Lin Ziqiang, Lawrence Wayne R, Gong Weiwei, Lin Lifeng, Hu Jianxiong, Zhu Sui, Meng Ruilin, He Guanhao, Xu Xiaojun, Liu Tao, Zhong Jieming, Yu Min, Reinhold Karin, Ma Wenjun
Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, 511443, China.
Department of Epidemiology and Biostatistics, School of Public Health, University at Albany, State University of New York, 1 University Place, Rensselaer, NY, 12144, United States.
Heliyon. 2023 Mar 21;9(4):e14648. doi: 10.1016/j.heliyon.2023.e14648. eCollection 2023 Apr.
Properly analyzing and reporting data remains a challenging task in epidemiologic research, as underreporting of data is often overlooked. The evaluation on the effect of underreporting remains understudied. In this study, we examined the effect of different scenarios of mortality underreporting on the relationship between PM, temperature, and mortality. Mortality data, PM, and temperature data in seven cities were obtained from Provincial Center for Disease Control and Prevention (CDC), China Meteorological Data Sharing Service System, and China National Environmental Monitoring Center, respectively. A time-series design with a distributed lag nonlinear model (DLNM) was used to examine the effects of five mortality underreporting scenarios: 1) Random underreporting of mortality; 2) Underreporting is monotonically increasing (MI) or monotonically decreasing (MD); 3) Underreporting due to holiday and weekends; 4) Underreporting occurs before the 20th day of each month, and these underreporting will be added after the 20th day of the month; and 5) Underreporting due to holiday, weekends, MI, and MD. We observed that underreporting at random (UAR) scenario had little effect on the association between PM, temperature, and daily mortality. However, other four underreporting not at random (UNAR) scenarios mentioned above had varying degrees of influence on the association between PM, temperature, and daily mortality. Additionally, in addition to imputation under UAR, the variation of minimum mortality temperature (MMT) and attributable fraction (AF) of mortality attributed to temperature in the same imputation scenarios is inconsistent in different cities. Finally, we observed that the pooled excess risk (ER) below MMT was negatively associated with mortality and the pooled ER above MMT was positively associated with mortality. This study showed that UNAR impacted the association between PM, temperature, and mortality, and potential underreporting should be dealt with before analyzing data to avoid drawing invalid conclusions.
在流行病学研究中，正确分析和报告数据仍然是一项具有挑战性的任务，因为数据漏报往往被忽视。对数据漏报影响的评估仍未得到充分研究。在本研究中，我们考察了不同死亡率漏报情形对颗粒物（PM）、温度与死亡率之间关系的影响。七个城市的死亡率数据、PM数据和温度数据分别来自中国省级疾病预防控制中心（CDC）、中国气象数据共享服务系统和中国国家环境监测中心。采用具有分布滞后非线性模型（DLNM）的时间序列设计，考察了五种死亡率漏报情形的影响：1）死亡率随机漏报；2）漏报呈单调增加（MI）或单调减少（MD）；3）因节假日和周末漏报；4）每月20日前发生漏报，并在当月20日后添加这些漏报数据；5）因节假日、周末、MI和MD漏报。我们观察到，随机漏报（UAR）情形对PM、温度与每日死亡率之间的关联影响较小。然而，上述其他四种非随机漏报（UNAR）情形对PM、温度与每日死亡率之间的关联有不同程度的影响。此外，除了UAR情形下的插补外，在相同插补情形下，不同城市的最低死亡率温度（MMT）变化以及温度所致死亡率的归因分数（AF）并不一致。最后，我们观察到，低于MMT的合并超额风险（ER）与死亡率呈负相关，高于MMT的合并ER与死亡率呈正相关。本研究表明，UNAR影响了PM、温度与死亡率之间的关联，在分析数据之前应处理潜在的漏报情况，以避免得出无效结论。
