动态清零结束后中国超额死亡的定量计算

先前，江苏2023年一季度火化数据发布后，网民根据这一数据（比2022年一季度增加7.2万）按比例扩展到全国，无论是按全部人口还是老龄人口，都得到150-160万的死亡数。

现在又有一篇发表于JAMA Network Open的文章Excess All-Cause Mortality in China After Ending the Zero COVID Policy，得到的结论是：An estimated 1.87 million (95% CI, 0.71 million-4.43 million; 1.33 per 1000 population) excess deaths occurred among individuals 30 years and older in China during the first 2 months after the end of the zero COVID policy. Excess deaths predominantly occurred among older individuals and were observed across all provinces in mainland China except Tibet.

估计的方法：
作者发现清，北，哈三校从2016年到2023年1月公布的每月教职工死亡数量和所在省份百度指数BI（和死亡相关的搜索项，如“火葬场”）有强正相关
死亡数的估计是负二项分布 E(ln(Death)) = β0 + β1 Month + β2 COVID + β3 ZeroCovid + offset(ln(P))
百度指数的模型是 E(ln(BI i)) = β0 + β1 Day + β2 COVID + β3 ZeroCovid.
(BI[RRref] − 1)/(MR[RRref] − 1) = (BI[RRi] − 1)/(MR[RRi] − 1)估计出每个省份的死亡率增长MR，再根据中国政府公布的人口数据就得到超额死亡
Limitations:
However, our study has limitations. The reliance on obituary data for employees from 3 universities in Beijing and Heilongjiang could result in an overestimation of excess mortality because university employees were older than the general population, or alternatively, an underestimation because the employees had higher socioeconomic status. Such biases may be especially pronounced if patterns of representation of these variables among those with obituary data changed over time. Also, increases in BI searches may not have fully reflected mortality increases outside the reference region, leading to underestimations of excess mortality in other
regions. Further validation of our estimate will be crucial once alternative data sources (eg, population-based mortality data at the national or subnational level) become available. In particular, data delineated by levels of age, sex, and socioeconomic status would allow covariate adjustment for these important demographic variables.

lemma_

支持定量研究。我猜的数量和这个结果碰巧在一个数量级