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

• DOI: 10.1186/s12911-021-01501-1
• Publication Date: 2021-06-02T09:04:59Z
• Authors: Sarah Charlotte J...; Matthew Cunningham; Ilse N. Dippenaar; Fablina Sharara; Eve E. Wool; Kareha M. Agesa; Chieh Han; Molly K. Miller-P...; Shadrach Wilson; John E. Fuller; Shelly Balassyano; Gregory J. Bertol...; Nicole Davis Weaver; ; Jalal Arabloo; Alaa Badawi; Akshaya Srikanth ...; Katrin Burkart; Luis Alberto Cámera; Felix Carvalho; Carlos A. Castañe...; Jee-Young Jasmine...; 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 Ib...; Maryam Keramati; Ejaz Ahmad Khan; Yun Jin Kim; Adnan Kisa; Hamidreza Komaki; Ai Koyanagi; Samantha Leigh La...; Kate E. LeGrand; Xuefeng Liu; Azeem Majeed; Reza Malekzadeh; Bahram Mohajer; Abdollah Mohammad...; Reza Mohammadpour...; 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 Pashazade...; David M. Pereira; Hai Quang Pham; Salman Rawaf; David Laith Rawaf; Andre M. N. Renzaho; Luca Ronfani; Abdallah M. Samy; Subramanian Senth...; Sadaf G. Sepanlou; Masood Ali Shaikh; David H. Shaw; Kenji Shibuya; Jasvinder A. Singh; Valentin Yurievic...; Anna Aleksandrovn...; Emma Elizabeth Sp...; Eyayou Girma Tadesse; Mohamad-Hani Temsah; Marcos Roberto To...; Bach Xuan Tran; Gebiyaw Wudie Tse...; Pascual R. Valdez; Prashant M. Vishw...; Giang Thu Vu; Yasir Waheed; Naohiro Yonemoto; Rafael Lozano; Alan D. Lopez; Christopher J. L....; Mohsen Naghavi
• Keywords: Male; Metric (unit); ACCURACY; Health, Toxicology and Mutagenesis; Social Sciences; Garbage codes; FOS: Health sciences; Global Health; 310; Global Burden of Disease; Engineering; 0302 clinical medicine; Japan; Sociology; Cause of Death; Pathology; Disease; Global Maternal and Child Health Outcomes; Public health; Statistics; Vital registration; STATISTICS; Data Accuracy; FOS: Sociology; Programming language; 3. Good health; Environmental health; Operations management; GBD Cause of Death Collaborators; Health; MORTALITY DATA; Physical Sciences; HEART-FAILURE; Medicine; Female; France; Life Sciences & Biomedicine; Algorithms; Brazil; Research Article; 330; COMPLETION; Computer applications to medicine. Medical informatics; R858-859.7; 610; Cause of death; Nursing; Garbage; Star ranking system; 03 medical and health sciences; XXXXXX - Unknown; Health Sciences; FOS: Mathematics; Humans; Mortality; UNDERLYING CAUSE; Demography; CERTIFICATION; Science & Technology; US; Data quality; Impact of Climate Change on Human Health; 1103 Clinical Sciences; Computer science; 0806 Information Systems; Redistribution; Environmental Science; Pediatrics, Perinatology and Child Health; Garbage collection; Impact of Social Factors on Health Outcomes; Medical Informatics; Mathematics

AbstractBackgroundAccurate, 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.MethodsWe 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.ResultsThe 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.ConclusionsWe 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.