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

Sarah Charlotte Johnson; Matthew Cunningham; Ilse N. Dippenaar; Fablina Sharara; Eve E. Wool; Kareha M. Agesa; Chieh Han; Molly K. Miller-Petrie; Shadrach Wilson; John E. Fuller; Shelly Balassyano; Gregory J. Bertolacci; Nicole Davis Weaver; Jalal Arabloo; Alaa Badawi; Akshaya Srikanth Bhagavathula; Katrin Burkart; Luis Alberto Cámera; Felix Carvalho; Carlos A. 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 Kisa; Hamidreza Komaki; Ai Koyanagi; Samantha Leigh Larson; Kate E. LeGrand; Xuefeng Liu; 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; David M. Pereira; Hai Quang Pham; Salman Rawaf; David Laith Rawaf; Andre M. N. Renzaho; Luca Ronfani; Abdallah M. Samy; Subramanian Senthilkumaran; Sadaf G. Sepanlou; Masood Ali Shaikh; David H. 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 R. Valdez; Prashant M. Vishwanath; Giang Thu Vu; Yasir Waheed; Naohiro Yonemoto; Rafael Lozano; Alan D. Lopez; Christopher J. L. Murray; Mohsen Naghavi

doi:10.1186/s12911-021-01501-1

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

Sarah Charlotte Johnson, Matthew Cunningham, Ilse N. Dippenaar, Fablina Sharara, Eve E. Wool, Kareha M. Agesa, Chieh Han, Molly K. Miller-Petrie, Shadrach Wilson, John E. Fuller, Shelly Balassyano, Gregory J. Bertolacci, Nicole Davis Weaver, Jalal Arabloo, Alaa Badawi, Akshaya Srikanth Bhagavathula, Katrin Burkart, Luis Alberto Cámera, Felix Carvalho, Carlos A. Castañeda-OrjuelaJee-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 Kisa, Hamidreza Komaki, Ai Koyanagi, Samantha Leigh Larson, Kate E. LeGrand, Xuefeng Liu, 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, David M. Pereira, Hai Quang Pham, Salman Rawaf, David Laith Rawaf, Andre M. N. Renzaho, Luca Ronfani, Abdallah M. Samy, Subramanian Senthilkumaran, Sadaf G. Sepanlou, Masood Ali Shaikh, David H. 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 R. Valdez, Prashant M. Vishwanath, Giang Thu Vu, Yasir Waheed, Naohiro Yonemoto, Rafael Lozano, Alan D. Lopez, Christopher J. L. Murray, Mohsen Naghavi

Western Sydney University

Research output: Contribution to journal › Article › peer-review

62 Citations (Scopus)

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.

Original language	English
Article number	175
Number of pages	20
Journal	BMC Medical Informatics and Decision Making
Volume	21
Issue number	1
DOIs	https://doi.org/10.1186/s12911-021-01501-1
Publication status	Published - 2021

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Johnson, S. C., Cunningham, M., Dippenaar, I. N., Sharara, F., Wool, E. E., Agesa, K. M., Han, C., Miller-Petrie, M. K., Wilson, S., Fuller, J. E., Balassyano, S., Bertolacci, G. J., Davis Weaver, N., Arabloo, J., Badawi, A., Bhagavathula, A. S., Burkart, K., Cámera, L. A., Carvalho, F., ... Naghavi, M. (2021). Public health utility of cause of death data : applying empirical algorithms to improve data quality. BMC Medical Informatics and Decision Making, 21(1), Article 175. https://doi.org/10.1186/s12911-021-01501-1

@article{e9000f553fe14549bfe2c3a0394d2e6a,

title = "Public health utility of cause of death data : applying empirical algorithms to improve data quality",

