Global Network for Women's and Children's Health Research: probable causes of stillbirth in low- and middle-income countries using a prospectively defined classification system.

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.

A prospective cause of death classification system for maternal deaths in low and middle-income countries: results from the Global Network Maternal Newborn Health Registry.

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.

An alternative strategy for perinatal verbal autopsy coding: single versus multiple coders.

OBJECTIVE: To determine the comparability between cause of death (COD) by a single physician coder and a two-physician panel, using verbal autopsy. METHODS: The study was conducted between May 2007 and June 2008. Within a week of a perinatal death in 38 rural remote communities in Guatemala, the Democratic Republic of Congo, Zambia and Pakistan, VA questionnaires were completed. Two independent physicians, unaware of the others decisions, assigned an underlying COD, in accordance with the causes listed in the chapter headings of the International classification diseases and related health problems, 10th revision (ICD-10). Cohen's kappa statistic was used to assess level of agreement between physician coders. RESULTS: There were 9461 births during the study period; 252 deaths met study enrolment criteria and underwent verbal autopsy. Physicians assigned the same COD for 75% of stillbirths (SB) (K = 0.69; 95% confidence interval: 0.61-0.78) and 82% early neonatal deaths (END) (K = 0.75; 95% confidence interval: 0.65-0.84). The patterns and proportion of SBs and ENDs determined by the physician coders were very similar compared to causes individually assigned by each physician. Similarly, rank order of the top five causes of SB and END was identical for each physician. CONCLUSION: This study raises important questions about the utility of a system of multiple coders that is currently widely accepted and speculates that a single physician coder may be an effective and economical alternative to VA programmes that use traditional two-physician panels to assign COD.

Using verbal autopsy to ascertain perinatal cause of death: are trained non-physicians adequate?

OBJECTIVES: To develop a standardized verbal autopsy (VA) training program and evaluate whether its implementation resulted in comparable knowledge required to classify perinatal cause of death (COD) by physicians and non-physicians. METHODS: Training materials, case studies, and written and mock scenarios for this VA program were developed using conventional VA and ICD-10 guidelines. This program was used to instruct physicians and non-physicians in VA methodology using a train-the-trainer model. Written tests of cognitive and applied knowledge required to classify perinatal COD were administered before and after training to evaluate the effect of the VA training program. RESULTS: Fifty-three physicians and non-physicians (nurse-midwives/nurses and Community Health Workers [CHW]) from Pakistan, Zambia, the Democratic Republic of Congo, and Guatemala were trained. Cognitive and applied knowledge mean scores among all trainees improved significantly (12.8 and 28.8% respectively, P < 0.001). Cognitive and applied knowledge post-training test scores of nurse-midwives/nurses were comparable to those of physicians. CHW (high-school graduates with 15 months or less formal health/nursing training) had the largest improvements in post-training applied knowledge with scores comparable to those of physicians and nurse-midwives/nurses. However, CHW cognitive knowledge post-training scores were significantly lower than those of physicians and nurses. CONCLUSIONS: With appropriate training in VA, cognitive and applied knowledge required to determine perinatal COD is similar for physicians and nurses-midwives/nurses. This suggests that midwives and nurses may play a useful role in determining COD at the community level, which may be a practical way to improve the accuracy of COD data in rural, remote, geographic areas.