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使用伤残调整生命年衡量伦敦两个内城区的疾病负担。

Measuring burden of disease in two inner London boroughs using Disability Adjusted Life Years.

作者信息

Dodhia H, Phillips K

机构信息

King's College London, University of London, UK.

出版信息

J Public Health (Oxf). 2008 Sep;30(3):313-21. doi: 10.1093/pubmed/fdn015. Epub 2008 Apr 8.

Abstract

BACKGROUND

This paper uses the Disability Adjusted Life Year (DALY) to estimate disease burden at a local level and relates this to programme budget (PB) data.

METHODS

We estimated DALY using the global burden of disease (GBD) template. For years of life lost, local mortality data were used and for years of life with disability, the GBD estimates from World Health Organization EURO A region (including the UK) were used. We used PB data to analyse how healthcare expenditure matched disease burden.

RESULTS

In 2005 the burden of disease in Lambeth was estimated at 36,368 DALYs (13,515 DALYs lost per 100,000) and in Southwark was 34,196 DALYs (13,244 DALYs lost per 100,000). There were gender and area differences. The ranking is different when mortality and morbidity are combined compared with mortality alone. We estimated that the average spend per DALY lost in 2005 was 11,066 pounds in Lambeth and 9390 pounds in Southwark.

CONCLUSIONS

We used a pragmatic approach to estimate overall disease burden providing a local, more comprehensive picture with important differences in spend by disease and health authority area. However, a more detailed approach to support decisions about prioritization based on modelling interventions that impact on avoidable burden of disease is recommended.

摘要

背景

本文使用伤残调整生命年(DALY)来估计地方层面的疾病负担,并将其与项目预算(PB)数据相关联。

方法

我们使用全球疾病负担(GBD)模板来估计DALY。对于生命损失年数,使用当地的死亡率数据;对于残疾生命年数,使用世界卫生组织欧洲A区域(包括英国)的GBD估计值。我们使用PB数据来分析医疗保健支出如何与疾病负担相匹配。

结果

2005年,兰贝斯区的疾病负担估计为36368个DALY(每10万人中有13515个DALY损失),南华克区为34196个DALY(每10万人中有13244个DALY损失)。存在性别和地区差异。与仅考虑死亡率相比,将死亡率和发病率合并时的排名有所不同。我们估计,2005年兰贝斯区每损失一个DALY的平均花费为11066英镑,南华克区为9390英镑。

结论

我们采用了一种务实的方法来估计总体疾病负担,提供了一幅局部的、更全面的图景,不同疾病和卫生当局区域的支出存在重要差异。然而,建议采用一种更详细的方法,通过对影响可避免疾病负担的干预措施进行建模,来支持有关优先事项的决策。

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