Suppr超能文献

疾病负担研究中全球、国家和次国家严重程度分布的影响:以苏格兰癌症为例。

The impact of worldwide, national and sub-national severity distributions in Burden of Disease studies: A case study of cancers in Scotland.

机构信息

Public Health Science Directorate, NHS Health Scotland, Glasgow, Scotland.

Information Services Division, NHS National Services Scotland, Edinburgh, Scotland.

出版信息

PLoS One. 2019 Aug 9;14(8):e0221026. doi: 10.1371/journal.pone.0221026. eCollection 2019.

Abstract

BACKGROUND

Increasingly Burden of Disease (BOD) measures are being used to influence policy decisions because they summarise the complete effects of morbidity and mortality in an equitable manner. An important element of producing non-fatal BOD estimates are severity distributions. The Global Burden of Disease (GBD) study use the same severity distributions across countries due to a lack of available country-specific data. In the Scottish BOD (SBOD) study we developed national severity distributions for cancer types. The main aim of this study was to consider the extent to which the use of worldwide severity distributions in BOD studies are influencing cross-country comparisons, by comparing weighted-average disability weights (DW) based on GBD severity distributions with nationally derived severity distributions in Scotland for cancer types.

METHODS

We obtained individual records from the Scottish Cancer Registry for 21 cancer types and linked these to registered deaths. We estimated prevalent cancer cases for 2016 and assigned each case to sequelae using GBD 2016 study definitions. We compared the impact of using severity distributions based on GBD 2016, a Scotland-wide distribution, and distributions specific to deprivation strata in Scotland, on the weighted-average DW for each cancer type.

RESULTS

The relative difference in point estimates of weighted-average DW based on GBD 2016 worldwide severity distributions compared with Scottish national severity distributions resulted in overestimates in the majority of cancers (17 out of 21 cancer types). The largest overestimates were for gallbladder and biliary tract cancer (70.8%), oesophageal cancer (31.6%) and pancreatic cancer (31.2%). Furthermore, the use of weighted-average DW based on Scottish national severity distributions rather than sub-national Scottish severity distributions stratified by deprivation quintile overestimated weighted-average DW in the least deprived areas (16 out of 18 cancer types), and underestimated in the most deprived areas (16 out of 18 cancer types).

CONCLUSION

Our findings illustrate a bias in point estimates of weighted-average DW created using worldwide severity distributions. This bias would have led to the misrepresentation of non-fatal estimates of the burden of individual cancers, and underestimated the scale of socioeconomic inequality in this non-fatal burden. This highlights the importance of not interpreting non-fatal estimates of burden of disease too precisely, especially for sub-national estimates and those comparing populations when relying on data inputs from other countries. It is essential to ensure that any estimates are based upon country-specific data as far as possible.

摘要

背景

越来越多的疾病负担(BOD)衡量标准被用于影响政策决策,因为它们以公平的方式总结了发病率和死亡率的全部影响。产生非致命性 BOD 估计的一个重要因素是严重程度分布。由于缺乏可用的特定国家数据,全球疾病负担(GBD)研究在各国使用相同的严重程度分布。在苏格兰 BOD(SBOD)研究中,我们为癌症类型开发了国家严重程度分布。这项研究的主要目的是通过比较基于 GBD 严重程度分布的加权平均残疾权重(DW)与苏格兰癌症类型的全国性严重程度分布,考虑在多大程度上使用 BOD 研究中的全球严重程度分布会影响跨国比较。

方法

我们从苏格兰癌症登记处获得了 21 种癌症类型的个体记录,并将这些记录与登记死亡联系起来。我们估计了 2016 年的现患癌症病例,并使用 GBD 2016 研究定义将每个病例分配给后遗症。我们比较了使用基于 GBD 2016 的严重程度分布、全苏格兰分布以及苏格兰贫困程度特定分布对每种癌症类型加权平均 DW 的影响。

结果

基于 GBD 2016 全球严重程度分布的加权平均 DW 的点估计值与苏格兰国家严重程度分布的相对差异导致大多数癌症(21 种癌症类型中的 17 种)的高估。高估幅度最大的是胆囊和胆道癌(70.8%)、食道癌(31.6%)和胰腺癌(31.2%)。此外,使用基于苏格兰国家严重程度分布而不是按贫困五分位数分层的苏格兰亚国家严重程度分布的加权平均 DW 会高估最不贫困地区(18 种癌症类型中的 16 种)的加权平均 DW,而低估最贫困地区(18 种癌症类型中的 16 种)的加权平均 DW。

结论

我们的研究结果说明了使用全球严重程度分布产生的加权平均 DW 的点估计值存在偏差。这种偏差会导致对个体癌症非致命性负担的估计不准确,并低估了这种非致命性负担的社会经济不平等程度。这突出表明,在依赖其他国家的数据输入时,特别是在进行亚国家估计和比较人群时,不要过于精确地解释非致命性疾病负担的估计值非常重要。必须尽可能确保任何估计值都基于特定国家的数据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f5a8/6688784/e6c3337b9b56/pone.0221026.g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验