确定非最佳健康状况调查问卷的最佳切断点:隐藏的个人资料分析
Lai Kun Tong1, Mio Leng Au2, Yong Bing Liu3
1Research Management and Development Department, Kiang Wu Nursing College of Macau, Macao, China.
BMC public health
|March 17, 2025
概括
这项研究确定了低于最佳健康状况问卷 (SHSQ-25) 的最佳切断分数为44分. 这一新门可以更准确地识别患有不理想健康状况的个体.
科学领域:
- 健康心理学 心理健康心理学
- 心理测量 心理测量 心理测量
- 公共卫生 公共卫生
背景情况:
- 非最佳健康状况问卷 (SHSQ-25) 是评估非最佳健康状况的关键工具.
- 确定SHSQ-25的最佳切断点对于准确的评估至关重要.
- 这项研究解决了对SHSQ-25.5的验证截止分数的需求.
研究的目的:
- 用先进的统计方法确定SHSQ-25的最佳切断点.
- 为了提高SHSQ-25的诊断准确度,识别不理想的健康状况.
- 探索生活安排和不理想的健康状况之间的关系.
主要方法:
- 根据SHSQ-25的反应,使用隐形形状分析 (LPA) 来识别研究群体内的不同子组.
- 采用接收器操作特征 (ROC) 曲线分析来确定最佳切断点.
- 在中国对4918名成年人进行了一项横截面的在线调查.
主要成果:
- LPA确定了三个不同的风险概况:高风险 (19.0%),中等风险 (51.4%) 和低风险 (29.6%).
- 为SHSQ-25确定了44的最佳切割点,实现了高灵敏度 (97.53%) 和特异性 (97.77%).
- 与他人一起生活与不理想健康的可能性降低有关 (OR=0.617,p<0.001).
结论:
- 为SHSQ-25建立了44的最佳切断得分,显著提高了它的实用性.
- 这种经过验证的切线可以提高在临床和公共卫生环境中对非最佳健康状况的识别和管理.
- 研究结果表明,生活安排所表明的社会支持可能起到保护性作用,防止健康状况不佳.
相关概念视频
01:00Detection of Gross Error: The Q Test
5.0K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
5.0K
01:20Comparing the Survival Analysis of Two or More Groups
113
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
113
01:24Kaplan-Meier Approach
74
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
74
01:02Quantifying and Rejecting Outliers: The Grubbs Test
1.4K
Sometimes, a data set can have a recorded numerical observation that greatly deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier. To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
1.4K
01:16Goodness-of-Fit Test
3.3K
The goodness-of-fit test is a type of hypothesis test which determines whether the data "fits" a particular distribution. For example, one may suspect that some anonymous data may fit a binomial distribution. A chi-square test (meaning the distribution for the hypothesis test is chi-square) can be used to determine if there is a fit. The null and alternative hypotheses may be written in sentences or stated as equations or inequalities. The test statistic for a goodness-of-fit test is given as...
3.3K
01:15Assumptions of Survival Analysis
81
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.
81