Interdisciplinary Centre on Population Dynamics, Syddansk Universitet, Odense, Denmark
Interdisciplinary Centre on Population Dynamics, Syddansk Universitet, Odense, Denmark.
BMJ Open. 2024 Aug 5;14(6):e079534. doi: 10.1136/bmjopen-2023-079534.
To quantify inequalities in lifespan across multiple social determinants of health, how they act in tandem with one another, and to create a scoring system that can accurately identify subgroups of the population at high risk of mortality.
Comparison of life tables across 54 subpopulations defined by combinations of four social determinants of health: sex, marital status, education and race, using data from the Multiple Cause of Death dataset and the American Community Survey.
United States, 2015-2019.
We compared the partial life expectancies (PLEs) between age 30 and 90 years of all subpopulations. We also developed a scoring system to identify subgroups at high risk of mortality.
There is an 18.0-year difference between the subpopulations with the lowest and highest PLE. Differences in PLE between subpopulations are not significant in most pairwise comparisons. We visually illustrate how the PLE changes across social determinants of health. There is a complex interaction among social determinants of health, with no single determinant fully explaining the observed variation in lifespan. The proposed scoring system adds clarification to this interaction by yielding a single score that can be used to identify subgroups that might be at high risk of mortality. A similar scoring system by cause of death was also created to identify which subgroups could be considered at high risk of mortality from specific causes. Even if subgroups have similar mortality levels, they are often subject to different cause-specific mortality risks.
Having one characteristic associated with higher mortality is often not sufficient to be considered at high risk of mortality, but the risk increases with the number of such characteristics. Reducing inequalities is vital for societies, and better identifying individuals and subgroups at high risk of mortality is necessary for public health policy.
量化多个健康社会决定因素对寿命的不平等影响,以及它们相互作用的方式,并创建一个评分系统,以准确识别高死亡率人群的亚组。
比较了由四个健康社会决定因素(性别、婚姻状况、教育和种族)组合定义的 54 个人口亚组的生命表,使用了来自多死因数据集和美国社区调查的数据。
美国,2015-2019 年。
我们比较了所有亚组从 30 岁到 90 岁的部分预期寿命(PLE)。我们还开发了一个评分系统来识别高死亡率的亚组。
PLE 最低和最高的亚组之间存在 18.0 年的差异。在大多数成对比较中,亚组之间的 PLE 差异不显著。我们直观地展示了 PLE 如何在社会决定因素之间变化。社会决定因素之间存在复杂的相互作用,没有单一决定因素能完全解释寿命的观察到的变化。所提出的评分系统通过生成一个可用于识别可能处于高死亡率风险的亚组的单一评分,为这种相互作用提供了更清晰的说明。还创建了一个按死因分类的类似评分系统,以确定哪些亚组可能因特定原因而处于高死亡率风险。即使亚组的死亡率水平相似,它们通常也面临不同的特定原因死亡率风险。
仅有一种与较高死亡率相关的特征通常不足以被认为处于高死亡率风险,但随着此类特征的数量增加,风险会增加。减少不平等对社会至关重要,更好地识别高死亡率风险的个体和亚组对于公共卫生政策是必要的。