RESUMO
OBJECTIVE: We sought to classify causes of stillbirth for six low-middle-income countries using a prospectively defined algorithm. DESIGN: Prospective, observational study. SETTING: Communities in India, Pakistan, Guatemala, Democratic Republic of Congo, Zambia and Kenya. POPULATION: Pregnant women residing in defined study regions. METHODS: Basic data regarding conditions present during pregnancy and delivery were collected. Using these data, a computer-based hierarchal algorithm assigned cause of stillbirth. Causes included birth trauma, congenital anomaly, infection, asphyxia, and preterm birth, based on existing cause of death classifications and included contributing maternal conditions. MAIN OUTCOME MEASURES: Primary cause of stillbirth. RESULTS: Of 109 911 women who were enrolled and delivered (99% of those screened in pregnancy), 2847 had a stillbirth (a rate of 27.2 per 1000 births). Asphyxia was the cause of 46.6% of the stillbirths, followed by infection (20.8%), congenital anomalies (8.4%) and prematurity (6.6%). Among those caused by asphyxia, 38% had prolonged or obstructed labour, 19% antepartum haemorrhage and 18% pre-eclampsia/eclampsia. About two-thirds (67.4%) of the stillbirths did not have signs of maceration. CONCLUSIONS: Our algorithm determined cause of stillbirth from basic data obtained from lay-health providers. The major cause of stillbirth was fetal asphyxia associated with prolonged or obstructed labour, pre-eclampsia and antepartum haemorrhage. In the African sites, infection also was an important contributor to stillbirth. Using this algorithm, we documented cause of stillbirth and its trends to inform public health programs, using consistency, transparency, and comparability across time or regions with minimal burden on the healthcare system. TWEETABLE ABSTRACT: Major causes of stillbirth are asphyxia, pre-eclampsia and haemorrhage. Infections are important in Africa.
Assuntos
Algoritmos , Sistema de Registros , Natimorto/epidemiologia , África/epidemiologia , Ásia/epidemiologia , Países em Desenvolvimento , Feminino , Saúde Global , Guatemala/epidemiologia , Humanos , Serviços de Saúde Materno-Infantil , Gravidez , Complicações na Gravidez/epidemiologia , Estudos ProspectivosRESUMO
OBJECTIVE: To describe the causes of maternal death in a population-based cohort in six low- and middle-income countries using a standardised, hierarchical, algorithmic cause of death (COD) methodology. DESIGN: A population-based, prospective observational study. SETTING: Seven sites in six low- to middle-income countries including the Democratic Republic of the Congo (DRC), Guatemala, India (two sites), Kenya, Pakistan and Zambia. POPULATION: All deaths among pregnant women resident in the study sites from 2014 to December 2016. METHODS: For women who died, we used a standardised questionnaire to collect clinical data regarding maternal conditions present during pregnancy and delivery. These data were analysed using a computer-based algorithm to assign cause of maternal death based on the International Classification of Disease-Maternal Mortality system (trauma, termination of pregnancy-related, eclampsia, haemorrhage, pregnancy-related infection and medical conditions). We also compared the COD results to healthcare-provider-assigned maternal COD. MAIN OUTCOME MEASURES: Assigned causes of maternal mortality. RESULTS: Among 158 205 women, there were 221 maternal deaths. The most common algorithm-assigned maternal COD were obstetric haemorrhage (38.6%), pregnancy-related infection (26.4%) and pre-eclampsia/eclampsia (18.2%). Agreement between algorithm-assigned COD and COD assigned by healthcare providers ranged from 75% for haemorrhage to 25% for medical causes coincident to pregnancy. CONCLUSIONS: The major maternal COD in the Global Network sites were haemorrhage, pregnancy-related infection and pre-eclampsia/eclampsia. This system could allow public health programmes in low- and middle-income countries to generate transparent and comparable data for maternal COD across time or regions. TWEETABLE ABSTRACT: An algorithmic system for determining maternal cause of death in low-resource settings is described.