Postmortem investigations and identification of multiple causes of child deaths: An analysis of findings from the Child Health and Mortality Prevention Surveillance (CHAMPS) network

Abstract

Background

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.

Methods and findings

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.

Conclusions

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.

Abstract

In an analysis of data from the Child Health and Mortality Prevention Surveillance (CHAMPS) network, Robert Breiman, Dianna Blau, and colleagues investigate how considering all conditions in the causal chain leading to death informs the identification of deaths attributable to various diagnoses.

Author summary

Why was this study done?

• More than 5 million deaths occur annually in children <5 years of age globally, mostly in low-income settings in Africa and South Asia.

• There is reason to believe that a substantial proportion of these deaths are preventable if there were effective programs that target the most common causes of death.

What did the researchers do and find?

• We studied the causes of death in 7 sites in Africa and South Asia where child mortality is high by conducting surveillance for mortality and then collecting samples of tissue and other diagnostic specimens to perform pathology and diagnostic tests.

• In each country, a panel of clinical, laboratory, and epidemiological experts determined underlying, intermediate, and immediate causes of death for each child by studying the clinical data available before death, postmortem questionnaire surveys done with parents of the deceased, and postmortem pathology and testing.

What do these findings mean?

• Up to now, most programs focus on a single cause of death (CoD) for each child referred to as the underlying condition.

• However, the findings suggest that most deaths have multiple conditions that contribute to death.

• Considering all of the conditions, rather than underlying condition alone, markedly changed the proportion of deaths attributed to various diagnoses, especially lower respiratory infection (LRI), sepsis, and meningitis.

• The findings suggest that considering the full chain of events leading to death can better guide priorities for research and strategies for mortality prevention.