Temporal Patterns in Youth Suicide Deaths Reported in the National Violent Death Reporting System
Victoria R Hatton1, Elaine Clark2, Rebekah S Huber3
1Department of Educational Psychology, University of Utah, Salt Lake City, Utah.
Summary
Youth suicide deaths show distinct seasonal and weekly patterns, varying significantly by individual age. Understanding these temporal trends is crucial for effective prevention strategies in child and adolescent mental health.
Area of Science:
- Public Health
- Epidemiology
- Child and Adolescent Psychiatry
Background:
- Suicide is a significant cause of mortality in children and adolescents.
- Examining temporal patterns and precipitating factors in youth suicides can inform prevention efforts.
Purpose of the Study:
- To investigate distinct temporal patterns in youth suicide deaths.
- To identify specific times of year and week when youth suicides are more prevalent.
Main Methods:
- Analysis of suicide death distributions across months and days of the week.
- Utilized data from the National Violent Death Reporting System for youth aged 10-18.
Main Results:
- Youth suicides peaked in March, April, and October, and on Mondays, Tuesdays, and Wednesdays.
- Distinct patterns emerged when disaggregated by age: 18-year-olds peaked in July, while 10-year-olds peaked on Saturdays.
Conclusions:
- Temporal suicide patterns in youth vary significantly by individual age.
- Aggregating age groups may obscure unique trends, highlighting the need for age-specific analysis in prevention.
More Related Videos
Related Concept Videos
01:18Introduction To Survival Analysis
236
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
The primary goal of survival analysis is to estimate survival time—the time...
236
02:20Longitudinal Research
12.0K
Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
12.0K
02:04Bullying
8.4K
A modern form of aggression is bullying. As you learn in your study of child development, socializing and playing with other children is beneficial for children’s psychological development. However, as you may have experienced as a child, not all play behavior has positive outcomes. Some children are aggressive and want to play roughly. Other children are selfish and do not want to share toys. One form of negative social interactions among children that has become a national concern is...
8.4K
01:20Actuarial Approach
78
The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
78
01:50Cross-Sectional Research
11.3K
In cross-sectional research, a researcher compares multiple segments of the population at the same time. If they were interested in people's dietary habits, the researcher might directly compare different groups of people by age. Instead of following a group of people for 20 years to see how their dietary habits changed from decade to decade, the researcher would study a group of 20-year-old individuals and compare them to a group of 30-year-old individuals and a group of 40-year-old...
11.3K
01:15Assumptions of Survival Analysis
127
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
127