背景:

准确衡量新冠肺炎大流行造成的死亡人数,对于每个国家和地区了解大流行对公共卫生的影响程度至关重要。死亡率统计是公共卫生决策的基础。报告死亡人数试图量化新冠肺炎大流行随时间在不同人口和地点的严重程度,它们被广泛视为跟踪大流行相对于报告病例比率更可靠的指标。死亡率因时间和地点而异,其测量受到众所周知的偏见的影响,这种偏见在新冠肺炎大流行期间加剧了。这份报告旨在估计2020年1月1日至2021年12月31日期间,191个国家和地区以及选定国家的252个国家以下单位因新冠肺炎大流行而导致的超额死亡率。

方法:

收集了74个国家和地区以及266个国家以下地点 (包括低收入和中等收入国家的31个地点) 的全原因死亡报告,这些国家和地区在2020年和2021年期间以及之前长达11年的时间里每周或每月报告死于各种原因。此外,我们还获得了印度12个邦的超额死亡率数据。一段时间内的超额死亡率被计算为观察死亡率,剔除了受延迟登记和热浪等异常情况影响的时期的数据减去预期死亡率。六个模型被用来估计预期死亡率;预期死亡率的最终估计是基于这些模型的集合。总体权重基于样本外预测有效性检验得出的均方根误差。由于世界各地的死亡率记录不完整,我们建立了一个统计模型,预测没有全因死亡数据的地点和时期的超额死亡率。我们使用最小绝对收缩和选择算子 (LASSO) 回归作为变量选择机制,选择了15个协变量,包括与新冠肺炎大流行有关的协变量 (如血清阳性率) 和与背景人群健康指标 (如医疗服务可获得性和质量指数) 有关的协变量,其对超额死亡率的影响方向与美国疾病控制和预防中心的荟萃分析一致。用选定的最佳模型,我们运行了一个预测过程,对每个协变量使用100个绘制以及估计系数和残差的100个绘制,这些估计系数和残差是使用关于超额死亡率和协变量的绘制水平输入数据在绘制水平运行的回归估计的。然后在国家、地区和全球层面生成平均值和95%的不确定区间。样本外预测有效性测试是在我们最终的模型规范的基础上进行的。

结果:

尽管 2020 年 1 月 1 日至 2021 年 12 月 31 日期间报告的 COVID-19 死亡人数总计为 5.9400 万,但我们估计全球有 18.2 百万(95% 的不确定性区间为 17.1-19.6)人死于在此期间的 COVID-19 大流行(以超额死亡率衡量)。全球因 COVID-19 大流行而导致的超额死亡率为每 10 万人中有 120.3 人死亡 (113.1–129.3),超额死亡率超过每 10 万人中有 300 人死亡在 21 个国家。在南亚、北非和中东以及东欧地区,因 COVID-19 导致的超额死亡人数最多。在国家层面,估计因 COVID-19 导致的累计超额死亡人数最多的国家是印度(4.07 万 [3.71–4.36])、美国(1.13 百万 [1.08–1 .18])、俄罗斯 (1.07 万 [1.06–1.08])、墨西哥 (798 000 [741 000–867 000])、巴西 (792 000 [730 000–847 000])、印度尼西亚 ( 736 000 [594 000–955 000])和巴基斯坦(664 000 [498 000–847 000])。在这些国家中,超额死亡率最高的是俄罗斯(每 10 万人中有 374.6 人死亡 [369.7–378.4])和墨西哥(每 10 万人中有 325.1 [301.6–353.3]),巴西(186.9 [172.2–199.8] 每 10 万人)和美国(179.3 [170.7–187.5] 每 10 万人)的情况相似。

图1 全因死亡率的后期登记及其对计算的长期超额死亡率的影响

图2 2020年至2021年累积期间新冠肺炎疫情造成的估计超额死亡率的全球分布

图3 2020-21年累计期间新冠肺炎疫情造成的估计超额死亡率与报告的新冠肺炎死亡率之间的比率的全球分布

结论:

这场大流行的全面影响远远大于仅因新冠肺炎而报告的死亡人数。长期以来,人们一直认为加强世界各地的死亡登记系统对全球公共卫生战略至关重要,这对于改进对这一大流行和未来大流行的监测是必要的。此外,有必要进行进一步研究,以帮助区分SARS-CoV-2感染直接造成的超额死亡率与作为大流行间接后果的死亡原因的变化。

Background

Mortality statistics are fundamental to public health decision making. Mortality varies by time and location, and its measurement is affected by well known biases that have been exacerbated during the COVID-19 pandemic. This paper aims to estimate excess mortality from the COVID-19 pandemic in 191 countries and territories, and 252 subnational units for selected countries, from Jan 1, 2020, to Dec 31, 2021.

