Abstract
BackgroundDetermining cause of death is needed to develop strategies to reduce maternal death, stillbirth, and newborn death, especially for low-resource settings where 98% of deaths occur. Most existing classification systems are designed for high income settings where extensive testing is available. Verbal autopsy or audits, developed as an alternative, are time-intensive and not generally feasible for population-based evaluation. Furthermore, because most classification is user-dependent, reliability of classification varies over time and across settings. Thus, we sought to develop classification systems for maternal, fetal and newborn mortality based on minimal data to produce reliable cause-of-death estimates for low-resource settings.ResultsIn six low-resource countries (India, Pakistan, Guatemala, DRC, Zambia and Kenya), we evaluated data which are collected routinely at antenatal care and delivery and could be obtained with interview, observation, or basic equipment from the mother, lay-health provider or family to inform causes of death. Using these basic data collected in a standard way, we then developed an algorithm to assign cause of death that could be computer-programmed. Causes of death for maternal (trauma, abortion, hemorrhage, infection and hypertensive disease of pregnancy), stillbirth (birth trauma, congenital anomaly, infection, asphyxia, complications of preterm birth) and neonatal death (congenital anomaly, infection, asphyxia, complications of preterm birth) are based on existing cause of death classifications, and compatible with the World Health Organization International Classification of Disease system.ConclusionsOur system to assign cause of maternal, fetal and neonatal death uses basic data from family or lay-health providers to assign cause of death by an algorithm to eliminate a source of inconsistency and bias. The major strengths are consistency, transparency, and comparability across time or regions with minimal burden on the healthcare system. This system will be an important contribution to determining cause of death in low-resource settings.
Highlights
Determining cause of death is needed to develop strategies to reduce maternal death, stillbirth, and newborn death, especially for low-resource settings where 98% of deaths occur
In many low-income countries, minimal resources are available for determining cause of death for mothers, much less cause of death for fetuses and newborns which occur much more frequently, and diagnostic tools such as autopsy, placental histology, x-ray, ultrasound and bacterial cultures are generally not available [13]
The stillbirth classification algorithm Stillbirths are generally considered to be deaths in utero occurring at 20 weeks gestation or greater, depending on the setting [40]
Summary
Determining cause of death is needed to develop strategies to reduce maternal death, stillbirth, and newborn death, especially for low-resource settings where 98% of deaths occur. We sought to develop classification systems for maternal, fetal and newborn mortality based on minimal data to produce reliable cause-of-death estimates for low-resource settings. More than 35 systems have been developed to classify the cause of stillbirths alone, and other classification schemes attempt to define causes of neonatal and maternal deaths [8,9,10,11,12]. Most of these classification systems are best suited for high income settings because the tests to define cause of death are extensive. In many low-income countries, minimal resources are available for determining cause of death for mothers, much less cause of death for fetuses and newborns which occur much more frequently, and diagnostic tools such as autopsy, placental histology, x-ray, ultrasound and bacterial cultures are generally not available [13]
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