Anna A Sordo, Anna A Do, Melissa J Irwin, David J Muscatello
{"title":"Development of a registration interval correction model for enhancing excess all-cause mortality surveillance during the COVID-19 pandemic","authors":"Anna A Sordo, Anna A Do, Melissa J Irwin, David J Muscatello","doi":"10.1093/ije/dyae145","DOIUrl":null,"url":null,"abstract":"Background Estimates of excess deaths provide critical intelligence on the impact of population health threats including seasonal respiratory infections, pandemics and environmental hazards. Timely estimates of excess deaths can inform the response to COVID-19. However, access to timely mortality data is challenging due to the time interval between the death occurring and the date the death is registered and available for analysis (‘registration interval’). Development Using data from the New South Wales, Australia, Births Deaths and Marriages Registry, we developed a Poisson regression model that estimated near-complete weekly counts, for a given week of death, from partially-complete death registration counts. A 10-weeks lag was considered, and a 2-year baseline of historical registration intervals was used to correct lag weeks. Application Validation of estimated counts found that the root-mean-square error (as a percentage of mean observed near-complete registrations) was less than 7% for lag week 3, and <5% for lag weeks 4–9. We incorporated this method utilizing an existing rapid weekly mortality surveillance system. Counts corrected for registration interval replaced observed values for the most recent weeks. Excess death estimates, based on corrected counts, were within 1.2% of near-complete counts available 9 weeks from the end of the analysis period. Conclusions This study demonstrates a method for estimating recent death counts to correct for registration intervals. Estimates obtained at a 3-week lag were acceptable, while those at greater than 3 weeks were optimal.","PeriodicalId":14147,"journal":{"name":"International journal of epidemiology","volume":null,"pages":null},"PeriodicalIF":6.4000,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International journal of epidemiology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1093/ije/dyae145","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH","Score":null,"Total":0}
引用次数: 0
Abstract
Background Estimates of excess deaths provide critical intelligence on the impact of population health threats including seasonal respiratory infections, pandemics and environmental hazards. Timely estimates of excess deaths can inform the response to COVID-19. However, access to timely mortality data is challenging due to the time interval between the death occurring and the date the death is registered and available for analysis (‘registration interval’). Development Using data from the New South Wales, Australia, Births Deaths and Marriages Registry, we developed a Poisson regression model that estimated near-complete weekly counts, for a given week of death, from partially-complete death registration counts. A 10-weeks lag was considered, and a 2-year baseline of historical registration intervals was used to correct lag weeks. Application Validation of estimated counts found that the root-mean-square error (as a percentage of mean observed near-complete registrations) was less than 7% for lag week 3, and <5% for lag weeks 4–9. We incorporated this method utilizing an existing rapid weekly mortality surveillance system. Counts corrected for registration interval replaced observed values for the most recent weeks. Excess death estimates, based on corrected counts, were within 1.2% of near-complete counts available 9 weeks from the end of the analysis period. Conclusions This study demonstrates a method for estimating recent death counts to correct for registration intervals. Estimates obtained at a 3-week lag were acceptable, while those at greater than 3 weeks were optimal.
期刊介绍:
The International Journal of Epidemiology is a vital resource for individuals seeking to stay updated on the latest advancements and emerging trends in the field of epidemiology worldwide.
The journal fosters communication among researchers, educators, and practitioners involved in the study, teaching, and application of epidemiology pertaining to both communicable and non-communicable diseases. It also includes research on health services and medical care.
Furthermore, the journal presents new methodologies in epidemiology and statistics, catering to professionals working in social and preventive medicine. Published six times a year, the International Journal of Epidemiology provides a comprehensive platform for the analysis of data.
Overall, this journal is an indispensable tool for staying informed and connected within the dynamic realm of epidemiology.