is a non-inferiority trial margin? To answer this, ask whether you have ever weighed a tradeoff between the best version of something and an acceptable alternative. Restated, in a choice between two decisions, is one option not inferior to the other? Watch an animated video that explores the basis for non-inferiority trials, the meaning of non-inferiority margins, and the interpretation of a non-inferiority trial's results.

NEJM Evidence - A New Journal in the NEJM Group Family.

NEJM Evidence - A New Journal in the NEJM Group Family In January 2022, the NEJM Group will be publishing a new journal, NEJM Evidence. This monthly, peer-reviewed, online-only, general medical journal will publish original research, along the full spectrum of clinical investigation, that takes ideas and turns them into reality.

Fossil-Fuel Pollution and Climate Change - A New NEJM Group Series.

Fossil-Fuel Pollution and Climate ChangeThe editors announce a new NEJM Group series on climate change and the increasingly urgent health and care delivery challenges we face. Articles will appear in the New England Journal of Medicine, in NEJM Evidence, and in NEJM Catalyst Innovations in Care Delivery.