Postmortem investigations and identification of multiple causes of child deaths: An analysis of findings from the Child Health and Mortality Prevention Surveillance (CHAMPS) network.
<h4>Background</h4>The current burden of >5 million deaths yearly is the focus of the Sustainable Development Goal (SDG) to end preventable deaths of newborns and children under 5 years old by 2030. To accelerate progression toward this goal, data are needed that accurately quantify t...
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Medicine R Robert F Breiman Dianna M Blau Portia Mutevedzi Victor Akelo Inacio Mandomando Ikechukwu U Ogbuanu Samba O Sow Lola Madrid Shams El Arifeen Mischka Garel Nana Bukiwe Thwala Dickens Onyango Antonio Sitoe Ima-Abasi Bassey Adama Mamby Keita Addisu Alemu Muntasir Alam Sana Mahtab Dickson Gethi Rosauro Varo Julius Ojulong Solomon Samura Ashka Mehta Alexander M Ibrahim Afruna Rahman Pio Vitorino Vicky L Baillie Janet Agaya Milagritos D Tapia Nega Assefa Atique Iqbal Chowdhury J Anthony G Scott Emily S Gurley Karen L Kotloff Amara Jambai Quique Bassat Beth A Tippett-Barr Shabir A Madhi Cynthia G Whitney CHAMPS Consortium Postmortem investigations and identification of multiple causes of child deaths: An analysis of findings from the Child Health and Mortality Prevention Surveillance (CHAMPS) network. |
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<h4>Background</h4>The current burden of >5 million deaths yearly is the focus of the Sustainable Development Goal (SDG) to end preventable deaths of newborns and children under 5 years old by 2030. To accelerate progression toward this goal, data are needed that accurately quantify the leading causes of death, so that interventions can target the common causes. By adding postmortem pathology and microbiology studies to other available data, the Child Health and Mortality Prevention Surveillance (CHAMPS) network provides comprehensive evaluations of conditions leading to death, in contrast to standard methods that rely on data from medical records and verbal autopsy and report only a single underlying condition. We analyzed CHAMPS data to characterize the value of considering multiple causes of death.<h4>Methods and findings</h4>We examined deaths identified from December 2016 through November 2020 from 7 CHAMPS sites (in Bangladesh, Ethiopia, Kenya, Mali, Mozambique, Sierra Leone, and South Africa), including 741 neonatal, 278 infant, and 241 child <5 years deaths for which results from Determination of Cause of Death (DeCoDe) panels were complete. DeCoDe panelists included all conditions in the causal chain according to the ICD-10 guidelines and assessed if prevention or effective management of the condition would have prevented the death. We analyzed the distribution of all conditions listed as causal, including underlying, antecedent, and immediate causes of death. Among 1,232 deaths with an underlying condition determined, we found a range of 0 to 6 (mean 1.5, IQR 0 to 2) additional conditions in the causal chain leading to death. While pathology provides very helpful clues, we cannot always be certain that conditions identified led to death or occurred in an agonal stage of death. For neonates, preterm birth complications (most commonly respiratory distress syndrome) were the most common underlying condition (n = 282, 38%); among those with preterm birth complications, 256 (91%) had additional conditions in causal chains, including 184 (65%) with a different preterm birth complication, 128 (45%) with neonatal sepsis, 69 (24%) with lower respiratory infection (LRI), 60 (21%) with meningitis, and 25 (9%) with perinatal asphyxia/hypoxia. Of the 278 infant deaths, 212 (79%) had ≥1 additional cause of death (CoD) beyond the underlying cause. The 2 most common underlying conditions in infants were malnutrition and congenital birth defects; LRI and sepsis were the most common additional conditions in causal chains, each accounting for approximately half of deaths with either underlying condition. Of the 241 child deaths, 178 (75%) had ≥1 additional condition. Among 46 child deaths with malnutrition as the underlying condition, all had ≥1 other condition in the causal chain, most commonly sepsis, followed by LRI, malaria, and diarrheal disease. Including all positions in the causal chain for neonatal deaths resulted in 19-fold and 11-fold increases in attributable roles for meningitis and LRI, respectively. For infant deaths, the proportion caused by meningitis and sepsis increased by 16-fold and 11-fold, respectively; for child deaths, sepsis and LRI are increased 12-fold and 10-fold, respectively. While comprehensive CoD determinations were done for a substantial number of deaths, there is potential for bias regarding which deaths in surveillance areas underwent minimally invasive tissue sampling (MITS), potentially reducing representativeness of findings.