澳大利亚人对产前查的偏好:一项区分选择实验,比较大都市和农村/区域地区
Amber Salisbury1,2, Sarah Norris3, Alison Pearce4,5
1Menzies Centre for Health Policy and Economics, Sydney School of Public Health, University of Sydney, Sydney, NSW, Australia. amber.salisbury@sydney.edu.au.
Applied health economics and health policy
|January 17, 2025
概括
澳大利亚对非侵入性产前检测 (NIPT) 的偏好因地点而异. 农村社区优先考虑更广泛的疾病查和更短的等待时间,从而影响支付遗传查服务的意愿.
科学领域:
- 生殖遗传学 生殖遗传学
- 卫生经济学 卫生经济学
- 公共卫生政策 公共卫生政策
背景情况:
- 非侵入性产前检测 (NIPT) 在澳大利亚具有作为遗传查工具的潜力.
- 人们对NIPT的成本,商业化,心理影响以及农村人口的获取差异存在担忧.
研究的目的:
- 为了确定澳大利亚对NIPT特征的偏好.
- 调查大都会和农村/区域个体之间的偏好差异.
- 使用支付意愿 (WTP) 和等待意愿量化权衡.
主要方法:
- 采用了一个离散选择实验 (DCE),其中有12个选择任务.
- 参与者从澳大利亚大都市 (n=160) 和农村/区域 (n=168) 招募.
- 混合逻辑和隐性类分析被用于计算WTP和等待意愿.
主要成果:
- 假阳性率,假阴性率和成本显著影响了这两组人的偏好.
- 农村偏好还受到条件范围,不确定的利率和等待时间的影响.
- WTP的估计有所不同,从13澳元以避免1%的假阳性率增加到323澳元用于广泛的条件查.
结论:
- 农村和大都市人口之间的不同偏好必须在NIPT交付中考虑.
- 为经济评估产前查计划提供澳大利亚特定的WTP估计.
相关概念视频
Regression Toward the Mean
6.3K
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...
6.3K
Testing a Claim about Population Proportion
2.9K
A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
2.9K


