Describing socio-economic variation in life expectancy according to an individual's education, occupation and wage in England and Wales: An analysis of the ONS Longitudinal Study.
Describing socio-economic variation in life expectancy according to an individual's education, occupation and wage in England and Wales: An analysis of the ONS Longitudinal Study.
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根据英格兰和威尔士个人的教育、职业和工资描述预期寿命的社会经济变化：对ONS纵向研究的分析。
DOI：
10.1016/j.ssmph.2021.100815
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发表时间：
2021-06
期刊：
影响因子：
--
通讯作者：
Belot A
中科院分区：
文献类型：
--
作者：
Ingleby FC;Woods LM;Atherton IM;Baker M;Elliss-Brookes L;Belot A
生活在更贫困地区的人的健康状况更差，这种不平等是健康和社会政策的主要驱动力。许多针对这些差距的干预措施都隐含着这样的假设，即健康状况较差主要与地区因素有关，而且这些不平等对男女都是一样的。然而，由于男女的个人社会经济地位（SES）而造成的健康差异却没有得到很好的记录。我们使用与ONS纵向研究相关的人口普查数据，从职业、教育和估计工资方面得出个人层面的SES，并研究了成人死亡率和预期寿命的差异。我们使用泊松回归对年龄、性别和SES特异性死亡率进行建模，并使用20岁时的预期寿命总结死亡率差异。我们将结果与使用区域级剥夺指标计算的结果进行了比较。观察到社会经济地位群体之间的预期寿命存在很大的不平等，尽管社会经济地位群体之间的差异妇女比男子小。最广泛的不平等现象出现在男子教育（两组之间预期寿命相差7.2年（95% CI：3.0-10.1））和工资（相差7.0年（95% CI：3.5-9.8））以及妇女教育（相差5.4年（95% CI：2.2-8.1））方面。在所有群体中，没有学历的男子的预期寿命最低。就预期寿命的年数差异而言，这里用个人一级数据衡量的不平等与以前用地区一级贫困指标确定的不平等程度相似。这些数据表明，健康不平等与个人社会经济地位和地区一级的贫困密切相关，突出了这些不同指标的互补作用。事实上，不良结果很可能是社区和个人影响的产物。目前的政策，根据地理区域之间的不平等证据的卫生支出的决定可能会忽视个人层面的SES不平等的那些生活在富裕地区，以及失踪的重要性别差异。减少不平等的卫生政策以个人为目标，尽管往往以地区一级的数据为基础。我们使用个人层面的社会经济数据显示了健康结果的广泛不平等。个人一级的不平等程度与地区一级的数据相似。我们发现重要的性别差异的不平等是无法检测到的地区一级的分析。在旨在减少不平等的政策中，应考虑到个人和地区两级的因素。
People who live in more deprived areas have poorer health outcomes, and this inequality is a major driver of health and social policy. Many interventions targeting these disparities implicitly assume that poorer health is predominantly associated with area-level factors, and that these inequalities are the same for men and women. However, health differentials due to individual socio-economic status (SES) of men and women are less well documented. We used census data linked to the ONS Longitudinal Study to derive individual-level SES in terms of occupation, education and estimated wage, and examined differences in adult mortality and life expectancy. We modelled age-, sex- and SES-specific mortality using Poisson regression, and summarised mortality differences using life expectancy at age 20. We compared the results to those calculated using area-level deprivation metrics. Wide inequalities in life expectancy between SES groups were observed, although differences across SES groups were smaller for women than for men. The widest inequalities were found across men's education (7.2-year (95% CI: 3.0–10.1) difference in life expectancy between groups) and wage (7.0-year (95% CI: 3.5–9.8) difference), and women's education (5.4-year (95% CI: 2.2–8.1) difference). Men with no qualifications had the lowest life expectancy of all groups. In terms of the number of years' difference in life expectancy, the inequalities measured here with individual-level data were of a similar magnitude to inequalities identified previously using area-level deprivation metrics. These data show that health inequalities are as strongly related to individual SES as to area-level deprivation, highlighting the complementary usefulness of these different metrics. Indeed, poor outcomes are likely to be a product of both community and individual influences. Current policy which bases health spending decisions on evidence of inequalities between geographical areas may overlook individual-level SES inequalities for those living in affluent areas, as well as missing important sex differences. Health policy to reduce inequalities targets individuals, although often based on area-level data. We show wide inequalities in health outcomes using individual-level socio-economic data. Individual-level inequalities are of a similar magnitude to those observed with area-level data. We find important sex differences in inequalities which are undetectable in area-level analyses. Both individual and area-level factors should be considered in policy aimed at reducing inequality.
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DOI：
10.1001/jama.2016.4226
发表时间：
2016-04-26
期刊：
JAMA
影响因子：
--
作者：
Chetty R;Stepner M;Abraham S;Lin S;Scuderi B;Turner N;Bergeron A;Cutler D
通讯作者：
Cutler D
影响因子：
50
作者：
Parker, Marty;Bucknall, Milica;Wilkie, Ross
通讯作者：
Wilkie, Ross
影响因子：
4.5
作者：
Rachet B;Maringe C;Woods LM;Ellis L;Spika D;Allemani C
通讯作者：
Allemani C
影响因子：
2.9
作者：
Ingleby FC;Belot A;Atherton I;Baker M;Elliss-Brookes L;Woods LM
通讯作者：
Woods LM
影响因子：
50
作者：
Bennett, James E.;Pearson-Stuttard, Jonathan;Ezzati, Majid
通讯作者：
Ezzati, Majid