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

Johnson, S.C. and Cunningham, M. and Dippenaar, I.N. and Sharara, F. and Wool, E.E. and Agesa, K.M. and Han, C. and Miller-Petrie, M.K. and Wilson, S. and Fuller, J.E. and Balassyano, S. and Bertolacci, G.J. and Davis Weaver, N. and Arabloo, J. and Badawi, A. and Bhagavathula, A.S. and Burkart, K. and Cámera, L.A. and Carvalho, F. and Castañeda-Orjuela, C.A. and Choi, J.-Y.J. and Chu, D.-T. and Dai, X. and Dianatinasab, M. and Emmons-Bell, S. and Fernandes, E. and Fischer, F. and Ghashghaee, A. and Golechha, M. and Hay, S.I. and Hayat, K. and Henry, N.J. and Holla, R. and Househ, M. and Ibitoye, S.E. and Keramati, M. and Khan, E.A. and Kim, Y.J. and Kisa, A. and Komaki, H. and Koyanagi, A. and Larson, S.L. and LeGrand, K.E. and Liu, X. and Majeed, A. and Malekzadeh, R. and Mohajer, B. and Mohammadian-Hafshejani, A. and Mohammadpourhodki, R. and Mohammed, S. and Mohebi, F. and Mokdad, A.H. and Molokhia, M. and Monasta, L. and Moni, M.A. and Naveed, M. and Nguyen, H.L.T. and Olagunju, A.T. and Ostroff, S.M. and Kan, F.P. and Pereira, D.M. and Pham, H.Q. and Rawaf, S. and Rawaf, D.L. and Renzaho, A.M.N. and Ronfani, L. and Samy, A.M. and Senthilkumaran, S. and Sepanlou, S.G. and Shaikh, M.A. and Shaw, D.H. and Shibuya, K. and Singh, J.A. and Skryabin, V.Y. and Skryabina, A.A. and Spurlock, E.E. and Tadesse, E.G. and Temsah, M.-H. and Tovani-Palone, M.R. and Tran, B.X. and Tsegaye, G.W. and Valdez, P.R. and Vishwanath, P.M. and Vu, G.T. and Waheed, Y. and Yonemoto, N. and Lozano, R. and Lopez, A.D. and Murray, C.J.L. and Naghavi, M. and of Death Collaborators, GBD Cause (2021) Public health utility of cause of death data: applying empirical algorithms to improve data quality. BMC Medical Informatics and Decision Making, 21 (1). ISSN 14726947

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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 1Â 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.

Item Type: Article
Additional Information: cited By 1
Uncontrolled Keywords: algorithm; Brazil; cause of death; female; France; global health; human; Japan; male; measurement accuracy, Algorithms; Brazil; Cause of Death; Data Accuracy; Female; France; Global Health; Humans; Japan; Male
Subjects: WA Public Health
WA Public Health > WA. 900 Vital Statistics
Divisions: Faculty of Health > Department of Epidemiology
Depositing User: zeynab . bagheri
Date Deposited: 12 Sep 2021 04:33
Last Modified: 12 Sep 2021 04:33
URI: http://eprints.skums.ac.ir/id/eprint/9180

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