Methods

All-cause mortality reports were collected for 74 countries and territories and 266 subnational locations (including 31 locations in low-income and middle-income countries) that had reported either weekly or monthly deaths from all causes during the pandemic in 2020 and 2021, and for up to 11 year previously. In addition, we obtained excess mortality data for 12 states in India. Excess mortality over time was calculated as observed mortality, after excluding data from periods affected by late registration and anomalies such as heat waves, minus expected mortality. Six models were used to estimate expected mortality; final estimates of expected mortality were based on an ensemble of these models. Ensemble weights were based on root mean squared errors derived from an out-of-sample predictive validity test. As mortality records are incomplete worldwide, we built a statistical model that predicted the excess mortality rate for locations and periods where all-cause mortality data were not available. We used least absolute shrinkage and selection operator (LASSO) regression as a variable selection mechanism and selected 15 covariates, including both covariates pertaining to the COVID-19 pandemic, such as seroprevalence, and to background population health metrics, such as the Healthcare Access and Quality Index, with direction of effects on excess mortality concordant with a meta-analysis by the US Centers for Disease Control and Prevention. With the selected best model, we ran a prediction process using 100 draws for each covariate and 100 draws of estimated coefficients and residuals, estimated from the regressions run at the draw level using draw-level input data on both excess mortality and covariates. Mean values and 95% uncertainty intervals were then generated at national, regional, and global levels. Out-of-sample predictive validity testing was done on the basis of our final model specification.

Findings

Although reported COVID-19 deaths between Jan 1, 2020, and Dec 31, 2021, totalled 5.94 million worldwide, we estimate that 18.2 million (95% uncertainty interval 17.1–19.6) people died worldwide because of the COVID-19 pandemic (as measured by excess mortality) over that period. The global all-age rate of excess mortality due to the COVID-19 pandemic was 120.3 deaths (113.1–129.3) per 100 000 of the population, and excess mortality rate exceeded 300 deaths per 100 000 of the population in 21 countries. The number of excess deaths due to COVID-19 was largest in the regions of south Asia, north Africa and the Middle East, and eastern Europe. At the country level, the highest numbers of cumulative excess deaths due to COVID-19 were estimated in India (4.07 million [3.71–4.36]), the USA (1.13 million [1.08–1.18]), Russia (1.07 million [1.06–1.08]), Mexico (798 000 [741 000–867 000]), Brazil (792 000 [730 000–847 000]), Indonesia (736 000 [594 000–955 000]), and Pakistan (664 000 [498 000–847 000]). Among these countries, the excess mortality rate was highest in Russia (374.6 deaths [369.7–378.4] per 100 000) and Mexico (325.1 [301.6–353.3] per 100 000), and was similar in Brazil (186.9 [172.2–199.8] per 100 000) and the USA (179.3 [170.7–187.5] per 100 000).

Interpretation

The full impact of the pandemic has been much greater than what is indicated by reported deaths due to COVID-19 alone. Strengthening death registration systems around the world, long understood to be crucial to global public health strategy, is necessary for improved monitoring of this pandemic and future pandemics. In addition, further research is warranted to help distinguish the proportion of excess mortality that was directly caused by SARS-CoV-2 infection and the changes in causes of death as an indirect consequence of the pandemic.

Wang, H., et al., Estimating excess mortality due to the COVID-19 pandemic: a systematic analysis of COVID-19-related mortality, 2020–21. The Lancet.

背景:

肝细胞癌是世界上与癌症相关的死亡的第三大原因。然而,非酒精性脂肪性肝病 (NAFLD) 相关肝细胞癌的患病率正在迅速上升。全球近三分之一的人口患有 NAFLD,其中约 20% 患有非酒精性脂肪性肝炎,可进展为肝硬化和肝细胞癌。NAFLD 是美国和欧洲部分地区肝细胞癌发病率上升最快的原因,预计与全球肥胖流行病同时呈指数增长。随着治疗乙肝病毒和丙型肝炎病毒感染的最新进展,由病毒性肝炎引起的肝细胞癌的负担正在下降;与其他原因的肝细胞癌相比,NAFLD相关肝细胞癌的临床表现和预后尚不清楚。我们的目标是确定NAFLD相关肝细胞癌的患病率、临床特征、监测率、治疗分配和结果。

方法:

在这项系统回顾和荟萃分析中,我们检索了MEDLINE和Embase,从最初到2022年1月17日,检索英文文章,比较NAFLD相关性肝细胞癌和其他原因引起的肝细胞癌的临床特征和预后。我们包括横断面和纵向观察性研究,排除儿科研究。研究水平的数据是从已发表的报告中提取的。主要结果是(1)继发于NAFLD的肝细胞癌的比例,(2)与NAFLD相关的肝细胞癌与其他原因的患者和肿瘤特征的比较,以及(3)NAFLD相关与非NAFLD相关的肝细胞癌的监测、治疗分配以及总体和无病生存结果的比较。我们使用推广的线性混合模型分析比例数据。对NAFLD相关肝癌与非NAFLD相关肝细胞癌进行配对Meta分析,得出优势比(OR)或均数差值,比较NAFLD相关肝癌与非NAFLD相关肝细胞癌的差异。我们使用风险比的合并分析来评估生存结果。

结果:

