Zhao Tianming, Liu Haixia, Bulloch Gabriella, Jiang Zhen, Cao Zhaobing, Wu Zunyou
Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, China.
National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Lancet Reg Health West Pac. 2023 Apr 5;36:100755. doi: 10.1016/j.lanwpc.2023.100755.
The COVID-19 pandemic has caused significant global public health challenges, and impacted HIV testing and reporting worldwide. We aimed to estimate the impact of COVID-19 polices on identifying HIV/AIDS cases in China from 2020 to 2022.
We used an interrupted time series (ITS) design and seasonal autoregressive integrated moving average intervention (SARIMA Intervention) model. Monthly reported data on HIV/AIDS cases were extracted from the National Bureau of Disease Control and Prevention of China from January 2004 to August 2022. Data on Stringency Index (SI) and Economic Support Index (ESI) from January 22, 2020 to August 31, 2022 were extracted from the Oxford COVID-19 Government Response Tracker (OxCGRT). Using these, a SARIMA-Intervention model was constructed to evaluate the association between COVID-19 polices and monthly reported HIV/AIDS case numbers from January 2004 to August 2022 using function from R. The absolute percentage errors (APEs) compared the expected numbers generated by the SARIMA-Intervention model with actual numbers of HIV/AIDS, and was the primary outcome of this study. A second counterfactual model estimated HIV/AIDS case numbers if COVID-19 hadn't occurred in December 2019, and the mean difference between actual and predicted numbers were calculated. All statistical analyses were performed in R software (version 4.2.1) and EmpowerStats 2.0 and a P < 0.05 was considered statistically significant.
The SARIMA-Intervention model indicated HIV/AIDS monthly reported cases were inversely and significantly correlated with stricter lockdown and COVID-19 related polices (Coefficient for SI = -231.24, 95% CI: -383.17, -79.32) but not with economic support polices (Coefficient for ESI = 124.27, 95% CI: -309.84, 558.38). APEs of the SARIMA-Intervention model for prediction of HIV/AIDS cases from January 2022 through August 2022, were -2.99, 5.08, -13.64, -34.04, -2.76, -1.52, -1.37 and -2.47 respectively, indicating good accuracy and underreporting of cases during COVID-19. The counterfactual model estimates between January 2020 and August 2022 an additional 1314 HIV/AIDS cases should have been established monthly if COVID-19 hadn't occurred.
The COVID-19 pandemic influenced the allocation and acquisition of medical resources which impacted accurate monthly reporting of HIV in China. Interventions that promote continuous HIV testing and ensure the adequate provision of HIV services including remote delivery of HIV testing services (HIV self-testing) and online sexual counseling services are necessary during pandemics in future.
Ministry of Science and Technology of the People's Republic of China (The grant number: 2020YFC0846300) and Fogarty International Center, National Institutes of Health, USA (The grant number: G11TW010941).
新冠疫情给全球公共卫生带来了重大挑战，影响了全球范围内的艾滋病毒检测和报告。我们旨在评估2020年至2022年新冠疫情防控政策对中国艾滋病毒/艾滋病病例识别的影响。
我们采用了中断时间序列（ITS）设计和季节性自回归积分滑动平均干预（SARIMA干预）模型。从中国国家疾病预防控制局提取了2004年1月至2022年8月艾滋病毒/艾滋病病例的月度报告数据。从牛津新冠疫情政府应对追踪器（OxCGRT）提取了2020年1月22日至2022年8月31日的疫情防控强度指数（SI）和经济支持指数（ESI）数据。利用这些数据，使用R语言的函数构建了一个SARIMA干预模型，以评估2004年1月至2022年8月新冠疫情防控政策与每月报告的艾滋病毒/艾滋病病例数之间的关联。绝对百分比误差（APE）将SARIMA干预模型生成的预期病例数与艾滋病毒/艾滋病实际病例数进行比较，是本研究的主要结果。第二个反事实模型估计了如果2019年12月没有发生新冠疫情，艾滋病毒/艾滋病病例数，并计算了实际病例数与预测病例数之间的平均差异。所有统计分析均在R软件（版本4.2.1）和EmpowerStats 2.0中进行，P < 0.05被认为具有统计学意义。
SARIMA干预模型表明，每月报告的艾滋病毒/艾滋病病例数与更严格的封锁措施和新冠疫情相关政策呈显著负相关（SI系数 = -231.24，95%置信区间：-383.17，-79.32），但与经济支持政策无关（ESI系数 = 124.27，95%置信区间：-309.84，558.38）。SARIMA干预模型对2022年1月至8月艾滋病毒/艾滋病病例预测的APE分别为-2.99、5.08、-13.64、-34.04、-2.76、-1.52、-1.37和-2.47，表明在新冠疫情期间病例报告的准确性良好，但存在漏报情况。反事实模型估计，在2020年1月至2022年8月期间，如果没有发生新冠疫情，每月应新增1314例艾滋病毒/艾滋病病例。
新冠疫情影响了医疗资源的分配和获取，进而影响了中国艾滋病毒每月报告的准确性。在未来的疫情期间，有必要采取干预措施，促进持续的艾滋病毒检测，并确保提供足够的艾滋病毒服务，包括远程提供艾滋病毒检测服务（艾滋病毒自我检测）和在线性咨询服务。
中华人民共和国科学技术部（资助编号：2020YFC0846300）和美国国立卫生研究院福格蒂国际中心（资助编号：G11TW010941）。