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.",

author = "Johnson, {Sarah Charlotte} and Matthew Cunningham and Dippenaar, {Ilse N.} and Fablina Sharara and Wool, {Eve E.} and Agesa, {Kareha M.} and Chieh Han and Miller-Petrie, {Molly K.} and Shadrach Wilson and Fuller, {John E.} and Shelly Balassyano and Bertolacci, {Gregory J.} and {Davis Weaver}, Nicole and Jalal Arabloo and Alaa Badawi and Bhagavathula, {Akshaya Srikanth} and Katrin Burkart and C{\'a}mera, {Luis Alberto} and Felix Carvalho and Casta{\~n}eda-Orjuela, {Carlos A.} and Choi, {Jee-Young Jasmine} and Dinh-Toi Chu and Xiaochen Dai and Mostafa Dianatinasab and Sophia Emmons-Bell and Eduarda Fernandes and Florian Fischer and Ahmad Ghashghaee and Mahaveer Golechha and Hay, {Simon I.} and Khezar Hayat and Henry, {Nathaniel J.} and Ramesh Holla and Mowafa Househ and Ibitoye, {Segun Emmanuel} and Maryam Keramati and Khan, {Ejaz Ahmad} and Kim, {Yun Jin} and Adnan Kisa and Hamidreza Komaki and Ai Koyanagi and Larson, {Samantha Leigh} and LeGrand, {Kate E.} and Xuefeng Liu and Azeem Majeed and Reza Malekzadeh and Bahram Mohajer and Abdollah Mohammadian-Hafshejani and Reza Mohammadpourhodki and Shafiu Mohammed and Farnam Mohebi and Mokdad, {Ali H.} and Mariam Molokhia and Lorenzo Monasta and Moni, {Mohammad Ali} and Muhammad Naveed and Nguyen, {Huong Lan Thi} and Olagunju, {Andrew T.} and Ostroff, {Samuel M.} and Kan, {Fatemeh Pashazadeh} and Pereira, {David M.} and Pham, {Hai Quang} and Salman Rawaf and Rawaf, {David Laith} and Renzaho, {Andre M. N.} and Luca Ronfani and Samy, {Abdallah M.} and Subramanian Senthilkumaran and Sepanlou, {Sadaf G.} and Shaikh, {Masood Ali} and Shaw, {David H.} and Kenji Shibuya and Singh, {Jasvinder A.} and Skryabin, {Valentin Yurievich} and Skryabina, {Anna Aleksandrovna} and Spurlock, {Emma Elizabeth} and Tadesse, {Eyayou Girma} and Mohamad-Hani Temsah and Tovani-Palone, {Marcos Roberto} and Tran, {Bach Xuan} and Tsegaye, {Gebiyaw Wudie} and Valdez, {Pascual R.} and Vishwanath, {Prashant M.} and Vu, {Giang Thu} and Yasir Waheed and Naohiro Yonemoto and Rafael Lozano and Lopez, {Alan D.} and Murray, {Christopher J. L.} and Mohsen Naghavi",

year = "2021",

doi = "10.1186/s12911-021-01501-1",

language = "English",

volume = "21",

journal = "BMC Medical Informatics and Decision Making",

issn = "1472-6947",

publisher = "BioMed Central",

number = "1",

}

Johnson, SC, Cunningham, M, Dippenaar, IN, Sharara, F, Wool, EE, Agesa, KM, Han, C, Miller-Petrie, MK, Wilson, S, Fuller, JE, Balassyano, S, Bertolacci, GJ, Davis Weaver, N, Arabloo, J, Badawi, A, Bhagavathula, AS, Burkart, K, Cámera, LA, Carvalho, F, Castañeda-Orjuela, CA, Choi, J-YJ, Chu, D-T, Dai, X, Dianatinasab, M, Emmons-Bell, S, Fernandes, E, Fischer, F, Ghashghaee, A, Golechha, M, Hay, SI, Hayat, K, Henry, NJ, Holla, R, Househ, M, Ibitoye, SE, Keramati, M, Khan, EA, Kim, YJ, Kisa, A, Komaki, H, Koyanagi, A, Larson, SL, LeGrand, KE, Liu, X, Majeed, A, Malekzadeh, R, Mohajer, B, Mohammadian-Hafshejani, A, Mohammadpourhodki, R, Mohammed, S, Mohebi, F, Mokdad, AH, Molokhia, M, Monasta, L, Moni, MA, Naveed, M, Nguyen, HLT, Olagunju, AT, Ostroff, SM, Kan, FP, Pereira, DM, Pham, HQ, Rawaf, S, Rawaf, DL, Renzaho, AMN, Ronfani, L, Samy, AM, Senthilkumaran, S, Sepanlou, SG, Shaikh, MA, Shaw, DH, Shibuya, K, Singh, JA, Skryabin, VY, Skryabina, AA, Spurlock, EE, Tadesse, EG, Temsah, M-H, Tovani-Palone, MR, Tran, BX, Tsegaye, GW, Valdez, PR, Vishwanath, PM, Vu, GT, Waheed, Y, Yonemoto, N, Lozano, R, Lopez, AD, Murray, CJL & Naghavi, M 2021, 'Public health utility of cause of death data : applying empirical algorithms to improve data quality', BMC Medical Informatics and Decision Making, vol. 21, no. 1, 175. https://doi.org/10.1186/s12911-021-01501-1

TY - JOUR

T1 - Public health utility of cause of death data : applying empirical algorithms to improve data quality

AU - Johnson, Sarah Charlotte

AU - Cunningham, Matthew

AU - Dippenaar, Ilse N.

