Facultad de Ciencias Económicas y Empresariales, Universidad de Navarra, Pamplona, España.
Front Public Health. 2022 Nov 8;10:950469. doi: 10.3389/fpubh.2022.950469. eCollection 2022.
During the COVID-19 pandemic, surveillance systems worldwide underestimated mortality in real time due to longer death reporting lags. In Spain, the mortality monitor "MoMo" published downward biased excess mortality estimates daily. I study the correction of such bias using polynomial regressions in data from January to March 2021 for Spain and the Comunitat Valenciana, the region with the highest excess mortality.
This adjustment for real-time statistics consisted of (1) estimating forthcoming revisions with polynomial regressions of past revisions, and (2) multiplying the daily-published excess mortality by these estimated revisions. The accuracy of the corrected estimates compared to the original was measured by contrasting their mean absolute errors (MAE) and root mean square errors (RMSE).
Applying quadratic and cubic regressions improved the first communication of cumulative mortality in Spain by 2-3%, on average, and the flow in registered deaths by 20%. However, for the Comunitat Valenciana, those corrections improved the first publications of the cumulative mortality by 36-45%, on average; their second publication, by 23-30%; and the third, by 15-21%. The flow of deaths registered each day improved by 62-63% on their first publication, by 19-36% on the second, and by 12-17% on the third.
It is recommended that MoMo's estimates for excess mortality be corrected from the effect of death reporting lags by using polynomial regressions. This holds for the flows in each date and their cumulative sum, as well as national and regional data. These adjustments can be applied by surveillance systems in other countries.
在 COVID-19 大流行期间,由于死亡报告的滞后时间更长,全球监测系统实时低估了死亡率。在西班牙,死亡率监测器“MoMo”每天发布的超额死亡率估计值都存在向下偏差。我使用 2021 年 1 月至 3 月西班牙和瓦伦西亚社区(超额死亡率最高的地区)的数据,研究了使用多项式回归来纠正这种偏差的方法。
这种实时统计数据的调整包括:(1)使用过去的修订版多项式回归来估计即将进行的修订;(2)用这些估计的修订版来乘以每日公布的超额死亡率。通过对比原始数据和校正数据的平均绝对误差(MAE)和均方根误差(RMSE)来衡量校正后估计值的准确性。
应用二次和三次回归平均将西班牙的累积死亡率的首次报告提高了 2-3%,并将注册死亡人数的流量提高了 20%。然而,对于瓦伦西亚社区,这些修正平均将累积死亡率的首次公布提高了 36-45%,第二次公布提高了 23-30%,第三次公布提高了 15-21%。每天注册死亡人数的流量在首次公布时提高了 62-63%,在第二次公布时提高了 19-36%,在第三次公布时提高了 12-17%。
建议使用多项式回归来修正 MoMo 对超额死亡率的估计,以消除死亡报告滞后的影响。这适用于每天的流量及其累积和,以及国家和地区的数据。这些调整可以由其他国家的监测系统应用。