在这项系统评价和荟萃分析中,我们搜索了 MEDLINE 和 Embase 从开始到 2022 年 1 月 17 日的英文文章,这些文章比较了 NAFLD 相关肝细胞癌与其他原因引起的肝细胞癌的临床特征和结果。我们纳入了横断面和纵向观察研究,排除了儿科研究。研究层面的数据是从已发表的报告中提取的。主要结果是(1)继发于 NAFLD 的肝细胞癌的比例,(2)NAFLD 相关肝细胞癌与其他原因的患者和肿瘤特征的比较,以及(3)监测、治疗分配以及总体和疾病的比较- NAFLD 相关与非 NAFLD 相关肝细胞癌的无生存结果。我们使用广义线性混合模型分析比例数据。进行配对荟萃分析以获得优势比 (OR) 或平均差,比较 NAFLD 相关与非 NAFLD 相关的肝细胞癌。我们使用风险比的汇总分析来评估生存结果。

图1 按世卫组织区域分列,世界各地继发于非酒精性脂肪性肝病的肝细胞癌的比例

图3 肝细胞癌的比例

结论:

与其他原因导致的肝细胞癌相比,NAFLD 相关的肝细胞癌与更高比例的无肝硬化患者和更低的监测率相关。应为患有肝细胞癌高风险的无肝硬化的 NAFLD 患者制定监测策略。

Background

The clinical presentation and outcomes of non-alcoholic fatty liver disease (NAFLD)-related hepatocellular carcinoma are unclear when compared with hepatocellular carcinoma due to other causes. We aimed to establish the prevalence, clinical features, surveillance rates, treatment allocation, and outcomes of NAFLD-related hepatocellular carcinoma.

Methods

In this systematic review and meta-analysis, we searched MEDLINE and Embase from inception until Jan 17, 2022, for articles in English that compared clinical features, and outcomes of NAFLD-related hepatocellular carcinoma versus hepatocellular carcinoma due to other causes. We included cross-sectional and longitudinal observational studies and excluded paediatric studies. Study-level data were extracted from the published reports. The primary outcomes were (1) the proportion of hepatocellular carcinoma secondary to NAFLD, (2) comparison of patient and tumour characteristics of NAFLD-related hepatocellular carcinoma versus other causes, and (3) comparison of surveillance, treatment allocation, and overall and disease-free survival outcomes of NAFLD-related versus non-NAFLD-related hepatocellular carcinoma. We analysed proportional data using a generalised linear mixed model. Pairwise meta-analysis was done to obtain odds ratio (OR) or mean difference, comparing NAFLD-related with non-NAFLD-related hepatocellular carcinoma. We evaluated survival outcomes using pooled analysis of hazard ratios.

Findings

Of 3631 records identified, 61 studies (done between January, 1980, and May, 2021; 94 636 patients) met inclusion criteria. Overall, the proportion of hepatocellular carcinoma cases secondary to NAFLD was 15.1% (95% CI 11.9–18.9). Patients with NAFLD-related hepatocellular carcinoma were older (p<0.0001), had higher BMI (p<0.0001), and were more likely to present with metabolic comorbidities (diabetes [p<0.0001], hypertension [p<0.0001], and hyperlipidaemia [p<0.0001]) or cardiovascular disease at presentation (p=0.0055) than patients with hepatocellular carcinoma due to other causes. They were also more likely to be non-cirrhotic (38.5%, 27.9–50.2 vs 14.6%, 8.7–23.4 for hepatocellular carcinoma due to other causes; p<0.0001). Patients with NAFLD-related hepatocellular carcinoma had larger tumour diameters (p=0.0087), were more likely to have uninodular lesions (p=0.0003), and had similar odds of Barcelona Clinic Liver Cancer stages, TNM stages, alpha fetoprotein concentration, and Eastern Cooperative Oncology Group (ECOG) performance status to patients with non-NAFLD-related hepatocellular carcinoma. A lower proportion of patients with NAFLD-related hepatocellular carcinoma underwent surveillance (32.8%, 12.0–63.7) than did patients with hepatocellular carcinoma due to other causes (55.7%, 24.0–83.3; p<0.0001). There were no significant differences in treatment allocation (curative therapy, palliative therapy, and best supportive care) between patients with NAFLD-related hepatocellular carcinoma and those with hepatocellular carcinoma due to other causes. Overall survival did not differ between the two groups (hazard ratio 1.05, 95% CI 0.92–1.20, p=0.43), but disease-free survival was longer for patients with NAFLD-related hepatocellular carcinoma (0.79, 0.63–0.99; p=0.044). There was substantial heterogeneity in most analyses (I2>75%), and all articles had low-to-moderate risk of bias.

Interpretation

NAFLD-related hepatocellular carcinoma is associated with a higher proportion of patients without cirrhosis and lower surveillance rates than hepatocellular carcinoma due to other causes. Surveillance strategies should be developed for patients with NAFLD without cirrhosis who are at high risk of developing hepatocellular carcinoma.

Tan, D.J.H., et al., Clinical characteristics, surveillance, treatment allocation, and outcomes of non-alcoholic fatty liver disease-related hepatocellular carcinoma: a systematic review and meta-analysis. The Lancet Oncology.