持续的贫困和儿童牙损伤:时间变化的暴露分析
Yusuke Matsuyama1,2, Aya Isumi3, Satomi Doi3
1Department of Global Health Promotion, Tokyo Medical and Dental University, Bunkyo-ku, Tokyo, Japan.
Journal of epidemiology and community health
|July 19, 2023
概括
持续的贫困显著增加了日本学生的牙损伤. 长期贫困对的累积影响比在单一时间点测量的贫困更为明显.
科学领域:
- 公共卫生 公共卫生
- 儿科牙科 儿科牙科
- 健康的社会经济决定因素
背景情况:
- 牙损是影响全球儿童的重大公共卫生问题.
- 众所周知,社会经济因素,特别是贫困,会影响口腔健康结果.
- 了解贫困对儿童牙健康的长期影响对于有针对性的干预至关重要.
研究的目的:
- 调查持续贫困对日本小学儿童牙损伤发生率的累积影响.
- 为了比较持续贫困的儿童和从未贫困过的儿童的牙损伤负担.
- 评估持续的贫困是否对牙损伤的影响大于在单一时间点测量的贫困.
主要方法:
- 东京阿达奇市4291名小学生的四波数据使用的纵向研究 (2015-2020年).
- 根据家庭收入,物质剥夺或公用事业支付困难定义的贫困,通过护理人员问卷进行评估.
- 由学校牙医评估的牙损伤;使用有针对性的最大概率估计来控制混因素的统计分析.
主要成果:
- 与从未经历过贫困 (平均2.39) 的儿童相比,持续贫困的儿童牙损伤明显增加 (平均3.81).
- 持续的贫困与1.54多颗腐烂的牙比从未经历过贫困有关,在控制混后.
- 持续的贫困和牙损伤之间的关联比在单一时间点评估的贫困更强.
结论:
- 持续的贫困对儿童牙的累积影响是巨大的.
- 长期贫困对儿童牙健康的风险比短期或间歇性贫困更大.
- 调查结果强调需要持续的社会经济支持,以改善儿童口腔健康结果.
更多相关视频
09:33Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India
Published on: December 23, 2022
2.3K
08:50In Vitro Rearing of Solitary Bees: A Tool for Assessing Larval Risk Factors
Published on: July 16, 2018
8.2K
相关概念视频
The Availability Heuristic
6.0K
A heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. Different types of heuristics are used in different types of situations, and the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):
6.0K
Introduction To Survival Analysis
287
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...
287
Study Designs in Epidemiology
275
Epidemiological study designs are fundamental tools for investigating the distribution, determinants, and control of health conditions in populations. They help researchers understand the relationships between exposures and outcomes, and they broadly fall into two categories: "observational" and "experimental" studies.
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and...
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and...
275
Longitudinal 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
Analysis of Population Pharmacokinetic Data
297
Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
297
Bias in Epidemiological Studies
361
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:
361
