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经合组织成员国医疗体系的时空分析:数据缺失和地理时空加权回归对推断的影响。

A Spatio-Temporal Analysis of OECD Member Countries' Health Care Systems: Effects of Data Missingness and Geographically and Temporally Weighted Regression on Inference.

机构信息

Statistics & Actuarial Sciences, Western University, London, ON N6A 5B7, Canada.

Computer Science, Western University, London, ON N6A 5B7, Canada.

出版信息

Int J Environ Res Public Health. 2023 Jun 30;20(13):6265. doi: 10.3390/ijerph20136265.

DOI:10.3390/ijerph20136265
PMID:37444112
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10341688/
Abstract

Determinants of health care quality and efficiency are of importance to researchers, policy-makers, and public health officials as they allow for improved human capital and resource allocation as well as long-term fiscal planning. Statistical analyses used to understand determinants have neglected to explicitly discuss how missing data are handled, and consequently, previous research has been limited in inferential capability. We study OECD health care data and highlight the importance of transparency in the assumptions grounding the treatment of data missingness. Attention is drawn to the variation in ordinary least squares coefficient estimates and performance resulting from different imputation methods, and how this variation can undermine statistical inference. We also suggest that parametric regression models used previously are limited and potentially ill-suited for analysis of OECD data due to the inability to deal with both spatial and temporal autocorrelation. We propose the use of an alternative method in geographically and temporally weighted regression. A spatio-temporal analysis of health care system efficiency and quality of care across OECD member countries is performed using four proxy variables. Through a forward selection procedure, medical imaging equipment in a country is identified as a key determinant of quality of care and health outcomes, while government and compulsory health insurance expenditure per capita is identified as a key determinant of health care system efficiency.

摘要

医疗保健质量和效率的决定因素对于研究人员、政策制定者和公共卫生官员来说非常重要,因为它们可以改善人力资本和资源配置,以及进行长期财政规划。用于了解决定因素的统计分析忽略了明确讨论如何处理缺失数据,因此,先前的研究在推理能力方面受到限制。我们研究了经合组织的医疗保健数据,并强调了在处理数据缺失问题的假设基础上保持透明度的重要性。我们还提请注意不同插补方法导致的普通最小二乘系数估计值和性能的变化,以及这种变化如何破坏统计推断。我们还建议,由于无法处理空间和时间自相关,以前使用的参数回归模型是有限的,并且可能不适合分析经合组织的数据。我们提出使用替代方法,即地理和时间加权回归。使用四个代理变量对经合组织成员国的医疗保健系统效率和护理质量进行了时空分析。通过向前选择过程,确定一个国家的医疗成像设备是护理质量和健康结果的关键决定因素,而政府和强制性医疗保险支出人均是医疗保健系统效率的关键决定因素。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c973/10341688/2580b29ee4e8/ijerph-20-06265-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c973/10341688/175fc9ded17a/ijerph-20-06265-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c973/10341688/f79fd464874a/ijerph-20-06265-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c973/10341688/e3be145f6192/ijerph-20-06265-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c973/10341688/2580b29ee4e8/ijerph-20-06265-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c973/10341688/175fc9ded17a/ijerph-20-06265-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c973/10341688/f79fd464874a/ijerph-20-06265-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c973/10341688/e3be145f6192/ijerph-20-06265-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c973/10341688/2580b29ee4e8/ijerph-20-06265-g004.jpg

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