超额死亡率 P 值与人口年龄结构的关系。

IF 3.2 2区 医学 Q2 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH Population Health Metrics Pub Date : 2024-09-27 DOI:10.1186/s12963-024-00346-w
Niklas Ullrich-Kniffka, Jonas Schöley
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引用次数: 0

摘要

背景:自 COVID-19 大流行爆发以来,超额死亡率 P 值作为衡量大流行负担的一个指标日益突出。P 分数表示观察到的死亡人数与预期死亡人数偏差的百分比。由于 P 分数经常用于比较国家间的超额死亡率,因此出现了有关该指标年龄依赖性的问题。在本文中,我们介绍了有关 P 分数的人口结构偏差的正式和实证结果,并特别关注 COVID-19 大流行期间欧洲的跨国比较:方法:利用联合国《世界人口展望》2024 年修订版和 HMDs 短期死亡率波动数据系列中的数据,计算了欧洲国家 2021 年、2022 年和 2023 年的 P 分数。2021 年、2022 年和 2023 年的预期死亡人数是使用 Lee-Carter 预测模型估算的,该模型假设了大流行前的情况。采用北川式分解法将国家间的 P 分数差异分解为超额死亡率和结构部分。为了研究 P 分数跨国排名对人口结构差异的敏感性,我们计算了年龄标准化 P 分数和传统 P 分数之间的等级相关性:P 分数是按预期死亡的年龄分布加权的特定年龄超额死亡百分比的平均值。结果表明,在欧洲比较中,死亡分布差异的影响微乎其微。在大多数情况下,超额死亡率效应是主要效应。在 COVID-19 大流行期间,欧洲各国的 P 分数排名在年龄标准化 P 分数和传统 P 分数下都很相似:尽管 P 分数在形式上取决于预期死亡人数的年龄分布,但由于整个欧洲大陆的死亡人数分布相似,因此这一结构性因素在欧洲的比较中只起到次要作用。因此,在欧洲比较中,P-分数适合作为超额死亡率的衡量标准,因为它主要反映了超额死亡率的差异。不过,这一结论不应推断到全球比较中,因为在全球比较中,各国的死亡分布可能会有很大不同。在 P 值比较存在偏差的情况下,可以采用年龄标准化作为解决方案。
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Population age structure dependency of the excess mortality P-score.

Background: Since the outbreak of the COVID-19 pandemic, the excess mortality P-score has gained prominence as a measure of pandemic burden. The P-score indicates the percentage by which observed deaths deviate from expected deaths. As the P-score is regularly used to compare excess mortality between countries, questions arise regarding the age dependency of the measure. In this paper we present formal and empirical results on the population structure bias of the P-score with a special focus on cross-country comparisons during the COVID-19 pandemic in Europe.

Methods: P-scores were calculated for European countries for 2021, 2022, and 2023 using data from the 2024 revision of the United Nations' World Population Prospects and the HMDs Short Term Mortality Fluctuations data series. The expected deaths for 2021, 2022, and 2023 were estimated using a Lee-Carter forecast model assuming pre-pandemic conditions. P-score differences between countries were decomposed using a Kitagawa-type decomposition into excess-mortality and structural components. To investigate the sensitivity of P-score cross-country rankings to differences in population structure we calculated the rank-correlation between age-standardized and classical P-scores.

Results: The P-score is an average of age-specific percent excess deaths weighted by the age-distribution of expected deaths. It can be shown that the effect of differences in the distribution of deaths only plays a marginal role in a European comparison. In most cases, the excess mortality effect is the dominant effect. P-score rankings among European countries during the COVID-19 pandemic are similar under both age-standardized and classical P-scores.

Conclusions: Although the P-score formally depends on the age-distribution of expected deaths, this structural component only plays a minor role in a European comparison, as the distribution of deaths across the continent is similar. Thus, the P-score is suitable as a measure of excess mortality in a European comparison, as it mainly reflects the differences in excess mortality. However, this finding should not be extrapolated to global comparisons, where countries could have very different death distributions. In situations were P-score comparisons are biased age-standardization can be applied as a solution.

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来源期刊
Population Health Metrics
Population Health Metrics PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH-
CiteScore
6.50
自引率
0.00%
发文量
21
审稿时长
29 weeks
期刊介绍: Population Health Metrics aims to advance the science of population health assessment, and welcomes papers relating to concepts, methods, ethics, applications, and summary measures of population health. The journal provides a unique platform for population health researchers to share their findings with the global community. We seek research that addresses the communication of population health measures and policy implications to stakeholders; this includes papers related to burden estimation and risk assessment, and research addressing population health across the full range of development. Population Health Metrics covers a broad range of topics encompassing health state measurement and valuation, summary measures of population health, descriptive epidemiology at the population level, burden of disease and injury analysis, disease and risk factor modeling for populations, and comparative assessment of risks to health at the population level. The journal is also interested in how to use and communicate indicators of population health to reduce disease burden, and the approaches for translating from indicators of population health to health-advancing actions. As a cross-cutting topic of importance, we are particularly interested in inequalities in population health and their measurement.
期刊最新文献
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