Predictors of singleton preterm birth using multinomial regression models accounting for missing data: A birth registry-based cohort study in northern Tanzania

Abstract

Background Preterm birth is a significant contributor of under-five and newborn deaths globally. Recent estimates indicated that, Tanzania ranks the tenth country with the highest preterm birth rates in the world, and shares 2.2% of the global proportion of all preterm births. Previous studies applied binary regression models to determine predictors of preterm birth by collapsing gestational age at birth to <37 weeks. For targeted interventions, this study aimed to determine predictors of preterm birth using multinomial regression models accounting for missing data. Methods We carried out a secondary analysis of cohort data from the KCMC zonal referral hospital Medical Birth Registry for 44,117 women who gave birth to singletons between 2000-2015. KCMC is located in the Moshi Municipality, Kilimanjaro region, northern Tanzania. Data analysis was performed using Stata version 15.1. Assuming a nonmonotone pattern of missingness, data were imputed using a fully conditional specification (FCS) technique under the missing at random (MAR) assumption. Multinomial regression models with robust standard errors were used to determine predictors of moderately to late ([32,37) weeks of gestation) and very/extreme (<32 weeks of gestation) preterm birth. Results The overall proportion of preterm births among singleton births was 11.7%. The trends of preterm birth were significantly rising between the years 2000-2015 by 22.2% (95%CI 12.2%, 32.1%, p<0.001) for moderately to late preterm and 4.6% (95%CI 2.2%, 7.0%, p = 0.001) for very/extremely preterm birth category. After imputation of missing values, higher odds of moderately to late preterm delivery were among adolescent mothers (OR = 1.23, 95%CI 1.09, 1.39), with primary education level (OR = 1.28, 95%CI 1.18, 1.39), referred for delivery (OR = 1.19, 95%CI 1.09, 1.29), with pre-eclampsia/eclampsia (OR = 1.77, 95%CI 1.54, 2.02), inadequate (<4) antenatal care (ANC) visits (OR = 2.55, 95%CI 2.37, 2.74), PROM (OR = 1.80, 95%CI 1.50, 2.17), abruption placenta (OR = 2.05, 95%CI 1.32, 3.18), placenta previa (OR = 4.35, 95%CI 2.58, 7.33), delivery through CS (OR = 1.16, 95%CI 1.08, 1.25), delivered LBW baby (OR = 8.08, 95%CI 7.46, 8.76), experienced perinatal death (OR = 2.09, 95%CI 1.83, 2.40), and delivered male children (OR = 1.11, 95%CI 1.04, 1.20). Maternal age, education level, abruption placenta, and CS delivery showed no statistically significant association with very/extremely preterm birth. The effect of (<4) ANC visits, placenta previa, LBW, and perinatal death were more pronounced on the very/extremely preterm compared to the moderately to late preterm birth. Notably, extremely higher odds of very/extreme preterm birth were among the LBW babies (OR = 38.34, 95%CI 31.87, 46.11). Conclusions The trends of preterm birth have increased over time in northern Tanzania. Policy decisions should intensify efforts to improve maternal and child care throughout the course of pregnancy and childbirth towards preterm birth prevention. For a positive pregnancy outcome, interventions to increase uptake and quality of ANC services should also be strengthened in Tanzania at all levels of care, where several interventions can easily be delivered to pregnant women, especially those at high-risk of experiencing adverse pregnancy outcomes.

Joint Modeling of Singleton Preterm Birth and Perinatal Death Using Birth Registry Cohort Data in Northern Tanzania

“…Inadequate ANC visits increased the risk of both preterm birth and perinatal death. Previous studies on independent predictors of these outcomes support this finding ( 14 , 16 , 17 , 37 , 50 , 51 ). A separate analysis including an interaction term between the number of antenatal care visits and maternal age groups in the co-occurrence model (results not shown) was statistically significant for preterm birth only for mothers aged 15–19 and 40+ compared to 25–29 years.…”

“…Other information criteria such as the conditional AIC can be used (43)(44)(45) but are not currently available in STATA software. Previous analyses related to this work accounted for missing data (14,37). However, for the joint modeling analysis, despite imputing the missing data, analysis of the imputed data could not be achieved due to model complexity (i.e., having additional parameters to estimate) and the machine's computational power.…”

“…Still, these trends should be interpreted with caution given the potential under-reporting of perinatal deaths events in this registry ( 14 , 49 ). Appropriate interventions to reduce the rising preterm birth rates are necessary ( 37 ) given its known contribution to perinatal and neonatal deaths ( 1 , 48 ). The UN Inter-agency Group for Child Mortality Estimation indicated that “the focus should be on maintaining high coverage of quality antenatal care, skilled care at birth, postnatal care for mother and baby, and care of small and sick newborns to address the main causes of neonatal mortality globally” ( 1 ).…”

“…Additionally, pre-eclampsia/eclampsia, PPH, and LBW increased the likelihood of co-occurrence. On top of these complications being among the common risk factors of preterm birth ( 16 , 22 , 37 , 58 ) and perinatal death ( 14 , 15 , 19 , 21 , 59 ), they also increase the risk of newborns transfer to intensive care units ( 60 ). Given their history, women at risk of these adverse pregnancy events should be given due public health and clinical attention and care during antenatal, intrapartum, and postnatal periods.…”

“…Ordinary least-squares linear regression assessed linear trends of proportions of the two outcomes for every year increase. Findings from previous analyses for the predictors of preterm birth ( 37 ) and perinatal death ( 14 , 15 ) informed selection of variables to include in the initial steps of multivariable analysis. The next step was a separate stepwise manual reduction of variables not significantly associated with preterm birth and perinatal death ( p < 0.05) using the mixed-effects generalized linear models with exchangeable correlation structure.…”