このページは機械翻訳されています。他のページは英語で表示される場合があります。 View in English

米国における自殺率の変化を分解する (2001年−2023年)

  • 0The Hong Kong Jockey Club Centre for Suicide Research and Prevention, Faculty of Social Sciences, The University of Hong Kong, Hong Kong, China.

|

|

まとめ

この要約は機械生成です。

アメリカの自殺率は 2001年から2023年にかけて 白人の男性による銃による自殺によって増加した. 薬物関連自殺は高齢の白人女性でも増加し 標的型予防戦略が必要になった.

科学分野

  • 公衆衛生
  • 流行病学について
  • メンタルヘルスの研究

背景

  • 自殺率の上昇は 人口の多様性を理解する必要があります
  • 効果的な予防策は 人種,年齢,性別,方法による傾向の分析に依存します

研究 の 目的

  • 2001年から2023年までの米国の自殺率を分析する.
  • 人口の違い (年齢,性別,民族) と自殺に使用される方法を解剖する.
  • 自殺率の全体的な変化に影響を与える傾向を特定する.

主な方法

  • CDC WISQARSデータベース (2001年,2018年2020年,2023年) を使用した遡及的観察研究.
  • 年齢,性別,民族,方法による自殺症例の階層化
  • 人口統計を分析して 10万人に当たりの割合を決定する

主要な成果

  • 米国全体の自殺率は10万人当たり10.7人から14.6人 (2001年−2023年) に増加し,2019年−2020年には減少した.
  • 銃による自殺,特に白人男性の自殺は,増加の主な要因でした.
  • また,白人女性 (45歳以上) の薬物関連自殺と少数民族の銃による自殺も増加しています.

結論

  • 銃による自殺は米国における自殺率の変化の主な要因である (2001年−2023年).
  • 45歳以上の白人女性の薬物関連自殺が増加するのは大きな懸念です.
  • 標的を絞った予防の取り組みは,人口特有の要因と方法のアクセシビリティを考慮する必要があります.

関連する概念動画

Applications of Life Tables 01:22

119

Life tables are versatile across various fields, providing a quantitative basis for analyzing mortality and survival rates. Whether used by demographers, actuaries, epidemiologists, or sociologists, life tables offer valuable insights into the dynamics of life and death, facilitating informed decisions in public health, insurance, conservation, and beyond. Their broad applicability highlights the interconnectedness of demographic data with practical outcomes in everyday life and strategic...

Actuarial Approach 01:20

131

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,...

Regression Toward the Mean 01:52

6.5K

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...

Survival Curves 01:18

306

Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...

Diagnostic and Statistical Manual of Mental Disorders (DSM) 01:27

141

The Diagnostic and Statistical Manual of Mental Disorders (DSM) serves as the primary classification system for mental health disorders, providing standardized diagnostic criteria for clinicians and researchers. First published by the American Psychiatric Association (APA) in 1952, the DSM has undergone several revisions to reflect evolving psychiatric understanding. The fifth edition, DSM-5, released in 2013, introduced key updates that expanded diagnostic categories and modified diagnostic...

Longitudinal Research 02:20

12.4K

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...