<h4>Conclusions</h4>Including conditions that appear anywhere in the causal chain, rather than considering underlying condition alone, markedly changed the proportion of deaths attributed to various diagnoses, especially LRI, sepsis, and meningitis. While CHAMPS methods cannot determine when 2 conditions cause death independently or may be synergistic, our findings suggest that considering the chain of events leading to death can better guide research and prevention priorities aimed at reducing child deaths. |
| format |
article |
| author |
Robert F Breiman Dianna M Blau Portia Mutevedzi Victor Akelo Inacio Mandomando Ikechukwu U Ogbuanu Samba O Sow Lola Madrid Shams El Arifeen Mischka Garel Nana Bukiwe Thwala Dickens Onyango Antonio Sitoe Ima-Abasi Bassey Adama Mamby Keita Addisu Alemu Muntasir Alam Sana Mahtab Dickson Gethi Rosauro Varo Julius Ojulong Solomon Samura Ashka Mehta Alexander M Ibrahim Afruna Rahman Pio Vitorino Vicky L Baillie Janet Agaya Milagritos D Tapia Nega Assefa Atique Iqbal Chowdhury J Anthony G Scott Emily S Gurley Karen L Kotloff Amara Jambai Quique Bassat Beth A Tippett-Barr Shabir A Madhi Cynthia G Whitney CHAMPS Consortium |
| author_facet |
Robert F Breiman Dianna M Blau Portia Mutevedzi Victor Akelo Inacio Mandomando Ikechukwu U Ogbuanu Samba O Sow Lola Madrid Shams El Arifeen Mischka Garel Nana Bukiwe Thwala Dickens Onyango Antonio Sitoe Ima-Abasi Bassey Adama Mamby Keita Addisu Alemu Muntasir Alam Sana Mahtab Dickson Gethi Rosauro Varo Julius Ojulong Solomon Samura Ashka Mehta Alexander M Ibrahim Afruna Rahman Pio Vitorino Vicky L Baillie Janet Agaya Milagritos D Tapia Nega Assefa Atique Iqbal Chowdhury J Anthony G Scott Emily S Gurley Karen L Kotloff Amara Jambai Quique Bassat Beth A Tippett-Barr Shabir A Madhi Cynthia G Whitney CHAMPS Consortium |
| author_sort |
Robert F Breiman |
| title |
Postmortem investigations and identification of multiple causes of child deaths: An analysis of findings from the Child Health and Mortality Prevention Surveillance (CHAMPS) network. |
| title_short |
Postmortem investigations and identification of multiple causes of child deaths: An analysis of findings from the Child Health and Mortality Prevention Surveillance (CHAMPS) network. |
| title_full |
Postmortem investigations and identification of multiple causes of child deaths: An analysis of findings from the Child Health and Mortality Prevention Surveillance (CHAMPS) network. |
| title_fullStr |
Postmortem investigations and identification of multiple causes of child deaths: An analysis of findings from the Child Health and Mortality Prevention Surveillance (CHAMPS) network. |
| title_full_unstemmed |
Postmortem investigations and identification of multiple causes of child deaths: An analysis of findings from the Child Health and Mortality Prevention Surveillance (CHAMPS) network. |
| title_sort |
postmortem investigations and identification of multiple causes of child deaths: an analysis of findings from the child health and mortality prevention surveillance (champs) network. |
| publisher |
Public Library of Science (PLoS) |
| publishDate |
2021 |
| url |
https://doaj.org/article/ef564d1c67214749a85426496f56e460 |
| work_keys_str_mv |
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oai:doaj.org-article:ef564d1c67214749a85426496f56e4602021-12-02T19:56:02ZPostmortem investigations and identification of multiple causes of child deaths: An analysis of findings from the Child Health and Mortality Prevention Surveillance (CHAMPS) network.1549-12771549-167610.1371/journal.pmed.1003814https://doaj.org/article/ef564d1c67214749a85426496f56e4602021-09-01T00:00:00Zhttps://doi.org/10.1371/journal.pmed.1003814https://doaj.org/toc/1549-1277https://doaj.org/toc/1549-1676<h4>Background</h4>The current burden of >5 million deaths yearly is the focus of the Sustainable Development Goal (SDG) to end preventable deaths of newborns and children under 5 years old by 2030. To accelerate progression toward this goal, data are needed that accurately quantify the leading causes of death, so that interventions can target the common causes. By adding postmortem pathology and microbiology studies to other available data, the Child Health and Mortality Prevention Surveillance (CHAMPS) network provides comprehensive evaluations of conditions leading to death, in contrast to standard methods that rely on data from medical records and verbal autopsy and report only a single underlying condition. We analyzed CHAMPS data to characterize the value of considering multiple causes of death.