AU - Sharara, Fablina

AU - Wool, Eve E.

AU - Agesa, Kareha M.

AU - Han, Chieh

AU - Miller-Petrie, Molly K.

AU - Wilson, Shadrach

AU - Fuller, John E.

AU - Balassyano, Shelly

AU - Bertolacci, Gregory J.

AU - Davis Weaver, Nicole

AU - Arabloo, Jalal

AU - Badawi, Alaa

AU - Bhagavathula, Akshaya Srikanth

AU - Burkart, Katrin

AU - Cámera, Luis Alberto

AU - Carvalho, Felix

AU - Castañeda-Orjuela, Carlos A.

AU - Choi, Jee-Young Jasmine

AU - Chu, Dinh-Toi

AU - Dai, Xiaochen

AU - Dianatinasab, Mostafa

AU - Emmons-Bell, Sophia

AU - Fernandes, Eduarda

AU - Fischer, Florian

AU - Ghashghaee, Ahmad

AU - Golechha, Mahaveer

AU - Hay, Simon I.

AU - Hayat, Khezar

AU - Henry, Nathaniel J.

AU - Holla, Ramesh

AU - Househ, Mowafa

AU - Ibitoye, Segun Emmanuel

AU - Keramati, Maryam

AU - Khan, Ejaz Ahmad

AU - Kim, Yun Jin

AU - Kisa, Adnan

AU - Komaki, Hamidreza

AU - Koyanagi, Ai

AU - Larson, Samantha Leigh

AU - LeGrand, Kate E.

AU - Liu, Xuefeng

AU - Majeed, Azeem

AU - Malekzadeh, Reza

AU - Mohajer, Bahram

AU - Mohammadian-Hafshejani, Abdollah

AU - Mohammadpourhodki, Reza

AU - Mohammed, Shafiu

AU - Mohebi, Farnam

AU - Mokdad, Ali H.

AU - Molokhia, Mariam

AU - Monasta, Lorenzo

AU - Moni, Mohammad Ali

AU - Naveed, Muhammad

AU - Nguyen, Huong Lan Thi

AU - Olagunju, Andrew T.

AU - Ostroff, Samuel M.

AU - Kan, Fatemeh Pashazadeh

AU - Pereira, David M.

AU - Pham, Hai Quang

AU - Rawaf, Salman

AU - Rawaf, David Laith

AU - Renzaho, Andre M. N.

AU - Ronfani, Luca

AU - Samy, Abdallah M.

AU - Senthilkumaran, Subramanian

AU - Sepanlou, Sadaf G.

AU - Shaikh, Masood Ali

AU - Shaw, David H.

AU - Shibuya, Kenji

AU - Singh, Jasvinder A.

AU - Skryabin, Valentin Yurievich

AU - Skryabina, Anna Aleksandrovna

AU - Spurlock, Emma Elizabeth

AU - Tadesse, Eyayou Girma

AU - Temsah, Mohamad-Hani

AU - Tovani-Palone, Marcos Roberto

AU - Tran, Bach Xuan

AU - Tsegaye, Gebiyaw Wudie

AU - Valdez, Pascual R.

AU - Vishwanath, Prashant M.

AU - Vu, Giang Thu

AU - Waheed, Yasir

AU - Yonemoto, Naohiro

AU - Lozano, Rafael

AU - Lopez, Alan D.

AU - Murray, Christopher J. L.

AU - Naghavi, Mohsen

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

UR - http://hdl.handle.net/1959.7/uws:59924

U2 - 10.1186/s12911-021-01501-1

DO - 10.1186/s12911-021-01501-1

M3 - Article

SN - 1472-6947

VL - 21

JO - BMC Medical Informatics and Decision Making

JF - BMC Medical Informatics and Decision Making

IS - 1

M1 - 175

ER -

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

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