Public health utility of cause of death data: applying empirical algorithms to improve data quality

BMC Medical Informatics and Decision Making - 2021
Sarah Johnson1, Matthew Cunningham1, Ilse N. Dippenaar1, Fablina Sharara1, Eve Wool1, Kareha M Agesa1, Chieh Han1, Molly K. Miller-Petrie2, Shadrach Wilson1, John E Fuller1, Shelly Balassyano1, Gregory J Bertolacci1, Nicole Davis Weaver1, Jalal Arabloo, Alaa Badawi, Akshaya Srikanth Bhagavathula, Katrin Burkart, Luis Cámera, Félix Carvalho, Carlos Castañeda-Orjuela, Jee‐Young Jasmine Choi, Dinh‐Toi Chu, Xiaochen Dai, Mostafa Dianatinasab, Sophia Emmons-Bell, Eduarda Fernandes, Florian Fischer, Ahmad Ghashghaee, Mahaveer Golechha, Simon I Hay, Khezar Hayat, Nathaniel J. Henry, Ramesh Holla, Mowafa Househ, Segun Emmanuel Ibitoye, Maryam Keramati, Ejaz Ahmad Khan, Yun Jin Kim, Adnan Kısa, Hamidreza Komaki, Ai Koyanagi, Samantha Leigh Larson, Kate E LeGrand, Xuefeng Li, Azeem Majeed, Reza Malekzadeh, Bahram Mohajer, Abdollah Mohammadian-Hafshejani, Reza Mohammadpourhodki, Shafiu Mohammed, Farnam Mohebi, Ali H Mokdad, Mariam Molokhia, Lorenzo Monasta, Mohammad Ali Moni, Muhammad Naveed, Huong Lan Thi Nguyen, Andrew T Olagunju, Samuel M Ostroff, Fatemeh Pashazadeh Kan, Paula B. Andrade, Hai Quang Pham, Salman Rawaf, David Laith Rawaf, Andre Μ Ν Renzaho, Luca Ronfani, Abdallah M. Samy, Subramanian Senthilkumaran, Sadaf G Sepanlou, Masood Ali Shaikh, David Shaw, Kenji Shibuya, Jasvinder A Singh, Valentin Yurievich Skryabin, Anna Aleksandrovna Skryabina, Emma Elizabeth Spurlock, Eyayou Girma Tadesse, Mohamad-Hani Temsah, Marcos Roberto Tovani‐Palone, Bach Xuan Tran, Gebiyaw Wudie Tsegaye, Pascual Valdéz, Prashant M. Vishwanath, Giang Thu Vu, Yasir Waheed, Naohiro Yonemoto, Rafael Lozano, Alan D. López1,3,4, Christopher J L Murray4,1, Mohsen Naghavi1,4,5
1Institute for Health Metrics and Evaluation, University of Washington, Seattle, USA
2Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, USA
3Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia
4Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, USA
5Department of Health Metrics Sciences, Director of Subnational Burden of Disease Estimation, Institute for Health Metrics and Evaluation School of Medicine, University of Washington, Seattle, USA

Tóm tắt

Abstract Background Accurate, comprehensive, cause-specific mortality estimates are crucial for informing public health decision making worldwide. Incorrectly or vaguely assigned deaths, defined as garbage-coded deaths, mask the true cause distribution. The Global Burden of Disease (GBD) study has developed methods to create comparable, timely, cause-specific mortality estimates; an impactful data processing method is the reallocation of garbage-coded deaths to a plausible underlying cause of death. We identify the pattern of garbage-coded deaths in the world and present the methods used to determine their redistribution to generate more plausible cause of death assignments. Methods We describe the methods developed for the GBD 2019 study and subsequent iterations to redistribute garbage-coded deaths in vital registration data to plausible underlying causes. These methods include analysis of multiple cause data, negative correlation, impairment, and proportional redistribution. We classify garbage codes into classes according to the level of specificity of the reported cause of death (CoD) and capture trends in the global pattern of proportion of garbage-coded deaths, disaggregated by these classes, and the relationship between this proportion and the Socio-Demographic Index. We examine the relative importance of the top four garbage codes by age and sex and demonstrate the impact of redistribution on the annual GBD CoD rankings. Results The proportion of least-specific (class 1 and 2) garbage-coded deaths ranged from 3.7% of all vital registration deaths to 67.3% in 2015, and the age-standardized proportion had an overall negative association with the Socio-Demographic Index. When broken down by age and sex, the category for unspecified lower respiratory infections was responsible for nearly 30% of garbage-coded deaths in those under 1 year of age for both sexes, representing the largest proportion of garbage codes for that age group. We show how the cause distribution by number of deaths changes before and after redistribution for four countries: Brazil, the United States, Japan, and France, highlighting the necessity of accounting for garbage-coded deaths in the GBD. Conclusions We provide a detailed description of redistribution methods developed for CoD data in the GBD; these methods represent an overall improvement in empiricism compared to past reliance on a priori knowledge.

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