Background
Research on long COVID in China is limited, particularly in terms of large-sample epidemiological data and the effects of recent SARS-CoV-2 sub-variants. China provides an ideal study environment owing to its large infection base, high vaccine coverage, and stringent pre-pandemic measures.
Methods
This retrospective study used an online questionnaire to investigate SARS-CoV-2 infection status and long COVID symptoms among 74,075 Chinese residents over one year. The relationships between baseline characteristics, vaccination status, pathogenic infection, and long COVID were analyzed using multinomial logistic regression, and propensity matching.
Findings
Analysis of 68,200 valid responses revealed that the most frequent long COVID symptoms include fatigue (30.53%), memory decline (27.93%), decreased exercise ability (18.29%), and brain fog (16.87%). These symptoms were less prevalent among those infected only once: fatigue (24.85%), memory decline (18.11%), and decreased exercise ability (12.52%), etc. Women were more likely to experience long COVID, with symptoms varying by age group, except for sleep disorders and muscle/joint pain, which were more common in older individuals. Northern China exhibits a higher prevalence of long COVID, potentially linked to temperature gradients. Risk factors included underlying diseases, alcohol consumption, smoking, and the severity of acute infection (OR > 1, FDR < 0.05). Reinfection was associated with milder symptoms but led to a higher incidence and severity of long COVID (OR > 1, FDR < 0.05). Vaccination, particularly multiple boosters, significantly reduced long-term symptoms by 30%–70% (OR < 1, FDR < 0.05). COVID-19 participants also self-reported more bacterial, influenza and mycoplasma infections, and 8%–10% of patients felt SARS-CoV-2-induced chronic diseases.
Interpretation
This survey provides valuable insights into long COVID situation among Chinese residents, with 10%–30% (including repeated infection) reporting symptoms. Monitoring at-risk individuals based on identified risk factors is essential for public health efforts.
Funding
This study was funded by the China Postdoctoral Science Foundation (2022M723344, 2023M743729), Guangdong Basic and Applied Basic Research Foundation (2023A1515110489), and the Bill & Melinda Gates Foundation (INV-027420).