Research article
A parsimonious explanation for intersecting perinatal mortality curves: understanding the effect of plurality and of parity
1 Perinatal Epidemiology Research Unit, Departments of Obstetrics & Gynecology and of Pediatrics, Dalhousie University, Halifax, Nova Scotia, Canada
2 Health Surveillance and Epidemiology Division, Centre for Healthy Human Development, Health Canada, Ottawa, Ontario, Canada
3 Department of Environmental and Community Medicine, Robert Wood Johnson Medical School, University of Medicine and Dentistry of New Jersey, Piscataway, NJ, USA
4 Departments of Pediatrics and of Epidemiology and Biostatistics, McGill University, Montreal, Quebec, Canada
5 Section of Epidemiology and Biostatistics, Department of Obstetrics, Gynecolgy and Reproductive Sciences, Robert Wood Johnson Medical School, University of Medicine and Dentistry of New Jersey, Piscataway, NJ, USA
6 Departments of Pediatrics and of Community Health, University of Calgary, Calgary, Alberta, Canada
7 Canadian Perinatal Surveillance System, Health Surveillance and Epidemiology Division, Centre for Healthy Human Development, Health Canada, Ottawa, Ontario, Canada
BMC Pregnancy and Childbirth 2003, 3:3 doi:10.1186/1471-2393-3-3
Published: 2 June 2003Abstract
Background
Birth weight- and gestational age-specific perinatal mortality curves intersect when compared across categories of maternal smoking, plurality, race and other factors. No simple explanation exists for this paradoxical observation.
Methods
We used data on all live births, stillbirths and infant deaths in Canada (1991–1997) to compare perinatal mortality rates among singleton and twin births, and among singleton births to nulliparous and parous women. Birth weight- and gestational age-specific perinatal mortality rates were first calculated by dividing the number of perinatal deaths at any given birth weight or gestational age by the number of total births at that birth weight or gestational age (conventional calculation). Gestational age-specific perinatal mortality rates were also calculated using the number of fetuses at risk of perinatal death at any given gestational age.
Results
Conventional perinatal mortality rates among twin births were lower than those among singletons at lower birth weights and earlier gestation ages, while the reverse was true at higher birth weights and later gestational ages. When perinatal mortality rates were based on fetuses at risk, however, twin births had consistently higher mortality rates than singletons at all gestational ages. A similar pattern emerged in contrasts of gestational age-specific perinatal mortality among singleton births to nulliparous and parous women. Increases in gestational age-specific rates of growth-restriction with advancing gestational age presaged rising rates of gestational age-specific perinatal mortality in both contrasts.
Conclusions
The proper conceptualization of perinatal risk eliminates the mortality crossover paradox and provides new insights into perinatal health issues.
