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用于估计英格兰地方当局层面预期寿命和发现异常趋势的时空模型。

Spatio-temporal model to estimate life expectancy and to detect unusual trends at the local authority level in England.

机构信息

MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.

MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK

出版信息

BMJ Open. 2020 Nov 12;10(11):e036855. doi: 10.1136/bmjopen-2020-036855.

Abstract

OBJECTIVES

To estimate life expectancy at the local authority level and detect those areas that have a substantially low life expectancy after accounting for deprivation.

DESIGN

We used registration data from the Office for National Statistics on mortality and population in England, by local authority, age group and socioeconomic deprivation decile, for both men and women over the period 2001-2018. We used a statistical model within the Bayesian framework to produce robust mortality rates, which were then transformed to life expectancy estimates. A rule based on exceedance probabilities was used to detect local authorities characterised by a low life expectancy among areas with a similar deprivation level from 2012 onwards.

RESULTS

We confirmed previous findings showing differences in the life expectancy gap between the most and least deprived areas from 2012 to 2018. We found variations in life expectancy trends across local authorities, and we detected a number of those with a low life expectancy when compared with others of a similar deprivation level.

CONCLUSIONS

There are factors other than deprivation that are responsible for low life expectancy in certain local authorities. Further investigation on the detected areas can help understand better the stalling of life expectancy which was observed from 2012 onwards and plan efficient public health policies.

摘要

目的

在考虑贫困因素后,估算地方当局的预期寿命,并发现那些预期寿命明显较低的地区。

设计

我们使用了英格兰国家统计局关于 2001 年至 2018 年期间按地方当局、年龄组和社会经济贫困程度十分位数划分的死亡率和人口登记数据,这些数据涉及男性和女性。我们在贝叶斯框架内使用统计模型生成稳健的死亡率,然后将其转化为预期寿命估计值。自 2012 年以来,我们使用基于超出概率的规则来检测那些在类似贫困水平的地区预期寿命较低的地方当局。

结果

我们证实了之前的发现,即 2012 年至 2018 年期间,最贫困和最不贫困地区之间的预期寿命差距存在差异。我们发现地方当局之间的预期寿命趋势存在差异,与其他类似贫困水平的地区相比,我们发现了一些预期寿命较低的地区。

结论

除了贫困因素外,还有其他因素导致某些地方当局的预期寿命较低。对所检测到的地区进行进一步调查,可以帮助更好地了解自 2012 年以来观察到的预期寿命停滞不前的原因,并制定有效的公共卫生政策。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e89d/7662413/21291b1f8996/bmjopen-2020-036855f01.jpg

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