A rise in all-cause mortality in China following termination of the Zero COVID Policy
In a recent study published in JAMA Network Open, researchers assess the impact of discontinuing the zero coronavirus disease 2019 (COVID-19) policy on all-cause mortality in China.
Study: Excess All-Cause Mortality in China After Ending the Zero COVID Policy. Image Credit: IHOR SULYATYTSKYY / Shutterstock.com
COVID-19 in China
For three years, China maintained low COVID-19 excess mortality through strict measures. Nevertheless, upon abandoning the zero COVID policy in December 2022, the rate of COVID-19 cases and hospitalizations increased.
Between early December 2022 to January 12, 2023, 60,000 COVID-19 deaths were reported in Chinese health facilities. Earlier predictions had foreseen a massive increase in excess deaths within the range of 0.97 to 2.10 million during the Omicron surge if the policy was dropped; however, these projections were not made using empirical data.
About the study
In the present study, mortality information between January 1, 2016, to January 31, 2023, was extracted from obituary data for Peking University (PU), Tsinghua University (TU) in Beijing, and Harbin Institute of Technology (HIT) in Harbin. These universities, as of 2022, employed 19,992, 19,898, and 7,293 individuals, respectively, including present and retired staff.
Each university consistently posted obituaries for their official employees, with a typical delay of around three days after the individual's death. This practice remained constant before and throughout the COVID-19 pandemic. All categories of employees were covered, excluding those from affiliated hospitals whose obituaries were not listed on the main university sites.
Syndromic surveillance data was also obtained using the Baidu Index (BI), which reflects search frequencies on China's major internet search engine, Baidu. The BI, particularly during infectious disease outbreaks, serves as a trusted data source for infodemiology and infoveillance studies.
For the current study, daily BI values related to mortality keywords like "funeral parlor," "cremation," "crematorium," and "burial" were collected for various Chinese regions between January 1, 2016, and January 31, 2023. The study relied solely on public data and published literature, thus preventing the need for institutional review approval.
Statistical analysis determined the relative mortality change for individuals 30 years and older in Beijing and Harbin between December 2022 and January 2023. To this end, an interrupted time-series design assessed the impact of distinct shocks or interventions at specific moments.
The entire time series was segmented into three distinct periods, including pre-COVID-19, the period with stringent mitigation measures, and the post-zero COVID-19 policy phase. These segments were factored into a segmented negative binomial regression model, which identified monthly death counts.
A significant positive correlation was observed between BI changes for mortality-related terms and mortality due to the relaxed zero COVID policies. This consistent pattern of BI shifts for mortality-related terms was observed throughout all Chinese regions. Consequently, the mortality increase in reference areas was used to infer proportional changes for the rest of the country.
Region-specific excess mortality was determined by multiplying the proportional mortality increase with expected deaths. This estimation used data from the 2020 census and China National Disease Surveillance Points.
Sensitivity analyses were implemented, in which one of the three universities was iteratively excluded and involved drawing 10,000 samples randomly from each parameter distribution.
Between December 2022 and January 2023, 130 and 42 deaths were reported among employees of PKU and THU in Beijing, respectively. Comparatively, Heilongjiang's HIT reported 12 and 19 deaths in these same months, respectively.
About 76% of the deceased in Beijing were male, and 80% were 85 years or older. This age distribution marked a significant increase from pre-pandemic times and the initial three years of the pandemic. HIT was associated with similar age and gender patterns in its death statistics.
Death counts peaked in both cities during the fourth week of December in 2022, which corresponds to the BI peak in most provinces that month. The death toll in Beijing's universities rose dramatically by 403% in December and 56% in January when juxtaposed with anticipated numbers. Similarly, the death counts in HIT for December (12 versus 3) and January (19 versus 3) significantly surpassed expectations.
Between December 2022 and January 2023, China experienced an estimated excess of 1.87 million deaths among those aged 30 and above. This spike was observed across all provinces, except Tibet, with increases ranging from 77% in Guangxi to 279% in Ningxia.
After China ended its zero COVID policy, a notable 1.87 million excess deaths were estimated within the subsequent two months, with older individuals primarily affected. These figures drastically surpassed China's official estimate of 60,000, although the peak of hospitalizations and deaths by the end of December 2022 aligned with government reports.
Various models had predicted such excess deaths, with projections ranging from 0.99 to 2.1 million fatalities if the zero COVID strategy ceased. The current estimates could indicate that the Chinese population's limited immunity had a more significant impact than previously thought.
- Xiao, H., Wang, Z., Liu, F., & Unger, J. M. (2023). Excess All-Cause Mortality in China After Ending the Zero COVID Policy. JAMA Network Open. doi:10.1001/jamanetworkopen.2023.30877
Posted in: Medical Science News | Medical Research News | Disease/Infection News | Healthcare News
Tags: Coronavirus, covid-19, immunity, Mortality, Omicron, Pandemic, Technology
Vijay Kumar Malesu
Vijay holds a Ph.D. in Biotechnology and possesses a deep passion for microbiology. His academic journey has allowed him to delve deeper into understanding the intricate world of microorganisms. Through his research and studies, he has gained expertise in various aspects of microbiology, which includes microbial genetics, microbial physiology, and microbial ecology. Vijay has six years of scientific research experience at renowned research institutes such as the Indian Council for Agricultural Research and KIIT University. He has worked on diverse projects in microbiology, biopolymers, and drug delivery. His contributions to these areas have provided him with a comprehensive understanding of the subject matter and the ability to tackle complex research challenges.