From the Department of Public Health, Aarhus University, Aarhus C, Denmark.
Department of Clinical Medicine, Aarhus University, Aarhus N, Denmark.
Epidemiology. 2019 Sep;30(5):706-712. doi: 10.1097/EDE.0000000000001040.
Despite the dual objectives of many health care systems of improving total health and reducing health inequality, trial designs seem to ignore the assessment of inequality effects. Our study aimed to illustrate an empirical framework for the assessment of inequality effects alongside policy-oriented trials to inform a possible efficiency versus equality trade-off.
We measured inequality in the concentrations of all-cause and disease-related mortality and hospital admissions across ranks of socioeconomic status in a randomized controlled trial that tested the efficacy of general population screening of men for vascular disease. We used alternative definitions of inequality (relative/absolute, in attainment/shortfall, ranked by education/income), and supplemented the classical "frequentist" approach to statistical inference with Bayesian posterior probabilities. Equality contours for health improvement that leave inequality unaffected are illustrated graphically. We used bootstrapping for interpretation.
We estimated the posterior probability of screening increasing inequality to be between 0.21 and 0.93 depending on the inequality definition. Income-ranked inequality appeared to be generally higher than education-ranked inequality but less affected by screening. For the shortfall-relative index based on education-rank, the mean health improvement of a 7% relative reduction in all-cause mortality generated by screening incurred a mean relative increase in inequality of 28%. For the income-based indices, there was no evidence of a trade-off.
We illustrated how decision uncertainty can be reduced by explicit assessment of inequality alongside trials and found some evidence of a possible equity-efficiency trade-off in the context of screening, although this depended on the definition of equality.
尽管许多医疗保健系统都有改善总体健康水平和减少健康不平等的双重目标，但试验设计似乎忽视了不平等效应的评估。我们的研究旨在展示一个评估不平等效应的实证框架，以及面向政策的试验，以了解可能的效率与平等之间的权衡。
我们在一项随机对照试验中测量了社会经济地位等级中全因和与疾病相关的死亡率和住院率的不平等程度，该试验测试了对男性血管疾病进行人群筛查的效果。我们使用了不平等的替代定义（相对/绝对，实现/差距，按教育/收入排名），并补充了贝叶斯后验概率的经典“频率主义”统计推断方法。图形说明了不影响不平等的健康改善平等线。我们使用自举法进行解释。
我们估计筛查增加不平等的后验概率在 0.21 到 0.93 之间，具体取决于不平等的定义。按收入排名的不平等似乎普遍高于按教育排名的不平等，但受筛查影响较小。对于基于教育排名的差距相对指数，筛查导致的全因死亡率降低 7%的相对减少带来的平均健康改善，导致不平等平均相对增加 28%。对于基于收入的指数，没有证据表明存在权衡。
我们通过在试验中明确评估不平等来展示如何减少决策不确定性，并在筛查背景下发现了可能存在的公平与效率权衡的一些证据，尽管这取决于平等的定义。