<h4>Methods and findings</h4>We examined deaths identified from December 2016 through November 2020 from 7 CHAMPS sites (in Bangladesh, Ethiopia, Kenya, Mali, Mozambique, Sierra Leone, and South Africa), including 741 neonatal, 278 infant, and 241 child <5 years deaths for which results from Determination of Cause of Death (DeCoDe) panels were complete. DeCoDe panelists included all conditions in the causal chain according to the ICD-10 guidelines and assessed if prevention or effective management of the condition would have prevented the death. We analyzed the distribution of all conditions listed as causal, including underlying, antecedent, and immediate causes of death. Among 1,232 deaths with an underlying condition determined, we found a range of 0 to 6 (mean 1.5, IQR 0 to 2) additional conditions in the causal chain leading to death. While pathology provides very helpful clues, we cannot always be certain that conditions identified led to death or occurred in an agonal stage of death. For neonates, preterm birth complications (most commonly respiratory distress syndrome) were the most common underlying condition (n = 282, 38%); among those with preterm birth complications, 256 (91%) had additional conditions in causal chains, including 184 (65%) with a different preterm birth complication, 128 (45%) with neonatal sepsis, 69 (24%) with lower respiratory infection (LRI), 60 (21%) with meningitis, and 25 (9%) with perinatal asphyxia/hypoxia. Of the 278 infant deaths, 212 (79%) had ≥1 additional cause of death (CoD) beyond the underlying cause. The 2 most common underlying conditions in infants were malnutrition and congenital birth defects; LRI and sepsis were the most common additional conditions in causal chains, each accounting for approximately half of deaths with either underlying condition. Of the 241 child deaths, 178 (75%) had ≥1 additional condition. Among 46 child deaths with malnutrition as the underlying condition, all had ≥1 other condition in the causal chain, most commonly sepsis, followed by LRI, malaria, and diarrheal disease. Including all positions in the causal chain for neonatal deaths resulted in 19-fold and 11-fold increases in attributable roles for meningitis and LRI, respectively. For infant deaths, the proportion caused by meningitis and sepsis increased by 16-fold and 11-fold, respectively; for child deaths, sepsis and LRI are increased 12-fold and 10-fold, respectively. While comprehensive CoD determinations were done for a substantial number of deaths, there is potential for bias regarding which deaths in surveillance areas underwent minimally invasive tissue sampling (MITS), potentially reducing representativeness of findings.<h4>Conclusions</h4>Including conditions that appear anywhere in the causal chain, rather than considering underlying condition alone, markedly changed the proportion of deaths attributed to various diagnoses, especially LRI, sepsis, and meningitis. While CHAMPS methods cannot determine when 2 conditions cause death independently or may be synergistic, our findings suggest that considering the chain of events leading to death can better guide research and prevention priorities aimed at reducing child deaths.Robert F BreimanDianna M BlauPortia MutevedziVictor AkeloInacio MandomandoIkechukwu U OgbuanuSamba O SowLola MadridShams El ArifeenMischka GarelNana Bukiwe ThwalaDickens OnyangoAntonio SitoeIma-Abasi BasseyAdama Mamby KeitaAddisu AlemuMuntasir AlamSana MahtabDickson GethiRosauro VaroJulius OjulongSolomon SamuraAshka MehtaAlexander M IbrahimAfruna RahmanPio VitorinoVicky L BaillieJanet AgayaMilagritos D TapiaNega AssefaAtique Iqbal ChowdhuryJ Anthony G ScottEmily S GurleyKaren L KotloffAmara JambaiQuique BassatBeth A Tippett-BarrShabir A MadhiCynthia G WhitneyCHAMPS ConsortiumPublic Library of Science (PLoS)articleMedicineRENPLoS Medicine, Vol 18, Iss 9, p e1003814 (2021) |