发表机构
University of Valencia; Norwegian University of Science and Technology（瓦伦西亚大学; 挪威科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用西欧欧洲社会调查数据，比较序数模型与二分化模型在自评健康空间分析中的差异，发现二分化截断点选择会影响地理结论，建议优先采用序数建模并论证截断点合理性。
AI 中文摘要
调查回答通常使用有序响应类别进行测量。在欧洲社会调查中，自评健康采用从非常好到非常差的五级量表测量，但分析中通常将回答二分为“良好”和“较差”健康的二元类别。二元指标提供了易于沟通的患病率度量，但二分化减少了信息量，可能限制所捕获的健康变异，同时截断点的选择可能影响估计和实质性结论。在空间背景下，关于这些效应的系统性证据仍然有限。我们利用欧洲社会调查第11轮（2023/2024年）西欧数据来填补这一空白。我们按年龄、教育程度和地区对男性受访者的自评健康进行建模。结合事后分层的贝叶斯空间个体层面模型提供了具有人口代表性的估计。我们将五类别结果的序数累积logit模型与使用两种二分化（在“一般”健康的分类上有所不同）的伯努利逻辑回归模型进行比较。年龄较大和教育程度较低在各规格中均与较差的自评健康持续相关，表明相对稳健的基本年龄和教育梯度。然而，其幅度和不确定性以及一些地理结论对结果的建模方式敏感。将“一般”健康归入二元截断点的任一侧，会改变被识别为拥有高于平均水平的不太健康状态的地区。序数模型保留了类别特定信息，并可通过聚合产生熟悉的二元患病率估计。二元指标仍然有用，特别是在监测和沟通方面。尽管如此，所选截断点应被证明合理，并应考虑替代截断点或序数建模方法的敏感性，尤其是在地理比较中。
英文摘要
Survey responses are often measured using ordered response categories. In the European Social Survey, self-rated health is measured on a five-point scale from very good to very bad, yet analyses commonly dichotomise responses into binary categories of "good" and "poor" health. Binary indicators provide prevalence measures that are straightforward to communicate, but dichotomisation reduces information and may limit captured health variation, while the cut-off may influence estimates and substantive conclusions. Systematic evidence on these effects in spatial settings remains limited. We address this gap using European Social Survey round 11 (2023/2024) for Western Europe. We model self-rated health among male respondents by age, education and region. Bayesian spatial individual-level models with poststratification provide population-representative estimates. We compare an ordinal cumulative logit model for the five-category outcome with Bernoulli logistic regression models using two dichotomisations differing in the classification of "fair" health. Older age and lower education are consistently associated with worse self-rated health across specifications, suggesting relatively robust fundamental age and educational gradients. However, their magnitude and uncertainty, and some geographical conclusions, are sensitive to how the outcome is modelled. Assigning "fair" health to either side of a binary cut-off changes the regions identified as having above-average levels of less favourable health. The ordinal model retains category-specific information and can produce familiar binary prevalence estimates through aggregation. Binary indicators remain useful, particularly for monitoring and communication. Nevertheless, the selected cut-off should be justified and sensitivity to alternative cut-offs or an ordinal modelling approach should be considered, especially for geographical comparisons.
