ished by a general population and housing census in 1991. The sampling frame was a list of all EAs (clusters). Within each domain, a two-stage sample was selected. The first stage involved selecting 466 clusters (primary sampling units) with a probability proportional to the size, the size being the number of households in the cluster. The second stage involved the systematic sampling of households from the selected clusters.
This study is based on an analysis of existing survey data with all identifier information removed. The survey was approved by the Ethics Committee of the ORC Macro at Calverton in the USA and by the National Ethics Committee in the Ministry of Health in Nigeria. All study participants gave informed consent before participation and all information was collected confidentially.
Each woman interviewed in the survey was asked to provide a detailed history of all her live births in chronological order, including whether a birth was single or multiple, sex of the child, date of birth, survival status, age of the child on the date of interview if alive, and if not alive, age at death of each live birth. These data from the birth histories were used to calculate infant mortality rate, defined as the probability of dying before completing 12 months of age, using a synthetic cohort life table. The rate was expressed as deaths per 1000 live births.
The multiple birth status was analysed as not multiple-birth (singleton) and multiple-birth (twin, triplet, quadruplet, or higher order). Each multiple birth child was analysed as an individual child, and the clustering effect of each group of multiple births was included in the analysis.
Because child survival is correlated with pregnancy care, delivery assistance, maternal nutrition, household living conditions, and other child, mother, and household characteristics and socio-economic factors that can also affect morbidity and mortality in children, the association between multiple birth status and infant mortality were estimated after adjusting for the effects of these other risk factors and potentially confounding factors. These factors include child's sex (boy, girl), professional assistance at delivery (no, yes), birth order (1, 2, 3, 4+), child's birth size (below average, average, above average), mother's age at childbirth (13–17, 18–24, 25–34, 35–48), mother's body mass index (BMI) (<18.5, 18.5–24.9, 25.0+ kg/m2), mother's education (no education, some primary, secondary or higher), household wealth index (highest, fourth, middle, second, lowest), household access to safe drinking water (yes, no), availability of a hygienic toilet (yes, no), cooking fuel type (low pollution fuel, high pollution fuel), ethnic group (Hausa/Fulania, Igbo, Yoruba, others) residence (urban, rural) and geographic division (North central, North East, North West, South East, South South, and South West).
We used univariable and multivariable survival regression procedure with Weibull hazard function in Stata version 10 to examine the relationship of multiple birth status and other factors on infant mortality. A number of unadjusted hazard regression models were used to assess the unadjusted effect of multiple births and different risk factor and confounding factor, and a full adjusted model to assess the adjusted effect of multiple births controlling for all other factors that were significant in the unadjusted analyses (p < .05). In our analysis, weights were used to restore the representativeness of the sample, in which certain categories of respondents were over-sampled and non-response rates varied from one geographical area to another. Results were presented as hazard ratios (HR) with 95% confidence intervals (CI).
The sample distribution of children born in the five years preceding the 2003 Nigeria Demographic and Health Survey (NDHS) by multiple birth status and other selected characteristics are shown in Table 1 (see additional file 1). About 4% of children are of multiple births. Fifty-one percent of all births were boys and 49% were girls.
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