Use of the International Classification of Diseases to Perinatal Mortality (ICD-PM) with verbal autopsy to determine the causes of stillbirths and neonatal deaths in rural Cambodia: a population-based, prospective, cohort study

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Abstract

Background

Perinatal mortality remains a significant global health challenge, particularly in low- and middle-income countries (LMICs). Accurate cause-of-death data are essential to inform effective interventions but is often scarce. This study aimed to identify causes of stillbirths and neonatal deaths in rural Cambodia using verbal autopsy (VA) and the WHO International Classification of Diseases to Perinatal Mortality (ICD-PM).

Methods

A four-year prospective study (2018-2022) in Preah Vihear province, Cambodia, established a community health worker-based pregnancy surveillance system. Verbal autopsy was conducted on stillbirths and neonatal deaths, with dual physician analysis to interpret VA data. To classify causes of death, ICD-PM was applied with adaptations made for stillbirths with unknown timing of death.

Results

A total of 522 deaths (229 stillbirths, 293 neonatal deaths) were recorded, and 79.1% (413) had a VA. Applying ICD-PM, primary causes of death were identified for 36.6% of stillbirths and 95.0% of neonatal deaths. The leading cause of death was hypoxia for intrapartum stillbirths (78.3%), low birth weight and prematurity for early neonatal deaths (40.9%), and infection for late neonatal deaths (51.4%). Complications during labour and delivery were the leading maternal contributing condition for intrapartum stillbirths (63.3%) and early neonatal deaths (42.4%). Unknown timing of death was assigned to 12.0% of stillbirths.

Conclusion

Application of ICD-PM with VA-derived data provides valuable insights into causes of stillbirths and neonatal deaths. However, adaptations are necessary to address ICD-PM’s limitations, particularly to classify unknown timing of death. Our findings can contribute to global efforts to improve the reporting of perinatal mortality data.

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